From 9fd5db63359895b830096c371ab1efa09d1964a3 Mon Sep 17 00:00:00 2001 From: MARCOP001 Date: Mon, 1 Jun 2026 19:31:31 +0200 Subject: [PATCH] Esecuzione log step-by-step --- app.py | 114 +- data/raw/openalex_20260601_190230.json | 111089 ++++++++++++++++++++++ data/raw/openalex_20260601_190825.json | 111089 ++++++++++++++++++++++ data/raw/openalex_20260601_191453.json | 111089 ++++++++++++++++++++++ data/raw/pubmed_20260601_190321.xml | 22274 +++++ log_esecuzione.txt | Bin 0 -> 17472 bytes www/services/api_retriever.py | 43 +- www/services/file_extractor.py | 18 +- www/services/parsers.py | 9 + www/services/standardizer.py | 33 + www/services/validation.py | 5 + 11 files changed, 355751 insertions(+), 12 deletions(-) create mode 100644 data/raw/openalex_20260601_190230.json create mode 100644 data/raw/openalex_20260601_190825.json create mode 100644 data/raw/openalex_20260601_191453.json create mode 100644 data/raw/pubmed_20260601_190321.xml create mode 100644 log_esecuzione.txt diff --git a/app.py b/app.py index 97d8c879f..6cbd95361 100644 --- a/app.py +++ b/app.py @@ -49,8 +49,10 @@ # Import necessary libraries for better performance - avoid importing everything import tempfile import os +import logging import requests import functools +import sys from datetime import datetime import pandas as pd import io @@ -71,6 +73,25 @@ from www.services.standardizer import convert2df from www.services.api_retriever import extract_data + +if hasattr(sys.stdout, "reconfigure"): + sys.stdout.reconfigure(encoding="utf-8", errors="replace") +if hasattr(sys.stderr, "reconfigure"): + sys.stderr.reconfigure(encoding="utf-8", errors="replace") + +LOG_FORMAT = "%(asctime)s | %(levelname)-8s | %(name)s | %(message)s" +LOG_DATE_FORMAT = "%Y-%m-%d %H:%M:%S" + +logging.basicConfig( + level=logging.INFO, + format=LOG_FORMAT, + datefmt=LOG_DATE_FORMAT, + stream=sys.stdout, + force=True, +) +logger = logging.getLogger("bibliometrix.app") +logger.info("Avvio applicazione Bibliometrix Shiny") + # MODIFICA rispetto alla versione fornita: le tabelle itables ricevono stringhe # CSS nel parametro `style`. Usare `width:100%;`, non `width=100%;`, evita # errori di parsing nel tab Table. @@ -767,6 +788,11 @@ def select_db(): def mostra(): selected_action = input.select() database_raw = input.database() if selected_action == "1A" else "Sample" + logger.info( + "Avvio caricamento dati da interfaccia: azione=%s, database=%s", + selected_action, + database_raw, + ) # Nasconde la sidebar ui.update_sidebar("sidebar_load_data", show=False) @@ -788,9 +814,15 @@ def mostra(): # file grezzi e file gia' standardizzati. I sample sono gia' # pronti per la dashboard, quindi non passano dalla pipeline ETL. if selected_action == "1C": # Dati Sample di Test + logger.info("Caricamento dataset sample: sources/samples/sample.xlsx") sample_data = pd.read_excel("sources/samples/sample.xlsx") df.set(sample_data) reset_all_analyses() + logger.info( + "Dataset sample caricato: righe=%s, colonne=%s", + len(sample_data), + len(sample_data.columns), + ) # MODIFICA rispetto alla versione fornita: per i file grezzi # usiamo la catena ETL esplicita richiesta: @@ -798,6 +830,7 @@ def mostra(): elif selected_action == "1A": # Dati Locali (Base Level) files = input.Dataset() if files: + logger.info("Import raw data: %s file ricevuti", len(files)) all_standardized_dfs = [] # --- FASE 1, 2, 3: EXTRACT & TRANSFORM PER FILE --- @@ -806,17 +839,37 @@ def mostra(): file_name = file_info["name"] # Extract the extension to pass to standardizer file_ext = os.path.splitext(file_name)[1].lower() + logger.info( + "Elaborazione file: nome=%s, estensione=%s, sorgente=%s", + file_name, + file_ext, + source_upper, + ) try: # Extract raw records from the specific file raw_records = extract_from_file(file_path, source=source_upper) + logger.info( + "Estrazione completata per %s: record_grezzi=%s", + file_name, + len(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) + logger.info( + "Standardizzazione completata per %s: righe=%s, colonne=%s", + file_name, + len(df_part), + len(df_part.columns), + ) + else: + logger.warning("Nessun record grezzo estratto da %s", file_name) except Exception as e: + logger.exception("Errore durante l'elaborazione del file %s", file_name) ui.notification_show(f"Errore di elaborazione per ({file_name}): {e}", type="error", duration=8) # --- FASE 4: MERGE E LOAD --- @@ -824,18 +877,31 @@ def mostra(): try: # Combine all standard dataframes into one standardized_df = pd.concat(all_standardized_dfs, ignore_index=True) + logger.info( + "Merge DataFrame standardizzati completato: righe=%s, colonne=%s", + len(standardized_df), + len(standardized_df.columns), + ) standardized_df = prepare_dataframe_for_app(standardized_df) + logger.info( + "Preparazione per dashboard completata: righe_valide=%s", + len(standardized_df), + ) # --- FASE LOAD: Salvataggio nel reattivo Shiny --- df.set(standardized_df) reset_all_analyses() + logger.info("Dataset caricato nello stato reattivo Shiny") 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 l'aggregazione dei dati: {e}", type="error", duration=10) else: + logger.warning("Import raw data terminato senza DataFrame standardizzati validi") ui.notification_show("Nessun record valido trovato nei file caricati.", type="warning", duration=8) + else: + logger.warning("Import raw data richiesto senza file caricati") elif selected_action == "1B": # File gia' standardizzati # Anche i file gia' esportati vengono fatti passare da @@ -843,23 +909,45 @@ def mostra(): # mantiene lo stesso contratto usato dalle funzioni. files = input.Dataset() if files: + logger.info("Load Bibliometrix file: %s file ricevuti", len(files)) loaded_dfs = [] for file_info in files: try: + logger.info("Lettura file standardizzato: %s", file_info["name"]) loaded = pd.read_excel(file_info["datapath"]) - loaded_dfs.append(convert2df(loaded.to_dict(orient="records"), source="WEB_OF_SCIENCE", validate=True)) + logger.info( + "File standardizzato letto: %s righe, %s colonne", + len(loaded), + len(loaded.columns), + ) + df_part = convert2df(loaded.to_dict(orient="records"), source="WEB_OF_SCIENCE", validate=True) + loaded_dfs.append(df_part) + logger.info( + "Validazione file standardizzato completata: %s righe", + len(df_part), + ) except Exception as e: + logger.exception("Errore nel caricamento del file standardizzato %s", file_info["name"]) ui.notification_show(f"Errore nel caricamento di ({file_info['name']}): {e}", type="error", duration=8) if loaded_dfs: try: standardized_df = pd.concat(loaded_dfs, ignore_index=True) + logger.info( + "Merge file standardizzati completato: righe=%s, colonne=%s", + len(standardized_df), + len(standardized_df.columns), + ) standardized_df = prepare_dataframe_for_app(standardized_df) df.set(standardized_df) reset_all_analyses() + logger.info("File Bibliometrix caricato nello stato reattivo Shiny") ui.notification_show(f"File Bibliometrix caricato: {len(standardized_df)} record.", duration=5) except Exception as e: + logger.exception("Errore durante il caricamento dei file standardizzati") ui.notification_show(f"Errore durante il caricamento: {e}", type="error", duration=10) + else: + logger.warning("Load Bibliometrix richiesto senza file caricati") # -------- ADVICE BUTTON -------- @render.ui @reactive.event(input.advice_modal_completeness) @@ -1013,8 +1101,10 @@ def show_data_table(): def esegui_pipeline_api(): query = input.api_query() source = input.api_source() + logger.info("Avvio pipeline API da interfaccia: source=%s, query=%s", source, query) if not query: + logger.warning("Pipeline API interrotta: query vuota") ui.notification_show("Inserisci una query valida prima di eseguire.", type="warning") return @@ -1023,8 +1113,14 @@ def esegui_pipeline_api(): try: # --- FASE 1: EXTRACT (via API) --- raw_records = extract_data(query=query, source=source) + logger.info( + "Estrazione API completata: source=%s, record_grezzi=%s", + source, + len(raw_records), + ) if not raw_records: + logger.warning("Pipeline API senza risultati compatibili: source=%s, query=%s", source, query) ui.notification_show("Nessun risultato compatibile trovato con la query.", type="warning") return @@ -1037,12 +1133,20 @@ def esegui_pipeline_api(): # `file_type="api"` evita di fingere che i record live siano # CSV/TXT: la standardizzazione sa che arrivano da API. standardized_df = convert2df(raw_records, source=source_mapped, file_type="api", validate=True) + logger.info( + "Standardizzazione API completata: source=%s, righe=%s, colonne=%s", + source_mapped, + len(standardized_df), + len(standardized_df.columns), + ) standardized_df = prepare_dataframe_for_app(standardized_df) + logger.info("Preparazione dashboard per dati API completata: righe_valide=%s", len(standardized_df)) # Assegna i dati al DataFrame reattivo df.set(standardized_df) reset_all_analyses() + logger.info("Dataset API caricato nello stato reattivo Shiny") ui.update_navs("hidden_tabs", selected="import") @@ -1050,6 +1154,7 @@ def esegui_pipeline_api(): ui.notification_show(f"✅ Download API completato! Creati e testati {len(standardized_df)} record uniformati.", duration=5) except Exception as e: + logger.exception("Fallimento del processo API") ui.notification_show(f"❌ Fallimento del processo API: {e}", type="error", duration=15) with ui.nav_panel("None", value="collections"): ui.h3("🚧 Warning: Merge Collection is under construction 🚧") @@ -2858,7 +2963,12 @@ async def handle_user_input(user_input: str): ) for i, table in enumerate(context['table']): prompt += f"\nTable {i}: {table.to_json(orient='records')}" - print(f"Prompt for Gemini: {prompt}") + logger.info( + "Richiesta Gemini preparata: pannello=%s, tabelle=%s, caratteri_prompt=%s", + context["panel"], + len(context["table"]), + len(prompt), + ) if client is not None: try: response = client.models.generate_content( diff --git a/data/raw/openalex_20260601_190230.json b/data/raw/openalex_20260601_190230.json new file mode 100644 index 000000000..0291a5324 --- /dev/null +++ b/data/raw/openalex_20260601_190230.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": 17397.934, + "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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+ + 2817-1705 + + 5 + + 2026 + Jun + 01 + + + JMIR AI + JMIR AI + + Estimating a Physiological Lung Function Score and Biological Sex Using Pulmonary Function Tests and Machine Learning: Retrospective Study. + + e89060 + e89060 + + 10.2196/89060 + + Sex and age have long been known to affect lung function. Several biological variables and anatomical factors may contribute to sex- and age-related differences in pulmonary metrics. + We hypothesized that a machine learning model could be trained to predict a person's lung age and self-reported sex using pulmonary function test data. + We retrospectively analyzed complete pulmonary function tests from 6392 healthy adults across 3 Mayo Clinic regions. Four models of increasing complexity were trained using gradient-boosted machines to predict chronological age and biological sex. Model interpretability was assessed using Shapley additive explanation values and partial dependence plots. Quantile regression was used to estimate reference percentiles for predicted lung age. + The best-performing age model (model 4, inclusive of time-series features) achieved a root mean square error of 7.01 years (95% CI 6.73-7.30) and a mean absolute error of 5.55 years (95% CI 5.32-5.80). The best-performing sex classification model (model 4) achieved an area under the curve of 0.981 (95% CI 0.975-0.988), sensitivity of 91.7% (95% CI 89.0%-93.9%), and specificity of 95.6% (95% CI 93.9%-97%). Key predictors for lung age included residual volume as a percentage of total lung capacity (TLC), forced expiratory volume in 1 second, and alveolar volume. For sex classification, peak expiratory flow, height, and age were among the most influential features. Age-stratified evaluation showed the overestimation of lung age in younger adults and underestimation in older adults. Predicted lung age increased broadly with chronological age, and quantile regression provided normative reference ranges. + Applying artificial intelligence to pulmonary function data allows the prediction of a patient's sex and estimation of lung age. The ability of an artificial intelligence algorithm to determine physiological lung age, with further validation, may serve as a measure of overall respiratory health. + © Patrick W Johnson, Zachary S Quicksall, Jieun Lee, Augustine S Lee, Kaiser G Lim, Victor E Ortega, Shivaram Poigai Arunachalam, Scott A Helgeson. Originally published in JMIR AI (https://ai.jmir.org). + + + + Johnson + Patrick W + PW + 0000-0001-8365-1375 + + Department of Quantitative Health Sciences, Mayo Clinic Hospital, 4500 San Pablo Road S, Jacksonville, FL, 32224, United States, 1 9049536728. + + + + Quicksall + Zachary S + ZS + 0000-0002-8791-0925 + + Department of Quantitative Health Sciences, Mayo Clinic Hospital, 4500 San Pablo Road S, Jacksonville, FL, 32224, United States, 1 9049536728. + + + + Lee + Jieun + J + 0000-0003-2376-4356 + + Department of Quantitative Health Sciences, Mayo Clinic Hospital, 4500 San Pablo Road S, Jacksonville, FL, 32224, United States, 1 9049536728. + + + + Lee + Augustine S + AS + 0000-0001-8018-5145 + + Department of Quantitative Health Sciences, Mayo Clinic Hospital, 4500 San Pablo Road S, Jacksonville, FL, 32224, United States, 1 9049536728. + + + + Lim + Kaiser G + KG + 0000-0002-4551-6559 + + Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic Hospital, Rochester, MN, United States. + + + + Ortega + Victor E + VE + 0000-0001-6361-7372 + + Department of Medicine, Division of Pulmonary Medicine, Mayo Clinic Hospital, Phoenix, AZ, United States. + + + + Arunachalam + Shivaram Poigai + SP + 0000-0003-3251-5415 + + Department of Quantitative Health Sciences, Mayo Clinic Hospital, 4500 San Pablo Road S, Jacksonville, FL, 32224, United States, 1 9049536728. + + + + Helgeson + Scott A + SA + 0000-0001-7590-2293 + + Department of Quantitative Health Sciences, Mayo Clinic Hospital, 4500 San Pablo Road S, Jacksonville, FL, 32224, United States, 1 9049536728. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + Canada + JMIR AI + 9918645789006676 + 2817-1705 + + + age + artificial intelligence + gender + machine learning + pulmonary function test + spirometry + + + + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 12 + 5 + + + 2026 + 5 + 7 + + + 2026 + 5 + 7 + + + 2026 + 6 + 1 + 11 + 53 + + + epublish + + 42224015 + 10.2196/89060 + v5i1e89060 + + + + + + 42223953 + + 2026 + 06 + 01 + +
+ + 1520-5215 + + + 2026 + Jun + 01 + + + The journal of physical chemistry. A + J Phys Chem A + + Benchmarking the UMA Foundation Interatomic Potential for Gas-Phase Chemical Kinetics. + 10.1021/acs.jpca.6c01748 + + Foundation machine-learned interatomic potentials promise rapid access to high-quality potential energy surfaces, but their fitness for gas-phase chemical kinetics remains largely untested. Here we benchmark the Universal Models for Atoms (UMA) foundation model for gas-phase kinetics applications relevant to combustion and atmospheric chemistry using our automated KinBot workflow across 12 representative systems. We compare optimized structures and ZPE-corrected energetics against the parent level of theory, ωB97M-V/def2-TZVPD, and assess pathway discovery, stationary-point fidelity, and downstream kinetic inputs such as conformer ordering and one-dimensional hindered rotor scans. UMA reliably identifies the expected reaction channels over broad regions of chemical space, including pathways that are also a challenge for ab initio methods. Single-point DFT corrections at UMA geometries are an efficient way to improve energies. Overall, we suggest a practical hybrid workflow in which UMA performs inexpensive exploration and sampling, while DFT refinement is reserved for the important regions of the PES. Our results indicate that UMA can substantially accelerate rate coefficient calculations for gas-phase systems, motivating future work on uncertainty quantification, targeted finetuning, and Δ-learning corrections toward a gas-phase kinetics-specialized foundation model. + + + + Kendall + Daniel T + DT + 0009-0000-4486-8723 + + Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States. + + + Department of Chemistry, University of California Los Angeles, Los Angeles, California 90095, United States. + + + + Zádor + Judit + J + 0000-0002-9123-8238 + + Combustion Research Facility, Sandia National Laboratories, Livermore, California 94551-0969, United States. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + United States + J Phys Chem A + 9890903 + 1089-5639 + + IM + + + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 11 + 33 + + + aheadofprint + + 42223953 + 10.1021/acs.jpca.6c01748 + + + + + + 42223918 + + 2026 + 06 + 01 + +
+ + 1520-5851 + + + 2026 + Jun + 01 + + + Environmental science & technology + Environ Sci Technol + + A Hybrid Data-Driven Intensified Mechanistic Modeling Framework for Accurate and Reliable Wastewater Treatment Process Simulation and Control. + 10.1021/acs.est.6c00540 + + Computational modeling guides the wastewater treatment (WWT) design. However, nonlinearities, stochastic influent fluctuations, and complex microbial interactions challenge model predictive accuracy and robustness. Mechanistic models lack fidelity under dynamic conditions due to static parametrization. Data-driven models have emerged to capture such dynamics, but their unconstrained flexibility can produce unstable predictions, which are unacceptable in high-stakes WWT. This study proposes a hybrid data-driven intensified mechanistic modeling (HyDIM) framework, which functionalizes kinetic parameters using a data-driven component to enable adaptive responses, while the mechanistic core ensures predictive robustness. HyDIM was validated in the high-stakes sulfide-laden WWT system, where precise control is critical to mitigating hydrogen sulfide release and optimizing elemental sulfur recovery. Compared to the mechanistic model, HyDIM improved prediction accuracy under dynamic conditions, with R + 2 values for sulfide, sulfur, and sulfate increasing from 0.30, 0.09, and 0.02 to 0.66, 0.77, and 0.74, respectively. Predictive robustness was also enhanced, with extreme deviations reduced by 78.2% relative to the data-driven model. Using HyDIM, a bi-objective optimization strategy achieved stable long-term performance, with 96% sulfide removal and 91% sulfur recovery, a 41% improvement in sulfur recovery over the mechanistic strategy. Overall, this work presents a novel modeling paradigm for reliable decision support and control in WWT. + + + + Xie + Wenlang + W + + Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. + + + + Guo + Jiahua + J + + Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. + + + + Li + Hao + H + + Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. + + + + Liang + Zhengsheng + Z + + Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. + + + School of Biological and Food Engineering, Guangdong University of Education, Guangzhou 510310, China. + + + + Qu + Shen + S + + Center for Energy & Environmental Policy Research, Beijing Institute of Technology, Beijing 100081, China. + + + + Jiang + Feng + F + 0000-0002-8198-7454 + + Guangdong Provincial Key Lab of Environmental Pollution Control and Remediation Technology, School of Environmental Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China. + + + Guangdong Provincial Engineering Research Center of Low-Carbon Technology for Water Pollution Control, Sun Yat-sen University, Guangzhou 510275, China. + + + Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 519000, China. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + United States + Environ Sci Technol + 0213155 + 0013-936X + + IM + + accuracy and interpretability + machine learning + mechanistic model + optimization and control + parameter functionalization + selective sulfide oxidation + + + + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 11 + 24 + + + aheadofprint + + 42223918 + 10.1021/acs.est.6c00540 + + + + + + 42223885 + + 2026 + 06 + 01 + +
+ + 1867-1462 + + + 2026 + Jun + 01 + + + Interdisciplinary sciences, computational life sciences + Interdiscip Sci + + Unveiling the Impact of Copper Metabolism on Epithelial-Mesenchymal Transition of Triple-Negative Breast Cancer: Identification of Therapeutic Targets. + 10.1007/s12539-026-00835-6 + + Triple-negative breast cancer (TNBC) is a biologically aggressive subtype of breast cancer marked by high heterogeneity and poor prognosis. Copper metabolism has been implicated in TNBC progression, but its functional contributions remain insufficiently defined. In this study, we analyzed transcriptomic data from 229 TNBC and adjacent normal samples from The Cancer Genome Atlas (TCGA) to identify 26 differentially expressed copper metabolism-related genes (DEGs-CM). A nine-gene Cox model (HEPHL1, COX7A1, COX4I2, JUN, MAPT, MT1A, AOC3, DCT, AOC2) demonstrated robust prognostic value, with time-dependent AUCs of 0.88, 0.84, and 0.80 at 1, 3, and 5 years. Functional enrichment analyses revealed epithelial-mesenchymal transition (EMT) and angiogenesis pathways enriched in high-risk groups. Four genes (AOC3, COX4I2, COX7A1, JUN) were further identified as copper metabolism-related metastasis genes (CMMRGs) through correlation with metastasis-associated programs. Based on these genes, machine learning classifiers were developed to predict TNBC presence and lymph node metastasis. Classifiers trained on the full dataset achieved consistently high performance, with most models showing AUCs greater than 0.97 (random forest, XGBoost, and AdaBoost classifier) even when using reduced gene panels (26-, 9-, and 4-gene sets), demonstrating stable classification across gene panels. In contrast, performance declined notably in metastasis-specific classification, largely due to the limited number of labeled metastatic samples. Among these, the 9-gene panel yielded the highest test AUCs across most models (gradient boosting machine AUC 0.64), suggesting that it may provide an optimal balance between model complexity and discriminative power, while also highlighting a key limitation related to the restricted sample size and incomplete clinical annotations in the metastasis-specific dataset. Single-cell RNA sequencing confirmed fibroblast-specific enrichment of CMMRGs and associated EMT signatures, suggesting a mechanistic link between copper metabolism, stromal remodeling, and metastasis. These results establish a copper-centered molecular framework for TNBC diagnosis and metastasis prediction, supporting the translational potential of copper metabolism-related genes in clinical applications. + © 2026. International Association of Scientists in the Interdisciplinary Areas. + + + + Zhuang + Kai + K + + Dongguan Key Laboratory of Computer-Aided Drug Design, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China. + + + Guangdong Medical University Key Laboratory of Big Data Mining and Precision Drug Design, Guangdong Provincial Key Laboratory for Research and Development of Natural Drugs, School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China. + + + School of Public Health, Guangdong Medical University, Dongguan, 523808, China. + + + + Yan + Lin + L + + Dongguan Key Laboratory of Computer-Aided Drug Design, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China. + + + + Waqas + Muhammad + M + + Dongguan Key Laboratory of Computer-Aided Drug Design, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China. + + + Guangdong Medical University Key Laboratory of Big Data Mining and Precision Drug Design, Guangdong Provincial Key Laboratory for Research and Development of Natural Drugs, School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China. + + + + Feng + Haixin + H + + Dongguan Key Laboratory of Computer-Aided Drug Design, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China. + + + Guangdong Medical University Key Laboratory of Big Data Mining and Precision Drug Design, Guangdong Provincial Key Laboratory for Research and Development of Natural Drugs, School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China. + + + School of Public Health, Guangdong Medical University, Dongguan, 523808, China. + + + + Tang + Siqi + S + + Guangdong Medical University Key Laboratory of Big Data Mining and Precision Drug Design, Guangdong Provincial Key Laboratory for Research and Development of Natural Drugs, School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China. + + + + He + Haoqi + H + + School of Public Health, Guangdong Medical University, Dongguan, 523808, China. + + + + Yu + Xinshi + X + + Dongguan Key Laboratory of Computer-Aided Drug Design, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China. + + + + Wang + Yanling + Y + + Department of Anesthesiology, the Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China. wangyl5@mail.sysu.edu.cn. + + + + Huang + Zunnan + Z + 0000-0002-5821-703X + + Dongguan Key Laboratory of Computer-Aided Drug Design, the First Dongguan Affiliated Hospital, Guangdong Medical University, Dongguan, 523710, China. zn_huang@gdmu.edu.cn. + + + Guangdong Medical University Key Laboratory of Big Data Mining and Precision Drug Design, Guangdong Provincial Key Laboratory for Research and Development of Natural Drugs, School of Pharmacy, Guangdong Medical University, Dongguan, 523808, China. zn_huang@gdmu.edu.cn. + + + + eng + + + No. PF100-2-02 + Talent Development Foundation of The First Dongguan Affiliated Hospital of Guangdong Medical University + + + + 2019268 + Higher Education Reform Project of Guangdong Province + + + + No. 2025KCXTD018 + Innovation Team Project of Guangdong Province Regular Higher Education Institutions + + + + + Journal Article + + + 2026 + 06 + 01 + +
+ + Germany + Interdiscip Sci + 101515919 + 1867-1462 + + IM + + Copper metabolism + Fibroblast + Machine learning + Metastasis + Prognosis + Transcriptomics + Triple-negative breast cancer + + Declarations. Conflict of interest: Author Zunnan Huang is a member of the Editorial Board for Interdisciplinary Sciences: Computational Life Sciences, and was not involved in the journal’s review of, or decisions related to, this manuscript. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2024 + 11 + 21 + + + 2026 + 3 + 10 + + + 2026 + 3 + 9 + + + 2026 + 6 + 1 + 11 + 22 + + + aheadofprint + + 42223885 + 10.1007/s12539-026-00835-6 + 10.1007/s12539-026-00835-6 + + + + Liu L, Wang Y, Miao L et al (2018) Combination immunotherapy of MUC1 mRNA nano-vaccine and CTLA-4 blockade effectively inhibits growth of triple negative breast cancer. 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+ + 1559-0305 + + + 2026 + Jun + 01 + + + Molecular biotechnology + Mol Biotechnol + + DNA-Based Boolean Logic Gates for Molecular Computation and Biosensing: A Critical Review. + 10.1007/s12033-026-01582-1 + + DNA-based Boolean logic gates represent a transformative platform in molecular computation, providing a bridge between biological processes. The field has advanced toward sophisticated designs incorporating spatiotemporal control. Distinguished by features such as massive parallelism, biocompatibility, energy efficiency, and programmability, DNA computing enables operations beyond the scope of silicon-based technologies. These qualities have enabled applications in biomedical diagnostics, autonomous biosensing, and therapeutic regulation. Furthermore, multi-layered circuit architectures, including neural network like designs, highlight the transition from basic proofs of concept to practical, application-oriented technologies. Key challenges remain particularly signal leakage, gate stability, scalability, and NOT gate constraints. Emerging solutions, such as photocaging for precise activation, enzyme-mediated processing for higher fidelity, and cost-effective array-based DNA synthesis, have improved reliability and scalability. Integration with artificial intelligence, machine learning, and bioelectronics is further advancing hybrid molecular electronic systems capable of coupling biological recognition with robust digital processing. Future progress depends on standardization, reproducibility, and economic scalability. The development of automated design frameworks, rigorous validation, and cost reduction strategies will be central to driving adoption. With immediate potential in diagnostics and environmental monitoring, DNA logic gates are shaping the foundation of molecular intelligence platforms. + © 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. + + + + Srinivasan + Abinaya + A + + Department of Physics, AMET University, Kanathur, Chennai, Tamil Nadu, 603112, India. + + + + Kesavan + Hema + H + + Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, 600089, India. + + + + Rajendran + Umamaheswari + U + + Department of Chemistry, C. Kandaswami Naidu College for Women, Cuddalore, Tamil Nadu, 607001, India. + + + + Palayam + Tejashree + T + + Department of Computer Science and Applications, Faculty of Science and Humanities, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, 600 089, India. + + + + Bakthavatchalam + Senthil + S + + Department of Chemistry, Faculty of Engineering and Technology, SRM Institute of Science and Technology, Ramapuram, Chennai, Tamil Nadu, 600089, India. senthilb4@srmist.edu.in. + + + + Vinayagam + Ramachandran + R + 0000-0003-3568-2358 + + Department of Biotechnology, College of Life and Applied Sciences, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongsangbuk-Do, 38541, Republic of Korea. rambio@yu.ac.kr. + + + Department of Research, Saveetha College of Nursing, Saveetha Institute of Medical and Technical Sciences, Saveetha University, Chennai, Tamil Nadu, India. rambio@yu.ac.kr. + + + + eng + + Journal Article + Review + + + 2026 + 06 + 01 + +
