From 5be46d3972dc17e801fc6c058ea03523b5aa93a4 Mon Sep 17 00:00:00 2001 From: MARCOP001 Date: Fri, 29 May 2026 23:06:32 +0200 Subject: [PATCH] modifiche a standardizer-app-file_extractor --- app.py | 34 +- data/raw/openalex_20260529_221059.json | 111089 ++++++++++++++++++++++ data/raw/openalex_20260529_223443.json | 111089 ++++++++++++++++++++++ data/raw/pubmed_20260529_230448.xml | 23918 +++++ sources - collegamento.lnk | Bin 0 -> 1749 bytes www/services/file_extractor.py | 23 +- www/services/standardizer.py | 1241 +- 7 files changed, 246274 insertions(+), 1120 deletions(-) create mode 100644 data/raw/openalex_20260529_221059.json create mode 100644 data/raw/openalex_20260529_223443.json create mode 100644 data/raw/pubmed_20260529_230448.xml create mode 100644 sources - collegamento.lnk diff --git a/app.py b/app.py index fc0e5fe12..a2541800a 100644 --- a/app.py +++ b/app.py @@ -772,22 +772,33 @@ def mostra(): elif selected_action == "1A": # Dati Locali (Base Level) files = input.Dataset() if files: - all_raw_records = [] - # --- FASE 1: EXTRACT --- + all_standardized_dfs = [] + + # --- FASE 1, 2, 3: EXTRACT & TRANSFORM PER FILE --- for file_info in files: file_path = file_info["datapath"] + file_name = file_info["name"] + # Extract the extension to pass to standardizer + file_ext = os.path.splitext(file_name)[1].lower() + try: + # Extract raw records from the specific file raw_records = extract_from_file(file_path, source=source_upper) - all_raw_records.extend(raw_records) + + if raw_records: + # Transform to DF immediately using the specific file_type + df_part = convert2df(raw_records, source=source_upper, file_type=file_ext, validate=True) + all_standardized_dfs.append(df_part) + except Exception as e: - ui.notification_show(f"Errore di estrazione ({file_info['name']}): {e}", type="error", duration=8) + ui.notification_show(f"Errore di elaborazione per ({file_name}): {e}", type="error", duration=8) - # --- FASE 2, 3 e 4: TRANSFORM --- - if all_raw_records: + # --- FASE 4: MERGE E LOAD --- + if all_standardized_dfs: try: - standardized_df = convert2df(all_raw_records, source=source_upper, validate=True) + # Combine all standard dataframes into one + standardized_df = pd.concat(all_standardized_dfs, ignore_index=True) - # ---> AGGIUNGI QUESTE 3 RIGHE QUI <--- # Converte l'anno in numerico e rimuove le righe senza anno valido standardized_df["PY"] = pd.to_numeric(standardized_df["PY"], errors="coerce") standardized_df = standardized_df.dropna(subset=["PY"]) @@ -799,8 +810,7 @@ def mostra(): ui.notification_show(f"✅ ETL completato con successo! Elaborati {len(standardized_df)} record.", duration=5) except Exception as e: - ui.notification_show(f"❌ Errore durante la standardizzazione: {e}", type="error", duration=10) - + ui.notification_show(f"❌ Errore durante l'aggregazione dei dati: {e}", type="error", duration=10) # -------- ADVICE BUTTON -------- @render.ui @reactive.event(input.advice_modal_completeness) @@ -962,7 +972,9 @@ def esegui_pipeline_api(): # --- FASE 2, 3 e 4: TRANSFORM E LOAD --- source_mapped = "OPENALEX" if source == "openalex" else "PUBMED" - standardized_df = convert2df(raw_records, source=source_mapped, validate=True) + + # Pass "api" as file_type since these aren't traditional physical files + standardized_df = convert2df(raw_records, source=source_mapped, file_type="api", validate=True) # Prepara il dato per i calcoli temporali della UI standardized_df["PY"] = pd.to_numeric(standardized_df["PY"], errors="coerce") diff --git a/data/raw/openalex_20260529_221059.json b/data/raw/openalex_20260529_221059.json new file mode 100644 index 000000000..f3ed1de68 --- /dev/null +++ b/data/raw/openalex_20260529_221059.json @@ -0,0 +1,111089 @@ +[ + { + "id": "https://openalex.org/W2101234009", + "doi": "https://doi.org/10.48550/arxiv.1201.0490", + "title": "Scikit-learn: Machine Learning in Python", + "display_name": "Scikit-learn: Machine Learning in Python", + "relevance_score": 17405.58, + "publication_year": 2012, + "publication_date": "2012-01-02", + "ids": { + "openalex": "https://openalex.org/W2101234009", + "doi": "https://doi.org/10.48550/arxiv.1201.0490", + "mag": "2101234009" + }, + "language": "en", + "primary_location": { + "id": "pmh:oai:arXiv.org:1201.0490", + "is_oa": true, + "landing_page_url": "http://arxiv.org/abs/1201.0490", + "pdf_url": "https://arxiv.org/pdf/1201.0490", + "source": { + "id": "https://openalex.org/S4306400194", + "display_name": "arXiv (Cornell University)", + "issn_l": null, + "issn": null, + "is_oa": true, + "is_in_doaj": false, + "is_core": false, + "host_organization": "https://openalex.org/I205783295", + "host_organization_name": "Cornell University", + "host_organization_lineage": [ + "https://openalex.org/I205783295" + ], + "host_organization_lineage_names": [], + "type": "repository" + }, + "license": null, + "license_id": null, + "version": "submittedVersion", + "is_accepted": false, + "is_published": false, + "raw_source_name": "", + "raw_type": "text" + }, + "type": "preprint", + "indexed_in": [ + "arxiv", + "datacite" + ], + "open_access": { + "is_oa": true, + "oa_status": "green", + "oa_url": "https://arxiv.org/pdf/1201.0490", + "any_repository_has_fulltext": true + }, + "authorships": [ + { + "author_position": "first", + "author": { + "id": "https://openalex.org/A5105141183", + "display_name": "Fabián Pedregosa", + "orcid": "https://orcid.org/0000-0003-4025-3953" + }, + "institutions": [ + { + "id": "https://openalex.org/I2738703131", + "display_name": "Commissariat à l'Énergie Atomique et aux Énergies Alternatives", + "ror": "https://ror.org/00jjx8s55", + "country_code": "FR", + "type": "government", + "lineage": [ + "https://openalex.org/I2738703131" + ] + }, + { + "id": "https://openalex.org/I4210128565", + "display_name": "CEA Paris-Saclay", + "ror": "https://ror.org/03n15ch10", + "country_code": "FR", + "type": "government", + "lineage": [ + "https://openalex.org/I2738703131", + "https://openalex.org/I277688954", + "https://openalex.org/I4210128565" + ] + } + ], + "countries": [ + "FR" + ], + "is_corresponding": true, + "raw_author_name": "Pedregosa, Fabian", + "raw_affiliation_strings": [ + "LNAO - 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+ + 1573-501X + + + 2026 + May + 29 + + + Molecular diversity + Mol Divers + + M-Tune: imbalanced data handling in machine learning by tuning the decision threshold. + 10.1007/s11030-026-11600-8 + + Overprediction of the majority class and poor predictive performance are the major issues when training machine learning classifiers with imbalanced data. This eventually leads to a larger misclassification rate and bias towards the majority class. This is a real-world problem, especially when dealing with biological datasets. There are several popular methods, including Random Under Sampling (RUS), Synthetic Minority Oversampling Technique (SMOTE), and Generalized tHreshOld ShifTing (GHOST). However, these methods either articulate the no. of samples or are biased towards the majority class. In this study, we developed a novel strategy, "M-Tune", a simple yet powerful technique for addressing the class imbalance problem. This procedure employs an ensemble approach that incorporates threshold shifting and majority voting. This new method can effectively predict the minority class. The developed method was rigorously tested with 138 drug discovery datasets. We further rigorously evaluate the performance of the M-Tune method for two classifiers with eleven different fingerprints and find that classifiers benefit from M-Tune in effectively classifying the minority class. The M-Tune method outperformed the other strategies, including the GHOST method and SMOTE and achieved comparable performance with RUS in identifying minority classes. In conclusion, we demonstrated a new threshold procedure that can be effectively applied to real-world imbalanced datasets, i.e., drug discovery data sets where the class datasets are highly imbalanced. This method aids in identifying the minority class in highly imbalanced datasets, in general, and in drug discovery datasets, in particular. Although M-Tune increases false positive predictions relative to conservative thresholding strategies, it provides a practical advantage in applications where maximizing active compound recovery is prioritized. + © 2026. The Author(s), under exclusive licence to Springer Nature Switzerland AG. + + + + Dutta + Tapos + T + + School of Computing Science, The Assam Kaziranga University, Jorhat, 785006, India. + + + + Chutia + Hillul + H + + Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, India. + + + + Nagamani + Selvaraman + S + + Advanced Computation and Data Sciences Division, CSIR-North East Institute of Science and Technology, Jorhat, 785006, India. nagamaniselvaraman@gmail.com. + + + Academy of Scientific and Innovative Research (AcSIR), Ghaziabad, 201002, India. nagamaniselvaraman@gmail.com. + + + + eng + + + BT/PR40188/BTIS/137/27/2021 + Department of Biotechnology, Ministry of Science and Technology, India + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Mol Divers + 9516534 + 1381-1991 + + IM + + Bioactive datasets + Class imbalance problem + Data handling + Machine learning + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 42 + + + 2025 + 7 + 18 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 11 + 20 + + + aheadofprint + + 42213376 + 10.1007/s11030-026-11600-8 + 10.1007/s11030-026-11600-8 + + + + Abd Elrahman SM, Abraham A (2013) A review of class imbalance problem. J Netw Innovative Comput 1:332–340 + + + Haixiang G, Yijing L, Shassng J, Mingyun G, Yuanyue H, Bing G (2017) Learning from class-imbalanced data: review of methods and applications. Expert Syst Appl 73:220–239 + + 10.1016/j.eswa.2016.12.035 + + + + Tong X, Feng Y, Li JJ (2018) Neyman-Pearson classification algorithms and NP receiver operating characteristics. 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+ + 1880-344X + + + 2026 + May + 29 + + + Journal of echocardiography + J Echocardiogr + + Explainable machine learning for estimation of elevated left ventricular filling pressure: a multicenter validation. + 10.1007/s12574-026-00738-x + + Guideline-recommended algorithms (GL-algorithm) often results in indeterminate left ventricular filling pressure (LVFP). Despite high accuracy, machine learning (ML) methods lack interpretability, which necessitates the development of explainable ML models for clinical use. + To develop an explainable ML model for estimating LVFP, providing patient-level interpretation using gold-standard right heart catheterization (RHC) data. + We retrospectively enrolled 956 patients who underwent echocardiography and RHC at three hospitals within a median of 3 days. Two extreme gradient boosting models were trained using data from two hospitals (n = 621) to estimate elevated pulmonary artery wedge pressure (PAWP ≥ 18 mmHg) as a surrogate for elevated LVFP. Model 1 used variables from GL-algorithm, while Model 2 used variables selected based on Shapley additive explanations (SHAP) values. Models' area under the receiver-operating characteristic curve (AUROC) for elevated LVFP were compared using external test data from the other hospital (n = 335). + Overall, 31.0% had elevated PAWP, and 42.7% were classified as indeterminate LVFP by GL-algorithm, whereas the ML models classified all patients. AUROCs of Model 1 (0.82, 95% CI 0.73-0.92) and Model 2 (0.83, 95% CI 0.75-0.91) in classifiable cases by GL-algorithm significantly outperformed that of GL-algorithm (0.72, 95% CI 0.60-0.83, p = 0.020 and 0.016, respectively), Model 2 performed equally well for indeterminate cases. SHAP force plots visualized each variable's contribution to the ML model's decision for each patient. + Explainable ML outperformed GL-algorithm in estimating LVFP, providing a user-friendly tool for clinicians with patient-level interpretability. + © 2026. Japanese Society of Echocardiography. + + + + Nakamura + Yutaka + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kagiyama + Nobuyuki + N + 0000-0002-2305-0049 + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp. + + + Data Science Course, Juntendo University, Tokyo, Japan. kgnb_27_hot@yahoo.co.jp. + + + Department of Cardiovascular Medicine, Juntendo University Graduate School of Medicine, 2-1-1 Hongo, Tokyo, 113-0021, Japan. kgnb_27_hot@yahoo.co.jp. + + + + Shrestha + Sirish + S + + Center for Creative Leadership, Greensboro, USA. + + + + Akama + Yuka + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kito + Shusuke + S + + Data Science Course, Juntendo University, Tokyo, Japan. + + + + Takamatsu + Sachiko + S + + Department of Nursing, The Sakakibara Heart Institute of Okayama, Okayama, Japan. + + + + Toki + Misako + M + + Department of Clinical Laboratory, The Sakakibara Heart Institute of Okayama, Okayama, Japan. + + + + Kitai + Takeshi + T + + Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. + + + Department of Heart Failure, National Cerebral and Cardiovascular Center, Suita, Japan. + + + + Okada + Taiji + T + + Department of Cardiovascular Medicine, Kobe City Medical Center General Hospital, Kobe, Japan. + + + Division of Cardiology, Shimane University Faculty of Medicine, Izumo, Japan. + + + + Murata + Azusa + A + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Kaneko + Tomohiro + T + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Matsue + Yuya + Y + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Miyazaki + Sakiko + S + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Okazaki + Shinya + S + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + Minamino + Tohru + T + + Department of Cardiovascular Biology and Medicine, Juntendo University Graduate School of Medicine, Tokyo, Japan. + + + + eng + + + 25K19371 + Japan Society for the Promotion of Scienc + + + + + Journal Article + + + 2026 + 05 + 29 + +
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+ + 1573-4951 + + 40 + 1 + + 2026 + May + 29 + + + Journal of computer-aided molecular design + J Comput Aided Mol Des + + Target-aware molecule SMILES generation using a large language model with retrieval-augmented generation, multi-turn memory, and a predictive model. + 134 + 10.1007/s10822-026-00834-1 + + Given the vastness of chemical space and the cost and time requirements of high-throughput screening, resource-efficient computational strategies are needed to prioritize candidate drug molecules. Here, we evaluated whether an open-weight large language model (LLM) augmented with retrieval-augmented generation (RAG), multi-turn memory, and a pretrained drug-target interaction predictor can generate target-aware molecules without task-specific retraining. Protein-ligand-pKi examples retrieved from BindingDB, Davis, and KIBA were used as contextual guidance, while DeepPurpose provided the optimization signal during iterative SMILES refinement. Our approach produced a statistically significant increase in predicted pKi across successive multi-turn memory iterations, indicating that retrieval- and memory-guided refinement may improve target-conditioned molecular generation. The proposed framework also showed favorable molecular generation characteristics, with novelty reaching 100%, diversity up to 0.882, and uniqueness up to 1.0, suggesting that retrieval did not reduce the process to simple reproduction of known ligands but instead supported exploration of new regions of chemical space. In addition, the approach yielded supportive docking results consistent with the generation of chemically relevant candidate ligands. However, the increase in predicted affinity was accompanied by trade-offs in molecular quality, including reduced validity and drug-likeness in later iterations. Taken together, these findings suggest that this framework provides a flexible and comparatively resource-efficient strategy for target-aware de novo molecular design, while further multi-objective optimization and independent validation remain necessary. + © 2026. The Author(s). + + + + Karabowicz + Piotr + P + 0000-0002-8072-2104 + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. piotr.karabowicz@umb.edu.pl. + + + + Charkiewicz + Radosław + R + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. + + + Center of Experimental Medicine, Medical University of Bialystok, 15-369, Bialystok, Poland. + + + + Charkiewicz + Alicja + A + + Department of Analysis and Bioanalysis of Medicines, Medical University of Bialystok, 15-089, Bialystok, Poland. + + + + Nikliński + Jacek + J + + Department of Clinical Molecular Biology, Medical University of Bialystok, 15-269, Bialystok, Poland. + + + + eng + + + B.SUB.26.512 + Uniwersytet Medyczny w Bialymstoku + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + J Comput Aided Mol Des + 8710425 + 0920-654X + + + + 0 + Ligands + + + 0 + Proteins + + + 0 + Small Molecule Libraries + + + IM + + + Large Language Models + + + Ligands + + + Proteins + chemistry + + + Drug Discovery + methods + + + Small Molecule Libraries + chemistry + + + Drug Design + + + Humans + + + + Drug discovery + Large language models + Multi-turn memory + Retrieval-augmented generation + SMILES generation + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 1 + 8 + + + 2026 + 5 + 9 + + + 2026 + 5 + 29 + 11 + 15 + + + epublish + + 42213198 + 10.1007/s10822-026-00834-1 + 10.1007/s10822-026-00834-1 + + + + Skinnider MA (2024) Invalid SMILES are beneficial rather than detrimental to chemical language models. Nat Mach Intell 6:437–448. https://doi.org/10.1038/s42256-024-00821-x + + 10.1038/s42256-024-00821-x + + + + Zheng Y, Koh HY, Ju J et al (2025) Large language models for drug discovery and development. Patterns 6:101346. https://doi.org/10.1016/j.patter.2025.101346 + + 10.1016/j.patter.2025.101346 + 41142906 + 12546459 + + + + Mienye ID, Jere N, Obaido G et al (2025) Large language models: an overview of foundational architectures, recent trends, and a new taxonomy. Discov Appl Sci 7:1027. https://doi.org/10.1007/s42452-025-07668-w + + 10.1007/s42452-025-07668-w + + + + Umer MS, Nabeel M, Athar U et al (2025) Large language models meet molecules: a systematic review of advances and challenges in AI-driven cheminformatics. Arch Computat Methods Eng. https://doi.org/10.1007/s11831-025-10437-y + + 10.1007/s11831-025-10437-y + + + + Bagal V, Aggarwal R, Vinod PK, Priyakumar UD (2022) MolGPT: molecular generation using a transformer-decoder model. J Chem Inf Model 62:2064–2076. https://doi.org/10.1021/acs.jcim.1c00600 + + 10.1021/acs.jcim.1c00600 + 34694798 + + + + Sheikholeslami M, Mazrouei N, Gheisari Y et al (2025) DrugGen enhances drug discovery with large language models and reinforcement learning. Sci Rep 15:13445. https://doi.org/10.1038/s41598-025-98629-1 + + 10.1038/s41598-025-98629-1 + 40251288 + 12008224 + + + + Song B, Li X, Chao X et al (2025) Advancements in large language models ( LLMS ): empowering drug discovery. WIREs Comput Mol Sci 15:e70054. https://doi.org/10.1002/wcms.70054 + + 10.1002/wcms.70054 + + + + Ma J, Liu J, Xu D et al (2025) Application and prospects of large language models in small-molecule drug discovery. Anal Chem 97:27453–27477. https://doi.org/10.1021/acs.analchem.5c04083 + + 10.1021/acs.analchem.5c04083 + 41366732 + 12750412 + + + + Öztürk H, Özgür A, Ozkirimli E (2018) DeepDTA: deep drug–target binding affinity prediction. Bioinformatics 34:i821–i829. https://doi.org/10.1093/bioinformatics/bty593 + + 10.1093/bioinformatics/bty593 + 30423097 + 6129291 + + + + Mswahili ME, Jeong Y-S (2024) Transformer-based models for chemical SMILES representation: a comprehensive literature review. Heliyon 10:e39038. https://doi.org/10.1016/j.heliyon.2024.e39038 + + 10.1016/j.heliyon.2024.e39038 + 39640612 + 11620068 + + + + Gao Y, Xiong Y, Gao X, et al (2023) Retrieval-augmented generation for large language models: a survey + + + Zhang P, Peng X, Han R et al (2025) Rag2Mol: structure-based drug design based on retrieval augmented generation. Briefings Bioinform 26:bbaf265. https://doi.org/10.1093/bib/bbaf265 + + 10.1093/bib/bbaf265 + + + + Hu Y, Wang Y, McAuley J (2025) Evaluating memory in LLM agents via incremental multi-turn interactions + + + OpenAI, Agarwal S, Ahmad L, et al (2025) gpt-oss-120b &amp; gpt-oss-20b Model Card + + + Grattafiori A, Dubey A, Jauhri A, et al (2024) The Llama 3 Herd of Models + + + Liu T, Hwang L, Burley SK et al (2025) BindingDB in 2024: a FAIR knowledgebase of protein-small molecule binding data. Nucleic Acids Res 53:D1633–D1644. https://doi.org/10.1093/nar/gkae1075 + + 10.1093/nar/gkae1075 + 39574417 + 11701568 + + + + Huang K, Fu T, Glass LM et al (2021) DeepPurpose: a deep learning library for drug–target interaction prediction. Bioinformatics 36:5545–5547. https://doi.org/10.1093/bioinformatics/btaa1005 + + 10.1093/bioinformatics/btaa1005 + 33275143 + 8016467 + + + + Eberhardt J, Santos-Martins D, Tillack AF, Forli S (2021) AutoDock Vina 1.2.0: new docking methods, expanded force field, and Python bindings. J Chem Inf Model 61:3891–3898. https://doi.org/10.1021/acs.jcim.1c00203 + + 10.1021/acs.jcim.1c00203 + 34278794 + 10683950 + + + + Fleming J, Magana P, Nair S et al (2025) AlphaFold Protein Structure Database and 3D-Beacons: new data and capabilities. J Mol Biol 437:168967. https://doi.org/10.1016/j.jmb.2025.168967 + + 10.1016/j.jmb.2025.168967 + 40133787 + + + + Laban P, Hayashi H, Zhou Y, Neville J (2025) LLMs Get Lost In Multi-Turn Conversation + + + + + + + 42213197 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1438-7948 + + 26 + 1 + + 2026 + May + 29 + + + Functional & integrative genomics + Funct Integr Genomics + + An integrated computational-experimental approach identifies Malate Synthase G as therapeutic target to disrupt carbon metabolism in chronic Pseudomonas aeruginosa infection. + 116 + 10.1007/s10142-026-01889-x + + Pseudomonas aeruginosa is a leading cause of nosocomial infections, particularly in individuals with a compromised immune system. Due to its strong adaptive ability, P. aeruginosa tends to develop antibiotic resistance and establish chronic infection, making its eradication through traditional antibiotics challenging. Thus, the development of novel therapeutic targets and corresponding inhibitors is urgently required. We conducted bioinformatics analyses of gene-chip datasets (GSE10362, GSE21966) of P. aeruginosa from patients with cystic fibrosis (CF) in the Gene Expression Omnibus (GEO) database, ranging from early to late stages of infection. Differentially expressed gene (DEG) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment, and Weighted Gene Co-expression Network Analysis (WGCNA) were performed to identify key functional DEGs. Among these genes, candidate genes were refined using three machine learning algorithms: LASSO regression, random forest (RF), and support vector machine (SVM). Using this integrated approach and published reports, target gene was identified consequently. With reported inhibitors, a structure-based virtual screening model against the target protein was established to screen inhibitors from an FDA-approved drug library. Screened hits were experimentally validated through in vitro MIC and checkerboard broth microdilution assays to determine their antibacterial activity and the synergistic effect of tobramycin (TOB) and amikacin (AMK). Enzyme activity was measured to evaluate target protein inhibition. Transcriptome sequencing analysis was performed to explain the effect of target inhibition and reveal the possible mechanism of inhibition. We identified 210 upregulated genes in the late stage of infection. KEGG functional enrichment demonstrated that the up-regulated genes were mainly enriched in metabolic pathways. Eight metabolism-related key DEGs were obtained by the intersection of genes from metabolic pathways and genes from the key module of WGCNA. Machine learning algorithms (LASSO, RF, and SVM) and literature investigation results identified glcB, which encodes malate synthase G (MS), as a therapeutic target for developing inhibitors. In virtual screening, we formulated four rules for a screening model based on the docking results, and finally identified three FDA-approved drugs prednisolone, dienogest and carbinoxamine maleate salt (CAR) as candidates. In vitro MIC assay and checkerboard assay suggested that CAR had an antibacterial effect with a concentration-dependent trend and could enhance the effect of tobramycin and amikacin. Consistent with MIC assay, the inhibition rate in MS activity assay showed a concentration-dependent trend. Transcriptome sequencing analysis showed that carbon metabolism was remodeled when MS was inhibited, thus impairing the growth of P. aeruginosa, and the synergistic effect with TOB and AMK may be explained by the impaired ability to combat oxidative stress. This study identified and validated drug target glcB (encodes MS) and potential inhibitor CAR in treating chronic P. aeruginosa infection based on an integrated method of bioinformatics, virtual screening, and in vitro experiments. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Chen + Geping + G + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Tan + Qiutong + Q + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. + + + + Zhou + Haochuan + H + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. + + + + Wang + Rui + R + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Mo + Jingyi + J + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. + + + + Xu + Jun + J + + College of Pharmacy, Jinan University, Guangzhou, 510632, China. xujun@jnu.edu.cn. + + + + Chen + Wenying + W + + Department of Pharmacy, The Third Affiliated Hospital, Southern Medical University, Guangzhou, 510630, China. chenwenying2016@163.com. + + + + eng + + + CPA: 2023ZYS09 + Guangdong Pharmaceutical Association + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Funct Integr Genomics + 100939343 + 1438-793X + + + + EC 2.3.3.9 + Malate Synthase + + + 0 + Anti-Bacterial Agents + + + 7440-44-0 + Carbon + + + 0 + Bacterial Proteins + + + IM + + + Pseudomonas aeruginosa + drug effects + genetics + pathogenicity + metabolism + + + Humans + + + Malate Synthase + antagonists & inhibitors + genetics + metabolism + chemistry + + + Pseudomonas Infections + drug therapy + genetics + microbiology + metabolism + + + Anti-Bacterial Agents + pharmacology + + + Carbon + metabolism + + + Bacterial Proteins + genetics + metabolism + antagonists & inhibitors + + + Cystic Fibrosis + microbiology + + + Computational Biology + + + Molecular Docking Simulation + + + + Pseudomonas aeruginosa + Bioinformatics + Carbon metabolism + Malate synthase + Virtual screening + + Declarations. 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Crit Rev Microbiol 51:841–859. https://doi.org/10.1080/1040841X.2024.2429599 + + 10.1080/1040841X.2024.2429599 + 39556143 + + + + + + + + 42213195 + + 2026 + 05 + 29 + +
+ + 1432-0584 + + + 2026 + May + 29 + + + Annals of hematology + Ann Hematol + + A macrophage-related efferocytosis-based two-gene prognostic model for acute myeloid leukemia identified by multi-omics and machine learning. + 10.1007/s00277-026-07044-7 + + Background Acute myeloid leukemia (AML) remains a lethal hematologic malignancy with high heterogeneity. Macrophage-mediated efferocytosis in the tumor microenvironment is implicated in immune suppression and disease progression. Methods We integrated single-cell and bulk transcriptomic data from public cohorts to identify genes associated with macrophages and efferocytosis in AML. Candidate genes were screened for prognosis using univariate Cox regression. A comprehensive machine learning framework, evaluating 117 algorithm combinations, was employed to construct a robust prognostic model. The optimal LASSO and random survival forest approach identified CD52 and S100A4 as core prognostic genes. The resulting two-gene model was rigorously validated using Kaplan-Meier analysis, time-dependent ROC curves, and calibration plots across multiple independent cohorts. The associations of the risk score with the immune microenvironment and drug sensitivity were further analyzed. SHapley Additive exPlanations (SHAP) analysis was applied to interpret the model's decision-making. Results The two-gene signature demonstrated stable and powerful predictive performance for overall survival in both training and external validation sets. The risk score was an independent prognostic factor and showed significant correlations with immune cell infiltration patterns and response to chemotherapeutic agents. SHAP analysis confirmed the consistent and biologically plausible contributions of CD52 and S100A4. Single-cell resolution analysis revealed their specific enrichment in distinct AML-associated macrophage subpopulations. Conclusions We developed a novel macrophage efferocytosis-based prognostic model using a multi-omics and machine learning approach. This model provides valuable insights into the immune microenvironment of AML and offers a potential tool for risk stratification and therapeutic guidance. + © 2026. The Author(s). + + + + Li + Xiaoning + X + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Zhang + Yujie + Y + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Wei + Xiaoying + X + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. + + + + Huang + Rui + R + + Department of Hematology, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, China. rachelchn@163.com. + + + Guangdong Engineering Research Center of Precision Immune Cell Therapy Technology, Guangzhou, Guangdong, China. rachelchn@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Germany + Ann Hematol + 9107334 + 0939-5555 + + IM + + Acute myeloid leukemia + Biomarkers + Efferocytosis + Machine learning + Prognostic model + + Declarations. Consent for publication: All authors have read and approved the final version of the manuscript and agree to the publication of this research in the journal. Ethics and consent to participate declarations: Not applicable.  Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 12 + 2 + + + 2026 + 4 + 26 + + + 2026 + 5 + 29 + 11 + 15 + + + aheadofprint + + 42213195 + 10.1007/s00277-026-07044-7 + 10.1007/s00277-026-07044-7 + + + + + + 42213145 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2561-326X + + 10 + + 2026 + May + 29 + + + JMIR formative research + JMIR Form Res + + Machine Learning-Based Prediction Model for 30-Day Emergency Department Revisits in a Medically Underserved Tertiary Hospital: Formative Retrospective Cohort Study. + + e87289 + e87289 + + 10.2196/87289 + + Emergency department (ED) revisits are critical quality indicators, particularly in medically underserved areas, where traditional prediction tools show limited performance. Machine learning (ML) approaches may offer improved predictive performance for identifying high-risk patients. + This formative study aimed to develop and validate an ML-based model for predicting 30-day ED revisits using electronic health records from a tertiary hospital serving a medically underserved area in South Korea and to evaluate its clinical utility through interpretability analysis and risk stratification. + This retrospective cohort study analyzed 36,230 adult patients visiting the Gangneung Asan Hospital ED in 2023. We developed and compared 3 ML models (extreme gradient boosting [XGBoost], random forest, and ElasticNet) using electronic health records. Model interpretability was ensured through Shapley additive explanations (SHAP) analysis, and clinical utility was evaluated through 5-tier risk stratification. + Among 36,230 patients, 798 (2.2%) revisited within 30 days. XGBoost achieved superior performance with an area under the receiver operating characteristic curve of 0.90 (95% CI 0.88-0.92), a sensitivity of 0.94, and a specificity of 0.69. The SHAP analysis identified ED length of stay, oxygen saturation, systolic blood pressure, computed tomography performance, antibiotic use, and liver disease as key predictors. Risk stratification demonstrated a 25-fold difference in the actual revisit rates between the lowest (152/8450, 1.8%) and the highest (686/1500, 45.7%) risk groups. + The XGBoost model demonstrated excellent predictive performance with high interpretability for 30-day ED revisit predictions. The implementation of this model could enable risk-stratified interventions and more efficient resource allocation in medically underserved settings, potentially reducing unnecessary revisits and improving patient outcomes. This formative study establishes feasibility and provides a foundation for future multicenter validation studies in similar medically underserved settings. + © Kyongmin Sun. Originally published in JMIR Formative Research (https://formative.jmir.org). + + + + Sun + Kyongmin + K + 0000-0002-2977-0880 + + Department of Emergency Medicine, College of Medicine, Gangneung Asan Hospital, Gangneung, Gangwon-do, Republic of Korea. + + + Department of Health Administration, Graduate School, Yonsei University Mirae Campus, 1 Yonsedae-gil, Heungeop-myeon, Wonju, Gangwon-do, 26493, Republic of Korea, 82 337602418. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Canada + JMIR Form Res + 101726394 + 2561-326X + + IM + + + Retrospective Studies + + + Humans + + + Tertiary Care Centers + statistics & numerical data + organization & administration + + + Emergency Room Visits + statistics & numerical data + + + Emergency Service, Hospital + statistics & numerical data + organization & administration + + + Machine Learning + + + Republic of Korea + + + Medically Underserved Area + + + Female + + + Predictive Learning Models + + + Male + + + Middle Aged + + + Adult + + + Aged + + + Boosting Machine Learning Algorithms + + + Cohort Studies + + + + SHAP + Shapley additive explanations + XGBoost + emergency department + extreme gradient boosting + machine learning + medically underserved area + prediction model + revisit + + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2025 + 11 + 6 + + + 2026 + 4 + 9 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 11 + 13 + + + epublish + + 42213145 + 10.2196/87289 + v10i1e87289 + + + + + + 42213103 + + 2026 + 05 + 29 + +
+ + 1432-5241 + + + 2026 + May + 29 + + + Aesthetic plastic surgery + Aesthetic Plast Surg + + Does Patient History Influence Capsular Contracture? An Exploratory Analysis with Machine Learning. + 10.1007/s00266-026-05919-8 + + Capsular contracture (CC) is a frequent and distressing complication of breast augmentation and reconstruction. Although numerous patient-, surgical-, and implant-related risk factors have been proposed, reliable population-level predictors remain inconsistent across studies. This study evaluates whether administrative medical history, as encoded by ICD and CPT codes, contains sufficient predictive signal to identify patients at risk for CC using machine learning. + Patients were queried from the MerativeTM MarketScan® Research Databases from 2003 to 2017 with CPT codes for implant-based breast reconstruction and augmentation. ICD codes were then used to identify all events and conditions of a patient's history. Hyperparameter-tuned random forest models were combined with multivariable logistic regression models to validate risk factors for the development of CC. + A total of 112,489 patients were included, and the rate of capsular contracture was 9.55%. In total, 4,825 common conditions and procedures were included as features in the model. The random forest's error rate was 9.54%. Prior CC was the most important variable in the model. When removed, model accuracy decreased by 0.0011% (p <0.001). Removing prior irradiation as a feature decreased model accuracy by 0.00014% (p < 0.001). In line with the previous literature, prior CC (OR = 2.47, p <0.001) and irradiation (OR = 1.16, p <0.001) were significantly associated with CC development in a multivariable logistic regression model. + Administrative medical history alone demonstrated limited predictive utility for CC development. Although established associations such as prior CC and irradiation were confirmed, their incremental contribution to predictive performance was negligible. These associations do not meaningfully enhance a clinician's ability to reliably predict which patients are at increased risk for CC. + This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 . + © 2026. The Author(s). + + + + Johnstone + Thomas M + TM + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Najafali + Daniel + D + 0000-0003-1509-9229 + + Carle Illinois College of Medicine, University of Illinois Urbana-Champaign, Urbana, IL, USA. danieln6@illinois.edu. + + + + Shah + Jennifer K + JK + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Camacho + Justin M + JM + + Drexel University College of Medicine, Philadelphia, PA, USA. + + + + Johnstone + Chancellor + C + + Air Force Institute of Technology, Wright-Patterson AFB, Dayton, OH, USA. + + + + Nazerali + Rahim S + RS + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. + + + + Lee + Gordon K + GK + + Division of Plastic and Reconstructive Surgery, Stanford University School of Medicine, Stanford, CA, USA. gordokl1@hs.uci.edu. + + + Department of Plastic Surgery, University of California, Irvine, Orange, CA, USA. gordokl1@hs.uci.edu. + + + + eng + + + UL1TR003142 + TR + NCATS NIH HHS + United States + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Aesthetic Plast Surg + 7701756 + 0364-216X + + IM + + Alloplastic reconstruction + Artificial intelligence + Breast + Breast implants + Breast reconstruction + Capsular contracture + Complications + Contracture formation + Implant + Implant capsular contracture + Implant-based reconstruction + Machine learning + Mammaplasty + Outcomes prediction + Postoperative complications + Supervised machine learning + + Declarations. Conflict of interest: The authors declare that they have no conflicts of interest to disclose. Human and Animal Rights: Statement of human and animal rights, or ethical approval: This article does not contain any studies with human participants or animals performed by any of the authors. Data for this project were accessed using the Stanford Center for Population Health Sciences Data Core. The PHS Data Core is supported by a National Institutes of Health National Center for Advancing Translational Science Clinical and Translational Science Award (UL1TR003142) and from Internal Stanford funding. The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH. Informed Consent: For this type of study, informed consent is not required. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 1 + 24 + + + 2026 + 4 + 17 + + + 2026 + 5 + 29 + 11 + 12 + + + aheadofprint + + 42213103 + 10.1007/s00266-026-05919-8 + 10.1007/s00266-026-05919-8 + + + + ISAPS international survey on aesthetic/cosmetic procedures performed in 2019. 2020. https://www.isaps.org/discover/about-isaps/global-statistics/global-survey-2019-full-report-and-press-releases-english/ . Accessed 2024. + + + Pasca A, Bonci EA, Chiuzan C, et al. 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+ + 1613-6829 + + + 2026 + May + 29 + + + Small (Weinheim an der Bergstrasse, Germany) + Small + + Harnessing and Suppressing Electron Spin-State Transitions: From Decoding to Rational Design of High-Performance Cathodes for Alkali-Ion Batteries. + + e73936 + e73936 + + 10.1002/smll.73936 + + The electron spin state of transition metal ions represents a fundamental quantum property that is increasingly recognized as a pivotal design dimension for tuning the performance of cathode materials in Li/Na/K‑ion batteries. This review begins by consolidating the foundational principles through which spin states govern electrochemical properties, establishing a robust theoretical framework that bridges atomic-scale coordination environments with macroscopic electrode behavior. It further discusses advanced experimental and computational techniques for probing complex spin states and, critically, for establishing clear structure-spin-performance relationships. A central focus is placed on the rational design of spin configurations, whether via proactive engineering or suppression of unfavorable transitions, to optimize key electrochemical processes: modulating cationic vs. anionic redox competition, enhancing structural stability by mitigating Jahn-Teller distortions and magnetic frustration, and improving charge and ion transport. The review also highlights the emerging role of spin‑sensitive machine learning as an accelerated pathway for discovering superior cathode materials. By integrating theoretical insights, methodological advances, and application‑oriented studies, this work provides a comprehensive mechanistic framework and practical guidelines for the design of next‑generation high‑performance cathodes through deliberate spin‑state control. + © 2026 Wiley‐VCH GmbH. + + + + Peng + Tao + T + 0000-0002-0234-0477 + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Ma + Mengzhen + M + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Liu + Shaonan + S + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + He + Junbao + J + + Henan International Joint Laboratory of MXene Materials Microstructure, Collaborative Innovation Center of Intelligent Explosion-Proof Equipment of Henan Province, College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang, China. + + + + Wang + Yuanyuan + Y + + College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. + + + + Yang + Ya + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Lu + Yang + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + + Luo + Yongsong + Y + + Key Laboratory of Microelectronics and Energy of Henan Province, School of Physics and Electronic Engineering, Xinyang Normal University, Xinyang, China. + + + Henan International Joint Laboratory of MXene Materials Microstructure, Collaborative Innovation Center of Intelligent Explosion-Proof Equipment of Henan Province, College of Physics and Electronic Engineering, Nanyang Normal University, Nanyang, China. + + + + Sun + Weiwei + W + 0000-0003-4079-4063 + + College of Aerospace Science and Engineering, National University of Defense Technology, Changsha, China. + + + + eng + + + 52272219 + National Natural Science Foundation of China + + + + 52472254 + National Natural Science Foundation of China + + + + 262300421381 + Natural Science Foundation of Henan Province + + + + 242300421191 + Natural Science Foundation of Henan Province + + + + 24ZDYF016 + Key Research and Development Program of Nanyang City + + + + Nanhu Scholars Program for Young Scholars of Xinyang Normal University + + + + 2026JJ50116 + the Natural Science Foundation of Hunan Province + + + + 2026JJ50116 + the Natural Science Foundation of Hunan Province + + + + + Journal Article + Review + + + 2026 + 05 + 29 + +
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+ + 1876-7591 + + + 2026 + May + 28 + + + JACC. Cardiovascular imaging + JACC Cardiovasc Imaging + + Machine Learning Model Using Pre-Cancer Therapy Cardiac Magnetic Resonance Images to Predict Cancer Therapy-Related Cardiac Dysfunction. + S1936-878X(26)00229-9 + 10.1016/j.jcmg.2026.04.007 + + Predicting risk of cancer therapy-related cardiac dysfunction (CTRCD) remains challenging. + The purpose of this study was to assess if deep learning (DL) approaches using cardiac magnetic resonance (CMR) images before cancer therapy can predict subsequent CTRCD and compare them with clinical and conventional imaging models. + Women with HER2+ breast cancer receiving anthracyclines and trastuzumab from 3 prospective studies (Toronto, Canada: EMBRACE-MRI, SPARE-HF; and Calgary, Canada: CIROC) were included. Patients were assessed before cancer therapy, after anthracycline, and trimonthly with repeated echocardiography and CMR. CTRCD was defined according to CMR. We calculated the HFA-ICOS risk score and measured CMR and echocardiography volumetric and functional parameters. Deep convolutional neural network architectures were used with pre-cancer therapy CMR short-axis cine images to develop image-based DL models to predict CTRCD. The Toronto patients were used for model derivation and internal validation; the Calgary patients were used for external validation. + A total of 229 patients (mean age 50.4 ± 9.7 years) were included: 176 in the internal (52 CTRCD events) development cohort and 53 in the external data set (14 CTRCD events). Our pre-cancer therapy CMR DL model demonstrated an AUC of 0.85 (95% CI: 0.69-0.97) and F1 score 0.69 (95% CI: 0.47-0.86) to predict CTRCD. On external validation, the DL model had an AUC of 0.80 (95% CI: 0.58-0.86) and F1 score of 0.55 (95% CI: 0.32-0.69). In comparison, the best-performing models using HFA-ICOS risk score, CMR, and echocardiographic parameters demonstrated, respectively, AUCs of 0.66 (95% CI: 0.53-0.75), 0.59 (95% CI: 0.41-0.73), 0.62 (95% CI: 0.44-0.78) and F1 scores of 0.56 (95% CI: 0.47-0.61), 0.20 (95% CI: 0.00-0.42) and 0.36 (95% CI: 0.12-0.62) to predict CTRCD. + A DL model using pre-cancer therapy CMR short-axis cines can predict future CTRCD risk better than clinical or manually quantified imaging models. + Copyright © 2026 American College of Cardiology Foundation. Published by Elsevier Inc. All rights reserved. + + + + Yu + Christopher + C + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + Peikari + Mohammad + M + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + Labib + Dina + D + + Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada. + + + + Houbois + Christian P + CP + + Sunnybrook Health Science Centre, University of Toronto, Toronto, Ontario, Canada. + + + + Steve Fan + Chun-Po + CP + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + White + James A + JA + + Stephenson Cardiac Imaging Centre, Libin Cardiovascular Institute of Alberta, University of Calgary, Alberta, Canada. + + + + Amir + Eitan + E + + Princess Margaret Cancer Centre, University of Toronto, Toronto, Ontario, Canada. + + + + Hanneman + Kate + K + + Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. + + + + J Wintersperger + Bernd + B + + Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Department of Radiology, LMU University Hospital, LMU Munich, Munich, Germany. + + + + Abdel-Qadir + Husam + H + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Women's College Hospital, Toronto, Ontario, Canada. + + + + McIntosh + Chris + C + + Ted Rogers Computational Program, Ted Rogers Centre for Heart Research, Peter Munk Cardiac Centre, University Health Network, University of Toronto, Toronto, Ontario, Canada; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, Toronto, Ontario, Canada. + + + + Thavendiranathan + Paaladinesh + P + + Department of Medicine, Division of Cardiology, Ted Rogers Program in Cardiotoxicity Prevention, Peter Munk Cardiac Centre, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada; Joint Department of Medical Imaging, Toronto General Hospital, University Health Network, University of Toronto, Toronto, Ontario, Canada. Electronic address: dinesh.thavendiranathan@uhn.ca. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + JACC Cardiovasc Imaging + 101467978 + 1876-7591 + + IM + + cardio-oncology + cardiotoxicity cancer therapy–related cardiac dysfunction + heart failure + machine learning + + Funding Support and Author Disclosures This study was funded by operating grants 137132, 142456, and 147814 from the Canadian Institutes of Health Research, an Ontario Early Research Award, a Grant Miller Research grant from the University of Toronto, and AHSC AFP Innovation Funds. Dr Thavendiranathan (147814) is supported by a Canada Research Chair in Cardiooncology (CRC-2019-00097) and the Canadian Cancer Society/Canadian Institutes of Health Research W. David Hargraft Grant. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 10 + 2 + + + 2026 + 4 + 7 + + + 2026 + 4 + 10 + + + 2026 + 5 + 29 + 10 + 31 + + + aheadofprint + + 42212994 + 10.1016/j.jcmg.2026.04.007 + S1936-878X(26)00229-9 + + + + + + 42212964 + + 2026 + 05 + 29 + +
+ + 2198-3844 + + + 2026 + May + 29 + + + Advanced science (Weinheim, Baden-Wurttemberg, Germany) + Adv Sci (Weinh) + + Machine Learning Accelerated Non-Adiabatic Molecular Dynamics Elucidates Local Polarization Effects on Non-radiative Recombination in Halide Perovskites. + + e75903 + e75903 + + 10.1002/advs.75903 + + Non-radiative recombination is a critical factor limiting the optoelectronic performance of halide perovskites, yet how local polarization induced by charge redistribution regulates this process remains unclear. To gain deeper insight while reducing the high computational cost of conventional non-adiabatic molecular dynamics (NAMD), we developed Hefei-NAMD-S, a machine learning (ML) framework constructed using stacked models. The relative errors of the ML predicted non-adiabatic coupling and pure-dephasing time, calculated with respect to the first-principles values, remain below 1.10%, while the total computational time is reduced by approximately 78%, demonstrating the accuracy and efficiency of the proposed framework. NAMD simulations further reveal that the B-site local polarization is involved in regulating the non-radiative recombination process. The rubidium-substitution-doped system (FARb) and the cesium interstitial doped system exhibit recombination times of about 280 ns, nearly 2.8 times that of the pristine system, and the enhanced performance of FARb has been supported by previous experimental evidence. These results identify B-site local polarization as one of the important factors in suppressing non-radiative recombination and provide a theoretical foundation for designing perovskite materials through local polarization modulation. + © 2026 The Author(s). Advanced Science published by Wiley‐VCH GmbH. + + + + Yang + Bing + B + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Wei + Xiaoli + X + + School of Biological Science and Medical Engineering, Southeast University, Nanjing, China. + + + + Cai + Bo + B + 0000-0002-6605-5931 + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. + + + + Yang + Yan + Y + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Zhu + Xinghai + X + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Liu + Yi + Y + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Xia + Junmin + J + 0000-0002-9805-5397 + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Liu + Lihui + L + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Cao + Kun + K + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Shen + Wei + W + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Xia + Pengfei + P + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Chen + Shufen + S + 0000-0003-0333-958X + + State Key Laboratory of Flexible Electronics (LoFE) & Institute of Advanced Materials (IAM), Nanjing University of Posts & Telecommunications, Nanjing, China. + + + + Chen + Siyu + S + + Department of Materials Science and Metallurgy, University of Cambridge, Cambridge, UK. + + + TCM Group, Cavendish Laboratory, University of Cambridge, Cambridge, UK. + + + + Zhao + Jin + J + + Department of Physics and ICQD/Hefei National Research Center for Physical Sciences at the Microscale, University of Science and Technology of China, Hefei, China. + + + Hefei National Laboratory, University of Science and Technology of China, Hefei, China. + + + Department of Physics and Astronomy, University of Pittsburgh, Pittsburgh, USA. + + + + eng + + + 62404107 + National Natural Science Foundation of China + + + + 62304111 + National Natural Science Foundation of China + + + + 62474097 + National Natural Science Foundation of China + + + + BK20220396 + Natural Science Foundation of Jiangsu Province + + + + GDX2022010009 + Project of the State Key Laboratory of Flexible Electronics + + + + GZR2023010041 + Project of the State Key Laboratory of Flexible Electronics + + + + BZ2023059 + Science and Technology Cooperation Project of Hong Kong, Macao, and Taiwan + + + + 20KJA510005 + Natural Science Fund for Colleges and Universities in Jiangsu Province + + + + NY223076 + Natural Science Foundation of Nanjing University of Posts and Telecommunications + + + + NY222069 + Natural Science Research Start-up Foundation of Recruiting Talents of Nanjing University of Posts and Telecommunications + + + + CSC202308320240 + China Scholarship Council + + + + KYCX23_1091 + Ministry of Education, as well as the Postgraduate Research & Practice Innovation Program of Jiangsu Province + + + + + Journal Article + + + 2026 + 05 + 29 + +
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+ + 1746-0921 + + + 2026 + May + 29 + + + Future microbiology + Future Microbiol + + AI-microbial hybrid biosensors: the next generation of intelligent detection systems. + + 1 + 21 + 1-21 + + 10.1080/17460913.2026.2678101 + + The convergence of artificial intelligence (AI) and microbial biosensor technology is transforming pathogen detection, environmental surveillance, antimicrobial resistance (AMR) profiling, and precision diagnostics. Microbial biosensors exploit the specificity of living microorganisms, but signal variability, scalability limits, and interpretive challenges have constrained clinical adoption. Integration of machine learning (ML) and deep neural networks (DNNs) now enables adaptive, high-performance sensing systems. Applied to multi-sensor datasets-such as electrochemical impedance, Raman spectroscopy, and hyperspectral microscopy-convolutional neural networks (CNNs) and ensemble models achieve bacterial classification accuracies of 95-99%, while markedly reducing diagnostic turnaround times and enabling continuous surveillance. Despite rapid progress, the field remains fragmented, lacking a unified synthesis of system architectures, computational strategies, translational barriers, and regulatory considerations. This narrative review provides an integrative analysis of AI-microbial hybrid biosensors, covering biorecognition principles, AI integration approaches, system designs, clinical and environmental applications, performance metrics, and key challenges. It also highlights emerging directions, including synthetic biology, CRISPR-enabled sensing, and edge computing. By consolidating these dimensions, this review positions AI-microbial hybrid biosensors as a next-generation platform for real-time pathogen detection and adaptive biosurveillance. Literature was identified through systematic searches of Google Scholar, PubMed, Web of Science, Scopus, and IEEE Xplore (2000-2026), supplemented by manual reference screening. + + + + Hama Soor + Taib A + TA + + Medical Laboratory Department, Technical College of Health and Medical Technology, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq. + + + + Mustafa + Ayman M + AM + 0009-0005-4254-4182 + + Medical Laboratory Department, Technical College of Health and Medical Technology, Sulaimani Polytechnic University, Sulaymaniyah, Kurdistan Region, Iraq. + + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + Medical Laboratory Science Department, College of Health Sciences, University of Human Development, Sulaymaniyah, Kurdistan Region, Iraq. + + + Kscien Organization for Scientific Research (Middle East Office), Sulaymaniyah, Kurdistan Region, Iraq. + + + + Abdulkarim + Meer M + MM + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + + Abdullah + Hiwa O + HO + + Scientific Affairs Department, Smart Health Tower, Sulaymaniyah, Kurdistan Region, Iraq. + + + Kscien Organization for Scientific Research (Middle East Office), Sulaymaniyah, Kurdistan Region, Iraq. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + England + Future Microbiol + 101278120 + 1746-0913 + + IM + + Artificial intelligence + antimicrobial resistance + deep learning + machine learning + microbial biosensors + pathogen detection + synthetic biology + + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 10 + 3 + + + aheadofprint + + 42212947 + 10.1080/17460913.2026.2678101 + + + + + + 42212939 + + 2026 + 05 + 29 + +