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+ + 1013-9052 + + 38 + 6 + + 2026 + Jun + 01 + + + The Saudi dental journal + Saudi Dent J + + What is the impact of AI-driven prosthodontic planning on dental implant positioning outcomes? A systematic review. + 77 + 10.1007/s44445-026-00184-6 + + This systematic review aimed to evaluate the role of artificial intelligence (AI)-based technologies, including machine learning algorithms, predictive analytics, and AI-assisted surgical guidance, in prosthodontic-driven planning for dental implant positioning, with particular attention to diagnostic accuracy, planning consistency, and workflow optimization. + This systematic review aimed to evaluate the role of artificial intelligence (AI)-based technologies, including machine learning algorithms, predictive analytics, and AI-assisted surgical guidance, in prosthodontic-driven planning for dental implant positioning, with particular attention to diagnostic accuracy, planning consistency, and workflow optimization. + The included studies suggest that AI-based approaches are associated with improved consistency in diagnostic image interpretation, enhanced accuracy of virtual implant planning, and reduced operator-dependent variability. Across predominantly moderate- to high-quality studies, AI-assisted workflows were reported to support more individualized implant positioning by integrating anatomical, prosthetic, and occlusal parameters. However, the evidence remains heterogeneous, and reported benefits are primarily derived from observational and pilot studies rather than large-scale clinical trials. + Current evidence indicates that AI has the potential to support prosthodontic-driven implant planning by enhancing diagnostic standardization and facilitating personalized treatment strategies. Nevertheless, the absence of quantitative synthesis, limited external validation of AI models, and variability in study quality warrant cautious interpretation. Further well-designed clinical studies and standardized validation protocols are required before definitive conclusions regarding clinical outcomes can be drawn. + © 2026. The Author(s). + + + + Sokoli + Dugagjin + D + 0000-0002-4674-4852 + + University of Business and Technology, Prishtina, Kosovo. + + + + Kiseri + Burim + B + 0000-0001-6017-4350 + + University of Business and Technology, Prishtina, Kosovo. + + + + Demjaha + Genc + G + 0000-0001-8090-1711 + + University of Business and Technology, Prishtina, Kosovo. + + + + Asllani-Hoxha + Flora + F + 0009-0004-9019-6082 + + University of Prishtina "Hasan Prishtina", Prishtina, Kosovo. + + + + Bimbashi + Venera + V + 0000-0002-7212-4972 + + Department of Prosthodontics, University Dentistry Clinical Center of Kosovo, Prishtinë, Republic of Kosovo. vbimbashi@gmail.com. + + + Department of Prosthodontics Alma Mater Europaea, Campus College Rezonanca, Prishtinë, Republic of Kosovo. vbimbashi@gmail.com. + + + + eng + + Journal Article + Review + + + 2026 + 06 + 01 + +
+ + Saudi Arabia + Saudi Dent J + 9313603 + 1013-9052 + + + Artificial intelligence + Dental implants + Implant positioning + Machine learning + Predictive analytics + Prosthodontics + + Declarations. Competing interests: The authors declare no conflict of interest. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 7 + 21 + + + 2026 + 4 + 26 + + + 2026 + 6 + 1 + 11 + 21 + + + epublish + + 42223831 + 10.1007/s44445-026-00184-6 + 10.1007/s44445-026-00184-6 + + + + Blake P, Thompson R (2018) AI-enhanced prosthodontics: from theory to practice. J Dent Sci 13(3):209–217 + + + Chen L, Xu X, Wang G (2019) Blockchain and AI in dental records management: ensuring security and traceability. J Dent Tech 12(4):245–252 + + + Feng C, Wu J, Zhao L (2021) Enhancing prosthodontic outcomes with AI: a clinical perspective. Prosthet Dent Int 68(3):197–205 + + + Garcia M, Fernandez E, Lopez J (2020) AI in prosthodontics: current applications and future directions. J Clin Exp Dent 12(5) + + + Hwang Y, Choi J, Kim S (2020) The future of AI in prosthodontics: emerging trends and innovations. J Dent Res 99(4):356–363 + + + Johnson T, Blake M (2018) Machine learning algorithms in dental radiography: enhancing diagnostic precision. Dentomaxillofac Radiol 47(2):20180012 + + + Kim SJ, Park SJ, Kang JH (2017) Predictive analytics in dental implantology: utilizing AI for outcome forecasting. J Prosthet Dent 118(5):633–640 + + + Lee JH, Kim DH, Lee SM et al (2019) AI-driven analysis of CBCT images for optimal dental implant placement. J Dent Res 98(5):456–462 + + + Liu Z, Huang Y, Xie J (2020) AI-based predictive models for dental implant longevity. Int J Comput Dent 23(3):165–172 + + + Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, Shamseer L, Tetzlaff JM, Akl EA, Brennan SE, Chou R, Glanville J, Grimshaw JM, Hróbjartsson A, Lalu MM, Li T, Loder EW, Mayo-Wilson E, McDonald S, McGuinness LA, Stewart LA, Thomas J, Tricco AC, Welch VA, Whiting P, Moher D (2021) The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ 372:n71. https://doi.org/10.1136/bmj.n71 . 2021 Mar 29 + + + Patel A, Singh K, Gupta R (2020) AI-assisted surgical guide design for dental implants: accuracy and clinical outcomes. J Oral Maxillofac Surg 78(9):1543–1550 + + + Reddy S, Thomas L, Zhang Q (2019) The impact of AI on prosthodontic treatment outcomes: a review. J Prosthodont 28(6):543–549 + + + Roberts D, Adams G (2016) The role of the clinician in AI-driven dental implantology. J Dent 55(2):120–126 + + + Simmons A, Rivera C, Huynh T (2017) AI-guided dental implant placement: benefits and challenges. Clin implant Dent Relat Res. 19(6):972–980 + + + Singh V, Patel K (2019) The role of AI in modern prosthodontics: a review. Int J Prosthodont 32(5):523–529 + + + Thomas M, Lee S, Patel N (2019) Application of AI in prosthodontics and implant dentistry: a systematic review. J Prosthet Dent 122(4):314–321 + + + Wang Y, Zhang H, Tang Z (2020) Integration of AI, and AR in dental education: a novel approach to training. Eur J Dent Educ 24(1):34–41 + + + Yang B, Sun Z, Shi J et al (2020) Artificial intelligence in dental imaging: diagnosis and prediction of implant outcomes. Clin Oral Implants Res 31(6):567–575 + + + Yasui T, Kimura M, Nagamine H, Yajima S, Karube T, Sato H, Asoda S, Hara S, Onizawa K (2021) Influence of prostate cancer status on the prevalence of medication-related osteonecrosis of the jaw. Oral Surg Oral Med Oral Pathol Oral Radiol 131(3):312–318. https://doi.org/10.1016/j.oooo.2020.12.018 + + 10.1016/j.oooo.2020.12.018 + 33431343 + + + + Zhang Y, Li H (2018) AI in treatment planning for dental implants: a comprehensive review. Int J Prosthodont 31(4):372–380 + + + Zhou X, Li Q, Wu W (2020) AI in dental implantology: a comprehensive review of its applications and benefits. Clin Oral Implants Res 31(7):735–743 + + + + + + + 42223828 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1534-6315 + + 26 + 1 + + 2026 + Jun + 01 + + + Current allergy and asthma reports + Curr Allergy Asthma Rep + + Targeting Type 2 and Non-type 2 Asthma: Emerging Biologics and Personalized Strategies. + 41 + 10.1007/s11882-026-01283-4 + + Asthma continues to pose a serious global health issue affecting billions of people and causing significant morbidity. It is immunologically heterogeneous disease, classified as Type 2 (Th2/ILC2-mediated, eosinophilic) or Type 1 (Th1-mediated, neutrophilic, steroid-resistant) inflammation. This review aims to evaluates current biologic therapies, emerging strategies, and challenges in asthma management and highlights the challenges and future directions in personalized asthma management. + Current biologics for Type 2 asthma like anti-IgE (omalizumab), anti-IL-5 (mepolizumab, reslizumab, benralizumab), anti-IL-4/IL-13 (dupilumab) and anti-TSLP (tezepelumab) were effectively controlling severe eosinophilic asthma. Emerging therapies for Type 1 and mixed phenotypes include anti-TNF-α agents, CXCR2 antagonists, IL-17 blockers, JAK-STAT inhibitors and microbiome-based approaches and upstream epithelial cytokine-targeting therapies such as anti-TSLP agents. Dual or broad-spectrum strategies, such as bispecific antibodies and endotype-guided biologic selection offer more targeted interventions. Despite these advances, challenges persist regarding high costs, limited accessibility, absence of robust biomarkers, and potential risks of immunosuppression. Biologics have transformed severe Type 2 asthma management, but effective treatments for Type 1 and steroid-resistant asthma remain limited. Future directions involve multi-omics, machine learning and gene therapy to optimize personalized therapy and develop inclusive strategies for the diverse inflammatory endotypes. + © 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. + + + + J + Geetha + G + + Department of Microbiology, Auxilium College (Autonomous), Vellore, 6, India. + + + + Ranganathan + Nathiya + N + + Department of Microbiology, Auxilium College (Autonomous), Vellore, 6, India. nathiyamb@auxiliumcollege.edu.in. + + + + A S + Vickram + V + + Department of Biotechnology, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, SIMATS, Chennai, India. + + + + Chopra + Hitesh + H + + Centre for Research Impact & Outcome, Chitkara College of Pharmacy, Chitkara University, Rajpura, Punjab, 140401, India. chopraontheride@gmail.com. + + + + eng + + Journal Article + Review + + + 2026 + 06 + 01 + +
+ + United States + Curr Allergy Asthma Rep + 101096440 + 1529-7322 + + + + 0 + Biological Products + + + 0 + Anti-Asthmatic Agents + + + 0 + Cytokines + + + IM + + + Humans + + + Asthma + immunology + drug therapy + therapy + + + Precision Medicine + methods + + + Biological Products + therapeutic use + + + Anti-Asthmatic Agents + therapeutic use + + + Cytokines + antagonists & inhibitors + + + Th2 Cells + immunology + + + + Asthma + Biologics + JAK-STAT inhibitors + Type 1 inflammation + Type 2 inflammation + + Declarations. Ethics Approval and Consent to Participate: This article is a review and does not report any original data involving human participants or animals. Therefore, ethical approval was not required. Consent for Publication: All authors have reviewed and approved the final version of the manuscript and consent to its submission for publication. 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+ + 1348-2246 + + + 2026 + Jun + 01 + + + Analytical sciences : the international journal of the Japan Society for Analytical Chemistry + Anal Sci + + A general framework for extrapolation-aware prediction reliability in forward and inverse analyses of Gaussian mixture regression models. + 10.1007/s44211-026-00924-y + + Gaussian mixture regression (GMR) and direct inverse analysis (DIA) are powerful tools for molecular, material, and process design, but their reliability decreases when extrapolation occurs beyond the training data. To address this challenge, an index of extrapolation (IoE) that evaluates extrapolation potential and prediction trustworthiness in both forward and inverse analyses of GMR models is proposed. Defined as the negative logarithm of the probability density function, the IoE enables stable identification of interpolation-like and extrapolation-like regions. Validation with numerical simulations confirmed its ability to distinguish these regions, while applications to data sets of organic solubility, inorganic superconductivity, and batch processes demonstrated that high-IoE regions correspond to reduced prediction reliability, where prediction errors may show larger dispersion. The proposed IoE provides a practical and generalizable framework to assess applicability domains, enhancing the interpretability and reliability of machine learning models. This advancement strengthens data-driven design and supports more efficient discovery of novel molecules, materials, and processes. + © 2026. The Author(s). + + + + Kaneko + Hiromasa + H + 0000-0001-8367-6476 + + Department of Applied Chemistry, School of Science and Technology, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, Kanagawa, 214-8571, Japan. hkaneko@meiji.ac.jp. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + Switzerland + Anal Sci + 8511078 + 0910-6340 + + IM + + Applicability domain + Direct inverse analysis + Gaussian mixture regression + Prediction reliability + Probability density + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 1 + 14 + + + 2026 + 5 + 7 + + + 2026 + 6 + 1 + 11 + 20 + + + aheadofprint + + 42223792 + 10.1007/s44211-026-00924-y + 10.1007/s44211-026-00924-y + + + + S. Greenhill, S. Rana, S. Gupta, P. Vellanki, S. Venkatesh, Bayesian optimization for adaptive experimental design: a review. IEEE Access 8, 13937–13948 (2020) + + 10.1109/ACCESS.2020.2966228 + + + + S. Amini, I. Vannieuwenhuyse, A. Morales-Hernández, Constrained Bayesian optimization: a review. IEEE Access 13, 1581–1593 (2025) + + 10.1109/ACCESS.2024.3522876 + + + + H. Kaneko, Adaptive design of experiments based on gaussian mixture regression. Chemometr. Intell. Lab. Syst. 208, 104226 (2021) + + 10.1016/j.chemolab.2020.104226 + + + + H. Kaneko, True gaussian mixture regression and genetic algorithm-based optimization with constraints for direct inverse analysis. Sci. Technol. Adv. Mater. Methods 2(1), 14–22 (2022) + + + N. Shimizu, H. Kaneko, Direct inverse analysis based on gaussian mixture regression for multiple objective variables in material design. Mater. Des. 196, 109168 (2020) + + 10.1016/j.matdes.2020.109168 + + + + https://scikit-learn.org/stable/modules/generated/sklearn.mixture.GaussianMixture.html . Accessed 19 Oct 2025 + + + https://scikit-learn.org/stable/modules/generated/sklearn.mixture.BayesianGaussianMixture.html . Accessed 19 Oct 2025 + + + https://datachemeng.com/dcekit/ . Accessed 19 Oct 2025 + + + H. Kaneko, Extended gaussian mixture regression for forward and inverse analysis. Chemometr. Intell. Lab. Syst. 213, 104325 (2021) + + 10.1016/j.chemolab.2021.104325 + + + + T.J. Hou, K. Xia, W. Zhang, X.J. Xu, ADME evaluation in drug discovery. 4. Prediction of aqueous solubility based on atom contribution approach. J. Chem. Inf. Comput. Sci. 44, 266–275 (2004) + + 10.1021/ci034184n + 14741036 + + + + H. Kam, A data-driven statistical model for predicting the critical temperature of a superconductor. Comput. Mater. Sci. 154, 346–354 (2018) + + 10.1016/j.commatsci.2018.07.052 + + + + https://archive.ics.uci.edu/ml/datasets/Superconductivty+Data# . Accessed 19 Oct 2025 + + + http://www.rdkit.org/ . Accessed 19 Oct 2025 + + + H. Kaneko, Direct prediction of the batch time and process variable profiles using batch process data based on different batch times. Comput. Chem. Eng. 169, 108072 (2023) + + 10.1016/j.compchemeng.2022.108072 + + + + https://github.com/jckantor/CBE30338 . Accessed 19 Oct 2025 + + + + + + + 42223752 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1863-2491 + + 20 + 1 + + 2026 + Jun + 01 + + + Journal of robotic surgery + J Robot Surg + + Interpretable machine learning model for predicting operative difficulty in robotic total mesorectal excision for mid-low rectal cancer. + 563 + 10.1007/s11701-026-03522-2 + + Precise preoperative prediction of surgical complexity in robot-assisted total mesorectal excision (R-TME) is essential for optimizing surgical strategies. The current study aimed to construct an interpretable machine learning (ML) model to anticipate operative difficulty in sphincter-preserving R-TME. Retrospective data from 449 patients diagnosed with mid-to-low rectal cancer undergoing R-TME at Center A were analyzed. The dataset was partitioned randomly into training (n = 314) and internal validation (n = 135) groups using a 7:3 ratio. Additionally, external validation was conducted with a prospective cohort (n = 100) from multiple centers. Operative difficulty was quantified using a scoring system ranging from 0 to 13. Feature selection was performed employing Least Absolute Shrinkage and Selection Operator (LASSO) regression, followed by evaluation of five ML algorithms. Model accuracy and robustness were measured using metrics including the Area Under the Curve (AUC), calibration plots, decision curve analysis (DCA), and supplementary indicators. Interpretability of the predictive model was enhanced using SHapley Additive exPlanations (SHAP). Critical predictive factors comprised BMI, neoadjuvant treatment status, clinical staging, tumor distance to the anal verge, interspinous diameter, lateral mesorectal width, posterior mesorectal thickness, and two specific pelvic angle measurements. Among evaluated ML methods, Gradient Boosting Machine (GBM) demonstrated superior performance, achieving an AUC of 0.874 in the training cohort and 0.835 in internal validation. Calibration plots and DCA indicated excellent robustness and significant clinical applicability of the GBM model. Furthermore, external validation presented an AUC of 0.809, confirming the model's generalizability. SHAP-based analysis delineated individual predictor impacts, facilitating the creation of an accessible online prediction instrument. This study successfully established and externally validated a transparent ML-based model to predict operative challenges in sphincter-preserving R-TME. This model can effectively aid surgeons in identifying anticipated difficulties prior to surgery, thereby enhancing clinical decision-making and improving surgical planning. + © 2026. The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature. + + + + Mao + Haoran + H + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Ma + Shuai + S + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Li + Yang + Y + + Department of General Surgery, State Key Lab of Digestive Health, National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China. + + + + Sun + Xuan + X + + Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, China. + + + + Fu + Yongqi + Y + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Zhang + Huaju + H + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Zhou + Quanbo + Q + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Guo + Shihao + S + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Duan + Xiaofei + X + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Li + Tengyu + T + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Sun + Haifeng + H + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Zhang + Hairong + H + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Zhang + Zhiyong + Z + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Wang + Guixian + G + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Hu + Junhong + J + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Li + Zhen + Z + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. + + + + Sun + Zhenqiang + Z + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. fccsunzq@zzu.edu.cn. + + + + Jing + Changqing + C + + Department of Gastrointestinal Surgery, Affiliated to Shandong Provincial Hospital, Shandong First Medical University, Jinan, 250021, China. jingchangqing@sdfmu.edu.cn. + + + Key Laboratory of Engineering of Shandong Province, Jinan, 250021, China. jingchangqing@sdfmu.edu.cn. + + + Medical Science and Technology Innovation Center, Shandong First Medical University, Shandong Academy of Medical Sciences, Jinan, 250021, China. jingchangqing@sdfmu.edu.cn. + + + Department of Gastrointestinal Surgery, Shandong Provincial Hospital, Shandong University, Jinan, 250021, China. jingchangqing@sdfmu.edu.cn. + + + + Wang + Quan + Q + + Department of Gastrocolorectal Surgery, General Surgery Center, The First Hospital of Jilin University, Changchun, 130021, China. wquan@jlu.edu.cn. + + + + Yuan + Weitang + W + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. yuanweitang@zzu.edu.cn. + + + + Yao + Hongwei + H + + Department of General Surgery, State Key Lab of Digestive Health, National Clinical Research Center for Digestive Diseases, Beijing Friendship Hospital, Capital Medical University, Beijing, 100050, China. yaohongwei@ccmu.edu.cn. + + + + Lian + Yugui + Y + + Department of Colorectal Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450000, China. fcclianyg@zzu.edu.cn. + + + + eng + + + 252300420535 + Natural Science Foundation of Henan Province + + + + U2004112 + National Natural Science Foundation of China + + + + 25A320072 + Key Scientific Research Project of Colleges and Universities in Henan Province + + + + 262102311098 + Henan Provincial Science and Technology Research Project + + + + + Journal Article + + + 2026 + 06 + 01 + +
+ + England + J Robot Surg + 101300401 + 1863-2483 + + IM + + + Humans + + + Rectal Neoplasms + surgery + pathology + + + Predictive Learning Models + + + Robotic Surgical Procedures + methods + + + Retrospective Studies + + + Female + + + Male + + + Middle Aged + + + Machine Learning + + + Colorectal Surgical Procedures + + + Rectum + surgery + + + Aged + + + Boosting Machine Learning Algorithms + + + + Machine learning + Operative difficulty prediction + Rectal cancer + Robot-assisted total mesorectal excision + SHAP + + Declarations. Consent for publication: This study integrated both retrospective and prospective data. Specifically, the requirement for informed consent for the retrospective cohort was waived by the Ethics Committee. For the prospective external validation cohort, written informed consent was obtained from all participants prior to enrollment. All data were anonymized to protect participant confidentiality and privacy. Competing interests: The authors declare no competing interests. Ethical approval: The study protocol was approved by the Ethics Committee of The First Affiliated Hospital of Zhengzhou University and the respective ethics committees of all participating centers. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 4 + 24 + + + 2026 + 5 + 15 + + + 2026 + 6 + 1 + 11 + 19 + + + epublish + + 42223752 + 10.1007/s11701-026-03522-2 + 10.1007/s11701-026-03522-2 + + + + Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I et al (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 71(3):209–249 + + 33538338 + + + + Heald RJ, Husband EM, Ryall RD (1982) The mesorectum in rectal cancer surgery–the clue to pelvic recurrence? 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Sci Rep 15(1):26931 + + 10.1038/s41598-025-12644-w + 40707590 + 12290098 + + + + Hanani AA, Donmez TB, Kutlu M, Mansour M (2025) Predicting thyroid cancer recurrence using supervised CatBoost: A SHAP-based explainable AI approach. Med (Baltim) 104(22):e42667 + + 10.1097/MD.0000000000042667 + + + + Hajjar M, Aldabbagh G, Albaradei S (2025) Interpretable Multi-Cancer Early Detection Using SHAP-Based Machine Learning on Tumor-Educated Platelet RNA. Diagnostics (Basel) 15(17):2216 + + + Agha RA, Borrelli MR, Vella-Baldacchino M, Thavayogan R, Orgill DP, STROCSS Group (2017) The STROCSS statement: Strengthening the Reporting of Cohort Studies in Surgery. Int J Surg 46:198–202 + + 10.1016/j.ijsu.2017.08.586 + 28890409 + 6040889 + + + + Ye C, Wang X, Sun Y, Deng Y, Huang Y et al (2021) A nomogram predicting the difficulty of laparoscopic surgery for rectal cancer. 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Colorectal Dis 7(3):232–240 + + 10.1111/j.1463-1318.2005.00819.x + 15859960 + + + + Shuqin Zang Q, Chen S, Jiang et al (2024) Analysis of the lateral mesorectum and its clinical value based on rectal high-resolution MRI[J/OL]. Chin J Colorec Dis (Electron Ed) 13(04):312–320 + + + Maliske S, Chau J, Ginader T, Byrn J, Bhatia S et al (2019) Timing of surgery following neoadjuvant chemoradiation in rectal cancer: a retrospective analysis from an academic medical center. J Gastrointest Oncol 10(4):597–604 + + 10.21037/jgo.2019.02.02 + 31392039 + 6657321 + + + + Lefevre JH, Mineur L, Kotti S, Rullier E, Rouanet P et al (2016) Effect of Interval (7 or 11 weeks) Between Neoadjuvant Radiochemotherapy and Surgery on Complete Pathologic Response in Rectal Cancer: A Multicenter, Randomized, Controlled Trial (GRECCAR-6). 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+ + 1591-9528 + + + 2026 + May + 31 + + + Clinical and experimental medicine + Clin Exp Med + + Morpho-functional plasticity of tumour-associated macrophages: linking cell shape, ultrastructure, and immune regulation in cancer. + 10.1007/s10238-026-02190-8 + + Macrophages are highly adaptable immune cells that play dual roles in the tumor microenvironment (TME), either promoting or restraining tumor progression. Their polarization into M1 and M2 tumor-associated macrophage (TAM) phenotypes is classically defined by molecular and biochemical cues. Recent evidence shows that morphology also significantly influences the macrophage function and behavior. This review highlights the correlation between macrophage shape and function, and how cytoskeletal remodeling, extracellular matrix (ECM) stiffness, and topographical patterning regulate macrophage polarization under in vitro and in vivo conditions. We further discuss the ultrastructural features of distinct phenotypes and their mechanistic links to tumor growth and metastasis. Therapeutic strategies that target morphology through ECM modulation, engineered biomaterials, and nanoparticle platforms are examined alongside modern imaging and machine learning tools that enable precise morphometric analysis. Finally, we address the translational progress and limitations of current 2D versus 3D model systems. Overall, this review emphasizes macrophage morphology as a critical yet underexplored determinant of immune function and tumor dynamics. + © 2026. The Author(s). + + + + Kizhakkanoodan + Keerthana Suresh + KS + + Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, India. + + + + Inigo + Vidhula Jessica + VJ + + Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, India. + + + + Koulmane Laxminarayana + Sindhura Lakshmi + SL + + Department of Pathology, Kasturba Medical College, Manipal Academy of Higher Education, Manipal, India. + + + + Seetharam + Raviraja N + RN + + Chandigarh University, Mohali, 140413, Punjab, India. + + + + Guruprasad + Kanive Parashiva + KP + + Department of Ageing Research, Manipal School of Life Sciences, Manipal Academy of Higher Education, Manipal, India. + + + + Guru + Bharath Raja + BR + 0000-0003-2542-7740 + + Manipal Institute of Technology (MIT), Manipal Academy of Higher Education, Manipal, India. bharath.guru@manipal.edu. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 31 + +
+ + Italy + Clin Exp Med + 100973405 + 1591-8890 + + IM + + Cytoskeleton + ECM stiffness + Macrophage morphology + Macrophage polarization + Tumor microenvironment + Tumor-associated macrophages (TAMs) + + Declarations. Competing interests: The authors declare no competing interests. Ethics declaration: Not applicable. Informed consent: Not applicable. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 4 + 6 + + + 2026 + 5 + 11 + + + 2026 + 6 + 1 + 11 + 19 + + + aheadofprint + + 42223750 + 10.1007/s10238-026-02190-8 + 10.1007/s10238-026-02190-8 + + + + + + 42223738 + + 2026 + 06 + 01 + +
+ + 2730-6011 + + + 2026 + Jun + 01 + + + Discover oncology + Discov Oncol + + Integrative multi-omics analysis identifies a circadian rhythm-associated gene signature for prognosis and therapeutic stratification in lung adenocarcinoma. + 10.1007/s12672-026-05279-4 + + Circadian rhythm disruption is increasingly implicated in tumor progression and therapy resistance. However, its prognostic value and impact on the tumor immune microenvironment in lung adenocarcinoma (LUAD) remain unclear. This study aimed to develop a robust circadian rhythm-based gene signature to improve risk stratification and inform personalized therapeutic strategies in LUAD. + We integrated multi-omics data from over 900 LUAD patients across TCGA and GEO databases. A circadian rhythm-related gene prognostic signature (CRGPS) was constructed from candidate genes using ten machine learning algorithms and validated externally. The tumor immune microenvironment, mutation landscape, and therapy response were analyzed using bioinformatics algorithms. Single-cell RNA sequencing data were utilized to explore gene expression at cellular resolution. + A 10-gene CRGPS was developed, which effectively stratified patients into high- and low-risk groups with significantly divergent overall survival in both training and validation cohorts. Unsupervised clustering based on these genes revealed two molecular subtypes (C1 and C2) with distinct characteristics. The two subgroups exhibited significant differences in terms of the tumor immune microenvironment, clinical prognosis, and therapeutic sensitivity. Single-cell analysis localized key signature genes to endothelial and epithelial cells and revealed enhanced endothelial-endothelial communication. + The CRGPS serves as a robust prognostic framework for risk stratification and provides hypothesis-generating insights into therapeutic vulnerabilities based on in silico predictions of immune profiles and drug sensitivity, pending prospective validation. + © 2026. The Author(s). + + + + Lu + Yiheng + Y + + Medical School of Chinese PLA, Beijing, China. + + + Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. + + + Department of Oncology, Hainan Hospital of Chinese PLA General Hospital, Hainan, Sanya, China. + + + + Dong + Yi + Y + + Medical School of Chinese PLA, Beijing, China. + + + Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. + + + + Sun + Cheng + C + + Medical School of Chinese PLA, Beijing, China. + + + Department of Neurosurgery, Chinese PLA General Hospital, Beijing, 100853, China. + + + + Yang + Fan + F + + Medical School of Chinese PLA, Beijing, China. + + + + Xiao + Shuyan + S + + Medical School of Chinese PLA, Beijing, China. + + + + Liu + Yining + Y + + Medical School of Chinese PLA, Beijing, China. + + + + Han + Xiao + X + + Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. hanxiaoplagh@126.com. + + + + Liu + Qiaowei + Q + + Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. dr_liuqiaowei@126.com. + + + + Hu + Yi + Y + + Department of Oncology, the First Medical Center, Chinese PLA General Hospital, Beijing, China. huyi301zlxb@sina.com. + + + Senior Department of Oncology, Chinese PLA General Hospital, Beijing, China. huyi301zlxb@sina.com. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + United States + Discov Oncol + 101775142 + 2730-6011 + + + Circadian rhythm + Immunotherapy response + Lung adenocarcinoma + Machine learning + Molecular subtyping + Multi-omics + Prognostic signature + Tumor immune microenvironment + scRNA sequencing + + Declarations. Ethics approval and consent to participate: This study utilized publicly available, fully anonymized data and did not involve any direct interaction with human participants or collection of personal information; therefore, no consent to participate was required. Consent for publication: Not applicable. This study did not include any identifiable patient data, clinical photographs, or case reports requiring individual consent for publication. Competing interests: The authors declare no competing interests. Conflict of interest: The authors declare no conflicts of interest. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 8 + + + 2026 + 5 + 20 + + + 2026 + 6 + 1 + 11 + 18 + + + aheadofprint + + 42223738 + 10.1007/s12672-026-05279-4 + 10.1007/s12672-026-05279-4 + + + + + + 42223736 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2150-5551 + + 18 + 1 + + 2026 + Jun + 01 + + + Nano-micro letters + Nanomicro Lett + + High Polarity Doping of CoFe Layered Hydroxides: Bifunctional and Corrosion-Resistant Anion Exchange Membrane Seawater Electrolyzers. + 393 + 10.1007/s40820-026-02230-8 + + Green hydrogen production through seawater electrolysis is a promising strategy, although challenges such as sluggish oxygen evolution reaction (OER) kinetics and chlorine (Cl-) corrosion hinder its practical applicability. A novel fluorine (F)-doped cobalt (Co) and iron (Fe) layered metal hydroxide (F-CoFe LMH-8) is developed as a robust bifunctional catalyst achieving 81.23 and 265.5 mV at 10 mA cm-2 for hydrogen evolution reaction (HER) and oxygen evolution reaction (OER), respectively. Theoretical and experimental studies demonstrate that the F-doping modulates the electronic structure, effectively tuning Fe sites toward a high-spin configuration that optimizes binding energies and induces a chlorophobic effect that repel corrosive (Cl-) ions. Notably, the F-CoFe LMH-8( +|| -) bifunctional catalyst integrated anion exchange membrane water electrolyzer (AEMWE) exhibited outstanding performance for continuous H2 production, achieves a current density of 1.2 A cm-2 in 1 M KOH, 1.02 A cm-2 in 1 M KOH + 0.5 M NaCl, and 1 A cm-2 in 1 M KOH in seawater at 2.3 V. Furthermore, a long short-term memory-based machine learning model was employed to forecast and predict the stability of F-CoFe LMH-8. This approach provides a comprehensive pathway for heuristic design of durable, chlorophobic, and advanced electrocatalyst for seawater-based AEMWE and large-scale hydrogen production. + © 2026. The Author(s). + + + + Saj + Anandhan Ayyappan + AA + + Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea. + + + + Prabhakaran + Sampath + S + + Division of AI Convergence Research, Korea Institute of Ceramic Engineering and Technology (KICET), Jinju, 52851, Republic of Korea. + + + + Rasool + Mohsin + M + + University of Ulsan, Ulsan, 44776, Republic of Korea. + + + + Bhunia + Kousik + K + + Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea. + + + + Lee + Dongho + D + + University of Ulsan, Ulsan, 44776, Republic of Korea. + + + + Ko + Hyunseok + H + + Division of AI Convergence Research, Korea Institute of Ceramic Engineering and Technology (KICET), Jinju, 52851, Republic of Korea. + + + + Dongale + Tukaram D + TD + + Computational Electronics and Nanoscience Research Laboratory, School of Nanoscience and Biotechnology, Shivaji University, Kolhapur, Maharashtra, 416004, India. + + + + Perumalsamy + Muthukumar + M + + Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea. + + + + Raaju Sundhar + Arul Saravanan + AS + + Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea. + + + + Kim + Do Hwan + DH + + Division of Science Education and Institute of Fusion Science, Department of Energy Storage/Conversion Engineering (BK21 FOUR), Jeonbuk National University, Jeonju, Jeonbuk, 54896, Republic of Korea. dhk201@jbnu.ac.kr. + + + + Kim + Sang Jae + SJ + + Nanomaterials & System Lab, Major of Mechatronics Engineering, Faculty of Applied Energy System, Jeju National University, Jeju, 63243, South Korea. kimsangj@jejunu.ac.kr. + + + Nanomaterials & System Lab, Major of Mechanical System Engineering, College of Engineering, Jeju National University, Jeju, 63243, South Korea. kimsangj@jejunu.ac.kr. + + + Green Hydrogen Glocal Leading Research Center (gH2-RC), Jeju National University, Jeju, 63243, Republic of Korea. kimsangj@jejunu.ac.kr. + + + Research Institute of New Energy Industry (RINEI), Jeju National University, Jeju, 63243, South Korea. kimsangj@jejunu.ac.kr. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + Germany + Nanomicro Lett + 101727940 + 2150-5551 + + + Corrosion resistance + F-doping + H2 generation + Long short-term memory (LSTM) + Seawater electrolysis + + Declarations. Conflict of interest: 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 26 + + + 2026 + 4 + 29 + + + 2026 + 6 + 1 + 11 + 18 + + + epublish + + 42223736 + 10.1007/s40820-026-02230-8 + 10.1007/s40820-026-02230-8 + + + + Y. Liu, Y. Wang, P. Fornasiero, G. Tian, P. Strasser et al., Long-term durability of seawater electrolysis for hydrogen: from catalysts to systems. Angew. Chem. Int. Ed. 63(47), e202412087 (2024). https://doi.org/10.1002/anie.202412087 + + 10.1002/anie.202412087 + + + + F.-Y. Gao, P.-C. Yu, M.-R. Gao, Seawater electrolysis technologies for green hydrogen production: challenges and opportunities. 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+ + 1438-7948 + + 26 + 1 + + 2026 + Jun + 01 + + + Functional & integrative genomics + Funct Integr Genomics + + Exploring the potential targets and mechanisms of artemisinin in the treatment of diabetic kidney disease using network pharmacology and molecular docking. + 120 + 10.1007/s10142-026-01894-0 + + Diabetic kidney disease (DKD) is a secondary glomerular disease caused by diabetes, and its incidence is increasing annually. Artemisinin is an organic compound with multiple pharmacological effects. This study aims to investigate the potential therapeutic effects of artemisinin on DKD and its molecular mechanisms. Common targets were identified using network pharmacology, transcriptome data from the Gene Expression Omnibus (GEO) database, and three machine learning algorithms, including Least Absolute Shrinkage and Selection Operator (LASSO), Support Vector Machine Recursive Feature Elimination (SVM-RFE), and Random Forest (RF). Target expression profiles were examined through single-cell sequencing data analysis. Molecular docking and cellular assays, including western blot, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), terminal deoxynucleotidyl transferase-mediated dUTP nick end labeling (TUNEL), enzyme-linked immunosorbent assay (ELISA), and reactive oxygen species (ROS) detection, validated the effects of artemisinin and Fms related receptor tyrosine kinase 1(FLT1) on human renal glomerular endothelial cells (HRGECs) under a high-glucose (HG) injury model. A total of 64 common targets between artemisinin and DKD were identified, and machine learning identified FLT1 as a key target. Single-cell analysis revealed high FLT1 expression in DKD endothelial cells. More importantly, artemisinin exhibited stable binding to FLT1. HG reduced FLT1 expression and cell viability in HRGECs. Besides, HG-induced decrease in cell viability, increase in proportion of TUNEL-positive cells, elevation in the levels of interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α) and lipid ROS production were reversed by FLT1 overexpression or artemisinin treatment, and these protective effects of artemisinin were significantly attenuated upon FLT1 knockdown. Artemisinin alleviates HG-induced HRGEC injury by restoring FLT1 expression, providing a potential therapeutic target for DKD treatment. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Li + Hongning + H + + Department of Endocrinology, Lishui District People's Hospital, No. 85 Chongwen Road Nanjing, Jiangsu, 211200, China. + + + + Cai + Xiangzeng + X + + Department of Endocrinology, Lishui District People's Hospital, No. 85 Chongwen Road Nanjing, Jiangsu, 211200, China. + + + + Yu + Shuqin + S + + Department of Endocrinology, Lishui District People's Hospital, No. 85 Chongwen Road Nanjing, Jiangsu, 211200, China. yushuqinzx20@163.com. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + Germany + Funct Integr Genomics + 100939343 + 1438-793X + + + + 0 + Artemisinins + + + 9RMU91N5K2 + artemisinin + + + 0 + Reactive Oxygen Species + + + IY9XDZ35W2 + Glucose + + + IM + + + Humans + + + Artemisinins + pharmacology + therapeutic use + chemistry + + + Molecular Docking Simulation + + + Diabetic Nephropathies + drug therapy + metabolism + genetics + pathology + + + Network Pharmacology + + + Reactive Oxygen Species + metabolism + + + Glucose + toxicity + + + Apoptosis + drug effects + + + + Artemisinin + Diabetic kidney disease + Fms related receptor tyrosine kinase 1 + Machine learning + Molecular docking + + Declarations. Ethics approval and consent to participate: Not applicable. 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+ + 1936-0568 + + 20 + 1 + + 2026 + Jun + 01 + + + Head and neck pathology + Head Neck Pathol + + Predicting Metastatic Potential from Histopathological Whole Slide Images of Primary Tumors in Head and Neck Squamous Cell Carcinoma Using Attention-Based Deep Learning and Signaling Pathway Alterations. + 59 + 10.1007/s12105-026-01920-4 + + Head and Neck Squamous Cell Carcinoma (HNSCC) ranks as the 6th most prevalent cancer worldwide, imposing a significant burden on global healthcare systems. The progression to metastasis poses a substantial challenge in the clinical management of HNSCC and is a leading cause of patient mortality. Patients diagnosed with distant metastasis (DM) often have limited treatment options and predominantly receive palliative care. Therefore, timely detection of HNSCC patients at risk of metastasis is paramount for proactive intervention and close monitoring, offering the potential for early-stage treatments and enhancing patient survival rates. + The pre-processing of Whole Slide Images (WSIs) involved patching, segmentation, background removal, and color normalization. A novel diagnostic framework was introduced using deep learning to predict the likelihood of DM in patients with HNSCC. The proposed model integrates a Self-Attention mechanism with a Residual Network (ResNet), enhancing performance and addressing challenges arising from patch-level variability during pre-processing. + The proposed model outperformed convolutional neural networks (CNNs), ensemble methods, and classical classifiers such as multilayer perceptrons (MLPs) and logistic regression. It achieved an accuracy of 82.14%, an AUC of 0.80, and an F1 score of 0.82 in predicting metastasis. Robustness and generalizability were further confirmed through stratified 10-fold cross-validation on the TCGA-HNSCC dataset and external validation on the TCGA-ESCA dataset, demonstrating consistent performance across diverse patient subgroups and cancer types. + The proposed framework showcases potential of using Deep Learning in enhancing clinical decision-making. These findings highlight the use of attention-based models when integrated with deep residual networks offering insights for oncological diagnostics. Future work may focus on improving explainability and interpretability, incorporating multimodal data, and generalizing the approach to other cancer types. + © 2026. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature. + + + + Majeed + Tabasum + T + + Centre for Artificial Intelligence, Islamic University of Science and Technology, Awantipora, India. tabasum.majeed@iust.ac.in. + + + + Aalam + Syed Wajid + SW + + Centre for Artificial Intelligence, Islamic University of Science and Technology, Awantipora, India. + + + + Masoodi + Tariq Ahmad + TA + + Human Immunology Department, Research Branch, Sidra Medicine, Doha, Qatar. + + + + Ahanger + Abdul Basit + AB + + Department of Computer Science, Islamic University of Science and Technology, Awantipora, India. + + + + Misra + Deepika + D + + Department of Oral Pathology and Microbiology, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India. + + + + Nayyar + Vivek + V + + Department of Oral Pathology and Microbiology, Centre for Dental Education and Research, All India Institute of Medical Sciences, New Delhi, India. + + + + Khan + Meraj Alam + MA + + DigiBiomics Inc, 3052 Owls Foot Drive, Mississauga, ON, Canada. + + + + Bhat + Ajaz A + AA + + Department of Human Genetics-Precision Medicine in Diabetes, Obesity and Cancer Program, Sidra Medicine, Doha, Qatar. + + + + Bhat + Muzafar Rasool + MR + + Department of Computer Science, Islamic University of Science and Technology, Awantipora, India. + + + + Macha + Muzafar Ahmad + MA + + Watson-Crick Centre for Molecular Medicine, Islamic University of Science and Technology, Awantipora, India. muzafar.macha@iust.ac.in. + + + + Assad + Assif + A + + Centre for Artificial Intelligence, Islamic University of Science and Technology, Awantipora, India. assif.assad@islamicuniversity.edu.in. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
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+ + 1528-0691 + + + 2026 + Jun + 01 + + + Chemical record (New York, N.Y.) + Chem Rec + + Heterojunction Engineering of Electrodes and Membranes in Redox Flow Batteries. + + e70183 + e70183 + + 10.1002/tcr.70183 + + Redox flow batteries (RFBs) have demonstrated considerable potential for application prospects in the domain of large-scale energy storage. This potential can be attributed to their notable advantages, which include the decoupling of power and capacity, a prolonged cycle life, and a high level of safety. However, the enhancement of their energy efficiency and power density remains significantly constrained by critical issues, including sluggish reaction kinetics, the shuttle effect of active species, and inadequate interfacial stability. In recent years, heterojunctions have emerged as an efficient interfacial engineering strategy, providing novel solutions for enhancing the key performance of RFBs. This enhancement is achieved through the reconstruction of the interfacial electronic structure and modulation of the built-in electric field. This study provides a comprehensive review of the research progress related to heterojunctions in RFBs. The study focuses on summarizing the construction strategies and action mechanisms of heterojunction electrodes and heterojunction ion exchange membranes. It also discusses the regulatory effects of these materials on redox reaction kinetics, ion transport behavior, and cycling stability of RFBs. Additionally, the development trend of machine learning-assisted rational design of heterojunctions is discussed in detail. The objective of this work is to establish a theoretical framework and to elucidate the research insights pertinent to the precision design and engineering application of heterojunction materials in high-performance RFBs. + © 2026 The Chemical Society of Japan and Wiley‐VCH GmbH. + + + + Dan + Xiaoxin + X + + Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, School of Resources, Environment and Materials, Guangxi University, Nanning, China. + + + + Li + Zanbin + Z + + Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, School of Resources, Environment and Materials, Guangxi University, Nanning, China. + + + + Chen + Mingying + M + + Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, School of Resources, Environment and Materials, Guangxi University, Nanning, China. + + + + Hou + Xianghua + X + 0000-0001-8973-5297 + + Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, School of Resources, Environment and Materials, Guangxi University, Nanning, China. + + + + Luo + Yang + Y + 0000-0003-4536-3457 + + Department of Physics, City University of Hong Kong, Hong Kong, China. + + + China Huadian Corporation Ltd. (CHD), Beijing, China. + + + + Zhuo + Longchao + L + 0000-0003-3396-3893 + + School of Materials Science and Engineering, Xi'an University of Technology, Xi'an, China. + + + + Liu + Wenxian + W + 0000-0002-2808-1864 + + College of Materials Science and Engineering, Zhejiang University of Technology, Hangzhou, China. + + + + Feng + Ligang + L + 0000-0001-9879-0773 + + Faculty of Materials Science and Engineering, Kunming University of Science and Technology, Kunming, China. + + + + Liu + Xijun + X + 0009-0007-7379-3901 + + Guangxi Key Laboratory of Processing for Non-ferrous Metals and Featured Materials, MOE Key Laboratory of New Processing Technology for Nonferrous Metals and Materials, School of Resources, Environment and Materials, Guangxi University, Nanning, China. + + + + eng + + + 22469002 + National Natural Science Foundation of China + + + + + Journal Article + Review + + + 2026 + 06 + 01 + +
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+ + 1573-4951 + + 40 + 1 + + 2026 + Jun + 01 + + + Journal of computer-aided molecular design + J Comput Aided Mol Des + + Reproducibility, validation, and failure modes across classical and AI-driven molecular docking. + 137 + 10.1007/s10822-026-00849-8 + + Molecular docking is indispensable across computer‑aided discovery. However, its conclusions often hinge more on modeling choices than on software brand or nominal score. In this review, we argue that docking should be treated explicitly as conditional modeling whose interpretability depends on structural provenance, ligand‑state definition, search‑space design, and validation under deployment‑relevant conditions. This framework is intended as a practical reference for evaluating docking rigor across both academic and applied workflows. We highlight recurrent failure modes, cross‑target score comparisons under non‑comparable states, over‑read scores as affinities, under‑modeled solvation/flexibility, and uncritical use of predicted structures, and show how AI both exacerbates and mitigates these risks. We then propose best practices for modern validation (self‑docking as necessary but insufficient; cross‑docking, decoys, apo/predicted structures, and out‑of‑distribution tests as essential complements) and offer a concise FAIR reporting checklist enabling reuse and audit. Looking forward, we contend that the most valuable advances are those that improve deployment‑relevant reliability, pose plausibility, enrich screening, and enhance robustness across receptor uncertainty, rather than tool novelty alone. This review reframes docking success from "obtaining a pose and a score" to earning confidence through transparent workflows and evaluation aligned with real use. In this context, the term "AI-driven" does not imply replacing physics-based docking, but rather expanding the workflow landscape in which classical and machine learning approaches coexist. This review, therefore, treats docking as a unified decision framework spanning both paradigms, with emphasis on how validation, generalization, and reproducibility requirements evolve in AI-assisted workflows. + © 2026. The Author(s). + + + + Kittelson + Katiana Simões + KS + 0000-0002-8082-4469 + + Department of Pharmaceutical Sciences, College of Health and Human Sciences, North Dakota State University, Fargo, ND, USA. + + + + Martins + Allana C F + ACF + 0000-0001-5647-2187 + + Department of Pharmaceutical Sciences, College of Health and Human Sciences, North Dakota State University, Fargo, ND, USA. + + + + Santos + Raquel Possemozer + RP + 0009-0005-1807-175X + + Department of Pharmaceutical Sciences, College of Health and Human Sciences, North Dakota State University, Fargo, ND, USA. + + + + Celante + Gizele + G + 0000-0003-2654-4332 + + Department of Pharmaceutical Sciences, College of Health and Human Sciences, North Dakota State University, Fargo, ND, USA. gizele.celante@ndsu.edu. + + + + da Silva Gomes + Roberto + R + 0000-0002-8075-9716 + + Department of Pharmaceutical Sciences, College of Health and Human Sciences, North Dakota State University, Fargo, ND, USA. roberto.gomes@ndsu.edu. + + + + eng + + + 3P20GM109024 + GM + NIGMS NIH HHS + United States + + + 3P20GM109024 + GM + NIGMS NIH HHS + United States + + + 3P20GM109024 + GM + NIGMS NIH HHS + United States + + + + Journal Article + Review + + + 2026 + 06 + 01 + +
+ + Netherlands + J Comput Aided Mol Des + 8710425 + 0920-654X + + + + 0 + Ligands + + + 0 + Proteins + + + IM + + + Molecular Docking Simulation + methods + + + Ligands + + + Reproducibility of Results + + + Software + + + Artificial Intelligence + + + Proteins + chemistry + + + Humans + + + Protein Binding + + + Computer-Aided Design + + + + Computer-aided drug design + Docking validation + Machine learning + Molecular docking + Protein-ligand interactions + Reproducibility benchmarking + Scoring functions + Virtual screening + + Declarations. 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J Chem Inf Model 58(7):1441–1458. https://doi.org/10.1021/acs.jcim.8b00228 + + 10.1021/acs.jcim.8b00228 + 29890081 + + + + Blaschke T, Arús-Pous J, Chen H, Margreitter C, Tyrchan C, Engkvist O, Papadopoulos K, Patronov A (2020) REINVENT 2.0: an AI tool for de novo drug design. J Chem Inf Model 60(12):5918–5922. https://doi.org/10.1021/ACS.JCIM.0C00915 + + 10.1021/ACS.JCIM.0C00915 + 33118816 + + + + Özçelik R, Brinkmann H, Criscuolo E, Grisoni F (2025) Generative deep learning for de novo drug designa chemical space odyssey. J Chem Inf Model 65(14):7352–7372. https://doi.org/10.1021/ACS.JCIM.5C00641 + + 10.1021/ACS.JCIM.5C00641 + 40632942 + 12308806 + + + + Jamal QMS, Khan MI, Alharbi AH, Ahmad V, Yadav BS (2023) Identification of natural compounds of the apple as inhibitors against cholinesterase for the treatment of Alzheimer’s disease: an in silico molecular docking simulation and ADMET study. Nutrients 15(7):1579. https://doi.org/10.3390/NU15071579/S1 + + 10.3390/NU15071579/S1 + 37049419 + 10097405 + + + + Luo L, Zhong A, Wang Q, Zheng T, Structure-Based Pharmacophore Modeling V, Screening (2021) Molecular docking, ADMET, and molecular dynamics (MD) simulation of potential inhibitors of PD-L1 from the library of marine natural products. Mar Drugs 20(1):29. https://doi.org/10.3390/md20010029 + + 10.3390/md20010029 + 35049884 + 8777599 + + + + Melancon K, Pliushcheuskaya P, Meiler J, Künze G (2024) Targeting Ion channels with ultra-large library screening for hit discovery. Front Mol Neurosci 16. https://doi.org/10.3389/fnmol.2023.1336004 + + + Yang C, Yang Z, Tong K, Wang J, Yang W, Yu R, Jiang F, Ji Y (2022) Homology modeling and molecular docking simulation of martentoxin as a specific inhibitor of the BK channel. Ann Transl Med 10(2):71–71. https://doi.org/10.21037/atm-21-6967 + + 10.21037/atm-21-6967 + 35282126 + 8848368 + + + + Minibaeva G, Ivanova A, Polishchuk P, EasyDock (2023) Customizable and scalable docking tool. J Cheminform 15(1):102. https://doi.org/10.1186/s13321-023-00772-2 + + 10.1186/s13321-023-00772-2 + 37915072 + 10619229 + + + + El-Sayed AF, Aboulthana WM, Sherief MA, El-Bassyouni GT, Mousa SM, Synthesis (2024) Structural, molecular docking, and in vitro biological activities of Cu-doped ZnO nanomaterials. Sci Rep 14(1):9027. https://doi.org/10.1038/s41598-024-59088-2 + + 10.1038/s41598-024-59088-2 + 38641640 + 11031592 + + + + Chen W, He H, Wang J, Wang J, Chang C-EA (2023) Uncovering water effects in protein-ligand recognition: importance in the second hydration shell and binding kinetics. Phys Chem Chem Phys 25(3):2098–2109. https://doi.org/10.1039/d2cp04584b + + 10.1039/d2cp04584b + 36562309 + 9970846 + + + + Chen T, Shu X, Zhou H, Beckford FA, Misir M (2023) Algorithm selection for protein–ligand docking: strategies and analysis on ACE. Sci Rep 13(1):8219. https://doi.org/10.1038/s41598-023-35132-5 + + 10.1038/s41598-023-35132-5 + 37217655 + 10201035 + + + + Li Y, Zhan R, Rao J, Liu M, Sang P, Zeng X, Zheng M, Li X, Yang L (2026) Structure-informed machine learning for drug discovery: a task-centric perspective. Brief Bioinform 27(1). https://doi.org/10.1093/bib/bbag081 + + + Wang Y, Jiao Q, Wang J, Cai X, Zhao W, Cui X (2023) Prediction of protein-ligand binding affinity with deep learning. Comput Struct Biotechnol J 21:5796–5806. https://doi.org/10.1016/J.CSBJ.2023.11.009 + + 10.1016/J.CSBJ.2023.11.009 + 38213884 + 10782002 + + + + Bayarri G, Andrio P, Gelpí JL, Hospital A, Orozco M (2024) Using interactive jupyter notebooks and bioconda for FAIR and reproducible biomolecular simulation workflows. PLoS Comput Biol 20(6):e1012173. https://doi.org/10.1371/JOURNAL.PCBI.1012173 + + 10.1371/JOURNAL.PCBI.1012173 + 38900779 + 11189206 + + + + Vitalis A, Winkler S, Zhang Y, Widmer J, Caflisch A (2025) A FAIR-compliant management solution for molecular simulation trajectories. J Chem Inf Model 65(5):2443–2455. https://doi.org/10.1021/ACS.JCIM.4C01301 + + 10.1021/ACS.JCIM.4C01301 + 39977657 + 11898051 + + + + Wilkinson MD, Dumontier M, Sansone S-A, Bonino da Silva Santos LO, Prieto M, Batista D, McQuilton P, Kuhn T, Rocca-Serra P, Crosas M, Schultes E (2019) Evaluating FAIR maturity through a scalable, automated, community-governed framework. Sci Data 6(1):174. https://doi.org/10.1038/s41597-019-0184-5 + + 10.1038/s41597-019-0184-5 + 31541130 + 6754447 + + + + + + + + 42223509 + + 2026 + 06 + 01 + +