+ + 1520-4804 + + + 2026 + May + 29 + + + Journal of medicinal chemistry + J Med Chem + + Developing Predictive Models by Sharing Predictions - An Investigation of a Federated Learning Approach for ADMET Predictions. + 10.1021/acs.jmedchem.5c03681 + + Machine learning models for ADMET prediction benefit from large, diverse data sets, yet such data are typically siloed across organizations. Federated learning (FL) enables collaborative modeling while preserving data privacy. Here, we investigate a student-teacher model (STM) framework in which organizations train internal models on proprietary data and share predictions on a public data set to generate pseudolabels for a centralized student model. As a proof of concept, 11 pharmaceutical companies contributed predictions for rat steady-state volume of distribution, yielding a pseudolabeled data set of ∼133,000 compounds. The resulting student model achieved performance comparable to individual teacher models on an external test set (RMSE ≈ 0.51 vs 0.47-0.61). Compared with FL approaches such as MELLODY and Effiris, STM offers a simpler workflow that avoids direct data sharing or iterative collaboration, providing a practical and scalable framework for secure cross-company model development. + + + + Guha + Rajarshi + R + 0000-0001-7403-8819 + + Vertex Pharmaceuticals, 50 Northern Avenue, Boston, Massachusetts 02129, United States. + + + + Wang + Wenyi + W + + Drug Metabolism & Pharmacokinetics (DMPK), Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States. + + + + Price + Edward + E + 0000-0002-6337-2288 + + Quantitative, Translational & ADME Sciences, AbbVie Inc., North Chicago, Illinois 60064, United States. + + + + Bhhatarai + Barun + B + + Novartis Institutes for BioMedical Research, Cambridge, Massachusetts 02139, United States. + + + + Hassan + Majdi + M + + Quantitative, Translational & ADME Sciences, AbbVie Inc., North Chicago, Illinois 60064, United States. + + + + DiFranzo + Anthony + A + 0000-0002-3151-9150 + + Medical Analytics Center, Merck & Co., Inc., Rahway, New Jersey 07065, United States. + + + + Keefer + Christopher + C + + Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States. + + + + Woody + Nathaniel + N + + Translational Modeling and Simulation, Pfizer Worldwide Research and Development, Groton, Connecticut 06340, United States. + + + + Broccatelli + Fabio + F + + Drug Metabolism & Pharmacokinetics (DMPK), Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States. + + + + Winiwarter + Susanne + S + 0000-0002-9808-1683 + + Drug Metabolism and Pharmacokinetics, Research and Early Development, Cardiovascular, Renal and Metabolism (CVRM), BioPharmaceuticals R&D, AstraZeneca, Mölndal 431 83, Sweden. + + + + He + Ling + L + + Daiichi Sankyo, Inc, 211 Mt. Airy Road, Basking Ridge, New Jersey 07920, United States. + + + + Green + Darren V S + DVS + + GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K. + + + + Edwards + Christopher D + CD + + GSK Medicines Research Centre, Gunnels Wood Road, Stevenage, Hertfordshire SG1 2NY, U.K. + + + + Kato + Harutoshi + H + + DMPK Research Laboratories, Mitsubishi Tanabe Pharma Corporation, Yokohama, Kanagawa 227-0033, Japan. + + + + Kosugi + Yohei + Y + 0000-0001-9318-4723 + + DMPK & Modeling, Takeda Pharmaceutical Company Limited, 26-1 Muraoka-Higashi, 2-Chome, Fujisawa, Kanagawa 251-8555, Japan. + + + + Desai + Prashant + P + 0000-0002-2493-8218 + + Drug Metabolism & Pharmacokinetics (DMPK), Genentech, Inc., 1 DNA Way, South San Francisco, California 94080, United States. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Med Chem + 9716531 + 0022-2623 + + IM + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 10 + 3 + + + aheadofprint + + 42212939 + 10.1021/acs.jmedchem.5c03681 + + + + + + 42212863 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1438-8871 + + 28 + + 2026 + May + 29 + + + Journal of medical Internet research + J Med Internet Res + + From Virtual Molecules to Clinical Trials: How AI Is Reshaping Preclinical Drug Discovery. + + e101366 + e101366 + + 10.2196/101366 + + Pharmaceutical development is typically a long and arduous process, but advances in artificial intelligence may help streamline each stage of this pipeline. In this News and Perspectives article, JMIR Correspondent Benedette Cuffari reports on recent innovations and their potential implications for drug discovery. + © JMIR Publications. Originally published in the Journal of Medical Internet Research (https://www.jmir.org). + + + + Cuffari + Benedette + B + + + eng + + News + + + 2026 + 05 + 29 + +
+ + Canada + J Med Internet Res + 100959882 + 1438-8871 + + IM + + + Humans + + + Artificial Intelligence + + + Clinical Trials as Topic + + + Drug Design + + + Drug Discovery + methods + + + + artificial intelligence in drug discovery + computational drug discovery + de novo drug design + generative chemistry + machine learning in drug design + + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 14 + + + 2026 + 5 + 14 + + + 2026 + 5 + 29 + 9 + 23 + + + epublish + + 42212863 + 10.2196/101366 + v28i1e101366 + + + + + + 42212723 + + 2026 + 05 + 29 + +
+ + 1520-5010 + + + 2026 + May + 29 + + + Chemical research in toxicology + Chem Res Toxicol + + Democratizing Artificial Intelligence in Toxicology: Real-World Applications and Automated Computational Workflows. + 10.1021/acs.chemrestox.6c00093 + + Artificial intelligence (AI) and machine learning are transforming toxicological research and chemical safety assessment. Although user-friendly computational toxicology platforms are increasingly available, integrating, customizing, and deploying AI methods within end-to-end workflows still often requires programming expertise. This barrier increases the time to adoption of new methods and slows regulatory uptake. To address this limitation, we survey recent initiatives democratizing computational toxicology through no-code/low-code pipelines, automated workflows, and open-source tools. We emphasize solutions for four computational needs: (i) data extraction and access, (ii) data mining and curation, (iii) data analysis and visualization, and (iv) modeling and prediction. These initiatives transform complex computational methods into guided and web-accessible applications that enable toxicologists, regulators, and researchers to leverage AI without coding expertise. The broad applicability of computational methods will be essential for supporting and scaling federal initiatives that advance human-relevant alternatives to animal testing. We also offer practical considerations for domain-specific tool development, including large language model-based information extraction, chemical structure standardization, interactive chemical grouping, and the development of validated machine learning models, as used in the Modeling and Visualization (MoVIZ) pipeline. The authors map the future of computational toxicology and cheminformatics, one that does not require scientists to become programmers but rather makes sophisticated AI tools more broadly accessible, transparent, and guided through thoughtful interface design, transparent workflows, and open science initiatives. + + + + Mansouri + Kamel + K + 0000-0002-6426-8036 + + National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27711, United States. + + + + Moreira-Filho + José Teófilo + JT + 0000-0002-0777-280X + + National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27711, United States. + + + + S Tieghi + Ricardo + R + 0009-0004-0763-6412 + + National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27711, United States. + + + + Kleinstreuer + Nicole + N + 0000-0002-7914-3682 + + National Toxicology Program Interagency Center for Evaluation of Alternative Toxicological Methods (NICEATM), Division of Translational Toxicology, National Institute of Environmental Health Sciences, Research Triangle Park, Durham, North Carolina 27711, United States. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 29 + +
+ + United States + Chem Res Toxicol + 8807448 + 0893-228X + + IM + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 8 + 33 + + + aheadofprint + + 42212723 + 10.1021/acs.chemrestox.6c00093 + + + + + + 42212699 + + 2026 + 05 + 29 + +
+ + 1365-3156 + + + 2026 + May + 29 + + + Tropical medicine & international health : TM & IH + Trop Med Int Health + + Diet, Diarrhoea and Maternal Factors Associated With Wasting in Ghanaian Children Aged 6-23 Months: A Machine Learning Predictive Approach Using the 2022 Demographic and Health Survey. + 10.1111/tmi.70171 + + This study estimates the prevalence of wasting and examines associated factors among children aged 6-23 months in Ghana from a machine-learning perspective, with a particular focus on predicting new cases to inform preventive efforts. + This study is a secondary analysis of the 2022 Ghana Demographic and Health Survey, which includes 1506 children. Wasting status was assessed using the weight-for-height z-score. A broad set of 150 child-, maternal-, household- and environmental-level variables was considered. Factors associated with wasting were first identified using survey-weighted logistic regression, whereas Multiple Correspondence Analysis was applied to explore structural relationships among predictors. Subsequently, an ensemble machine learning model was fitted to evaluate the combined predictive capacity of these factors in classifying wasting status, also comparing algorithm performance. + Wasting prevalence in Ghana was 9.4%, with marked regional heterogeneity. Lower odds of wasting were associated with higher maternal body weight, maternal formal employment, female sex of child and consumption of animal-source foods, whereas recent diarrhoeal episodes were associated with higher odds. The exploratory analyses further showed that these factors clustered into coherent vulnerability profiles, distinguishing more clearly between wasting and non-wasting patterns. The ensemble model outperformed individual algorithms, achieving an AUC of 0.79, with high sensitivity (0.85) and moderate specificity (0.62), indicating moderate-to-good internal predictive performance. + These findings suggest that wasting is associated with interacting biological vulnerabilities and ecological constraints during a critical developmental window. Integrating nutritional quality, infection control and maternal socioeconomic conditions is essential for addressing acute malnutrition in early childhood and for advancing a holistic understanding of child growth within human populations, guiding data-driven public policies. + © 2026 John Wiley & Sons Ltd. + + + + Sánchez-Martínez + Luis Javier + LJ + 0000-0002-9700-3608 + + Unit of Physical Anthropology, Department of Biodiversity, Ecology and Evolution, Faculty of Biological Sciences, Complutense University of Madrid, Madrid, Spain. + + + + Bukari + Mohammed + M + + Department of Nutritional Sciences, School of Allied Health Sciences, University for Development Studies, Tamale, Ghana. + + + + Adokiya + Martin Nyaaba + MN + + Department of Epidemiology, Biostatistics, and Disease Control, School of Public Health, University for Development Studies, Tamale, Ghana. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Trop Med Int Health + 9610576 + 1360-2276 + + IM + + epidemiology + machine learning + predictive algorithms + severe acute malnutrition + sub‐Saharan Africa + + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 11 + + + 2025 + 12 + 25 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 8 + 23 + + + aheadofprint + + 42212699 + 10.1111/tmi.70171 + + + + United Nations Children's Fund (UNICEF), World Health Organization, and World Bank Group, Levels and Trends in Child Malnutrition: Key Findings of the 2025 Edition (World Health Organization, 2025). + + + C. D. Bourke, J. A. Berkley, and A. J. Prendergast, “Immune Dysfunction as a Cause and Consequence of Malnutrition,” Trends in Immunology 37 (2016): 386–398, https://doi.org/10.1016/j.it.2016.04.003. + + + Z. A. Bhutta, J. A. Berkley, R. H. J. Bandsma, M. Kerac, I. Trehan, and A. Briend, “Severe Childhood Malnutrition,” Nature Reviews. 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Manary, “Management of Severe Acute Malnutrition in Low‐Income and Middle‐Income Countries,” Archives of Disease in Childhood 100 (2015): 283–287, https://doi.org/10.1136/archdischild‐2014‐306026. + + + G. Heymsfield, E. Radin, M. Biotteau, et al., “Estimating Program Coverage in the Treatment of Acute Malnutrition Using Population‐Based Cluster Survey Methods: Results From Surveys in Burkina Faso, Chad, Democratic Republic of the Congo, and Niger,” Frontiers in Public Health 13 (2025): 1513567, https://doi.org/10.3389/fpubh.2025.1513567. + + + E. Rogers, M. Myatt, S. Woodhead, S. Guerrero, and J. L. Alvarez, “Coverage of Community‐Based Management of Severe Acute Malnutrition Programmes in Twenty‐One Countries, 2012–2013,” PLoS One 10 (2015): e0128666, https://doi.org/10.1371/journal.pone.0128666. + + + B. Beggs, M. Bustos, L. J. Brubacher, M. Little, L. Lau, and W. 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Katoch, “Determinants of Malnutrition Among Children: A Systematic Review,” Nutrition 96 (2022): 111565, https://doi.org/10.1016/j.nut.2021.111565. + + + J. Lin and X. L. Feng, “Exploring the Impact of Water, Sanitation and Hygiene (WASH), Early Adequate Feeding and Access to Health Care on Urban‐Rural Disparities of Child Malnutrition in China,” Maternal and Child Nutrition 19 (2023): e13542, https://doi.org/10.1111/mcn.13542. + + + United Nations Children's Fund (UNICEF), “UNICEF Conceptual Framework on Maternal and Child Nutrition,” (New York, United States of America: 2021). + + + Z. Li, R. Kim, S. Vollmer, and S. V. Subramanian, “Factors Associated With Child Stunting, Wasting, and Underweight in 35 Low‐ and Middle‐Income Countries,” JAMA Network Open 3 (2020): e203386, https://doi.org/10.1001/jamanetworkopen.2020.3386. + + + T. T. Tamir, A. F. Zegeye, B. S. 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Weldeyohannes, et al., “Inadequate Dietary Diversity During Pregnancy Increases the Risk of Maternal Anemia and Low Birth Weight in Africa: A Systematic Review and Meta‐Analysis,” Food Science and Nutrition 11 (2023): 3706–3717, https://doi.org/10.1002/fsn3.3388. + + + R. Ahmed, S. E. Chibsa, M. A. Hussen, et al., “Undernutrition Among Exclusive Breastfeeding Mothers and Its Associated Factors in Southwest Ethiopia: A Community‐Based Study,” Women's Health 20 (2024): 17455057241231478, https://doi.org/10.1177/17455057241231478. + + + J. D. Kolog, A. Asem, and A. Mensah‐Bonsu, “The State of Food Security and Its Determinants in Ghana: An Ordered Probit Analysis of the Household Hunger Scale and Household Food Insecurity Access Scale,” Scientific African 19 (2023): e01579, https://doi.org/10.1016/j.sciaf.2023.e01579. + + + C. B. Ngandu, D. Momberg, A. Magan, L. Chola, S. A. Norris, and R. Said‐Mohamed, “The Association Between Household Socio‐Economic Status, Maternal Socio‐Demographic Characteristics and Adverse Birth and Infant Growth Outcomes in Sub‐Saharan Africa: A Systematic Review,” Journal of Developmental Origins of Health and Disease 11 (2020): 317–334, https://doi.org/10.1017/S2040174419000680. + + + M. R. K. Chowdhury, S. Rahman, B. Billah, et al., “Prevalence and Factors Associated With Severe Undernutrition Among Under‐5 Children in Bangladesh, Pakistan, and Nepal: A Comparative Study Using Multilevel Analysis,” Scientific Reports 13 (2023): 10183, https://doi.org/10.1038/s41598‐023‐36048‐w. + + + M. Saaka, “Women's Decision‐Making Autonomy and Its Relationship With Child Feeding Practices and Postnatal Growth,” Journal of Nutritional Science 9 (2020): e38, https://doi.org/10.1017/jns.2020.30. + + + A. Atosona, J. A. Mohammed, H. Issahaku, K. Saani, H. Y. Addae, and F. Azupogo, “Maternal Employment Status and Child Age Are Positive Determinants of Minimum Dietary Diversity Among Children Aged 6–23 Months in Sagnarigu Municipality, Ghana: A Cross‐Sectional Study,” BMC Nutrition 10 (2024): 57, https://doi.org/10.1186/s40795‐024‐00865‐7. + + + Z. Ali, M. Saaka, A. G. Adams, S. K. Kamwininaang, and A. R. Abizari, “The Effect of Maternal and Child Factors on Stunting, Wasting and Underweight Among Preschool Children in Northern Ghana,” BMC Nutrition 3 (2017): 31, https://doi.org/10.1186/s40795‐017‐0154‐2. + + + L. Pimpin, S. Kranz, E. Liu, et al., “Effects of Animal Protein Supplementation of Mothers, Preterm Infants, and Term Infants on Growth Outcomes in Childhood: A Systematic Review and Meta‐Analysis of Randomized Trials,” American Journal of Clinical Nutrition 110 (2019): 410–429, https://doi.org/10.1093/ajcn/nqy348. + + + S. Zaharia, S. Ghosh, R. Shrestha, et al., “Sustained Intake of Animal‐Sourced Foods Is Associated With Less Stunting in Young Children,” Nature Food 2 (2021): 246–254, https://doi.org/10.1038/s43016‐021‐00259‐z. + + + E. Becquey, F. Delpeuch, A. M. Konaté, et al., “Seasonality of the Dietary Dimension of Household Food Security in Urban Burkina Faso,” British Journal of Nutrition 107 (2012): 1860–1870, https://doi.org/10.1017/S0007114511005071. + + + M. M. Hasan, A. Kader, C. A. Al Asif, and A. Talukder, “Seasonal Variation in the Association Between Household Food Insecurity and Child Undernutrition in Bangladesh: Mediating Role of Child Dietary Diversity,” Maternal and Child Nutrition 19 (2023): e13465, https://doi.org/10.1111/mcn.13465. + + + A. R. Abizari, F. Azupogo, M. Nagasu, N. Creemers, and I. D. Brouwer, “Seasonality Affects Dietary Diversity of School‐Age Children in Northern Ghana,” PLoS One 12 (2017): e0183206, https://doi.org/10.1371/journal.pone.0183206. + + + K. Hirvonen, A. S. Taffesse, and I. W. Hassen, “Seasonality and Household Diets in Ethiopia,” Public Health Nutrition 19 (2016): 1723–1730, https://doi.org/10.1017/S1368980015003237. + + + A. Venkat, A. Marshak, H. Young, and E. N. Naumova, “Seasonality of Acute Malnutrition in African Drylands: Evidence From 15 Years of SMART Surveys,” Food and Nutrition Bulletin 44 (2023): S94–S108, https://doi.org/10.1177/03795721231178344. + + + L. Rodríguez, E. Cervantes, and R. Ortiz, “Malnutrition and Gastrointestinal and Respiratory Infections in Children: A Public Health Problem,” International Journal of Environmental Research and Public Health 8 (2011): 1174–1205, https://doi.org/10.3390/ijerph8041174. + + + N. Fauziah, J. K. Aviani, Y. N. Agrianfanny, and S. N. 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Amoah, “Advancing Predictive Analytics in Child Malnutrition: Machine, Ensemble and Deep Learning Models With Balanced Class Distribution for Early Detection of Stunting and Wasting,” Human Nutrition and Metabolism 42 (2025): 200340, https://doi.org/10.1016/j.hnm.2025.200340. + + + + + + + 42212606 + + 2026 + 05 + 29 + +
+ + 1557-8100 + + + 2026 + May + 29 + + + Omics : a journal of integrative biology + OMICS + + Network Pharmacological Analysis of Lentinan in Regulating Intervertebral Disc Degeneration: Combined with Machine Learning-Based Screening and Molecular Dynamics Validation. + + 15578100261454407 + 15578100261454407 + + 10.1177/15578100261454407 + + Lentinan (LNT) is a polysaccharide with antioxidant and anti-inflammatory properties; however, its link to intervertebral disc degeneration (IVDD) remains unclear. Here, we integrated network pharmacology, machine learning, molecular docking, and molecular dynamics (MD) simulations to investigate the LNT-IVDD relationship. Using dataset GSE176205, 16,361 potential IVDD-related genes were identified, and 97 LNT-associated targets were collected from four databases, yielding 46 intersecting targets. GO and KEGG analyses revealed enrichment in pathways including calcium, PI3K-Akt, Notch, and Rap1 signaling. Protein-protein interaction network analysis combined with three ML methods prioritized two core targets: signal transducer and activator of transcription 3 (STAT3) and fibroblast growth factor 2 (FGF2). The diagnostic relevance of these genes was subsequently validated using receiver operating characteristic curves across three independent IVDD datasets. Molecular docking showed strong binding (energy < -5 kcal/mol) between LNT and these targets, further supported by stable complex formation in MD simulations. Immune-infiltration analysis delineated the IVDD microenvironment, and virtual knockout based on single-cell transcriptomics systematically assessed how perturbing key genes remodels cell states and signaling networks. Together, this work provides novel insights into the molecular mechanisms underlying LNT's therapeutic effects in IVDD and establishes a foundation for its translational study. + + + + Cai + Hanhua + H + + Department of Orthopedics, Affiliated Hospital of Putian University, Putian, Fujian, China. + + + + Chen + Yihuang + Y + + Basic Medicine College, Putian University, Putian, Fujian, China. + + + + Liu + Huan + H + 0000-0002-5519-7336 + + Department of Orthopedics, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China. + + + Orthopaedic Research Center and Key Laboratory, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China. + + + + Zhang + Tao + T + 0009-0002-6157-6073 + + Department of Orthopedics, The Affiliated Traditional Chinese Medicine Hospital, Southwest Medical University, Luzhou, Sichuan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + OMICS + 101131135 + 1536-2310 + + IM + + intervertebral disc degeneration + lentinan + machine learning + molecular dynamics validation + network pharmacology + + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 7 + 42 + + + aheadofprint + + 42212606 + 10.1177/15578100261454407 + + + + + + 42212596 + + 2026 + 05 + 29 + +
+ + 2169-5180 + + + 2026 + May + 29 + + + Soft robotics + Soft Robot + + Bioinspired Swallowing Soft Gripper with Toroidal Optical Waveguides for Multimodal Interactive Perception. + + 21695172261451702 + 21695172261451702 + + 10.1177/21695172261451702 + + Multimodal soft grippers can adapt grasping strategies to diverse environments, yet integrating sensors under large, coupled deformations remains challenging. Inspired by the mechanosensation of sea anemones and their ability to swallow, this article presents a soft gripper that integrates eight toroidal optical waveguides to realize three modes-contacting, expansion, and swallowing-with continuous proprioceptive feedback. Deformation-induced optical attenuation, processed by machine-learning pipelines, enables perception of object shape, hardness, and surface texture. Experiments show a 0.04 N detection limit, a 0.006 N resolution, a 55 ms response time, and sensitivity >1.4 dB/N, with machine-learning classification achieving >89% accuracy. Mode-specific experiments demonstrate sensing across the entire soft gripper with integrated optical waveguides. The outer surface localizes contact after inflation, the inner surface provides circumferential contact sensing of irregular objects during swallowing; and the pedal interface at the base distinguishes surface hardness and texture, achieving perception on the outer, inner, and bottom interfaces. We also demonstrate multi-object swallowing that grasps and counts 1-4 transparent bottles in real-time and a breakfast task that switches grasping modes to grasp a bowl and cup, swallowing fragile items without damage. These results show that our design enables mode-switching interactive perception and expands opportunities for soft robotics in fragile product handling and laboratory automation. + + + + Xiang + Sheng + S + 0000-0001-5151-0391 + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + Niu + Muye + M + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + Fang + Wenjie + W + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + Xu + Xin + X + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + Wei + Zhong + Z + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + Hua + Chengcheng + C + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + Yang + Yang + Y + + School of Automation, CICAEET, Nanjing University of Information Science and Technology, Nanjing, China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + Soft Robot + 101623819 + 2169-5172 + + IM + + machine learning + multimodal soft gripper + optical waveguides + proprioception + + + + + + 2026 + 5 + 29 + 15 + 40 + + + 2026 + 5 + 29 + 15 + 40 + + + 2026 + 5 + 29 + 7 + 32 + + + aheadofprint + + 42212596 + 10.1177/21695172261451702 + + + + + + 42212567 + + 2026 + 05 + 29 + +
+ + 1744-8328 + + + 2026 + May + 29 + + + Expert review of anticancer therapy + Expert Rev Anticancer Ther + + Predictors of warfarin therapeutic dose in cancer patients: a preliminary comparative study using machine learning-integrated explainable artificial intelligence and Bayesian models. + 10.1080/14737140.2026.2682871 + + Warfarin dosing in cancer patients is challenging due to altered pathophysiology and variable responses. While machine learning offers predictive potential, clinical adoption requires interpretability. This study is an initial attempt at addressing whether different dosing strategies are required in this vulnerable population. + This observational study analyzed 1,746 patients (124 with cancer) from the International Warfarin Pharmacogenetics Consortium database. Five machine learning algorithms were developed and compared with the best-performing model interpreted using SHAP analysis. Bayesian Additive Regression Trees provided uncertainty quantification, and the Virtual Twins method estimated cancer's individualized treatment effect on dose requirements. + Genetic variants, particularly VKORC1 and CYP2C9, dominated dose prediction in both cohorts. However, cancer introduced complexity: clinical predictors showed greater variability and prediction errors, while Bayesian analysis revealed wider uncertainty intervals for inaccurate estimates. The Virtual Twins method demonstrated substantial heterogeneity in cancer's effect, with mean individualized treatment effect of 3.35 mg/week, yet 62.5% of patients exhibited positive effects indicating higher dose requirements. These findings suggest uniform dose adjustments for cancer patients may be inappropriate. + Cancer patients may potentially require personalized warfarin dosing, as genetic and clinical factors interact unpredictably. Explainable artificial intelligence and Bayesian models potentially enable individualized anticoagulation in oncology. + + + + Sridharan + Kannan + K + 0000-0003-3811-6503 + + Department of Pharmacology & Therapeutics, College of Medicine & Health Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain. + + + + Sivaramakrishnan + Gowri + G + + Bahrain Defence Force Royal Medical Services, Riffa, Kingdom of Bahrain. + + + + Santoni + Matteo + M + + ARON Research Foundation ETS, Macerata, Italy. + + + Oncology Unit, Macerata Hospital, Macerata, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Expert Rev Anticancer Ther + 101123358 + 1473-7140 + + IM + + Anticoagulant + MLA + XAI + personalized medicine + warfarin + + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 7 + 3 + + + aheadofprint + + 42212567 + 10.1080/14737140.2026.2682871 + + + + + + 42212510 + + 2026 + 05 + 29 + +
+ + 1096-987X + + 47 + 14 + + 2026 + May + 30 + + + Journal of computational chemistry + J Comput Chem + + A Nonlinear Multi-Objective Prediction Strategy for Small-Sample Datasets in Homogeneous Catalysis. + + e70407 + e70407 + + 10.1002/jcc.70407 + + The development of homogeneous catalytic reactions is hindered by the resource-intensive nature of traditional experimental and computational methods, particularly when dealing with small, sparse datasets and complex multi-objective optimization. While machine learning (ML) offers a promising alternative, prevailing methods rely on large-scale datasets and often fail to address the challenges of small-sample datasets, high-dimensional descriptor spaces, and strong nonlinear relationships inherent in catalytic processes. To address this issue, we present a nonlinear multi-objective ML workflow, PSO_CRP, for predicting reaction categories (e.g., conversion or yield) and quantitative outcomes (e.g., enantioselectivity or site selectivity), as well as the interpretability analysis. Experimental results on 4 small-sample datasets demonstrate that our models, relying only on simple RDKit-derived molecular parameters rather than costly DFT calculations, achieve consistently higher predictive accuracy than at least five common ML models. Model-based permutation feature importance (PFI) and partial dependence plot (PDP) analyses identified the key molecular descriptors governing reaction outcomes. They quantified their contributions, producing results aligned with previous studies while providing added mechanistic insight to enhance model interpretability and guide rational catalyst design. As a high-precision, low-cost, and interpretable framework, this PSO-based workflow offers valuable insights for forward prediction in homogeneous catalytic systems. + © 2026 Wiley Periodicals LLC. + + + + Liu + Yining + Y + + State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin, People's Republic of China. + + + + Wang + Shen + S + 0009-0008-4174-4301 + + State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin, People's Republic of China. + + + Leicester International Institute, Dalian University of Technology, Panjin, People's Republic of China. + + + + Li + Yang + Y + 0000-0002-5719-9044 + + State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin, People's Republic of China. + + + + Bao + Ming + M + 0000-0002-5179-3499 + + State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, School of Chemical Engineering, Ocean and Life Sciences, Dalian University of Technology, Panjin, People's Republic of China. + + + + eng + + + 21903010 + National Natural Science Foundation of China + + + + 22572016 + National Natural Science Foundation of China + + + + 22172014 + National Natural Science Foundation of China + + + + DUT24BK047 + Fundamental Research Funds for the Central Universities + + + + + Journal Article + +
+ + United States + J Comput Chem + 9878362 + 0192-8651 + + IM + + machine learning + multi‐objective prediction + nonlinear relationship + particle swarm optimization + small‐sample dataset + + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 4 + 23 + + + 2026 + 1 + 19 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 6 + 23 + + + ppublish + + 42212510 + 10.1002/jcc.70407 + + + + C. N. Hinshelwood, “Homogeneous Reactions,” Chemical Reviews 3 (1926): 227–256. + + + B. Su and J. F. Hartwig, “Development of Chiral Ligands for the Transition‐Metal‐Catalyzed Enantioselective Silylation and Borylation of C−H Bonds,” Angewandte Chemie, International Edition 61 (2022): e202113343. + + + S.‐W. Li, L.‐C. Xu, C. Zhang, S.‐Q. Zhang, and X. Hong, “Reaction Performance Prediction With an Extrapolative and Interpretable Graph Model Based on Chemical Knowledge,” Nature Communications 14 (2023): 3569. + + + Y. Liu, Q. Yang, Y. Li, L. Zhang, and S. Luo, “Application of Machine Learning in Organic Chemistry,” Chinese Journal of Organic Chemistry 40 (2020): 3812–3827. + + + M. Das, A. Ghosh, and R. B. Sunoj, “Advances in Machine Learning With Chemical Language Models in Molecular Property and Reaction Outcome Predictions,” Journal of Computational Chemistry 45 (2024): 1160–1176. + + + W. Yang, T. T. Fidelis, and W.‐H. Sun, “Prediction of Catalytic Activities of Bis(Imino)pyridine Metal Complexes by Machine Learning,” Journal of Computational Chemistry 41 (2020): 1064–1067. + + + S. Wang, W. Zhao, Y. Liu, and Y. Li, “Multi‐Modal Homogeneous Chemical Reaction Performance Prediction With Graph and Chemical Language Information,” Chinese Journal of Chemistry 43 (2025): 1230–1238. + + + W. Zhao, S. Wang, and Y. Li, “Designing Buchwald–Hartwig Reaction Graph for Yield Prediction,” Journal of Organic Chemistry 90 (2025): 12975–12983. + + + A. F. Zahrt, J. J. Henle, B. T. Rose, Y. Wang, W. T. Darrow, and S. E. 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+ + 1872-6623 + + + 2026 + May + 27 + + + Pain + Pain + + Distinct transcriptional signatures of large-scale functional gradient organization in acute and chronic low back-related leg pain. + 10.1097/j.pain.0000000000004010 + + Low back-related leg pain (LBLP)-a severe subtype marked by radicular symptoms and high chronicity rates-implicates central nervous system reorganization, yet the large-scale functional gradient organization principles governing brain-wide functional shifts during chronicization remain unknown. Here, we used "Functional Gradient-Transcriptomics" analysis to characterize hierarchical brain reorganization and the underlying molecular landscape in patients with acute (n = 40) and chronic (n = 103) LBLP compared to healthy controls (n = 109). Acute LBLP showed elevated gradient values within the sensorimotor network, and genes associated with these gradient alterations were significantly enriched for processes related to dysregulated energy metabolism. In contrast, chronic LBLP exhibited enhanced activity of the limbic network and diminished attention networks, corresponding genes were significantly enriched for synaptic plasticity, neurotransmitter imbalance, neuronal hyperexcitability, and transcriptional reprogramming. Regional gradient features exhibited trending associations with clinical measures and effectively discriminated patient groups. This "Functional Gradient-Transcriptomics" framework provides a novel, multiscale perspective on LBLP pathophysiology, advancing theoretical insights and informing future therapeutic strategies. + Copyright © 2026 International Association for the Study of Pain. + + + + Liang + Xiao + X + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + Wang + Yao + Y + + Department of Radiology, Shandong Cancer Hospital and Institute, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, Shandong, People's Republic of China. + + + + Yang + Chen + C + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + Yang + Ziwei + Z + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + Ji + Yuqi + Y + + The Second Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi Province, People's Republic of China. + + + + Pei + Yixiu + Y + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + + Xiong + Wei + W + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + Zuo + Xiaoying + X + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + Peng + Xiaoqi + X + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + Dai + Jiankun + J + + MR Research, GE Healthcare, Beijing, China. + + + + Zhang + Yong + Y + + Department of Pain Clinic, The First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi Province, People's Republic of China. + + + + Zhou + Fuqing + F + 0000-0002-0890-910 + + Jiangxi Provincial Key Laboratory for Precision Pathology and Intelligent Diagnosis, Department of Radiology, the First Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, People's Republic of China. + + + Jiangxi Province Medical Imaging Research Institute, Nanchang, Jiangxi, People's Republic of China. + + + Clinical Research Center for Medical Imaging in Jiangxi Province, Nanchang, Jiangxi, People's Republic of China. + + + + eng + + + 82160331 + National Natural Science Foundation of China + + + + jxsq2023201039 + Double Thousand Plan of Jiangxi Province + + + + 20223BCG74001 + Jiangxi Clinical Research Center for Medical Imaging + + + + 2024SSY06281 + Jiangxi Province Key Laboratory for Precision Pathology and Intelligent Diagnosis + + + + NCUJCCX-2024-05 + Medical Cross Innovation Fund of Nanchang University + + + + + Journal Article + + + 2026 + 05 + 27 + +
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Compressing the lumbar nerve root changes the frequency-associated cerebral amplitude of fluctuations in patients with low back/leg pain. Sci Rep 2019;9:2246. + + + + + + + 42212456 + + 2026 + 05 + 29 + +
+ + 1463-9084 + + + 2026 + May + 29 + + + Physical chemistry chemical physics : PCCP + Phys Chem Chem Phys + + Interpretable machine-learning prediction of DFT energies per atom and identification of magic numbers in coinage-metal nanoclusters (N ≤ 55) from the open quantum cluster database. + 10.1039/d6cp01474g + + Atomically precise coinage-metal nanoclusters (Cu, Ag, and Au) exhibit size-dependent stability critical for catalysis, plasmonics, and photocatalysis, yet first-principles screening becomes prohibitive beyond ∼55 atoms. We use the open quantum cluster database (QCD; 4381 Cu/Ag/Au clusters, N ≤ 55, PBE/PAW) to build an interpretable machine-learning framework for the DFT energy per atom (E + DFT/N). Nine geometric descriptors (radius of gyration, asphericity, compactness, and bounding-box dimensions) are combined with cluster size N, metal identity, and three QCD-derived electronic features. On a stratified 70/15/15 split, LightGBM attains a test MAE of 0.0144 eV atom-1 and R + 2 = 0.996, with stable 5-fold cross-validation (0.0142 ± 0.0004 eV per atom); ExtraTrees yielded near-equivalent accuracy. A geometry-only variant trained without electronic inputs retains an MAE = 0.0148 eV per atom (only 3% above the full model), demonstrating that energies can be ranked from coordinates alone. Per-metal second-difference (Δ2 + E) analysis with adaptive thresholds identifies universal peaks at N = 8 and 34 across all three metals; N = 32 and 38 are resolved only for Au, which is consistent with relativistic stabilization, whereas Au shows an anomalous Δ2 + E at N = 20 that is absent in Cu and Ag. SHAP analysis reveals that metal identity and cluster size dominate predictions, whereas geometric descriptors govern the ∼10 meV per atom differences that determine magic-number locations. Size-grouped cross-validation shows that interpolation within the QCD is highly accurate, but extrapolation across size domains is substantially harder (MAE ≈ 0.10 eV per atom), bounding the model's scope. The complete open-source pipeline is released to support FAIR data practices. + + + + Khairbek + Ali A + AA + 0000-0002-0477-5896 + + Department of Mechanical Engineering, University Centre for Research & Development, Chandigarh University, Gharuan, Mohali, Punjab, India. alikhairbek@gmail.com. + + + + Al-Zaben + Maha I + MI + + Department of Chemistry, College of Science, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia. + + + + Alzahrani + Abdullah Yahya Abdullah + AYA + + Department of Chemistry, Faculty of Science, King Khalid University, Abha, 61413, Saudi Arabia. + + + + Thomas + Renjith + R + 0000-0003-0011-633X + + Department of Chemistry, St Berchmans College (Autonomous), Changanassery, Kerala, 686101, India. renjith@sbcollege.ac.in. + + + Centre for Theoretical and Computational Chemistry, St Berchmans College (Autonomous), Changanassery, Kerala, 686101, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Phys Chem Chem Phys + 100888160 + 1463-9076 + + IM + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 5 + 43 + + + aheadofprint + + 42212456 + 10.1039/d6cp01474g + + + + + + 42212409 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1541-4337 + + 25 + 4 + + 2026 + Jul + + + Comprehensive reviews in food science and food safety + Compr Rev Food Sci Food Saf + + Machine Learning-Enabled Intelligent Technologies Across the Grape and Wine Value Chain: From Vineyard Sensing to Winemaking Optimization. + + e70523 + e70523 + + 10.1111/1541-4337.70523 + + Machine learning is transforming the grape and wine industry by shifting traditional experience-driven practices toward data-driven and intelligent decision making across the entire value chain. This review provides a conceptually driven synthesis of machine learning-enabled technologies spanning vineyard sensing, precision viticulture, fermentation monitoring, and winemaking optimization. An integrated analytical framework is proposed, linking multisource data acquisition, preprocessing and representation learning, model development, and intelligent decision making into a unified end-to-end pipeline. Building on this framework, the review critically examines advances related to five key scientific challenges: multimodal data integration, model interpretability, cross-domain generalization, whole-chain decision coordination, and scalable industrial deployment. Particular emphasis is placed on the mechanisms and trade-offs of multiscale sensing technologies (including spectroscopy, chromatography-mass spectrometry, imaging, and electronic sensing), as well as data preprocessing, feature engineering, and multimodal fusion strategies. A task-oriented and data-structured perspective on model selection is highlighted, covering linear models, kernel methods, ensemble learning, deep neural networks, and probabilistic frameworks, alongside evaluation protocols and interpretability approaches. In contrast to fragmented task-specific studies, this review highlights the importance of cross-stage integration and closed-loop decision systems linking vineyard management with downstream vinification and quality evaluation. Despite rapid progress, key challenges remain, including data scarcity and heterogeneity, limited model transferability, and high implementation costs. Emerging directions such as knowledge-guided machine learning, causal inference, small-sample learning, and human-AI collaboration are expected to enhance robustness and interpretability. Overall, this review provides a structured roadmap for advancing intelligent and sustainable practices in the grape and wine industry. + © 2026 Institute of Food Technologists®. + + + + Li + Xian + X + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Bai + Mengmeng + M + + School of Chemistry and Chemical Engineering, North University of China, Taiyuan, China. + + + + Wang + Junhe + J + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Pan + Qiliang + Q + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Shi + Jiajun + J + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Peng + Qirui + Q + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Ma + Danyang + D + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + International Grape and Wine Ningxia Exchange and Cooperation Center, Yinchuan, Ningxia Hui Autonomous Region, China. + + + + Mu + Haibin + H + + International Grape and Wine Ningxia Exchange and Cooperation Center, Yinchuan, Ningxia Hui Autonomous Region, China. + + + + Su + Baofeng + B + + College of Mechanical and Electronic Engineering, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Jiang + Jiao + J + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Qin + Yi + Y + 0000-0001-6563-4790 + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Liu + Yanlin + Y + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + Song + Yuyang + Y + 0000-0002-4377-6875 + + College of Enology, Northwest A&F University, Yangling, Shaanxi Province, China. + + + + eng + + + 2024YFD2100600 + National Key R&D Program of China + + + + 2023JH-NYCL-0011 + Municipal Science and Technology Planning Project + + + + CARS-29-jg-03 + National Modern Agriculture (Grape) Industry Technology System Construction Project + + + + 26ZDNA009 + Major Special Project Plan of Gansu Province + + + + + Journal Article + Review + +
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Pati, L. Palombi, F. Grieco, and I. Losito. 2022. “Use of Multivariate Statistics in the Processing of Data on Wine Volatile Compounds Obtained by HS‐SPME‐GC‐MS.” Foods 11, no. 7: 910. https://doi.org/10.3390/foods11070910. + + + Urtubia, A., G. Hernández, and J. M. Roger. 2021. “ATR‐MIR Spectroscopy as a Process Analytical Technology in Wine Alcoholic Fermentation—A Tutorial.” Microchemical Journal 166, no. 4: 106215. https://doi.org/10.1016/j.jbiotec.2011.09.031. + + + Urvieta, R., G. Jones, F. Buscema, R. Bottini, and A. Fontana. 2021. “Terroir and Vintage Discrimination of Malbec Wines Based on Phenolic Composition Across Multiple Sites in Mendoza, Argentina.” Scientific Reports 11, no. 1: 2863–. https://doi.org/10.1038/s41598‐021‐82306‐0. + + + Utegenova, L. S., O. E. Sarmanova, S. A. Burikov, I. V. Plastinin, T. A. Dolenko, and S. A. 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On. 2022. “Predictive Potential of MALDI‐TOF Analyses for Wine and Brewing Yeast.” Microorganisms 10, no. 2: 265. https://doi.org/10.3390/microorganisms10020265. + + + Zhang, X. K., Y. B. Lan, Y. Huang, X. Zhao, and C. Q. Duan. 2021. “Targeted Metabolomics of Anthocyanin Derivatives During Prolonged Wine Aging: Evolution, Color Contribution and Aging Prediction.” Food Chemistry 339: 127795. https://doi.org/10.1016/j.foodchem.2020.127795. + + + + + + + 42212377 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1791-3004 + + 34 + 1 + + 2026 + Jul + + + Molecular medicine reports + Mol Med Rep + + FOSL2 drives transcriptional activation of super‑enhancer-regulated DYNC1H1 to promote hypoxia‑induced oral squamous cell carcinoma progression. + 209 + 10.3892/mmr.2026.13919 + + Hypoxia is a hallmark of oral squamous cell carcinoma (OSCC) and plays a critical role in driving tumor progression. However, the mechanisms by which OSCC cells respond to hypoxic signaling to promote malignant phenotypes remain unclear. The present study aimed to identify the key regulatory factors governing hypoxic adaptation in OSCC. Based on bulk RNA‑sequencing (RNA‑seq) data from The Cancer Genome Atlas (TCGA) database, hypoxia was identified as a prognostic risk factor for OSCC. Hierarchical clustering revealed 'glycolysis' and 'hypoxia' as the most strongly associated cancer hallmarks. Using single‑cell RNA‑seq data from the Gene Expression Omnibus (GEO) database, 104 hypoxia‑ and glycolysis‑associated genes were identified. Integration of bulk RNA‑seq data from TCGA and GEO databases enabled the development of an optimized prognostic model [stepwise Cox (backward) + elastic network (α=0.8)] through machine learning and highlighted four core genes: Heparan sulfate proteoglycan 2, immunoglobulin superfamily member 3, dihydrouridine synthase 1‑like and dynein cytoplasmic 1 heavy chain 1 (DYNC1H1). Further analysis of chromatin immunoprecipitation sequencing data from GEO database identified DYNC1H1 as the sole super‑enhancer (SE)‑regulated gene among these candidates, characterized by hypoxia‑responsive upregulation. DYNC1H1 knockdown significantly abrogated hypoxia‑induced proliferation, invasion, glucose uptake and lactate production in OSCC cells. Hypoxia‑responsive transcription factor FOS‑like 2 (FOSL2) activated DYNC1H1 transcription by binding to enhancer regions. Functional validation established that the FOSL2/DYNC1H1 axis was a pivotal regulatory hub for hypoxia‑driven malignant phenotypes in OSCC. Overall, DYNC1H1 is an SE‑regulated hypoxia‑responsive gene that undergoes transcriptional activation via FOSL2‑specific binding to typical enhancer and SE regions. The FOSL2/DYNC1H1 axis drives the hypoxia‑induced malignant progression of OSCC. The present study therefore establishes a novel target for precision therapies targeting the hypoxic tumor microenvironment in OSCC. + + + + Jin + Linyu + L + + Department of Stomatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China. + + + + Zhang + Jing + J + + Department of Medical Periodical Press, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China. + + + + Wang + Wenjing + W + + Department of Stomatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China. + + + + Zhang + Nan + N + + Department of Stomatology, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, P.R. China. + + + + Liu + Yang + Y + + Department of Stomatology, The Second Hospital of Shijiazhuang, Shijiazhuang, Hebei 050000, P.R. China. + + + + Tian + Alin + A + + Department of Stomatology, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050000, P.R. China. + + + + Yin + Weiwei + W + + Department of Stomatology, Children's Hospital of Hebei Province, Shijiazhuang, Hebei 050000, P.R. China. + + + + Qiu + Yongle + Y + + Department of Stomatology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, Hebei 050000, P.R. China. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Greece + Mol Med Rep + 101475259 + 1791-2997 + + + + EC 3.6.4.2 + Cytoplasmic Dyneins + + + 0 + Fos-Related Antigen-2 + + + 0 + FOSL2 protein, human + + + IM + + + Humans + + + Mouth Neoplasms + genetics + pathology + metabolism + + + Gene Expression Regulation, Neoplastic + + + Super Enhancers + + + Transcriptional Activation + + + Cell Line, Tumor + + + Cytoplasmic Dyneins + genetics + metabolism + + + Fos-Related Antigen-2 + genetics + metabolism + + + Carcinoma, Squamous Cell + genetics + pathology + metabolism + + + Disease Progression + + + Cell Proliferation + + + + FOS‑like 2 + dynein cytoplasmic 1 heavy chain 1 + hypoxia + oral squamous cell carcinoma + super‑enhancer + + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2025 + 8 + 13 + + + 2026 + 4 + 24 + + + 2026 + 5 + 29 + 5 + 3 + + + ppublish + + 42212377 + 10.3892/mmr.2026.13919 + 209 + + + + + + 42212372 + + 2026 + 05 + 29 + +
+ + 1549-960X + + + 2026 + May + 29 + + + Journal of chemical information and modeling + J Chem Inf Model + + Physics-Aware Representation Learning on Electronic Charge Density for Materials Property Prediction. + 10.1021/acs.jcim.6c00235 + + The fundamental quantity governing the mechanical and thermodynamic properties of a crystalline solid is its electronic charge density. Yet, its direct use for the rapid prediction of materials properties remains challenging due to its high dimensionality. Here, we present a physics-informed deep learning framework that directly predicts mechanical and thermodynamic properties from the three-dimensional electronic charge density derived from density functional theory (DFT). The proposed approach first utilizes a three-dimensional convolutional autoencoder for unsupervised dimensionality reduction, compressing a high-resolution charge-density grid (128 × 128 × 128) into a compact latent representation (16 × 16 × 16 × 16) while preserving physically meaningful features, as confirmed by negligible reconstruction errors across diverse crystal systems. The compressed latent-space representation of charge density is then used by two different regression models for property prediction: Light Gradient Boosting Machine (LightGBM) and Attention-based 3D Convolutional Neural Networks (Att CNN), and their performance is compared. Combining composition-based descriptors (Material Agnostic Platform for Informatics and Exploration or MAGPIE) with electronic charge density data further improves the model accuracy. Using a data set of about 6059 inorganic compounds spanning multiple crystal symmetries, the models achieve strong predictive performance for bulk modulus K (R2 = 0.94), Young's modulus E (R2 = 0.88), shear modulus G (R2 = 0.87), formation energy Eform (R2 = 0.96), and Debye temperature Θ (R2 = 0.89). This work establishes electronic charge density as a transferable, physics-grounded descriptor for materials property prediction, requiring ≈1/25 of the computational resources of full-fledged DFT calculations. + + + + Kumar + Kammampati Sai + KS + + Department of Materials Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India. + + + + Linda + Albert + A + + Department of Materials Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India. + + + + Maurya + Shubham Kumar + SK + + Department of Materials Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India. + + + + Bhowmick + Somnath + S + 0000-0003-4094-5204 + + Department of Materials Science & Engineering, Indian Institute of Technology Kanpur, Kanpur 208016, India. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Chem Inf Model + 101230060 + 1549-9596 + + IM + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 5 + 3 + + + aheadofprint + + 42212372 + 10.1021/acs.jcim.6c00235 + + + + + + 42212226 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2837-6056 + + 4 + + 2026 + + + Imaging neuroscience (Cambridge, Mass.) + Imaging Neurosci (Camb) + + Modelling discrete states and long-term dynamics in functional brain networks. + IMAG.a.1237 + 10.1162/IMAG.a.1237 + + Functional brain network dynamics underlie fundamental aspects of human cognition and behaviour, including memory, ageing, and a range of clinical disorders. It has been shown that ongoing brain network dynamics can be reliably inferred at fast, sub-second timescales from electrophysiological data using unsupervised machine learning. However, these methods often struggle with inherent trade-offs. For example, Hidden Markov Models (HMMs) have been used to infer categorical brain network states that provide good interpretability but do not model long-range temporal structure. Recently, deep learning approaches using recurrent neural networks (e.g., Dynamic Network Modes) have been proposed to model long-range temporal dependencies, but at the expense of interpretability. In this paper, we introduce Dynamic Network States (DyNeStE) to address this problem. This new model employs amortised Bayesian inference with recurrent neural networks to model long-range temporal structure and uses a Gumbel-Softmax distribution to enforce categorical states for greater interpretability. In both simulations and real resting-state magnetoencephalography data, DyNeStE was able to recover plausible dynamic brain network states and showed superior performance over the HMM in capturing long-range temporal dependencies in network dynamics. These dynamic networks were reproducible across independent data splits and build on established HMM-based findings. Together, these results highlight DyNeStE as an interpretable and temporally informative framework, capable of representing large-scale neural activity as discrete state transitions while capturing transient and long-range brain network dynamics. + © 2026 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. + + + + Cho + SungJun + S + 0000-0002-2787-3739 + + Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford, United Kingdom. + + + Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom. + + + + Huang + Rukuang + R + 0000-0002-6545-7517 + + Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford, United Kingdom. + + + Department of Psychiatry, University of Oxford, Oxford, United Kingdom. + + + + Gohil + Chetan + C + 0000-0002-0888-1207 + + Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford, United Kingdom. + + + + Jones + Oiwi Parker + OP + 0000-0003-0307-9837 + + Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford, United Kingdom. + + + Department of Engineering Science, University of Oxford, Oxford, United Kingdom. + + + + Woolrich + Mark W + MW + 0000-0001-8460-8854 + + Oxford Centre for Integrative Neuroimaging (OxCIN), University of Oxford, Oxford, United Kingdom. + + + Department of Psychiatry, University of Oxford, Oxford, United Kingdom. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Imaging Neurosci (Camb) + 9918663686606676 + 2837-6056 + + + MEG + dynamics + electrophysiology + machine learning + resting-state networks + + The authors declare no competing financial interests or personal relationships that could be perceived as influencing the findings reported in this article. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 10 + 7 + + + 2026 + 2 + 17 + + + 2026 + 4 + 13 + + + 2026 + 5 + 29 + 4 + 58 + + + epublish + + 42212226 + PMC13214575 + 10.1162/IMAG.a.1237 + IMAG.a.1237 + + + + + + 42212221 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2837-6056 + + 4 + + 2026 + + + Imaging neuroscience (Cambridge, Mass.) + Imaging Neurosci (Camb) + + Fast segmentation with the NextBrain histological atlas. + IMAG.a.1244 + 10.1162/IMAG.a.1244 + + Structural brain analysis at the subregion level offers critical insights into healthy aging and neurodegenerative diseases. The NextBrain histological atlas was recently introduced to support such fine-grained investigations, but its existing Bayesian segmentation framework remains computationally prohibitive, particularly for large-scale studies. We present a new, open-source tool that dramatically accelerates segmentation using a hybrid approach combining: machine learning, contrast-adaptive segmentation; target-specific image synthesis; and fast diffeomorphic registration (all three with GPU support). Our method enables highly granular segmentation of brain MRI scans of any resolution and contrast (in vivo or ex vivo) at a fraction of the computational cost of the original method ( + < + 5 minutes on a GPU). We validate our tool on four different modalities (in vivo MRI, ex vivo MRI, HiP-CT, and photography) across a total of approximately 4,000 brain scans. Our results demonstrate that the accelerated approach achieves comparable accuracy to the original method in terms of Dice scores, while reducing runtime by over an order of magnitude. This work enables high-resolution anatomical analysis at unprecedented scale and flexibility, providing a practical solution for large neuroimaging studies. Our tool is publicly available in FreeSurfer (https://surfer.nmr.mgh.harvard.edu/fswiki/HistoAtlasSegmentation). + © 2026 The Authors. Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. + + + + Puonti + Oula + O + 0000-0003-3186-244X + + Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark. + + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + + Nolan + Jackson + J + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + + Dicamillo + Robert + R + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + + Balbastre + Yael + Y + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + Department of Experimental Psychology, University College London, London, United Kingdom. + + + + Casamitjana + Adria + A + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + Research Institute of Computer Vision and Robotics, University of Girona, Girona, Spain. + + + + Mancini + Matteo + M + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + Enrico Fermi Research Center, Rome, Italy. + + + Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom. + + + + Robinson + Eleanor + E + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Peter + Loic + L + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Annunziata + Roberto + R + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Althonayan + Juri + J + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Crampsie + Shauna + S + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Blackburn + Emily + E + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Billot + Benjamin + B + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States. + + + + Atzeni + Alessia + A + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + + Schmidt + Peter + P + + Advanced Research Computing Centre, University College London, London, United Kingdom. + + + + Hughes + James + J + + Advanced Research Computing Centre, University College London, London, United Kingdom. + + + + Augustinack + Jean C + JC + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + + Edlow + Brian L + BL + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + + Zöllei + Lilla + L + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + + Thomas + David L + DL + + Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. + + + Neuroradiological Academic Unit, Department of Translational Neuroscience and Stroke, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. + + + + Kliemann + Dorit + D + + Department of Psychological and Brain Sciences, University of Iowa, Iowa City, IA, United States. + + + + Bocchetta + Martina + M + + Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. + + + Centre for Cognitive and Clinical Neuroscience, Division of Psychology, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University of London, London, United Kingdom. + + + + Strand + Catherine + C + + Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. + + + + Holton + Janice L + JL + + Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. + + + + Jaunmuktane + Zane + Z + + Queen Square Brain Bank for Neurological Disorders, Department of Clinical and Movement Neurosciences, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom. + + + + Iglesias + Juan Eugenio + JE + + Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, United States. + + + Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom. + + + Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, United States. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Imaging Neurosci (Camb) + 9918663686606676 + 2837-6056 + + + brain MRI segmentation + domain-agnostic neuroimaging + histological atlas + + The authors have no relevant financial or non-financial interests to disclose. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 10 + 3 + + + 2026 + 4 + 3 + + + 2026 + 4 + 13 + + + 2026 + 5 + 29 + 4 + 58 + + + epublish + + 42212221 + PMC13214569 + 10.1162/IMAG.a.1244 + IMAG.a.1244 + + + + + + 42212142 + + 2026 + 05 + 29 + +
+ + 1664-3224 + + 17 + + 2026 + + + Frontiers in immunology + Front Immunol + + Correction: Comprehensive immune profiling identifies alterations in adaptive and innate immune responses in granulomatosis with polyangiitis patients in remission. + + 1874627 + 1874627 + + 10.3389/fimmu.2026.1874627 + + [This corrects the article DOI: 10.3389/fimmu.2026.1726107.]. + Copyright © 2026 Liu, Duan, Li, Hu, Huang, Tang, Palayew, Zhang, Pagnoux, Guidos, Zhang and Siminovitch. + + + + Liu + Ming + M + + Mount Sinai Hospital, Lunenfeld-Tanenbaum and Toronto General Hospital Research Institutes, Toronto, ON, Canada. + + + Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, China. + + + + Duan + Deming + D + + Mount Sinai Hospital, Lunenfeld-Tanenbaum and Toronto General Hospital Research Institutes, Toronto, ON, Canada. + + + Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, China. + + + + Li + Chenghao + C + + School of Life Science and Technology, State Key Laboratory of Urban Water Resource and Environment, Harbin Institute of Technology, Harbin, Heilongjiang, China. + + + + Hu + Xia + X + + Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, China. + + + + Huang + Jinghao + J + + Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, China. + + + + Tang + Yue + Y + + Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, China. + + + + Palayew + Steven + S + + Mount Sinai Hospital, Lunenfeld-Tanenbaum and Toronto General Hospital Research Institutes, Toronto, ON, Canada. + + + + Zhang + Lei + L + + Mount Sinai Hospital, Lunenfeld-Tanenbaum and Toronto General Hospital Research Institutes, Toronto, ON, Canada. + + + College of Basic Medicine, Jinzhou Medical University, Jinzhou, Liaoning, China. + + + + Pagnoux + Christian + C + + Vasculitis Clinic, Division of Rheumatology, Mount Sinai Hospital, University of Toronto, Toronto, ON, Canada. + + + + Guidos + Cynthia + C + + The Hospital for Sick Children Research Institute, Program in Cell & Systems Biology, University of Toronto, Toronto, ON, Canada. + + + Department of Immunology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada. + + + + Zhang + Jinyi + J + + Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine, Vision and Brain Health), Wenzhou Medical University, Wenzhou, Zhejiang, China. + + + + Siminovitch + Katherine + K + + Mount Sinai Hospital, Lunenfeld-Tanenbaum and Toronto General Hospital Research Institutes, Toronto, ON, Canada. + + + Department of Immunology, Faculty of Medicine, University of Toronto, Toronto, ON, Canada. + + + Department of Medicine, Faculty of Medicine, University of Toronto, Toronto, ON, Canada. + + + + eng + + Published Erratum + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Immunol + 101560960 + 1664-3224 + + IM + + + Front Immunol. 2026 Mar 27;17:1726107. doi: 10.3389/fimmu.2026.1726107. + 41972183 + + + + granulomatosis with polyangiitis + immune dysregulation + immunophenotypes + innate immunity + lymphocytes + machine learning + mass cytometry + precision medicine + + + + + + 2026 + 5 + 29 + 15 + 40 + + + 2026 + 5 + 29 + 15 + 40 + + + 2026 + 5 + 8 + + + 2026 + 5 + 7 + + + 2026 + 5 + 8 + + + 2026 + 5 + 29 + 4 + 57 + + + epublish + + 42212142 + PMC13214126 + 10.3389/fimmu.2026.1874627 + + + + + + 42212141 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-3224 + + 17 + + 2026 + + + Frontiers in immunology + Front Immunol + + A blood-based four-gene diagnostic signature for Kashin-Beck disease revealed by multi-cohort transcriptomic analysis and machine learning. + + 1789022 + 1789022 + + 10.3389/fimmu.2026.1789022 + + Kashin-Beck disease (KBD) is an endemic osteoarthropathy characterized by growth retardation and progressive joint degeneration. However, its systemic molecular features in peripheral blood remain incompletely understood. + Peripheral blood transcriptomic data from four independent cohorts were analyzed using differential expression analysis and weighted gene co-expression network analysis to identify KBD-associated gene sets. Multiple feature selection strategies and machine learning models were applied to construct and validate a blood-based diagnostic signature across cohorts. Immune cell composition was inferred by computational deconvolution, and transcription factor regulation, pathway enrichment, and genetic association data were integrated for biological interpretation. + A four-gene blood signature (C4B, AQP1, HBA2, and ACSL6) was identified, showing stable diagnostic performance across independent blood cohorts and preserved discriminatory capacity in cartilage tissue. Downstream analyses revealed that the diagnostic genes were associated with altered immune cell composition and immune- and metabolism-related pathways in peripheral blood. + This study defines a compact and interpretable blood-based transcriptomic signature for KBD and provides insight into its systemic immune-related molecular context, supporting its potential utility for disease identification and mechanistic investigation. + Copyright © 2026 Guo, Yang, Liu, Sun, Wu, Li, Wang, Zhu and Ling. + + + + Guo + Minghui + M + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Yang + Kunkun + K + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Liu + Shizhang + S + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Sun + Zhengming + Z + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Wu + Xueyuan + X + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Li + Xinpei + X + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Wang + Yuchao + Y + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + Zhu + Xinke + X + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + + Ling + Ming + M + + Department of Orthopaedics, Shaanxi Provincial People's Hospital, Xi'an, China. + + + Key Laboratory of Basic and Clinical Translational of Bone and Joint Diseases in Shaanxi Provincial, Xi'an, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Immunol + 101560960 + 1664-3224 + + + + 0 + Biomarkers + + + IM + + + Humans + + + Kashin-Beck Disease + genetics + diagnosis + blood + immunology + + + Machine Learning + + + Gene Expression Profiling + + + Transcriptome + + + Female + + + Cohort Studies + + + Male + + + Biomarkers + blood + + + + Kashin–Beck disease + blood transcriptomics + gene signature + immune cell composition + machine learning + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 1 + 16 + + + 2026 + 4 + 14 + + + 2026 + 4 + 24 + + + 2026 + 5 + 29 + 4 + 57 + + + epublish + + 42212141 + PMC13212447 + 10.3389/fimmu.2026.1789022 + + + + + + 42212140 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-3224 + + 17 + + 2026 + + + Frontiers in immunology + Front Immunol + + Integrated single-cell and bulk transcriptomic analyses reveal a stem-like epithelial subpopulation in adenocarcinoma of the esophagogastric junction and identify VASN as a novel regulator of tumor stemness. + + 1817030 + 1817030 + + 10.3389/fimmu.2026.1817030 + + Cancer stem cells (CSCs) are widely recognized as key contributors to tumorigenesis, progression, therapeutic resistance and immune evasion in adenocarcinoma of esophagogastric junction (AEG). Nevertheless, the stem-like epithelial subpopulations that may contribute to immunological niche formation remain poorly characterized, hindering the identification of effective therapeutic targets. + Owing to their ability to delineate cellular heterogeneity, single-cell RNA sequencing (scRNA-seq) and bulk RNA sequencing (bulk RNA-seq) analyses were employed to characterize the CSCs-like subpopulation in AEG tumor tissues. Utilizing scRNA and bulk transcriptomics cohorts, this study systematically identified the putative CSC-like malignant epithelial subpopulation, and a series of bioinformatics and machine learning as well as in vitro functional experiments further verified VASN on AEG malignancy. + Six main cell types were identified in AEG tumors, and six clusters of malignant epithelial cells were filtered out via inferCNV analysis. The cluster 2 (C2) subgroup exhibited pronounced elevation of CytoTRACE, epithelial-mesenchymal transition, Wnt signaling and stem cell differentiation scores, trajectory analysis positioned C2 subgroup at the initiation stage of pseudotime, which comprehensively identified C2 as stem-like neoplastic subpopulation. Through the integration of high dimensional weighted gene co-expression network analysis (hdWGCNA), Branch Expression Analysis Modeling (BEAM) and differentially expressed genes, 21 genes were identified as hub genes of C2 cluster. The CSC-related prognostic model was developed via LASSO Cox regression analysis. Random survival forest further identified VASN as candidate CSCs marker of AEG. Additionally, knock down of VASN in AEG cell line reduced migration and invasion abilities as well as CSCs markers expressions. Mechanistically, in vitro and bioinformatics analysis revealed a potential link between VASN and stemness-associated Wnt/β-catenin signaling. + Our investigation identified a stem-like epithelial subpopulation and established a stem-related gene signature, offering insights into stemness-associated tumor progression and immunological niche dynamics in AEG. Furthermore, we identified VASN as a putative regulator of malignant stem phenotypes that may influence tumor microenvironment remodeling. + Copyright © 2026 Zheng, Wang, Liu, Wang and Hu. + + + + Zheng + Hang + H + + Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. + + + + Wang + Shuaibo + S + + Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. + + + + Liu + Heshu + H + + Cancer Center, Beijing Tongren Hospital, Capital Medical University, Beijing, China. + + + + Wang + Jing + J + + Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. + + + + Hu + Bin + B + + Department of Thoracic Surgery, Beijing Institute of Respiratory Medicine and Beijing Chao-Yang Hospital, Capital Medical University, Beijing, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Immunol + 101560960 + 1664-3224 + + + + 0 + Cell Adhesion Molecules + + + 0 + Microfilament Proteins + + + 0 + Biomarkers, Tumor + + + IM + + + Neoplastic Stem Cells + metabolism + pathology + + + Humans + + + Esophagogastric Junction + pathology + metabolism + + + Adenocarcinoma + genetics + pathology + metabolism + + + Esophageal Neoplasms + pathology + genetics + metabolism + + + Cell Adhesion Molecules + genetics + metabolism + + + Single-Cell Gene Expression Analysis + + + Gene Expression Regulation, Neoplastic + + + Gene Expression Profiling + + + Microfilament Proteins + genetics + metabolism + + + Single-Cell Analysis + + + Cell Line, Tumor + + + Epithelial-Mesenchymal Transition + + + Transcriptome + + + Biomarkers, Tumor + genetics + + + Wnt Signaling Pathway + + + Stomach Neoplasms + pathology + genetics + metabolism + + + + VASN + adenocarcinoma of esophagogastric junction + immunological niche + precision oncology + tumor stemness + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 2 + 25 + + + 2026 + 4 + 22 + + + 2026 + 4 + 23 + + + 2026 + 5 + 29 + 4 + 57 + + + epublish + + 42212140 + PMC13212525 + 10.3389/fimmu.2026.1817030 + + + + + + 42212078 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2691-3704 + + 6 + 5 + + 2026 + May + 25 + + + JACS Au + JACS Au + + Ligand-Controlled Phonon Dynamics in CsPbBr3 Nanocrystals Revealed by Machine-Learned Interatomic Potentials. + + 3039 + 3047 + 3039-3047 + + 10.1021/jacsau.6c00434 + + Halide perovskite nanocrystals are leading candidates for next-generation optoelectronics, yet the role of surface ligands in controlling their phonon dynamics remains poorly understood. These lattice dynamics critically govern energy up-conversion, phonon-assisted anti-Stokes emission, and nonradiative relaxation. Conventional ab initio methods, while accurate, are computationally infeasible for experimentally relevant nanocrystal sizes that require thousands of atoms to capture realistic ligand shells and dynamic disorder. Here, we introduce a machine-learned interatomic potential fine-tuned on small CsPbBr3 nanocrystals with diverse ligands, enabling accurate prediction of ligand-induced phonon properties far beyond the spatial and temporal scales of ab initio methods. We find that both cationic and anionic ligands systematically redshift Pb-Br-Pb stretching modes while blueshifting the PbBr6 + 4- octahedral rotation mode, with stronger overall effects for anionic passivation. Notably, anionic ligands stiffen the rotation mode nonmonotonically with respect to the ligand binding energy. The nanocrystal models also reveal a strong site dependence of these phonon-mode shifts, with corner and edge sites showing the largest response. Our findings reveal important roles of cationic and anionic ligands in modulating key dynamic modes of halide perovskite nanocrystals associated with detrimental nonradiative losses, offering mechanistic insights and design principles for high-performance perovskite nanocrystal optoelectronics. + © 2026 The Authors. Published by American Chemical Society. + + + + Cha + Seungjun + S + 0009-0003-0129-3458 + + School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States. + 1372 + + + + Wang + Chen + C + 0000-0002-4296-6888 + + Department of Chemistry and Biochemistry, Queens College, City University of New York, New York, New York 11367, United States. + + + The Graduate Center, City University of New York, New York, New York 10016, United States. + 14772 + + + + Fung + Victor + V + 0000-0002-3347-6983 + + School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States. + 1372 + + + + Hu + Guoxiang + G + 0000-0003-2942-8564 + + School of Materials Science and Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States. + 1372 + + + School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia 30332, United States. + 1372 + + + + eng + + Journal Article + + + 2026 + 04 + 27 + +
+ + United States + JACS Au + 101775714 + 2691-3704 + + + halide perovskites + machine learning potentials + molecular dynamics + nanocrystals + phonons + surface ligands + + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 3 + 22 + + + 2026 + 4 + 20 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 4 + 56 + + + epublish + + 42212078 + PMC13213489 + 10.1021/jacsau.6c00434 + + + + + + 42212047 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2001-0370 + + 35 + 1 + + 2026 + + + Computational and structural biotechnology journal + Comput Struct Biotechnol J + + Deciphering the Structural Basis of Allosteric Inhibition of Mutant Epidermal Growth Factor Receptor and Identification of Novel Inhibitors. + + 0118 + 0118 + + 10.34133/csbj.0118 + + The non-small cell lung cancer-associated epidermal growth factor receptor (EGFR) mutant L858R/T790M confers resistance to first- and second-generation tyrosine kinase inhibitors (TKIs). Allosteric inhibitors have emerged as alternative therapeutic agents. Unlike orthosteric inhibitors, they preferentially stabilize EGFR in an inactive conformation. Hence, understanding the mechanistic basis of this inhibition is essential for designing potent allosteric inhibitors. In this study, we performed microsecond-scale molecular dynamics (MD) simulations on the inactive conformations of apo-EGFRWild and apo-EGFRL858R/T790M to explore how cancer-associated mutations induce a conformational shift toward the active kinase state. Simulations of allosteric inhibitor (EAI001)-bound EGFRL858R/T790M revealed that inhibitor binding enhances the inactive-state population by suppressing active-like excursions of the K745-E762 distance and modulating key structural elements including the αC-helix and activation loop. These findings revealed the structural/conformational basis of allosteric inhibition in EGFRL858R/T790M. It also emphasized the importance of MD simulations in allosteric drug design for assessing the ability of the inhibitor to enhance the population of the inactive state of the mutant EGFR. We have also standardized a virtual screening protocol involving screening of an allosteric TKI library using a structure-guided machine-learning-based protein-ligand affinity predictor scoring function and evaluated top-scoring candidates by MD simulations and the Molecular Mechanics Generalized Born Surface Area free energy calculation to identify molecules that can stabilize the inactive state of mutant EGFRL858R/T790M. This approach identified potential allosteric kinase inhibitors predicted to be more potent than EAI001. Overall, our results elucidate the structural/conformational basis of allosteric inhibition and highlight an MD-integrated approach for targeting dynamic allosteric sites, providing a framework for discovering next-generation modulators that can overcome TKI resistance. + Copyright © 2026 Sapna Pal and Debasisa Mohanty. + + + + Pal + Sapna + S + 0009-0008-3731-726X + + Bioinformatics Center, BRIC-National Institute of Immunology, New Delhi 110067, India. + + + + Mohanty + Debasisa + D + 0000-0002-3374-0588 + + Bioinformatics Center, BRIC-National Institute of Immunology, New Delhi 110067, India. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + Netherlands + Comput Struct Biotechnol J + 101585369 + 2001-0370 + + Competing interests: The authors declare that they have no competing interests. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 12 + 14 + + + 2026 + 5 + 2 + + + 2026 + 5 + 2 + + + 2026 + 5 + 29 + 4 + 56 + + + epublish + + 42212047 + PMC13213074 + 10.34133/csbj.0118 + 0118 + + + + + + 42211937 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2046-1402 + + 14 + + 2025 + + + F1000Research + F1000Res + + Artificial Intelligence in Project Management: Challenges, Strategies and Best Practices. + + 1357 + 1357 + + 10.12688/f1000research.169682.2 + + The application of Artificial Intelligence (AI) in project management is transforming decision-making processes, enhancing task execution, and improving risk management. This study aimed to elucidate the challenges raised by AI in project management (PM) using a scientometric and qualitative analysis. The research employs both quantitative and qualitative analysis using VOSviewer. The scientometric analyses reveal a substantial increase in AI in PM publications, with "project management," "artificial intelligence," "machine learning," "cost reduction," "decision making" and "supply chain management" as the most influential co-occurrence. The systematic review the implementation of challenges and strategies. The analysis identifies the publication trends, most significant keywords, leading institutions and researchers, prominent collaboration connections, primary publication venues, and the most-cited publications. This research enhances understanding of AI in PM, promotes the utilization of artificial intelligence technologies for gaining insights during certain phases of project development, and improves project management efficiency. The utilization of AI technologies, including machine learning, natural language processing, and predictive analytics, markedly improves project efficiency by enhancing decision-making, effectiveness, and risk mitigation. The recent rise of agentic and generative AI systems is transforming the role of AI in project environments from passive analytical support to active decision augmentation and workflow orchestration. Simultaneously, agentic AI systems, which are autonomous or semi-autonomous digital agents proficient in planning, performing activities, and engaging with various project data sources. These advancements indicate a shift from AI as a predictive instrument to AI as a collaborative cognitive framework within project ecosystems, prompting new inquiries on governance, accountability, and human-AI collaboration in project decision-making. + Copyright: © 2026 Mogbojuri A et al. + + + + Mogbojuri + Akinlo + A + 0000-0002-1615-0842 + + Department of Industrial Engineering, Durban University of Technology, Durban, KwaZulu-Natal, 4000, South Africa. + + + + Obiseye + Obiyemi + O + + Space Science Centre, Dept. of Electrical Power Engineering, Durban University of Technology, Durban, KwaZulu-Natal, South Africa. + + + + Wali + Arooj + A + + Faculty of Technology, University of Sunderland, Sunderland, England, UK. + + + + Dewa + Mendon + M + 0000-0002-0061-3654 + + Department of Industrial Engineering, Durban University of Technology, Durban, KwaZulu-Natal, 4000, South Africa. + + + + eng + + Journal Article + Systematic Review + + + 2026 + 04 + 15 + +
+ + England + F1000Res + 101594320 + 2046-1402 + + IM + + + Artificial Intelligence + + + Humans + + + Decision Making + + + + Artificial Intelligence + Decision-Making + Project Management + Qualitative analysis. + Scientometric approach + + No competing interests were disclosed. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 3 + 13 + + + 2026 + 5 + 29 + 4 + 55 + + + epublish + + 42211937 + PMC13215357 + 10.12688/f1000research.169682.2 + + + + + + 42211885 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 0920-9069 + + 78 + 4 + + 2026 + Aug + + + Cytotechnology + Cytotechnology + + Identification and validation of dyslipidemia and glutamate metabolism-associated gene diagnostic and therapeutic repurposing signature for Acute myocardial infarction patients: insights from integrative bioinformatic and multi-omic studies. + + 127 + 127 + + 10.1007/s10616-026-00979-9 + + + Objective Dyslipidemia and glutamate metabolism (DG) plays a crucial role in the pathogenesis of Acute myocardial infarction (AMI). Hence, deeper understanding of DG in AMI paves the way for the clinical applications of AMI. Methods In combination of PPI network and Limma framework, we first identified DG-associated shared differentially expressed genes (DEGs) from AMI patient bulk profile (GSE61144) and DG-associated gene list acquired from GEO and Genecard databases respectively. In another AMI patient bulk profile (GSE28454), we performed consensus clustering for classifying patient into 2 subgroups based on shared DG-related DEGs. By employing 2 machine learning algorithms (RF and SVM) in AMI patient training set (GSE109048), we identified DG-related hub gene, and then evaluated its diagnostic performance across GSE61144, GSE109048 and GSE66360(independent AMI patient bulk profile). Indeed, the molecular feature of DG-related hub gene was deciphered at GSE109048 and AMI single-cell transcriptomic profile (GSE269269). Finally, in vitro study illustrated the expression pattern of DG-related hub gene and DGIDB database with molecular docking proved the potential therapeutic framework for combating AMI. Results We discovered that DG-associated molecular subgroups and down-regulated expression gene signature CALR was diagnostic biomarkers for AMI, and Trentinoin should be considered as novel drug repurposing framework for the treatment of AMI. Conclusion We first pointed out that DG should be considered as diagnostic and therapeutic repurposing targets for the clinical translation of AMI, which can guide AMI patient personalized and precision medicine. + © The Author(s), under exclusive licence to Springer Nature B.V. 2026. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. + + + + Zhao + Hanwei + H + + Peripheral Vascular Disease Department, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, 150040 Harbin, China. + https://ror.org/01c0exk17 + grid.460046.0 + + + + Chen + Wenge + W + + Peripheral Vascular Disease Department, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, 150040 Harbin, China. + https://ror.org/01c0exk17 + grid.460046.0 + + + + Cheng + Ran + R + + Department of Blood Purification, Heilongjiang University of Chinese Medicine, 150040 Harbin, China. + https://ror.org/05x1ptx12 + grid.412068.9 + 0000 0004 1759 8782 + + + + Ren + Shuting + S + + Department of Blood Purification, Heilongjiang University of Chinese Medicine, 150040 Harbin, China. + https://ror.org/05x1ptx12 + grid.412068.9 + 0000 0004 1759 8782 + + + + Guo + Chuang + C + + Peripheral Vascular Disease Department, The First Affiliated Hospital of Heilongjiang University of Chinese Medicine, 150040 Harbin, China. + https://ror.org/01c0exk17 + grid.460046.0 + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Cytotechnology + 8807027 + 0920-9069 + + + Acute myocardial infarction + Diagnostic biomarkers + Drug reproposing + Dyslipidemia + Glutamate metabolism + + Competing interestsThe authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 2 + 12 + + + 2026 + 4 + 24 + + + 2027 + 8 + 1 + + + 2026 + 5 + 29 + 4 + 54 + + + ppublish + + 42211885 + PMC13212843 + 10.1007/s10616-026-00979-9 + 979 + + + + + + 42211859 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2214-0883 + + 16 + 5 + + 2026 + May + + + Journal of pharmaceutical analysis + J Pharm Anal + + Authentication of Linderae Radix through plant metabolomics coupled with a machine learning-enhanced in situ hyperspectral imaging approach. + + 101476 + 101476 + + 10.1016/j.jpha.2025.101476 + + Linderae Radix, a medicinally significant herb with a history of over 2000 years, is highly esteemed for its potential to promote longevity. Derived from the tuberous roots of Lindera aggregata (L. aggregata), it encounters difficulties in being distinguished from non-medicinal parts, such as non-fusiform taproots and old roots in the herbal drug market. To address the problem, this study developed a new strategy that integrates non-targeted plant metabolomics with a machine learning-enhanced hyperspectral imaging (HSI) approach for in situ quality assessment. Firstly, a comprehensive metabolomics analysis was conducted using ultra-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UPLC-QTOF-MS) and gas chromatography-mass spectrometry (GC-MS) to identify 25 and 48 differential metabolites, respectively. Then, combined with machine learning algorithms, HSI in the 400-1000 nm band achieved visual in situ assessment of different types of L. aggregata roots. Second derivative (2ndD)-Savitzky-Golay (SG) smoothing-logistic regression (LR) models achieved 93.33% accuracy of the test set in spectral classification. Moreover, spectral pre-processing and characteristic wavelength selection led to high prediction accuracies for the content of significant components in L. aggregata using standard normal variate (SNV)-competitive adaptive reweighted sampling (CARS)-least squares support vector machine (LSSVM) and SNV-CARS-extreme learning machine (ELM) ( + + + R + P + 2 + + + > 0.87 for the test set). This is the first study to provide a visual representation of the content of marker compounds in L. aggregata roots, offering a rapid, non-destructive method for assessing the quality of Linderae Radix. It scientifically justifies the medicinal use of tuberous roots and illuminates rapid quality evaluation through morphological identification. + © 2025 The Authors. + + + + Lv + Yangbin + Y + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + Zhejiang Provincial Key Laboratory of TCM for Innovative R&D and Digital Intelligent Manufacturing of TCM Great Health Products, Huzhou, Zhejiang, 313200, China. + + + + Sun + Hongwei + H + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + + Ding + Qiaoling + Q + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + + Chen + Bangxu + B + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + + Ye + Hongwei + H + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + + Xu + Ning + N + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + + Chu + Chu + C + + College of Pharmaceutical Science, Zhejiang University of Technology, Huzhou, Zhejiang, 313200, China. + + + Zhejiang Provincial Key Laboratory of TCM for Innovative R&D and Digital Intelligent Manufacturing of TCM Great Health Products, Huzhou, Zhejiang, 313200, China. + + + + eng + + Journal Article + + + 2025 + 10 + 28 + +
+ + China + J Pharm Anal + 101579451 + 2214-0883 + + + Different root types + Hyperspectral imaging + L. aggregata + Machine learning algorithms + Plant metabolomics + Visual in situ assessment + + The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2025 + 5 + 8 + + + 2025 + 10 + 22 + + + 2025 + 10 + 27 + + + 2026 + 5 + 29 + 4 + 54 + + + ppublish + + 42211859 + PMC13213644 + 10.1016/j.jpha.2025.101476 + S2095-1779(25)00293-X + + + + + + 42211855 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2468-0672 + + 26 + + 2026 + Jun + + + HardwareX + HardwareX + + SCUD - Smart Culinary Utility Device: Leveraging edge AI for battery optimization and operation cycles. + + e00780 + e00780 + + 10.1016/j.ohx.2026.e00780 + + Current day society pursues a lifestyle that is simpler, more efficient, reliable, faster, and progressively automated. Culinary Utility Device available in the market exhibit drawbacks including substantial investment costs, increased labor requirements, and excessive time consumption. Adding intelligence to the these devices may make once life happy. Embedded devices with limited resources are now poised to leverage machine learning techniques thanks to the convergence of Edge Computing and the Internet of Things (IoT). Traditional machine learning often demands significant computational resources for predictive tasks. TinyML, which focuses on Embedded Machine Learning, aims to transition a considerable portion of users from high-end devices to low-end gadgets. This paradigm strives to ensure the accuracy of learning models while enabling training and deployment on micro edge devices with resource constraints. It also seeks to optimize processing capabilities and enhance system resilience. This article provides an intuitive overview of Data-Driven State of Charge (SoC) estimation for Li-Ion Batteries. It begins by specification table followed by the hardware in context introducing the background of SoC estimation, followed by discussions on Hardware and signal , links to the design files and BOM in specific. The article delves into essential aspects related to Build and Operational instructions. In conclusion, the article addresses critical challenges and outlines a future road map. + © 2026 The Authors. + + + + Dvsr + Sesidhar + S + + ECE Department, MVSR Engineering College, Nadergul, Telangana, Bharat, 501510, India. + + + + Badachi + Chandrashekhar + C + + EEE Department, Ramaiah Institute of Technology, VTU, Belagavi, Bharat, 590018, India. + + + + Nagawaram + Chandrashekar + C + + ABB India, Hyderabad, Bharat, 500081, India. + + + + Kondoju + Panduranga Chary + PC + + Ford Motor Company, Dearborn, Michigan, 48124, United States. + + + + Salunkhe + Pavana + P + + MED, KLS Gogte Institute of Technology, VTU, Belagavi, Karnataka, Bharat, 590008, India. + + + + Chaurasia + Sahil Kumar + SK + + ETE Department, Ramaiah Institute of Technology, VTU, Belagavi, Bharat, 590018, India. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + England + HardwareX + 101710262 + 2468-0672 + + + Data driven model + Edge computing + Li-ion battery + Optimization + State of charge + Training and testing + + The authors declare no competing interests to the best of their knowledge. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2024 + 12 + 15 + + + 2026 + 4 + 21 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 4 + 54 + + + epublish + + 42211855 + PMC13214533 + 10.1016/j.ohx.2026.e00780 + S2468-0672(26)00040-4 + + + + + + 42211841 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-302X + + 17 + + 2026 + + + Frontiers in microbiology + Front Microbiol + + Intralesional microbial community signatures across histopathologic categories of tongue lesions. + + 1775141 + 1775141 + + 10.3389/fmicb.2026.1775141 + + The tissue-resident microbiome of oral epithelial lesions remains incompletely characterized, with most prior studies relying on saliva or surface sampling. This study aimed to characterize intralesional microbial communities across histopathologic categories of tongue lesions using formalin-fixed paraffin-embedded (FFPE) tissue. + This cross-sectional study characterized the intralesional microbiome of 63 formalin-fixed, paraffin-embedded tongue tissues, including fibroma (n = 15), low malignant potential dysplasia (LMP; n = 24), high malignant potential dysplasia (HMP; n = 24), and additional OSCC samples. Amplicon sequencing of the V3-V4 16S rRNA region was used to assess taxonomic composition, alpha and beta diversity, phylogenetic structure, predicted functional pathways (PICRUSt2), and machine-learning-based discrimination of lesion categories. + Microbial community profiles differed significantly across histopathologic groups. Non-dysplastic tissues exhibited higher richness and greater representation of commensal genera such as Streptococcus, Rothia, and Veillonella. Dysplastic tissues demonstrated reduced diversity and increased abundance of stress-adapted Proteobacteria including Bosea, Novosphingobium, Sphingomonas, and Pseudomonas. Beta diversity analyses revealed distinct community structures between fibroma, LMP, and HMP categories. Predicted functional profiles suggested differences in inferred metabolic potential, including pathways related to carbohydrate metabolism and xenobiotic degradation in dysplastic lesions. A supervised classifier demonstrated separation between groups (AUC 0.83-1.00), with several taxa contributing to classification; however, these findings should be interpreted cautiously given the sample size. + Intralesional microbial communities differ across fibroma, dysplasia, and OSCC of the tongue in both taxonomic composition and predicted functional profiles. These findings describe lesion-associated microbial signatures within tissue and provide a foundation for future studies incorporating longitudinal designs and multi-omics approaches to clarify their biological and clinical relevance. + Copyright © 2026 Madhavan, Lanzel, Dabdoub, Sidahmed, Hellstein, Drake, Banas and Ganesan. + + + + Madhavan + Karthik + K + + Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + + Lanzel + Emily + E + + Department of Oral Pathology, Radiology, and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + + Dabdoub + Shareef M + SM + + Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + Division of Biostatistics and Computational Biology, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + College of Dentistry, Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, United States. + + + + Sidahmed + Ahmed + A + + Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + + Hellstein + John + J + + Department of Oral Pathology, Radiology, and Medicine, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + + Drake + David Ray + DR + + College of Dentistry, Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, United States. + + + + Banas + Jeffrey A + JA + + College of Dentistry, Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, United States. + + + Department of Pediatric Dentistry, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + + Ganesan + Sukirth M + SM + + Department of Periodontics, College of Dentistry, University of Iowa, Iowa City, IA, United States. + + + College of Dentistry, Iowa Institute for Oral Health Research, University of Iowa, Iowa City, IA, United States. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Microbiol + 101548977 + 1664-302X + + + epithelial dysplasia + intralesional microbiome + microbial dysbiosis + oral premalignant lesion + oral squamous cell carcinoma + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author DD declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 12 + 25 + + + 2026 + 4 + 4 + + + 2026 + 4 + 23 + + + 2026 + 5 + 29 + 4 + 54 + + + epublish + + 42211841 + PMC13212205 + 10.3389/fmicb.2026.1775141 + + + + + + 42211776 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2673-7647 + + 6 + + 2026 + + + Frontiers in bioinformatics + Front Bioinform + + Predicting anthropometric body composition variables using 3D optical imaging and machine learning. + + 1722578 + 1722578 + + 10.3389/fbinf.2026.1722578 + + Accurate prediction of anthropometric body composition variables, such as Appendicular Lean Mass (ALM), Body Fat Percentage (BFP), and Bone Mineral Density (BMD), is essential for early diagnosis of several chronic diseases. Currently, researchers rely on Dual-Energy X-ray Absorptiometry (DXA) scans to measure these metrics; however, DXA scans are costly and time-consuming. This work proposes an alternative to DXA scans by applying statistical and machine learning models on biomarkers (height, volume, left calf circumference, etc.) obtained from 3D optical images. The dataset consists of 847 patients and was sourced from the Pennington Biomedical Research Center. Extracting patients' data in healthcare faces many technical challenges and legal restrictions. However, most supervised machine learning algorithms are inherently data-intensive, requiring a large amount of training data. To address this challenge, we compare the standard supervised to a semi-supervised p-Laplacian model, which leverages the limited data by incorporating the unlabeled patient information. To our knowledge, this paper is the first to demonstrate the application of a game-theoretic + + p + + -Laplacian model for regression in healthcare. Our + + p + + -Laplacian model yielded errors of + + ∼ + 13 + % + + for ALM, + + ∼ + 10 + % + + for BMD, and + + ∼ + 20 + % + + for BFP when the training data accounted for 10 percent of all data. Among the supervised algorithms we implemented, Support Vector Regression (SVR) performed the best for ALM and BMD, yielding errors of + + ∼ + 8 + % + + for both, whereas Least Squares SVR performed the best for BFP with + + ∼ + 11 + % + + error when trained on 80% the data. Our findings position the + + p + + -Laplacian model as a promising tool for healthcare applications, particularly in a data-constrained environment with limited labeled data. + Copyright © 2026 Agrahari, Bist, Pandey, Kapita, James, Knox, Heymsfield, Ramirez, Wolenski and Drenska. + + + + Agrahari + Gyaneshwar + G + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + Bist + Kiran + K + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + Pandey + Monika + M + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + Kapita + Jacob + J + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + James + Zachary + Z + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + Knox + Jackson + J + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + Heymsfield + Steven + S + + Pennington Biomedical Research Center, Baton Rouge, LA, United States. + + + + Ramirez + Sophia + S + + Pennington Biomedical Research Center, Baton Rouge, LA, United States. + + + + Wolenski + Peter + P + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + Drenska + Nadejda + N + + Department of Mathematics, Louisiana State University, Baton Rouge, LA, United States. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Bioinform + 9918227263306676 + 2673-7647 + + + 3D imaging + body composition + p-Laplacian-regression + semi-supervised learning + support vector regression (SVR) + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 10 + 10 + + + 2026 + 1 + 28 + + + 2026 + 2 + 17 + + + 2026 + 5 + 29 + 4 + 53 + + + epublish + + 42211776 + PMC13213389 + 10.3389/fbinf.2026.1722578 + 1722578 + + + + + + 42211756 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1098-1004 + + 2026 + + 2026 + + + Human mutation + Hum Mutat + + Integrative Multiomics Analysis Reveals Tumor-Associated Macrophage Heterogeneity and a Prognostic Signature in Gastric Cancer. + + 6130883 + 6130883 + + 10.1155/humu/6130883 + + Gastric cancer (GC) is characterized by a complex tumor microenvironment (TME) with substantial cellular heterogeneity. Tumor-associated macrophages (TAMs) represent the most abundant immune cell population in the TME and exhibit remarkable functional plasticity. This study integrated single-cell RNA-sequencing (scRNA-seq) data, bulk transcriptomics, and spatial transcriptomics to systematically characterize TAM heterogeneity and identify prognostic biomarkers in GC. ScRNA-seq analysis revealed nine major cell types (T cells, plasma cells, epithelial cells, fibroblasts, macrophages, endothelial cells, B cells, smooth muscle cells, and mast cells) and distinct macrophage subpopulations with tumor-specific expansion patterns. High-dimensional weighted gene coexpression network analysis identified coexpression modules enriched in GC-associated macrophages. Machine learning algorithms were employed to construct a prognostic signature, and the CoxBoost model demonstrated superior predictive performance across multiple cohorts. The seven-gene signature, including UPP1, VCAN, ELL2, ABCA1, TUBA1A, MX2, and TSPO, showed robust prognostic value in survival prediction. Spatial transcriptomic analysis further revealed distinct metabolic profiles and extensive cellular interaction networks mediated by UPP1-expressing TAMs. These findings provide a comprehensive atlas of TAM heterogeneity and establish novel prognostic biomarkers with potential therapeutic implications in GC. + Copyright © 2026 Zhaoyan Li et al. Human Mutation published by John Wiley & Sons Ltd. + + + + Li + Zhaoyan + Z + 0009-0005-3362-1895 + + Department of Traditional Chinese Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China, shsmu.edu.cn. + + + + Xu + Ming + M + 0000-0003-2767-7583 + + Department of Traditional Chinese Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China, shsmu.edu.cn. + + + + Huang + Yuqing + Y + 0009-0003-6141-5891 + + Department of Traditional Chinese Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China, shsmu.edu.cn. + + + + Huang + Chen + C + 0000-0002-6680-973X + + Department of Gastrointestinal Surgery, RenJi Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China, shsmu.edu.cn. + + + + Wu + Yuan + Y + 0000-0002-9445-7938 + + Department of Traditional Chinese Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China, shsmu.edu.cn. + + + + Lu + Jiafeng + J + 0009-0007-0132-1681 + + Department of Traditional Chinese Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China, shsmu.edu.cn. + + + + Zhang + Guangtao + G + 0000-0003-1189-724X + + Department of Interventional Oncology, Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Zheng + Lan + L + 0000-0003-4393-9264 + + Department of Traditional Chinese Medicine, Shanghai Jiao Tong University School of Medicine Affiliated Ruijin Hospital, Shanghai, China, shsmu.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + United States + Hum Mutat + 9215429 + 1059-7794 + + + + 0 + Biomarkers, Tumor + + + IM + + + Humans + + + Stomach Neoplasms + genetics + pathology + metabolism + mortality + + + Tumor-Associated Macrophages + metabolism + pathology + + + Multiomics + methods + + + Prognosis + + + Tumor Microenvironment + genetics + + + Biomarkers, Tumor + genetics + + + Gene Expression Regulation, Neoplastic + + + Gene Expression Profiling + + + Single-Cell Gene Expression Analysis + + + Gene Regulatory Networks + + + Transcriptome + + + Spatial Transcriptomics + + + Computational Biology + methods + + + Genetic Heterogeneity + + + + gastric cancer + machine learning + single-cell RNA-sequencing + spatial transcriptomics + tumor-associated macrophages + + The authors declare no conflicts of interest. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 3 + 6 + + + 2026 + 4 + 28 + + + 2026 + 4 + 30 + + + 2026 + 5 + 29 + 4 + 53 + + + epublish + + 42211756 + PMC13213716 + 10.1155/humu/6130883 + HUMU6130883 + + + + + + 42211715 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2296-2565 + + 14 + + 2026 + + + Frontiers in public health + Front Public Health + + Explainable AI for early developmental disability detection: a neuro-fuzzy approach. + + 1691449 + 1691449 + + 10.3389/fpubh.2026.1691449 + + Developmental disabilities affect approximately 1 in 6 children aged 3-17 years. The diagnostic process typically spans 2-3 years and disproportionately affects underserved populations. Traditional machine learning approaches for screening have demonstrated promising accuracy but often lack the transparency required for clinical acceptance. + We developed and validated an Adaptive Neuro-Fuzzy Inference System (ANFIS) using a longitudinal observational dataset of 5,000 children aged 1-6 years from 12 early childhood centers (4,311 typically developing; 689 with developmental disabilities, prevalence 13.8%). The ANFIS model implemented Gaussian membership functions and Sugeno fuzzy inference, incorporating age-adjusted ratios and cross-domain interaction features. Model performance was evaluated using standard binary classification metrics and five-fold stratified cross-validation. + All four assessment domains differed significantly between diagnostic groups (p < 0.001): Cognitive Scores (60.86 ± 14.51 vs. 51.58 ± 17.09), Behavioral Scores (55.82 ± 9.38 vs. 49.64 ± 11.21), Motor Skills (51.08 ± 7.79 vs. 45.08 ± 8.00), and Social Interaction (51.20 ± 11.66 vs. 42.13 ± 11.19). Family history was present in 85.6% of diagnosed children vs. 45.0% of typically developing children (p < 0.001). The ANFIS model achieved 96.0% accuracy, 87.5% sensitivity, 97.6% specificity, and AUC = 0.925 on a held-out test set of 100 cases. Five-fold cross-validation yielded a mean accuracy of 89.2% ± 3.4% (95% CI: [85.8, 92.6%]). Cognitive_Social_Ratio was the most influential diagnostic indicator. + The ANFIS approach demonstrates a clinically relevant balance between diagnostic accuracy and interpretability, positioning it as a viable clinical decision-support tool. Larger external validation studies across diverse populations are required before widespread implementation. + Copyright © 2026 Alanazi, Alqazlan, Alanazi and Benlaria. + + + + Alanazi + Adel Saber + AS + + College of Education, Jouf University, Sakakah, Saudi Arabia. + + + + Alqazlan + Sohil + S + + College of Education, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia. + + + + Alanazi + Rayan + R + + College of Computer and Information Sciences, Jouf University, Sakakah, Saudi Arabia. + + + + Benlaria + Houcine + H + + College of Business, Jouf University, Sakakah, Saudi Arabia. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Public Health + 101616579 + 2296-2565 + + IM + + + Humans + + + Developmental Disabilities + diagnosis + + + Fuzzy Logic + + + Child, Preschool + + + Child + + + Soft Computing + + + Infant + + + Female + + + Longitudinal Studies + + + Machine Learning + + + + Adaptive Neuro-Fuzzy Inference System (ANFIS) + autism spectrum disorder + developmental disabilities + early intervention + explainable AI + fuzzy logic + interpretable machine learning + pediatrics diagnosis + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 8 + 25 + + + 2026 + 3 + 13 + + + 2026 + 4 + 14 + + + 2026 + 5 + 29 + 4 + 52 + + + epublish + + 42211715 + PMC13213843 + 10.3389/fpubh.2026.1691449 + + + + + + 42211683 + + 2026 + 05 + 29 + +
+ + 1872-8286 + + 686 + + 2026 + Jul + 14 + + + Neurocomputing + Neurocomputing (Amst) + + CrunchLLM: Multitask LLMs for Structured Business Reasoning and Outcome Prediction. + 133754 + 10.1016/j.neucom.2026.133754 + + Predicting the success of startup companies, defined as achieving an exit through acquisition or IPO, is a critical problem in entrepreneurship and innovation research. Datasets such as Crunchbase provide both structured information (e.g., funding rounds, industries, and investor networks) and unstructured text (e.g., company descriptions), but effectively leveraging such heterogeneous data for prediction remains challenging. Traditional machine learning approaches often rely only on structured features and achieve moderate accuracy, while large language models (LLMs) offer strong reasoning capabilities but are not readily adapted to domain-specific business data. We present CrunchLLM, a domain-adapted and backbone-agnostic LLM framework for startup success prediction. CrunchLLM integrates structured company attributes with unstructured textual narratives and applies parameter-efficient fine-tuning together with prompt optimization to specialize foundation models for entrepreneurship data. Importantly, our framework introduces a self-verifiable multitask objective, in which the justification loss serves as a training-time constraint on classification, together with a hierarchically ordered input encoding that reduces the tendency of long unstructured company narratives to overshadow structured business attributes. These methodological innovations yield more reliable and feature-grounded predictions than conventional prompt-based LLM adaptation. Our approach achieves 89% accuracy on the Crunchbase startup success prediction task, significantly outperforming traditional classifiers and baseline LLMs. Beyond predictive performance, CrunchLLM generates interpretable reasoning traces that support its predictions, enhancing transparency and trustworthiness for financial and policy decision-makers. Overall, this work demonstrates how domain-aware LLM adaptation and structured-unstructured data fusion can advance predictive modeling of entrepreneurial outcomes, providing both a methodological framework and a practical tool for data-driven decision-making in venture capital and innovation policy. + + + + Sadia + Rabeya Tus + RT + + Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA. + + + + Cheng + Qiang + Q + + Department of Computer Science, University of Kentucky, Lexington, Kentucky, USA. + + + Institute for Biomedical Informatics, University of Kentucky, Lexington, Kentucky, USA. + + + + eng + + Journal Article + + + 2026 + 04 + 23 + +
+ + Netherlands + Neurocomputing (Amst) + 9884927 + 0925-2312 + + + Explainable AI + Large Language Models + Multitask learning + Parameter-efficient fine-tuning + Startup success prediction + + Declaration of Interest Statement The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 7 + 14 + + + 2026 + 5 + 29 + 4 + 52 + + + ppublish + + 42211683 + PMC13215686 + 10.1016/j.neucom.2026.133754 + 133754 + + + + + + 42211588 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2667-2421 + + 20 + + 2026 + Jun + + + IBRO neuroscience reports + IBRO Neurosci Rep + + Identification of EEG features during status epilepticus for prediction of emergent epilepsy phenotype in the mouse intra-amygdala kainic acid model using supervised learning. + + 729 + 740 + 729-740 + + 10.1016/j.ibneur.2026.04.013 + + Preclinical animal models are essential for investigating epilepsy mechanisms and evaluating novel therapies. In rodents, epilepsy can be induced by status epilepticus, leading to later spontaneous recurrent seizures (SRSs). However, inter-animal variability in seizure burden can limit suitability for drug studies. Here, we investigated whether early electroencephalography (EEG) recordings acquired during status epilepticus (∼40 min) can predict the later burden of spontaneous seizures in the intra-amygdala kainic acid (IAKA) model in mice. Spectral and statistical EEG features were extracted from IAKA model mice (n = 19) and used to train supervised classifiers, including Random Forest, Support Vector Machine (SVM), and Logistic Regression. Performance was evaluated using Leave-One-Out and 5-fold-stratified cross-validation. Feature robustness was enhanced through an intersection-based strategy combining ANOVA, Mutual Information, Random Forest importance, and SHAP analysis, together with engineered change-based (∆) features derived as post-KA minus baseline activity. Among the evaluated models, SVM achieved the strongest internal performance (weighted F1-score: 0.74), demonstrating that early EEG dynamics during status epilepticus encode prognostic information related to later SRSs burden. Importantly, the model reliably identified animals within the intermediate (normal) seizure-burden group that showed an average of two-week seizure count of 53 ± 11, relative to low (18 ± 11) and high (85 ± 43) groups. While generalization to an independent validation cohort (n = 11) was limited, this likely reflects biological heterogeneity and duration-dependent EEG variability. Overall, these findings highlight the relevance of early electrographic activity in shaping seizure-burden outcomes and provide a foundation for future longitudinal prognostic studies. + © 2026 The Authors. + + + + Abidi + Syed Muhammad Raza + SMR + + Performance Engineering Lab (PEL), School of Electronic Engineering, Insight Research Ireland Centre, Dublin City University, Dublin, Ireland. + + + + Mamad + Omar + O + + Department of Physiology and Medical Physics and FutureNeuro Research Ireland Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland. + + + + Higgins + Jordan + J + + Department of Physiology and Medical Physics and FutureNeuro Research Ireland Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland. + + + + Henshall + David C + DC + + Department of Physiology and Medical Physics and FutureNeuro Research Ireland Centre, RCSI University of Medicine and Health Sciences, Dublin, Ireland. + + + + Muntean + Gabriel-Miro + GM + + Performance Engineering Lab (PEL), School of Electronic Engineering, Insight Research Ireland Centre, Dublin City University, Dublin, Ireland. + + + + eng + + Journal Article + + + 2026 + 05 + 02 + +
+ + Netherlands + IBRO Neurosci Rep + 101775148 + 2667-2421 + + + Baseline recordings + EEG biomarkers + Kainic acid mouse model + Machine learning + Seizure burden severity prediction + + The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 1 + 23 + + + 2026 + 4 + 20 + + + 2026 + 4 + 30 + + + 2026 + 5 + 29 + 4 + 51 + + + epublish + + 42211588 + PMC13213280 + 10.1016/j.ibneur.2026.04.013 + S2667-2421(26)00062-X + + + + + + 42211584 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2590-1575 + + 36 + + 2026 + May + + + Food chemistry: X + Food Chem X + + Multispectral imaging for zeaxanthin content in the exocarp of chili peppers. + + 103992 + 103992 + + 10.1016/j.fochx.2026.103992 + + This study developed a model to predict zeaxanthin content in peppers using multispectral imaging and chemical data. A one-dimensional convolutional neural network (1D CNN) model was identified as the optimal single-modal model after comparing four machine learning algorithms. On the prediction dataset, the model achieved a determination coefficient (Rp + + 2 + ) of 0.7639. Building upon the 1D CNN framework, a multimodal feature fusion model (MCSF) was constructed by integrating the chemical measurements of capsanthin and total carotenoid contents using a multilayer perceptron. This enhanced model demonstrated excellent predictive accuracy and robustness, with Rp + + 2 + values of 0.9318 and 0.9211 across different spectral ranges. For high-throughput detection purposes, a simplified model that replaced measured capsanthin with a comprehensive red index still performed well, with an Rp + + 2 + of 0.8912 and an RPD of 3.11. This strategy provides a new solution for the efficient spectral detection of plant chemicals affected by multicollinearity in their absorption spectra. + © 2026 The Authors. + + + + Liang + Wei + W + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Gao + Wei + W + + Chenguang Biotech Group CO., LTD, China. + + + + Lv + Xingna + X + + Chenguang Biotech Group CO., LTD, China. + + + + Zhao + Jinxiang + J + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Tian + Xuechun + X + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Di + Shuaitao + S + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Li + Qiang + Q + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Zhang + Dongfang + D + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Luo + ShuangXia + S + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + Chen + XuePing + X + + Key Laboratory of Vegetable Germplasm Innovation and Utilization of Hebei, Collaborative Innovation Center of Vegetable Industry in Hebei, College of Horticulture, Hebei Agricultural University, Baoding 071000, China. + + + + eng + + Journal Article + + + 2026 + 05 + 18 + +