+ + 1741-0134 + + + 2026 + Jun + 01 + + + Protein engineering, design & selection : PEDS + Protein Eng Des Sel + + Examining Selection Dynamics and Limitations in Multi-round Protein Selection of High Diversity Libraries. + gzag012 + 10.1093/protein/gzag012 + + Proteins and peptides underpin essential biological functions and technological applications, from targeting disease-relevant interactions to providing broad enzymatic activities. However, engineering molecules with desired properties remains difficult, owing to complex sequence-structure-function relationships and the lack of data on specific systems. Experimental selection strategies, including directed evolution, phage display, and mRNA display, address this challenge by leveraging high diversity libraries and iterative enrichment under defined selection pressures. This allows for the identification of candidates without requiring extensive prior knowledge, and can generate extensive datasets for use in machine learning. While many selection systems exist, comparisons across different selection approaches are hindered by the lack of a unifying analytical framework. Here, we developed a toolset of broadly applicable analyses for assessing selection dynamics in multi-round or multi-condition experiments, ranging from position level analysis of sequence properties to full sequence space mappings through protein language model embeddings. Performing analyses across different systems, we identify desirable traits in selection experiments including enrichment of distinct sequence patterns and correlation between enrichment and final desired functions. Notably, even under weak selection regimes with all sequences <1% frequency, functional sequences (e.g., 70nM IC50 binder to SARS-CoV-2 main protease) are still consistently enriched. We also find repeated selections of the same starting library can help differentiate selection effects of varying conditions (e.g., different delivery of metal ligand) from system noise. These findings, along with the toolset, can be used to guide experimental design, interpretation, and troubleshooting across protein and peptide discovery platforms. + © The Author(s) 2026. Published by Oxford University Press. + + + + Chen + John Z + JZ + 0000-0002-2628-5820 + + Research School of Chemistry, Australian National University, Canberra, Australia. + + + ARC Centre of Excellence in Synthetic Biology, Australia. + + + + Gall + Barnabas + B + + Research School of Chemistry, Australian National University, Canberra, Australia. + + + ARC Centre for Innovations in Peptide & Protein Science, Australia. + + + + Lu + Tommy Y + TY + + ARC Centre for Innovations in Peptide & Protein Science, Australia. + + + School of Chemistry, The University of Sydney, Sydney, Australia. + + + + Heslop + Isabella + I + + Research School of Chemistry, Australian National University, Canberra, Australia. + + + + Hesselson + Daniel + D + + Centenary Institute and Faculty of Medicine and Health, Charles Perkins Centre, The University of Sydney, Sydney, Australia. + + + + Nitsche + Christoph + C + + Research School of Chemistry, Australian National University, Canberra, Australia. + + + + Tham + Wai-Hong + WH + + The Walter and Eliza Hall Institute of Medical Research, Parkville, Australia. + + + Department of Medical Biology, The University of Melbourne, Melbourne, Australia. + + + + Payne + Richard J + RJ + + ARC Centre for Innovations in Peptide & Protein Science, Australia. + + + School of Chemistry, The University of Sydney, Sydney, Australia. + + + + Jackson + Colin J + CJ + 0000-0001-6150-3822 + + Research School of Chemistry, Australian National University, Canberra, Australia. + + + ARC Centre of Excellence in Synthetic Biology, Australia. + + + ARC Centre for Innovations in Peptide & Protein Science, Australia. + + + Research School of Biology, Australian National University, Canberra, Australia. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + England + Protein Eng Des Sel + 101186484 + 1741-0126 + + IM + + Computational pipeline + Deep sequencing + Protein engineering + Protein libraries + Selection + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 12 + 20 + + + 2026 + 4 + 24 + + + 2026 + 5 + 9 + + + 2026 + 6 + 1 + 11 + 4 + + + aheadofprint + + 42223509 + 10.1093/protein/gzag012 + 8699381 + + + + + + 42223485 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1433-0350 + + 42 + 1 + + 2026 + Jun + 01 + + + Child's nervous system : ChNS : official journal of the International Society for Pediatric Neurosurgery + Childs Nerv Syst + + The role of machine learning, deep learning, and MRI findings in the classification of pediatric posterior fossa tumors. + 237 + 10.1007/s00381-026-07316-7 + + Pediatric posterior fossa tumors represent a major subset of childhood central nervous system neoplasms; however, overlapping MRI features often hinder accurate non-invasive characterization. This study aimed to evaluate machine learning (ML) and deep learning (DL) models for classifying these tumors using MRI-derived radiomic features. + This retrospective study analyzed MRI data from 63 pediatric patients with confirmed posterior fossa tumors, including 21 medulloblastoma (MB), 20 pilocytic astrocytoma (PA), 11 ependymoma (EP), and 11 diffuse midline glioma (DMG) cases. T2-weighted imaging, diffusion-weighted imaging, and apparent diffusion coefficient sequences showing the best single-model performance were used to construct gradient boosting machine (GBM), decision tree (DT), and random forest (RF) models and their ensemble combinations. Model performance was evaluated using standard classification metrics. A ResNet101V2-based DL model was developed using multiple MRI sequences. ML models were validated using fivefold cross-validation, whereas the DL model was trained using a 67/33 train-test split with data augmentation. + The RF + GBM ensemble achieved the highest ML performance, with an overall accuracy of 78%, and showed the strongest classification for MB and PA, whereas EP and DMG remained difficult to distinguish. The DL model demonstrated high performance on T1-weighted imaging and contrast-enhanced T1-weighted imaging, achieving accuracies of 98% and 96%, respectively, but showed lower performance on diffusion-based sequences. + ML and DL approaches improve MRI-based classification of pediatric posterior fossa tumors; however, accurate differentiation of EP and DMG remains challenging. These findings support the potential of AI-driven methods as clinically relevant, non-invasive decision-support tools in pediatric neuro-oncology. + © 2026. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. + + + + Çapar + Emre + E + + Department of Radiology, School of Medicine, Erciyes University, Kayseri, Turkey. + + + + Karaman + Zehra Filiz + ZF + + Department of Radiology, School of Medicine, Erciyes University, Kayseri, Turkey. dr.fkaraman@gmail.com. + + + + Ünalan + Serhat + S + + Department of Biomedical Engineering, Yildiz Technical University, Istanbul, Turkey. + + + + Aytan + Miray + M + + Department of Biomedical Engineering, Yildiz Technical University, Istanbul, Turkey. + + + + Topkaraoğlu + Mehmet Kutalmış + MK + + Department of Biomedical Engineering, Yildiz Technical University, Istanbul, Turkey. + + + + Akkuş + Tekin + T + + Department of Biomedical Sciences and Engineering, Koc University, Istanbul, Turkey. + + + + Keserci + Ali + A + + Department of Biomedical Engineering, Erciyes University, Kayseri, Turkey. + + + + Coşkun + Abdulhakim + A + + Department of Radiology, School of Medicine, Erciyes University, Kayseri, Turkey. + + + + Keserci + Bilgin + B + + Department of Biomedical Engineering, Yildiz Technical University, Istanbul, Turkey. + + + Department of Radiology, School of Medical Sciences, Universiti Sains Malaysia, Kelantan, Malaysia. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + Germany + Childs Nerv Syst + 8503227 + 0256-7040 + + IM + + + Humans + + + Child + + + Magnetic Resonance Imaging + methods + + + Machine Learning + + + Infratentorial Neoplasms + diagnostic imaging + classification + + + Female + + + Deep Learning + + + Retrospective Studies + + + Male + + + Child, Preschool + + + Adolescent + + + Random Forest + + + Radiomics + + + Classification Algorithms + + + Predictive Learning Models + + + Boosting Machine Learning Algorithms + + + + Artificial intelligence + Magnetic resonance imaging + Pediatric neuro-oncology + Radiomics + + Declarations. Competing interests: The authors declare no competing interests. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 12 + 19 + + + 2026 + 5 + 9 + + + 2026 + 6 + 1 + 11 + 3 + + + epublish + + 42223485 + 10.1007/s00381-026-07316-7 + 10.1007/s00381-026-07316-7 + + + + Gurney JG, Kadan-Lottick N (2001) Brain and other central nervous system tumors: rates, trends, and epidemiology. Curr Opin Oncol 13:160–166. https://doi.org/10.1097/00001622-200105000-00005 + + 10.1097/00001622-200105000-00005 + 11307058 + + + + Ostrom QT, Gittleman H, Kruchko C et al (2019) Primary brain and other central nervous system tumors in Appalachia: regional differences in incidence, mortality, and survival. J Neurooncol 142:27–38. https://doi.org/10.1007/s11060-018-03073-z + + 10.1007/s11060-018-03073-z + 30543034 + + + + Ostrom QT, Cioffi G, Waite K et al (2021) CBTRUS statistical report: primary brain and other central nervous system tumors diagnosed in the United States in 2014–2018. Neuro Oncol 23:iii1–iii105. https://doi.org/10.1093/neuonc/noab200 + + 10.1093/neuonc/noab200 + 34608945 + 8491279 + + + + Orphanidou‐Vlachou E, Vlachos N, Davies NP et al (2014) Texture analysis of T1‐and T2‐weighted MR images and use of probabilistic neural network to discriminate posterior fossa tumours in children. NMR Biomed 27:632–639. https://doi.org/10.1002/nbm.3099 + + 10.1002/nbm.3099 + 24729528 + 4529665 + + + + Di Ieva A, Russo C, Liu S et al (2021) Application of deep learning for automatic segmentation of brain tumors on magnetic resonance imaging: a heuristic approach in the clinical scenario. Neuroradiology 63:1253–1262. https://doi.org/10.1007/s00234-021-02649-3 + + 10.1007/s00234-021-02649-3 + 33501512 + + + + Bodalal Z, Trebeschi S, Nguyen-Kim TDL et al (2019) Radiogenomics: bridging imaging and genomics. Abdom Radiol 44:1960–1984. https://doi.org/10.1007/s00261-019-02028-w + + 10.1007/s00261-019-02028-w + + + + Gillies RJ, Kinahan PE, Hricak H (2016) Radiomics: images are more than pictures, they are data. Radiology 278:563–577. https://doi.org/10.1148/radiol.2015151169 + + 10.1148/radiol.2015151169 + 26579733 + + + + Grech‐Sollars M, Hales PW, Miyazaki K et al (2015) Multi‐centre reproducibility of diffusion MRI parameters for clinical sequences in the brain. NMR Biomed 28:468–485. https://doi.org/10.1002/nbm.3269 + + 10.1002/nbm.3269 + 25802212 + 4403968 + + + + Formentin C, Joaquim AF, Ghizoni E (2023) Posterior fossa tumors in children: current insights. Eur J Pediatr 182:4833–4850. https://doi.org/10.1007/s00431-023-05189-5 + + 10.1007/s00431-023-05189-5 + 37679511 + + + + Plaza MJ, Borja MJ, Altman N et al (2013) Conventional and advanced MRI features of pediatric intracranial tumors: posterior fossa and suprasellar tumors. AJR Am J Roentgenol 200:1115–1124. https://doi.org/10.2214/AJR.12.9725 + + 10.2214/AJR.12.9725 + 23617498 + + + + Panigrahy A, Blüml S (2009) Neuroimaging of pediatric brain tumors: from basic to advanced magnetic resonance imaging (MRI). J Child Neurol 24:1343–1365. https://doi.org/10.1177/0883073809342129 + + 10.1177/0883073809342129 + 19841424 + + + + Poretti A, Meoded A, Huisman TA (2012) Neuroimaging of pediatric posterior fossa tumors including review of the literature. J Magn Reson Imaging 35:32–47. https://doi.org/10.1002/jmri.22722 + + 10.1002/jmri.22722 + 21989968 + + + + Koral K, Mathis D, Gimi B et al (2013) Common pediatric cerebellar tumors: correlation between cell densities and apparent diffusion coefficient metrics. Radiology 268:532–537. https://doi.org/10.1148/radiol.13121362 + + 10.1148/radiol.13121362 + 23564715 + + + + Hohm A, Karremann M, Gielen GH et al (2022) Magnetic resonance imaging characteristics of molecular subgroups in pediatric H3 K27M mutant diffuse midline glioma. Clin Neuroradiol 32:249–258. https://doi.org/10.1007/s00062-021-01120-3 + + 10.1007/s00062-021-01120-3 + 34919158 + + + + Szychot E, Youssef A, Ganeshan B et al (2021) Predicting outcome in childhood diffuse midline gliomas using magnetic resonance imaging based texture analysis. J Neuroradiol 48:243–247. https://doi.org/10.1016/j.neurad.2020.02.005 + + 10.1016/j.neurad.2020.02.005 + 32184119 + + + + Tanyel T, Nadarajan C, Duc NM et al (2023) Deciphering machine learning decisions to distinguish between posterior fossa tumor types using MRI features: what do the data tell us? Cancers (Basel) 15:4015. https://doi.org/10.3390/cancers15164015 + + 10.3390/cancers15164015 + 37627043 + 10452543 + + + + Zitouni S, Koc G, Doganay S et al (2017) Apparent diffusion coefficient in differentiation of pediatric posterior fossa tumors. Jpn J Radiol 35:448–453. https://doi.org/10.1007/s11604-017-0652-9 + + 10.1007/s11604-017-0652-9 + 28550357 + + + + Gutierrez DR, Awwad A, Meijer L et al (2014) Metrics and textural features of MRI diffusion to improve classification of pediatric posterior fossa tumors. AJNR Am J Neuroradiol 35:1009–1015. https://doi.org/10.3174/ajnr.A3784 + + 10.3174/ajnr.A3784 + + + + Novak J, Zarinabad N, Rose H et al (2021) Classification of paediatric brain tumours by diffusion weighted imaging and machine learning. Sci Rep 11:2987. https://doi.org/10.1038/s41598-021-82214-3 + + 10.1038/s41598-021-82214-3 + 33542327 + 7862387 + + + + Fetit AE, Novak J, Peet AC et al (2015) Three‐dimensional textural features of conventional MRI improve diagnostic classification of childhood brain tumours. NMR Biomed 28:1174–1184. https://doi.org/10.1002/nbm.3353 + + 10.1002/nbm.3353 + 26256809 + + + + Zhou H, Hu R, Tang O et al (2020) Automatic machine learning to differentiate pediatric posterior fossa tumors on routine MR imaging. AJNR Am J Neuroradiol 41:1279–1285. https://doi.org/10.3174/ajnr.A6621 + + 10.3174/ajnr.A6621 + 32661052 + 7357647 + + + + Ünalan S, Onat FE, Şahin F, et al. (2024) Revealing distinctive insights: machine learning-enhanced ensembled MRI sequences for pediatric posterior fossa tumor classification. https://doi.org/10.21203/rs.3.rs-3831163/v1 + + + Garaba A, Ponzio F, Grasso EA et al (2023) Radiomics for differentiation of pediatric posterior fossa tumors: a meta-analysis and systematic review of the literature. Cancers (Basel) 15:5891. https://doi.org/10.3390/cancers15245891 + + 10.3390/cancers15245891 + 38136435 + 10742196 + + + + Yearley AG, Blitz SE, Patel RV et al (2022) Machine learning in the classification of pediatric posterior fossa tumors: a systematic review. Cancers (Basel) 14:5608. https://doi.org/10.3390/cancers14225608 + + 10.3390/cancers14225608 + 36428701 + 9688156 + + + + Quon J, Bala W, Chen L et al (2020) Deep learning for pediatric posterior fossa tumor detection and classification: a multi-institutional study. AJNR Am J Neuroradiol 41:1718–1725. https://doi.org/10.3174/ajnr.A6704 + + 10.3174/ajnr.A6704 + 32816765 + 7583118 + + + + Yimit Y, Yasin P, Hao Y et al (2025) MRI-based deep learning with clinical and imaging features to differentiate medulloblastoma and ependymoma in children. Front Mol Biosci 12:1570860. https://doi.org/10.3389/fmolb.2025.1570860 + + 10.3389/fmolb.2025.1570860 + 40356719 + 12066621 + + + + Yimit Y, Yasin P, Tuersun A et al (2024) Multiparametric MRI-based interpretable radiomics machine learning model differentiates medulloblastoma and ependymoma in children: a two-center study. Acad Radiol 31:3384–3396. https://doi.org/10.1016/j.acra.2024.02.040 + + 10.1016/j.acra.2024.02.040 + 38508934 + + + + Button KS, Ioannidis JP, Mokrysz C et al (2013) Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14:365–376. https://doi.org/10.1038/nrn3475 + + 10.1038/nrn3475 + 23571845 + + + + + + + + 42223356 + + 2026 + 06 + 01 + +
+ + 1535-3907 + + + 2026 + Jun + 01 + + + Journal of proteome research + J Proteome Res + + In-Depth Plasma Proteomics Identifies SNCA as a Discriminating Biomarker of PDAC. + 10.1021/acs.jproteome.5c01286 + + Pancreatic ductal adenocarcinoma (PDAC) carries a poor prognosis largely due to lack of efficient diagnostic means. We applied mass spectrometry-based high-coverage plasma proteome analysis accompanying with machine learning to develop a 5-protein diagnostic model: SNCA, GCLC, LBP, ALAD, and SORD. For differentiating PDAC from healthy controls (HCs), this model reached an area under the curve (AUC) of 0.973 with 100% sensitivity and 85% specificity in the discovery cohort, with nested cross-validation confirming robust performance (AUC = 0.958). Further validation centered on SNCA achieved an AUC of 0.835 in an independent validation cohort. SNCA also showed good diagnostic performance in PDAC patients with low CA19-9 level (AUC = 0.868), underscoring its potential value for this subgroup. Overall, these findings indicate SNCA as a promising candidate plasma diagnostic marker for PDAC. + + + + Yu + Jiaqi + J + 0009-0001-5047-7531 + + School of Basic Medicine, Qingdao University, Qingdao, Shandong Province 266071, China. + + + + Luo + Wenhui + W + + The Second Department of Hepatobiliary Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong Province 264000, China. + + + + Zhang + Jian + J + + Beijing Proteome Research Center, Beijing 102206, China. + + + State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Academy of Military Medical Sciences, Beijing 102206, China. + + + + Qin + Baoyi + B + + Beijing Proteome Research Center, Beijing 102206, China. + + + + Wang + Xiaoxin + X + + Central Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong Province 264000, China. + + + + Yang + Guoqian + G + + Beijing Proteome Research Center, Beijing 102206, China. + + + + Hou + Wenhao + W + + Beijing Proteome Research Center, Beijing 102206, China. + + + + Li + Chaoying + C + + Beijing Proteome Research Center, Beijing 102206, China. + + + + Liu + Fujun + F + + Central Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong Province 264000, China. + + + + Sun + Shijie + S + + The Second Department of Hepatobiliary Surgery, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong Province 264000, China. + + + + Liu + Xin + X + + Central Laboratory, Yantai Yuhuangding Hospital, Qingdao University, Yantai, Shandong Province 264000, China. + + + + Ying + Wantao + W + 0000-0001-8309-2198 + + School of Basic Medicine, Qingdao University, Qingdao, Shandong Province 266071, China. + + + Beijing Proteome Research Center, Beijing 102206, China. + + + State Key Laboratory of Medical Proteomics, National Center for Protein Sciences (Beijing), Academy of Military Medical Sciences, Beijing 102206, China. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + United States + J Proteome Res + 101128775 + 1535-3893 + + IM + + diagnosis biomarker + machine learning + pancreatic ductal adenocarcinoma + plasma proteomics + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 10 + 2 + + + aheadofprint + + 42223356 + 10.1021/acs.jproteome.5c01286 + + + + + + 42223265 + + 2026 + 06 + 01 + +
+ + 1520-8524 + + 159 + 6 + + 2026 + Jun + 01 + + + The Journal of the Acoustical Society of America + J Acoust Soc Am + + Estimating mechanical impedance from hydrophone measurements. + + 4904 + 4913 + 4904-4913 + + 10.1121/10.0043957 + + Integrating machine learning or deep learning models into autonomous underwater vehicles often requires extra steps with an accuracy trade-off, and the scarcity of training datasets makes it inconvenient. This work introduces a reduced-order identification approach for thin spherical shells using sparse hydrophone measurements. Because these impedances are unique to a given scatterer, the central hypothesis is that they provide strong identification potential. The method begins by using established techniques to analytically reconstruct the scattered field from hydrophone measurements. From this field, the surface pressures and velocities corresponding to each spherical harmonic are obtained and used to compute the in vacuo mechanical impedance. The method's effectiveness is then evaluated using synthetic data with added noise. Results demonstrate that Modal Mechanical Impedance Estimation can approximate the first two modal mechanical impedances with absolute percentage error less than 10% in the low-frequency range (ka ≤ 2.1) with only 10 hydrophones. Its performance is limited by directivity due to increasing frequency. By enabling efficient computation without any training phase, the proposed method stands out as a promising candidate for real-time and low-energy applications. + © 2026 Acoustical Society of America. + + + + Polat + Halim + H + 0009-0007-6041-0916 + + Department of Mechanical Engineering, Boston University, Boston, Massachusetts 02215, USA. + + + + McDaniel + James G + JG + 0000-0002-7980-2051 + + Department of Mechanical Engineering, Boston University, Boston, Massachusetts 02215, USA. + + + + Gilbert + Jeff + J + + Triton Systems, Chelmsford, Massachusetts 01824, USA. + + + + Lucifredi + Irena + I + + SOFAR Acoustics, LLC, Woburn, Massachusetts 01801, USA. + + + + eng + + Journal Article + +
+ + United States + J Acoust Soc Am + 7503051 + 0001-4966 + + IM + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 9 + 12 + + + 2026 + 5 + 14 + + + 2026 + 6 + 1 + 9 + 3 + + + ppublish + + 42223265 + 10.1121/10.0043957 + 3393437 + + + + + + 42223163 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1934-6638 + + 134 + 6 + + 2026 + Jun + + + Cancer cytopathology + Cancer Cytopathol + + Artificial intelligence-assisted urine cytology based on the Paris System for Reporting Urothelial Carcinoma. + + e70120 + e70120 + + 10.1002/cncy.70120 + + Urine cytology is a noninvasive and valuable tool for detecting urothelial carcinoma but suffers from variable sensitivity and observer dependency. Artificial intelligence (AI) may enhance the diagnostic accuracy and efficiency of urine cytology. The objective of this study was to develop and validate an AI-based cytology system for urothelial carcinoma detection in both clinical and screening contexts. + In total, 328 retrospective clinical cases and 1489 prospective health screening samples were analyzed. All specimens underwent liquid-based cytology and were digitized at ×20 magnification. For model development, ed 269 annotated training slides (56,710 cells) were used. The AI pipeline mimicked cytopathologist workflow, integrating deep learning-based cell detection and segmentation with feature extraction and support vector machine classification into AI-negative, AI-atypical, and AI-positive categories according to The Paris System for Reporting the Urinary Cytology. Agreement was assessed using weighted κ values and prevalence-adjusted, bias-adjusted κ values. + In the clinical cohort, AI achieved 83.0% agreement with histopathology and 82.6% agreement with expert cytology. Weighted κ values (κ, 0.631-0.661) reflected substantial agreement, with discrepancies mainly between adjacent categories. In the health screening cohort, the prevalence-adjusted, bias-adjusted κ was 0.647 despite low prevalence. AI demonstrated a high negative predictive value of 99.7% and negative percent agreement of 82.3%, with a minimal false-omission rate (0.25%). + AI-assisted urine cytology exhibited substantial concordance with expert interpretation and histopathologic standards. Its capability of screen-out and high negative predictive value support its potential as a reliable triage tool, improving workflow efficiency while maintaining diagnostic reliability in large-scale screening. + © 2026 American Cancer Society. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies. + + + + Chen + Lan + L + 0000-0002-9247-4988 + + Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China. + + + + Cao + Xinyi + X + + Department of Medical Development, Hangzhou Zhiwei Information Technology Co., Ltd., Hangzhou, China. + + + + Lu + Zongyue + Z + + Department of Medical Development, Hangzhou Zhiwei Information Technology Co., Ltd., Hangzhou, China. + + + + Liu + Longteng + L + + Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China. + + + + Sun + Mingjun + M + + Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China. + + + + Wu + Yulong + Y + + Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China. + + + + Zhang + Wei + W + 0000-0003-1515-964X + + Department of Pathology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China. + + + + eng + + + BJ2022-14 + National High Level Hospital Clinical Research Funding + + + + mnzl202024 + Bethune·Urinary Tumor Special Research Fund Project + + + + + Journal Article + +