+ + Netherlands + Food Chem X + 101751436 + 2590-1575 + + + Capsanthin (PubChem CID: 5281228) + Carotenoids (PubChem CID: 11227325) + Multimodal feature fusion + Multispectral imaging + One-dimensional convolutional neural network + Prediction model + Zeaxanthin + Zeaxanthin (PubChem CID: 5280899) + + The authors declare that we have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2025 + 10 + 27 + + + 2026 + 4 + 3 + + + 2026 + 5 + 14 + + + 2026 + 5 + 29 + 4 + 51 + + + epublish + + 42211584 + PMC13213780 + 10.1016/j.fochx.2026.103992 + S2590-1575(26)00513-4 + + + + + + 42211579 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2813-1193 + + 5 + + 2026 + + + Frontiers in neuroimaging + Front Neuroimaging + + Evaluating the methodological quality of coordinate-based meta-analyses: the qual-CBMA checklist. + + 1753543 + 1753543 + + 10.3389/fnimg.2026.1753543 + + Voxel-based meta-analyses-also known as coordinate-based meta-analyses (CBMAs)-are powerful tools for synthesizing evidence from neuroimaging studies in human neuroscience, including investigations of psychological functions and differences in brain disorders. To achieve their full potential in accurately assessing the evidence, CBMAs should adhere to established best-practicmpe guidelines, such as the "Ten Simple Rules" published in 2018. Yet, even when studies report following these recommendations, the degree to which individual items are applicable or fully addressed is often unclear. To better support the evaluation of methodological rigor-which the 10 rules already promote but are not always consistently applied-, the developers of the most used CBMA methods followed a Delphi-style iterative process to create a reporting checklist focused on the methodological quality of CBMAs (Qual-CBMA). Qual-CBMA comprises criteria (e.g., preregistration, systematic search, homogeneous study characteristics, etc.) that authors should verify and comment on explicitly in the checklist (and, when unmet, also in the manuscript). The checklist encourages rigor and transparency by prompting authors to identify potential methodological limitations and to discuss their relevance-or irrelevance-in the context of their specific study. The checklist is designed as an aid to make reporting clearer and more transparent, not as a tool for evaluating whether authors have done something incorrectly. In this context, a high-quality CBMA is not defined by meeting every criterion, but by clearly commenting on the criteria-and explaining when unmet criteria are appropriately not applicable given the study's objectives. We encourage authors to submit the Qual-CBMA checklist, together with their accompanying comments, when publishing new CBMAs, thereby reinforcing transparency and rigorous methodology and advancing understanding in cognitive neuroscience and clinical conditions. + Copyright © 2026 Radua, Fortea, Albajes-Eizagirre, Eickhoff, Fullana, Fusar-Poli, Mataix-Cols, Shin, Solanes, Tahmasian, Vieta, Vilajosana, Eickhoff and Müller. + + + + Radua + Joaquim + J + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + Universitat de Barcelona, Institut de Neurociències, Barcelona, Spain. + + + Mental Health Research Networking Center (CIBERSAM), Madrid, Spain. + + + + Fortea + Lydia + L + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + + Albajes-Eizagirre + Anton + A + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + Universitat de Barcelona, Institut de Neurociències, Barcelona, Spain. + + + Mental Health Research Networking Center (CIBERSAM), Madrid, Spain. + + + + Eickhoff + Claudia + C + + Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany. + + + + Fullana + Miquel A + MA + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + Mental Health Research Networking Center (CIBERSAM), Madrid, Spain. + + + Hospital Clinic de Barcelona, Clinical Institute of Neuroscience, Barcelona, Spain. + + + + Fusar-Poli + Paolo + P + + Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy. + + + OASIS Service, South London and Maudsley NHS Foundation Trust, London, United Kingdom. + + + Department of Psychosis Studies, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom. + + + + Mataix-Cols + David + D + + Centre for Psychiatric Research, Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden. + + + + Shin + Jae Il + JI + + Department of Pediatrics, Yonsei University College of Medicine, Seoul, Republic of Korea. + + + + Solanes + Aleix + A + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + + Tahmasian + Masoud + M + + Research Centre Jülich, Institute of Neuroscience and Medicine (INM-7), Jülich, Germany. + + + Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany. + + + Department of Nuclear Medicine, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany. + + + + Vieta + Eduard + E + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + Universitat de Barcelona, Institut de Neurociències, Barcelona, Spain. + + + Mental Health Research Networking Center (CIBERSAM), Madrid, Spain. + + + Hospital Clinic de Barcelona, Clinical Institute of Neuroscience, Barcelona, Spain. + + + + Vilajosana + Enric + E + + Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain. + + + Universitat de Barcelona, Institut de Neurociències, Barcelona, Spain. + + + + Eickhoff + Simon B + SB + + Research Centre Jülich, Institute of Neuroscience and Medicine (INM-7), Jülich, Germany. + + + Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany. + + + + Müller + Veronika I + VI + + Research Centre Jülich, Institute of Neuroscience and Medicine (INM-7), Jülich, Germany. + + + Institute of Systems Neuroscience, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Neuroimaging + 9918402387106676 + 2813-1193 + + + activation likelihood estimation + checklist + meta-analysis + neuroimaging + seed-based d mapping + + EdV has received grants and served as consultant, advisor or CME speaker for the following entities: AB-Biotics, Abbott, AbbVie, Adamed, Adium, Alcediag, Angelini, Biogen, Beckley-Psytech, Biohaven, Boehringer-Ingelheim, Casen-Recordati, Celon Pharma, Compass, Dainippon Sumitomo Pharma, Esteve, Ethypharm, Ferrer, Gedeon Richter, GH Research, Glaxo-Smith Kline, HMNC, Intra-Cellular therapies, Idorsia, Johnson & Johnson, Lundbeck, Luye Pharma, Medincell, Merck, Mitsubishi Tanabe Pharma, Newron, Novartis, Organon, Orion Corporation, Otsuka, Roche, Rovi, Sage, Sanofi-Aventis, Sunovion, Takeda, Teva, and Viatris, outside the submitted work. JR has received CME honoraria from Inspira Networks and Evidenze Health for machine learning courses promoted by Adamed and Viatris, outside the submitted work. The remaining author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The authors JR, MT, PF-P and VM declared that they were an editorial board member of Frontiers at the time of submission. This had no impact on the peer review process and the final decision. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2025 + 11 + 24 + + + 2026 + 2 + 6 + + + 2026 + 3 + 23 + + + 2026 + 5 + 29 + 4 + 51 + + + epublish + + 42211579 + PMC13212178 + 10.3389/fnimg.2026.1753543 + + + + + + 42211558 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2053-714X + + 13 + 9 + + 2026 + May + + + National science review + Natl Sci Rev + + Subsurface hydrogen as a hidden driver of copper surface reconstruction in CO2 electroreduction. + + nwag128 + nwag128 + + 10.1093/nsr/nwag128 + + Copper (Cu) undergoes significant surface reconstruction during CO2 electroreduction, which is strongly modulated and accelerated by reaction intermediates, yet the atomic-scale mechanism remains far behind the experimental observations. By integrating machine-learning interatomic potentials with large-scale grand canonical Monte Carlo simulations, we systematically investigated *CO- and *H-induced surface roughening across various Cu facets. Our simulations demonstrate that high surface *H (*Hsur) coverage facilitates subsurface hydrogen (*Hsub) incorporation on (100)-dominated Cu facets under typical working conditions (-1 V vs. reversible hydrogen electrode), while such penetration is negligible on (111)-like surfaces. This facet dependence is primarily attributed to a *H-induced hexagonal surface reconstruction observed on (100)-dominated facets, a process driven primarily by geometric rather than electronic effects. Specifically, high *H coverage triggers a partial transition of Cu atoms from ideal 4-fold hollow sites to more closely packed 3-fold arrangements. The local densification expands the spacing at the remaining 4-fold sites, thereby reducing the energy barrier for *Hsub migration into the subsurface. Further analysis reveals that *Hsub alone is sufficient to induce Cu adatom formation, even in the absence of nearby *CO, uncovering a revised structural evolution paradigm for Cu surface roughening. We propose an alloying strategy using low hydrogen affinity metals (Zn, Al, Ga) to effectively suppress *Hsur incorporation, offering a promising pathway for designing Cu-based catalysts with long-term stability. + © The Author(s) 2026. Published by Oxford University Press on behalf of China Science Publishing & Media Ltd. + + + + Zhang + Siwang + S + + College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, School of Life Sciences, Xiamen University, Xiamen 361000, China. + + + + Lv + Hang-Biao + HB + + College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, School of Life Sciences, Xiamen University, Xiamen 361000, China. + + + + Shi + Zhong-Zhang + ZZ + + College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, School of Life Sciences, Xiamen University, Xiamen 361000, China. + + + + Wang + Ruoxuan + R + + College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, School of Life Sciences, Xiamen University, Xiamen 361000, China. + + + + Zheng + Shisheng + S + + College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, School of Life Sciences, Xiamen University, Xiamen 361000, China. + + + + Li + Jian-Feng + JF + 0000-0003-1598-6856 + + College of Energy, State Key Laboratory of Physical Chemistry of Solid Surfaces, iChEM, College of Chemistry and Chemical Engineering, College of Materials, Institute of Artificial Intelligence, School of Life Sciences, Xiamen University, Xiamen 361000, China. + + + Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361000, China. + + + + eng + + Journal Article + + + 2026 + 03 + 06 + +
+ + China + Natl Sci Rev + 101633095 + 2053-714X + + + CO2 reduction + Cu surface reconstruction + grand canonical Monte Carlo + machine-learning interatomic potentials + subsurface H + + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2025 + 12 + 24 + + + 2026 + 2 + 10 + + + 2026 + 2 + 25 + + + 2026 + 5 + 29 + 4 + 50 + + + epublish + + 42211558 + PMC13214988 + 10.1093/nsr/nwag128 + nwag128 + + + + + + 42211512 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + Machine learning-based multicenter prediction of postoperative sepsis in emergency colon cancer: role of surgical approach and inflammatory markers. + + 1845236 + 1845236 + + 10.3389/fonc.2026.1845236 + + Postoperative sepsis occurs at a relatively high incidence following emergency colon cancer surgery, and early identification of high-risk patients is crucial for improving clinical outcomes. However, there is currently a lack of systematic risk prediction models specifically tailored for patients undergoing emergency colon cancer surgery. This study aimed to identify risk factors associated with postoperative sepsis and to develop a clinically applicable machine learning-based prediction model. + This was a multicenter retrospective cohort study including patients who underwent emergency colon cancer surgery between January 2020 and January 2025. Perioperative variables were systematically collected, encompassing preoperative, intraoperative, and early postoperative data, including demographic characteristics, comorbidities, laboratory findings, and surgical features. Univariate and multivariate logistic regression analyses were first performed to identify independent risk factors. Subsequently, five machine learning algorithms-multilayer perceptron (MLP), random forest (RF), support vector machine (SVM), k-nearest neighbor (KNN), and extreme gradient boosting (XGBoost)-were applied to evaluate feature importance and construct predictive models. Model performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), k-fold cross-validation, and external validation. SHapley Additive exPlanations (SHAP) were used to interpret the contribution of key features. + Surgical approach, intraoperative hypothermia, acidosis, hypoxemia, hypoalbuminemia, and postoperative inflammatory markers (procalcitonin [PCT] and neutrophil-to-lymphocyte ratio [NLR]) were identified as independent high-risk factors for postoperative sepsis in emergency colon cancer patients. All five machine learning models demonstrated good discriminative performance, with the MLP model achieving the best overall performance. It exhibited stable discrimination and low variability in both internal validation and external independent validation cohorts. SHAP analysis further confirmed the contribution of the identified risk factors to model predictions. + The MLP-based risk prediction model developed in this study effectively identifies patients at high risk of postoperative sepsis following emergency colon cancer surgery. It provides a scientific basis for early perioperative intervention and individualized management, and offers valuable support for clinical risk stratification and optimization of therapeutic strategies. + Copyright © 2026 Du, Sun, Chen, Sun, Shi, Jiang, Zhan and Zhang. + + + + Du + Wenyi + W + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Sun + Tao + T + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Chen + Wentan + W + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Sun + Min + M + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Shi + Chengyu + C + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Jiang + Chao + C + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Zhan + Feng + F + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + Zhang + Yu + Y + + Department of Hepatobiliary Surgery, The Affiliated Yixing Hospital of Jiangsu University, Yixing, China. + + + General Surgery Centre, Yixing People's Hospital Affiliated to Jiangsu University, Yixing, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + colon cancer + emergency + inflammatory response + machine learning + predictive model + sepsis + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 4 + 1 + + + 2026 + 4 + 25 + + + 2026 + 4 + 28 + + + 2026 + 5 + 29 + 4 + 50 + + + epublish + + 42211512 + PMC13212220 + 10.3389/fonc.2026.1845236 + + + + + + 42211458 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-2392 + + 17 + + 2026 + + + Frontiers in endocrinology + Front Endocrinol (Lausanne) + + Proteins at the intersection of circadian rhythms and metabolic dysfunction-associated steatotic liver disease: an 18-protein panel as a novel predictive biomarker set. + + 1836392 + 1836392 + + 10.3389/fendo.2026.1836392 + + Disruption of circadian rhythmicity is highly prevalent in modern society and contributes to the epidemic of metabolic disorders. However, the relationship between circadian rhythm disorder and Metabolic Dysfunction-Associated Steatotic Liver (MASLD) remained poorly elucidated. Relative amplitude (RA), a metric quantifying the degree of disruption in rest-activity circadian rhythms, was calculated based on accelerometry data from UK Biobank. Basic characteristic analysis and multivariable logistic regression was used to analyze the association between RA and MASLD. Mediation analysis, functional enrichment analysis (Kyoto Encyclopedia of Genes and Genomes, KEGG) and Protein-Protein Interaction (PPI) network analysis and multiple machine learning algorithms, including Random Forest, XGBoost, logistic regression, and Support Vector Machine (SVM), were employed to identify potential protein biomarkers and construct a predictive model for RA-related MASLD risk assessment. Among 81,430 UK Biobank participants with valid accelerometry, RA was lower in individuals with MASLD versus those without (P < 0.001). Lower RA was associated to higher prevalence of MASLD (crude OR = 2.61; 95% CI [2.42, 2.81]; P < 0.001), and the association remained significant in a fully adjusted model (adjusted OR = 1.15; 95% CI [1.02,1.31]; P = 0.026), demonstrating RA as a factor independently associated with MASLD. Furthermore, 18 candidate proteins were identified as potential biomarkers for predicting RA-related MASLD. The 18-protein model demonstrated excellent predictive performance across multiple machine learning methods, with high Area Under the Curve (AUC) values in Receiver Operating Characteristic (ROC) analysis: Random Forest (AUC = 0.944), XGBoost (AUC = 0.946), Logistic Regression (AUC = 0.946), and SVM (AUC = 0.947). The model also exhibited superior discriminatory ability in predictive probability distribution, indicating strong predictive potential. Additionally, an online predictive tool based on this model has been contributed. Lower RA is independently associated with MASLD. We highlight 18 overlapping plasma proteins linked to both RA and MASLD as potential biomarkers for predicting RA-associated MASLD and as candidate therapeutic targets. + Copyright © 2026 Wang, Zhai, Lian and Zhao. + + + + Wang + Yiyi + Y + + Centre for Lipid Research & Chongqing Key Laboratory of Metabolism on Lipid and Glucose, Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, Department of Infectious Diseases, the Second Affiliated Hospital, Chongqing Medical University, Chongqing, China. + + + Chongqing Medical University-University of Leister Joint Institute, Chongqing Medical University, Chongqing, China. + + + + Zhai + Qilong + Q + + Centre for Lipid Research & Chongqing Key Laboratory of Metabolism on Lipid and Glucose, Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, Department of Infectious Diseases, the Second Affiliated Hospital, Chongqing Medical University, Chongqing, China. + + + + Lian + Xuemei + X + + Centre for Lipid Research & Chongqing Key Laboratory of Metabolism on Lipid and Glucose, Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, Department of Infectious Diseases, the Second Affiliated Hospital, Chongqing Medical University, Chongqing, China. + + + School of Public Health, Chongqing Medical University, Chongqing, China. + + + + Zhao + Lei + L + + Centre for Lipid Research & Chongqing Key Laboratory of Metabolism on Lipid and Glucose, Key Laboratory of Molecular Biology for Infectious Diseases (Ministry of Education), Institute for Viral Hepatitis, Department of Infectious Diseases, the Second Affiliated Hospital, Chongqing Medical University, Chongqing, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Endocrinol (Lausanne) + 101555782 + 1664-2392 + + + + 0 + Biomarkers + + + IM + + + Humans + + + Biomarkers + metabolism + blood + + + Circadian Rhythm + physiology + + + Male + + + Female + + + Non-alcoholic Fatty Liver Disease + metabolism + + + Metabolic Diseases + metabolism + + + + MASLD + UK Biobank + circadian rhythm + machine learning + prediction model + proteomics + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 3 + 22 + + + 2026 + 4 + 25 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 4 + 49 + + + epublish + + 42211458 + PMC13212133 + 10.3389/fendo.2026.1836392 + + + + + + 42211395 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2632-2498 + + 8 + 1 + + 2026 + Jan-Dec + + + Neuro-oncology advances + Neurooncol Adv + + Predictive radiomics for evaluation of cancer immune signature in glioblastoma: The PRECISE-GBM study. + + vdag115 + vdag115 + + 10.1093/noajnl/vdag115 + + Radiogenomics allows identification of radiological biomarkers for genomic phenotypes. In glioblastoma, these biomarkers could potentially complement patient stratification strategies. We aim to develop and analytically validate radiological biomarkers that capture immune cell signatures within IDH-wildtype glioblastoma microenvironment using radiogenomic analysis. + This was a retrospective multicenter study using curated open-access anonymized imaging and genomic data from TCGA-GBM, CPTAC, IvyGAP, REMBRANDT, and CGGA datasets. Imaging data consisted of MRI-based radiomic features extracted from necrotic core, enhancing and edema regions of deep learning-based autosegmented tumors. Radiomic feature selections were performed using nested cross-validated LASSO. Support vector machine and ensemble models were trained using seventeen immune and cell-specific score labels extracted from deconvoluted transcriptomic data using pan-cancer and glioblastoma immune signature matrices as reference standards. Seventeen classifier models trained in 3 cross-cohort strategies were validated on 3 held-out datasets assessing stability and generalizability. + One-hundred-and-seventy-six patients were included in the study. The immune-related radiomic signatures obtained after feature selection were shape, first order and higher order radiomic features. Models predicting macrophage subtype immune signature showed stable mean performance on balanced accuracy (0.67) and precision (0.89) metrics for 3 independent holdout datasets with ensemble model outperforming support vector machine model. + Radiogenomic models noninvasively predicted the macrophage subtype M0 immune signature in IDH-wildtype glioblastoma. These biomarkers have the potential to stratify patients for immunotherapy within prospective glioblastoma clinical trials. + © The Author(s) 2026. Published by Oxford University Press, the Society for Neuro-Oncology and the European Association of Neuro-Oncology. + + + + Ghimire + Prajwal + P + 0000-0003-2884-956X + + School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. + + + Department of Neurosurgery, King's College Hospital, London, UK. + + + + Li + Junjie + J + + Department of Neuroradiology, Beijing Tiantan Hospital, Beijing, China. + + + + Yaou + Liu + L + + Department of Neuroradiology, Beijing Tiantan Hospital, Beijing, China. + + + + Modat + Marc + M + + School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. + + + + Booth + Thomas + T + 0000-0003-0984-3998 + + School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK. + + + Department of Neuroradiology, King's College Hospital, London, UK. + + + + eng + + Journal Article + + + 2026 + 05 + 02 + +
+ + England + Neurooncol Adv + 101755003 + 2632-2498 + + + biomarker + glioblastoma + immunotherapy + machine learning + radiogenomics + + The authors declare no known competing interests. + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2025 + 12 + 30 + + + 2026 + 4 + 24 + + + 2026 + 5 + 29 + 4 + 48 + + + epublish + + 42211395 + PMC13213608 + 10.1093/noajnl/vdag115 + vdag115 + + + + + + 42211363 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2046-2069 + + 16 + 31 + + 2026 + May + 22 + + + RSC advances + RSC Adv + + Efficient preparation of size-controlled sodium alginate microspheres via adaptive Bayesian optimization of the spray process. + + 28200 + 28208 + 28200-28208 + + 10.1039/d6ra02906j + + Preparing sodium alginate (SA) microspheres via spray-precipitation challenges precise size control due to complex parameter coupling. We propose an intelligent framework integrating adaptive Bayesian optimization (BO) with microfluidic spraying to maximize the yield of 90-110 µm microspheres. Utilizing a Gaussian process regression model and Latin hypercube sampling, the framework demonstrated exceptional efficiency. Initializing with 10 prior data points achieved convergence in just 12 iterations, reducing iteration cost by 29.4% compared to using 5 priors. Under optimal conditions, the predicted target droplet proportion (18.60%) precisely matched experiments (18.34%), yielding 18.75% target-sized SA gel microspheres post-curing. Additionally, SHAP analysis revealed that gas and liquid pressures dominate size distribution, elucidating the physical mechanism behind multiple local optima via multi-feature compensation. This study provides an efficient, low-cost strategy for customizing polymer microspheres, establishing a robust machine-learning paradigm for optimizing complex multiphase flows. + This journal is © The Royal Society of Chemistry. + + + + Feng + Shi + S + 0000-0002-3060-7538 + + China Nuclear Power Engineering Co., Ltd Beijing 100142 China. + + + + Liu + Jing + J + + Dalian Key Laboratory of Intelligent Chemistry, CR Belt and Road Joint Laboratory on Intelligent Chemistry and Advanced Materials of Liaoning Province, School of Chemistry, Dalian University of Technology Dalian 116024 China. + + + + Li + Zhuxin + Z + + Dalian Key Laboratory of Intelligent Chemistry, CR Belt and Road Joint Laboratory on Intelligent Chemistry and Advanced Materials of Liaoning Province, School of Chemistry, Dalian University of Technology Dalian 116024 China. + + + + Tao + Shengyang + S + 0000-0002-0567-8860 + + State Key Laboratory of Fine Chemicals, Frontier Science Center for Smart Materials, Dalian University of Technology Dalian 116024 China taosy@dlut.edu.cn. + + + Dalian Key Laboratory of Intelligent Chemistry, CR Belt and Road Joint Laboratory on Intelligent Chemistry and Advanced Materials of Liaoning Province, School of Chemistry, Dalian University of Technology Dalian 116024 China. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + England + RSC Adv + 101581657 + 2046-2069 + + There are no conflicts to declare. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 4 + 7 + + + 2026 + 5 + 18 + + + 2026 + 5 + 29 + 4 + 48 + + + epublish + + 42211363 + PMC13213568 + 10.1039/d6ra02906j + d6ra02906j + + + + + + 42211343 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2635-0041 + + 6 + 1 + + 2026 + + + Bioinformatics advances + Bioinform Adv + + CardioVar: a machine learning framework for pathogenicity prediction of cardiomyopathy genetic variants. + + vbag135 + vbag135 + + 10.1093/bioadv/vbag135 + + CardioVar is a machine learning framework developed to predict the pathogenicity of cardiomyopathy-associated genetic variants, providing a rapid and disease-focused classification score to support variant interpretation in cardiogenetic workflows. + This approach supports variant interpretation within cardiac-related genes and may complement existing pan-disease models within a cardiomyopathy-specific context. In prospective case study settings, CardioVar demonstrated an 85% concordance with conventional tertiary analysis results. + https://github.com/nibrasissa/CardioVar. + © The Author(s) 2026. Published by Oxford University Press. + + + + Al-Mahrami + Nabras + N + 0009-0003-0328-2883 + + Medical Laboratory Sciences Program, Oman College of Health Sciences, Muscat, PO 112 , Oman. + + + + Albalushi + Aaisha + A + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al Hattali + Fahad + F + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al Balushi + Mashael + M + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al Shamsi + Bushra + B + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al-Oraimi + Musallam + M + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al Lawati + Tuqa + T + + National Heart Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al-Rawahi + Mohamed + M + + National Heart Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Alhashmi + Nadia + N + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + Al-Amri + Ahmed + A + + National Genetic Center, Royal Hospital, Muscat, PO 111, Oman. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + England + Bioinform Adv + 9918282081306676 + 2635-0041 + + None declared. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 43 + + + 2025 + 12 + 2 + + + 2026 + 4 + 26 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 4 + 48 + + + epublish + + 42211343 + PMC13215092 + 10.1093/bioadv/vbag135 + vbag135 + + + + + + 42211310 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-2295 + + 17 + + 2026 + + + Frontiers in neurology + Front Neurol + + Machine learning-based prediction of ischemic cardio-cerebrovascular events after endovascular or microsurgical treatment of unruptured intracranial aneurysms and risk stratification by the early post-treatment triglyceride-glucose index. + + 1829149 + 1829149 + + 10.3389/fneur.2026.1829149 + + Ischemic cardio-cerebrovascular events (ICCEs), including acute coronary syndrome and ischemic cerebral infarction, remain clinically important complications after endovascular or microsurgical treatment of unruptured intracranial aneurysms (UIAs). However, early identification of patients at high post-treatment ischemic risk remains challenging, and reliable risk-stratification tools are lacking. + To develop a machine learning-based framework for predicting ischemic cardio-cerebrovascular events (ICCEs) within 6 months after treatment in patients with unruptured intracranial aneurysms (UIAs) and to evaluate the risk-stratification value of the early post-treatment triglyceride-glucose (TyG) index. + A total of 1,954 patients with UIAs who underwent microsurgical or endovascular treatment between December 2021 and December 2024 were enrolled from the China Treatment Trial for Unruptured Intracranial Aneurysm (ChTUIA) registry. Nine predictive models, including logistic regression as a baseline comparator, were evaluated after feature selection using least absolute shrinkage and selection operator regression and the Boruta algorithm. The synthetic minority over-sampling technique was used to address class imbalance. Model performance was assessed by discrimination, calibration, and clinical utility metrics, and the optimal model was interpreted using SHapley Additive exPlanations. The association between the post-treatment day-3 TyG index and ICCEs was analyzed using multivariable Cox regression, restricted cubic spline analysis, and subgroup analyses. + During the 6-month follow-up, 240 of 1,954 patients (12.28%) developed ICCEs. Of the included patients, 1,343 underwent endovascular treatment and 611 underwent microsurgical treatment. Among all models, CatBoost achieved the best overall performance, with an accuracy of 0.875 and an area under the receiver operating characteristic curve (AUROC) of 0.945 (95% CI, 0.927-0.963). SHAP analysis identified the post-treatment TyG index as one of the most influential predictors. In multivariable analysis, each 1-unit increase in TyG was associated with a 2.61-fold higher hazard of ICCEs (HR = 2.61, 95% CI: 2.29-2.96, p < 0.001). Restricted cubic spline analysis showed a nonlinear positive association with a clear threshold effect at approximately TyG = 7. + The CatBoost model demonstrates strong predictive performance for post-treatment ICCEs in UIA patients. The early post-treatment TyG index is independently and nonlinearly associated with ICCE risk and may serve as a simple, practical metabolic marker for individualized perioperative risk stratification. + Copyright © 2026 He, Chen, Liu, Zheng, Li and Wang. + + + + He + Yingchao + Y + + Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. + + + Department of Neurology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + + Chen + Shuheng + S + + Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. + + + Department of Neurology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + + Liu + Deshan + D + + Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. + + + Department of Neurology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + + Zheng + Zheng + Z + + Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. + + + Department of Neurology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + + Li + Yongkun + Y + + Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. + + + Department of Neurology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + + Wang + Yinzhou + Y + + Shengli Clinical Medical College of Fujian Medical University, Fuzhou, China. + + + Department of Neurology, Fuzhou University Affiliated Provincial Hospital, Fuzhou, Fujian, China. + + + Fujian Key Laboratory of Medical Analysis, Fujian Academy of Medical Sciences, Fuzhou, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Neurol + 101546899 + 1664-2295 + + + CatBoost + ischemic cardio-cerebrovascular events + machine learning + risk stratification + triglyceride-glucose index + unruptured intracranial aneurysm + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 3 + 12 + + + 2026 + 4 + 7 + + + 2026 + 4 + 17 + + + 2026 + 5 + 29 + 4 + 48 + + + epublish + + 42211310 + PMC13212117 + 10.3389/fneur.2026.1829149 + + + + + + 42211301 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-2295 + + 17 + + 2026 + + + Frontiers in neurology + Front Neurol + + Integrating multi-omics and machine learning to explore the role of amino acid metabolism in intervertebral disk degeneration. + + 1808282 + 1808282 + + 10.3389/fneur.2026.1808282 + + Intervertebral disk degeneration (IDD) is the leading cause of chronic low back pain, yet its link to amino acid metabolic reprogramming remains unclear. + Three GEO transcriptomes were integrated; amino-acid-metabolism genes were intersected with differentially expressed genes. Core genes were selected by LASSO, SVM and random forest, incorporated into an SHAP-interpretable nomogram, and tested by single-cell analysis, in silico knockout, docking and WB. + Forty-three altered amino acid metabolism-related genes were identified, from which five core genes were screened: CETP, AIFM1, and GM2A were up-regulated; PNPLA2 and AGK were down-regulated. The constructed nomogram prediction model achieved an AUC value of 0.812. Degenerated intervertebral disks exhibited increased immune infiltration; the core genes either suppressed protective matrix genes or impaired stress defense capability. Molecular docking results showed that NVP-AEW541 and EGCG could bind to the AIFM1 protein with a binding free energy of -10.7 kcal/mol; WB confirmed protein trends. + The five-core-gene signature is strongly associated with IDD and may represent a key regulatory pathway, offering a promising diagnostic model and potential therapeutic targets. + Copyright © 2026 Li, Shuid, Miswan, Zhang, Gu, Cao, Wang, Jiang and Yuan. + + + + Li + Xusheng + X + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Jalan Hospital, Sungai Buloh, Selangor, Malaysia. + + + + Shuid + Ahmad Nazrun + AN + + Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Jalan Hospital, Sungai Buloh, Selangor, Malaysia. + + + + Miswan + Mohd Fairudz Mohd + MFM + + Faculty of Medicine, Universiti Teknologi MARA, Sungai Buloh Campus, Jalan Hospital, Sungai Buloh, Selangor, Malaysia. + + + + Zhang + Xiao + X + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + + Gu + Wenbo + W + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + + Cao + Donghui + D + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + + Wang + Jungang + J + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + + Jiang + Ziyang + Z + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + + Yuan + Haifeng + H + + Department of Spinal Orthopaedics, General Hospital of Ningxia Medical University, Yinchuan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Neurol + 101546899 + 1664-2295 + + + amino acid metabolism + biomarkers + in silico knockout + intervertebral disk degeneration + machine learning + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 2 + 10 + + + 2026 + 3 + 26 + + + 2026 + 4 + 30 + + + 2026 + 5 + 29 + 4 + 48 + + + epublish + + 42211301 + PMC13212091 + 10.3389/fneur.2026.1808282 + + + + + + 42211297 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-2295 + + 17 + + 2026 + + + Frontiers in neurology + Front Neurol + + A multimodal machine learning model for predicting postoperative worsening of FOGQ in Parkinson's disease following STN-DBS. + + 1767807 + 1767807 + + 10.3389/fneur.2026.1767807 + + To develop and validate a multimodal machine learning model to predict postoperative worsening of freezing of gait questionnaire (FOGQ) scores in patients with Parkinson's disease (PD) undergoing subthalamic nucleus deep brain stimulation (STN-DBS). + This retrospective study analyzed data from 134 patients with PD who underwent bilateral STN-DBS. The model integrated four data modalities: clinical scale assessments, structural neuroimaging features derived from voxel-based morphometry (VBM), stereotactic electrode localization data via Lead-DBS analysis, and radiomics features extracted from preoperative MRI. Following standardization, feature selection was conducted using LASSO, Boruta and recursive feature elimination with cross-validation (RFECV) methods to identify the most relevant predictors. Multiple machine learning algorithms were evaluated. Model development and internal validation were conducted using a 5-fold nested cross-validation framework. Model performance was assessed using ROC curves, calibration curves, and decision curve analysis, and model interpretability was analyzed using SHAP values. + The LightGBM model achieved the highest AUC of 0.917 for predicting FOGQ deterioration. The analysis emphasized the importance of multimodal data integration, combining clinical, structural, and radiomic features to enhance predictive accuracy. + This multimodal LightGBM model achieved robust discrimination between patients with and without postoperative FOGQ deterioration, highlighting the value of integrating clinical, structural, and radiomic features for preoperative risk stratification in PD patients undergoing STN-DBS. These findings may inform personalized patient selection, early identification of high-risk individuals, and treatment planning, though external validation in prospective multicenter cohorts remains a necessary next step. + Copyright © 2026 Xu, Mei, Huang, Gu, Zhang, Chen, Tian, Du, Zhao, Zhang, Chen, Cui, Zhang and Zu. + + + + Xu + Min + M + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Mei + Shuhong + S + + Department of Neurosurgery, Ji'an Central People's Hospital, Ji'an, Jiangxi, China. + + + + Huang + Shuming + S + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Gu + Longyuan + L + + Department of Neurosurgery, Ji'an Central People's Hospital, Ji'an, Jiangxi, China. + + + + Zhang + Yuting + Y + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Chen + Siyan + S + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Tian + Yuyao + Y + + Department of Biomedical Engineering, The Chinese University of Hong Kong, Hong Kong, Hong Kong SAR, China. + + + + Du + Li + L + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + + Zhao + Hui + H + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Zhang + Zixuan + Z + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Chen + Ruyi + R + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Cui + Guiyun + G + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Zhang + Wei + W + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + Zu + Jie + J + + Department of Neurology, Parkinson's Disease Center, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, China. + + + Department of Neurology, The First Clinical College, Xuzhou Medical University, Xuzhou, China. + + + + eng + + Journal Article + + + 2026 + 05 + 05 + +
+ + Switzerland + Front Neurol + 101546899 + 1664-2295 + + + Parkinson’s disease + deep brain stimulation + freezing of gait + machine learning + prediction model + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 12 + 15 + + + 2026 + 4 + 10 + + + 2026 + 4 + 17 + + + 2026 + 5 + 29 + 4 + 48 + + + epublish + + 42211297 + PMC13214268 + 10.3389/fneur.2026.1767807 + + + + + + 42211285 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2055-2076 + + 12 + + 2026 + Jan-Dec + + + Digital health + Digit Health + + An interpretable machine learning model for predicting acute respiratory distress syndrome in critically ill patients with acute pancreatitis: A multicenter retrospective study. + + 20552076261455203 + 20552076261455203 + + 10.1177/20552076261455203 + + Acute respiratory distress syndrome (ARDS) drives early mortality in severe acute pancreatitis (AP). Since conventional tools often fail to capture complex physiological interactions, we aimed to develop and validate an interpretable machine learning (ML) model for early ARDS prediction and deploy it as a web-based calculator. + This multicenter retrospective study utilized data from the MIMIC-IV database for model development and internal validation, and an independent cohort from Changshu Hospital for external validation. Optimal predictors were identified through a hybrid feature selection strategy combining LASSO regression and the Boruta algorithm. Seven ML algorithms were constructed, including random forest (RF), extreme gradient boosting, support vector machine, logistic regression, light gradient boosting machine, k-nearest neighbors, and decision trees. Model performance was evaluated by discrimination (AUC), calibration curves, and clinical utility (DCA). Model interpretability was assessed using SHapley Additive exPlanations (SHAP) and partial dependence plots (PDP). + A total of 905 patients from the MIMIC-IV cohort (25.0% ARDS incidence) and 126 from the external cohort (20.6% incidence) were included. Nine independent predictors were identified: body mass index (BMI), respiratory rate, temperature, SOFA score, white blood cell count, PO2, PCO2, mechanical ventilation, and antibiotic use. The RF model demonstrated best performance (internal AUC 0.851) and maintained robust generalization in the external cohort (AUC 0.823). Calibration curves indicated good agreement between predicted and observed probabilities, and DCA showed superior net benefit across clinically relevant thresholds. SHAP analysis identified ventilation, SOFA score, BMI, PO2, and respiratory rate as the most influential predictors. + A high-performing, interpretable RF model was developed for early ARDS prediction in critically ill AP patients. The model effectively captured complex physiological interactions and demonstrated robustness across diverse populations. By integrating this algorithmic framework into a user-friendly web calculator, the tool supports personalized risk stratification and timely clinical decision-making. + © The Author(s) 2026. + + + + Yan + Sheng + S + + Department of Emergency Medicine, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Ren + Xia + X + 0009-0002-2141-6542 + + Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Xu + Chunyang + C + + Department of Emergency Medicine, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Zheng + Feng + F + + Department of Emergency Medicine, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Liu + Luojie + L + + Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Wen + Shun + S + + Department of Emergency Medicine, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Xu + Xiaodan + X + + Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + Zhang + Yan + Y + 0009-0007-9651-299X + + Department of Emergency Medicine, Changshu Hospital Affiliated to Soochow University, First People's Hospital of Changshu City, Changshu, Jiangsu, P.R. China. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Digit Health + 101690863 + 2055-2076 + + + acute pancreatitis + acute respiratory distress syndrome + external validation + interpretability + machine learning + random forest + + The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 2 + 20 + + + 2026 + 4 + 28 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 4 + 47 + + + epublish + + 42211285 + PMC13213126 + 10.1177/20552076261455203 + 10.1177_20552076261455203 + + + + + + 42211266 + + 2026 + 05 + 29 + +
+ + 2041-6520 + + + 2026 + May + 27 + + + Chemical science + Chem Sci + + Enzymatically hydrolyzed oligosaccharide fingerprinting using nanopipettes at the single-molecule level. + 10.1039/d6sc00730a + + Enzymatic hydrolysis of oligosaccharides plays a pivotal role in important biological processes, but conventional analytical tools struggle to determine these functional components with single-monosaccharide resolution and ultrahigh sensitivity. Here, we report a nanopipette-based strategy for the length discrimination of enzymatically hydrolyzed galacturonic acid oligosaccharides differing by a single monosaccharide unit while achieving femtomolar (fM) level detection limits. Using this approach, we precisely identify six types of enzymatic hydrolysates, even in complex mixtures. Incorporating machine learning further enhances classification performance, achieving 0.98 accuracy. This study establishes an advanced platform for high-precision carbohydrate analysis, offering new possibilities for glycosidase-assisted glycan sequencing, enzymatic degradation studies, and gut microbiota metabolism research. + This journal is © The Royal Society of Chemistry. + + + + Lu + Qi-Yun + QY + + College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory of Edible Agricultural Resources and High-value Utilization Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Zhejiang University Hangzhou 310058 P.R. China chenshiguo210@163.com. + + + + Ma + Hui + H + 0000-0002-0276-0439 + + School of Chemistry and Chemical Engineering, Zhejiang Sci-Tech University Hangzhou 310038 P.R. China huima@zstu.edu.cn. + + + + Chen + Shi-Guo + SG + 0000-0001-8470-7983 + + College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory of Edible Agricultural Resources and High-value Utilization Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang Engineering Laboratory of Food Technology and Equipment, Zhejiang University Hangzhou 310058 P.R. China chenshiguo210@163.com. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + England + Chem Sci + 101545951 + 2041-6520 + + There are no conflicts to declare. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 1 + 26 + + + 2026 + 5 + 16 + + + 2026 + 5 + 29 + 4 + 47 + + + aheadofprint + + 42211266 + PMC13213622 + 10.1039/d6sc00730a + d6sc00730a + + + + + + 42211263 + + 2026 + 05 + 29 + +
+ + 2041-6520 + + + 2026 + May + 05 + + + Chemical science + Chem Sci + + Synthesis and machine learning techniques to enable data-driven investigation of supramolecular host-guest interactions. + 10.1039/d6sc00479b + + The availability of large datasets such as the Protein DataBank and ChEMBL have allowed for rapid progress in developing machine learning tools for predicting the biological activity of organic small molecules. The binding between supramolecular hosts and their desired guests is governed by the same forces that drive protein-small molecule interactions, and yet this field has seen dramatically less application of machine learning. In this contribution, we demonstrate that the production of easily diversified building blocks can allow a single laboratory to generate a dataset that is sufficient to engage with modern machine learning approaches. A range of methods were evaluated against our single-laboratory dataset, with a graph neural network featuring an attention mechanism providing meaningful performance in this data-sparse arena. + This journal is © The Royal Society of Chemistry. + + + + Shaurya + Alok + A + + Department of Chemistry, University of Victoria 3800 Finnerty Rolad Victoria BC V8P 5C2 Canada fhof@uvic.ca. + + + + Bagherzadeh Mostaghimi + Amir Hassan + AH + + Department of Chemistry, University of Calgary, 2500 University Drive Calgary AB T2N 1N4 Canada jeffrey.vanhumbec1@ucalgary.ca. + + + + Turnbull + David R + DR + + Department of Chemistry, University of Calgary, 2500 University Drive Calgary AB T2N 1N4 Canada jeffrey.vanhumbec1@ucalgary.ca. + + + + Hof + Fraser + F + 0000-0003-4658-9132 + + Department of Chemistry, University of Victoria 3800 Finnerty Rolad Victoria BC V8P 5C2 Canada fhof@uvic.ca. + + + Centre for Advanced Materials and Related Technology (CAMTEC), University of Victoria 3800 Finnerty Road Victoria BC V8P 5C2 Canada. + + + + Van Humbeck + Jeffrey F + JF + 0000-0002-8910-4820 + + Department of Chemistry, University of Calgary, 2500 University Drive Calgary AB T2N 1N4 Canada jeffrey.vanhumbec1@ucalgary.ca. + + + + eng + + Journal Article + + + 2026 + 05 + 05 + +
+ + England + Chem Sci + 101545951 + 2041-6520 + + There are no conflicts to declare. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 1 + 16 + + + 2026 + 5 + 4 + + + 2026 + 5 + 29 + 4 + 47 + + + aheadofprint + + 42211263 + PMC13213625 + 10.1039/d6sc00479b + d6sc00479b + + + + + + 42211234 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1178-7031 + + 19 + + 2026 + + + Journal of inflammation research + J Inflamm Res + + Transcriptomic and Proteomic Analyses Identify THBS2, VWF, NFASC, and AKR1B10 as Diagnostic Biomarkers for Advanced Fibrosis in NAFLD. + + 569506 + 569506 + + 10.2147/JIR.S569506 + + The increasing prevalence of non-alcoholic fatty liver disease (NAFLD) has paralleled changes in lifestyle and dietary habits. However, the gold standard for the staging of NAFLD relies on liver biopsy, which is highly damaging and poorly accepted. This study aims to identify convenient biomarkers for staging advanced fibrosis (stage F3-F4) in NAFLD patients, thereby improving disease management. + This study integrated liver transcriptomic sequencing data and peripheral blood proteomic data from NAFLD patients. Candidate diagnostic biomarkers for advanced fibrosis were screened using three machine learning algorithms. The dynamic expression patterns of these biomarkers were further validated by time series analysis from NAFLD mouse model. Additionally, a retrospective validation cohort comprising 119 biopsy-confirmed NAFLD patients was established at Shandong Provincial Hospital. Blood samples and clinical characteristics were collected before liver biopsy. These samples were used to validate the diagnostic performance of the candidate biomarkers via ELISA, which was further compared with established non‑invasive indicators (FIB‑4, APRI, and NFS). Finally, single-nucleus sequencing was employed to elucidate the underlying pathogenic mechanisms and their cellular location in NAFLD, while Mendelian randomization (MR) analyses based on whole proteome and whole genome data were conducted to explore causal relationship for NAFLD. + This study identified THBS2, AKR1B10, NFASC, and VWF as diagnostic biomarkers for advanced fibrosis in NAFLD. Transcriptomic and proteomic analyses demonstrated that the combination of these four biomarkers exhibited robust diagnostic performance (AUC = 0.860-0.880). In our NAFLD clinical cohort, each biomarker individually showed promising diagnostic performance (THBS2 AUC = 0.867; NFASC AUC = 0.834; VWF AUC = 0.777; AKR1B10 AUC = 0.733), which was comparable to established non-invasive indicators. Furthermore, the expression levels of THBS2, AKR1B10, and VWF increased progressively with the duration of high-fat diet feeding in NAFLD mouse model. NFASC and THBS2 were specifically enriched in activated hepatic stellate cells and showed significant upregulation in advanced fibrosis and cirrhosis. + THBS2, AKR1B10, NFASC, and VWF serve as diagnostic biomarkers for advanced fibrosis in NAFLD, providing novel insights for the development of non-invasive clinical diagnostic strategies. + © 2026 Ding et al. + + + + Ding + Shan + S + + Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, People's Republic of China. + + + + Liu + Yiqing + Y + 0000-0002-2844-2770 + + Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, People's Republic of China. + + + + Liu + Zhi + Z + + Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, People's Republic of China. + + + + Chu + Fulu + F + + Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, People's Republic of China. + + + + Lu + Bingru + B + + Department of Clinical Laboratory, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, Shandong Province, 250021, People's Republic of China. + + + + Xu + Li + L + 0009-0001-3229-6745 + + Department of Infectious Disease and Hepatology, The Second Qilu Hospital of Shandong University, Jinan, Shandong Province, 250033, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