+ + United States + Cancer Cytopathol + 101499453 + 1934-662X + + IM + + + Humans + + + Female + + + Artificial Intelligence + + + Retrospective Studies + + + Cytodiagnosis + methods + + + Male + + + Urologic Neoplasms + urine + diagnosis + pathology + + + Aged + + + Middle Aged + + + Intelligent Systems + + + Carcinoma, Transitional Cell + urine + diagnosis + pathology + + + Prospective Studies + + + Urine + cytology + + + Urothelium + pathology + + + Deep Learning + + + Urinary Bladder Neoplasms + urine + diagnosis + pathology + + + + The Paris System for Reporting Urinary Cytology (TPS) + artificial intelligence (AI) + deep learning + urine cytology + urothelial carcinoma (UC) + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 5 + 7 + + + 2026 + 2 + 9 + + + 2026 + 5 + 11 + + + 2026 + 6 + 1 + 8 + 4 + + + ppublish + + 42223163 + 10.1002/cncy.70120 + + + + Gontero P, Birtle A, Capoun O, et al. European Association of Urology guidelines on non–muscle‐invasive bladder cancer (TaT1 and carcinoma in situ)—a summary of the 2024 guidelines update. Eur Urol. 2024;86(6):531‐549. doi:10.1016/j.eururo.2024.07.027 + + + Jubber I, Ong S, Bukavina L, et al. Epidemiology of bladder cancer in 2023: a systematic review of risk factors. Eur Urol. 2023;84(2):176‐190. doi:10.1016/j.eururo.2023.03.029 + + + Antoni S, Ferlay J, Soerjomataram I, Znaor A, Jemal A, Bray F. Bladder cancer incidence and mortality: a global overview and recent trends. Eur Urol. 2017;71(1):96‐108. doi:10.1016/j.eururo.2016.06.010 + + + Bray F, Laversanne M, Sung H, et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2024;74(3):229‐263. doi:10.3322/caac.21834 + + + Rosenthal DL, Wojcik EM, Kurtycz DFI, eds. The Paris System for Reporting Urinary Cytology. 1st ed. Springer International Publishing; 2016. + + + Wojcik EM, Kurtycz DFI, Rosenthal DL, eds. The Paris System for Reporting Urinary Cytology. 2nd ed. Springer International Publishing; 2022. + + + Nikas IP, Seide S, Proctor T, Kleinaki Z, Kleinaki M, Reynolds JP. The Paris System for Reporting Urinary Cytology: a meta‐analysis. J Pers Med. 2022;12(2):170. doi:10.3390/jpm12020170 + + + Bakkar R, Mirocha J, Fan X, et al. Impact of the Paris system for reporting urine cytopathology on predictive values of the equivocal diagnostic categories and interobserver agreement. Cytojournal. 2019;16:21. doi:10.4103/cytojournal.cytojournal_30_19 + + + Khorsandi N, Porten S, VandenBussche CJ, Greenland N, Vohra P. Bridging perspectives: integrating urologist and cytopathologist insights of urine cytology. Semin Diagn Pathol. 2025;42(6):150960. doi:10.1016/j.semdp.2025.150960 + + + Sahoo SR, Sahu S, Bhuyan P. Role of urine cytology and Paris system in detection of high‐grade urothelial carcinoma: a diagnostic re‐evaluation. J Contemp Clin Pract. 2025;11(8):616‐621. doi:10.61336/jccp/25‐08‐87 + + + Long T, Layfield LJ, Esebua M, Frazier SR, Giorgadze DT, Schmidt RL. Interobserver reproducibility of The Paris System for Reporting Urinary Cytology. Cytojournal. 2017;14:17. doi:10.4103/cytojournal.cytojournal_12_17 + + + Niazi MKK, Parwani AV, Gurcan MN. Digital pathology and artificial intelligence. Lancet Oncol. 2019;20(5):e253‐e261. doi:10.1016/s1470‐2045(19)30154‐8 + + + Capitanio A, Dina RE, Treanor D. Digital cytology: a short review of technical and methodological approaches and applications. Cytopathology. 2018;29(4):317‐325. doi:10.1111/cyt.12554 + + + Allahqoli L, Laganà AS, Mazidimoradi A, et al. Diagnosis of cervical cancer and pre‐cancerous lesions by artificial intelligence: a systematic review. Diagnostics (Basel). 2022;12(11):2771. doi:10.3390/diagnostics12112771 + + + Giarnieri E, Scardapane S. Towards artificial intelligence applications in next generation cytopathology. Biomedicines. 2023;11(8):2225. doi:10.3390/biomedicines11082225 + + + Li J, Chong TW, Fong KY, et al. Will artificial intelligence (AI) replace cytopathologists: a scoping review of current applications and evidence of A.I. in urine cytology. World J Urol. 2025;43(1):200. doi:10.1007/s00345‐025‐05583‐8 + + + Wu S, Shen R, Hong G, et al. Development and validation of an artificial intelligence‐based model for detecting urothelial carcinoma using urine cytology images: a multicentre, diagnostic study with prospective validation. EClinicalMedicine. 2024;71:102566. doi:10.1016/j.eclinm.2024.102566 + + + Zhang Z, Fu X, Liu J, et al. Developing a machine learning algorithm for identifying abnormal urothelial cells: a feasibility study. Acta Cytol. 2021;65(4):335‐341. doi:10.1159/000510474 + + + Muralidaran C, Dey P, Nijhawan R, Kakkar N. Artificial neural network in diagnosis of urothelial cell carcinoma in urine cytology. Diagn Cytopathol. 2015;43(6):443‐449. doi:10.1002/dc.23244 + + + Sanghvi AB, Allen EZ, Callenberg KM, Pantanowitz L. Performance of an artificial intelligence algorithm for reporting urine cytopathology. Cancer Cytopathol. 2019;127(10):658‐666. doi:10.1002/cncy.22176 + + + Lyu C, Zhang W, Huang H, et al. RTMDet: an empirical study of designing real‐time object detectors (version 2). arXiv. 2022. doi:10.48550/ARXIV.2212.07784 + + + Liu S, Qi L, Qin H, Shi J, Jia J. Path aggregation network for instance segmentation (version 4). arXiv. 2018. doi:10.48550/ARXIV.1803.01534 + + + Bodla N, Singh B, Chellappa R, Davis LS. Soft‐NMS ‐‐ improving object detection with one line of code (version 2). arXiv. 2017. doi:10.48550/ARXIV.1704.04503 + + + Dumitru R‐G, Peteleaza D, Craciun C. Using DUCK‐net for polyp image segmentation. Sci Rep. 2023;13(1). doi:10.1038/s41598‐023‐36940‐5 + + + Skacel M, Fahmy M, Brainard JA, et al. Multitarget fluorescence in situ hybridization assay detects transitional cell carcinoma in the majority of patients with bladder cancer and atypical or negative urine cytology. J Urol. 2003;169(6):2101‐2105. doi:10.1097/01.ju.0000066842.45464.cc + + + Chen L, Liu L, Sun M, He S, Liu D. Practical application of The Paris System for Reporting Urinary Cytology. Chin J Pathol. 2024;53(5):470‐476. doi:10.3760/cma.j.cn112151‐20231012‐00251 + + + Danakas A, Sweeney M, Cheris S, Agrawal T. Urinary tract cytology: a cytologic‐histopathologic correlation with The Paris System, an institutional study. J Am Soc Cytopathol. 2021;10(1):56‐63. doi:10.1016/j.jasc.2020.09.013 + + + Zare S, Mirsadraei L, Reisian N, et al. A single institutional experience with The Paris System for Reporting Urinary Cytology: correlation of cytology and histology in 194 cases. Am J Clin Pathol. 2018;150(2):162‐167. doi:10.1093/ajcp/aqy043 + + + Phruttinarakorn B, Plumworasawat S, Kayankarnnavee J, Lualon J, Pongtippan A. Application of the Paris reporting system for urine cytology: the three‐year experience of a single tertiary care institute in Thailand. Acta Cytol. 2022;66(2):134‐141. doi:10.1159/000521139 + + + Zhang ML, Miki Y, Hang J, et al. A review of upper urinary tract cytology performance before and after the implementation of The Paris System. Cancer Cytopathol. 2021;129(4):264‐274. doi:10.1002/cncy.22343 + + + Sokolova IA, Halling KC, Jenkins RB, et al. The development of a multitarget, multicolor fluorescence in situ hybridization assay for the detection of urothelial carcinoma in urine. J Mol Diagn. 2000;2(3):116‐123. doi:10.1016/s1525‐1578(10)60625‐3 + + + Lobo N, Afferi L, Moschini M, et al. Epidemiology, screening, and prevention of bladder cancer. Eur Urol Oncol. 2022;5(6):628‐639. doi:10.1016/j.euo.2022.10.003 + + + U.S. Preventive Services Task Force. Recommendation: Bladder Cancer in Adults: Screening. U.S. Preventive Services Taskforce; 2011. Accessed February 6, 2026. https://www.uspreventiveservicestaskforce.org/uspstf/recommendation/bladder‐cancer‐in‐adults‐screening。 + + + Nojima S, Terayama K, Shimoura S, et al. A deep learning system to diagnose the malignant potential of urothelial carcinoma cells in cytology specimens. Cancer Cytopathol. 2021;129(12):984‐995. doi:10.1002/cncy.22443 + + + Vaickus LJ, Suriawinata AA, Wei JW, Liu X. Automating the Paris system for urine cytopathology—a hybrid deep‐learning and morphometric approach. Cancer Cytopathol. 2019;127(2):98‐115. doi:10.1002/cncy.22099 + + + Kaneko M, Tsuji K, Masuda K, et al. Urine cell image recognition using a deep‐learning model for an automated slide evaluation system. BJU Int. 2022;130(2):235‐243. doi:10.1111/bju.15518 + + + Lebret T, Pignot G, Colombel M, et al. Artificial intelligence to improve cytology performances in bladder carcinoma detection: results of the VisioCyt test. BJU Int. 2022;129(3):356‐363. doi:10.1111/bju.15382 + + + Lebret T, Paoletti X, Pignot G, et al. Artificial intelligence to improve cytology performance in urothelial carcinoma diagnosis: results from validation phase of the French, multicenter, prospective VISIOCYT1 trial. World J Urol. 2023;41(9):2381‐2388. doi:10.1007/s00345‐023‐04519‐4 + + + + + + + 42223123 + + 2026 + 06 + 01 + +
+ + 1096-3669 + + + 2026 + Jun + 01 + + + Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA + Waste Manag Res + + Analysis of waste disposal and recycling processes in Türkiye with machine learning and artificial neural networks. + + 734242X261453689 + 734242X261453689 + + 10.1177/0734242X261453689 + + This study aims to analyze waste disposal and recovery data for 2022 and 2024 in Türkiye to reveal changes in national waste management and to make future predictions using multivariate machine learning and artificial neural network (ANN) models. Trends in disposal and recovery capacity were identified, and the main variables affecting waste processing volumes were modeled. Multiple linear regression and feedforward ANN models were created using the capacity and processing quantities obtained from Türkiye İstatistik Kurumu's data. The ANN model was trained with three hidden layers and a 64-32-16 neuron structure, with a learning rate of 0.001. The dataset was divided into 80% training and 20% testing. The machine learning model's test set accuracy coefficient was calculated as R + 2 = 0.87, and the mean absolute error (MAE) was 1.96 million tons. The ANN model demonstrated even higher performance, reaching R + 2 = 0.93 and an MAE of 1.41 million tons. ANNs accurately predicted total recycled waste to be 51.5 million tons in 2024, while the amount of landfilled waste was predicted with only a 3.2% margin of error. Model analyses revealed that increasing landfill capacity significantly increases disposal volume, while increasing the number of recycling facilities affects recovery volume through a strong, nonlinear relationship. The results show that ANN models can predict Türkiye's waste management trends with high accuracy and prove that they provide a powerful decision support tool that can be used in future policy planning. + + + + Parlakkiliç + Alaattin + A + 0000-0002-6834-6839 + + Management Information Systems, Faculty of Economics and Administrative Sciences, Ufuk University, Ankara, Turkey. + + + + eng + + Journal Article + + + 2026 + 06 + 01 + +
+ + England + Waste Manag Res + 9881064 + + IM + + artificial neural networks + disposal + machine learning + recycling + waste management + + + + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 7 + 52 + + + aheadofprint + + 42223123 + 10.1177/0734242X261453689 + + + + + + 42223102 + + 2026 + 06 + 01 + +
+ + 1089-7690 + + 164 + 21 + + 2026 + Jun + 07 + + + The Journal of chemical physics + J Chem Phys + + Machine learning accelerated nonadiabatic molecular dynamics of defect-mediated recombination in alkali metal passivated Cu2ZnSnS4. + 214111 + 10.1063/5.0336824 + + Nonradiative electron-hole recombination mediated by defects is a critical loss mechanism in semiconductors and requires computationally demanding nonadiabatic (NA) molecular dynamics simulations. In this work, we develop a machine learning-accelerated framework to model long-time NA couplings in Cu2ZnSnS4 containing CuZn antisites and CuZn + ZnCu antisite pairs, with and without alkali metal passivation. By formulating NA coupling evolution as a time-series prediction problem, we benchmark 37 representative deep learning architectures, including recurrent neural networks, convolutional neural networks, transformers, and hybrid models. Among them, the extended long short-term memory model achieves the best overall performance, yielding an average test set R2 of 0.98 while maintaining high computational efficiency. This approach enables accurate reconstruction of long-time NA coupling trajectories at a prediction cost reduced by over five orders of magnitude relative to repeated direct first-principles NA coupling evaluations. The application of the framework shows that alkali metal doping systematically reduces the NA coupling strength and accelerates decoherence, with Li exhibiting the strongest suppression of recombination, which is associated with enhanced charge localization and lattice fluctuations. The predicted recombination lifetimes agree closely with first-principles results, validating the reliability of the machine learning approach. This work establishes a generalizable strategy for machine learning-assisted NA dynamics simulations and provides mechanistic insights into defect-mediated recombination processes. + © 2026 Author(s). Published under an exclusive license by AIP Publishing. + + + + Zhang + Zhaosheng + Z + 0000-0002-4080-0884 + + College of Chemistry and Materials Science, Hebei University, Baoding 071002, People's Republic of China. + + + + Xiong + Qing + Q + + College of Chemistry and Materials Science, Hebei University, Baoding 071002, People's Republic of China. + + + + Liu + Yanbo + Y + 0009-0002-3717-9262 + + College of Chemistry and Materials Science, Hebei University, Baoding 071002, People's Republic of China. + + + + Liu + Jiadong + J + + College of Chemistry and Materials Science, Hebei University, Baoding 071002, People's Republic of China. + + + + Zhang + Wutong + W + + College of Chemistry and Materials Science, Hebei University, Baoding 071002, People's Republic of China. + + + + eng + + Journal Article + +
+ + United States + J Chem Phys + 0375360 + 0021-9606 + + IM + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 4 + 2 + + + 2026 + 5 + 19 + + + 2026 + 6 + 1 + 7 + 33 + + + ppublish + + 42223102 + 10.1063/5.0336824 + 3393121 + + + + + + 42223099 + + 2026 + 06 + 01 + +
+ + 1089-7690 + + 164 + 21 + + 2026 + Jun + 07 + + + The Journal of chemical physics + J Chem Phys + + Physics-informed machine learning model for accurate prediction of electron affinities. + 214108 + 10.1063/5.0334579 + + Accurate wavefunction methods such as CCSD(T) can predict chemical and thermodynamic properties of small molecules with near-experimental precision. However, their steep computational cost limits their use for large systems or extensive datasets. In contrast, density functional theory (DFT) is faster and more practical for large molecules but often fails to accurately capture electronic changes with quantitative accuracy. To address these limitations, we have developed a machine learning model for the prediction of electron affinities with physics-based structural features (RDKit and SMARTS) and quantum chemistry-based electronic features (Mulliken charge analysis). These features, which capture both local bonding motifs and global electronic context, are used as input descriptors to an XGBR (eXtreme Gradient Boosting Regressor) model in a ΔML framework. By embedding QM-based electronic features to the ΔML model, our enhanced ΔML+ model attains a mean absolute error of 0.03 eV with respect to G4MP2 values, surpassing conventional chemical accuracy targets while exhibiting markedly reduced dependence on the underlying DFT functional. By analyzing both vertical and adiabatic EAs within the same framework, we highlight the crucial role of geometry relaxation in predictive modeling. Overall, our approach offers an efficient and transferable route to benchmark accurate electron affinity predictions, pointing toward next generation computational protocols that overcome the limitations of standalone DFT. + © 2026 Author(s). Published under an exclusive license by AIP Publishing. + + + + Swain + Debashis + D + 0009-0000-3233-2382 + + Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA. + + + + Manna + Surya Sekhar + SS + 0000-0002-8496-109X + + Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA. + + + + Maier + Sarah + S + + Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA. + + + + Raghavachari + Krishnan + K + 0000-0003-3275-1426 + + Department of Chemistry, Indiana University, Bloomington, Indiana 47405, USA. + + + + eng + + Journal Article + +
+ + United States + J Chem Phys + 0375360 + 0021-9606 + + IM + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 18 + + + 2026 + 5 + 13 + + + 2026 + 6 + 1 + 7 + 33 + + + ppublish + + 42223099 + 10.1063/5.0334579 + 3393116 + + + + + + 42223029 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1600-0625 + + 35 + 6 + + 2026 + Jun + + + Experimental dermatology + Exp Dermatol + + Identification of Polyamine Metabolism-Related Biomarkers in Psoriasis: A Study Based on Transcriptomics and Single-Cell Sequencing. + + e70289 + e70289 + + 10.1111/exd.70289 + + Psoriasis (PS) is linked to altered polyamine metabolism, which may drive chronic inflammation and abnormal keratinocyte proliferation. We integrated transcriptomics and single-cell RNA sequencing (scRNA-seq) to identify polyamine metabolism-related genes (PMRGs) as biomarkers for PS and elucidate their mechanisms. Through the application of differential expression analysis, three machine learning algorithms, combined with receiver operating characteristic (ROC) analysis and expression quantification, biomarkers related to polyamine metabolism in PS were identified. Furthermore, the expression of these biomarkers was verified through western blotting. These biomarkers were subsequently incorporated into a diagnostic nomogram. Further investigations of these biomarkers included enrichment analysis and immune infiltration analysis. Additionally, scRNA-seq analysis was conducted to annotate the cell types in PS, providing insights into the cellular mechanisms underlying the progression of PS. In PS samples, biomarkers PSME2, PSMB5, PSMC4, PSMB10 and SMOX were significantly upregulated, achieving an area under the ROC curve (AUC) > 0.9 in GSE13355. Western blotting confirmed their upregulation, aligning with bioinformatics results. These biomarkers were closely associated with inflammation, cell proliferation, energy metabolism and signalling pathways. scRNA-seq identified 11 cell types, with mature dendritic cell (mDC) showing notable proportional differences between PS patients and controls, marking them as key cells. mDC exhibited distinct developmental trajectories with branching heterogeneity, featuring a reduction in early stages and an increase in later stages. Biomarker expression levels were higher in the later stages of mDC differentiation. The five PMRGs-PSME2, PSMB5, PSMC4, PSMB10 and SMOX-identified through comprehensive analyses, emerged as significant biomarkers for PS, providing promising diagnostic targets and mechanistic insights for the early detection and targeted treatment of PS. + © 2026 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd. + + + + Zhang + Li + L + 0009-0005-6305-0712 + + Department of Dermatology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China. + + + + Jia + Xiaoqiang + X + 0009-0005-1471-7269 + + Department of Dermatology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China. + + + + Liu + Xia + X + 0000-0002-6951-3520 + + Department of Dermatology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China. + + + + Yang + Yanni + Y + 0009-0005-5427-2372 + + Department of Dermatology, The Second Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, China. + + + + eng + + Journal Article + +
+ + Denmark + Exp Dermatol + 9301549 + 0906-6705 + + + + 0 + Biomarkers + + + 0 + Polyamines + + + IM + + + Psoriasis + metabolism + genetics + + + Humans + + + Biomarkers + metabolism + + + Single-Cell Analysis + + + Polyamines + metabolism + + + Single-Cell Gene Expression Analysis + + + Transcriptome + + + Gene Expression Profiling + + + Sequence Analysis, RNA + + + Dendritic Cells + metabolism + + + + biomarkers + mature dendritic cell + polyamine metabolism + psoriasis + single‐cell RNA sequencing + transcriptomic + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 5 + 6 + + + 2025 + 5 + 22 + + + 2026 + 5 + 19 + + + 2026 + 6 + 1 + 7 + 3 + + + ppublish + + 42223029 + 10.1111/exd.70289 + + + + E. Christophers, “Psoriasis–Epidemiology and Clinical Spectrum,” Clinical and Experimental Dermatology 26, no. 4 (2001): 314–320. + + + A. Rendon and K. 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+ + 1944-8252 + + + 2026 + Jun + 01 + + + ACS applied materials & interfaces + ACS Appl Mater Interfaces + + Emerging Trends in Additive Manufacturing for Thermoelectric Devices: Materials, Structures, and Engineering Approaches. + 10.1021/acsami.6c04499 + + Energy harvesting is gaining importance in the 21st century, with thermoelectric (TE) technology offering a promising method for converting thermal energy into electrical energy. However, the application of TE devices remains relatively low because of the limitations of conventional manufacturing methods. Fabricating complex-shaped TE devices using traditional manufacturing processes is challenging and leads to a low efficiency. Unlike conventional subtractive methods, additive manufacturing (AM) builds three-dimensional (3D) objects layer by layer, enabling the creation of intricate and complex structures with precision. This study explores recent trends in AM of thermoelectric systems, with a focus on materials, synthesis methods, and device fabrication. It also discusses the challenges associated with these AM techniques and explores potential areas for improvement. Recent studies have shown that AM can produce thermoelectric units with hollow and layered structures, enhancing temperature gradients and power density compared with conventional designs. The ability to customize geometries through AM offers promising opportunities to enhance the performance of the TE materials and devices. AM technologies can produce highly efficient, functionally graded TE devices. By enabling rapid prototyping and high-performance structures, AM can improve the efficiency and application of the TE materials. Future work focuses on further advancing these AM techniques by integration with machine learning (ML) and development of multimaterial TE devices. + + + + Kondaguli + R S + RS + + B.L.D.E A's V.P Dr. P G Halakatti College of Engg & Technology, (Affiliated to Visvesvaraya Technological University), Vijayapur, Karnataka586103, India. + + + + Kumar + Inder + I + + Thermoelectric Materials and Devices Laboratory, Department of Physics, Indian Institute of Science, Bangalore560012, India. + + + + Hiremath + C R + CR + + B.L.D.E A's V.P Dr. P G Halakatti College of Engg & Technology, (Affiliated to Visvesvaraya Technological University), Vijayapur, Karnataka586103, India. + + + + Malaji + P V + PV + + B.L.D.E A's V.P Dr. P G Halakatti College of Engg & Technology, (Affiliated to Visvesvaraya Technological University), Vijayapur, Karnataka586103, India. + + + + Adinarayanappa + Somashekara Makireddypalli + SM + + Additive Manufacturing and 4D Printing Laboratory, Department of Mechanical, Materials and Aerospace Engineering, Indian Institute of Technology Dharwad (IIT Dh) Dharwad, Dharwad580011, India. + + + + Mallik + Ramesh Chandra + RC + 0000-0002-8383-7812 + + Thermoelectric Materials and Devices Laboratory, Department of Physics, Indian Institute of Science, Bangalore560012, India. + + + + eng + + Journal Article + Review + + + 2026 + 06 + 01 + +
+ + United States + ACS Appl Mater Interfaces + 101504991 + 1944-8244 + + IM + + 3D printing + additive manufacturing + direct ink writing + sustainable energy + thermoelectric devices + thermoelectric materials + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 6 + 52 + + + aheadofprint + + 42223010 + 10.1021/acsami.6c04499 + + + + + + 42222992 + + 2026 + 06 + 01 + +
+ + 0009-4293 + + 80 + 5 + + 2026 + May + 27 + + + Chimia + Chimia (Aarau) + + Delta-Augmented Subsystem Density Functional Theory: A Study Across Diverse Systems. + + 319 + 326 + 319-326 + + 10.2533/chimia.2026.319 + + In this study, we expand upon and benchmark the Kim-Gordon method (KG), a subsystem density functional theory (DFT) approach appended with a machine-learned correction to compensate for errors in the kinetic energy term and thereby match Kohn-Sham (KS) DFT accuracy. This correction is obtained through 'delta- learning' based on KS-DFT data. The method promises sampling of configurations for condensed molecular systems at the Kohn-Sham DFT level of accuracy at a fraction of the computational cost. Despite encouraging results for liquid water, it was not obvious whether the scheme had more general appeal. In this work, we show that the approach allows for a broad range of applications. In particular, we successfully apply it to complex molecular liquids, such as bulk ammonia and methanol. As a bonus, the correction trained on the bulk KS data is applicable to clusters, illustrating its transferability. By focusing on 'delta-learning'-predicting small corrections rather than full Kohn-Sham (KS) energies and forces-we significantly reduce the required training data. This approach, especially when combined with linear-scaling self-consistent field (LS-SCF) techniques, establishes the method as a highly efficient computational tool for molecular dynamics. + Copyright 2026 Michela Pauletti, Marcella Iannuzzi, Vladimir V. Rybkin. License: This work is licensed under a Creative Commons Attribution 4.0 International License. + + + + Pauletti + Michela + M + 0009-0001-3518-6277 + + Physical Chemistry Institute, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland. michela.pauletti@live.it. + + + Current address: Interventional and Experimental Endoscopy (InExEn), Department of Internal Medicine 2, University Hospital Würzburg, Würzburg, Germany. + + + + Iannuzzi + Marcella + M + + Physical Chemistry Institute, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland. marcella.iannuzzi@chem.uzh.ch. + + + + Rybkin + Vladimir V + VV + 0000-0001-5136-6035 + + Physical Chemistry Institute, University of Zurich, Winterthurerstrasse 190, Zurich, Switzerland. vladimir.rybkin@quantumsimulations.de. + + + Current address: HQS Quantum Simulations GmbH, Rintheimer Strasse 23, 76131 Karlsruhe, Germany. + + + + eng + + + 200021_162432 + SNSF_ + Swiss National Science Foundation + Switzerland + + + PZ00P2_174227 + SNSF_ + Swiss National Science Foundation + Switzerland + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + Switzerland + Chimia (Aarau) + 0373152 + 0009-4293 + + IM + + Delta-learning + Kim-Gordon method + Machine-learning potentials + Molecular liquids + Subsystem DFT + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 3 + 8 + + + 2026 + 3 + 24 + + + 2026 + 6 + 1 + 6 + 44 + + + epublish + + 42222992 + 10.2533/chimia.2026.319 + + + + + + 42222991 + + 2026 + 06 + 01 + +
+ + 0009-4293 + + 80 + 5 + + 2026 + May + 27 + + + Chimia + Chimia (Aarau) + + Physics-Informed and Equivariant Machine Learning for Molecular Dipole Moment Prediction. + + 314 + 318 + 314-318 + + 10.2533/chimia.2026.314 + + Accurate prediction of molecular electric dipole moments is crucial for determining molecular properties and understanding molecular reactivity. Recent developments in equivariant machine learning enable direct prediction of electric dipole moments as vector quantities. This raises the question as to whether it is still necessary to consider physics-informed charge-based principles in the model to achieve satisfactory performance. In this work, we systematically compare direct equivariant electric dipole prediction using MACE with charge-based approaches using adapted MACE which incorporates a variant charge equilibrium equation (QEq) into the MACE framework. Across diverse datasets including QM7b, QM9, as well as subsets of the SPICE and SN2 data sets, we demonstrate that both direct equivariant electric dipole prediction and physics-informed QEq approaches show good performance for short-to-medium range interaction datasets. The charge-based QEq model shows slightly better performance than direct prediction beyond small training data sizes. Notably, the charge-based QEq model outperforms direct electric dipole prediction for the systems with long range interactions. Our results highlight the importance of incorporating physics into the model for improved model interpretability and transferability. + Copyright 2026 Ke Chen, Sandra Luber. License: This work is licensed under a Creative Commons Attribution 4.0 International License. + + + + Chen + Ke + K + + Department of Chemistry, University of Zurich, Zurich, CH-8057, Switzerland. ke.chen2@chem.uzh.ch. + + + + Luber + Sandra + S + + Department of Chemistry, University of Zurich, Zurich, CH-8057, Switzerland. sandra.luber@chem.uzh.ch. + + + + eng + + + lp11 + Centro Svizzero di Calcolo Scientifico + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + Switzerland + Chimia (Aarau) + 0373152 + 0009-4293 + + IM + + Charge equilibrium equation + Dipole moment + Direct equivariant prediction + Machine learning + Physics-informed model + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 3 + 3 + + + 2026 + 3 + 18 + + + 2026 + 6 + 1 + 6 + 44 + + + epublish + + 42222991 + 10.2533/chimia.2026.314 + + + + + + 42222990 + + 2026 + 06 + 01 + +
+ + 0009-4293 + + 80 + 5 + + 2026 + May + 27 + + + Chimia + Chimia (Aarau) + + From Experimental Planning to Autonomous Discovery: The Changing Role of Design of Experiments in Nanotechnology. + + 305 + 313 + 305-313 + + 10.2533/chimia.2026.305 + + Design of Experiments (DoE) is increasingly reshaping how nanomaterials are discovered, optimized, and understood, enabling a shift from empirical trial-and-error toward predictive, knowledge-driven design. As nanotechnology advances toward multifunctional and highly coupled systems, unstructured experimentation struggles to deliver reproducibility, efficiency, or transferability. This perspective highlights the evolution of DoE from classical factorial designs and response surface methodology to Bayesian, adaptive, and machine-learning-enabled frameworks. We discuss how structured experimentation reveals hidden interactions, supports multi-objective optimization, and enables uncertainty-aware decision-making across complex synthesis spaces. + Copyright 2026 Miroslava Nedyalkova, Vasil Simeonov, Marco Lattuada. License: This work is licensed under a Creative Commons Attribution 4.0 International License. + + + + Nedyalkova + Miroslava + M + 0000-0003-0793-3340 + + Department of Chemistry, University of Fribourg, Fribourg, CH-1700, Switzerland. miroslava.nedyalkova@unifr.ch. + + + + Simeonov + Vasil + V + + University of Sofia. vsimeonov@chem.uni-sofia.bg. + + + + Lattuada + Marco + M + + Department of Chemistry, University of Fribourg, Fribourg, CH-1700, Switzerland. marco.lattuada@unifr.ch. + + + + eng + + + BG-RRP-2.004-0008 + NextGenerationEU + + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + Switzerland + Chimia (Aarau) + 0373152 + 0009-4293 + + IM + + Design of experiments + Nanoparticles + Nanotechnology + Self-driving labs + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 20 + + + 2026 + 3 + 16 + + + 2026 + 6 + 1 + 6 + 44 + + + epublish + + 42222990 + 10.2533/chimia.2026.305 + + + + + + 42222989 + + 2026 + 06 + 01 + +
+ + 0009-4293 + + 80 + 5 + + 2026 + May + 27 + + + Chimia + Chimia (Aarau) + + Leveraging the Potential of Machine-Learning Interatomic Potentials for QM/MM Simulations. + + 298 + 304 + 298-304 + + 10.2533/chimia.2026.298 + + Machine-learning interatomic potentials (MLIPs) are increasingly used to replace computationally expensive quantum-mechanical (QM) calculations to obtain the energies and forces in ab initio or multiscale molecular dynamics (MD) simulations. While the computational cost of MLIPs lies between that of QM methods and classical force fields (molecular mechanics, MM), their accuracy is close to that of the chosen reference method (e.g. density functional theory, DFT) with sufficient training data. However, for large biological systems in solution, MLIPs are still too costly to perform long MD simulations, where the full system (i.e. including the solvent) is described by the MLIP. Instead, multiscale approaches analogous to QM/MM (i.e. ML/MM) offer a viable compromise between computational effort and accessible system size and time scales. In this review, we provide a brief overview of recent advances and current developments in this field. + Copyright 2026 Antonia S. Kuhn, Igor Gordiy, Felix Pultar, Sereina Riniker. License: This work is licensed under a Creative Commons Attribution 4.0 International License. + + + + Kuhn + Antonia S + AS + 0000-0002-4616-4428 + + Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland. antonia.kuhn@phys.chem.ethz.ch. + + + + Gordiy + Igor + I + 0000-0002-6540-1804 + + Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland. igor.gordiy@phys.chem.ethz.ch. + + + + Pultar + Felix + F + 0000-0001-8900-4734 + + Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland. felix.pultar@alumni.ethz.ch. + + + + Riniker + Sereina + S + + Department of Chemistry and Applied Biosciences, ETH Zürich, Vladimir-Prelog-Weg 2, 8093 Zürich, Switzerland. sriniker@ethz.ch. + + + + eng + + + 200021_212732 + SNSF_ + Swiss National Science Foundation + Switzerland + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + Switzerland + Chimia (Aarau) + 0373152 + 0009-4293 + + IM + + Machine learning + Molecular dynamics + Multiscale simulations + Neural network potentials + QM/MM + + There are no conflicts of interest to declare. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 2 + 15 + + + 2026 + 3 + 16 + + + 2026 + 6 + 1 + 6 + 44 + + + epublish + + 42222989 + 10.2533/chimia.2026.298 + + + + + + 42222988 + + 2026 + 06 + 01 + +
+ + 0009-4293 + + 80 + 5 + + 2026 + May + 27 + + + Chimia + Chimia (Aarau) + + High-Accuracy Molecular Simulations with Machine-Learning Potentials and Semiclassical Approximations to Quantum Dynamics. + + 292 + 297 + 292-297 + + 10.2533/chimia.2026.292 + + Accurate simulations of molecules require high-level electronic-structure theory in combination with rigorous methods for approximating the quantum dynamics. Machine-learning approaches can significantly reduce the computational expense of this workflow without any loss of accuracy. We discuss various methods for constructing potential energy surfaces including transfer learning, which requires a minimal number of expensive training points. In this way, we can study chemical reactions at a high level but at a low cost. In particular, as the potentials are smooth and differentiable, they enable the use of more advanced semiclassical approximations to quantum dynamics, such as perturbatively corrected instanton theory, which can capture both tunnelling and anharmonicity. + Copyright 2026 Valerii Andreichev, Jindra Dušek, Markus Meuwly, Jeremy O. Richardson. License: This work is licensed under a Creative Commons Attribution 4.0 International License. + + + + Andreichev + Valerii + V + 0000-0003-2071-2964 + + Department of Chemistry, University of Basel, CH-4056 Basel, Switzerland. valerii.andreichev@unibas.ch. + + + + Dušek + Jindra + J + 0009-0008-5168-1623 + + Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland. jindra.dusek@phys.chem.ethz.ch. + + + + Meuwly + Markus + M + 0000-0001-7930-8806 + + Department of Chemistry, University of Basel, CH-4056 Basel, Switzerland. m.meuwly@unibas.ch. + + + + Richardson + Jeremy O + JO + 0000-0002-9429-151X + + Department of Chemistry and Applied Biosciences, ETH Zurich, CH-8093 Zurich, Switzerland. jeremy.richardson@phys.chem.ethz.ch. + + + + eng + + + 207772 + SNSF_ + Swiss National Science Foundation + Switzerland + + + 200020_219779 + SNSF_ + Swiss National Science Foundation + Switzerland + + + 200021_215088 + SNSF_ + Swiss National Science Foundation + Switzerland + + + + Journal Article + + + 2026 + 05 + 27 + +
+ + Switzerland + Chimia (Aarau) + 0373152 + 0009-4293 + + IM + + Machine learning + Reaction dynamics + Tunnelling + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 2 + 16 + + + 2026 + 3 + 17 + + + 2026 + 6 + 1 + 6 + 44 + + + epublish + + 42222988 + 10.2533/chimia.2026.292 + + + + + + 42222902 + + 2026 + 06 + 01 + +
+ + 1349-7006 + + + 2026 + Jun + 01 + + + Cancer science + Cancer Sci + + Organ-Specific Cancer-Associated Fibroblast Subtypes Across the Digestive System Linked to Cancer Hallmarks. + 10.1111/cas.70435 + + Cancer-associated fibroblasts (CAFs) are major stromal components of the tumor microenvironment (TME) and play diverse roles in gastrointestinal (GI) cancer progression, immunity, and therapeutic resistance. However, the heterogeneity, tissue specificity, and clinical relevance of CAFs across GI cancers remain incompletely defined. We integrated 14 independent single-cell RNA-sequencing datasets comprising 239 GI adenocarcinoma samples to define the subtypes of CAFs. Machine learning-based deconvolution was applied to 18 bulk transcriptomic cohorts (3396 patients) to estimate CAF subtype abundance and assess prognostic associations by meta-analysis. Functional states, differentiation trajectories, cell-cell communication, and spatial organization were analyzed using gene-set enrichment, transcription factor activity inference, pseudotime modeling, ligand-receptor analysis, and spatial transcriptomics. We identified eight CAF subtypes with distinct transcriptional programs and organ-specific distributions. Two myofibroblastic CAF subtypes (myCAF1 and myCAF2) were consistently associated with poor prognosis, characterized by extracellular matrix remodeling, TGF-β signaling, hypoxia adaptation, and close crosstalk with immunosuppressive macrophages, as well as tumor cells displaying epithelial-mesenchymal transition and hypoxia signatures. In contrast, inflammatory CAF (iCAF) 1 was associated with a favorable prognosis and increased infiltration of antitumor immune cells. Spatial transcriptomic analyses further revealed that distinct CAF subtypes preferentially occupied discrete spatial domains within the GI TME. Our study demonstrated that the functional state and spatial context of CAFs jointly affect tumor progression and outcomes in patients with GI cancers. myCAF-driven stromal niches promote immune suppression and poor prognosis, whereas a balanced inflammatory CAF program may support antitumor immunity. + © 2026 The Author(s). Cancer Science published by John Wiley & Sons Australia, Ltd on behalf of Japanese Cancer Association. + + + + Wang + Huaitao + H + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan. + + + Department of General Surgery, Shengjing Hospital of China Medical University, Shenyang, China. + + + + Semba + Takashi + T + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + + Yonemura + Atsuko + A + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + + Naito + Hiroshi + H + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + + Fu + Lingfeng + L + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + + Tajiri + Takuya + T + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan. + + + + Iwatsuki + Masaaki + M + + Department of Gastroenterological Surgery, Graduate School of Medical Sciences, Kumamoto University, Kumamoto, Japan. + + + + Ishimoto + Takatsugu + T + 0000-0003-1852-1835 + + Division of Carcinogenesis, The Cancer Institute, Japanese Foundation for Cancer Research, Tokyo, Japan. + + + International Research Center of Medical Sciences, Kumamoto University, Kumamoto, Japan. + + + + eng + + + JPMJFR200H + Fusion Oriented REsearch for disruptive Science and Technology + + + + 25ama221442 + Japan Agency for Medical Research and Development + + + + 25ama221446 + Japan Agency for Medical Research and Development + + + + JP256f0137010 + Japan Agency for Medical Research and Development + + + + 23K27463 + Japan Society for the Promotion of Science + + + + 23K27689 + Japan Society for the Promotion of Science + + + + 24K22151 + Japan Society for the Promotion of Science + + + + 24KK0162 + Japan Society for the Promotion of Science + + + + 25K19750 + Japan Society for the Promotion of Science + + + + + Journal Article + + + 2026 + 06 + 01 + +
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+ + 1463-9262 + + + 2026 + May + 14 + + + Green chemistry : an international journal and green chemistry resource : GC + Green Chem + + Machine learning to predict plasma-based CO2 conversion in dielectric barrier discharge reactors. + 10.1039/d6gc01077f + + Plasma-based CO2 conversion is an emerging defossilization technology that converts a potent greenhouse gas into valuable chemical feedstocks, yet its optimization is hampered by complex nonlinear behavior and resource-intensive experimentation. In this work, we collected a comprehensive database, comprising 358 data points with six key operational and geometric parameters, published in literature between 2010 and 2025. Leveraging this dataset, we developed a hybrid machine learning (ML) model integrating physics-informed neural network (PINN), random forest (RF) and extreme gradient boost (XGB) algorithms to predict CO2 conversion and energy efficiency (EE) in dielectric barrier discharge (DBD) reactors. Under a rigorous group 5-fold cross-validation (CV) protocol, the ensemble consistently outperformed all individual models, with the best-fold model achieving an R + 2 of 0.791. Error-correlation analysis revealed that the ensemble weights adapt to the pairwise error correlation structure: PINN consistently provides complementary information, while RF and XGB, being largely interchangeable, are selected according to their individual performance. When applied to prospective experimental validation, the hybrid model achieves an R + 2 of 0.92 on unseen data within the explored domain, and it eliminates unphysical predictions in data-sparse regimes, yielding strictly non-negative CO2 conversion estimates. SHapley Additive exPlanations (SHAP) analysis further identified flow rate and power as the dominant input features, collectively accounting for 61%-71% of the model's predictions. This work establishes a robust and interpretable framework while quantifying the generalizability of ML models in heterogeneous data environments, offering a practical tool to accelerate plasma-based gas conversion optimization. + This journal is © The Royal Society of Chemistry. + + + + Li + Jiayin + J + 0000-0002-1179-1077 + + Research Group PLASMANT and Center of Excellence PLASMA, University of Antwerp, Department of Chemistry Antwerp 2610 Belgium annemie.bogaerts@uantwerpen.be. + + + Electrification Institute, University of Antwerp Olieweg 97 2020 Antwerp Belgium. + + + + Lu + Xinpei + X + + School of Electrical and Electronic Engineering, Huazhong University of Science and Technology Wuhan Hubei 430074 China. + + + + Arun + Pranav + P + 0009-0002-0312-3519 + + Department of Chemical Engineering and Chemistry, Eindhoven University of Technology Eindhoven 5612 AZ the Netherlands S.Li1@tue.nl. + + + + Xu + Jing + J + + School of Electronic Information and Communications, Huazhong University of Science and Technology Wuhan Hubei 430074 People's Republic of China. + + + + Gallucci + Fausto + F + + Department of Chemical Engineering and Chemistry, Eindhoven University of Technology Eindhoven 5612 AZ the Netherlands S.Li1@tue.nl. + + + + Li + Sirui + S + 0000-0002-2267-4335 + + Department of Chemical Engineering and Chemistry, Eindhoven University of Technology Eindhoven 5612 AZ the Netherlands S.Li1@tue.nl. + + + + Bogaerts + Annemie + A + 0000-0001-9875-6460 + + Research Group PLASMANT and Center of Excellence PLASMA, University of Antwerp, Department of Chemistry Antwerp 2610 Belgium annemie.bogaerts@uantwerpen.be. + + + Electrification Institute, University of Antwerp Olieweg 97 2020 Antwerp Belgium. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + England + Green Chem + 101307052 + 1463-9262 + + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 19 + + + 2026 + 5 + 11 + + + 2026 + 6 + 1 + 6 + 18 + + + aheadofprint + + 42222869 + PMC13216855 + 10.1039/d6gc01077f + d6gc01077f + + + + + + 42222848 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2624-8212 + + 9 + + 2026 + + + Frontiers in artificial intelligence + Front Artif Intell + + Federated learning with dynamic weighted aggregation for multi-crop disease detection: a hybrid CNN-transformer approach. + + 1751118 + 1751118 + + 10.3389/frai.2026.1751118 + + Crop diseases pose a significant threat to agriculture globally, causing a loss of 220 billion dollars annually. The problem can be solved through traditional machine learning methods which run on central servers but face two main obstacles: farmers refuse to provide their farming information and the uneven distribution of crop diseases across different regions. This paper introduces a federated learning framework that addresses these challenges through a novel approach that combines hybrid CNN-Transformer architectures with dynamic weighted aggregation. Our system applies EfficientNet-B0 and MobileNetV2 lightweight models which use MobileViT blocks together with CBAM and ESA attention mechanisms to extract detailed features from crop images. The innovation lies in the dynamic aggregation strategy, "AdaClass"-Adaptive Class-Aware aggregation, that identifies the underperforming classes in each round using class-wise F1 score and emphasizes the clients that are having a stronger performance on challenging disease classes.This approach helps in promoting a balanced performance across different disease classes. This particularly benefits the disease classes that are difficult to detect. Extensive experiments on two standard datasets demonstrate strong results: the proposed EfficientNet-B0 hybrid model achieves 99.32% accuracy on PlantVillage and 92.5% on CCMT datasets, while the MobileNetV2 achieves 99.17% and 91.4% respectively. Importantly, these models remain efficient enough for deployment on edge devices, with the MobileNetV2 hybrid requiring only 7.63 MB storage and processing images in 41.2 milliseconds. + Copyright © 2026 Shashank and Bhavadharini. + + + + Shashank + Ragoor + R + + School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. + + + + Bhavadharini + R M + RM + + School of Computer Science and Engineering, Vellore Institute of Technology, Chennai, India. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Artif Intell + 101770551 + 2624-8212 + + + CNN-transformer + agriculture + crop disease detection + dynamic aggregation + federated learning + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 11 + 21 + + + 2026 + 4 + 2 + + + 2026 + 4 + 9 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222848 + PMC13219276 + 10.3389/frai.2026.1751118 + + + + + + 42222846 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2624-8212 + + 9 + + 2026 + + + Frontiers in artificial intelligence + Front Artif Intell + + Dynamic nested hierarchies: self-evolving machine learning architectures for lifelong learning. + + 1804338 + 1804338 + + 10.3389/frai.2026.1804338 + + Contemporary machine learning models, including large language models, exhibit remarkable capabilities in static tasks yet falter in non-stationary environments due to rigid architectures that hinder continual adaptation and lifelong learning. Building upon the nested learning (NL) paradigm, which decomposes models into multi-level optimization problems with fixed update frequencies, this work proposes Dynamic Nested Hierarchies (DNH) as an extension enabling autonomous structural adaptation. Unlike static nested learning where hierarchy depth and update frequencies are fixed at initialization, DNH introduces three biologically-grounded mechanisms: (1) level addition triggered by meta-loss thresholds, analogous to adult neurogenesis in the hippocampal dentate gyrus; (2) level pruning based on gradient contribution, analogous to synaptic elimination; and (3) frequency modulation driven by local surprise signals, analogous to neural oscillation adaptation. We provide explicit mappings between these mechanisms and neuroplasticity processes, moving beyond superficial analogy to principled design. Through rigorous mathematical formulations, we prove convergence bounds of O(1/T + δ2) in non-stationary environments, expressivity improvements bounded by ϵ ≤ O(1/L + + t + ) + γδ, and sublinear regret + O + + ( + + + + T + + + + ) + + compared to static architectures' linear regret. Empirical evaluations on language modeling, continual learning benchmarks-including comparisons with Elastic Weight Consolidation (EWC) and Synaptic Intelligence (SI) as well as modern methods (DER++, MEMO) on Split ImageNet, CLEAR-100, and CORe50-and long-context reasoning validate the theoretical advantages. Comprehensive ablation studies verify the contribution of each component, including the Self-Modifying Memory (SMM) module and Evolutionary Adam (EAdam) optimizer. We provide detailed computational cost analysis and parameter trajectory visualizations demonstrating bounded growth through self-regulating pruning. + Copyright © 2026 Jafari, Ozcinar and Anbarjafari. + + + + Jafari + Akbar Anbar + AA + + Institute of Technology, University of Tartu, Tartu, Estonia. + + + + Ozcinar + Cagri + C + + Institute of Technology, University of Tartu, Tartu, Estonia. + + + + Anbarjafari + Gholamreza + G + + Estonian Business School, Tallinn, Estonia. + + + 3S Holding, Tartu, Estonia. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Artif Intell + 101770551 + 2624-8212 + + + catastrophic forgetting + class-incremental learning + continual learning + dynamic nested hierarchies + lifelong learning + nested optimization + neurogenesis + neuroplasticity-inspired AI + + GA is employed by 3S Holding. 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. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 5 + + + 2026 + 4 + 1 + + + 2026 + 4 + 7 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222846 + PMC13216184 + 10.3389/frai.2026.1804338 + + + + + + 42222842 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2624-8212 + + 9 + + 2026 + + + Frontiers in artificial intelligence + Front Artif Intell + + Artificial intelligence and data analytics in human talent management. + + 1793296 + 1793296 + + 10.3389/frai.2026.1793296 + + Digital transformation has reshaped human talent management, with Artificial Intelligence (AI) and Data Analytics emerging as key tools for optimizing recruitment, retention, performance evaluation, and employee development. Despite growing interest, the literature remains fragmented across technical, managerial, and ethical perspectives. This study provides an integrated bibliometric and qualitative analysis of research on AI and Data Analytics in human talent management from 2015 to 2025, aiming to map evolution, thematic trends, methodological developments, and research gaps. + A mixed-methods approach combined bibliometric analysis with qualitative synthesis. A structured search in Scopus and Web of Science retrieved 137 records using Boolean operators combining AI/machine learning terms with People/HR Analytics and talent management concepts. After removing duplicates and irrelevant studies based on predefined inclusion criteria (peer-reviewed articles and reviews in English or Spanish), a final dataset of 82 documents was analyzed. Bibliometric techniques in R (v4.4.2) and VOSviewer (v1.6.20) examined scientific productivity, citation patterns, collaboration networks, and keyword co-occurrence. Additionally, the 30 most cited articles underwent qualitative synthesis to extract key conceptual and methodological contributions. + Scientific production showed exponential growth, particularly after 2020, with peaks in 2024 (21 articles) and 2025 (27 articles). Research concentrated in India, Germany, the United States, and the United Kingdom, with limited contributions from Latin America and Africa. Four thematic clusters emerged: (1) People Analytics and process optimization, (2) predictive models and machine learning, (3) data governance and ethical considerations, and (4) convergence of AI, Big Data, and advanced analytics. Highly cited studies highlighted advances in predictive modeling for turnover and recruitment, while also emphasizing ethical risks such as algorithmic bias and the need for explainable AI (XAI). + The field is evolving toward the integration of predictive capabilities with organizational decision-making and ethical frameworks, shifting human talent management from reactive to proactive, data-driven practices. However, gaps persist in empirical validation of models, standardization of ethical guidelines, interdisciplinary collaboration, and geographic diversity. This study contributes a unified socio-technical perspective that links technological innovation with organizational processes and governance. The findings offer a foundation for future research on context-specific, ethically grounded AI applications in emerging markets and support organizations in leveraging analytics for more adaptive and responsible talent management. + Copyright © 2026 Sarmiento Orna, Apolo-Silva, Solís-Naranjo, Borja-Salinas, Espinoza-Solis and Peralta-Gamboa. + + + + Sarmiento Orna + Diana Alexandra + DA + + Universidad Estatal de Milagro, Milagro, Ecuador. + + + + Apolo-Silva + Mariuxi Fernanda + MF + + Universidad Tecnica Estatal de Quevedo, Quevedo, Ecuador. + + + + Solís-Naranjo + Alvaro Paul + AP + + Universidad Estatal de Bolívar, Bolívar, Ecuador. + + + + Borja-Salinas + Ely Israel + EI + + Universidad Estatal de Milagro, Milagro, Ecuador. + + + + Espinoza-Solis + Eduardo Javier + EJ + + Universidad Estatal de Milagro, Milagro, Ecuador. + + + + Peralta-Gamboa + Dennis Alfredo + DA + + Universidad Estatal de Milagro, Milagro, Ecuador. + + + + eng + + Journal Article + Systematic Review + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Artif Intell + 101770551 + 2624-8212 + + + data-driven decision-making + digital transformation + emerging technologies + human resources innovation + intelligent automation + predictive analytics + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 1 + 21 + + + 2026 + 4 + 8 + + + 2026 + 4 + 13 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222842 + PMC13219358 + 10.3389/frai.2026.1793296 + + + + + + 42222841 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2624-8212 + + 9 + + 2026 + + + Frontiers in artificial intelligence + Front Artif Intell + + Transformer-based NLP approaches for credit risk prediction: a systematic review. + + 1819994 + 1819994 + + 10.3389/frai.2026.1819994 + + This study systematically reviews transformer based Natural Language Processing (NLP) and Large Language Model (LLM) approaches for credit risk prediction, addressing limitations of traditional structured data credit scoring models. + A PRISMA guided systematic literature review was conducted across Scopus, ScienceDirect, and Web of Science, covering English language studies published between 2015 and 2025. A total of 284 records were identified and screened using semantic similarity filtering, resulting in 63 eligible studies for qualitative synthesis. + The findings show that transformer based architectures, including Bidirectional Encoder Representations from Transformers (BERT), Robustly Optimized BERT Pretraining Approach (RoBERTa), and Large Language Model Meta AI (LLaMA), consistently outperform traditional statistical and machine learning baselines across financial prediction tasks. Attention based Long Short Term Memory (LSTM) models reported Area Under the Curve (AUC) improvements of 3.08% and KS increases of 10.3% over classical methods, while hybrid Convolutional Neural Network (CNN) Transformer architectures achieved accuracy levels up to 96.9% and F1 scores of 0.995 in credit risk datasets. Multimodal and transformer based systems also reported accuracy levels exceeding 95% in financial risk monitoring tasks. However, only a limited subset of studies incorporates formal explainability frameworks or fairness evaluations. + Transformer based NLP approaches improve credit risk prediction by effectively leveraging unstructured textual data. Nevertheless, challenges remain in interpretability, transparency, regulatory alignment, and ethical deployment. Future research should prioritize bias mitigation and governance aware model design to support responsible use in regulated financial environments. + Copyright © 2026 Raliphada, Olukanmi and Olusanya. + + + + Raliphada + Pfarelo + P + + School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, Gauteng, South Africa. + + + + Olukanmi + Seun + S + + School of Computer Science and Applied Mathematics, University of the Witwatersrand, Johannesburg, Gauteng, South Africa. + + + + Olusanya + Micheal + M + + Department of Computer Science and Information Technology, Sol Plaatje University, Kimberley, Northern Cape, South Africa. + + + + eng + + Journal Article + Systematic Review + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Artif Intell + 101770551 + 2624-8212 + + + BERT + LLaMA + Natural Language Processing (NLP) + RoBERTa + classification + credit risk + sentiment analysis + transformer models + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 28 + + + 2026 + 4 + 13 + + + 2026 + 4 + 20 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222841 + PMC13219308 + 10.3389/frai.2026.1819994 + + + + + + 42222818 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2470-1343 + + 11 + 20 + + 2026 + May + 26 + + + ACS omega + ACS Omega + + Machine Learning in Non-fullerene Organic Solar Cells: Accelerating Discovery, Design, and Understanding. + + 29307 + 29333 + 29307-29333 + + 10.1021/acsomega.6c01194 + + The emergence of nonfullerene acceptors (NFAs) has transformed the performance of organic solar cells (OSCs), driving laboratory power conversion efficiencies beyond 19%. Yet the immense combinatorial space of possible donor-acceptor materials renders exhaustive experimental exploration impractical. Machine learning (ML) and artificial intelligence have therefore become powerful tools for accelerating materials discovery, performance prediction, and molecular design in organic photovoltaics. This review provides a critical and systematic assessment of predictive and generative ML approaches applied to OSCs, encompassing methods ranging from random forests and gradient boosting to deep learning architectures such as graph neural networks, transformers, variational autoencoders, generative adversarial networks, and genetic algorithms. We examine advances in molecular representation and feature engineering, evaluate model performance in predicting key device metricsincluding power conversion efficiency, open-circuit voltage, short-circuit current density, and fill factorand assess the extent to which computational predictions translate into experimentally validated devices. We further discuss key limitations, including data set bias, distribution shift, morphology-related effects, chemical validity, and the synthetic accessibility of generated candidates, as well as the persistent gap between predicted and realized performance. Finally, we highlight emerging directions poised to shape the next phase of ML-guided OSC research, including physics-informed learning, multiobjective optimization, interpretable models, active learning coupled with first-principles calculations, and human-in-the-loop discovery pipelines. Collectively, these advances position machine learning as a central component in the rational design and accelerated development of next-generation organic photovoltaic materials. + © 2026 The Authors. Published by American Chemical Society. + + + + Das + Bibhas + B + 0000-0002-9671-5275 + + Department of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar 382355, Gujarat, India. + 242275 + + + + Mondal + Anirban + A + 0000-0003-3029-8840 + + Department of Chemistry, Indian Institute of Technology Gandhinagar, Gandhinagar 382355, Gujarat, India. + 242275 + + + + eng + + Journal Article + Review + + + 2026 + 05 + 11 + +