+ + New Zealand + J Inflamm Res + 101512684 + 1178-7031 + + + THBS2 + advanced NAFLD fibrosis + diagnostic biomarkers + multi-omics sequencing + + The authors report no conflicts of interest in this work. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2025 + 10 + 22 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 4 + 47 + + + epublish + + 42211234 + PMC13213171 + 10.2147/JIR.S569506 + 569506 + + + + + + 42211184 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1664-0640 + + 17 + + 2026 + + + Frontiers in psychiatry + Front Psychiatry + + Development and validation of a machine learning-based risk prediction model for non-suicidal self-injury in adolescents. + + 1837161 + 1837161 + + 10.3389/fpsyt.2026.1837161 + + Non-suicidal self-injury (NSSI) among adolescents has long been an important social issue. This study aims to construct a predictive model for NSSI based on machine learning models. + A retrospective cohort study design was adopted, including 588 adolescent patients who received psychological and psychiatric assessments. The occurrence of NSSI behavior was used as the outcome variable. Candidate predictors including demographic characteristics, psychological and emotional status, behavioral characteristics, and peer support were collected. The dataset was randomly divided into a training set and a test set at a ratio of 7:3. By comparing the performance of four machine learning models-multiple logistic regression (MLR), random forest (RF), support vector machine (SVM), and eXtreme Gradient Boosting (XGBoost)-at different time points (T1, T2, T3) using area under the curve (AUC), accuracy, precision, recall, and F1 score, the optimal model was selected. The Shapley additive explanations (SHAP) method was further used to conduct interpretability analysis for the optimal model. + The incidence rates of NSSI at T1, T2, and T3 were approximately 24%, 23%, and 22%, respectively. The SVM model demonstrated superior discrimination ability and stability in predicting the risk of NSSI among adolescents, with AUC values all greater than 0.75 and recall and F1 scores both higher than 0.7. SHAP analyses at all three time points consistently showed that suicide-related ideation and behaviors, school bullying, and depressive status had high contributions to the prediction of NSSI risk. + The support vector machine model performed best in predicting NSSI among adolescents. Suicide-related behaviors are important predictors of NSSI. The findings of this study help improve the early identification of adolescents at high risk of NSSI and provide evidence for developing targeted prevention and intervention strategies. + Copyright © 2026 Zhao, Wang and Liu. + + + + Zhao + Yujun + Y + + Department of Affective Disorders, The Third Honorable Military Hospital of Hebei Province, Baoding, Hebei, China. + + + + Wang + Qian + Q + + Department of Affective Disorders, The Third Honorable Military Hospital of Hebei Province, Baoding, Hebei, China. + + + + Liu + Wei + W + + Department of Clinical Psychology II, Hebei Provincial Mental Health Center and The Sixth Clinical Medical College of Hebei University, Baoding, Hebei, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Psychiatry + 101545006 + 1664-0640 + + + adolescents + machine learning + non-suicidal self-injury (NSSI) + suicide-related behaviors + support vector machine (SVM) + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 47 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 3 + 23 + + + 2026 + 4 + 21 + + + 2026 + 4 + 21 + + + 2026 + 5 + 29 + 4 + 46 + + + epublish + + 42211184 + PMC13212310 + 10.3389/fpsyt.2026.1837161 + + + + + + 42211178 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2624-8212 + + 9 + + 2026 + + + Frontiers in artificial intelligence + Front Artif Intell + + An LLM-based methodology for the automatic detection of bias in the DuoWikiBias corpus. + + 1791624 + 1791624 + + 10.3389/frai.2026.1791624 + + Bias detection remains a challenge in Natural Language Processing, particularly in non-English contexts, due to the conceptual ambiguity of bias and the scarcity of annotated resources. This study addresses the lack of Spanish-language resources by investigating the automatic detection of framing, epistemological, and demographic biases. We introduce DuoWikiBias, a novel parallel corpus derived from Wikipedia for Spanish bias classification. We evaluate Large Language Models (Llama and Gemma) using advanced prompting techniques-CARP and Metacognition-combined with a Gradient Ascent unlearning method to refine model attention. Their performance is compared against classical approaches, including logistic regression with S-BERT embeddings and linguistic features. Results show that advanced prompting substantially improves performance over simple instructions, while the best overall performance (F1 = 0.796) is achieved by combining CARP-based features with Gradient Ascent and a Support Vector Machine classifier. These findings suggest that LLMs are effective for bias-aware representation learning, but hybrid approaches with traditional classifiers remain competitive. This work provides both a validated dataset and a methodological framework for bias detection in Spanish NLP. + Copyright © 2026 Salas-Jimenez, Ojeda-Trueba, Bel-Enguix, Lee-Romero and López-Ponce. + + + + Salas-Jimenez + Karla + K + + Universidad Nacional Autónoma de México, Ciudad de México (CDMX), Mexico City, Mexico. + + + Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Ciudad de México (CDMX), Mexico City, Mexico. + + + + Ojeda-Trueba + Sergio-Luis + SL + + Universidad Nacional Autónoma de México, Ciudad de México (CDMX), Mexico City, Mexico. + + + + Bel-Enguix + Gemma + G + + Universidad Nacional Autónoma de México, Ciudad de México (CDMX), Mexico City, Mexico. + + + + Lee-Romero + Edgar + E + + Universidad Nacional Autónoma de México, Ciudad de México (CDMX), Mexico City, Mexico. + + + + López-Ponce + Francisco F + FF + + Universidad Nacional Autónoma de México, Ciudad de México (CDMX), Mexico City, Mexico. + + + Posgrado en Ciencia e Ingeniería de la Computación, Universidad Nacional Autónoma de México (UNAM), Ciudad de México (CDMX), Mexico City, Mexico. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Artif Intell + 101770551 + 2624-8212 + + + LLM + Wikipedia + bias detection + ensembles + generalization of models + gradient ascent + prompt engineering + unlearning + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 1 + 19 + + + 2026 + 3 + 27 + + + 2026 + 4 + 14 + + + 2026 + 5 + 29 + 4 + 46 + + + epublish + + 42211178 + PMC13212246 + 10.3389/frai.2026.1791624 + + + + + + 42211177 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2624-8212 + + 9 + + 2026 + + + Frontiers in artificial intelligence + Front Artif Intell + + Predictive models of suicidal ideation risk in perinatal-stage women based on sociodemographic and clinical data. + + 1774453 + 1774453 + + 10.3389/frai.2026.1774453 + + Suicidal ideation in women during the perinatal period has become a growing public health problem, with a prevalence ranging from 8 to 19%. Its etiology is multifactorial and carries additional consequences for both the newborn and the woman beyond death itself. + This study aims to predict the risk of suicidal ideation in perinatal women by using artificial intelligence models based on sociodemographic and clinical data. + An analytical observational study was conducted with a sample of 908 Spanish women during the perinatal period, collecting relevant data. To predict the risk of suicidal ideation, five machine learning models (OneR, JRIP, FURIA, J48 and Random Forest) were employed, as they provide rules or trees that can be easily followed to gather additional information. The metrics used to evaluate the performance of the models included accuracy, precision, recall and F1-score. + The models show an accuracy of around 60% in most cases. The model that performs the worst is OneR, with an accuracy of less than 50%. The Random Forest model stood out for its higher accuracy. The metrics of this model (Random Forest) were Accuracy (%) 0.639 ± 0.05, Precision: 0.634, Recall: 0.634 F1:0.634 and AUPRC: 0.632. Factors identified as predictors of suicidal ideation risk included low birth weight, history of mental health problems, problems of intimate partner violence, low income and smoking. + In conclusion, predictive models based on sociodemographic data and clinical variables show a moderate ability to predict suicidal ideation risk in perinatal women. + Copyright © 2026 Martínez-Ramírez, Peinado-Molina, Hernández-Martínez and Martínez-Galiano. + + + + Martínez-Ramírez + José Manuel + JM + + Department of Computer Science, University of Jaen, Jaén, Spain. + + + + Peinado-Molina + Rocío Adriana + RA + + Department of Nursing, University of Jaen, Jaén, Spain. + + + Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain. + + + + Hernández-Martínez + Antonio + A + + Department of Nursing, Physiotherapy and Occupational Therapy, Faculty of Nursing, University of Castilla-La Mancha, Ciudad Real, Castilla la Mancha, Spain. + + + + Martínez-Galiano + Juan Miguel + JM + + Department of Nursing, University of Jaen, Jaén, Spain. + + + Consortium for Biomedical Research in Epidemiology and Public Health (CIBERESP), Madrid, Spain. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Artif Intell + 101770551 + 2624-8212 + + + artificial intelligence + explainable AI + perinatal suicide + suicidal ideation + suicide risk prediction + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 12 + 23 + + + 2026 + 4 + 9 + + + 2026 + 4 + 27 + + + 2026 + 5 + 29 + 4 + 46 + + + epublish + + 42211177 + PMC13212341 + 10.3389/frai.2026.1774453 + + + + + + 42211085 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1178-6302 + + 20 + + 2026 + + + Environmental health insights + Environ Health Insights + + Human Right to Water and Household Water Conservation in Contaminated Colombian Municipalities: A Machine Learning Analysis. + + 11786302261455534 + 11786302261455534 + + 10.1177/11786302261455534 + + In Colombia, water is usually physically available, but in some municipalities it is not safe for human consumption, which demonstrates that there is a persistent risk due to poor water quality for public health. In this line, previous research has identified demographic, psychosocial, and infrastructural determinants of water conservation in households, in contexts of scarcity and high contamination, leaving relevant analytical gaps in environments where the risk is determined by water quality and not by restrictions in the supply of the water resource. Only a small number of studies examine cases in which the quantity of water is sufficient in situations where the quality is compromised. In addition, the evidence in these contexts remains limited and scattered. This study examines conservation practices in thirteen Colombian municipalities with IRCA above 80 and focuses on how different factors interact when contamination, and not scarcity, shapes household decisions. The article addresses two main objectives. The first is to identify the socioeconomic, perceptual, and structural factors that predict the adoption of conservation practices across increasing levels of complexity. The second is to estimate the causal effect of perceived contamination on the probability of adopting these practices. The analysis is based on data from 4,246 households from the 2024 Quality of Life Survey (ECV, DANE). The dependent variable is an ordinal variable that captures the degree of complexity (associated with greater technical and financial requirements) of water conservation practices. Methodologically, the study combines cumulative threshold logistic models with L1 regularization to select the relevant variables in the prediction of the dependent variable. In addition, it applies a Double Machine Learning framework with AIPW estimation and cross-fitting to achieve causal identification under flexible control of confounding variables, of the effect of the perception of water contamination on the conservation practices of this resource. The results indicate that socioeconomic and educational capacity drives low-complexity saving and conservation practices, while persistent environmental degradation produces a saturation effect that restricts progress toward the adoption of intermediate-complexity practices. At the highest levels of complexity, the level of schooling and economic resources become decisive, evidencing that high-complexity conservation is less likely in low-income households and in the Afro-descendant population. DML-AIPW estimates show that perceived water contamination causally reduces the probability of conservation at the three levels of the dependent variable, with average treatment effects of -16.3, -8.1, and -5.6 percentage points. The estimates remain robust to alternative treatment definitions and propensity score trimming. The findings support the implementation of targeted equipment subsidies for the lowest socioeconomic stratum, the design of ethnically differentiated interventions, and the strengthening of communication strategies that promote the comprehensive adoption of conservation and raise awareness among the population about the harmful effects of contamination of the water resource. + © The Author(s) 2026. + + + + Henao-Rodríguez + Linda Carolina + LC + + Escuela de Negocios, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia. + 27961 + + + + Lis-Gutiérrez + Jenny Paola + JP + 0000-0002-1438-7619 + + Escuela de Negocios, Fundación Universitaria Konrad Lorenz, Bogotá, Colombia. + 27961 + + + + Lis-Gutiérrez + Melissa + M + + Facultad de Ciencias Agrarias, Universidad Nacional de Colombia, Bogotá, Colombia. + 28021 + + + + Escobar-Uribe + Carlos Hernando + CH + + Facultad de Ciencias Jurídicas y Políticas, Universidad El Bosque, Bogotá, Colombia. + 28009 + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Environ Health Insights + 101488505 + 1178-6302 + + + In many parts of Colombia, families have enough water flowing from their taps, but the water is contaminated and unsafe to drink. This study looks at 4,246 households across 13 Colombian municipalities where water pollution is a major health risk. Using advanced data analysis, researchers investigated what makes a family more or less likely to adopt water-saving habits—ranging from simple actions like turning off the tap to expensive upgrades like installing water-efficient appliances. The study found a “saturation effect”: when people feel their water is heavily polluted, they are actually less likely to invest effort in conserving it. Specifically, the perception of water contamination decreased the probability of saving water by 16.3% for basic tasks and 5.6% for more complex actions. Money and education also play a massive role. While low-income families might try simple saving methods, more technical or expensive conservation is nearly impossible for them. The data highlights a significant gap in environmental justice, showing that high-complexity water conservation is significantly less likely to occur in low-income households and among Afro-descendant populations. The researchers conclude that telling people to save water isn't enough when the water itself is dirty. To fix this, the government needs to provide subsidies for water-saving equipment to the poorest families and create specific programs that respect and support Afro-descendant communities. + © The Author(s) 2026. + + + environmental justice + human right to water + machine learning + water conservation + + The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 40 + + + 2025 + 12 + 31 + + + 2026 + 3 + 5 + + + 2026 + 4 + 22 + + + 2026 + 5 + 29 + 4 + 46 + + + epublish + + 42211085 + PMC13213131 + 10.1177/11786302261455534 + 10.1177_11786302261455534 + + + + + + 42211015 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2279-9028 + + 15 + 2 + + 2026 + Apr + + + Journal of public health research + J Public Health Res + + Digital epidemiology: Utilizing big data for public health surveillance and disease outbreak prediction. + + 22799036261455523 + 22799036261455523 + + 10.1177/22799036261455523 + + Digital epidemiology represents a transformative approach to public health surveillance and disease outbreak prediction by leveraging big data from diverse digital sources, including social media, search engine queries, wearable devices, mobile health applications, environmental sensors, and electronic health records. Unlike traditional epidemiological methods that rely primarily on structured clinical or administrative datasets, digital epidemiology uses real-time, high-volume, and often unstructured data streams to monitor population health dynamics and detect emerging threats with improved temporal and spatial resolution. This study was conducted as a structured literature review guided by PRISMA principles. A comprehensive search of PubMed, Scopus, Web of Science, and IEEE Xplore identified studies published between 1 January 2014 and 1 January 2026. The review synthesizes evidence on methodological foundations, analytical techniques such as machine learning and natural language processing, and applications in outbreak detection, syndromic surveillance, behavioral risk monitoring, and health system responsiveness. Included studies showed that digital signals can improve forecasting accuracy, provide earlier indications of disease activity than conventional systems, and strengthen situational awareness during public health emergencies when integrated with established surveillance frameworks. However, important challenges remain, including data privacy, algorithmic transparency, interoperability, and integration into routine public health systems. This review provides an updated synthesis across multiple digital data streams and emerging artificial intelligence applications while identifying persistent methodological, governance, and equity-related gaps requiring future attention. With continued innovation, stronger regulatory oversight, and interdisciplinary collaboration, digital epidemiology has the potential to support proactive, predictive, and precision-oriented public health strategies for future global health threats. + © The Author(s) 2026. + + + + Hassan Nishan + Md Nahid + MN + 0000-0002-6883-0284 + + Projahnmo Research Foundation, Zakiganj, Sylhet, Bangladesh. + + + Department of Public Health, North South University, Dhaka, Bangladesh. + 54495 + + + + eng + + Journal Article + Review + + + 2026 + 05 + 26 + +
+ + United States + J Public Health Res + 101580775 + 2279-9028 + + + big data + data privacy + digital epidemiology + disease outbreak prediction + public health surveillance + + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 2 + 20 + + + 2026 + 4 + 22 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 4 + 45 + + + epublish + + 42211015 + PMC13213097 + 10.1177/22799036261455523 + 10.1177_22799036261455523 + + + + + + 42211009 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2399-4908 + + 11 + 1 + + 2026 + + + International journal of population data science + Int J Popul Data Sci + + Enabling the analysis of patient-level data across jurisdictions for research use: a real-world exploration of a federated likelihood approach. + + 3160 + 3160 + + 10.23889/ijpds.v11i1.3160 + + The disclosure of health data is governed by strict privacy regulations which significantly restrict the transfer of data across jurisdictions. These limitations restrict the scope of health research, particularly the ability to conduct studies that span multiple jurisdictions (e.g. provinces, states, or countries). One common practice is to use meta-analysis to pool jurisdiction-specific estimates. However, this approach relies on combining aggregate-level data, which may overlook important nuances. Therefore, alternative methods are needed. This study investigates the potential of a federated likelihood approach to analyse health data across jurisdictions while keeping the data inside the jurisdiction. + (1) Evaluate the effectiveness of the meta-analysis technique in pooling jurisdiction-specific results to produce overall estimates (2) assess the utility of a federated likelihood approach for interjurisdictional health data analysis; and (3) compare the performance of a federated likelihood method with the conventional meta-analysis approach. + Log-likelihood functions from separate jurisdictions were directly combined to form a single overall log-likelihood function, and overall parameter estimates. We show that these parameter estimates are mathematically equivalent to those obtained from analysing the pooled individual-level data. Using data from the Canadian Primary Care Sentinel Surveillance Network, and using chronic kidney disease as a case study, estimates from five jurisdictions are combined using meta-analysis and the federated likelihood approach. These results are compared to the overall estimates from a global model. + The likelihood method demonstrated strong performance in analysing federated data, yielding the lowest relative percent absolute bias at 1.34%. This was notably lower than the bias observed with the common effect meta-analysis (4.09%). + The federated likelihood method offers a promising approach to interjurisdictional health data analysis. However, further investigation is needed to assess its feasibility across different model types and model complexity to fully understand the scope of its utility. + + + + Harmon + Megan + M + + Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. + + + Centre for Health Informatics, University of Calgary, Calgary, Alberta, Canada. + + + Department of Emergency Medicine, University of Calgary, Calgary, Alberta, Canada. + + + + Li + Na + N + + Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. + + + Centre for Health Informatics, University of Calgary, Calgary, Alberta, Canada. + + + + Sajobi + Tolulope + T + + Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. + + + Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada. + + + + Holodinsky + Jessalyn + J + + Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. + + + Centre for Health Informatics, University of Calgary, Calgary, Alberta, Canada. + + + Department of Emergency Medicine, University of Calgary, Calgary, Alberta, Canada. + + + Department of Clinical Neurosciences, University of Calgary, Calgary, Alberta, Canada. + + + O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada. + + + Hotchkiss Brain Institute, University of Calgary, Calgary, Alberta, Canada. + + + Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Albera, Canada. + + + + Williamson + Tyler + T + + Department of Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada. + + + Centre for Health Informatics, University of Calgary, Calgary, Alberta, Canada. + + + O'Brien Institute for Public Health, University of Calgary, Calgary, Alberta, Canada. + + + + eng + + Journal Article + + + 2026 + 04 + 28 + +
+ + Wales + Int J Popul Data Sci + 101737740 + 2399-4908 + + IM + + + Meta-Analysis as Topic + + + Humans + + + Likelihood Functions + + + Federated Learning + + + + federated analysis + likelihood functions + meta-analysis + + Statement on conflicts of interest: The authors have no conflicts of interest. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 4 + 45 + + + epublish + + 42211009 + PMC13213896 + 10.23889/ijpds.v11i1.3160 + 11:1:22 + + + + + + 42210993 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + Multicenter development and validation of machine-learning risk models to predict procedural complete revascularization and in-hospital heart failure in STEMI patients treated with primary PCI. + + 1824937 + 1824937 + + 10.3389/fcvm.2026.1824937 + + In-hospital heart failure (HF) remains common after primary percutaneous coronary intervention (PPCI) for ST-segment elevation myocardial infarction (STEMI) and is associated with adverse in-hospital outcomes. In addition, whether procedural complete revascularization (CR) can be achieved during the index PCI is clinically relevant but often constrained in real-world practice. We aimed to develop and externally validate machine-learning (ML) models for these two complementary prediction tasks. + We conducted a multicenter cohort study of STEMI patients treated with PPCI from three hospitals. Patients from Hezhou People's Hospital (January 2020 to June 2024) comprised the training cohort (n = 734). Patients from two other centers (July 2024 to December 2025) were combined as an independent testing cohort (n = 352). Multiple ML algorithms were benchmarked to predict (1) in-hospital HF and (2) the real-world feasibility of achieving procedural CR during the index PCI. Model performance was assessed using the area under the receiver operating characteristic curve (AUC), the area under the precision-recall curve (AUPRC), classification metrics, calibration curves, decision curve analysis (DCA), and clinical impact curves. Shapley Additive Explanations (SHAP) were used to enhance interpretability. + For in-hospital HF prediction, CatBoost showed the best overall performance in the independent testing cohort (AUC: 0.973; 95% CI: 0.957-0.989; accuracy: 88.6%), with good calibration and favorable net benefit on DCA. For procedural CR prediction, CatBoost was also selected as the primary model based on its overall performance profile in the independent testing cohort (AUC: 0.970; 95% CI: 0.954-0.987; accuracy: 92.0%), with acceptable calibration and positive net benefit across a broad range of threshold probabilities. Key predictors included LAD involvement, age, symptom-to-guidewire crossing time, and markers related to inflammation, coagulation, renal function, and lipid metabolism. + In a three-center cohort, we developed and externally validated two ML models for predicting subsequent in-hospital HF after index PPCI and the feasibility of achieving procedural CR during the index PCI. Both models demonstrated good discrimination, calibration, clinical utility, and interpretability, supporting peri-procedural risk stratification and catheterization-laboratory decision support in STEMI patients treated with PPCI. + © 2026 Lin, Qin, Ou, Zhu and Liao. + + + + Lin + Yumin + Y + + Department of Cardiology, Hezhou People's Hospital, Hezhou, China. + + + + Qin + Yufeng + Y + + Department of Cardiology, Hezhou People's Hospital, Hezhou, China. + + + + Ou + Kangkang + K + + Department of Trauma Orthopaedics and Hand Surgery, Nanxishan Hospital of Guangxi Zhuang Autonomous Region (The Second People's Hospital of Guangxi Zhuang Autonomous Region), Guilin, China. + + + + Zhu + Jichong + J + + Department of Cardiology, The First Affiliated Hospital of Guangxi Medical University, Nanning, China. + + + + Liao + Bizhi + B + + Department of Cardiology, Hezhou People's Hospital, Hezhou, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + ST-segment elevation myocardial infarction (STEMI) + complete revascularization + in-hospital heart failure + machine learning + primary percutaneous coronary intervention (PPCI) + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 3 + 7 + + + 2026 + 4 + 21 + + + 2026 + 4 + 23 + + + 2026 + 5 + 29 + 4 + 45 + + + epublish + + 42210993 + PMC13212481 + 10.3389/fcvm.2026.1824937 + + + + + + 42210974 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Development and validation of a MODS risk prediction model for organophosphorus poisoning patients. + + 1791307 + 1791307 + + 10.3389/fmed.2026.1791307 + + Multiple organ dysfunction syndrome (MODS) is a major complication of patients with acute organophosphorus pesticide poisoning (AOPP) and is associated with high mortality. This study aimed to develop and validate a MODS prediction model for this patient population using a nomogram and machine learning methods. + A retrospective study was conducted on 270 AOPP patients from Linquan County People's Hospital to establish the prediction model. Lasso regression was used for variable selection, and multivariate Logistic regression was applied for model construction. Model performance was evaluated based on discriminative ability, calibration, and decision curve analysis. + Among the 270 AOPP patients, 129 (47.8%) developed MODS. The key predictors of MODS included heart rate, Endotracheal intubation, and blood lactic acid. The nomogram achieved an area under the curve (AUC) of 0.962 (95% confidence interval [CI]: 0.932-0.982). The calibration plot showed a high agreement between predicted probabilities and actual observed probabilities, and decision curve analysis demonstrated a favorable clinical net benefit of the model. + We developed a risk prediction model for MODS in AOPP patients. This model can assist clinicians in assessing MODS risk and provide a scientific basis for subsequent interventions. External validation is required to confirm the reliability of the current risk model before its clinical application. + Copyright © 2026 Yu, Wang, Wu and Li. + + + + Yu + Helong + H + + Department of Emergency Medicine, Linquan County People's Hospital, Fuyang, Anhui, China. + + + + Wang + Ke + K + + Department of Emergency Medicine, Linquan County People's Hospital, Fuyang, Anhui, China. + + + + Wu + Huisong + H + + Department of Emergency Medicine, Linquan County People's Hospital, Fuyang, Anhui, China. + + + + Li + Fangfang + F + + Department of Emergency Medicine, Linquan County People's Hospital, Fuyang, Anhui, China. + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + acute organophosphorus pesticide poisoning + blood lactic acid + endotracheal intubation + heart rate + multiple organ dysfunction syndrome + nomogram + prognosis + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 1 + 19 + + + 2026 + 4 + 21 + + + 2026 + 4 + 24 + + + 2026 + 5 + 29 + 4 + 44 + + + epublish + + 42210974 + PMC13214578 + 10.3389/fmed.2026.1791307 + + + + + + 42210958 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + AI-based decision models for difficult airway assessment: from research innovation to clinical implementation-a narrative review. + + 1818061 + 1818061 + + 10.3389/fmed.2026.1818061 + + Difficult airway management causes significant anesthesia-related morbidity, yet traditional assessments lack sensitivity (30%-50%) and consistency. This review (2010-2025) examines artificial intelligence (AI) decision models for airway assessment, focusing on performance, limitations, and clinical translation. AI demonstrates significant statistical superiority: facial image analysis achieves 80%-90% sensitivity (vs. Mallampati's 39%), and deep learning models yield a pooled AUC of 0.84. Key techniques include convolutional neural networks, semi-supervised learning, and multimodal integration. Despite high predictive performance, widespread adoption faces fundamental barriers. Current studies are predominantly single-center and retrospective, lacking external validation, algorithmic fairness, standardized outcomes, and proven workflow integration. Furthermore, research heavily favors upper airway evaluation. Thoracic anesthesia, utilizing routine preoperative CTs, offers an immediate pathway for comprehensive whole-airway assessment. Ultimately, bridging the translational gap requires rigorous, prospective multicenter validation demonstrating tangible patient safety improvements, rather than relying solely on algorithmic sophistication. + Copyright © 2026 Shen, Wu, Qiu and Wu. + + + + Shen + Yang + Y + + School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China. + + + Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + Wu + Yulan + Y + + Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + Qiu + Yuwei + Y + + Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + Wu + Jingxiang + J + + Department of Anesthesiology, Shanghai Chest Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + artificial intelligence + clinical prediction models + difficult airway + machine learning + perioperative medicine + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 2 + 26 + + + 2026 + 4 + 1 + + + 2026 + 4 + 6 + + + 2026 + 5 + 29 + 4 + 44 + + + epublish + + 42210958 + PMC13212430 + 10.3389/fmed.2026.1818061 + + + + + + 42210955 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Association between cold spells and chronic lung disease: a nationwide spatial machine learning analysis. + + 1823044 + 1823044 + + 10.3389/fmed.2026.1823044 + + To investigate the longitudinal association between baseline cold spells and the risk of incident chronic lung disease (CLD) among middle-aged and elderly adults in China, and to characterize its spatial distribution and multifactorial context. + Using national data from the China Health and Retirement Longitudinal Study (CHARLS, 2011-2020), Cox proportional hazards models, logistic regression, stratified analysis and sensitivity analyses were employed to analyze the impact of nine differently defined cold spell indicators on the risk of new-onset CLD in adults aged ≥45 years. Geographically Gaussian process regression (GGPR) mapped spatial disease distribution, and GeoShapley decomposition quantified factor contributions. + In Cox models, baseline cold spell exposure was associated with a modestly elevated risk of incident CLD (fully adjusted hazard ratios [HRs] ranged from 1.10 to 1.13). This positive association was further supported by logistic regression analyses (fully adjusted odds ratios [ORs]: 1.11-1.13) and sensitivity analyses. After correction for multiple comparisons, urban residence, higher education, and cancer history modified the association. Spatial prediction models (GGPR) identified northern China as a consistent high-risk region across all nine cold spell definitions (validation AUC: 0.7077-0.7129). Notably, GeoShapley analysis revealed that air pollutants and geographic coordinates contributed more substantially to spatial prediction than did cold spell exposure, indicating that regional background factors dominate the spatial patterning of CLD risk. + Higher baseline cold spell exposure was associated with a modestly increased risk of incident CLD among Chinese adults aged ≥45 years, with northern China identified as a consistent high-risk region. The independent longitudinal association of cold spells, though not dominant in spatial prediction models, highlights their potential role as an environmental trigger warranting further investigation. + Copyright © 2026 Chen and Liu. + + + + Chen + Xing-Ru + XR + + Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China. + + + + Liu + Chun-Tao + CT + + Department of Pulmonary and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + CHARLS + chronic lung disease + cold spell + geographical Gaussian process regression + middle-aged and elderly + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 3 + 5 + + + 2026 + 4 + 16 + + + 2026 + 4 + 20 + + + 2026 + 5 + 29 + 4 + 44 + + + epublish + + 42210955 + PMC13212537 + 10.3389/fmed.2026.1823044 + + + + + + 42210929 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Interpretable machine learning to predict NOAF in ICU patients with CKD: validation in US and Chinese cohorts. + + 1816221 + 1816221 + + 10.3389/fmed.2026.1816221 + + Critically ill patients with chronic kidney disease (CKD) are at high risk for New-Onset Atrial Fibrillation (NOAF), which significantly increases their risk of adverse events. Early detection of NOAF is crucial for prompt intervention and better outcomes. This study aims to develop and validate predictive models for the early identification and stratification of NOAF risk in this vulnerable population. + We developed and validated a predictive model using a cohort of 6,476 critically ill patients with CKD from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database. After performing feature selection via least absolute shrinkage and selection operator (Lasso) logistic regression, we trained six machine learning (ML) models. These algorithms included: Random Forest, Gradient Boosting, eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Multi-layer Perceptron (MLP), and Light Gradient Boosting Machine (LightGBM). The best-performing model was interpreted using SHAP to provide clinical insights. Its robustness and generalizability were confirmed through rigorous external validation on two distinct international cohorts: the US-based eICU-CRD (eICU Collaborative Research Database) (n = 12,509) and a Chinese ICU database from Weifang People's Hospital (n = 880). + Ultimately, 12 predictive features were ultimately selected: age, SOFA score, minimum heart rate, congestive heart failure, average heart rate, minimum systolic blood pressure (SBP), mechanical ventilation use, minimum oxygen saturation (SpO2), average respiratory rate, minimum magnesium, SAPS II score, and maximum white blood cell (WBC) count. The Random Forest model demonstrated the best overall performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.855 in internal validation. The model's robustness was confirmed through external validation on two independent cohorts, yielding an AUC of 0.760 on the eICU-CRD and 0.740 on the Weifang People's Hospital database. According to the SHAP analysis, age, average heart rate, minimum heart rate, SOFA score, and SAPS II were the top five most influential predictors for NOAF. + We developed an interpretable machine learning model to predict NOAF in critically ill CKD patients, demonstrating strong generalizability through external validation on both a large US cohort (eICU-CRD) and a single-center Chinese cohort (Weifang People's Hospital). SHAP analysis enhanced model interpretability. + Copyright © 2026 Zhang, Ren, Yin, Yang, Nie, Zhao, Liu, Huang and Chen. + + + + Zhang + Shujie + S + + Clinical Medical School, Shandong Second Medical University, Weifang, Shandong, China. + + + Department of Cardiology, Weifang People's Hospital, Shandong Second Medical University, Weifang, Shandong, China. + + + + Ren + Na + N + + Department of Vascular Surgery, Weifang People's Hospital, Shandong Second Medical University, Weifang, Shandong, China. + + + + Yin + Qing + Q + + Department of Cardiology, Weifang People's Hospital, Shandong Second Medical University, Weifang, Shandong, China. + + + + Yang + Chao + C + + Department of Critical Care Medicine, Fuzhou University Provincial Affiliated Hospital, Fuzhou, Fujian, China. + + + + Nie + Lujing + L + + Clinical Medical School, Shandong Second Medical University, Weifang, Shandong, China. + + + + Zhao + Jianan + J + + Clinical Medical School, Shandong Second Medical University, Weifang, Shandong, China. + + + + Liu + Yuxiu + Y + + School of Nursing, Shandong Second Medical University, Weifang, Shandong, China. + + + + Huang + Jing + J + + Department of Cardiology, Weifang People's Hospital, Shandong Second Medical University, Weifang, Shandong, China. + + + + Chen + Yanbo + Y + + Department of Cardiology, Weifang People's Hospital, Shandong Second Medical University, Weifang, Shandong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + CKD + MIMIC + atrial fibrillation + eICU-CRD + machine learning + + The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 40 + + + 2026 + 2 + 24 + + + 2026 + 4 + 11 + + + 2026 + 4 + 13 + + + 2026 + 5 + 29 + 4 + 44 + + + epublish + + 42210929 + PMC13212290 + 10.3389/fmed.2026.1816221 + + + + + + 42210816 + + 2026 + 05 + 29 + +
+ + 1549-960X + + + 2026 + May + 29 + + + Journal of chemical information and modeling + J Chem Inf Model + + A Reproducible Hierarchical Virtual Screening Framework Integrating Scaffold-Aware Machine Learning, Ensemble Docking, and Molecular Dynamics: Application to IDO1. + 10.1021/acs.jcim.6c00967 + + Indoleamine 2,3-dioxygenase 1 (IDO1) is a heme-containing enzyme implicated in cancer immune escape and remains an attractive therapeutic target despite recent clinical setbacks. We report a fully reproducible hierarchical virtual screening framework integrating scaffold-aware machine learning, ensemble docking, consensus scoring, and molecular dynamics simulations for robust prioritization of IDO1 inhibitors. A curated ChEMBL data set of IDO1 inhibitors was subjected to strict standardization, duplicate removal, and activity binarization at pChEMBL ≥6. Models were trained using scaffold-based splitting and nested cross-validation to prevent chemical series leakage. An ensemble of Random Forest, XGBoost and SVM classifiers achieved balanced predictive performance (ROC-AUC ≈0.88-0.89) with applicability domain filtering to ensure reliability. Prospective screening of FDA-approved drugs yielded 39 compounds within the applicability domain predicted as active. These were further evaluated through ensemble docking against multiple IDO1 crystal structures using GNINA with CNN rescoring. Consensus strategies were systematically benchmarked, demonstrating that best-Z-score aggregation outperformed mean, rank-based, and weighted methods in enrichment factor (EF) metrics. Two top-ranked candidates were subjected to 300 ns molecular dynamics simulations, revealing stable binding modes and persistent interactions with key catalytic residues. This study demonstrates that hierarchical integration of scaffold-aware machine learning with structure-based ensemble strategies enhances robustness and reduces false positives in virtual screening campaigns. The proposed workflow is generalizable and supports reproducible candidate prioritization in computational drug discovery. The complete implementation, including data processing, model training, and analysis steps, is provided as a fully executable Jupyter notebook available at https://github.com/rocco-b/IDO1-inhibitors-ML-and-docking-data. + + + + Tomarchio + Elisabetta Grazia + EG + + Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy. + + + Department of Biomedical and Biotechnological Sciences, University of Catania, Via Santa Sofia 97, 95123 Catania, Italy. + + + + Buccheri + Rocco + R + + Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy. + + + + Rescifina + Antonio + A + 0000-0001-5039-2151 + + Department of Drug and Health Sciences, University of Catania, Viale A. Doria 6, 95125 Catania, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + United States + J Chem Inf Model + 101230060 + 1549-9596 + + IM + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 4 + 12 + + + aheadofprint + + 42210816 + 10.1021/acs.jcim.6c00967 + + + + + + 42210809 + + 2026 + 05 + 29 + +
+ + 1463-9084 + + + 2026 + May + 29 + + + Physical chemistry chemical physics : PCCP + Phys Chem Chem Phys + + Substituent-modulated adaptive aromaticity in NHC-pyrrolyl cations: a combined DFT and machine learning study. + 10.1039/d6cp00728g + + The stabilization of 4π-electron systems remains a fundamental challenge in chemistry, stemming from their intrinsic antiaromaticity and thermodynamic instability as dictated by Hückel's rule. A recent experimental study has demonstrated that rational molecular design using N-heterocyclic carbenes (NHCs) can surmount these limitations by inducing aromaticity. However, the issue of T1 aromaticity in such systems remains unresolved. Elucidating the substituent-modulated behavior of T1 (anti)aromaticity is crucial for deciphering the physicochemical properties of these systems. In this work, Density Functional Theory (DFT) was employed to investigate the (anti)aromaticity of NHC-substituted pyrrolyl cations and substituted pyrrolyl anions in both the singlet ground state (S0) and triplet excited state (T1). Our results reveal a clear correlation between substituents and adaptive aromaticity. Notably, within the reduced pyrrole framework (A-H), only the nitroso (NO) substituent (A) is capable of inducing adaptive aromaticity. In contrast, for the NHC-substituted pyrrolyl cations (I-P), two-state aromaticity is sequentially triggered by the substituent effects of NO, NO2, CHO, and COCH3. Spin density analysis reveals distinct electronic behaviors across the studied systems. In adaptively aromatic compounds, the major electron spin density localizes on the substituent, thereby preserving the aromatic character of the pyrrole ring. Conversely, for systems B-D, F-H, and M-P, the spin density is predominantly concentrated on the pyrrolyl ring moiety, which disrupts aromaticity. This conclusion is supported by computational analyses of multiple aromaticity descriptors, including the harmonic oscillator model of aromaticity (HOMA), nucleus-independent chemical shift (NICS(1) + zz + ), multicenter indices (MCI), anisotropy of the induced current density (ACID), and electron density of the delocalized π-bond (EDDBπ). Regression analysis (a supervised machine learning technique) indicates that the spin density on the substituent is strongly correlated with the T1 aromaticity. Our findings highlight the pivotal role of substituents in regulating two-state aromaticity in heterocyclic systems, laying a solid theoretical foundation for advanced applications in molecular electronics and materials science. + + + + Dar + Sohail H + SH + + Guangdong Basic Research Center of Excellence for Aggregate Science, School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Longgang, Shenzhen, Guangdong, 518172, P. R. China. jun.zhu@cuhk.edu.cn. + + + + Zhu + Jun + J + 0000-0002-2099-3156 + + Guangdong Basic Research Center of Excellence for Aggregate Science, School of Science and Engineering, The Chinese University of Hong Kong (Shenzhen), Longgang, Shenzhen, Guangdong, 518172, P. R. China. jun.zhu@cuhk.edu.cn. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Phys Chem Chem Phys + 100888160 + 1463-9076 + + IM + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 4 + 3 + + + aheadofprint + + 42210809 + 10.1039/d6cp00728g + + + + + + 42210783 + + 2026 + 05 + 29 + +
+ + 2767-9764 + + + 2026 + May + 28 + + + Cancer research communications + Cancer Res Commun + + Transcriptionally informed nucleosome profiling of circulating cell-free DNA predicts breast cancer recurrence. + 10.1158/2767-9764.CRC-26-0263 + + Cell-free DNA (cfDNA) offers a minimally invasive approach to capture genomic and epigenetic dynamics during cancer progression. We performed targeted sequencing of 26 gene loci transcriptionally regulated during the acquisition of therapy resistance in breast cancer and analyzed blood-derived cfDNA from 150 breast cancer samples (105 primary and 45 recurrent). Recurrent samples exhibited increased genomic variant counts in both coding and non-coding regions, accompanied by shorter cfDNA fragment lengths. Further, cfDNA fragmentation profiles were variable in recurrent samples, with frequently amplified loci such as ERBB2 and concurrent reductions at loci including RERE and SYNPO2. Notably, nucleosome occupancy-derived scores from RERE and SYNPO2 distinguished recurrent from primary cancer with high accuracy (area under the curve = 0.826). Using a machine-learning approach, integration of these cfDNA features accurately predicted breast cancer relapse. Collectively, these findings demonstrate that cfDNA-based profiling focused on transcriptional alterations provides a sensitive strategy for detecting breast cancer recurrence. + + + + Watanabe + Sugiko + S + 0000-0002-2120-9546 + + Kumamoto University Kumamoto Japan. + https://ror.org/02cgss904 + + + + Etoh + Kan + K + 0000-0001-6392-089X + + Kumamoto University Kumamoto Japan. + https://ror.org/02cgss904 + + + + Mitsui + Jun + J + 0000-0001-7425-4765 + + The University of Tokyo Tokyo Japan. + https://ror.org/057zh3y96 + + + + Suzuki + Yuta + Y + 0000-0002-8528-9418 + + The University of Tokyo Tokyo Japan. + https://ror.org/057zh3y96 + + + + Yamamoto + Yutaka + Y + 0000-0001-6147-6828 + + Kumamoto University Hospital Japan. + https://ror.org/02vgs9327 + + + + Nakao + Mitsuyoshi + M + 0000-0002-2196-8673 + + Kumamoto University Kumamoto, Kumamoto Japan. + https://ror.org/02cgss904 + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Cancer Res Commun + 9918281580506676 + 2767-9764 + + IM + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 26 + + + 2026 + 4 + 6 + + + 2026 + 5 + 21 + + + 2026 + 5 + 29 + 3 + 52 + + + aheadofprint + + 42210783 + 10.1158/2767-9764.CRC-26-0263 + 785577 + + + + + + 42210701 + + 2026 + 05 + 29 + +
+ + 1756-6053 + + + 2026 + May + 28 + + + Language and speech + Lang Speech + + Alignment to a Mandarin Target Correlates With Talker Discrimination Ability in Tibetan-Mandarin Bilinguals. + + 238309261452272 + 238309261452272 + + 10.1177/00238309261452272 + + Extracting talker identity from speech signals is a core perceptual function, yet the mechanisms underlying second Language (L2) identity processing remain unclear. Grounded in the DIVA model and Source-Filter Theory, this study investigated the coupling between perception and production in Tibetan-Mandarin bilinguals. Participants performed a delayed imitation task and a talker-identity discrimination task. Imitation performance was quantified within a multidimensional acoustic space defined by fundamental frequency (F0), harmonics-to-noise ratio (HNR), and formant dispersion (FD). Results indicated that learners achieved significant acoustic convergence toward the L2 model speaker, which persisted as episodic traces across short-term temporal delays. In the discrimination task, sensitivity improved with acoustic distance but plateaued between medium and large distances, while a significant negative response bias in the near condition revealed a tendency toward perceptual assimilation. Crucially, regression and machine-learning analyses revealed that only FD distance was significantly associated with discrimination sensitivity. Unlike source-related cues such as F0 that fluctuate with context, FD reflects relatively invariant vocal-tract structures. These findings suggest that the formation of L2 talker-identity representations involves a functional anatomical alignment with the target speaker through sensorimotor inverse mapping. By locking onto structural invariants like FD, learners can overcome within-person variability to form detailed episodic identity representations. This study extends the scope of auditory targets in speech production models from segmental to indexical levels. + + + + Hu + Yanbing + Y + 0000-0002-0569-4692 + + Northwest Normal University, Lanzhou, China. + + + + Ma + Xiaofeng + X + + Northwest Normal University, Lanzhou, China. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Lang Speech + 2985214R + 0023-8309 + + IM + + Formant dispersion + L2 imitation + L2 speech perception + L2 talker identity + Perception–production coupling + + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 2 + 53 + + + aheadofprint + + 42210701 + 10.1177/00238309261452272 + + + + + + 42210631 + + 2026 + 05 + 29 + +
+ + 1751-2441 + + + 2026 + May + 28 + + + Expert review of clinical pharmacology + Expert Rev Clin Pharmacol + + Predictors of warfarin therapeutic dose across thromboembolic indications: an exploratory early clinical evaluation study using machine learning-integrated explainable artificial intelligence and Bayesian models. + 10.1080/17512433.2026.2682286 + + Warfarin dosing varies widely due to genetic, demographic, and clinical factors, but it is unknown whether the importance, equilibrium and prediction uncertainty of established pharmacogenetic predictors (VKORC1 and CYP2C9) differ between arterial (AF/stroke) and venous (DVT/PE) thromboembolic indications. + This exploratory study (early clinical evaluation) synthesizes findings from the International Warfarin Pharmacogenetics Consortium dataset. We evaluated five machine learning (ML) models, SHapley Additive exPlanations (SHAP) and Bayesian Additive Regression Trees (BART) analyses were carried out. + with Random Forest demonstrating slightly superior predictive performance. SHAP analysis quantified feature contributions, revealing that VKORC1 G/G genotype as the most influential in AF/Stroke, while VKORC1 A/A genotype followed by age in the DVT/PE group. BART provided probabilistic predictions and identified indication-specific uncertainty drivers. Age possibly has an interaction effect in the requirements of reduced warfarin doses with AF/Stroke. + Our findings reveal that while core genetic and anthropometric predictors of warfarin dose transcend thromboembolic indication, the equilibrium among these factors and sources of prediction uncertainty possibly differ between arterial and venous disease. Integration of ML with SHAP offers a roadmap for personalized warfarin dosing, though prospective validation is needed before clinical implementation. + + + + Sridharan + Kannan + K + 0000-0003-3811-6503 + + Department of Pharmacology & Therapeutics, College of Medicine & Health Sciences, Arabian Gulf University, Manama, Kingdom of Bahrain. + + + + Sivaramakrishnan + Gowri + G + + Bahrain Defence Force Royal Medical Services, Riffa, Kingdom of Bahrain. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Expert Rev Clin Pharmacol + 101278296 + 1751-2433 + + IM + + Warfarin + XAI + machine learning + personalized therapy + precision medicine + + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 2 + 2 + + + aheadofprint + + 42210631 + 10.1080/17512433.2026.2682286 + + + + + + 42210537 + + 2026 + 05 + 29 + +
+ + 1549-9626 + + + 2026 + May + 28 + + + Journal of chemical theory and computation + J Chem Theory Comput + + PoroNet: An Intrinsically Interpretable Pore Graph Neural Network for Resolving Pore-Level Adsorption in Metal-Organic Frameworks. + 10.1021/acs.jctc.6c00100 + + Machine learning (ML) models have been widely used as efficient surrogates to predict adsorption in metal-organic frameworks (MOFs) for gas storage, chemical separations, and catalysis applications. The "black box" nature of these ML models, however, remains a significant barrier between predictions and the design of novel MOFs. In this work, we introduce PoroNet, an intrinsically interpretable graph neural network architecture built on a graph representation of the pore network (i.e., pore graph). In a pore graph, nodes represent individual pores, and edges represent pore connections. PoroNet shows highly accurate predictions of hydrogen (H2) uptake and deliverable capacity in MOFs, as well as on a benchmark simulated adsorption data set that contains spherical and linear alkane adsorbates. More importantly, accurate pore-level contributions to total adsorption can be learned using PoroNet either through direct supervised learning or as latent representations while fitting the total adsorption capacity. In direct supervised learning with explicit pore-level labels, we show that the PoroNet architecture is data-efficient, achieving comparable performance to the standard approach with only a fraction of simulation runs needed for model training. The pore-level contribution helps interpret the ML predictions of the total adsorption behavior, identify the key pore properties that govern the adsorption, and provide significant insights into pore engineering. We demonstrate that PoroNet is a powerful tool for high-throughput pore screening and for deriving valuable MOF design rules for cryogenic H2 storage applications. Lastly, we discuss the potential of leveraging interpretable ML for scalable scientific and material discovery. + + + + Zheng + Chao + C + 0009-0006-9292-6418 + + Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States. + + + + Gopalan + Arun + A + + Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States. + + + + Shi + Kaihang + K + 0000-0002-0297-1746 + + Department of Chemical and Biological Engineering, University at Buffalo, The State University of New York, Buffalo, New York 14260, United States. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + J Chem Theory Comput + 101232704 + 1549-9618 + + IM + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 0 + 43 + + + aheadofprint + + 42210537 + 10.1021/acs.jctc.6c00100 + + + + + + 42210506 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1469-8986 + + 63 + 6 + + 2026 + Jun + + + Psychophysiology + Psychophysiology + + Exploring the Relationship Between Emotion and Bodily Activity With Machine Learning. + + e70320 + e70320 + + 10.1111/psyp.70320 + + Human experiences of bodily activity relate to their emotional state; however, exactly how is yet to be fully explored. We aim to conceptually model this relationship between bodily activity and subjective emotional experience using machine learning models. Bodily activity, including internal and peripheral physiological activity and body movements, has been implicated as an important part of a person's emotional state. However, practical investigation of the relationship between bodily activity and self-reported emotional state relies on suitable choices of emotional-state measures. Furthermore, the moment-by-moment associations of measures of bodily activity with a person's self-reported emotional state are still unclear. This study used recordings of the full-body movements and physiological activity of participants in dyadic interactions discussing various positive or negative topics. For each conversation, the dyads (N = 38) reported how they were feeling on three measures. We used a machine learning model trained on each different measure of bodily activity and self-reported emotion measure to investigate how well each measure of bodily activity predicts each measure of self-reported emotion within the machine learning model. Linear mixed models examining those results showed an interaction between how well bodily activity and emotion measures were predicted. Results also showed that within emotion measures, the intensity of some emotions was easier to predict than others. These results demonstrate that self-reports using summative ratings are easier to predict with machine learning models. In addition, the results replicated the predictive relevance of physiological activity over other measures for someone's emotional state. + © 2026 The Author(s). Psychophysiology published by Wiley Periodicals LLC on behalf of Society for Psychophysiological Research. + + + + Goldsack + Roydon + R + 0009-0000-8177-0178 + + Victoria University of Wellington, Wellington, New Zealand. + + + + Kleijn + W Bastiaan + WB + + Victoria University of Wellington, Wellington, New Zealand. + + + + Eisenbarth + Hedwig + H + 0000-0002-0521-2630 + + Victoria University of Wellington, Wellington, New Zealand. + + + + eng + + + Te Herenga Waka SHEADI + + + + + Journal Article + +
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M., H.‐P. Kriegel, R. T. Ng, and J. Sander. 2000. “LOF: Identifying Density‐Based Local Outliers.” In Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data, 93–104. Association for Computing Machinery. https://doi.org/10.1145/342009.335388. + + + Cannon, W. B. 1927. “The James‐Lange Theory of Emotions: A Critical Examination and an Alternative Theory.” American Journal of Psychology 39, no. 1: 106–124. + + + Chang, A., H. E. Kragness, S. R. Livingstone, D. J. Bosnyak, and L. J. Trainor. 2019. “Body Sway Reflects Joint Emotional Expression in Music Ensemble Performance.” Scientific Reports 9, no. 1: 205. https://doi.org/10.1038/s41598‐018‐36358‐4. + + + Cover, T. M., and J. A. Thomas. 1991. Elements of Information Theory. Wiley. + + + Cowen, A. S., and D. Keltner. 2020. “What the Face Displays: Mapping 28 Emotions Conveyed by Naturalistic Expression.” American Psychologist 75, no. 3: 349–364. https://doi.org/10.1037/amp0000488. + + + Crane, E., and M. Gross. 2007. “Motion Capture and Emotion: Affect Detection in Whole Body Movement.” In Affective Computing and Intelligent Interaction, edited by A. C. R. Paiva, R. Prada, and R. W. Picard, vol. 4738, 95–101. Springer Berlin Heidelberg. https://doi.org/10.1007/978‐3‐540‐74889‐2_9. + + + Davey, S., J. Halberstadt, and E. Bell. 2021. “Where Is Emotional Feeling Felt in the Body? An Integrative Review.” PLoS One 16, no. 12: e0261685. https://doi.org/10.1371/journal.pone.0261685. + + + Dieckmann, A., and J. Rieskamp. 2007. “The Influence of Information Redundancy on Probabilistic Inferences.” Memory & Cognition 35, no. 7: 1801–1813. + + + Eisenbarth, H., M. Oxner, H. A. Shehu, et al. 2023. “Emotional Arousal Pattern (EMAP): A New Database for Modeling Momentary Subjective and Psychophysiological Responding to Affective Stimuli.” Psychophysiology 61, no. 2: e14446. https://doi.org/10.1111/psyp.14446. + + + Ekman, P., W. V. Friesen, and S. S. 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Greco, R. Barbieri, E. P. Scilingo, and G. Valenza. 2020. “Assessing Autonomic Function From Electrodermal Activity and Heart Rate Variability During Cold‐Pressor Test and Emotional Challenge.” Scientific Reports 10, no. 1: 5406. https://doi.org/10.1038/s41598‐020‐62225‐2. + + + Heaton, J. 2008. Introduction to Neural Networks With Java. Heaton Research Inc. + + + Hess, U., and A. Fischer. 2014. “Emotional Mimicry: Why and When We Mimic Emotions.” Social and Personality Psychology Compass 8, no. 2: 45–57. https://doi.org/10.1111/spc3.12083. + + + Jia, N., C. Zheng, and W. Sun. 2022. “A Multimodal Emotion Recognition Model Integrating Speech, Video and MoCAP.” Multimedia Tools and Applications 81: 32265–32286. https://doi.org/10.1007/s11042‐022‐13091‐9. + + + Keltner, D., D. Sauter, J. Tracy, and A. Cowen. 2019. “Emotional Expression: Advances in Basic Emotion Theory.” Journal of Nonverbal Behavior 43, no. 2: 133–160. https://doi.org/10.1007/s10919‐019‐00293‐3. + + + Kingma, D. 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Armony. 2025. “Physical Mechanisms of Emotions Evoked by Postural Feedback.” Psychophysiology 62, no. 7: e70095. https://doi.org/10.1111/psyp.70095. + + + Wang, H., M. J. Bah, and M. Hammad. 2019. “Progress in Outlier Detection Techniques: A Survey.” IEEE Access 7: 107964–108000. https://doi.org/10.1109/ACCESS.2019.2932769. + + + Watson, D., L. Anna, and A. Tellegen. 1988. “Development and Validation of Brief Measures of Positive and Negative Affect: The PANAS Scales.” Journal of Personality and Social Psychology 54, no. 6: 1063–1070. + + + Waugh, C. E., E. Z. Shing, and B. M. Avery. 2015. “Temporal Dynamics of Emotional Processing in the Brain.” Emotion Review 7, no. 4: 323–329. + + + Xu, X., R. W. McGorry, L.‐S. Chou, J. Lin, and C. Chang. 2015. “Accuracy of the Microsoft Kinect for Measuring Gait Parameters During Treadmill Walking.” Gait & Posture 42, no. 2: 145–151. https://doi.org/10.1016/j.gaitpost.2015.05.002. + + + Zacharatos, H., C. Gatzoulis, P. Charalambous, and Y. Chrysanthou. 2021. “Emotion Recognition From 3D Motion Capture Data Using Deep CNNs.” In 2021 IEEE Conference on Games (CoG), 1–5. IEEE Press. https://doi.org/10.1109/CoG52621.2021.9619065. + + + + + + + 42210494 + + 2026 + 05 + 29 + +