+ + United States + ACS Omega + 101691658 + 2470-1343 + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 1 + 31 + + + 2026 + 4 + 24 + + + 2026 + 5 + 5 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222818 + PMC13216995 + 10.1021/acsomega.6c01194 + + + + + + 42222805 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2470-1343 + + 11 + 20 + + 2026 + May + 26 + + + ACS omega + ACS Omega + + Leveraging Neural Networks to Correct FoldX Free Energy Estimates. + + 30003 + 30013 + 30003-30013 + + 10.1021/acsomega.6c01409 + + Fast and accurate prediction of folding stability and binding affinity due to protein mutations is a critical need in structural biology. While experimental methods are accurate, they are often time-consuming and costly. Computational approaches like FoldX offer fast and inexpensive alternatives by estimating free energy changes (ΔΔG) from a single 3D structure, but their accuracy remains limited. In this work, we present dFX, a neural-network-based residual correction framework that improves FoldX free energy predictions by learning from its underlying energy terms. We first assembled a collection of experimentally measured ΔΔG values and their corresponding 3D structures. Using those structures, we obtained baseline ΔΔG predictions with FoldX, along with their contributing energy terms. We then trained dFX using these FoldX energy terms as input features and a correction factor as the target, where the correction factor is the difference between experimental and FoldX ΔΔG. Our dFX models improve the prediction accuracy relative to FoldX. The dFX correction leads to improvements in Pearson correlation between experimental and predicted ΔΔG relative to FoldX across folding and binding tasks. Models trained on lower-order mutations retain predictive capability for higher-order mutations in binding. Our dFX models also lead to improvements in epistasis prediction relative to FoldX, suggesting a better capture of nonadditive effects. To test the generalizability of our models, we used an external SARS-CoV-2 data set and found that the dFX model trained on single mutations for binding outperformed FoldX and other machine-learning approaches. Once trained, our neural network models add minimal computational time but provide improvements in accuracy, making them a valuable addition to any FoldX free energy prediction pipeline. Our dFX approach could be further optimized to predict antibody escape, aiding in the efficient development of watch lists. + © 2026 The Authors. Published by American Chemical Society. + + + + Barnes + Jonathan E + JE + 0000-0002-5434-1391 + + Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States. + 5640 + + + + Chi + L América + LA + 0000-0002-2606-0357 + + Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States. + 5640 + + + Department of Chemical and Biological Engineering, University of Idaho, Moscow, Idaho 83844, United States. + + + Department of Physics, University of Idaho, Moscow, Idaho 83844, United States. + + + + Pandey + Shubham Kumar + SK + 0000-0001-6138-0738 + + Department of Chemical and Biological Engineering, University of Idaho, Moscow, Idaho 83844, United States. + + + + Ytreberg + F Marty + FM + 0000-0001-7439-8224 + + Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States. + 5640 + + + Department of Physics, University of Idaho, Moscow, Idaho 83844, United States. + + + + Patel + Jagdish Suresh + JS + 0000-0003-4999-5347 + + Institute for Modeling Collaboration and Innovation, University of Idaho, Moscow, Idaho 83844, United States. + 5640 + + + Department of Chemical and Biological Engineering, University of Idaho, Moscow, Idaho 83844, United States. + + + + eng + + Journal Article + + + 2026 + 05 + 11 + +
+ + United States + ACS Omega + 101691658 + 2470-1343 + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 2 + 5 + + + 2026 + 5 + 4 + + + 2026 + 5 + 5 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222805 + PMC13216946 + 10.1021/acsomega.6c01409 + + + + + + 42222797 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2470-1343 + + 11 + 20 + + 2026 + May + 26 + + + ACS omega + ACS Omega + + Investigating the Optical Properties of Gold Nanorods Using Forward and Inverse Design. + + 29374 + 29389 + 29374-29389 + + 10.1021/acsomega.5c05623 + + Nanoparticles exhibit distinct optical properties due to their size, shape, and material composition, making them useful in photonics, sensing, and biological applications. Mie and Mie-Gans theory can be used to compute the optical properties of spherical and rod-shaped nanoparticles, which are limited by surface scattering and radiation damping; however, it cannot explain the optical properties of bipyramid, dumbbell, cube, and rectangular-shaped nanoparticles. Advanced computational techniques like Finite Difference Time Domain (FDTD) could be used to simulate the optical properties of complex-shaped nanoparticles, which is time-consuming and complex. To address this, we proposed to use a machine and deep learning-based forward and inverse design to explore the optical properties of gold nanorods. In forward design, we evaluated five different models and identified the XGB Regressor as the best performing model (MSE: 0.0041, MAE: 0.0190, R2: 0.994), which accurately predicts the absorption and scattering cross sections of the gold nanorod across a wavelength range of 400-1500 nm. For inverse design, we found the tandem model (MSE:0.000116, MAE: 0.004476, R2: 0.9521), which accurately identifies the length (L), width (W), and aspect ratio (AR) of the gold nanorod for achieving desired optical responses. The results provide critical insights into the underlying physics of optical effects in nanoparticles and demonstrate the capability of the integrated forward-inverse methodology for the systematic design of photonic devices and applications based on nanoparticles. The study's findings will aid in investigating the optical properties of complex-shaped nanoparticles by leveraging the synthesis and numerical complexity and optical response. The findings will pave the way for the use of specific-size-and-shape nanoparticles with specific optical properties (such as absorption and scattering) in photothermal therapy, bioimaging, biological sensing, solar cells, photonic devices, nanoantennas, and cell biology. + © 2026 The Authors. Published by American Chemical Society. + + + + Islam + Sabrina + S + 0009-0006-3584-6796 + + Nanotechnology, AI, IoT, and Applied Machine Learning Research Group, Department of Electrical and Electronic Engineering, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh. + 118864 + + + + Mohsin + Abu S M + ASM + 0000-0001-9281-9088 + + Nanotechnology, AI, IoT, and Applied Machine Learning Research Group, Department of Electrical and Electronic Engineering, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh. + 118864 + + + + Bhuian + Mohammed Belal Hossain + MBH + + Nanotechnology, AI, IoT, and Applied Machine Learning Research Group, Department of Electrical and Electronic Engineering, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh. + 118864 + + + + Rahman + Md Mosaddequr + MM + + Nanotechnology, AI, IoT, and Applied Machine Learning Research Group, Department of Electrical and Electronic Engineering, BRAC University, Kha 224 Bir Uttam Rafiqul Islam Avenue, Merul Badda, Dhaka 1212, Bangladesh. + 118864 + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + United States + ACS Omega + 101691658 + 2470-1343 + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 7 + 20 + + + 2025 + 11 + 30 + + + 2025 + 12 + 2 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222797 + PMC13216947 + 10.1021/acsomega.5c05623 + + + + + + 42222787 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2470-1343 + + 11 + 20 + + 2026 + May + 26 + + + ACS omega + ACS Omega + + Deciphering Molecular and Solvent Effects on Aqueous and Organic Solubility through Interpretable Machine Learning Approaches. + + 29757 + 29771 + 29757-29771 + + 10.1021/acsomega.5c13630 + + Solubility of a chemical compound is highly dependent on both its molecular structure and the experimental conditions at which the solubility is measured, which makes it a challenging task to accurately predict solubility across aqueous and organic media. In this study, an interpretable machine learning framework was developed for predicting the solubility of drug-like compounds using large-scale curated data sets, including AqSolDB, AqSolDBc, BigSolDB, and BigSolDB 2.0. CatBoost models were trained and rigorously validated using repeated 5-fold cross-validation, employing scaffold-based splitting for aqueous solubility and cold solute-solvent pair splitting for organic solubility. Feature selection and hyperparameter tuning were systematically applied, with hyperparameter optimization emerging as the primary contributor to performance improvement. The optimized models demonstrated strong and statistically significant performance improvement over baseline configurations. Similarity-based domain-of-applicability analysis on the external data set revealed that prediction error increases with structural dissimilarity from the training set. SHAP analysis provided mechanistic insights, showing that aqueous solubility is primarily governed by polarity and hydrogen-bonding descriptors, whereas organic solubility is influenced by solvent characteristics, temperature, and molecular topology. The consistency of results across data sets demonstrates the robustness and transferability of the learned structure-property relationships and the applicability of the proposed approach for real-world solubility prediction tasks. + © 2026 The Authors. Published by American Chemical Society. + + + + Gopichand + Boinapalli + B + 0009-0001-6652-6326 + + Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amrithapuri, Kerala 690525, India. + + + + Nair + Gopika S + GS + + Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amrithapuri, Kerala 690525, India. + + + + Nair + Bipin G + BG + 0000-0002-4944-8805 + + Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amrithapuri, Kerala 690525, India. + + + + Melethadathil + Nidheesh + N + 0000-0002-1451-979X + + Amrita School of Biotechnology, Amrita Vishwa Vidyapeetham, Amrithapuri, Kerala 690525, India. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + United States + ACS Omega + 101691658 + 2470-1343 + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 3 + + + 2026 + 4 + 21 + + + 2026 + 4 + 29 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222787 + PMC13216938 + 10.1021/acsomega.5c13630 + + + + + + 42222777 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2470-1343 + + 11 + 20 + + 2026 + May + 26 + + + ACS omega + ACS Omega + + Design of Multifunctional Bioactive Peptides for Potential Application in Food Packaging: A Machine-Learning-Integrated Optimization Framework. + + 29979 + 29991 + 29979-29991 + + 10.1021/acsomega.6c01238 + + Multifunctional bioactive peptides with antioxidant, antifungal, and antibacterial properties can be highly effective for the development of active packaging systems, thereby playing a pivotal role in alleviating foodborne diseases. With peptide design being a combinatorial problem, it is an extremely arduous process to screen the entire sequence space and discover peptides with multifunctional properties. In this study, a computational framework that integrates machine learning and optimization for the design of multifunctional bioactive peptides was proposed. Three machine-learning-based binary classification models were developed to screen antioxidant, antifungal, and antibacterial peptides, respectively. Accuracies for models 1, 2, and 3 were found to be 82.37%, 95.49%, and 96.75%, respectively. These models are then incorporated into an optimization framework to design new bioactive peptides with antioxidant, antifungal, and antibacterial properties. Analysis of amino acid compositions for these newly generated peptides revealed that amino acids that are characteristic of antioxidant, antifungal, and antibacterial peptides are present in significant proportions in these newly generated peptide sequences. This shows that the proposed computational framework indeed generates peptides with multifunctional properties, thereby indicating their potential applications in food packaging. A free-to-use executable tool that can expedite the design of such multifunctional bioactive peptides has also been developed. + © 2026 The Authors. Published by American Chemical Society. + + + + Gunapati + Sree Nithish Reddy + SNR + + Chemical Engineering Department Birla, Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Jawahar Nagar, Medchal District, Hyderabad 500078, Telangana, India. + 209298 + + + + Gunaseelan + Sathish Kumar + SK + + Chemical Engineering Department Birla, Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Jawahar Nagar, Medchal District, Hyderabad 500078, Telangana, India. + 209298 + + + + Dutta + Arnab + A + 0000-0001-8596-0112 + + Chemical Engineering Department Birla, Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Jawahar Nagar, Medchal District, Hyderabad 500078, Telangana, India. + 209298 + + + + Mitra + Debirupa + D + 0000-0001-5280-0761 + + Chemical Engineering Department Birla, Institute of Technology and Science (BITS) Pilani, Hyderabad Campus, Jawahar Nagar, Medchal District, Hyderabad 500078, Telangana, India. + 209298 + + + + eng + + Journal Article + + + 2026 + 05 + 12 + +
+ + United States + ACS Omega + 101691658 + 2470-1343 + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 2 + + + 2026 + 4 + 20 + + + 2026 + 5 + 4 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222777 + PMC13216959 + 10.1021/acsomega.6c01238 + + + + + + 42222760 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2752-6542 + + 5 + 5 + + 2026 + May + + + PNAS nexus + PNAS Nexus + + Uncovering controlling factors on rock glacier velocities in the Pamir-Karakoram-Kunlun region using explainable machine learning. + + pgag177 + pgag177 + + 10.1093/pnasnexus/pgag177 + + Rock glaciers are ice-debris landforms commonly found in high mountain environments. Shaped by long-term creep of ice-rich permafrost, they provide critical information for permafrost studies, mountain hydrology, and hazard assessment. Although the characteristics and controlling factors of rock glacier velocities across various temporal scales have been studied at individual sites, their environmental drivers in the spatial domain over large regions remain poorly understood. In this study, we employ four machine learning methods, i.e. support vector machine, extreme gradient boost, random forest, and backpropagation neural network, to model the relationship between rock glacier velocities and environmental variables for 5,163 rock glaciers in the Pamir-Karakoram-Kunlun region. Subsequently, we use SHapley Additive exPlanations to quantify variable importance. Results show that the upslope connection to a glacier is a critical factor controlling rock glacier velocities. In our study area, glacier-connected rock glaciers exhibit on average faster movement (median velocity = 38 cm/year) than talus-connected ones (median velocity = 28 cm/year). We also find that geomorphological properties exert stronger controls on the spatial variability of rock glacier velocities than regional climate variability. Rock glacier area and slope are identified as the second and third most important variables, with larger areas and steeper slopes associated with higher velocities. The snow cover duration ranks fourth, followed by precipitation, while air temperature shows minimal influence on velocity. Overall, these findings bridge a critical knowledge gap regarding the environmental controls on rock glacier dynamics at the regional scale, extending our understanding of rock glacier kinematics beyond site-specific investigations. + © The Author(s) 2026. Published by Oxford University Press on behalf of National Academy of Sciences. + + + + Sun + Zhangyu + Z + 0009-0009-9762-3311 + + Jiangsu Key Laboratory of Soil and Water Processes in Watershed, College of Geography and Remote Sensing, Hohai University, Nanjing 211100, China. + + + Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong 999077, China. + + + Institute of Geodesy, Graz University of Technology, Graz 8010, Austria. + + + + Liu + Lin + L + 0000-0002-9581-1337 + + Department of Earth and Environmental Sciences, Faculty of Science, The Chinese University of Hong Kong, Hong Kong 999077, China. + + + Institute of Environment, Energy and Sustainability, The Chinese University of Hong Kong, Hong Kong 999077, China. + + + + Bolch + Tobias + T + 0000-0002-8201-5059 + + Institute of Geodesy, Graz University of Technology, Graz 8010, Austria. + + + + eng + + Journal Article + + + 2026 + 05 + 20 + +
+ + England + PNAS Nexus + 9918367777906676 + 2752-6542 + + + High Mountain Asia + SHAP + control factor + machine learning + rock glacier velocity + + The authors declare no competing interests. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 3 + + + 2026 + 5 + 5 + + + 2026 + 6 + 1 + 6 + 18 + + + epublish + + 42222760 + PMC13220748 + 10.1093/pnasnexus/pgag177 + pgag177 + + + + + + 42222720 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2771-9316 + + 4 + 5 + + 2026 + May + 25 + + + Precision chemistry + Precis Chem + + Benchmarking Foundation Potentials against Quantum Chemistry Methods for Predicting Molecular Redox Potentials. + + 612 + 621 + 612-621 + + 10.1021/prechem.5c00258 + + Computational high-throughput virtual screening is essential for identifying redox-active molecules for sustainable applications, such as electrochemical carbon capture. A primary challenge in this approach is the high computational cost associated with accurate quantum chemistry calculations. Machine learning foundation potentials (FPs) trained on extensive density functional theory (DFT) calculations offer a computationally efficient alternative. Here, we benchmark the MACE-OMol-0 and UMA FPs against a hierarchy of DFT functionals for predicting experimental molecular redox potentials for both electron transfer (ET) and proton-coupled electron transfer (PCET) reactions. We find that these FPs achieve exceptional accuracy for PCET processes, rivaling their target DFT method. However, the performance is diminished for ET reactions, particularly for multielectron transfers involving reactive ions that are underrepresented in the OMol25 training data, revealing a key out-of-distribution limitation. To overcome this, we propose an optimal hybrid workflow that uses the FPs for efficient geometry optimization and thermochemical analysis, followed by a crucial single-point DFT energy refinement and an implicit solvation correction. This pragmatic approach provides a robust and scalable strategy for accelerating high-throughput virtual screening in sustainable chemistry. + © 2026 The Authors. Co-published by University of Science and Technology of China and American Chemical Society. + + + + Chen + Yicheng + Y + + Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore. + 37580 + + + + Cheng + Lixue + L + 0000-0002-7329-0585 + + Department of Chemistry, Hong Kong University of Science and Technology, Kowloon, Hong Kong 999077, China. + 58207 + + + + Jing + Yan + Y + 0000-0002-5669-4609 + + Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore. + 37580 + + + + Zhong + Peichen + P + 0000-0003-1921-1628 + + Department of Materials Science and Engineering, National University of Singapore, Singapore 117575, Singapore. + 37580 + + + + eng + + Journal Article + + + 2026 + 01 + 23 + +
+ + United States + Precis Chem + 9918574281706676 + 2771-9316 + + + carbon capture + high-throughput screening + machine learning interatomic potential + proton-coupled electron transfer + redox potential + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 10 + 28 + + + 2026 + 1 + 17 + + + 2026 + 1 + 18 + + + 2026 + 6 + 1 + 6 + 17 + + + epublish + + 42222720 + PMC13217336 + 10.1021/prechem.5c00258 + + + + + + 42222666 + + 2026 + 06 + 01 + +
+ + 2667-3215 + + 7 + + 2025 + Jun + + + SSM. Qualitative research in health + SSM Qual Res Health + + Anticipatory moral distress in machine learning-based clinical decision support tool development: A qualitative analysis. + 100540 + 10.1016/j.ssmqr.2025.100540 + + Ongoing interest in machine learning systems include the emerging capability to integrate electronic health records to develop clinical decision support (CDS) tools that improve medical care, diagnostics, and therapy. Such CDS tools, which can handle a large quantity of data sources, can advise clinicians and amplify insights on diverse patient risk factors, from physiological challenges to psychosocial vulnerabilities. Despite a growing interest, there are various challenges that hinder the successful use of CDS tools in clinical practice. Among these, a key challenge is hesitance or resistance among end-users to take up tools and integrate their use into practice. The current inquiry applied a framework of the symbolic interaction of participatory experience-based co-design and used an interpretive descriptive approach to analysis of qualitative data, investigating the ethical issues brought to light by clinicians participating in three participatory experience-based co-design focus groups, as a part of the initial development of a CDS tool for detecting risk factors for adverse health outcomes in outpatient obstetric care at a single academically affiliated medical institution. Findings revealed that participants describe their anticipated symbolic relationship with a ML-based CDS tool as either promising or morally distressing. Anticipatory moral distress includes three separate sub-categories: 1) clinical conflict with clinical assessment and judgment, 2) partial conflict with comprehensive clinical considerations, and 3) resource conflict with structural barriers related to care delivery. Future work should include utilizing participatory experience-based co-design with end users to identify relevant context and institution-specific priorities and concerns from the beginning of CDS tool development and to continue co-design throughout the development process. + + + + Whitney + Clare + C + 0000-0003-2219-8911 + + School of Nursing, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA. + + + + Preis + Heidi + H + 0000-0002-0459-290X + + Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA. + + + Department of Psychology, Stony Brook University, 100 Nicolls Rd, Stony Brook, NY, 11794, USA. + + + + Vargas + Alessa Ramos + AR + + Department of Obstetrics, Gynecology and Reproductive Medicine, Renaissance School of Medicine, Stony Brook University, 101 Nicolls Rd, Stony Brook, NY, 11794, USA. + + + + eng + + Journal Article + + + 2025 + 02 + 21 + +
+ + England + SSM Qual Res Health + 9918300877606676 + 2667-3215 + + + Artificial intelligence + Bioethics + Clinical decision-making + Machine learning + Moral distress + Perinatal health + Qualitative research + + Declaration of competing interest 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 6 + 17 + + + ppublish + + 42222666 + PMC13218729 + 10.1016/j.ssmqr.2025.100540 + 100540 + + + + + + 42222663 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1179-5549 + + 20 + + 2026 + + + Clinical Medicine Insights. Oncology + Clin Med Insights Oncol + + Machine Learning-Based Identification and Validation of PYCR1 and PYGM as Prognostic Biomarkers for Osteosarcoma. + + 11795549261452567 + 11795549261452567 + + 10.1177/11795549261452567 + + Osteosarcoma (OS) is a malignant tumor originating in the bones, predominantly affecting children and adolescents, characterized by high aggressiveness and poor prognosis. Identifying new prognostic biomarkers is crucial for improving the diagnosis and treatment of OS. + In this study, we collected gene expression data from 88 OS samples from the UCSC Xena platform and normal tissue expression data from 396 Genotype-Tissue Expression (GTEx) samples. Prognosis-related genes were first screened by univariate Cox regression and then further selected using the Least Absolute Shrinkage and Selection Operator (LASSO) regression. Based on these candidate genes, non-negative matrix factorization (NMF) was used for molecular subtype identification, and the Kaplan-Meier analysis was applied to compare survival among subtypes. Tumor microenvironment and immune cell infiltration analyses were performed to characterize differences between risk groups. In addition, the expression patterns of key genes were validated by quantitative real-time polymerase chain reaction (qRT-PCR), hematoxylin-eosin staining, immunohistochemistry, and immunofluorescence. + Pyrroline-5-carboxylate reductase 1 (PYCR1) was consistently upregulated in OS and was associated with poor prognosis. In contrast, glycogen phosphorylase, muscle-associated (PYGM) showed analysis-level-dependent expression patterns: it was downregulated at the bulk transcriptomic and tumor cell levels compared with normal controls, whereas within the OS cohort, relatively higher PYGM expression was observed in the high-risk group. Tumor microenvironment and immune cell infiltration analyses revealed significant immune differences between high- and low-risk groups. Histological and protein-level assays further confirmed the presence and cellular localization of PYCR1 and PYGM in OS tissues. + This study systematically identified and validated PYCR1 and PYGM as potential prognostic biomarkers for OS using integrated statistical and machine learning approaches. The PYCR1 showed a consistently tumor-promoting expression pattern, whereas PYGM demonstrated context-dependent expression changes across bulk tissue, risk-stratified tumor samples, and tumor cell lines, highlighting the biological complexity of metabolic biomarkers in OS. + © The Author(s) 2026. + + + + Xu + Guoyong + G + 0000-0003-0636-6831 + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + + Liu + Chong + C + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China. + + + + Xue + Jiang + J + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + + Chen + Jiarui + J + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + + Zou + Zhuan + Z + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + + Mo + Sen + S + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + + Zhou + Zhongxian + Z + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + + Zhan + Xinli + X + + Guangxi Medical University, The First Clinical Medical College, Nanning, P. R. China. + + + Spine and Osteopathy Ward, The First Affiliated Hospital of Guangxi Medical University, Nanning, P. R. China. + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + United States + Clin Med Insights Oncol + 101525771 + 1179-5549 + + + PYCR1 + PYGM + immune cell infiltration + osteosarcoma + prognostic biomarkers + tumor microenvironment + + The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 7 + 23 + + + 2026 + 5 + 4 + + + 2026 + 6 + 1 + 6 + 17 + + + epublish + + 42222663 + PMC13219957 + 10.1177/11795549261452567 + 10.1177_11795549261452567 + + + + + + 42222599 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-8021 + + 17 + + 2026 + + + Frontiers in genetics + Front Genet + + Tumor ecosystem subtyping of breast cancer based on somatic mutations and network propagation reveals distinct prognostic and genomic landscapes. + + 1847952 + 1847952 + + 10.3389/fgene.2026.1847952 + + Breast cancer is the most common malignancy in women worldwide, exhibiting high heterogeneity that complicates diagnosis, treatment, and prognosis. While somatic mutations stably reveal genetic characteristics of tumor cells, their application in breast cancer subtyping remains underexplored. + A total of 2,526 breast cancer patients from Memorial Sloan Kettering Cancer Center were classified into different tumor ecosystem subtypes (TESs) based on somatic mutation profiles using a network propagation algorithm. + The prognosis of breast cancer patients in TES 1 was significantly better than that of those in TES 2. Immunological characterization further revealed that the tumor microenvironment contained significantly more tumor immune cells in TES 1 than in TES 2, and that TES 2 had lower response to immunotherapy but was more sensitive to chemotherapeutic agents. Moreover, our tumor ecosystem subtyping method effectively classified patients across 20 cancer cohorts with good generalization. + This study proposes a stable, reproducible, and clinically applicable subtyping strategy based on somatic mutation data for tumor ecosystem subtyping, which can be used to guide personalized treatment for breast cancer patients and promote the development of precision medicine. + Copyright © 2026 Ding, Zhu, Huo, Li, Chen, Wang and Yang. + + + + Ding + Ke + K + + The First Affiliated Hospital of Harbin Medical University, School of Stomatology, Harbin Medical University, Harbin, China. + + + + Zhu + Zixuan + Z + + The First Affiliated Hospital of Harbin Medical University, School of Stomatology, Harbin Medical University, Harbin, China. + + + + Huo + Zhengchun + Z + + College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. + + + + Li + Honghao + H + + College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. + + + + Chen + Haoran + H + + The First Affiliated Hospital of Harbin Medical University, School of Stomatology, Harbin Medical University, Harbin, China. + + + + Wang + Shiyuan + S + + College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. + + + + Yang + Lei + L + + College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Genet + 101560621 + 1664-8021 + + + breast cancer + immunotherapy response + machine learning + network propagation + somatic mutation profile + tumor ecosystem + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 4 + 5 + + + 2026 + 4 + 19 + + + 2026 + 4 + 22 + + + 2026 + 6 + 1 + 6 + 16 + + + epublish + + 42222599 + PMC13218690 + 10.3389/fgene.2026.1847952 + 1847952 + + + + + + 42222576 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2832-3637 + + 4 + 5 + + 2026 + May + 25 + + + Chemical & biomedical imaging + Chem Biomed Imaging + + Machine-Learning-Based Automated Schlemm's Canal Volumetric Segmentation for Optical Coherence Tomography. + + 794 + 805 + 794-805 + + 10.1021/cbmi.5c00156 + + Volumetric segmentation of Schlemm's Canal (SC) in optical coherence tomography (OCT) is time-consuming, creating a barrier to experiments studying glaucoma and the anatomy of the trabecular outflow pathways in vivo. To this end, we developed an automated segmentation tool, Schlemm's Canal-Localization and Semantic Segmentation (SC-LSS), for the volumetric segmentation of SC in in vivo mice eyes from visible-light OCT (vis-OCT). SC-LSS first localizes the boundaries of SC and subsequently determines the boundaries of SC within the localized region. We used 324 B-scans from 16 mouse eyes for training, validation, and testing the model, and 203 additional B-scans to evaluate the model's accuracy. We found that the Dice coefficient between segmentations generated by SC-LSS and manual expert graders was 0.70 ± 0.20 and that the Dice coefficient between two expert graders was 0.73 ± 0.18 (p = 0.10). Furthermore, SC-LSS captured decreases in SC size with increasing intraocular pressure, yielding a 51.5% decrease in SC size at 20 mmHg compared to 5 mmHg. SC-LSS also identified a 20.1% increase in SC size following the administration of pilocarpine. We anticipate that SC-LSS will accelerate studies on factors regulating the trabecular outflow pathways and their role in glaucoma development and management. + © 2025 The Authors. Co-published by Nanjing University and American Chemical Society. + + + + Fang + Raymond + R + 0000-0002-0647-8537 + + Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States. + 3270 + + + Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States. + + + + Xu + Fengyuanshan + F + + Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States. + 3270 + + + + Yan + Zihang + Z + + Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States. + 3270 + + + + Sun + Cheng + C + 0000-0002-2744-0896 + + Department of Mechanical Engineering, Northwestern University, Evanston, Illinois 60208, United States. + + + + Kume + Tsutomu + T + + Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States. + + + + Huang + Alex S + AS + + Department of Ophthalmology, University of California San Diego, La Jolla, California 92093, United States. + 8784 + + + + Zhang + Hao F + HF + + Department of Biomedical Engineering, Northwestern University, Evanston, Illinois 60208, United States. + 3270 + + + Department of Ophthalmology, Northwestern University Feinberg School of Medicine, Chicago, Illinois 60611, United States. + + + + eng + + Journal Article + + + 2025 + 11 + 11 + +