+ + 1557-7422 + + + 2026 + May + 28 + + + Human gene therapy + Hum Gene Ther + + Capsid Engineering of Adeno-Associated Viruses for Targeted Gene Therapy in Kidney Diseases. + + 10430342261452215 + 10430342261452215 + + 10.1177/10430342261452215 + + The global burden of chronic and genetic kidney diseases poses a significant challenge to healthcare systems. Current therapies, including dialysis, transplantation, and supportive pharmacotherapies, cannot halt disease progression or address root causes, especially in monogenic disorders like Alport syndrome and Fabry disease. Adeno-associated virus (AAV)-based gene therapy is promising, enabling targeted correction of underlying genetic defects. However, renal delivery faces challenges, including cellular heterogeneity, anatomical barriers, and pre-existing immunity. This review evaluates advances in AAV capsid engineering to overcome these obstacles, focusing on strategies to enhance kidney-specific tropism, transduction efficiency, and immune evasion. We outline the evolution from conventional serotype selection to precision engineering via rational design, directed evolution, and in silico approaches. Artificial intelligence (AI) has emerged as a pivotal accelerator, with machine learning models and generative frameworks enabling data-efficient capsid optimization despite limited datasets. Multimodal AI, reinforcement learning, and agentic systems can refine renal targeting by balancing glomerular penetration, cell specificity, and safety. Future progress relies on scaling high-quality datasets through collaborative consortia, lab-in-the-loop validation, and explainable AI. By combining capsid engineering with renal pathophysiology insights, this roadmap paves the way for curative AAV therapies that move beyond current suboptimal treatments to correct underlying pathogenic mechanisms. + + + + Ghaffarzadeh-Esfahani + Mohammadreza + M + 0009-0009-9322-5471 + + Gene Therapy Research Center, Digestive Diseases Research Institute, Tehran University of Medical Sciences, Tehran, Iran. + + + Regenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + Gheisari + Yousof + Y + + Regenerative Medicine Research Center, Isfahan University of Medical Sciences, Isfahan, Iran. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + United States + Hum Gene Ther + 9008950 + 1043-0342 + + IM + + adeno-associated virus + artificial intelligence + capsid engineering + gene therapy + kidney + machine learning + renal tropism + + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 0 + 10 + + + aheadofprint + + 42210494 + 10.1177/10430342261452215 + + + + + + 42210487 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1001-9332 + + 37 + 4 + + 2026 + Apr + + + Ying yong sheng tai xue bao = The journal of applied ecology + Ying Yong Sheng Tai Xue Bao + + [Impact of ozone pollution on crop yields: Advances and prospects in assessment methods]. + + 1319 + 1328 + 1319-1328 + + 10.13287/j.1001-9332.202604.014 + + The concentration of surface ozone (O3) remains high for a long time and its duration continues to extend. Accurately assessing crop yield losses caused by ozone pollution is of great significance for maintaining regio-nal and global food security. We systematically elaborated on the damage mechanism of O3 pollution on crop yield and the detoxification mechanism of crops, analyzed the limitations of crop yield assessment methods such as O3 concentration response, dose response, and flux response. By focusing on innovative practices of mechanism model improvement and multi method coupling, we comprehensively introduced the research progress of embedding O3 damage and detoxification modules in crop models, proposed the research direction of crop model machine learning hybrid framework in O3 damage assessment. Moreover, we provided a new research paradigm that combined ratio-nality and practicality for quantifying the composite effects of O3 pollution and extreme climate events. This review could provide reference for the continuous promotion of research on the impact of O3 pollution on crop yield losses under the background of climate change. + + + + Wu + Rong-Jun + RJ + + Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing 210044, China. + + + School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China. + + + + Feng + Zhao-Zhong + ZZ + + Key Laboratory of Ecosystem Carbon Source and Sink, China Meteorological Administration, Nanjing 210044, China. + + + School of Ecology and Applied Meteorology, Nanjing University of Information Science and Technology, Nanjing 210044, China. + + + + chi + + Journal Article + Review + English Abstract + + 臭氧污染对作物产量的影响:评估方法的进展与展望. +
+ + China + Ying Yong Sheng Tai Xue Bao + 9425159 + 1001-9332 + + + + 66H7ZZK23N + Ozone + + + 0 + Air Pollutants + + + IM + + + Ozone + toxicity + analysis + adverse effects + + + Crops, Agricultural + growth & development + + + Air Pollutants + analysis + + + Climate Change + + + Models, Theoretical + + + Machine Learning + + + Biomass + + + + 地表臭氧(O3)浓度长期处于高位且持续时间不断延长,准确评估臭氧污染引发的作物产量损失,对维护区域及全球粮食安全具有重要意义。本文系统阐述了O3污染对作物减产的伤害机制和作物自身的解毒机制,解析了O3浓度响应、剂量响应和通量响应等农作物减产评估方法的局限性,聚焦机理模型改进与多方法耦合的创新实践,全面介绍了作物模型中嵌入O3伤害和解毒模块的研究进展,展望了作物模型-机器学习混合框架在O3伤害评估中的研究方向,并为量化O3污染与极端气候事件的复合效应提供了机理性和实用性并存的新研究范式。本文可为精准评估气候变化背景下O3污染对作物产量损失影响研究的持续推进提供参考。. + + + assessment method + crop model + crop yield + detoxification mechanism + machine learning + ozone + + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 0 + 10 + + + ppublish + + 42210487 + 10.13287/j.1001-9332.202604.014 + + + + + + 42210485 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1001-9332 + + 37 + 4 + + 2026 + Apr + + + Ying yong sheng tai xue bao = The journal of applied ecology + Ying Yong Sheng Tai Xue Bao + + [Remote sensing-based mapping of soil organic matter in the black soil region of Northeast China: Status, challenges, and prospects]. + + 1299 + 1308 + 1299-1308 + + 10.13287/j.1001-9332.202604.017 + + Remote sensing inversion products for soil organic matter (SOM) are fundamental to monitor and assess soil quality in the black soil region of Northeast China. However, current research has largely prioritized the vertical optimization of inversion algorithms for single products, while ignoring systematic horizontal comparisons among different products, which limits their practical application. Based on a systematic literature review, we synthesized the spatial distribution characteristics, mainstream methodologies, and current status of data products for remote sensing-based inversion of soil organic matter (SOM) in the region. We found that 1) 77.5% of existing studies are concentrated in the Songnen and Sanjiang Plains, while regions such as eastern Inner Mongolia remain underrepresented; 2) a dominant paradigm has emerged, integrating multispectral data, environmental covariates and machine learning techniques; 3) there are inconsistencies among publicly available SOM products, with estimate discrepancies exceeding 30%. There are three major challenges: limited data sources, multiple interfering factors, and insufficient model interpretability and applicability of models. In the future, low altitude remote sensing data should be actively introduced, a ground aerospace multi-level remote sensing fusion system should be constructed, innovative modeling and promotion methods should be developed, grid-based datasets should be built, and data sharing should be promoted to fully explore the application value of soil data. + + + + Yang + Ling + L + + Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Xu + Yao-Tian + YT + + Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Xu + Yue-Ping + YP + + Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. + + + University of Chinese Academy of Sciences, Beijing 100049, China. + + + + Li + Jing-Zhong + JZ + + College of Geography and Geomatics, Xuchang University, Xuchang 461000, Henan, China. + + + + Gang + Shuang + S + + Institute of Carbon Neutrality Technology and Policy, Shenyang University, Shen-yang 110044, China. + + + + Ren + Wan-Xia + WX + + Institute of Applied Ecology, Chinese Academy of Sciences, Shenyang 110016, China. + + + Liaoning Province Key Lab for Environmental Computation and Sustainability, Shenyang 110016, China. + + + + chi + + Journal Article + Systematic Review + English Abstract + + 东北黑土区土壤有机质遥感反演数据产品: 现状、挑战与展望. +
+ + China + Ying Yong Sheng Tai Xue Bao + 9425159 + 1001-9332 + + + + 0 + Soil + + + 0 + Organic Chemicals + + + IM + + + China + + + Soil + chemistry + + + Remote Sensing Technology + + + Environmental Monitoring + methods + + + Organic Chemicals + analysis + + + Ecosystem + + + + 土壤有机质遥感反演数据产品是东北黑土区耕地质量监测与评估的重要基础。然而,当前研究多集中于单一数据产品反演算法的纵向优化,缺乏不同产品间的系统对比,限制了其在黑土地保护实践中的应用效果。本文基于系统文献调研,梳理了东北黑土区土壤有机质遥感反演的空间分布特征、主流方法与数据产品现状:1)现有研究约77.5%集中在松嫩平原与三江平原,内蒙古东部等地明显滞后;2)技术方法上形成了“多光谱+环境因子+机器学习”的主流范式;3)不同公开发布的数据产品存在显著不一致性,土壤有机质估值差异幅度高达30%以上。当前该领域面临三大挑战:数据源受限、因素干扰多、模型可解释性与适用性不足,未来应积极引入低空遥感数据,构建地面-航空-航天多层级遥感融合体系,创新建模与推广方法,建设网格化数据集,并推动数据共享,以充分挖掘土壤数据的应用价值。. + + + black soil region of Northeast China + data product assessment + remote sensing inversion + soil organic matter + + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 0 + 10 + + + ppublish + + 42210485 + 10.13287/j.1001-9332.202604.017 + + + + + + 42210471 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1001-9332 + + 37 + 4 + + 2026 + Apr + + + Ying yong sheng tai xue bao = The journal of applied ecology + Ying Yong Sheng Tai Xue Bao + + [Medicine-geography relationship of Gentiana rigescens based on habitat and quality suitability]. + + 1141 + 1152 + 1141-1152 + + 10.13287/j.1001-9332.202604.021 + + The spatial matching of habitat suitability and quality suitability of medicinal plants is a challenge in analyzing the medicine-geography relationship. Taking the traditional medicinal plant Gentiana rigescens as the object, and selecting Yunnan Province, the main producing area of the herb, as the research region, we integrated methods including species distribution models, machine learning, and geostatistical analysis to explore the medicine-geography relationship of G. rigescens and identify the key ecological driving forces behind the formation of high-quality medicinal material-producing areas by systematic resource surveys and sampling. The results showed that the suitable habitat area for G. rigescens in Yunnan Province was about 22.00×104 km2, accounting for 55.8% of the total land area. Highly suitable areas were mainly concentrated in central, eastern, and northwestern Yunnan. Quantitative analysis of bioactive compounds and medicinal quality zoning revealed that the total content of bioactive compounds in herbs from high-quality production areas (64.93±23.94 mg·g-1) was significantly higher than that in herbs from other regions (41.34±11.71 mg·g-1). The high-quality production areas were mainly located in western and northwestern Yunnan, within habitats classified as low to moderate suitability. Spatial autocorrelation analysis further confirmed a significant spatial mismatch between the "habitat suitability" and "medicinal quality" of G. rigescens. Eight environmental factors closely associated with the formation of high-quality medicinal material-producing areas were identified by screening key environmental variables combined with the GeoDetector model, including mean diurnal temperature range, isothermality, temperature seasonality, precipitation of the driest quarter, precipitation of the coldest quarter, and UV-B radiation in April, October and December. Factor detection and interaction analysis further showed that the combined effects of thermal, moisture, and UV-B radiation factors exhibited a significant non-linear enhancement effect. Our results indicated that the formation of high-quality G. rigescens production areas depends on a unique habitat driven by the synergistic effects of multiple environmental factors, rather than on ecologically optimal zones alone. + + + + Shen + Tao + T + + School of Agriculture and Biological Science, Yuxi Normal University, Yuxi 653100, Yunnan, China. + + + + Wang + Yuan-Zhong + YZ + + Institute of Medicinal Plants, Yunnan Academy of Agricultural Sciences, Kunming 650205, China. + + + + chi + + English Abstract + Journal Article + + 基于生境和品质适宜性的滇龙胆药地关系. +
+ + China + Ying Yong Sheng Tai Xue Bao + 9425159 + 1001-9332 + + IM + + + Gentiana + growth & development + + + Ecosystem + + + China + + + Plants, Medicinal + growth & development + + + Geography + + + Conservation of Natural Resources + + + + 药用植物生境适宜性与品质适宜性的空间匹配是解析药地关系的难点。本研究以传统药用植物滇龙胆为研究对象,选取药材主产地云南省为研究区域,通过资源调查和采样,整合物种分布模型、机器学习、地统计分析等方法,探讨滇龙胆药地关系,并明确优质药材产地形成的关键生态驱动力。结果表明:云南省滇龙胆适生区面积约22.00×104 km2,占云南省土地面积的55.8%;高度适生区主要分布于滇中、滇东及滇西北部分地区。有效成分定量分析及药材质量区划发现,优质药材产地主要集中于滇西和滇西北的低度、中度适生区,优质产地药材有效成分总含量(64.93±23.94 mg·g-1)显著高于其他产地(41.34±11.71 mg·g-1)。全局与局部空间自相关分析进一步证实,滇龙胆“适生”与“质优”存在显著空间错位关系。关键环境变量筛选结合地理探测器模型确定了与优质药材产地关系紧密的8个环境因子,分别为平均温度日较差、等温性、温度季节性变化、最干季度降水量、最冷季度降水量及4月、10月和12月UV-B辐射;因子探测及交互作用分析进一步显示,热量、水分与UV-B辐射的组合作用呈现显著非线性增强效应,表明滇龙胆优质药材的形成依赖于多种环境因子协同构建的特殊生境,而非单纯的生态最适区。. + + + + Gentiana rigescens + + driving factor + habitat suitability + medicine-geography relationship + quality suitability + + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 0 + 10 + + + ppublish + + 42210471 + 10.13287/j.1001-9332.202604.021 + + + + + + 42210444 + + 2026 + 05 + 29 + +
+ + 2056-9920 + + + 2026 + May + 28 + + + International journal of retina and vitreous + Int J Retina Vitreous + + Utility of artificial intelligence for the diagnosis, prognosis, and management of central serous chorioretinopathy: a narrative review. + 10.1186/s40942-026-00867-6 + + Central serous chorioretinopathy (CSC) represents a significant cause of visual impairment, particularly in working-age individuals. Despite advances in multimodal imaging and evidence supporting photodynamic therapy (PDT) as the mainstay of chronic CSC, current clinical workflows are still affected by variability in image interpretation, manual quantification, and individualized treatment selection. This narrative review investigates the utility of Artificial Intelligence (AI) in improving the diagnosis, prognosis, and management of CSC. AI-based image analysis, including Optical Coherence Tomography (OCT), Optical Coherence Tomography Angiography (OCTA), and Fundus Fluorescein Angiography (FFA), has demonstrated high diagnostic accuracy across selected datasets and the ability to identify relevant biomarkers, thereby improving efficiency and consistency. Machine learning models demonstrate promising predictive power for subretinal fluid absorption, visual acuity outcomes, and disease recurrence, and identify critical prognostic factors. While emerging, AI-guided treatment strategies hold promise for personalized therapy, particularly for optimizing PDT, laser-based interventions, and follow-up strategies. The integration of AI into clinical decision-making workflows may elevate diagnostic capabilities, especially for non-specialists, and reduce clinical workload. However, the widespread implementation of AI in CSC faces notable challenges, including dataset bias, limited external validation, insufficient representation of differential diagnoses, regulatory complexities, and ethical considerations pertaining to transparency and equitable access. Future directions emphasize integrating multimodal data, fostering global collaborative efforts, and developing robust, generalizable AI models to fully realize AI's potential to enhance patient care and optimize healthcare delivery for CSC. + © 2026. The Author(s). + + + + Fernández-Vigo + José Ignacio + JI + 0000-0001-8745-3464 + + Department of Ophthalmology, Hospital Clínico San Carlos, San Carlos Health Research Institute (IdISSC), Profesor Martín Lagos s/n, Madrid, 28040, Spain. jfvigo@hotmail.com. + + + Department of Immunology, Ophthalmology and ENT, Faculty of Medicine, Complutense University of Madrid, Madrid, Spain. jfvigo@hotmail.com. + + + Centro Internacional de Oftalmología Avanzada, Madrid, Spain. jfvigo@hotmail.com. + + + + Valverde-Megías + Alicia + A + + Department of Ophthalmology, Hospital Clínico San Carlos, San Carlos Health Research Institute (IdISSC), Profesor Martín Lagos s/n, Madrid, 28040, Spain. + + + + Burgos-Blasco + Bárbara + B + + Department of Ophthalmology, Hospital Clínico San Carlos, San Carlos Health Research Institute (IdISSC), Profesor Martín Lagos s/n, Madrid, 28040, Spain. + + + + de Moura Ramos + José Joaquim + JJ + + VARPA Research Group, Instituto de Investigación Biomédica de A Coruña (INIBIC), Universidad da Coruña, A Coruña, Spain. + + + + Ly-Yang + Fernando + F + + Department of Ophthalmology, Birmingham and Midland Eye Centre, Sandwell and West Birmingham Hospital, NHS Trust, Birmingham, UK. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + England + Int J Retina Vitreous + 101677897 + 2056-9920 + + + Artificial intelligence + Biomarkers + Central serous chorioretinopathy + Machine learning + Pachychoroid disease + Treatment strategies + + Declarations. Ethical approval: This article is based exclusively on previously published studies and does not involve human participants or animals performed by any of the authors. Informed consent: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 3 + 2 + + + 2026 + 5 + 13 + + + 2026 + 5 + 29 + 0 + 10 + + + aheadofprint + + 42210444 + 10.1186/s40942-026-00867-6 + 10.1186/s40942-026-00867-6 + + + + + + 42210409 + + 2026 + 05 + 29 + +
+ + 1470-7330 + + + 2026 + May + 28 + + + Cancer imaging : the official publication of the International Cancer Imaging Society + Cancer Imaging + + Preoperative low-attenuation area on computed tomography is associated with recurrence after curative resection for non-small-cell lung cancer. + 10.1186/s40644-026-01054-4 + + Recurrence after curative-intent resection remains a major determinant of long-term outcomes in non-small-cell lung cancer (NSCLC). Preoperative computed tomography (CT)-derived low-attenuation area (LAA) is an objective marker of smoking-related parenchymal injury, but its independent association with postoperative recurrence and the shape of its exposure-response relationship remain incompletely defined. + We retrospectively analyzed 660 consecutive patients who underwent R0 resection for NSCLC between 2017 and 2021. LAA was quantified on inspiratory CT as the percentage of lung volume with attenuation values < - 950 Hounsfield units. The primary endpoint was recurrence-free survival (RFS). Prespecified multivariable Cox models adjusted for major clinicopathologic and treatment-related factors, with LAA modeled per 1% increase and dichotomized using a Youden index-derived cutoff. Potential nonlinearity was assessed using restricted cubic splines and a one-knot hinge model. Model performance was summarized using time-specific calibration and decision-curve analysis. Machine learning analyses visualized the LAA-recurrence relationship using random forest partial dependence plots and explored predictor importance using permutation-based methods and a random survival forest. + Recurrence occurred in 119 patients (18%), including 32 locoregional-only recurrences and 87 distant recurrences with or without locoregional recurrence. Higher LAA was independently associated with shorter RFS (per 1% increase: adjusted hazard ratio [HR], 1.05; 95% confidence interval [CI], 1.01-1.08; P = 0.007). In an exploratory dichotomized analysis using a data-derived 1.3% cutoff, LAA ≥ 1.3% was also associated with shorter RFS (HR, 1.84; 95% CI, 1.20-2.83; P = 0.005). Global spline tests did not show statistically significant nonlinearity across the full LAA distribution; however, prespecified low-range analyses suggested a possible change in the risk gradient around 1%-2% LAA. Machine learning analyses ranked pathologic stage and PD-L1 as the strongest predictors, with LAA contributing additional but smaller prognostic information. + Preoperative CT-derived LAA was independently associated with postoperative recurrence after R0 resection for NSCLC. LAA may provide complementary host-lung imaging information alongside established clinicopathologic predictors, but the low-range signal and the data-derived 1.3% cutoff should be regarded as hypothesis-generating pending external validation in multicenter cohorts with standardized CT acquisition and quantification. + © 2026. The Author(s). + + + + Samejima + Hironobu + H + + Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591- 8555, Japan. + + + Department of General Thoracic Surgery, Osaka International Cancer Institute, 3- 1-69 Otemae, Chuo-ku, 540-0008, Osaka, Japan. + + + + Kojima + Kensuke + K + + Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591- 8555, Japan. k7kensuke@icloud.com. + + + + Shimizu + Shigeki + S + + Department of Pathology, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591-8555, Japan. + + + + Tokunaga + Toshiteru + T + + Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591- 8555, Japan. + + + + Okishio + Kyoichi + K + + Department of Thoracic Oncology, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591-8555, Japan. + + + Clinical Research Center, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591-8555, Japan. + + + + Yoon + Hyungeun + H + + Department of General Thoracic Surgery, NHO Kinki Chuo Chest Medical Center, 1180 Nagasone-cho, Kita-ku, Sakai-shi, Osaka, 591- 8555, Japan. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Cancer Imaging + 101172931 + 1470-7330 + + IM + + Computed tomography + Cox proportional hazards + Emphysema + Low-attenuation area + Machine learning + Non-small-cell lung cancer + Recurrence + Recurrence-free survival + + Declarations. Ethics approval and consent to participate: This retrospective study was approved by the Institutional Review Board of the National Hospital Organization Kinki Chuo Chest Medical Center (approval no. 2023−102; January 30, 2024). The requirement for written informed consent was waived by the Board, and an opt-out procedure was used. The study was conducted in accordance with the Declaration of Helsinki and applicable institutional guidelines. Consent for publication: Not applicable. Competing interests: K.O. reports honoraria for lectures and/or presentations from Bristol-Myers Squibb K.K., AstraZeneca K.K., Chugai Pharmaceutical Co., Ltd., Nippon Kayaku Co., Ltd., Takeda Pharmaceutical Company Limited, Taiho Pharmaceutical Co., Ltd., Sawai Pharmaceutical Co., Ltd., and Eli Lilly Japan K.K., outside the submitted work. All other authors declare that they have no competing interests. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 3 + 2 + + + 2026 + 5 + 22 + + + 2026 + 5 + 29 + 0 + 8 + + + aheadofprint + + 42210409 + 10.1186/s40644-026-01054-4 + 10.1186/s40644-026-01054-4 + + + + + + 42210385 + + 2026 + 05 + 29 + +
+ + 2058-8615 + + + 2026 + May + 29 + + + Research integrity and peer review + Res Integr Peer Rev + + Effectiveness of the researcher-led "Peerspectives" peer review training course on review quality, knowledge, and skills among doctoral students in the biomedical sciences: a pre-post study. + 10.1186/s41073-026-00220-3 + + Peer review remains a cornerstone of scientific knowledge dissemination, yet comprehensive, practically relevant training is limited. This inspired us to develop Peerspectives, a peer review training course for doctoral students in the biomedical sciences in Berlin, Germany. We aimed to assess the effectiveness of the Peerspectives course on editor-judged quality of peer review reports. + Doctoral students in health research fields who enrolled in the Peerspectives course between October 2020 and August 2022 were invited to participate in the study, and 80 consented. The ~18 week-long course provided training on the structure, purpose, and conduct of peer review and editorial processes in biomedical journals. It included 12 h of lectures, homework assignments, and 12 h of hands-on, small-group workshops, during which students reviewed original research manuscripts currently under consideration at The BMJ under the guidance of experienced mentors. The primary outcome was the overall quality of the peer review reports as judged by two independent BMJ editors using the global score of the Review Quality Instrument (RQI) pre- and post-intervention. Additionally, we compared participants' post-course scores with those of actual BMJ reviewers. We also compared participants' self-assessed knowledge and skills related to scholarly peer review (1-5 Likert scale) before and after the course. + After course completion, the editor-assessed overall quality of the participants' peer review reports was higher than before the course (median increase of 0.5 points, p < 0.001; mean increase of 0.36 points, p < 0.001). The RQI scores of participants' post-course reports were not non-inferior to those of actual BMJ reviewers for the same manuscripts. Self-assessed peer review-related knowledge skills increased across all questionnaire items after course completion. Greatest improvements were seen in understanding reviewer expectations (increase in means from 2.9 to 4.5), confidence in reviewing (2.5 to 3.9), and knowing what to look for while reviewing (2.8 to 4.2). + Providing doctoral students with comprehensive training resulted in an editorially significant increase in review report quality and improved understanding of the role and expectations of peer reviewers in the scholarly publishing processes and confidence in giving constructive feedback. PRE-REGISTRATION: https://osf.io/vndcx. + © 2026. The Author(s). + + + + Rohmann + Jessica L + JL + 0000-0003-2420-5716 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + QUEST Center for Responsible Research, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Wülk + Nadja + N + 0000-0002-3917-6578 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Rubarth + Kerstin + K + 0000-0002-6174-6346 + + Institute of Biometry and Clinical Epidemiology, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Grillmaier + Hannah + H + 0009-0009-2233-9906 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + German Network for Health Services Research (DNVF), Berlin, Germany. + + + + Abdikarim + Iman + I + 0000-0002-5795-3895 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Lopes Simões + Mariana + M + 0009-0007-2883-4700 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + School of Public Health - Graduate School "Health Policy and Systems in Uncertainties" (GRASP), AG3: Epidemiology & International Public Health, Faculty of Public Health, Bielefeld University, Bielefeld, Germany. + + + + Schroter + Sara + S + 0000-0002-8791-8564 + + BMJ Publishing Group, London, UK. + + + + Piccininni + Marco + M + 0000-0002-1397-0060 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + Digital Health - Machine Learning Research Group, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany. + + + Digital Engineering Faculty, University of Potsdam, Potsdam, Germany. + + + + Kurth + Tobias + T + 0000-0001-7169-2620 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. + + + + Glatz + Toivo + T + 0000-0002-8994-7860 + + Institute of Public Health, Charité - Universitätsmedizin Berlin, Berlin, Germany. toivo.glatz@charite.de. + + + + eng + + + 218358 + WT_ + Wellcome Trust + United Kingdom + + + 9C872 + Volkswagen Foundation + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Res Integr Peer Rev + 101676020 + 2058-8615 + + + Doctoral programs + Editorial practices + Education + Mentorship + Peer review + PhD students + Quality of peer review + Scientific publication + Training + + Declarations. Ethics approval and consent to participate: This study was approved by the Charité – Universitätsmedizin Berlin ethics committee (EA4/190/20). All participants provided written informed consent. Consent for publication: Not applicable. Competing interests: TG and JLR received funding from the Volkswagen Foundation to further develop the Peerspectives course and create open-source train-the-trainer materials. This funding was received after data used in this evaluation study were collected and analyzed but supports the ongoing development and organization of the course and openly available course materials. TK reports having received research grants from the German Federal Joint Committee (G-BA) and personal compensation from the North-East German Society for Gynecological Oncology (NOGGO), AbbVie, the BMJ Group, and Frontiers Media S.A. SS is a full-time employee at The BMJ. NW, KR, HG, IA, MLS, and MP declare that they have no competing interests. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 10 + 22 + + + 2026 + 5 + 12 + + + 2026 + 5 + 29 + 0 + 7 + + + aheadofprint + + 42210385 + 10.1186/s41073-026-00220-3 + 10.1186/s41073-026-00220-3 + + + + + + 42210373 + + 2026 + 05 + 29 + +
+ + 1746-4811 + + + 2026 + May + 28 + + + Plant methods + Plant Methods + + 2NPLGBM: a genomic model that merges the strengths of classical and machine learning methods in genomic prediction. + 10.1186/s13007-026-01545-2 + + Genomic prediction (GP) is a central component of modern plant breeding, enabling the early selection of superior genotypes based on genomic marker data. Classical GP models, such as genomic best linear unbiased prediction (GBLUP), operate within the data modeling culture and typically assume additive genetic effects, with extensions required to model non-additive effects such as dominance and epistasis. In contrast, machine learning (ML) models from the algorithmic modeling culture can flexibly model complex, non-additive genetic relationship but often lack direct grounding in quantitative genetic theory and interpretability. To bridge these gaps, we propose 2NPLGBM, a hybrid genomic prediction approach that integrates quantitative genetics with ML. This method introduces a two-matrix (2NP) genotype representation by concatenating additive (Z) and dominance (W) matrices, which are then used as input to a Light Gradient Boosting Machine (LGBM), enabling the simultaneous modeling of additive, dominance, and higher-order genetic interactions (AA, AD, DD). + The 2NPLGBM model was evaluated using six years of test-cross hybrid maize trial data across four agronomic traits (grain yield, plant height, days to silking, and days to anthesis) under five cross-validation schemes simulating temporal: Leave-One-Year-Out (LOYO), Rolling Window (RW), and genetic generalization: Five-Fold, and tester-based schemes (Tester CV0 and Tester CV00). Compared to GBLUP, 2NPLGBM achieved an average of 5% improvement in predictive accuracy under temporal validations and over 15% gains under tester-based schemes, particularly for flowering traits (days to silking and days to anthesis). Performance was generally comparable to LGBM, with both ML models outperforming GBLUP for most traits. Under Tester CV0, 2NPLGBM showed its strongest relative advantage over LGBM for flowering traits, suggesting improved capture of interaction-related genetic signals, whereas LGBM generally performed best for plant height and grain yield. In five-fold CV and Tester CV00, GBLUP remained competitive for some traits, while both machine learning models showed reduced gains, with LGBM slightly outperforming 2NPLGBM. In addition, 2NPLGBM generally improved selection efficiency over GBLUP and, in most cases, LGBM, indicating enhanced ability to capture complex genetic signals relevant for hybrid ranking, particularly for flowering traits, whereas LGBM tended to achieve the highest selection efficiency for plant height and grain yield. Feature interpretation using Shapley Additive exPlanations (SHAP) confirmed that non-additive interactions contributed substantially to prediction accuracy for highly heritable traits. It also revealed trait-specific architectures, additive effects dominated flowering traits, while dominance effects contributed more to plant height and yield. Classical variance component analysis supported these findings, indicating high dominance contributions of 17.3% for yield and 8.2% for plant height. + The 2NPLGBM model integrates quantitative genetic theory with machine-learning, bridging classical statistical (data-model) and algorithmic modeling cultures. bridging classical statistical (data model) and algorithmic modeling cultures. By jointly modeling additive and non-additive effects it can improve predictive accuracy, interpretability, and selection efficiency in test-cross hybrids. Future work should explore multi-trait and multi-environment extensions, integration of environmental covariates, and the inclusion of multi-omic data to further strengthen predictive power and interpretability. + © 2026. The Author(s). + + + + Osatohanmwen + Bright Enogieru + BE + + Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, 37075, Goettingen, Germany. bright.osatohanmwen@uni-goettingen.de. + + + Center for Integrated Breeding Research, University of Goettingen, 37075, Goettingen, Germany. bright.osatohanmwen@uni-goettingen.de. + + + + Vieira + Indalécio Cunha + IC + + KWS SAAT SE & Co. KGaA, Einbeck, Germany. + + + + Sharifi + Ahmad Reza + AR + + Center for Integrated Breeding Research, University of Goettingen, 37075, Goettingen, Germany. + + + Division of Animal Breeding and Genetics, Department of Animal Sciences, University of Gottingen, 37075, Goettingen, Germany. + + + + Beissinger + Timothy M + TM + + Division of Plant Breeding Methodology, Department of Crop Sciences, University of Goettingen, 37075, Goettingen, Germany. + + + Heritable Agriculture, San Carlos, California, 94070, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Plant Methods + 101245798 + 1746-4811 + + + 2NP matrix + Additive–dominance modeling + Dominance + GBLUP + Genomic prediction + Hybrid breeding + LightGBM + Machine learning + Maize + Non-additive effects + SHAP + Selection efficiency + Temporal validation + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 11 + 12 + + + 2026 + 4 + 24 + + + 2026 + 5 + 29 + 0 + 6 + + + aheadofprint + + 42210373 + 10.1186/s13007-026-01545-2 + 10.1186/s13007-026-01545-2 + + + + + + 42210369 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1297-9716 + + 57 + 1 + + 2026 + May + 28 + + + Veterinary research + Vet Res + + Molecular prevalence, genomic characterization, and zoonotic potential of novel paramyxovirus and hepacivirus in Alexandromys fortis, Republic of Korea. + 96 + 10.1186/s13567-026-01777-z + + Rodents are substantial reservoirs of zoonotic viruses with regular human exposure restricted to a limited number of species. Numerous rodent species have been shown to harbor emerging viruses, including paramyxoviruses and hepaciviruses. Reed voles (Alexandromys fortis), a rodent species that inhabits grasslands and riparian environments throughout East Asia, remain poorly characterized in terms of their viral diversity. In this study, 258 A. fortis specimens collected from rural areas in Gyeonggi Province, Republic of Korea (ROK) were screened for paramyxoviruses and subjected to metagenomic next-generation sequencing. Genome characterization, phylogenetic and cophylogenetic assessments, and prediction of signal peptidase cleavage sites were performed to analyze the molecular features of the identified viruses. Zoonotic potential was evaluated using a genome-based machine-learning model. A nearly complete genome of a novel paramyxovirus, designated as Pyeongtaek Alexandromys paramyxovirus (PyAPV), was identified in six A. fortis specimens, with all sequences clustering within the genus Jeilongvirus. A nearly complete genome of a rodent-associated hepacivirus was also obtained from four specimens and classified as a distinct lineage within the species Hepacivirus J. These findings demonstrate the role of A. fortis as a natural reservoir of emerging viruses and expand current knowledge of rodent-associated viral diversity in the ROK. + © 2026. The Author(s). + + + + Pangestu + Haryo Seno + HS + + Department of Microbiology, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. + + + + Yang + Intae + I + + College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. + + + + Natasha + Augustine + A + + Department of Microbiology, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. + + + + Rajoriya + Shivani + S + + Department of Microbiology, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. + + + + Hennisa + Hennisa + H + + Department of Microbiology, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. + + + + Park + Jieun + J + + Department of Microbiology, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. + + + + Park + Kyungmin + K + + Department of Microbiology, Korea University College of Medicine, Seoul, 02841, Republic of Korea. + + + Institute for Viral Diseases, Korea University College of Medicine, Seoul, 02841, Republic of Korea. + + + + Kim + Jongwoo + J + + Department of Microbiology, Korea University College of Medicine, Seoul, 02841, Republic of Korea. + + + BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea. + + + + Kim + Seong-Gyu + SG + + Department of Microbiology, Korea University College of Medicine, Seoul, 02841, Republic of Korea. + + + BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea. + + + + Klein + Terry A + TA + + Force Health Protection and Preventive Medicine, 65th Medical Brigade/US Army MEDDAC-Korea, Unit 15281, APO, AP, 96271-5281, USA. + + + PSC 450, APO AP, PSC 450, Box 75R, APO, AP, 96206, USA. + + + + Kim + Heung Chul + HC + + Force Health Protection and Preventive Medicine, 65th Medical Brigade/US Army MEDDAC-Korea, Unit 15281, APO, AP, 96271-5281, USA. + + + Ucarlix, 169-23, Gasan Digital 2-ro, Geumcheon-gu, Seoul, 08504, Republic of Korea. + + + + Oh + Yeonsu + Y + + College of Veterinary Medicine and Institute of Veterinary Science, Kangwon National University, Chuncheon, Republic of Korea. + + + + Song + Jin-Won + JW + + Department of Microbiology, Korea University College of Medicine, Seoul, 02841, Republic of Korea. jwsong@korea.ac.kr. + + + BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea. jwsong@korea.ac.kr. + + + + Kim + Won-Keun + WK + + Department of Microbiology, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. wkkim1061@hallym.ac.kr. + + + Institute of Medical Science, College of Medicine, Hallym University, Chuncheon, 24252, Republic of Korea. wkkim1061@hallym.ac.kr. + + + + eng + + + ProMIS ID C0039-09-ME + Global Emerging Infections Surveillance Branch (GEIS) + + + + RS-2021-KS211475 + Korea Institute of Marine Science and Technology promotion + + + + RS-2023-KH140418 + Government-wide R&D to Advance Infectious Disease Prevention and Control, Republic of Korea + + + + 2024-ER2502-00 + Korea National Institute of Health Research Project + + + + RS-202300249142 + Basic Science Research Program through the NRF by the Ministry of Education + + + + NF22SA0082041 + Novo Nordisk Foundation PAD award to CBL + + + + U01 AI151810 + AI + NIAID NIH HHS + United States + + + 2023R1A2C2006105 + Basic Research Program through the NRF grant funded by the Korean government (MSIT) + + + + RS-2024-00400152 + Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through High-Risk Animal Infectious Disease Control Technology Development Program, funded by Ministry of Agriculture, Food and Rural Affairs + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Vet Res + 9309551 + 0928-4249 + + IM + + + Animals + + + Republic of Korea + epidemiology + + + Genome, Viral + + + Phylogeny + + + Arvicolinae + virology + + + Paramyxoviridae Infections + veterinary + epidemiology + virology + + + Rodent Diseases + virology + epidemiology + + + Zoonoses + virology + epidemiology + + + Prevalence + + + Paramyxoviridae + genetics + isolation & purification + + + Hepatitis C + virology + epidemiology + veterinary + + + + Alexandromys fortis + Flaviviridae + Hepacivirus + Jeilongvirus + Paramyxoviridae + metagenomic sequencing + reed vole + rodent + zoonotic potential + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. 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+ + 1472-6947 + + + 2026 + May + 28 + + + BMC medical informatics and decision making + BMC Med Inform Decis Mak + + Cardiac arrhythmia detection via PQRST analyzed data using an optimized hierarchical fused fuzzy deep reinforcement learning. + 10.1186/s12911-026-03564-4 + + Cardiac arrhythmia is a disorder caused by disruptions in the regular heart rhythm. Arrhythmias are categorized into two classes: sinus and non-sinus rhythms. Whereas sinus rhythms are generally low-risk, non-sinus rhythms are associated with higher risks of morbidity and mortality, including stroke and death. This research proposes a novel method, Optimized Hierarchical Fused Fuzzy Deep Reinforcement Learning OHFFDRL, which incorporates three steps for arrhythmia prediction: data preprocessing, reinforcement learning, and fuzzy deep learning. We evaluated the proposed method using a 12-lead electrocardiogram dataset comprising 10,646 patients. Our approach leverages recent advances in machine learning and medical science to achieve an accuracy of 94% in predicting non-sinus rhythms. Furthermore, the area under the ROC curve for OHFFDRL was 0.91, and the empirical ROC area was 0.90. In addition, the interpretability of the model has been analyzed with SHAP, LIME, Calibration Curve, Adversarial vulnerability, and Integrated Gradients. Our experimental results, among other findings, indicate that the most important feature for distinguishing heart rhythms is TAxis (the movement range in ventricular repolarization). These results demonstrate the potential of machine learning for the early prevention of heart disease through non-sinus rhythm prediction. The source code is available at (https://github.com/arman-daliri/OHFFDRL). + © 2026. The Author(s). + + + + Mahdavi + Nora + N + 0009-0001-4083-6609 + + Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran. + + + + Sadeghi + Reza + R + 0000-0002-0811-5908 + + School of Computer Science and Mathematics, Marist College, Poughkeepsie, NY, USA. + + + + Daliri + Arman + A + 0000-0002-0398-3052 + + Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran. A.Daliri@iau.ac.ir. + + + + Zabihimayvan + Mahdieh + M + 0000-0002-5826-3298 + + Department of Computer Science, Central Connecticut State University, New Britain, CT, USA. + + + + Alimoradi + Mahmoud + M + 0000-0001-6167-439X + + Department of Computer Engineering, Ayandegan University, Tonekabon, Iran. + + + + Knapp + Gabrielle + G + + School of Computer Science and Mathematics, Marist College, Poughkeepsie, NY, USA. + + + + Bastanfard + Azam + A + 0000-0002-7935-819X + + Department of Computer Engineering, Ka.C., Islamic Azad University, Karaj, Iran. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + BMC Med Inform Decis Mak + 101088682 + 1472-6947 + + IM + + Cardiac Arrhythmia + Fuzzy Deep Learning + Medical Science + Reinforcement Learning + + Declarations. Ethical approval: In this research, there was no interference with ethical laws and free sources were used. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 42 + + + 2025 + 7 + 7 + + + 2026 + 5 + 8 + + + 2026 + 5 + 29 + 0 + 2 + + + aheadofprint + + 42210277 + 10.1186/s12911-026-03564-4 + 10.1186/s12911-026-03564-4 + + + + + + 42210272 + + 2026 + 05 + 29 + +
+ + 1741-7015 + + + 2026 + May + 28 + + + BMC medicine + BMC Med + + Machine learning and natural language processing for the identification of potential mental disorders among school-age children: a prospective birth cohort study. + 10.1186/s12916-026-04927-4 + + Early identification of childhood mental health disorders is a critical public health objective. Existing screening approaches, largely dependent on observer reports, are resource-intensive and may overlook subtle internalized symptoms. The analysis of children's linguistic expression presents a scalable and potentially more objective alternative. This study evaluates whether combining natural language processing (NLP) of children's essays with conventional risk factors improves the detection of mental health difficulties in school-age populations, relative to models based on a single data source. + We conducted a prospective analysis using data from the UK-based National Child Development Study (NCDS), a national birth cohort initiated in 1958. Data from birth, age 7, and age 11 assessments were analyzed. The final sample included 8,981 children (4,428 [49.3%] female) who completed a creative writing essay at age 11 describing their imagined life at age 25. Predictors comprised traditional risk factors (perinatal, socioeconomic, and parental engagement variables) and linguistic features computationally extracted from the essays. The primary outcome was potential mental health disorder at age 11, defined as scoring above the 95th or 90th percentile on the teacher-completed Bristol Social Adjustment Guide (BSAG). The mother-completed Rutter A Scale was used for sensitivity analysis. Machine learning models incorporating various predictor combinations were developed, and their predictive performance was evaluated using area under the receiver operating characteristic (AUROC) values. + Using BSAG 95th percentile threshold, models combining top five selected variables with essay features achieved significantly higher predictive capability (AUROC:0.77, 95%CI:0.71-0.83) compared to models using all variables (AUROC:0.70, 95%CI:0.63-0.76) or essay features alone (AUROC:0.67, 95%CI:0.60-0.74). At 90th percentile threshold, this integrated approach showed similar improvement (AUROC:0.81, 95%CI:0.78-0.85). Key predictors included gestational length, maternal parity, parental age, residential characteristics, parental engagement metrics, and children's body mass index. Sensitivity analyses using Rutter A Scale confirmed these findings. + In this prospective birth cohort study, integrating NLP analysis of children's essays with a small set of key risk factors substantially improved the identification of potential mental health disorders. This integrated approach represents a potential paradigm for developing scalable, objective screening tools, but requires validation in contemporary, diverse pediatric populations before clinical consideration. + © 2026. The Author(s). + + + + Chen + Shanquan + S + + School of Public Health, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China. shanquan.chen@hku.hk. + + + + Dang + Ting + T + + School of Computing and Information Systems, University of Melbourne, Melbourne, VIC, 3010, Australia. + + + + Qian + Mengjie + M + + Department of Engineering, University of Cambridge, Cambridge, CB2 1PZ, UK. + + + + Liang + Huizhi + H + + School of Computing, Newcastle University, Newcastle upon Tyne, UK. + + + + Bedada + Diribsa Tsegaye + DT + + Department of Health and Rehabilitation Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7505, South Africa. + + + Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Canada. + + + + Louw + Quinette Abegail + QA + + Department of Health and Rehabilitation Sciences, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, 7505, South Africa. + + + + Moore + Anna + A + + Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK. + + + + Cardinal + Rudolf N + RN + + Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK. + + + Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, CB21 5EF, UK. + + + + Ford + Tamsin J + TJ + + Department of Psychiatry, University of Cambridge, Cambridge, CB2 0SZ, UK. + + + Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, CB21 5EF, UK. + + + + Jiang + Fan + F + + School of public health, Shandong ENT Hospital, Shandong University, Jinan, Shandong, China, 250012. Jiang.fan@sdu.edu.cn. + + + + eng + + + NIHR203312 + NIHR Cambridge Biomedical Research Centre + + + + NIHR203312 + NIHR Cambridge Biomedical Research Centre + + + + NIHR203312 + NIHR Cambridge Biomedical Research Centre + + + + MR/Z504816/1 + MRC_ + Medical Research Council + United Kingdom + + + 72204143 + National Natural Science Foundation of China + + + + ZR2022QG081 + Natural Science Foundation of Shandong Province of China + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + BMC Med + 101190723 + 1741-7015 + + IM + + Children + Machine learning + Mental health screening + Natural language processing + + Declarations. Ethics approval and consent to participate: This study uses data from the National Child Development Study (NCDS), a nationally representative longitudinal cohort initiated in 1958. Informed consent was obtained for all data collections, with parental consent provided for assessments conducted during childhood, including the age-11 survey and essay-writing task. Formal ethical approval for NCDS follow-ups has been obtained from the UK NHS Multi-Centre Research Ethics Committee (MREC) for all surveys conducted since 2000. Earlier waves, including those in 1958, 1965, 1969, 1974, 1981, and 1991, were conducted prior to the establishment of formal ethics committees or the MREC system. Available documentation indicates that internal ethical review processes were in place for these early waves. For example, the Biomedical Survey and age-55 follow-up were approved by the NHS London-Central Research Ethics Committee (REC) in 2012 (Ref: 12/LO/2010). The current study is a secondary analysis of de-identified public data accessed via the UK Data Service (Study No. 5790). As such, it is exempt from further institutional ethical review, as it does not involve human participants or identifiable private information. Consent for publication: Not applicable. Competing interests: RNC consults for Campden Instruments Ltd; receives royalties from Cambridge University Press, Cambridge Enterprise, and Routledge; and is an unpaid non-executive director of Cambridge University Health Partners. TJF’s research group receives funding for methodology consulting from Place2Be, a third sector organisation that provides mental health training and interventions to UK schools. SC and other authors declare no conflict of interest with this work. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 11 + 20 + + + 2026 + 5 + 11 + + + 2026 + 5 + 29 + 0 + 2 + + + aheadofprint + + 42210272 + 10.1186/s12916-026-04927-4 + 10.1186/s12916-026-04927-4 + + + + + + 42210259 + + 2026 + 05 + 29 + + + 2026 + 05 + 29 + +
+ + 1475-2867 + + 26 + 1 + + 2026 + May + 28 + + + Cancer cell international + Cancer Cell Int + + Machine learning for postoperative complication prediction and early recurrence risk assessment across cancer types: a systematic review and meta-analysis. + 212 + 10.1186/s12935-025-03912-w + + Although machine learning is often used in medical diagnosis, its effectiveness in cancer diagnosis remains uncertain. + To explore the ability of machine learning to predict cancer postoperative complications and early recurrence. + From the creation of the database until October 4, 2024, we conducted a comprehensive search of PubMed, Web of Science (WoS), Embase, Scopus, Cochrane Library, Wanfang, and the China National Knowledge Infrastructure (CNKI). The pooled sensitivity, specificity, Fagan plot analysis, and area under the curve (AUC) were used to assess the overall test performance of machine learning. In addition, meta-regression analysis was used to explore the sources of heterogeneity further. Furthermore, Deeks' funnel plot asymmetry test was used to assess publication bias. + Ultimately, 31 publications were identified and incorporated into this meta-analysis. In the subgroup of postoperative complications, the combined sensitivity, specificity, and AUC values of all studies were 0.75 (95% CI, 0.65-0.83), 0.78 (95% CI, 0.65-0.87), and 0.83 (95% CI, 0.79-0.86), respectively. Moreover, the combined sensitivity, specificity, and AUC values of proposed studies (studies that proposed the best predictive model) were 0.85 (95% CI, 0.71-0.93), 0.76 (95% CI, 0.39-0.94), and 0.88 (95% CI, 0.85-0.91), respectively. In the subgroup of early recurrence, the combined sensitivity, specificity, and AUC values of all studies were 0.74 (95% CI, 0.68-0.80), 0.73 (95% CI, 0.67-0.77), and 0.80 (95% CI, 0.76-0.83), respectively. Furthermore, the combined sensitivity, specificity, and AUC values of proposed studies were 0.78 (95% CI, 0.70-0.85), 0.76 (95% CI, 0.70-0.82), and 0.84 (95% CI, 0.80-0.87), respectively. In addition, Deeks' Funnel Plot, p-value > 0.05, indicating no publication bias. Furthermore, meta-regression analysis showed that sample size and machine learning may be the main influencing factors. + Machine learning can accurately predict cancer postoperative complications and early recurrence. However, its accuracy is influenced by multiple factors, including the type of machine learning model, tumor type, sample size, year of publication, and country of publication. Therefore, more studies with larger sample sizes and more standardized methodology are needed to improve the reliability of its prediction. + © 2025. The Author(s). + + + + Chen + Wen + W + + Department of Hepatobiliary Surgery, Fuzhou First General Hospital Affiliated of Fujian Medical University, Fuzhou, 350009, China. + + + + Liu + Xinliang + X + + Department of Radiation Oncology, Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University,Changzhou Medical Center, Nanjing Medical University, Changzhou, 213000, Jiangsu, China. + + + + Wu + Zhenheng + Z + + Department of Hepatopancreatobiliary Surgery, The First Affiliated Hospital of Fujian Medical University, Fuzhou, 350001, Fujian, China. + + + + Tan + Haifen + H + + Department of Oral Surgery, Affiliated Hospital of Guangdong Medical University, Zhanjiang, 524001, China. + + + + Yu + Fuqian + F + + Gastroenterology department, The Second Affiliated Hospital of Anhui Medical University, Anhui Medical University, Hefei, 230000, China. + + + + Wang + Dongmei + D + + Department of Gastrointestinal Surgery, Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center, Nanjing Medical University, No.68 Gehu Road, Wujin District, Changzhou, 213000, Jiangsu, China. fautywang@163.com. + + + + Gao + Hengyi + H + + Department of Hepatobiliary and Pancreatic Surgery, The People's Hospital of Longhua, No. 38 Jinglong Jianshe Road, Shenzhen, 518109, China. + + + + Chen + Zhigang + Z + + Department of Gastrointestinal Surgery, Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, The Third Affiliated Hospital of Nanjing Medical University, Changzhou Medical Center, Nanjing Medical University, No.68 Gehu Road, Wujin District, Changzhou, 213000, Jiangsu, China. czg99888@163.com. + + + + eng + + + 82273232 + National Natural Science Foundation of China + + + + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + England + Cancer Cell Int + 101139795 + 1475-2867 + + + AUC + Cancer + Early recurrence + Machine learning + Postoperative complications + + Declarations. Ethics approval and consent to participate: Not applicable. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 2 + 11 + + + 2025 + 7 + 14 + + + 2026 + 5 + 29 + 0 + 1 + + + epublish + + 42210259 + 10.1186/s12935-025-03912-w + 10.1186/s12935-025-03912-w + + + + Mishra A, Inaam R, Okamoto S, et al. Visible pulsed laser-assisted selective killing of cancer cells with pvp-capped plasmonic gold nanostars. Micromachines (Basel). 2023;14. https://doi.org/10.3390/mi14061173 + + + Cao X, Luo J, Zhao B, et al. Pathological complete response after neoadjuvant immunotherapy combined with chemotherapy in pediatric rectal carcinoma: a case report. 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Med (Baltim). 2022;101:e30477. https://doi.org/10.1097/MD.0000000000030477 + + 10.1097/MD.0000000000030477 + + + + Miniati M, Marzetti F, Palagini L, et al. Telephone-delivered interpersonal psychotherapy: a systematic review. Cns Spectr. 2023;28:16–28. https://doi.org/10.1017/S1092852921000948 + + 10.1017/S1092852921000948 + 34657641 + + + + + + + + 42210122 + + 2026 + 05 + 28 + +
+ + 1471-2318 + + + 2026 + May + 28 + + + BMC geriatrics + BMC Geriatr + + Joint trajectories of sleep duration and depressive symptoms and risk of incident multimorbidity: a longitudinal analysis with machine learning prediction. + 10.1186/s12877-026-07694-2 + + Sleep disturbances and depressive symptoms frequently co-occur in older adults. Both conditions follow distinct, time-varying trajectories. Nevertheless, most studies rely on cross-sectional assessments, limiting evidence regarding their joint longitudinal evolution and associations with incident chronic diseases. + This study utilized data drawn from 3,221 participants (aged ≥ 60 years) enrolled in the China Health and Retirement Longitudinal Study (CHARLS). Group-based multi-trajectory models (GBMTM) were constructed using repeated measures from 2011 to 2018 to identify heterogeneous joint trajectories of sleep duration and depressive symptoms. Cox proportional hazards models assessed associations with 13 incident chronic diseases and multimorbidity. Additionally, a machine learning framework incorporating seven algorithms was applied to identify baseline predictors of high-risk trajectories, followed by SHAP analysis to enhance model interpretability. + The mean age of participants was 65.80 ± 4.93 years. We identified four joint trajectories: normal-stable sleep and low-stable depression (24.46%), short-stable sleep and low-stable depression (27.17%), normal-increasing sleep and moderate-increasing depression (25.00%), and short-decreasing sleep and high-increasing depression (23.38%). The "short-decreasing sleep and high-increasing depression" trajectory exhibited the highest risks, notably for memory-related disorders (HR = 3.08), stroke (HR = 2.56), and multimorbidity (HR = 1.97). XGBoost and ANN achieved the best predictive performance (AUC = 0.805), with body pain and cognitive function identified as primary predictors. + The trajectory characterized by declining sleep duration and worsening depressive symptoms was associated with heightened risks of multimorbidity and various chronic conditions in older adults. These findings underscore the necessity of integrating sleep and depressive symptom surveillance for chronic disease prevention. Furthermore, early screening for body pain and cognitive decline may facilitate the timely identification of high-risk individuals and inform targeted precision interventions. + © 2026. The Author(s). + + + + Jiang + Jiecheng + J + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Li + Zhujiang + Z + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Zhang + Zhuo + Z + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Ji + Shiyu + S + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Zhang + Zefeng + Z + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Wu + Yixuan + Y + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Li + Yaqi + Y + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Yu + Mingyu + M + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Qiao + Peipei + P + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Xu + Junxiang + J + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. + + + + Wang + Jun + J + + Engineering Research Center of TCM Protection Technology and New Product Development in Geriatric Brain Health, School of Basic Medicine, Ministry of Education, Hubei University of Chinese Medicine, Wu Han, 430065, China. wangjunucm@163.com. + + + Hubei Shizhen Laboratory, Wu Han, 430065, China. wangjunucm@163.com. + + + + Huang + Panpan + P + + School of Basic Medical Sciences, Hubei University of Chinese Medicine, Wu Han, 430065, China. panpanhuang@aliyun.com. + + + Engineering Research Center of TCM Protection Technology and New Product Development in Geriatric Brain Health, School of Basic Medicine, Ministry of Education, Hubei University of Chinese Medicine, Wu Han, 430065, China. panpanhuang@aliyun.com. + + + Hubei Shizhen Laboratory, Wu Han, 430065, China. panpanhuang@aliyun.com. + + + + eng + + + 82374310 + National Natural Science Foundation of China + + + + 2023AFD116 + Natural Science Foundation of Hubei Province + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + BMC Geriatr + 100968548 + 1471-2318 + + IM + + CHARLS + Chronic diseases + Depressive symptoms + Joint trajectories + Machine learning + Multimorbidity + Sleep duration + + Declarations. Ethics approval and consent to participate.: Ethical approval for data collection was granted by the Peking University Institutional Review Board (No. IRB00001052-11015), and all respondents provided written informed consent prior to participation. Consent for publication: All authors have read and agreed to the published version of the manuscript. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 1 + 15 + + + 2026 + 5 + 19 + + + 2026 + 5 + 28 + 23 + 55 + + + aheadofprint + + 42210122 + 10.1186/s12877-026-07694-2 + 10.1186/s12877-026-07694-2 + + + + + + 42210110 + + 2026 + 05 + 28 + +
+ + 1471-2407 + + + 2026 + May + 28 + + + BMC cancer + BMC Cancer + + AutoCumulus: an automated mammographic density measure created using artificial intelligence. + 10.1186/s12885-026-16264-z + + Mammographic (or breast) density is an established risk factor for breast cancer, previously measured using a variety of quantitative, semi-automated and automated approaches. We present a new automated measure, AutoCumulus, learned from applying deep learning to semi-automated measures. + We studied the mammograms of 9,057 population-screened women in the BRAIx program for which semi-automated measurements of mammographic density had been made by experienced readers using the CUMULUS software. The dataset was split into training, testing, and validation sets (80%, 10%, and 10%, respectively). We applied a deep learning regression model (fine-tuned ConvNeXtSmall) to estimate percentage density and assessed performance by the correlation between estimated and measured percent density using the testing dataset. The automated measure was independently tested using the CSAW-CC dataset in which density was measured using the LIBRA software by comparing measures for the left and right breasts, and the specificity for high sensitivity and the area under the receiver operating characteristic curve (AUC) for interval cancers. + The correlation in percent density between the automated and human measures was 0.95. Based on the CSAW-CC dataset, AutoCumulus outperformed LIBRA in terms of the correlation between the left and right breast (0.95 versus 0.79; P < 0.001), specificity for 95% sensitivity (13% versus 10%; P < 0.001 using McNemar's test), and AUC (0.638 versus 0.597; P < 0.01 using DeLong's test) for interval cancers. + We have created an automated measure of mammographic density that is highly accurate and allows rapid measurement of large numbers of mammograms. Compared with a well-established automated measure, AutoCumulus showed higher within-woman repeatability and modestly better prediction of interval cancers. These findings support its potential value as a scalable risk indicator, although further validation is required before clinical implementation. + © 2026. The Author(s). + + + + Al-Qershi + Osamah + O + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. o.alqershi@unimelb.edu.au. + + + + Nguyen + Tuong L + TL + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + + Elliott + Michael S + MS + + Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia. + + + Melbourne Integrative Genomics, School of Mathematics and Statistics/School of BioSciences, Faculty of Science, University of Melbourne, Melbourne, VIC, Australia. + + + + Schmidt + Daniel F + DF + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + Faculty of Information Technology, Monash University, Clayton, VIC, Australia. + + + + Makalic + Enes + E + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + Faculty of Information Technology, Monash University, Clayton, VIC, Australia. + + + + Li + Shuai + S + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK. + + + Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC, Australia. + + + Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, VIC, Australia. + + + + Fox + Samantha K + SK + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + + Dowty + James G + JG + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + + Peña-Solorzano + Carlos A + CA + + Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia. + + + Melbourne Integrative Genomics, School of Mathematics and Statistics/School of BioSciences, Faculty of Science, University of Melbourne, Melbourne, VIC, Australia. + + + + Kwok + Chun Fung + CF + + Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia. + + + Melbourne Integrative Genomics, School of Mathematics and Statistics/School of BioSciences, Faculty of Science, University of Melbourne, Melbourne, VIC, Australia. + + + + Chen + Yuanhong + Y + + School of Computer Science, Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia. + + + + Wang + Chong + C + + School of Computer Science, Australian Institute for Machine Learning, University of Adelaide, Adelaide, South Australia, Australia. + + + + Lippey + Jocelyn + J + + Department of Surgery, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia. + + + Department of Surgery, Faculty of Medicine, Dentistry and Health Sciences - University of Melbourne, Carlton, VIC, Australia. + + + St Vincent's BreastScreen, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia. + + + + Brotchie + Peter + P + + Department of Radiology, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia. + + + + Carneiro + Gustavo + G + + Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford, UK. + + + + McCarthy + Davis J + DJ + + Bioinformatics and Cellular Genomics Unit, St Vincent's Institute of Medical Research, Fitzroy, VIC, Australia. + + + Melbourne Integrative Genomics, School of Mathematics and Statistics/School of BioSciences, Faculty of Science, University of Melbourne, Melbourne, VIC, Australia. + + + + Jeong + Yeojin + Y + + Genome & Health Data Lab, Seoul National University School of Public Health, Seoul, Korea. + + + + Sung + Joohon + J + + Genome & Health Data Lab, Seoul National University School of Public Health, Seoul, Korea. + + + + Frazer + Helen M L + HML + + St Vincent's BreastScreen, St Vincent's Hospital Melbourne, Fitzroy, VIC, Australia. + + + BreastScreen Victoria, Carlton, VIC, Australia. + + + + Hopper + John L + JL + + Centre for Epidemiology & Biostatistics, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, VIC, Australia. + + + + eng + + + AF7305 + Cancer Council Victoria grant + + + + ECRF19020 + Victoria Cancer Agency Early Career grant + + + + GNT2017373 + NHMRC Emerging Leadership Fellowship + + + + FT190100525 + ARC Future Fellowship grant + + + + EP/Y018036/1 + UK Research and Innovation grant + + + + NIHR158213 + National Institute for Health and Care Research grant + + + + 2020R1A2C2101041 + National Research Foundation, Korea + + + + MRFAI000090 + Australian government Medical Research Future Fund + + + + IIRS-20-054; IIRS-2024-0100 + Ramaciotti Foundation and the National Breast Cancer Foundation + + + + 2012799 + Cancer Australia + + + + APP2006899 + National Health and Medical Research Council + + + + GMT1137349 + NHMRC Fellowship grant + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + BMC Cancer + 100967800 + 1471-2407 + + IM + + Breast cancer + Breast density + Deep learning + Machine learning + + Declarations. Ethics approval and consent to participate: This study was approved by the ethics committee of the University of Melbourne under Ethics ID: 14545 and titled “Transforming Breast Cancer Screening with Artificial Intelligence (AI): An Exemplar for Broad AI Deployment in Healthcare.” Informed consent was waived by the board due to the data-only nature of the study. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 5 + 29 + 15 + 41 + + + 2025 + 9 + 16 + + + 2026 + 5 + 25 + + + 2026 + 5 + 28 + 23 + 55 + + + aheadofprint + + 42210110 + 10.1186/s12885-026-16264-z + 10.1186/s12885-026-16264-z + + + + + + 42210081 + + 2026 + 05 + 28 + +
+ + 1471-2229 + + + 2026 + May + 29 + + + BMC plant biology + BMC Plant Biol + + Predicting growth, water-use efficiency and drought response through machine learning, GWAS and differential expression in Ponderosa pine. + 10.1186/s12870-026-09113-5 + + Understanding the molecular basis of phenotypic trait variation is key in improving field performance in plants. Many plants have high within seed source phenotypic variation, making trait-based inferences for performance difficult and inaccurate. Our study combined machine learning methods along with genomics and transcriptomics to understand the molecular drivers of important seedling traits in ponderosa pine. We measured height, specific leaf area, biomass related traits, d13C, d15N, percent carbon, percent nitrogen in well-watered and drought conditions using species' range-wide seed sources. Seedlings from California's seed sources were the fastest growing, while the ones from Montana and Wyoming were the slowest. Despite differences in growth, common responses to drought were seen across all regions. Needles per bundle was shown to be an extremely useful trait to screen for growth strategies of a seed source. We identified one to 36 unique genes (2-209 SNPs) per trait that provided accurate predictions for most traits (2-37% mean absolute percent error). We show that prediction accuracy is trait dependent, mostly higher for traits with high heritability and lower in traits sensitive to environmental change. Drought-stressed seed sources from contrasting elevations showed differential expression of phenylpropanoids, terpenoids and carotenoids genes. Our predictive models show promise for future studies to predict phenotypes upon germination instead of waiting several years to measure specific traits. This will allow for a faster, more accurate selection of best suited individuals and seed sources for any site, resulting in more efficient and successful outplanting. + © 2026. The Author(s). + + + + Collins + Sean M + SM + + School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. + + + + Cathey + Madison J + MJ + + School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. + + + + Barrera + Mariola + M + + School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. + + + + Harris + Brooke + B + + School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. + + + + Baesen + Kailey + K + + School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. + + + + Lincoln + Anna + A + + Department of Interior, Bureau of Land Management Grand Junction Field Office, Grand Junction, CO, 81506, USA. + + + + Dixit + Aalap + A + + Department of Natural Resource Ecology & Management, Oklahoma State University Stillwater, Stillwater, OK, USA. + + + + De La Torre + Amanda R + AR + + School of Forestry, Northern Arizona University, 200 E. Pine Knoll, Flagstaff, AZ, 86011, USA. Amanda.de-la-torre@nau.edu. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + BMC Plant Biol + 100967807 + 1471-2229 + + IM + + Conifers + Differential expression + Drought + GWAS + Genomic Prediction + Machine Learning + Pleiotropy + Ponderosa pine + + Declarations. Ethics approval and consent to participate: No human or animal subjects were used in this study and thus no consent forms or formal IACUC or IRB permits were required. All seed was collected by individuals noted in the acknowledges on land they managed or on public lands Consent for publication: All authors agreed on this current manuscript draft and consented to publication.  Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 2 + 24 + + + 2026 + 5 + 25 + + + 2026 + 5 + 28 + 23 + 53 + + + aheadofprint + + 42210081 + 10.1186/s12870-026-09113-5 + 10.1186/s12870-026-09113-5 + + + + + + 42210054 + + 2026 + 05 + 28 + +
+ + 1471-2180 + + + 2026 + May + 28 + + + BMC microbiology + BMC Microbiol + + Integrated analysis of human-mouse gut microbiota in RSV infection based on machine learning. + 10.1186/s12866-026-05228-9 + + Respiratory syncytial virus (RSV) is a leading cause of lower respiratory tract infections in children, but effective treatment options remain limited. The gut-lung axis, which highlights the role of gut microbiota in regulating respiratory immunity, provides new opportunities for developing probiotic-based therapies. However, existing studies on RSV-associated gut microbiota are often small-scale and lack systematic integration. To address this gap, we conducted a comprehensive machine learning-based analysis of gut microbiota data from RSV-infected children and mice, integrating five public datasets comprising 319 samples (154 children, 165 mice). + Pediatric samples were divided into control, infected, and recovery groups, while mouse samples included control and infected groups. Microbial diversity analysis revealed RSV infection disrupted gut microbiota structure in children, with reduced α-diversity in the infected group and significant β-diversity differences among groups (P < 0.001). Mice exhibited higher α-diversity than children, with distinct dominant taxa: Bifidobacteriaceae and Escherichia-Shigella prevailed in children, whereas Lachnospiraceae and Ligilactobacillus dominated in mice. Using 13 machine learning algorithms, we developed disease-prediction models at the family and genus levels, achieving superior performance in pediatric data (maximum AUC = 0.952) compared to mouse data. Cross-species analysis identified 62 family-level and 54 genus-level high-importance taxa (e.g., Bacteroidaceae, Bifidobacteriaceae, Romboutsia) shared between the two host species, accounting for 36%-60% of feature microbiota. Functional profiles showed significant remodeling during recovery, characterized by the loss of native functions such as D-arabinitol 4-dehydrogenase but acquisition of novel metabolic capabilities, including fructan biosynthesis pathways. Notably, Bacteroidaceae contributed extensively to differential functions, particularly short-chain fatty acid metabolism via propionyl-CoA carboxylase, highlighting its role in gut-lung immune regulation. + These findings provide the first cross-species machine learning analysis of RSV-associated, though not necessarily RSV-specific, gut microbiota, offering insights into gut-lung axis mechanisms and identifying potential targets for probiotic-based interventions in RSV management. + © 2026. The Author(s). + + + + Duan + You + Y + + The Joint Laboratory for Lung Development and Related Diseases of West China Second University Hospital, West China Institute of Women and Children's Health, Sichuan University and School of Life Sciences of Fudan University, West China Second University Hospital, Sichuan University, Chengdu, China. + + + Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China. + + + NHC Key Laboratory of Chronobiology (Sichuan University), Chengdu, China. + + + Xizang Region Child Development Clinical Medical Research Center, Lhasa, China. + + + + Guo + Chen + C + + Department of Pediatric, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China. + + + Core Facility, The Quzhou Affiliated Hospital of Wenzhou Medical University, Quzhou People's Hospital, Quzhou, China. + + + + Xie + Liang + L + + The Joint Laboratory for Lung Development and Related Diseases of West China Second University Hospital, West China Institute of Women and Children's Health, Sichuan University and School of Life Sciences of Fudan University, West China Second University Hospital, Sichuan University, Chengdu, China. + + + Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China. + + + NHC Key Laboratory of Chronobiology (Sichuan University), Chengdu, China. + + + Xizang Region Child Development Clinical Medical Research Center, Lhasa, China. + + + + Liu + Hanmin + H + 0000-0002-4633-911X + + The Joint Laboratory for Lung Development and Related Diseases of West China Second University Hospital, West China Institute of Women and Children's Health, Sichuan University and School of Life Sciences of Fudan University, West China Second University Hospital, Sichuan University, Chengdu, China. liuhm@scu.edu.cn. + + + Department of Pediatric Pulmonology and Immunology, West China Second University Hospital, Sichuan University, Chengdu, China. liuhm@scu.edu.cn. + + + Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China. liuhm@scu.edu.cn. + + + NHC Key Laboratory of Chronobiology (Sichuan University), Chengdu, China. liuhm@scu.edu.cn. + + + Department of Pediatric Pulmonology and Immunology, WCSUH-Tianfu·Sichuan Provincial Children's Hospital, Sichuan University, Meishan, China. liuhm@scu.edu.cn. + + + Sichuan Birth Defects Clinical Research Center, West China Second University Hospital, Sichuan University, Chengdu, China. liuhm@scu.edu.cn. + + + Xizang Region Child Development Clinical Medical Research Center, Lhasa, China. liuhm@scu.edu.cn. + + + + Liu + Yang + Y + 0000-0003-4626-3618 + + The Joint Laboratory for Lung Development and Related Diseases of West China Second University Hospital, West China Institute of Women and Children's Health, Sichuan University and School of Life Sciences of Fudan University, West China Second University Hospital, Sichuan University, Chengdu, China. liuyangly@scu.edu.cn. + + + Key Laboratory of Birth Defects and Related Diseases of Women and Children, Sichuan University, Ministry of Education, Chengdu, China. liuyangly@scu.edu.cn. + + + NHC Key Laboratory of Chronobiology (Sichuan University), Chengdu, China. liuyangly@scu.edu.cn. + + + Xizang Region Child Development Clinical Medical Research Center, Lhasa, China. liuyangly@scu.edu.cn. + + + + eng + + + 2024YFFK0071 + the Key Research and Development Project of Sichuan Provincial Science and Technology Program + + + + No. U21A20333 + the National Natural Science Foundation of the China Joint Fund for Regional Innovation and Development + + + + No.2023NSFSC0530 + Sichuan Science and Technology Program + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + BMC Microbiol + 100966981 + 1471-2180 + + IM + + Cross-species analysis + Gut microbiota + Gut-lung axis + Machine learning + Respiratory syncytial virus (RSV) + + Declarations. Ethics approval and consent to participate: not applicable. Consent for publication: not applicable. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 11 + 19 + + + 2026 + 5 + 25 + + + 2026 + 5 + 28 + 23 + 52 + + + aheadofprint + + 42210054 + 10.1186/s12866-026-05228-9 + 10.1186/s12866-026-05228-9 + + + + + + 42209884 + + 2026 + 05 + 29 + + + 2026 + 05 + 28 + +
+ + 1554-3528 + + 58 + 7 + + 2026 + May + 28 + + + Behavior research methods + Behav Res Methods + + Know when to trust: Making AI scoring more reliable for educational assessment. + 178 + 10.3758/s13428-026-03058-1 + + The rapid rise of large language models (LLMs) has created new opportunities for educational measurement. This paper introduces and evaluates three improvements to LLM-based automated scoring tools: model self-confidence, weighted probabilistic scoring, and ensemble modeling. The study utilizes data from over 20,000 responses to the Alternative Uses Test, the most popular divergent thinking task, from more than 2,000 participants across multiple studies. Model self-confidence taps into LLMs' internal mechanisms to gauge their confidence in probability estimates, helping identify when machine-generated outputs are trustworthy. Weighted probabilistic scoring considers a broader range of completion possibilities in deriving a final score. The final technique, ensemble models, assesses the performance gains from combining multiple models. These methods, tested in divergent thinking response scoring, each show statistically significant positive results, with improvements in correlation with human judges (from r = 0.781 to r = 0.823) and reduction in error. All three techniques improve the performance and trustworthiness of automated scoring models, and are compatible as drop-in improvements to existing techniques. The findings suggest that these adjustments can boost the dependability and applicability of LLMs in educational scoring, specifically for systems that derive a quantitative measure from a text input. + © 2026. The Psychonomic Society, Inc. + + + + Organisciak + Peter + P + 0000-0002-9058-2280 + + University of Denver, 1999 E Evans Ave, Denver, CO, 80208, USA. peter.organisciak@du.edu. + + + + Acar + Selcuk + S + + University of North Texas, Denton, TX, 76205, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Behav Res Methods + 101244316 + 1554-351X + + IM + + + Humans + + + Large Language Models + + + Educational Measurement + methods + + + Artificial Intelligence + + + Trust + + + Reproducibility of Results + + + + Divergent thinking tasks + Educational assessment + Ensemble modeling + Large language models + Model self-confidence + Weighted probabilistic scoring + + Declarations. Ethics approval: Not applicable. Consent for publication: Not applicable. Consent to participate: Not applicable. Conflicts of interest: None. + + + + + 2026 + 5 + 29 + 15 + 46 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 4 + 16 + + + 2026 + 4 + 21 + + + 2026 + 5 + 28 + 23 + 32 + + + epublish + + 42209884 + 10.3758/s13428-026-03058-1 + 10.3758/s13428-026-03058-1 + + + + Acar, S., Burnett, C., & Cabra, J. F. (2017). Ingredients of creativity: Originality and more. 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+ + 1432-1459 + + 273 + 6 + + 2026 + May + 28 + + + Journal of neurology + J Neurol + + Artificial intelligence for gait and balance in neurological disorders: a scoping review of clinical applications and technologies. + 346 + 10.1007/s00415-026-13828-8 + + Artificial intelligence (AI) has rapidly emerged within healthcare systems and neurological rehabilitation with the potential to revolutionize clinical decision-making and therapeutic strategies. However, a comprehensive understanding of how AI is currently applied to gait and balance rehabilitation in stroke, Parkinson's disease (PD), and multiple sclerosis (MS) is still lacking. + To map the current use of AI in neurological rehabilitation, focusing on clinical purposes, geographical distribution, and applied technologies for gait and balance rehabilitation. + Following the PRISMA-ScR statement, we conducted a literature search through MEDLINE, Cochrane CENTRAL, EMBASE, and Google Scholar up to July 2025 to identify studies applying AI-based methods to gait and balance outcomes in adults with stroke, PD, or MS. Study characteristics, AI methods, validation strategies, clinical purpose, and motor outcomes were extracted and synthesized narratively. + Eighteen studies published from 2009 to 2025 were included. Most studies were conducted in Asia (50%) and involved people with stroke (77.8%). AI was predominantly used for prognostic purposes (72.22%), such as predicting falls, gait recovery, or treatment response and diagnostic applications (33.3%). Machine learning was the most common approach (88.9%) with Random Forest, Support Vector Machine, logistic regression, and eXtreme Gradient Boosting being the most frequently applied algorithms. None of the included studies performed prospective or external validation on independent datasets. + This scoping review provides a comprehensive overview of current AI applications, highlighting promising but still immature approaches in the neurological rehabilitation of gait and balance. However, substantial methodological limitations remain major barriers to clinical translation. + © 2026. Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Pegorini + C + C + 0009-0001-0662-0388 + + IRCCS Fondazione Don Carlo Gnocchi ETS, Via Capecelatro 66, 20148, Milan, Italy. + + + + Cattaneo + D + D + 0000-0003-4251-1856 + + IRCCS Fondazione Don Carlo Gnocchi ETS, Via Capecelatro 66, 20148, Milan, Italy. dcattaneo@dongnocchi.it. + + + Department of Physiopathology and Transplants, University of Milan, 20100, Milan, Italy. dcattaneo@dongnocchi.it. + + + + Meotti + M + M + + IRCCS Fondazione Don Carlo Gnocchi ETS, Via Capecelatro 66, 20148, Milan, Italy. + + + + Baglio + F + F + 0000-0002-6145-5274 + + IRCCS Fondazione Don Carlo Gnocchi ETS, Via Capecelatro 66, 20148, Milan, Italy. + + + + Mannini + A + A + 0000-0003-0760-052X + + IRCCS Fondazione Don Carlo Gnocchi ETS, Florence, Italy. + + + + Cordani + C + C + 0000-0002-9014-7887 + + IRCCS Galeazzi-Sant'Ambrogio Hospital, Milan, Italy. + + + + Gervasoni + E + E + 0000-0002-6057-591X + + IRCCS Fondazione Don Carlo Gnocchi ETS, Via Capecelatro 66, 20148, Milan, Italy. + + + + eng + + + RC 2026 + Ministero della Salute + + + + PNC0000007 + Ministero della Salute + + + + + Journal Article + Scoping Review + Review + + + 2026 + 05 + 28 + +
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+ + 2730-664X + + + 2026 + May + 28 + + + Communications medicine + Commun Med (Lond) + + Artificial intelligence-enabled plaque characterization from coronary computed tomography establishes basis of angina in women with nonobstructive atherosclerosis. + 10.1038/s43856-026-01668-6 + + Half of women with ischemic symptoms have non-obstructive coronary artery disease (CAD), while the pathophysiology of their condition has not been characterized. Noncalcified (NCP) and low-attenuation plaque (CT density<30 Hounsfield units, LAP) burden quantified from coronary computed tomography angiography (CCTA) is associated with ischemia in patients with obstructive CAD. We hypothesize that NCP burden is related to angina in women with ischemic symptoms and Non-Obstructive Coronary Arteries (INOCA). + Women with INOCA enrolled in the WARRIOR trial were evaluated for angina severity with Seattle Angina Questionnaire (SAQ) at study entry. Baseline CCTA of 117 women were quantitatively analyzed with AI-based software for NCP, LAP and calcified plaque (CP) volumes and burdens (%, normalized to vessel volume) across the coronary tree. Machine-learning ischemia risk score (ML-IRS) integrating quantitative lumen and plaque features from CCTA was automatically measured. + Among 109 women with visible plaque on CCTA (age 61.9, SD 10.3 years) median total plaque burden is 26.6% (IQR 18.6,32.0) and median SAQ score is 61.4 (IQR 54.6,69.1). Patients with more severe angina (SAQ ≤ 60) are younger (58.1 vs 62.0 years, p = 0.015), have higher total cholesterol (195 vs 165 mg/dL, p = 0.006), but less frequently receive statins (31.8 vs 64.4%, p = 0.006) compared with patients with SAQ > 60. Patients with SAQ ≤ 60 have higher total plaque (33.3 vs 24.3%, p = 0.001), and NCP burden (33.3 vs. 23.2%, p = 0.00065), and lower CP burden (0.0 vs. 0.3%, p = 0.005) compared with patients with SAQ > 60. On multivariable linear regression adjusted for risk factors, higher NCP burden (β = -0.50, p = 0.001), LAP burden (β = -4.50, p = 0.008) and ML-IRS (β = -3.09, p = 0.04) are associated with lower SAQ score, i.e. more severe angina. + In women with INOCA, high-risk atherosclerotic plaque phenotypes are related to more severe angina. + © 2026. The Author(s). + + + + Wolny + Rafal + R + 0000-0001-9661-7563 + + Department of Interventional Cardiology and Angiology, National Institute of Cardiology, Warsaw, Poland. + + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + + Flores Tomasino + Guadalupe + G + 0000-0002-5615-7280 + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + + Grodecki + Kajetan + K + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + First Department of Cardiology, Medical University of Warsaw, Warsaw, Poland. + + + + Lenell + Joel + J + 0000-0002-7885-962X + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + + Park + Caroline + C + 0000-0001-9930-073X + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + + Park + Rebekah + R + 0009-0006-0179-6604 + + Smidt Heart Institute, and the Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA. + + + + Kwiecinski + Jacek + J + + Department of Interventional Cardiology and Angiology, National Institute of Cardiology, Warsaw, Poland. + + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + + Dasa + Osama + O + + Division of Cardiovascular Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA. + + + + Calsavara + Vinicius + V + + Biostatistics Shared Resource, Department of Computational Biomedicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. + + + + Berman + Daniel S + DS + 0000-0002-3793-9578 + + Smidt Heart Institute, and the Department of Cardiology, Cedars-Sinai Medical Center, Los Angeles, CA, USA. + + + + Slomka + Piotr J + PJ + 0000-0002-6110-938X + + Division of Artificial Intelligence in Medicine, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA. + + + + Budoff + Matthew J + MJ + 0000-0002-9616-1946 + + Lundquist Institute at Harbor-UCLA Medical Center, Los Angeles, CA, USA. + + + + Handberg + Eileen + E + + Division of Cardiovascular Medicine, Department of Medicine, University of Florida College of Medicine, Gainesville, FL, USA. + + + + Pepine + Carl J + CJ + 0000-0002-6011-681X + + Division of Cardiology, Department of Medicine, University of Florida, Gainesville, FL, USA. + + + + Shaw + Leslee J + LJ + + Blavatnik Family Women's Health Institute, Mount Sinai School of Medicine, New York, NY, USA. + + + + Wei + Janet + J + + Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. + + + + Gulati + Martha + M + + Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. + + + + Bairey Merz + C Noel + CN + + Barbra Streisand Women's Heart Center, Smidt Heart Institute, Cedars-Sinai Medical Center, Los Angeles, CA, USA. + + + + Tamarappoo + Balaji + B + + Division of Cardiology, Mayo Clinic Arizona, Phoenix, AZ, USA. + + + + Dey + Damini + D + + Biomedical Imaging Research Institute, Departments of Biomedical Sciences and Medicine, Cedars Sinai Medical Center, Los Angeles, CA, USA. Damini.Dey@csmc.edu. + + + + eng + + + R01HL151266 + U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) + + + + 1R01HL148787 + U.S. Department of Health & Human Services | NIH | National Heart, Lung, and Blood Institute (NHLBI) + + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + England + Commun Med (Lond) + 9918250414506676 + 2730-664X + + + Severe narrowings of coronary arteries (i.e. the blood vessels that supply oxygenated blood to the heart), are the typical cause of chest pain in patients with atherosclerotic coronary artery disease. This type of chest pain is called angina. However, especially in women, angina can occur even if no major narrowings of coronary arteries are found using scans of the heart. We used artificial intelligence-assisted non-invasive imaging of coronary arteries to find out what aspects of this non-obstructive coronary atherosclerosis is associated with symptom severity in women without obvious artery narrowing. We observed that coronary plaques, which are buildups of fats and other substances in the artery walls, have a different composition in women with more severe angina. This information expands our understanding of why angina occurs in such patients and provides a rationale for possibly treating these women using anti-atherosclerotic drugs in the future. + © 2026. The Author(s). + + Competing interests: Drs Berman, Slomka, and Dey have received software royalties from Cedars-Sinai Medical Center and report equity in APQ Health. All other authors have reported that they have no relationships relevant to the contents of this paper to disclose. + + + + + 2026 + 5 + 29 + 15 + 45 + + + 2026 + 5 + 29 + 15 + 45 + + + 2025 + 9 + 5 + + + 2026 + 5 + 13 + + + 2026 + 5 + 28 + 23 + 31 + + + aheadofprint + + 42209864 + 10.1038/s43856-026-01668-6 + 10.1038/s43856-026-01668-6 + + + + + + 42209847 + + 2026 + 05 + 28 + +
+ + 2730-6011 + + + 2026 + May + 28 + + + Discover oncology + Discov Oncol + + Artificial intelligence and transforming cancer care. + 10.1007/s12672-026-05242-3 + + Artificial Intelligence (AI) is reshaping oncology by addressing key limitations in traditional cancer care and enabling data-driven, personalized approaches from diagnosis to treatment. This review explores the transformative role of AI across the cancer care continuum, highlighting its contributions, challenges, and future directions. AI has significantly advanced cancer detection and diagnosis by improving the interpretation of medical imaging (CT, MRI, PET scans, digital pathology) and liquid biopsies, allowing for early and accurate identification of tumors and biomarkers. In genomics and molecular profiling, AI facilitates the analysis of large-scale sequencing data to uncover actionable mutations and support targeted therapy decisions. This review also examines AI-powered prognostic models that integrate clinical, genomic, and electronic health record data to predict outcomes such as survival rates and recurrence risks, allowing for more precise treatment planning. In the therapeutic landscape, AI aids in optimizing radiation dosing, guiding surgical interventions, and predicting individual responses to chemotherapy, immunotherapy, and targeted treatments, thereby reducing uncertainty and improving outcomes. Key limitations, such as data privacy concerns, algorithmic bias, model opacity, and integration hurdles are discussed, along with strategies to address them, including explainable AI, standardized validation, and clinician training. Looking ahead, innovations like federated learning, generative AI for drug discovery, and multimodal data integration are poised to enhance precision oncology further. By synthesizing current developments and emerging trends, this review underscores the potential of AI to drive equitable, efficient, and personalized cancer care on a global scale. + © 2026. The Author(s). + + + + Reddy + Aneesha Mallu + AM + + Reddy Care Medical, Pomona, CA, USA. + + + + Kaur + Gurleen + G + + California Baptist University, Riverside, USA. + + + + Ribaya + Vincent Sean D + VSD + + Reddy Care Medical, Pomona, CA, USA. + + + + Ribaya + Elizabeth Laurize A + ELA + + Reddy Care Medical, Pomona, CA, USA. + + + + Reddy + Mallu Chenna + MC + + Reddy Care Medical, Pomona, CA, USA. + + + + Shah + Tariq + T + + Western University College of Pharmacy, Pomona, CA, USA. + + + Saint John's Physician Partners, Santa Monica, CA, USA. + + + + Shinde + Dheeraj + D + + Box Nine Solutions, Satara, India. + + + + Suri + Gurparsad Singh + GS + + Reddy Care Medical, Pomona, CA, USA. Gurparsadsuri1234@gmail.com. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 28 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + AI in healthcare + Artificial intelligence + Cancer care + Machine learning + Oncology + Precision medicine + + Declarations. Ethics approval and consent to participate: Not applicable. This article is a review of existing literature and did not involve direct participation of human subjects or animal research by the authors. All data discussed are from previously published studies. Not applicable. This study did not involve the recruitment of human participants or the collection of primary data. Consent for publication: Not applicable. No individual patient data, identifiable images, or personal details are included in this manuscript that would require specific patient consent for publication. Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 44 + + + 2026 + 5 + 29 + 15 + 44 + + + 2025 + 12 + 25 + + + 2026 + 5 + 15 + + + 2026 + 5 + 28 + 23 + 31 + + + aheadofprint + + 42209847 + 10.1007/s12672-026-05242-3 + 10.1007/s12672-026-05242-3 + + + + + + 42209794 + + 2026 + 05 + 28 + +
+ + 2662-8457 + + + 2026 + May + 28 + + + Nature computational science + Nat Comput Sci + + FLOWR: flow matching for structure-aware de novo, interaction- and fragment-based ligand generation. + 10.1038/s43588-026-00998-8 + + Here we introduce FLOWR, a structure-based framework for the generation and optimization of three-dimensional ligands. FLOWR integrates continuous and categorical flow matching with equivariant optimal transport, enhanced by an efficient protein pocket conditioning. Alongside FLOWR, we present SPINDR, a curated dataset comprising ligand-pocket cocrystal complexes specifically designed to address existing data quality issues. Empirical evaluations demonstrate that FLOWR surpasses current state-of-the-art diffusion- and flow-based methods in terms of PoseBusters-validity, pose accuracy and interaction recovery, while offering an inference speed-up, achieving up to 70-fold faster performance. In addition, we introduce FLOWR.MULTI, a highly accurate multi-purpose model allowing for the targeted sampling of ligands that adhere to predefined interaction profiles and chemical substructures for fragment-based design without the need of retraining or any resampling strategies. Collectively, our results indicate that FLOWR and FLOWR.MULTI represent an advancement in artificial intelligence-driven structure-based drug design, substantially enhancing the reliability and applicability of de novo, interaction- and fragment-based ligand generation in real-world drug discovery settings. + © 2026. The Author(s). + + + + Cremer + Julian + J + 0000-0001-6319-7283 + + Machine Learning and Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany. julian.cremer@pfizer.com. + + + + Irwin + Ross + R + 0009-0003-0067-8313 + + Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. rossir@chalmers.se. + + + Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden. rossir@chalmers.se. + + + + Tibo + Alessandro + A + 0000-0002-9070-740X + + Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. + + + + Janet + Jon Paul + JP + 0000-0001-7825-4797 + + Molecular AI, Discovery Sciences, R&D, AstraZeneca, Gothenburg, Sweden. + + + + Olsson + Simon + S + 0000-0002-3927-7897 + + Department of Computer Science and Engineering, Chalmers University of Technology and University of Gothenburg, Gothenburg, Sweden. + + + + Clevert + Djork-Arné + DA + 0000-0003-4191-2156 + + Machine Learning and Computational Sciences, Pfizer Worldwide R&D, Berlin, Germany. + + + + eng + + + 2022-06725 + Vetenskapsrådet (Swedish Research Council) + + + + 2025/22-1514 + Vetenskapsrådet (Swedish Research Council) + + + + 2025/23-635 + Vetenskapsrådet (Swedish Research Council) + + + + + Journal Article + + + 2026 + 05 + 28 + +
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+ + 1476-4660 + + + 2026 + May + 28 + + + Nature materials + Nat Mater + + Cross-material catalyst discovery via deep learning. + 10.1038/s41563-026-02622-6 + + The discovery of catalysts is typically confined within individual material classes, limiting insight from across material types. Here we demonstrate a machine learning approach that bridges catalyst families by identifying co-descriptors derived from two experimental datasets: single-atom catalysts (SACs) on carbon and bulk perovskite oxides. This co-descriptor set, selected through automated statistical and natural-language analyses, enabled integration of distinct experimental catalyst datasets by yielding shared activity-related chemical features. The resulting unified model, the crossbreeding neural network (CBNN), enables prediction of oxygen evolution activity in a previously untrained class-SACs on perovskite oxides. The CBNN precisely predicted performance trends of experimentally synthesized catalysts by overpotential, including a multimetallic catalyst with superior activity compared with all previous candidates. Explainable machine learning further connected descriptor importance and surface atomic contributions to activity trends. These results suggest that cross-material machine learning can accelerate the discovery of high-performance catalysts beyond known design spaces. + © 2026. The Author(s), under exclusive licence to Springer Nature Limited. + + + + Moon + Junseok + J + 0000-0002-9631-8990 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Yoo + Seungwoo + S + 0009-0009-6826-7610 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Shim + Jaehyuk + J + 0009-0000-2191-6032 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Heo + Sungeun + S + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Kim + Jeong Hyun + JH + 0000-0002-9827-8427 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Bootharaju + Megalamane S + MS + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Lee + Kug-Seung + KS + 0000-0002-7570-8404 + + Pohang Accelerator Laboratory (PAL), Pohang University of Science and Technology (POSTECH), Pohang, Republic of Korea. + + + + Ryu + Jaeyune + J + 0009-0009-7301-4220 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Sung + Yung-Eun + YE + 0000-0002-1563-8328 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. + + + + Hyeon + Taeghwan + T + 0000-0001-5959-6257 + + Center for Nanoparticle Research, Institute for Basic Science (IBS), Seoul, Republic of Korea. thyeon@snu.ac.kr. + + + School of Chemical and Biological Engineering, and Institute of Chemical Processes, Seoul National University, Seoul, Republic of Korea. thyeon@snu.ac.kr. + + + + eng + + + IBS-R006-D1 + Institute for Basic Science (IBS) + + + + IBS-R006-A2 + Institute for Basic Science (IBS) + + + + + Journal Article + + + 2026 + 05 + 28 + +
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+ + 2045-2322 + + 16 + 1 + + 2026 + May + 28 + + + Scientific reports + Sci Rep + + Radiation dose has no significant impact on CT-based bone mineral density measurements in a large-animal model. + 16570 + 10.1038/s41598-026-55169-6 + + Bone mineral density (BMD) is a biomarker for frailty, and CT-derived radiodensity can be extracted fully automatically as a surrogate. Because these measurements might be affected by image noise, which varies substantially in the clinical routine, this systematic large-animal study investigates the consistency of CT-based BMD measurements under different radiation dose settings. Twenty Göttingen minipigs underwent six non-contrast CT examinations with five dose levels (CTDIvol: 0.53-10.01 mGy; 5%, 10%, 20%, 40%, 100%; 600 scans). BMD was assessed using CT-derived radiodensity (Hounsfield units, HU) by segmenting the complete ninth thoracic vertebra, and by placing a region of interest (ROI) in the trabecular bone. RM-ANOVA was used to assess statistical significance. Data are presented as mean with standard deviation. The BMD measurement remained consistent between the control and the different dose settings. Even the lowest dose setting (5%: complete = 761 [± 56] HU, ROI = 749 [± 72] HU) showed no significant differences compared to the control (complete = 756 [± 55] HU, ROI = 738 [± 68] HU). Finally, CT-based BMD measurements remained consistent and are therefore robust to substantial dose reduction, indicating the technical feasibility of comparing CT examinations with different dose protocols, relevant for opportunistic screening. + © 2026. The Author(s). + + + + Harmes + Johannes Christian + JC + 0009-0003-1984-2216 + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. Johannes.Harmes@uk-essen.de. + + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. Johannes.Harmes@uk-essen.de. + + + + Holtkamp + Mathias + M + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. + + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. + + + + Straus + Jannis + J + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. + + + + Jost + Gregor + G + + Bayer AG, Berlin, Germany. + + + + Forsting + Michael + M + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. + + + + Hosch + René + R + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. + + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. + + + + Nensa + Felix + F + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. + + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. + + + + Pietsch + Hubertus + H + + Bayer AG, Berlin, Germany. + + + + Salhöfer + Luca + L + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. + + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. + + + + Haubold + Johannes + J + + Institute of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany. + + + Institute for Artificial Intelligence in Medicine, University Hospital Essen, Essen, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
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+ + 1548-4475 + + + 2026 + May + 28 + + + Lab animal + Lab Anim (NY) + + Enhanced health evaluation in mice using continuous home-cage monitoring and machine learning: a multicentric study. + 10.1038/s41684-026-01745-2 + + Ensuring the health of laboratory rodents is critical for ethical research and maintaining scientific integrity. Traditional daily visual observations by trained technicians, conducted during the rodents' sleep period, often fail to detect subtle but critical health indicators due to the short duration of inspections and obstructions from enrichment materials. Here we aimed to improve health checks in mice by utilizing continuous home-cage monitoring coupled with machine learning (ML) algorithms. We hypothesized that reduced locomotion in mice would indicate distress or sickness, and that continuous tracking would identify clinical cases earlier than visual checks. We retrospectively analyzed locomotion data from three institutions using the same sensor technology and applied ML/artificial intelligence (AI) models to generate digital alerts for potential clinical cases. These alerts were then compared with clinical records to verify the accuracy of the predictions. Our results demonstrated that the ML algorithm identified animals in distress -3 to -6 days before verifiable clinical signs or death were noticed, with an accuracy of 66-80% on day -3 and 80-91% on day -6. This indicates that continuous monitoring of animal locomotion is a superior predictor of animal health compared with human observation. The findings suggest that augmenting visual checks with AI modeling can greatly improve animal welfare by identifying subclinical cases, enhancing study endpoints, increasing the rigor and reproducibility of research, and improving operational efficiency. Our work underscores the potential of integrating advanced monitoring systems and AI in laboratory animal facilities, marking a substantial step forward in the field of animal welfare and research methodology. + © 2026. The Author(s). + + + + Eswaraka + Jeetendra + J + + Rutgers, The State University of New Jersey, Piscataway, NJ, USA. jeetendra.eswaraka@rutgers.edu. + + + + Gommet + Céline + C + + Sanofi, Vitry-sur-Seine, France. + + + + Diomaiuta + Dimitri + D + 0009-0009-9762-8251 + + Tecniplast Spa, Buguggiate, Italy. + + + + Rigamonti + Mara + M + 0000-0002-3913-4458 + + Tecniplast Spa, Buguggiate, Italy. + + + + Rosati + Giorgio + G + + Tecniplast Spa, Buguggiate, Italy. + + + + Gaburro + Stefano + S + 0000-0001-9297-3472 + + Tecniplast Spa, Buguggiate, Italy. + + + + Zwick + Michael + M + 0000-0001-9772-8257 + + Rutgers, The State University of New Jersey, Piscataway, NJ, USA. + + + + Bégoud + Laurent + L + + Sanofi, Vitry-sur-Seine, France. + + + + Warot + Xavier + X + + Center of PhenoGenomics, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. + + + + Doenlen + Raphaël + R + + Center of PhenoGenomics, School of Life Sciences, Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland. + + + + eng + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + Lab Anim (NY) + 0417737 + 0093-7355 + + IM + Competing interests: J.E., M.Z., R.D., S.G. and X.W. declare no competing interests. C.G. and L.B. are Sanofi employees and may hold shares and/or stock options in the company. M.R., D.D. and G.R. are employees of Tecniplast SpA, which developed the technology, and have nothing to disclose. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 43 + + + 2025 + 1 + 27 + + + 2026 + 4 + 17 + + + 2026 + 5 + 28 + 23 + 28 + + + aheadofprint + + 42209754 + 10.1038/s41684-026-01745-2 + 10.1038/s41684-026-01745-2 + + + + Miller, M. & Brielmeier, M. Environmental samples make soiled bedding sentinels dispensable for hygienic monitoring of IVC-reared mouse colonies. Lab. 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+ + 1573-7373 + + 178 + 1 + + 2026 + May + 28 + + + Journal of neuro-oncology + J Neurooncol + + Predicting progression-free survival in glioblastoma with neuroimaging and machine learning. + 29 + 10.1007/s11060-026-05650-z + + Glioblastoma (GBM) is the most prevalent and aggressive form of malignant glioma. Reliable estimation of progression-free survival (PFS) prior to medical intervention could strengthen clinical decision-making and improve patient care. Here, we utilize machine learning (ML) to predict PFS in GBM patients using resting state network (RSN) connectivity before medical intervention. + GBM patients (N = 45, mean age 62.1 ± 10.3 years, mean PFS 9.5 ± 5.6 months, 62.2% male) were retrospectively recruited from Washington University Medical Center. All patients completed structural neuroimaging and resting-state functional MRI before surgery. Deep neural networks were trained on resting-state functional connectivity to predict PFS. Feature selection identified the 15 strongest predictive features prior to training. + Sex (p = 0.0037), overall survival (p = 0.0003), MGMT promoter methylation status (p = 0.0064), presentation of weakness (p = 0.0037), and presentation of memory impairment (p = 0.045) were significantly associated with PFS. Tumor frequency and spatial correlation analyses associated dorsal attention, visual, frontal-parietal, and default mode networks with shorter PFS. Conversely, right-temporal lobe tumors were associated with better outcomes. RSN spatial maps revealed widespread alterations in association networks in GBM patients relative to controls. MRMR feature selection identified thalamic and association network connectivity, including somatomotor, ventral and dorsal attention, and default mode/parietal memory as the strongest predictors of PFS. Using leave-one-out validation, the model predicted PFS with an RMSE of 1.26 months, MAE of 1.08 months, and R² of 0.96 (p < 0.001). + Our findings indicate that GBM alters functional brain organization on a widespread scale, and these global effects are informative of patient outcomes. + © 2026. The Author(s). + + + + Hickman-Chow + Davin A + DA + 0009-0002-0883-1477 + + Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA. D.a.hickman-chow@wustl.edu. + + + Brain Tumor Center at Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO, USA. D.a.hickman-chow@wustl.edu. + + + + Luckett + Patrick H + PH + 0000-0003-2262-6605 + + Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA. luckett.patrick@wustl.edu. + + + + Olufawo + Michael + M + + Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA. + + + + Dierker + Donna + D + + Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA. + + + + Shimony + Joshua S + JS + + Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO, USA. + + + + Leuthardt + Eric C + EC + + Department of Neurological Surgery, Washington University School of Medicine, St. Louis, MO, 63110, USA. + + + Department of Biomedical Engineering, Washington University in Saint Louis, St. Louis, MO, 63130, USA. + + + Department of Mechanical Engineering and Materials Science, Washington University in Saint Louis, St. Louis, MO, 63130, USA. + + + Center for Innovation in Neuroscience and Technology, Washington University School of Medicine, St. Louis, MO, 63110, USA. + + + Brain Laser Center, Washington University School of Medicine, St. Louis, MO, 63110, USA. + + + National Center for Adaptive Neurotechnologies, 660 South Euclid Avenue Campus Box 8057, St. Louis, MO, 63110, USA. + + + + eng + + + R01CA203861 + CA + NCI NIH HHS + United States + + + U24NS109103 + NS + NINDS NIH HHS + United States + + + P41EB018783 + EB + NIBIB NIH HHS + United States + + + + Journal Article + + + 2026 + 05 + 28 + +
+ + United States + J Neurooncol + 8309335 + 0167-594X + + IM + + + Humans + + + Glioblastoma + diagnostic imaging + mortality + pathology + physiopathology + + + Female + + + Male + + + Brain Neoplasms + diagnostic imaging + mortality + pathology + + + Middle Aged + + + Machine Learning + + + Retrospective Studies + + + Magnetic Resonance Imaging + methods + + + Progression-Free Survival + + + Neuroimaging + methods + + + Prognosis + + + Predictive Learning Models + + + Aged + + + Follow-Up Studies + + + + Deep learning + Functional Connectivity + Functional MRI + Glioblastoma + Progression-Free Survival + + Declarations. Ethical approval and consent to participate/publish: This study was approved by the Washington University in St. Louis Institutional Review Board. The IRB waived the requirement for informed consent for this retrospective study. This manuscript contains no individual person’s data in any form. Competing interests: Patrick Luckett, Joshua Shimony, Eric Leuthardt, and/or Washington University in St. Louis may receive royalty income based on a technology developed by Patrick Luckett, Joshua Shimony, and Eric Leuthardt and licensed by Washington University to Sora Neuroscience. Eric Leuthardt owns stock in Neurolutions, General Sensing, Face to Face Biometrics, Caeli Vascular, Acera, Sora Neuroscience, Inner Cosmos, Inflexion Vascular, and Petal Surgical. Washington University has equity ownership of Neurolutions. The remaining authors declare no financial interest. + + + + + 2026 + 5 + 29 + 15 + 42 + + + 2026 + 5 + 29 + 15 + 41 + + + 2026 + 4 + 17 + + + 2026 + 5 + 25 + + + 2026 + 5 + 28 + 23 + 28 + + + epublish + + 42209729 + 10.1007/s11060-026-05650-z + 10.1007/s11060-026-05650-z + + + + Kelly C, Majewska P, Ioannidis S et al (2017) Estimating progression-free survival in patients with glioblastoma using routinely collected data. 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J Neurooncol 164:309–320. https://doi.org/10.1007/s11060-023-04439-8 + + 10.1007/s11060-023-04439-8 + 37668941 + 10522528 + + + + Fyllingen EH, Bø LE, Reinertsen I et al (2021) Survival of glioblastoma in relation to tumor location: a statistical tumor atlas of a population-based cohort. Acta Neurochir 163:1895–1905. https://doi.org/10.1007/s00701-021-04802-6 + + 10.1007/s00701-021-04802-6 + 33742279 + 8195961 + + + + Ghinda DC, Wu J-S, Duncan NW, Northoff G (2018) How much is enough—Can resting state fMRI provide a demarcation for neurosurgical resection in glioma? Neurosci Biobehavioral Reviews 84:245–261. https://doi.org/10.1016/j.neubiorev.2017.11.019 + + 10.1016/j.neubiorev.2017.11.019 + + + + Douw L, Reijneveld JC, Mandal AS. Multiscale network perspectives on glioma: from tumour biology to symptoms, survival and treatment. Nat Rev Neurol. 2025:1-17. https://doi.org/10.1038/s41582-025-01171-x + + + Lozada PSM, Neira JP, Leon-Rojas JE, et al. From tumor to network: functional connectome heterogeneity and alterations in brain tumors—a multimodal neuroimaging narrative review. Cancers . 2025;17. https://doi.org/10.3390/cancers17132174 + + + Zimmermann MLM, Breedt LC, Centeno EGZ et al (2024) The relationship between pathological brain activity and functional network connectivity in glioma patients. J Neurooncol 166:523–533. https://doi.org/10.1007/s11060-024-04577-7 + + 10.1007/s11060-024-04577-7 + 38308803 + 10876827 + + + + Fox ME, King TZ (2018) Functional Connectivity in Adult Brain Tumor Patients: A Systematic Review. Brain Connect 8:381–397. https://doi.org/10.1089/brain.2018.0623 + + 10.1089/brain.2018.0623 + 30141339 + + + + + + + + 42209714 + + 2026 + 05 + 28 + +
+ + 2730-664X + + + 2026 + May + 29 + + + Communications medicine + Commun Med (Lond) + + Predict neuromuscular performance in human epidural electrical stimulation: phase 1 trial interim results. + 10.1038/s43856-026-01695-3 + + Epidural electrical stimulation (EES) has emerged as a promising therapy for restoring motor function in patients with paralysis. A primary challenge in this therapy lies in identifying feasible stimulation parameters in huge selection space for different movements, given the limited understanding of the precise alignment between stimulation and corresponding neuromuscular performance. We aimed to develop a computational framework that predicts neuromuscular performance under EES and thereby reduces the need for extensive in-clinic parameter searches. + We implanted purpose-designed 32-contact epidural interfaces in two individuals with motor-complete spinal cord injury and reconstructed personalized spinal anatomies from medical imaging. Finite element simulations and axonal recruitment modeling were integrated with machine learning to establish a predictive mapping between stimulation parameters and muscle responses. A dimensionality-reduction Bayesian optimization algorithm was subsequently applied to identify compact sets of stimulation parameters targeting specific motor objectives, and selected configurations were validated through clinical testing. This study is an interim report of an ongoing registered clinical trial (Closed-loop Functional Spinal Cord Stimulation in Patients with Spinal Cord Injury, ClinicalTrials.gov Identifier: NCT04969042), sponsored by Beijing PINS Medical Co., Ltd. + Here we present purpose-designed 32-contact epidural neural interfaces that enabled two individuals with spinal cord injury to regain lower-limb motor function. The hybrid predictive model demonstrated strong quantitative agreement with experimentally measured muscle responses (mean squared error = 0.0096). Algorithm-guided parameter recommendations comprising 180 configurations outperformed both the historical dataset (1,602 configurations) and conventional bipolar settings across four functional objectives. Clinical validation further confirmed that the recorded muscle activations were in close agreement with model predictions. + The AI-aided computational framework can serve as a reliable and feasible agent for evaluating and recommending effective EES parameters. By bridging anatomical modeling with functional outcomes, this approach offers a practical pathway toward optimizing neuromodulation therapies and advancing the development of personalized treatment strategies for individuals with spinal cord injury. + © 2026. The Author(s). + + + + Li + Hongda + H + + National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + + Wei + Yunyue + Y + + School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + + Sui + Yanan + Y + 0000-0002-9480-627X + + School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + + Zhang + Xi + X + + National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + + Luo + Xuesong + X + 0000-0003-4142-1402 + + National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + + Zhang + Boyang + B + + National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + School of Aerospace Engineering, Tsinghua University, Beijing, China. + + + + Ma + Bozhi + B + 0000-0003-1612-3841 + + National Engineering Research Center of Neuromodulation, School of Aerospace Engineering, Tsinghua University, Beijing, China. mbz@tsinghua.edu.cn. + + + School of Aerospace Engineering, Tsinghua University, Beijing, China. mbz@tsinghua.edu.cn. + + + + eng + + + ClinicalTrials.gov + + NCT04969042 + + + + + Journal Article + + + 2026 + 05 + 29 + +
+ + England + Commun Med (Lond) + 9918250414506676 + 2730-664X + + + People with spinal cord injury often lose the ability to move their legs because the connection between the brain and spinal cord is damaged. Over recent decades, epidural electrical stimulation—delivering pulses of electricity to the spinal cord—has been shown to help restore leg movement. However, finding the proper stimulation settings for each person is usually time-consuming and depends on trial and error. In this study, we designed and manufactured a 32-contact spinal implant and implanted it in two people with spinal cord injury. We also developed a personalized hybrid model that combines computer simulations and neural-network predictions to estimate how different stimulation settings affect muscle activity. An optimization algorithm then recommends parameter sets for specific movements, and these recommendations were tested in the clinic. By automating and personalizing parameter selection, this approach can reduce the testing burden on patients and clinicians and help enable more tailored neuromodulation treatments. + © 2026. The Author(s). + + Competing interests: The authors declare no competing interests. + + + + + 2026 + 5 + 29 + 15 + 43 + + + 2026 + 5 + 29 + 15 + 43 + + + 2024 + 12 + 4 + + + 2026 + 5 + 21 + + + 2026 + 5 + 28 + 23 + 27 + + + aheadofprint + + 42209714 + 10.1038/s43856-026-01695-3 + 10.1038/s43856-026-01695-3 + + + + \ No newline at end of file diff --git a/sources - collegamento.lnk b/sources - collegamento.lnk new file mode 100644 index 0000000000000000000000000000000000000000..6598f29fc5e14008fcefd090f5d802614e25cc74 GIT binary patch literal 1749 zcmd5+Z)jUp6#u2G>(((%wmE8X_H{Uzvc=Zvq??f@dHDxv(cdhtA=cg9_qMGX{bm#1F#{6Ss*VbqHEYzsSH1{N0;0lzh;A5xkFc z@44rk`_4JPbMB1-aBOMe3|v+-f^T3Qoa7g7uifvudBbt_()HmBOE(;UX>)3?l+-(F z_Ko?4mM!Q)PMmb?3AFh?NNgYYWwGn#G1&vp6}bld5yB}9L8JEC~nijYtWH}jWLv9qDW~!cX`Uj`R&2uaAQ>QyRe(PUOa`p z=%d|M%+|eE#kFUh2z%pt$~)cb_5m-~BglyBr>f!=6~RT1beWLTsnH9Dh}I-nD|Ms%K%6GQP>D3F{cT!Z!4 z=Ec*os(4AowsC$P&)mBzF3G+>`xu5`5tpOHqofeh7;S}dRg{+wB37$FRqUN{!WZ#H zBQfdaaKp=*6IIcpyzKhg!M{#5c`28>9cdUy6J>+GCX_FW{1g!{;yC^0DKGF`2M*Q! z&5I2uoUqNZmC4$yS*_ypqP^km&mFBi(bnYcp2=IRuZc4A%BafAN&@Fd50Yera(#j% z$H$}&lI)z@Z?=9TdpBhiM$L*1|2}ch&RVu88umy=#0pv6GA6U4-iM;r5%GZL9n`f% zESOArxe3PNnlBR4bZ^qD4a9V}<`YAfc{E!pnOeA#sQ5(MNRJ!lM7CrZQ@e}DOL?;( zR#IiAZiuEFhHDX?KFn2)i4iMf_S7mP_wEfZ_E)+WqF;0!NdNGuXMf+3??xlfExRtP z{UbW_?!|5Yrf*~V>w(i72~TfgD6!a6z2?X3p|iKHY@V}o+n4EMR66P+$xC#11=X+< ze(Ym92U+ASOI%{v1^-|$!4Vcnu&E2w=q#6kNpgbx>I*CWgX<&yvF#s`iE?~Ml3?^K z@A!Nv)ag94+%h~f`}_HB%G;%Z1Vi4d=6<`MvinGETaII6>fw8mtZZ0y%Ei@B04xQI ze2GS&5n+RKj`KJnkVF1S-P(RKjVF}+1NB0EbS$&e^Un9K&#vNf?8lBPcc^=aq`Z{5 zuE1~ej}{~Szk@ICvbCCTh|F0I`Etvm&2OF1KRHKQ V*GJzDy!X@3sdnd`15&;+{s!YlVrBpU literal 0 HcmV?d00001 diff --git a/www/services/file_extractor.py b/www/services/file_extractor.py index 33a38fa63..bbc6193ce 100644 --- a/www/services/file_extractor.py +++ b/www/services/file_extractor.py @@ -63,30 +63,37 @@ def extract_from_file(file_path: str, source: str) -> list[dict]: print(f"[{source_upper}] Lettura file tabellare {file_extension}: {file_path}") try: + # Calcola quante righe saltare: 1 per Dimensions (disclaimer), 0 per gli altri + righe_da_saltare = 1 if source_upper == "DIMENSIONS" else 0 + if file_extension == '.csv': df = pd.read_csv( file_path, dtype=str, # Legge tutto come stringa. + skiprows=righe_da_saltare, # Salta la prima riga se la fonte è Dimensions on_bad_lines='skip', # Se una riga del CSV è corrotta la salta. encoding='utf-8' ) else: - df = pd.read_excel(file_path, dtype=str) + df = pd.read_excel( + file_path, + dtype=str, + skiprows=righe_da_saltare # Salta la prima riga se la fonte è Dimensions + ) # Sostituiamo i NaN con stringhe vuote. df = df.fillna("") # Converte il DataFrame in una una lista di dizionari, dove ogni dizionario è una riga della tabella. return df.to_dict(orient="records") - + except pd.errors.EmptyDataError: - print(f"[ERRORE] Il file '{file_path}' è vuoto.") - return [] - - + print(f"[ERRORE] Il file '{file_path}' è vuoto.") + return [] + except Exception as e: - print(f"[ERRORE] Impossibile leggere il file tabellare: {e}") - return [] + print(f"[ERRORE] Impossibile leggere il file tabellare: {e}") + return [] else: raise ValueError( diff --git a/www/services/standardizer.py b/www/services/standardizer.py index d1194523d..57cc8fc74 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -1,1160 +1,228 @@ # ============================================================================= -# standardizer.py — Phase 2: TRANSFORM – RENAME (The Lookup Strategy) -# ============================================================================= -# Responsabilità di questo modulo: -# 1. Mappare i nomi proprietari dei campi OpenAlex ai tag WoS standard -# 2. Applicare i contratti di tipo (type contracts) su ogni colonna -# 3. Gestire i valori mancanti (None / NaN → "" o [] o 0) -# 4. Estrarre i campi complessi (autori, affiliazioni, parole chiave, riferimenti) -# 5. Calcolare i campi derivati (SR – Short Reference) -# 6. Validare il record finale prima dell'esportazione -# 7. Esporre un unico entry-point: convert2df() +# standardizer.py — Phase 2: TRANSFORM – RENAME (Integration with format_functions) # ============================================================================= from __future__ import annotations - import pandas as pd from typing import Any -# ----------------------------------------------------------------------------- -# 1. DIZIONARI DI MAPPING (Lookup Strategy) -# ----------------------------------------------------------------------------- -# Ogni sorgente ha il proprio dizionario → . -# Aggiungere una nuova sorgente significa aggiungere solo un nuovo dict qui. - -# Campi scalari diretti (stringa o intero) provenienti dal top-level del record -OPENALEX_SCALAR_MAP: dict[str, str] = { - "id": "UT", # Identificatore univoco articolo - "doi": "DI", # DOI - "title": "TI", # Titolo documento - "publication_year": "PY", # Anno di pubblicazione (4 cifre) - "cited_by_count": "TC", # Numero di citazioni ricevute (int) - "language": "LA", # Lingua dell'articolo - "type": "DT", # Tipo documento (Article, Review, …) -} - -# ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER OPENALEX (VERSIONE CSV) -# ----------------------------------------------------------------------------- -OPENALEX_CSV_SCALAR_MAP: dict[str, str] = { - "id": "UT", # Identificatore Univoco - "doi": "DI", # DOI - "title": "TI", # Titolo documento - "publication_year": "PY", # Anno di pubblicazione - "type": "DT", # Tipo documento - "cited_by_count": "TC", # Citazioni - "host_venue": "SO", # Nome rivista (nei CSV OA a volte è source_display_name) - "source_display_name": "SO" # Inseriamo entrambe per robustezza -} - -# ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER SCOPUS (CSV EXPORT) -# ----------------------------------------------------------------------------- -SCOPUS_SCALAR_MAP: dict[str, str] = { - "EID": "UT", # Scopus Unique ID - "DOI": "DI", # DOI - "Title": "TI", # Titolo - "Source title": "SO", # Nome Rivista - "Abbreviated Source Title": "JI", # Abbreviazione Rivista - "Year": "PY", # Anno - "Document Type": "DT", # Tipo documento - "Cited by": "TC", # Citazioni - "Abstract": "AB", # Abstract - "Volume": "VL", # Volume - "Issue": "IS", # Fascicolo - "Page start": "BP", # Pagina iniziale - "Page end": "EP", # Pagina finale - "PubMed ID": "PMID", # PMID (se presente) - "Language of Original Document": "LA", # Lingua - "Correspondence Address": "RP", # Indirizzo di Reprint/Corrispondenza -} - -# ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER WEB OF SCIENCE -# ----------------------------------------------------------------------------- -# WoS usa nativamente i tag corretti, ma li mappiamo per coerenza -# e per applicare i type contracts. -WOS_SCALAR_MAP: dict[str, str] = { - "UT": "UT", - "DI": "DI", - "PM": "PMID", # In WoS a volte PubMed ID è 'PM' - "TI": "TI", - "SO": "SO", - "JI": "JI", - "PY": "PY", - "DT": "DT", - "LA": "LA", - "RP": "RP", - "AB": "AB", - "VL": "VL", - "IS": "IS", - "BP": "BP", - "EP": "EP", - "TC": "TC", # In WoS (Z9 o TC dipendentemente dall'export) - "Z9": "TC" -} - -# ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER DIMENSIONS (CSV / XLSX EXPORT) -# ----------------------------------------------------------------------------- -DIMENSIONS_SCALAR_MAP: dict[str, str] = { - "Publication ID": "UT", # Dimensions Unique ID - "DOI": "DI", # DOI - "PMID": "PMID", # PubMed ID - "Title": "TI", # Titolo - "Source title": "SO", # Nome Rivista (o Venue) - "PubYear": "PY", # Anno - "Publication Type": "DT", # Tipo documento - "Times cited": "TC", # Citazioni - "Abstract": "AB", # Abstract - "Volume": "VL", # Volume - "Issue": "IS", # Fascicolo - # La colonna Pagination la mappiamo temporaneamente su BP, - # poi la splittiamo in BP ed EP nella funzione di trasformazione - "Pagination": "BP", -} - -# ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER LENS (CSV EXPORT) -# ----------------------------------------------------------------------------- -LENS_SCALAR_MAP: dict[str, str] = { - "Lens ID": "UT", # Lens Unique ID - "DOI": "DI", # DOI - "PMID": "PMID", # PubMed ID - "Title": "TI", # Titolo - "Publication Year": "PY", # Anno di pubblicazione - "Publication Type": "DT", # Tipo documento - "Source Title": "SO", # Nome Rivista - "Volume": "VL", # Volume - "Issue": "IS", # Fascicolo - "Start Page": "BP", # Pagina iniziale - "End Page": "EP", # Pagina finale - "Abstract": "AB", # Abstract - "Citing Works Count": "TC", # Citazioni -} +# Importiamo le funzioni di formattazione dal file fornito +from . import format_functions as ff # ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER COCHRANE (TXT EXPORT) +# 1. CONTRATTI DI TIPO E DEFAULT # ----------------------------------------------------------------------------- -COCHRANE_SCALAR_MAP: dict[str, str] = { - "TI": "TI", # Titolo - "SO": "SO", # Source / Nome Rivista - "YR": "PY", # Cochrane spesso usa YR per l'anno - "PY": "PY", # Alternativa per l'anno - "DO": "DI", # DOI in Cochrane è DO - "DI": "DI", - "AB": "AB", # Abstract - "VL": "VL", # Volume - "NO": "IS", # Issue number in Cochrane è spesso NO o IS - "IS": "IS", - "PT": "DT", # Publication Type -} - -# Campi scalari annidati: (percorso_nested, tag_WoS) -# Il percorso è una lista di chiavi da seguire nel dict raw. -OPENALEX_NESTED_SCALAR_MAP: list[tuple[list[str], str]] = [ - (["primary_location", "source", "display_name"], "SO"), # Nome rivista - (["primary_location", "source", "abbreviated_title"],"JI"), # Abbreviazione ISO - (["biblio", "volume"], "VL"), # Volume - (["biblio", "issue"], "IS"), # Fascicolo - (["biblio", "first_page"], "BP"), # Pagina iniziale - (["biblio", "last_page"], "EP"), # Pagina finale - (["ids", "pmid"], "PMID"),# PubMed ID -] - -# ----------------------------------------------------------------------------- -# 2. CONTRATTI DI TIPO (Type Contracts) -# ----------------------------------------------------------------------------- -# Definisce il tipo atteso per ogni colonna del glossario WoS. -# Usato sia durante la trasformazione sia nella fase di validazione. - COLUMN_TYPE_CONTRACTS: dict[str, type] = { - # Scalari stringa - "DB": str, - "UT": str, - "DI": str, - "PMID": str, - "TI": str, - "SO": str, - "JI": str, - "PY": str, - "DT": str, - "LA": str, - "RP": str, - "AB": str, - "VL": str, - "IS": str, - "BP": str, - "EP": str, - "SR": str, - # Scalare numerico - "TC": int, - # Campi multi-valore - "AU": list, - "AF": list, - "C1": list, - "CR": list, - "DE": list, - "ID": list, + "DB": str, "UT": str, "DI": str, "PMID": str, + "TI": str, "SO": str, "JI": str, "PY": str, + "DT": str, "LA": str, "RP": str, "AB": str, + "VL": str, "IS": str, "BP": str, "EP": str, + "SR": str, "TC": int, + "AU": list, "AF": list, "C1": list, + "CR": list, "DE": list, "ID": list, } -# ----------------------------------------------------------------------------- -# DIZIONARIO DI MAPPING PER PUBMED (MEDLINE FORMAT) -# ----------------------------------------------------------------------------- -PUBMED_SCALAR_MAP: dict[str, str] = { - "PMID": "UT", # Identificatore Univoco - "LID": "DI", # Location ID (Spesso contiene il DOI) - "TI": "TI", # Titolo - "JT": "SO", # Journal Title (Nome della rivista) - "TA": "JI", # Journal Title Abbreviation - "PT": "DT", # Publication Type - "LA": "LA", # Language - "AB": "AB", # Abstract - "VI": "VL", # Volume - "IP": "IS", # Issue - "PG": "BP", # Paginazione (richiederà uno split per BP e EP) -} - -# Valore di default per ogni tipo (usato in caso di campo mancante/None) -_TYPE_DEFAULTS: dict[type, Any] = { - str: "", - int: 0, - list: [], -} +_TYPE_DEFAULTS: dict[type, Any] = {str: "", int: 0, list: []} +CSV_DELIMITER: str = ";" def _get_default_value(expected_type: type) -> Any: - """Restituisce una nuova istanza del valore di default per prevenire mutazioni condivise.""" if expected_type is list: - return [] # Genera una lista nuova ogni volta + return [] return _TYPE_DEFAULTS.get(expected_type, "") -# Delimitatore standard per la serializzazione CSV (Phase 2 spec) -CSV_DELIMITER: str = ";" - -# ----------------------------------------------------------------------------- -# 3. HELPER: accesso a campi nested -# ----------------------------------------------------------------------------- - -def _get_nested(record: dict, path: list[str]) -> Any: - """ - Naviga un dict annidato seguendo il percorso `path`. - Restituisce None se una qualunque chiave non esiste o il valore è None. - - Esempio: - _get_nested(rec, ["primary_location", "source", "display_name"]) - → rec["primary_location"]["source"]["display_name"] - """ - current: Any = record - for key in path: - if not isinstance(current, dict): - return None - current = current.get(key) - if current is None: - return None - return current - - -# ----------------------------------------------------------------------------- -# 4. CLEAN SCALAR FIELDS -# ----------------------------------------------------------------------------- - -def clean_scalar_fields(raw_record: dict) -> dict: - """ - Mappa e pulisce tutti i campi scalari (str / int) del record OpenAlex. - - Applica: - - La Lookup Strategy tramite OPENALEX_SCALAR_MAP e OPENALEX_NESTED_SCALAR_MAP - - I contratti di tipo di COLUMN_TYPE_CONTRACTS - - La gestione dei valori nulli (None → "" o 0) - - Args: - raw_record: dizionario grezzo proveniente dall'API OpenAlex. - - Returns: - dict con le sole chiavi WoS scalari valorizzate correttamente. - """ - result: dict = {} - - # Provenienza del database — sempre esplicita (spec §4.2 DB) - result["DB"] = "OPENALEX" - - # --- Campi scalari top-level --- - for oa_key, wos_tag in OPENALEX_SCALAR_MAP.items(): - val = raw_record.get(oa_key) - expected_type = COLUMN_TYPE_CONTRACTS[wos_tag] - result[wos_tag] = _cast_scalar(val, expected_type) - - # PY deve essere stringa a 4 cifre - if result.get("PY"): - result["PY"] = str(result["PY"])[:4] - - # --- Campi scalari nested --- - for path, wos_tag in OPENALEX_NESTED_SCALAR_MAP: - val = _get_nested(raw_record, path) - expected_type = COLUMN_TYPE_CONTRACTS[wos_tag] - result[wos_tag] = _cast_scalar(val, expected_type) - - # SO: spec richiede uppercase per convenzione bibliometrix - if result.get("SO"): - result["SO"] = result["SO"].upper() - - return result - - def _cast_scalar(value: Any, expected_type: type) -> Any: - """ - Converte `value` nel tipo atteso rispettando i contratti di tipo. - - Regole: - - None → valore di default per il tipo ("" per str, 0 per int) - - int → int(value) con fallback a 0 - - str → str(value).strip() - - Args: - value: valore grezzo (può essere None). - expected_type: tipo atteso (str o int). - - Returns: - Valore castato e pulito. - """ - if value is None: - return _TYPE_DEFAULTS[expected_type] + if value is None or (isinstance(value, float) and pd.isna(value)): + return _get_default_value(expected_type) if expected_type is int: try: - return int(value) + return int(float(value)) except (ValueError, TypeError): return 0 + elif expected_type is list: + return value if isinstance(value, list) else [str(value)] - # str — convertiamo e rimuoviamo spazi superflui return str(value).strip() - -# ----------------------------------------------------------------------------- -# 5. EXTRACT COMPLEX FIELDS -# ----------------------------------------------------------------------------- - -def extract_authors(raw_record: dict) -> list[str]: - """ - Estrae i nomi degli autori da OpenAlex e li formatta come 'Cognome, Nome'. - - Il formato "Cognome, Nome" è lo standard WoS per il tag AU. - Se il nome è una sola parola viene lasciato invariato. - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Lista di stringhe nel formato "Cognome, Nome". - Lista vuota se il campo è assente o privo di dati validi. - """ - authorships = raw_record.get("authorships") - if not authorships: - return [] - - author_list: list[str] = [] - for auth in authorships: - author_data = auth.get("author", {}) - name = author_data.get("display_name") - if not name: - continue - - name_str = str(name).strip() - parts = name_str.split() - if len(parts) > 1: - surname = parts[-1] - first_names = " ".join(parts[:-1]) - formatted = f"{surname}, {first_names}" - else: - formatted = name_str - - author_list.append(formatted) - - return author_list - - -def extract_affiliations(raw_record: dict) -> list[str]: - """ - Estrae le affiliazioni degli autori (tag C1) come lista di stringhe univoche. - - Itera su tutti gli autori e raccoglie il display_name di ogni istituzione, - evitando duplicati a livello di articolo. - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Lista di nomi di istituzioni (senza duplicati). - Lista vuota se il campo è assente. - """ - authorships = raw_record.get("authorships") - if not authorships: - return [] - - affiliation_list: list[str] = [] - seen: set[str] = set() - - for auth in authorships: - institutions = auth.get("institutions", []) - for inst in institutions: - inst_name = inst.get("display_name") - if inst_name: - name_str = str(inst_name).strip() - if name_str not in seen: - seen.add(name_str) - affiliation_list.append(name_str) - - return affiliation_list - - -def extract_reprint_address(raw_record: dict) -> str: - """ - Estrae l'indirizzo di reprint (RP) dal primo autore corrispondente. - - In OpenAlex, l'autore corrispondente si identifica tramite - 'is_corresponding': True in authorships. Se non presente, viene - usato il primo autore con almeno un'istituzione. - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Stringa con l'istituzione del corrispondente o "" se non trovata. - """ - authorships = raw_record.get("authorships", []) - if not authorships: - return "" - - # Prima scelta: autore corrispondente esplicito - for auth in authorships: - if auth.get("is_corresponding"): - institutions = auth.get("institutions", []) - if institutions: - name = institutions[0].get("display_name", "") - return str(name).strip() if name else "" - - # Fallback: primo autore con istituzione valorizzata - for auth in authorships: - institutions = auth.get("institutions", []) - if institutions: - name = institutions[0].get("display_name", "") - return str(name).strip() if name else "" - - return "" - - -def extract_keywords(raw_record: dict) -> list[str]: - """ - Estrae le parole chiave dell'autore (tag DE) dal campo 'keywords'. - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Lista di keyword come stringhe. - Lista vuota se il campo è assente o vuoto. - """ - keywords_data = raw_record.get("keywords") - if not keywords_data: - return [] - - return [ - str(kw["display_name"]).strip() - for kw in keywords_data - if kw.get("display_name") - ] - - -def extract_index_keywords(raw_record: dict) -> list[str]: - """ - Estrae gli Index Keywords / Keywords Plus (tag ID) dai topic di OpenAlex. - - OpenAlex espone i concetti tematici nel campo 'topics'. - Questi mappano al tag WoS ID (Index Keywords assegnati dall'indicizzatore). - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Lista di nomi di topic come stringhe. - Lista vuota se il campo è assente. - """ - topics_data = raw_record.get("topics") - if not topics_data: - # Fallback su 'concepts' (versione precedente dell'API OpenAlex) - concepts_data = raw_record.get("concepts") - if not concepts_data: - return [] - return [ - str(c["display_name"]).strip() - for c in concepts_data - if c.get("display_name") - ] - - return [ - str(t["display_name"]).strip() - for t in topics_data - if t.get("display_name") - ] - - -def extract_references(raw_record: dict) -> list[str]: - """ - Estrae i riferimenti citati (tag CR) come lista di ID OpenAlex. - - In OpenAlex i referenced_works sono forniti come URL/ID del tipo - 'https://openalex.org/W...'. Vengono restituiti come stringhe grezze - poiché la normalizzazione completa nel formato WoS richiederebbe - chiamate API aggiuntive (fuori dallo scope di questa fase). - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Lista di stringhe (ID OpenAlex dei lavori citati). - Lista vuota se il campo è assente. - """ - references = raw_record.get("referenced_works") - if not references: - return [] - - return [str(ref).strip() for ref in references if ref] - - -def reconstruct_abstract(raw_record: dict) -> str: - """ - Ricostruisce l'abstract di OpenAlex dall'indice invertito (InvertedIndex). - - OpenAlex non fornisce l'abstract come testo lineare ma come dizionario - { parola: [lista_di_posizioni] }. Questa funzione inverte la struttura - e restituisce il testo ricostruito. - - Args: - raw_record: dizionario grezzo OpenAlex. - - Returns: - Stringa con l'abstract ricostruito, oppure "" se assente o - in caso di errore nella ricostruzione. - """ - inverted_index = raw_record.get("abstract_inverted_index") - if not inverted_index: - return "" - - try: - max_index = max( - max(positions) - for positions in inverted_index.values() - ) - words: list[str] = [""] * (max_index + 1) - for word, positions in inverted_index.items(): - for pos in positions: - words[pos] = word - return " ".join(words).strip() - - except Exception as exc: - # Safe fallback: meglio restituire stringa vuota che bloccare la pipeline - print(f"[WARN] Errore nella ricostruzione dell'abstract: {exc}") - return "" - - # ----------------------------------------------------------------------------- -# 7. TRASFORMAZIONE PRINCIPALE -# ----------------------------------------------------------------------------- - -def transform_openalex_record(raw_record: dict) -> dict: - """ - Orchestra la trasformazione di un singolo record OpenAlex nel formato WoS. - """ - # 1. Scheletro completo - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } - - # 2. Campi scalari - standardized.update(clean_scalar_fields(raw_record)) - - # 3. Campi complessi - standardized["AU"] = extract_authors(raw_record) - standardized["AF"] = standardized["AU"] - standardized["C1"] = extract_affiliations(raw_record) - standardized["RP"] = extract_reprint_address(raw_record) - standardized["DE"] = extract_keywords(raw_record) - standardized["ID"] = extract_index_keywords(raw_record) - standardized["CR"] = extract_references(raw_record) - standardized["AB"] = reconstruct_abstract(raw_record) - - # Se JI è vuoto o non esiste, usiamo il nome completo della rivista (SO) come ripiego - if not standardized.get("JI") and standardized.get("SO"): - standardized["JI"] = standardized["SO"] +# 2. INTEGRAZIONE CON FORMAT_FUNCTIONS (Il Bridge) +# ----------------------------------------------------------------------------- +# Mappa dei tag WoS alle specifiche funzioni di format_functions.py +FORMAT_FUNCTIONS_MAP = { + 'AB': ff.format_ab_column, + 'AF': ff.format_af_column, + 'AU': ff.format_au_column, + 'BP': ff.format_bp_column, + 'C1': ff.format_c1_column, + 'CR': ff.format_cr_column, + 'DE': ff.format_de_column, + 'DI': ff.format_di_column, + 'DT': ff.format_dt_column, + 'EP': ff.format_ep_column, + 'IS': ff.format_is_column, + 'JI': ff.format_ji_column, + 'ID': ff.format_id_column, + 'LA': ff.format_la_column, + 'PMID': ff.format_pmid_column, + 'PY': ff.format_py_column, + 'RP': ff.format_rp_column, + 'SO': ff.format_so_column, + 'TC': ff.format_tc_column, + 'TI': ff.format_ti_column, + 'UT': ff.format_ut_column, + 'VL': ff.format_vl_column, + 'SR': ff.format_sr_column +} - return standardized +# Mappatura dei nomi dei DB da uppercase a quelli attesi da format_functions +SOURCE_NAME_MAP = { + "WEB_OF_SCIENCE": "Web_of_Science", + "SCOPUS": "Scopus", + "PUBMED": "PubMed", + "DIMENSIONS": "Dimensions", + "LENS": "The_Lens", + "COCHRANE": "Cochrane" +} -# ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER OPENALEX CSV -# ----------------------------------------------------------------------------- -def transform_openalex_csv_record(raw_record: dict) -> dict: +def transform_via_format_functions(raw_record: dict, source_upper: str, file_type: str) -> dict: """ - Converte una riga piatta di un CSV di OpenAlex nei tag WoS standard. - Si occupa di splittare le stringhe separate da virgola o punto e virgola in liste. + Funzione universale che delega l'estrazione dei campi a format_functions.py + e assicura il rispetto dei Type Contracts di standardizer. """ standardized: dict = { tag: _get_default_value(contract) for tag, contract in COLUMN_TYPE_CONTRACTS.items() } - # Manteniamo il nome del DB corretto per le analisi a valle - standardized["DB"] = "OPENALEX" - - # 1. Mappatura dei campi scalari diretti - for csv_key, wos_tag in OPENALEX_CSV_SCALAR_MAP.items(): - if csv_key in raw_record and raw_record[csv_key]: - standardized[wos_tag] = _cast_scalar(raw_record[csv_key], COLUMN_TYPE_CONTRACTS[wos_tag]) - - # 2. Gestione dei campi Multi-Valore (Split delle stringhe) - - # Autori (AU e AF): Nel CSV di solito sono in una colonna "authors" o "author_display_names" - authors_str = str(raw_record.get("authors", raw_record.get("author_display_names", ""))) - if authors_str and authors_str.strip(): - # I CSV possono usare la virgola o il punto e virgola come separatore interno - separator = ";" if ";" in authors_str else "," - # Splittiamo e rimuoviamo gli spazi vuoti extra - authors_list = [a.strip() for a in authors_str.split(separator) if a.strip()] - - standardized["AU"] = authors_list - standardized["AF"] = authors_list + standardized["DB"] = source_upper + ff_source_name = SOURCE_NAME_MAP.get(source_upper, source_upper) - # Concetti / Index Keywords (ID): Di solito in "concepts" o "topics" - concepts_str = str(raw_record.get("concepts", "")) - if concepts_str and concepts_str.strip(): - separator = ";" if ";" in concepts_str else "," - standardized["ID"] = [c.strip() for c in concepts_str.split(separator) if c.strip()] + for tag, extract_func in FORMAT_FUNCTIONS_MAP.items(): + try: + # Invoca la funzione specifica del campo passando i tre parametri richiesti + raw_value = extract_func(raw_record, ff_source_name, file_type) + except Exception as e: + raw_value = None # Fallback sicuro in caso di KeyError interni - # Riferimenti Citati (CR): Di solito in "referenced_works" - refs_str = str(raw_record.get("referenced_works", "")) - if refs_str and refs_str.strip(): - separator = ";" if ";" in refs_str else "," - standardized["CR"] = [r.strip() for r in refs_str.split(separator) if r.strip()] - - return standardized - -# ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER PUBMED -# ----------------------------------------------------------------------------- -def transform_pubmed_record(raw_record: dict) -> dict: - """ - Converte un record estratto dal formato MEDLINE nei tag WoS standard. - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } - - standardized["DB"] = "PUBMED" - standardized["PMID"] = raw_record.get("PMID", "") - - # Mappatura campi scalari diretti - for medline_key, wos_tag in PUBMED_SCALAR_MAP.items(): - val = raw_record.get(medline_key) - # Alcuni campi scalari in Medline potrebbero essere stati parsati come list - # se presenti più volte per errore, forziamo l'uso del primo elemento - if isinstance(val, list): - val = val[0] - standardized[wos_tag] = _cast_scalar(val, COLUMN_TYPE_CONTRACTS[wos_tag]) - - # --- Estrazioni Specifiche per PubMed --- - - # Anno di pubblicazione (DP in Medline è solitamente "2024 Oct 15", prendiamo le prime 4 cifre) - dp = raw_record.get("DP", "") - if dp and len(dp) >= 4: - standardized["PY"] = dp[:4] - - # DOI (LID in Medline contiene spesso "10.xxx [doi]", dobbiamo pulirlo) - lid = raw_record.get("LID", "") - if "[doi]" in str(lid): - standardized["DI"] = str(lid).split("[doi]")[0].strip() - - # Pagine (PG in Medline è spesso "123-145") - pg = raw_record.get("PG", "") - if "-" in pg: - parts = pg.split("-") - standardized["BP"] = parts[0].strip() - standardized["EP"] = parts[1].strip() - else: - standardized["BP"] = pg - - # Autori (AU). In Medline sono già nel formato "Cognome Iniziali" (es. "Smith J") - au = raw_record.get("AU", []) - standardized["AU"] = au if isinstance(au, list) else [au] - - # Autori Completi (FAU) - fau = raw_record.get("FAU", []) - standardized["AF"] = fau if isinstance(fau, list) else [fau] - - # Affiliazioni (AD) - ad = raw_record.get("AD", []) - standardized["C1"] = ad if isinstance(ad, list) else [ad] - - # Parole Chiave (OT - Other Term) - ot = raw_record.get("OT", []) - standardized["DE"] = ot if isinstance(ot, list) else [ot] - - return standardized - -# ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER SCOPUS -# ----------------------------------------------------------------------------- -def transform_scopus_record(raw_record: dict) -> dict: - """ - Converte una riga del CSV esportato da Scopus nei tag WoS standard. - Scopus esporta i campi multi-valore separandoli tipicamente con una virgola - o un punto e virgola. - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } - - standardized["DB"] = "SCOPUS" - - # 1. Mappatura campi scalari - for scopus_key, wos_tag in SCOPUS_SCALAR_MAP.items(): - if scopus_key in raw_record and raw_record[scopus_key]: - standardized[wos_tag] = _cast_scalar(raw_record[scopus_key], COLUMN_TYPE_CONTRACTS[wos_tag]) - - # 2. Campi Multi-Valore (Split) + # Cast al tipo atteso (int, str, list) per evitare disallineamenti + standardized[tag] = _cast_scalar(raw_value, COLUMN_TYPE_CONTRACTS[tag]) - # Autori (AU e AF): Scopus li fornisce come "Smith J., Doe A." - authors_str = str(raw_record.get("Authors", "")) - if authors_str and authors_str.strip() and authors_str.lower() != "[no author name available]": - # Split intelligente: Scopus usa spesso la virgola, ma a volte il punto e virgola. - separator = ";" if ";" in authors_str else "," - authors_list = [a.strip() for a in authors_str.split(separator) if a.strip()] - standardized["AU"] = authors_list - standardized["AF"] = authors_list # In Scopus l'export base non ha i nomi completi estesi, usiamo AU - - # Affiliazioni (C1) - affiliations_str = str(raw_record.get("Affiliations", "")) - if affiliations_str and affiliations_str.strip(): - standardized["C1"] = [aff.strip() for aff in affiliations_str.split(";") if aff.strip()] - - # Parole chiave dell'autore (DE) - auth_kw_str = str(raw_record.get("Author Keywords", "")) - if auth_kw_str and auth_kw_str.strip(): - standardized["DE"] = [kw.strip() for kw in auth_kw_str.split(";") if kw.strip()] - - # Parole chiave di indicizzazione (ID) - idx_kw_str = str(raw_record.get("Index Keywords", "")) - if idx_kw_str and idx_kw_str.strip(): - standardized["ID"] = [kw.strip() for kw in idx_kw_str.split(";") if kw.strip()] - - # Riferimenti Citati (CR): Scopus spesso ha un lungo blocco di testo separato da ';' - refs_str = str(raw_record.get("References", "")) - if refs_str and refs_str.strip(): - standardized["CR"] = [r.strip() for r in refs_str.split(";") if r.strip()] - - # Pulizia standard su Nome Rivista (in maiuscolo come da convenzione) + # Pulizia standard su Nome Rivista (SO) if standardized.get("SO"): standardized["SO"] = standardized["SO"].upper() return standardized # ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER WEB OF SCIENCE (Migliorata per TXT e CSV) +# 3. LOGICHE RESIDUE (Solo per OpenAlex, non presente in format_functions) # ----------------------------------------------------------------------------- -def transform_wos_record(raw_record: dict) -> dict: - """ - Converte e pulisce un record grezzo di WoS. - Supporta sia l'output del parser testuale (liste) sia quello CSV (stringhe piatte). - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } - - standardized["DB"] = "WEB_OF_SCIENCE" - - # 1. Mappatura campi scalari diretti - for wos_key, standard_tag in WOS_SCALAR_MAP.items(): - if wos_key in raw_record: - val = raw_record[wos_key] - - # Se arriva dal parser testuale, i valori singoli sono intrappolati in liste - if isinstance(val, list) and len(val) > 0: - val = val[0] - - standardized[standard_tag] = _cast_scalar(val, COLUMN_TYPE_CONTRACTS[standard_tag]) - - # 2. Funzione helper per estrarre liste (gestisce sia input TXT che CSV) - def extract_list_field(field_key: str) -> list[str]: - raw_val = raw_record.get(field_key, []) - if isinstance(raw_val, list): - return [str(v).strip() for v in raw_val if v] - elif isinstance(raw_val, str) and raw_val.strip(): - # I CSV di WoS usano il punto e virgola come separatore - return [v.strip() for v in raw_val.split(";") if v.strip()] - return [] - - # 3. Campi multi-valore - standardized["AU"] = extract_list_field("AU") - standardized["AF"] = extract_list_field("AF") or standardized["AU"] - standardized["C1"] = extract_list_field("C1") - standardized["CR"] = extract_list_field("CR") - standardized["DE"] = extract_list_field("DE") - standardized["ID"] = extract_list_field("ID") - - return standardized - -# ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER DIMENSIONS -# ----------------------------------------------------------------------------- -def transform_dimensions_record(raw_record: dict) -> dict: - """ - Converte una riga del file CSV o XLSX esportato da Dimensions - nei tag WoS standard. - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } - - standardized["DB"] = "DIMENSIONS" - - # 1. Mappatura campi scalari diretti - for dim_key, wos_tag in DIMENSIONS_SCALAR_MAP.items(): - if dim_key in raw_record and raw_record[dim_key]: - standardized[wos_tag] = _cast_scalar(raw_record[dim_key], COLUMN_TYPE_CONTRACTS[wos_tag]) - - # 2. Gestione Speciale: Paginazione - # Dimensions spesso fornisce i numeri di pagina come "123-145" - pagination = str(raw_record.get("Pagination", "")) - if "-" in pagination: - parts = pagination.split("-", 1) # Splittiamo solo sul primo trattino - standardized["BP"] = parts[0].strip() - standardized["EP"] = parts[1].strip() - - # 3. Campi Multi-Valore (Split) - - # Autori (AU e AF): Dimensions li separa tipicamente con il punto e virgola - authors_str = str(raw_record.get("Authors", "")) - if authors_str and authors_str.strip(): - authors_list = [a.strip() for a in authors_str.split(";") if a.strip()] - standardized["AU"] = authors_list - standardized["AF"] = authors_list - - # Affiliazioni (C1) - affiliations_str = str(raw_record.get("Authors Affiliations", "")) - if affiliations_str and affiliations_str.strip(): - standardized["C1"] = [aff.strip() for aff in affiliations_str.split(";") if aff.strip()] - - # Concepts (DE) - I concetti estratti dall'AI di Dimensions - concepts_str = str(raw_record.get("Concepts", "")) - if concepts_str and concepts_str.strip(): - standardized["DE"] = [c.strip() for c in concepts_str.split(";") if c.strip()] - - # MeSH Terms (ID) - Termini di indicizzazione medica - mesh_str = str(raw_record.get("MeSH terms", "")) - if mesh_str and mesh_str.strip(): - standardized["ID"] = [m.strip() for m in mesh_str.split(";") if m.strip()] - - # References (CR) - Dimensions di solito esporta un elenco di Reference IDs - refs_str = str(raw_record.get("Reference IDs", "")) - if refs_str and refs_str.strip(): - # A volte usa la virgola, a volte il punto e virgola - separator = ";" if ";" in refs_str else "," - standardized["CR"] = [r.strip() for r in refs_str.split(separator) if r.strip()] - - # Pulizia standard su Nome Rivista (in maiuscolo come da convenzione) - if standardized.get("SO"): - standardized["SO"] = standardized["SO"].upper() - - return standardized +def clean_scalar_fields_oa(raw_record: dict, mapping_dict: dict) -> dict: + res = {} + for oa_key, wos_tag in mapping_dict.items(): + val = raw_record.get(oa_key) + res[wos_tag] = _cast_scalar(val, COLUMN_TYPE_CONTRACTS[wos_tag]) + return res -# ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER COCHRANE -# ----------------------------------------------------------------------------- -def transform_cochrane_record(raw_record: dict) -> dict: - """ - Converte un record estratto dal file di testo Cochrane nei tag WoS standard. - Il parser originale 'parse_cochrane_data' concatena già i campi multipli - con il punto e virgola, quindi applichiamo uno split. - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } +def transform_openalex_record(raw_record: dict, file_type: str = "") -> dict: + # (Mantieni qui la logica di base per OpenAlex JSON originale) + standardized = {t: _get_default_value(c) for t, c in COLUMN_TYPE_CONTRACTS.items()} + standardized["DB"] = "OPENALEX" - standardized["DB"] = "COCHRANE" - - # 1. Mappatura campi scalari diretti - for coch_key, wos_tag in COCHRANE_SCALAR_MAP.items(): - if coch_key in raw_record and raw_record[coch_key]: - standardized[wos_tag] = _cast_scalar(raw_record[coch_key], COLUMN_TYPE_CONTRACTS[wos_tag]) - - # 2. Gestione Speciale: Paginazione - # In Cochrane le pagine (PG) possono essere "1-10" o singole - pg = str(raw_record.get("PG", "")) - if "-" in pg: - parts = pg.split("-", 1) - standardized["BP"] = parts[0].strip() - standardized["EP"] = parts[1].strip() - elif pg: - standardized["BP"] = pg.strip() - - # 3. Campi Multi-Valore (Split) + mapping = {"id": "UT", "doi": "DI", "title": "TI", "publication_year": "PY", "type": "DT", "cited_by_count": "TC"} + standardized.update(clean_scalar_fields_oa(raw_record, mapping)) - # Autori (AU e AF): Il parser li ha uniti con "; " - au_str = str(raw_record.get("AU", "")) - if au_str and au_str.strip(): - authors_list = [a.strip() for a in au_str.split(";") if a.strip()] - standardized["AU"] = authors_list - standardized["AF"] = authors_list - - # Parole chiave (KW in Cochrane diventa DE in WoS) - kw_str = str(raw_record.get("KW", "")) - if kw_str and kw_str.strip(): - standardized["DE"] = [k.strip() for k in kw_str.split(";") if k.strip()] - - # Pulizia standard su Nome Rivista - if standardized.get("SO"): - standardized["SO"] = standardized["SO"].upper() - - # Nota: Cochrane raramente esporta References strutturate (CR) o Affiliazioni (C1) - # in un formato facilmente parsabile nel txt base. I Type Contracts - # assicureranno che rimangano liste vuote [] senza causare crash. - + # Autori (estrazione semplificata) + authorships = raw_record.get("authorships", []) + if authorships: + standardized["AU"] = [str(a.get("author", {}).get("display_name", "")) for a in authorships] + standardized["AF"] = standardized["AU"] + standardized["C1"] = [inst.get("display_name", "") for auth in authorships for inst in auth.get("institutions", [])] return standardized -# ----------------------------------------------------------------------------- -# FUNZIONE DI TRASFORMAZIONE PER LENS -# ----------------------------------------------------------------------------- -def transform_lens_record(raw_record: dict) -> dict: - """ - Converte una riga del file CSV esportato da Lens nei tag WoS standard. - Lens separa quasi esclusivamente i campi multi-valore con il punto e virgola. - """ - standardized: dict = { - tag: _get_default_value(contract) - for tag, contract in COLUMN_TYPE_CONTRACTS.items() - } +def transform_openalex_csv_record(raw_record: dict, file_type: str = "") -> dict: + # (Mantieni qui la logica di base per OpenAlex CSV originale) + standardized = {t: _get_default_value(c) for t, c in COLUMN_TYPE_CONTRACTS.items()} + standardized["DB"] = "OPENALEX" - standardized["DB"] = "LENS" - - # 1. Mappatura campi scalari diretti - for lens_key, wos_tag in LENS_SCALAR_MAP.items(): - if lens_key in raw_record and raw_record[lens_key]: - standardized[wos_tag] = _cast_scalar(raw_record[lens_key], COLUMN_TYPE_CONTRACTS[wos_tag]) - - # 2. Campi Multi-Valore (Split) + mapping = {"id": "UT", "doi": "DI", "title": "TI", "publication_year": "PY", "type": "DT", "cited_by_count": "TC", "host_venue": "SO"} + standardized.update(clean_scalar_fields_oa(raw_record, mapping)) - # Autori (AU e AF): Lens li esporta nella colonna "Author/s" separati da ';' - authors_str = str(raw_record.get("Author/s", "")) - if authors_str and authors_str.strip(): - authors_list = [a.strip() for a in authors_str.split(";") if a.strip()] - standardized["AU"] = authors_list - standardized["AF"] = authors_list - - # Parole Chiave Autore (DE): Lens usa "Keywords" - kw_str = str(raw_record.get("Keywords", "")) - if kw_str and kw_str.strip(): - standardized["DE"] = [k.strip() for k in kw_str.split(";") if k.strip()] - - # Index Keywords (ID): Lens usa "Fields of Study" (basato su concetti AI come Dimensions) - fos_str = str(raw_record.get("Fields of Study", "")) - if fos_str and fos_str.strip(): - standardized["ID"] = [f.strip() for f in fos_str.split(";") if f.strip()] - - # Riferimenti Citati (CR): Lens usa "References" o "Lens ID delle referenze" - refs_str = str(raw_record.get("References", "")) - if refs_str and refs_str.strip(): - standardized["CR"] = [r.strip() for r in refs_str.split(";") if r.strip()] - - # Affiliazioni (C1): Spesso esportate come "Affiliations" in Lens - aff_str = str(raw_record.get("Affiliations", "")) - if aff_str and aff_str.strip(): - standardized["C1"] = [aff.strip() for aff in aff_str.split(";") if aff.strip()] - - # Pulizia standard su Nome Rivista (in maiuscolo come da convenzione) - if standardized.get("SO"): - standardized["SO"] = standardized["SO"].upper() - + authors_str = str(raw_record.get("authors", raw_record.get("author_display_names", ""))) + if authors_str: + sep = ";" if ";" in authors_str else "," + standardized["AU"] = [a.strip() for a in authors_str.split(sep) if a.strip()] + standardized["AF"] = standardized["AU"] return standardized # ----------------------------------------------------------------------------- -# 8. DISPATCHER (estensibilità multi-sorgente) -# ----------------------------------------------------------------------------- -# Mappa il nome della sorgente alla funzione di trasformazione corrispondente. -# Per aggiungere una nuova sorgente (es. Scopus) basta: -# 1. Implementare transform_scopus_record(raw_record) in questo modulo -# (o in un sottomodulo apposito) -# 2. Aggiungere la voce "SCOPUS": transform_scopus_record qui sotto - -_TRANSFORM_DISPATCHER: dict[str, Any] = { - "WEB_OF_SCIENCE": transform_wos_record, - "SCOPUS": transform_scopus_record, - "PUBMED": transform_pubmed_record, - "OPENALEX": transform_openalex_record, - "OPENALEX_CSV": transform_openalex_csv_record, - "DIMENSIONS": transform_dimensions_record, - "COCHRANE": transform_cochrane_record, - "LENS": transform_lens_record, -} - - -# ----------------------------------------------------------------------------- -# 9. VALIDAZIONE (Phase 5) +# 4. VALIDAZIONE E SERIALIZZAZIONE # ----------------------------------------------------------------------------- - -class ValidationError(Exception): - """Eccezione sollevata quando un record non supera la validazione.""" - pass - +class ValidationError(Exception): pass def validate_record(record: dict) -> None: - """ - Verifica che il record standardizzato rispetti tutti i contratti di tipo. - - Controlla: - - Presenza di tutte le colonne obbligatorie definite in COLUMN_TYPE_CONTRACTS - - Assenza di valori None o NaN (pandas.isna) - - Correttezza del tipo Python per ogni colonna - - Args: - record: dizionario standardizzato da validare. - - Raises: - ValidationError: se almeno un controllo fallisce, con messaggio - descrittivo di tutti gli errori trovati. - """ - errors: list[str] = [] - + errors = [] for tag, expected_type in COLUMN_TYPE_CONTRACTS.items(): - # Colonna presente? if tag not in record: - errors.append(f"[MISSING_COLUMN] Tag '{tag}' assente nel record.") + errors.append(f"[MISSING_COLUMN] Tag '{tag}' assente.") continue - val = record[tag] - - # Valore nullo? - try: - if pd.isna(val): - errors.append( - f"[NULL_VALUE] Tag '{tag}' contiene NaN/None." - ) - continue - except (TypeError, ValueError): - # pd.isna lancia TypeError su liste — in quel caso non è None - pass - - # Tipo corretto? if not isinstance(val, expected_type): - errors.append( - f"[TYPE_ERROR] Tag '{tag}': atteso {expected_type.__name__}, " - f"trovato {type(val).__name__} (valore: {repr(val)[:60]})." - ) - + errors.append(f"[TYPE_ERROR] Tag '{tag}': atteso {expected_type.__name__}, trovato {type(val).__name__}.") if errors: - raise ValidationError( - f"Validazione fallita con {len(errors)} errore/i:\n" - + "\n".join(f" • {e}" for e in errors) - ) - - -# ----------------------------------------------------------------------------- -# 10. SERIALIZZAZIONE CSV -# ----------------------------------------------------------------------------- + raise ValidationError("\n".join(errors)) def serialize_for_csv(record: dict) -> dict: - """ - Converte le liste in stringhe delimitate da ';' per la serializzazione CSV. - - I campi multi-valore (list[str]) vengono uniti con il delimitatore - standard CSV_DELIMITER (';'), come richiesto dalla spec §Phase 2. - I campi scalari non vengono modificati. - - Args: - record: dizionario standardizzato (post-validazione). - - Returns: - Nuovo dizionario con tutti i valori serializzabili come celle CSV. - """ - serialized = {} - for tag, val in record.items(): - if isinstance(val, list): - serialized[tag] = CSV_DELIMITER.join(str(item) for item in val) - else: - serialized[tag] = val - return serialized - + return {tag: (CSV_DELIMITER.join(str(i) for i in val) if isinstance(val, list) else val) for tag, val in record.items()} # ----------------------------------------------------------------------------- -# 11. ENTRY POINT PRINCIPALE: convert2df() +# 5. ENTRY POINT PRINCIPALE # ----------------------------------------------------------------------------- - def convert2df( raw_records: list[dict], source: str = "OPENALEX", + file_type: str = ".csv", # Aggiunto parametro per format_functions validate: bool = True, for_csv_export: bool = False, ) -> pd.DataFrame: """ - Entry point principale del modulo — trasforma una lista di record grezzi - in un DataFrame standardizzato pronto per le analisi bibliometriche. - - Replica concettualmente la funzione `convert2df()` di Bibliometrix-R. - - Pipeline interna: - 1. Seleziona la funzione di trasformazione tramite il Dispatcher - 2. Applica la trasformazione a ogni record - 3. (Opzionale) Valida ogni record standardizzato - 4. (Opzionale) Serializza le liste per l'esportazione CSV - 5. Assembla e restituisce il DataFrame - - Args: - raw_records: lista di dizionari grezzi dalla sorgente (API o file). - source: identificatore della sorgente dati (default: "OPENALEX"). - Deve corrispondere a una chiave in _TRANSFORM_DISPATCHER. - validate: se True (default), esegue la validazione su ogni record - e solleva ValidationError al primo errore trovato. - for_csv_export: se True, serializza le liste con il delimitatore ';' - per compatibilità con pandas.to_csv(). - - Returns: - pd.DataFrame con colonne ordinate secondo il glossario WoS §4.2. - - Raises: - ValueError: se `source` non è registrata nel Dispatcher. - ValidationError: se un record non supera la validazione (solo se - validate=True). - - Esempio: - >>> records = fetch_openalex_records(query="machine learning") - >>> df = convert2df(records, source="OPENALEX") - >>> df.to_csv("standardized_output.csv", index=False) + Trasforma una lista di record grezzi in un DataFrame standardizzato. """ + # --- SONDE DI DEBUG (da rimuovere una volta risolto) --- + print("\n" + "="*40) + print("🎯 DEBUG CONVERT2DF CHIAMATO!") + print(f"🔹 Source ricevuta: '{source}'") + print(f"🔹 File type ricevuto: '{file_type}'") + print(f"🔹 Tipo di raw_records: {type(raw_records)}") + + if isinstance(raw_records, list): + print(f"🔹 Numero di record: {len(raw_records)}") + if len(raw_records) > 0: + print(f"🔹 Chiavi del primo record: {list(raw_records[0].keys())[:5]}...") + else: + print("⚠️ ERRORE: raw_records NON è una lista!") + print("="*40 + "\n") + # ------------------------------------------------------- + source_upper = source.upper() + # Routing dinamico per OpenAlex vs Format Functions if source_upper == "OPENALEX" and raw_records: - # Se non c'è la chiave complessa 'authorships' ma ci sono chiavi piatte da CSV, - # scambiamo dinamicamente il dispatcher verso la versione CSV. first_record = raw_records[0] if "authorships" not in first_record and ("author_display_names" in first_record or "publication_year" in first_record): source_upper = "OPENALEX_CSV" - if source_upper not in _TRANSFORM_DISPATCHER: - registered = list(_TRANSFORM_DISPATCHER.keys()) - raise ValueError( - f"Sorgente '{source}' non registrata nel Dispatcher. " - f"Sorgenti disponibili: {registered}" - ) - - transform_fn = _TRANSFORM_DISPATCHER[source_upper] - standardized_records: list[dict] = [] + for idx, raw in enumerate(raw_records): try: - record = transform_fn(raw) + # Logica di Routing + if source_upper == "OPENALEX": + record = transform_openalex_record(raw, file_type) + elif source_upper == "OPENALEX_CSV": + record = transform_openalex_csv_record(raw, file_type) + else: + # Per Scopus, WoS, PubMed, Dimensions, Cochrane e Lens usa il Bridge + record = transform_via_format_functions(raw, source_upper, file_type) except Exception as exc: print(f"[ERROR] Trasformazione fallita per il record {idx}: {exc}") continue @@ -1164,54 +232,25 @@ def convert2df( validate_record(record) except ValidationError as ve: print(f"[WARN] Validazione fallita per il record {idx}:\n{ve}") - # Non blocchiamo l'intera pipeline: inseriamo comunque il record - # con un flag di warning. Per un comportamento strict, - # sostituire 'continue' a 'print' sopra. if for_csv_export: record = serialize_for_csv(record) standardized_records.append(record) - # Ordine delle colonne conforme al glossario §4.2 column_order = list(COLUMN_TYPE_CONTRACTS.keys()) - if not standardized_records: return pd.DataFrame(columns=column_order) df = pd.DataFrame(standardized_records) - # Garantiamo che tutte le colonne del glossario siano presenti + # Assicuriamo che tutte le colonne siano presenti for col in column_order: if col not in df.columns: if for_csv_export: - df[col] = "" # Se è per CSV, tutto deve essere testo vuoto + df[col] = "" else: default_val = _TYPE_DEFAULTS[COLUMN_TYPE_CONTRACTS[col]] - # Se è una lista vuota, usiamo una list comprehension per coprire tutte le righe - if isinstance(default_val, list): - df[col] = [[] for _ in range(len(df))] - else: - df[col] = default_val - - # ========================================================================= - # FASE 4: CALCULATED FIELDS (SR) - Applicazione sul DataFrame - # ========================================================================= - try: - from www.services.metatagextraction import SR - df = SR(df) - - except ImportError: - print("[WARN] Funzione SR non trovata in www.services.metatagextraction. Applicazione fallback.") - if not df.empty: - # Fallback avanzato: gestisce sia le liste (memoria) sia le stringhe serializzate per CSV - first_author = df["AU"].apply( - lambda x: x[0].split(",")[0].strip() if isinstance(x, list) and len(x) > 0 - else (str(x).split(";")[0].split(",")[0].strip() if isinstance(x, str) and str(x).strip() else "Unknown") - ) - df["SR"] = first_author + ", " + df["PY"].astype(str) + ", " + df["SO"].astype(str) - df["SR"] = df["SR"].str.strip(", ") - - # Restituiamo il DataFrame ordinato secondo il glossario - return df[column_order] + df[col] = [[] for _ in range(len(df))] if isinstance(default_val, list) else default_val + return df[column_order] \ No newline at end of file