+ + United States + Chem Biomed Imaging + 9918574388106676 + 2832-3637 + + + Schlemm’s Canal + aqueous outflow + image processing + machine learning + optical coherence tomography + optical imaging + segmentation + volumetric + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 9 + 6 + + + 2025 + 11 + 3 + + + 2025 + 11 + 5 + + + 2026 + 6 + 1 + 6 + 16 + + + epublish + + 42222576 + PMC13217338 + 10.1021/cbmi.5c00156 + + + + + + 42222575 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2832-3637 + + 4 + 5 + + 2026 + May + 25 + + + Chemical & biomedical imaging + Chem Biomed Imaging + + An Automated Workflow Leveraging Machine Learning for Physical Titer Determination from Cryo-TEM Images of Adeno-Associated Virus Capsids. + + 767 + 775 + 767-775 + + 10.1021/cbmi.5c00070 + + The application of gene therapies could be life-changing for many previously untreatable health conditions, but it is currently both financially expensive and time-intensive. Controlling the quality of these gene products involves monitoring impurities, such as partially filled and empty viral vectors, aggregates, and debris from the desired productfull viral capsids containing the genetic material for the gene of interest. Transmission electron microscopy (TEM), with its ability to identify nanometer-sized structures, provides a good approximation of ground truth to determine the presence of each of these species in production samples. Unfortunately, TEM methods are limited by the potential for sample damage during preparation and the difficulty in distinguishing viruses from cellular debris. They can also incur significant monetary and labor costs, as the images are often manually screened by trained individuals using subjective classification. Here, computer vision (CV) has been utilized to automate the labeling and classification steps within this analysis process, thereby improving the accuracy of impurity measurements in TEM imaging techniques. The manual analysis step may be superseded by a machine-learned model that predicts the classification of objects in TEM images. In this study, a CV model was trained to classify individual capsid images into one of three categoriesfull, partially full, or emptybased on the amount of DNA they contained. This model was then used to predict capsid category distributions with a 72% accuracy. A graphical user interface, Capsidize, was developed as an accessible and standardized method of labeling full, partial, and empty capsid physical titers in cryo-TEM images, reducing the active working time for labeling by 95%. + © 2025 The Authors. Co-published by Nanjing University and American Chemical Society. + + + + Gutierrez + Lilianna C + LC + + Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3815, United States. + 6612 + + + + Robinson + Anne Skaja + AS + 0000-0001-7235-1481 + + Department of Chemical Engineering, Carnegie Mellon University, Pittsburgh, Pennsylvania 15213-3815, United States. + 6612 + + + + eng + + Journal Article + + + 2025 + 12 + 16 + +
+ + United States + Chem Biomed Imaging + 9918574388106676 + 2832-3637 + + + TEM imaging + adeno-associated virus capsids + computer vision + machine learning + physical titer + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 5 + 30 + + + 2025 + 11 + 23 + + + 2025 + 11 + 24 + + + 2026 + 6 + 1 + 6 + 16 + + + epublish + + 42222575 + PMC13217355 + 10.1021/cbmi.5c00070 + + + + + + 42222567 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2832-3637 + + 4 + 5 + + 2026 + May + 25 + + + Chemical & biomedical imaging + Chem Biomed Imaging + + Review of Machine Learning for Single-Particle Tracking: Methods, Challenges, and Biophysical Insights. + + 695 + 718 + 695-718 + + 10.1021/cbmi.5c00146 + + Single-particle tracking (SPT) provides a powerful approach for probing dynamic molecular processes in living cells with high spatial and temporal resolution. Yet traditional analysis pipelines, which often rely on manual tuning or simplified models, are limited by the complexity, noise, and heterogeneity inherent to biological systems. Recent advances in machine learning (ML), especially deep learning (DL), have reshaped the SPT workflow, including particle detection, trajectory linking, motion classification, denoising, and biophysical inference. In this review, we systematically assess how ML/DL methods, including convolutional neural networks (CNNs), recurrent architectures, and Bayesian deep learning, improve the accuracy, robustness, and interpretability of SPT analyses. We survey techniques ranging from CNN-based detection and linking to statistically principled frameworks for uncertainty quantification, highlighting the versatility and effectiveness of ML/DL in overcoming persistent challenges and revealing new biological insights. We also discuss practical considerations for deployment, including selection of suitable problem domains and construction of large, high-quality training data sets. This review aims to provide a comprehensive and accessible guide to the current landscape of ML in SPT, offering both a critical evaluation of existing state-of-the-art methods and a reference for future development. + © 2025 The Authors. Co-published by Nanjing University and American Chemical Society. + + + + Zhang + Chen + C + + Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China. + 551667 + + + Department of Computer Science, Duke University, Durham, North Carolina 27705, United States. + + + + Liu + Ran + R + + Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China. + 551667 + + + School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China. + 47822 + + + + Ding + Zichen + Z + + Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China. + 551667 + + + School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150001, China. + + + + Lu + Peng + P + + Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China. + 551667 + + + School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China. + 47822 + + + + Tian + Weiming + W + 0000-0003-4958-4118 + + School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China. + 47822 + + + + Zhao + Yan + Y + + Institute of Biomedical Health Technology and Engineering, Shenzhen Bay Laboratory, Shenzhen 518132, China. + + + + He + Jiaye + J + + National Innovation Center for Advanced Medical Devices, Shenzhen 518131, China. + + + Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China. + + + Shenzhen University of Advanced Technology, Shenzhen 518107, China. + + + + Hou + Shangguo + S + 0000-0001-6394-6467 + + Institute of Systems and Physical Biology, Shenzhen Bay Laboratory, Shenzhen 518132, China. + 551667 + + + + eng + + Journal Article + Review + + + 2025 + 12 + 01 + +
+ + United States + Chem Biomed Imaging + 9918574388106676 + 2832-3637 + + + biophysical inference + deep learning + machine learning + motion classification + noise reduction + particle detection + single-particle tracking + uncertainty quantification + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 8 + 28 + + + 2025 + 11 + 14 + + + 2025 + 11 + 24 + + + 2026 + 6 + 1 + 6 + 16 + + + epublish + + 42222567 + PMC13217339 + 10.1021/cbmi.5c00146 + + + + + + 42222507 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2405-5808 + + 46 + + 2026 + Jun + + + Biochemistry and biophysics reports + Biochem Biophys Rep + + Integrative single-cell and bulk transcriptomic analysis identifies lesion-associated gene signatures for prognostic stratification and therapeutic guidance in head and neck squamous cell carcinoma. + + 102641 + 102641 + + 10.1016/j.bbrep.2026.102641 + + Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous malignancy with poor prognosis and variable response to therapy. Effective biomarkers for prognosis and treatment guidance remain limited. + We performed integrative analysis of single-cell and bulk RNA-seq data to dissect the tumor microenvironment and identify lesion-associated transcriptional changes. Gene modules related to lesion progression were identified using weighted gene co-expression network analysis (WGCNA) and integrated with differential expression and genomic data. Prognostic modeling was conducted using multiple machine learning algorithms, and the best-performing model was validated across independent cohorts. A nomogram combining the risk score and clinical features was developed. Multi-omics analyses were applied to characterize the genomic landscape, immune infiltration, and treatment response associated with the risk groups. + The final prognostic model effectively stratified patients by survival and was validated in an external cohort. The nomogram showed high predictive accuracy and clinical utility. High-risk tumors were enriched in oncogenic pathways such as hypoxia, glycolysis, and epithelial-mesenchymal transition, and displayed elevated genomic instability. Immune profiling revealed suppressed antitumor activity and increased infiltration of immunosuppressive cells in high-risk patients. Risk score was negatively associated with immunotherapy response and correlated with differential sensitivity to chemotherapy and targeted agents. + This study presents a robust prognostic model based on lesion-associated gene expression signatures in HNSCC. It offers valuable tools for individualized prognosis and therapeutic decision-making, with implications for advancing precision oncology in this challenging cancer type. + © 2026 Published by Elsevier B.V. + + + + Xiong + Meihua + M + + Jiangxi Province Hospital of Integrated Chinese&Western Medicine, Jiangxi, 330002, China. + + + + Luo + Yong + Y + + Nanchang Economic and Technological Development Zone People's Hospital, Nanchang, Jiangxi, 330013, China. + + + + Hu + Shiping + S + + Jiangxi Province Hospital of Integrated Chinese&Western Medicine, Jiangxi, 330002, China. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
+ + Netherlands + Biochem Biophys Rep + 101660999 + 2405-5808 + + + Head and neck squamous cell carcinoma + Single-cell RNA sequencing + Tumor microenvironment + Weighted gene co-expression network analysis + + This study does not involve any conflict of interest. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 9 + 7 + + + 2026 + 4 + 24 + + + 2026 + 5 + 18 + + + 2026 + 6 + 1 + 6 + 15 + + + epublish + + 42222507 + PMC13217476 + 10.1016/j.bbrep.2026.102641 + S2405-5808(26)00201-3 + + + + + + 42222437 + + 2026 + 06 + 01 + +
+ + 2771-165X + + + 2026 + Mar + 19 + + + Journal of clinical and translational pathology + J Clin Transl Pathol + + Associations of Normalization and Regularization with Machine Learning Overfitting in Cross-dataset Classification of Deaths Using Transcriptomic and Clinical Data: A Secondary Analysis of Publicly Available Databases. + 10.14218/jctp.2025.00051 + + Normalization can standardize and improve machine learning (ML) performance on omics data. However, it is unclear whether normalization is associated with overfitting (i.e., worse cross-dataset performance than intra-dataset performance). Therefore, we aimed to examine associations of normalization and regularization with overfitting of ML on omics data. + Using three paired transcriptomic and clinical datasets (lung adenocarcinoma: the Cancer Genome Atlas (TCGA)/Oncology Singapore; melanoma: TCGA/Dana-Farber Cancer Institute; glioblastoma: TCGA/Clinical Proteomic Tumor Analysis Consortium), we applied ANOVA-based gene selection methods, six normalization methods, and six ML models to classify cancer patients' deaths. Balanced accuracy (BA) and area under the curve (AUC) in intra- and cross-dataset settings were compared using inferential analyses. + Normalization consistently improved intra-dataset performance (median BA/AUC changes: 0.035-0.214/0.115-0.279) on all data, particularly with Z_Raw, but decreased or slightly increased cross-dataset performance (median BA/AUC changes: -0.029-0.079/0.029-0.064). Least Absolute Shrinkage and Selection Operator (LASSO) model without normalization consistently outperformed most of the ML models in cross-dataset testing across cancer types. ML models on all and molecular-alone data showed similar best performances. + Normalization increases ML's intra-dataset performance and overfitting in three paired cancer transcriptomic and clinical datasets. Regularized models such as LASSO appear to mitigate overfitting and achieve robust cross-dataset performance. Therefore, cross-dataset evaluation and regularized models are recommended to assess and reduce overfitting, while normalization should be used cautiously. Adding clinical data seems to have little impact on ML models' performance. However, future work on other diseases and datasets is warranted. + + + + Deng + Fei + F + + Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA. + + + + Zhang + Lanjing + L + 0000-0001-5436-887X + + Department of Chemical Biology, Ernest Mario School of Pharmacy, Rutgers University, Piscataway, NJ, USA. + + + Department of Pathology, Princeton Medical Center, Plainsboro, NJ, USA. + + + Rutgers Cancer Institute of New Jersey, New Brunswick, NJ, USA. + + + Department of Pathology and Laboratory Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA. + + + + eng + + Journal Article + + + 2026 + 03 + 19 + +
+ + United States + J Clin Transl Pathol + 9918227267706676 + 2771-165X + + + Cancer + Clinical feature + Machine learning + Normalization + Overfitting + Regularization + Transcriptomics + + Conflict of interest Lanjing Zhang is a deputy editor-in-chief of Journal of Clinical and Translational Pathology. The authors declare no other conflicts of interest. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 6 + 14 + + + aheadofprint + + 42222437 + PMC13221090 + 10.14218/jctp.2025.00051 + + + + + + 42222428 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2352-0477 + + 16 + + 2026 + Jun + + + European journal of radiology open + Eur J Radiol Open + + Radiomics analysis of restaging MRI for detection of pathological complete response in locally advanced rectal cancer. + + 100762 + 100762 + + 10.1016/j.ejro.2026.100762 + + magnetic resonance imaging (MRI)-based radiomics has emerged as a promising approach for non-invasive prediction of treatment response in rectal cancer. This study aimed to develop and validate a machine learning model based on radiomic features extracted from restaging MRI after neoadjuvant therapy in patients with locally advanced rectal cancer (LARC), to identify those achieving pathological complete response (pCR). + In this retrospective single-center study, patients with histologically confirmed rectal cancer treated between 2017 and 2022 were included if they underwent neoadjuvant therapy, staging and restaging MRI, and surgery. Tumor segmentation was performed on oblique axial T2-weighted images. Radiomic features were extracted using PyRadiomics following image resampling and intensity discretization. Feature selection was performed using the least absolute shrinkage and selection operator (LASSO), and a logistic regression model with Elastic Net regularization was trained. Model performance was evaluated using repeated stratified 5-fold cross-validation. Performance metrics included area under the receiver operating characteristic curve (AUC-ROC), accuracy, sensitivity, and specificity. + The final cohort included 86 patients (mean age 63 ± 12 years; 65% male) with 33 patients achieving pCR (38.37%) and 53 who did not (61.63%). The model demonstrated moderate discriminative performance, with a mean AUC-ROC of 0.74, accuracy of 67.4%, sensitivity of 60.1%, and specificity of 71.0%. Performance was higher for identifying non-responders compared with patients achieving pCR. + MRI-based radiomics from restaging MRI shows potential to predict treatment response in LARC, particularly in identifying non-responders to neoadjuvant therapy. Although performance remains moderate, this approach may support treatment stratification and personalized management. Further validation in larger, multicenter cohorts is warranted. + © 2026 The Authors. + + + + Ambrosini + Ilaria + I + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Francischello + Roberto + R + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Fanni + Salvatore Claudio + SC + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Faggioni + Lorenzo + L + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Aringhieri + Giacomo + G + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Fruzza + Rachele + R + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Cwiklinska + Karolina + K + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Caputo + Francesca Pia + FP + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Aghakhanyan + Gayane + G + + Nuclear Medicine Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Neri + Emanuele + E + + Academic Radiology Unit, Department of Translational Research, University of Pisa, Pisa, Italy. + + + + Lencioni + Riccardo + R + + Academic Radiology Unit, Department of Surgical, Medical, and Molecular Pathology, University of Pisa, Pisa, Italy. + + + + Cioni + Dania + D + + Academic Radiology Unit, Department of Surgical, Medical, and Molecular Pathology, University of Pisa, Pisa, Italy. + + + + eng + + Journal Article + + + 2026 + 05 + 21 + +
+ + England + Eur J Radiol Open + 101650225 + 2352-0477 + + + Radiomics + Rectal Cancer + Restaging + + The authors declare no conflicts of interest. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 31 + + + 2026 + 5 + 6 + + + 2026 + 5 + 7 + + + 2026 + 6 + 1 + 6 + 14 + + + epublish + + 42222428 + PMC13217831 + 10.1016/j.ejro.2026.100762 + S2352-0477(26)00039-0 + + + + + + 42222408 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + Correction: Editorial: National Cancer Research Month 2025: Advances in Detection, Treatment and Therapies in Oncology. + + 1867092 + 1867092 + + 10.3389/fonc.2026.1867092 + + [This corrects the article DOI: 10.3389/fonc.2026.1834671.]. + Copyright © 2026 Chow, Bezerra, Colussi, Khan, Suárez and Kaoud. + + + + Chow + James C L + JCL + + Department of Radiation Oncology, University of Toronto, Toronto, ON, Canada. + + + Radiation Medicine Program, Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada. + + + + Bezerra + Daniel P + DP + + Gonçalo Moniz Institute, Oswaldo Cruz Foundation (IGM-FIOCRUZ/BA), Salvador, Bahia, Brazil. + + + + Colussi + Valdir C + VC + + Department of Radiation Oncology, Case Western Reserve University, Cleveland, OH, United States. + + + Seidman Cancer Center, University Hospitals (UH) Cleveland, Cleveland, OH, United States. + + + + Khan + Matiullah + M + + Department of Pathology, Faculty of Medicine, AIMST University, Bedong, Malaysia. + + + + Suárez + Cecilia A + CA + + Physics Department, University of Buenos Aires, Buenos Aires, Argentina. + + + Interdisciplinary and Applied Physics Institute, University of Buenos Aires - National Scientific and Technical Research Council, Buenos Aires, Argentina. + + + + Kaoud + Tamer S + TS + + Department of Pharmaceutical Sciences, College of Pharmacy, University of Arkansas for Medical Sciences, Little Rock, AR, United States. + + + Winthrop P. Rockefeller Cancer Institute, University of Arkansas for Medical Sciences, Little Rock, AR, United States. + + + + eng + + Published Erratum + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + + Front Oncol. 2026 Apr 20;16:1834671. doi: 10.3389/fonc.2026.1834671. + 42088199 + + + + cancer detection + genomic profiling + machine learning in oncology + molecular mechanisms + precision oncology + radiotherapy advances + rare cancers + translational cancer research + + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 4 + 28 + + + 2026 + 4 + 29 + + + 2026 + 4 + 30 + + + 2026 + 6 + 1 + 6 + 14 + + + epublish + + 42222408 + PMC13220092 + 10.3389/fonc.2026.1867092 + + + + + + 42222405 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + Integrated perspectives on colorectal carcinogenesis: molecular pathogenesis, genomic alterations, diagnostic paradigms, therapeutic interventions and AI-driven directions in precision oncology. + + 1770430 + 1770430 + + 10.3389/fonc.2026.1770430 + + Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide, with a notable rise in incidence among younger populations and in developing nations. This review provides an integrative perspective on the molecular pathogenesis, epidemiology, classification, diagnosis, and therapeutic strategies in CRC. We emphasize the pivotal role of three major molecular pathways - chromosomal instability (CIN), microsatellite instability (MSI), and CpG island methylator phenotype (CIMP) and their association with tumor heterogeneity and clinical outcomes. The analysis highlights key risk factors including lifestyle-related variables and genetic predisposition, alongside emerging molecular subtypes that influence prognosis and therapeutic decision-making. Advances in non-invasive diagnostics, such as fecal immunochemical testing (FIT) and novel biomarker discovery, demonstrate potential for improving early detection and screening compliance. Preventive strategies encompassing lifestyle modification, chemoprevention, and vitamin D supplementation, coupled with tailored therapeutic interventions including monoclonal antibodies and targeted therapies, offer promising avenues for reducing disease burden. The review also explores recent advances in multi-omics integration and artificial intelligence driven analytics, which have transformed biomarker discovery, subtype classification, and therapeutic prediction in CRC. Emerging applications of machine learning (ML), radiogenomics, and quantum computing further highlight a paradigm shift toward precision oncology. This review underscores the need for precision oncology approaches that integrate molecular profiling, patient stratification, and personalized treatment to enhance health care delivery and clinical outcomes in CRC. + Copyright © 2026 N., S. and Ramasamy. + + + + N + Sidharth Kumar + SK + + Department of Biotechnology, Faculty of Biomedical Sciences & Technology, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai, India. + + + + S + Sai Kiruthiga + SK + + Department of Biotechnology, Faculty of Biomedical Sciences & Technology, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai, India. + + + + Ramasamy + Magesh + M + + Department of Biotechnology, Faculty of Biomedical Sciences & Technology, Sri Ramachandra Institute of Higher Education and Research (DU), Chennai, India. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + chromosomal instability + colorectal cancer + health care + machine learning + microsatellite instability + quantum computing + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 12 + 18 + + + 2026 + 4 + 1 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 14 + + + epublish + + 42222405 + PMC13215890 + 10.3389/fonc.2026.1770430 + + + + + + 42222384 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + Prediction of myelosuppression risk in postoperative cervical cancer patients undergoing concurrent chemoradiotherapy using machine learning models. + + 1800585 + 1800585 + + 10.3389/fonc.2026.1800585 + + By integrating clinical features and pelvic bone marrow dose-volume parameters, this study developed and compared multiple machine learning models to predict the risk of bone marrow suppression (BMS) in postoperative cervical cancer patients receiving concurrent chemoradiotherapy (CCRT). + A total of 415 cervical cancer patients who received postoperative CCRT at Siyang Hospital and Shanxian Central Hospital between March 2022 and April 2025 were retrospectively enrolled. The primary outcome was BMS. Candidate predictors included baseline clinical characteristics, laboratory indices, and pelvic bone marrow dose-volume parameters. Feature selection was performed using the Least Absolute Shrinkage and Selection Operator (LASSO). Logistic regression (LR), random forest (RF), Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), support vector machines (SVM), and artificial neural networks (ANN) were constructed. Model performance was evaluated in an independent test set using the area under the curve (AUC), calibration curves, and decision curve analysis, and model interpretability was assessed using SHAP. + Among the 415 patients, 220 (53.0%) developed BMS. Significant differences were observed between patients with and without BMS in lymph node boost irradiation, body mass index (BMI), aspartate aminotransferase (AST), serum albumin (ALB), and low-dose pelvic bone marrow irradiated volume parameters (V10, V20, and V30). LASSO retained BMI, AST, ALB, V10, V20, and lymph node boost irradiation as key predictors. Among the six models, the RF model achieved the best performance (AUC = 0.799). Calibration curves and decision curve analysis demonstrated good calibration and potential clinical net benefit of the RF model. + Machine learning models integrating routine clinical indicators and pelvic bone marrow dose-volume parameters can effectively predict the risk of myelosuppression in postoperative cervical cancer patients receiving CCRT. The random forest model demonstrated optimal performance and may serve as a practical tool for individualised risk stratification and early intervention. + Copyright © 2026 Wang, Li, Shen, Zhang, Shi, Shen and Qiu. + + + + Wang + Qingkai + Q + + Department of Laboratory Medicine, Shanxian Central Hospital, Heze, China. + + + + Li + Yaling + Y + + Department of Clinical Medicine, School of Medicine, Nantong University, Nantong, China. + + + + Shen + Liran + L + + Department of Laboratory Medicine, Shanxian Central Hospital, Heze, China. + + + + Zhang + Yunbiao + Y + + Department of Laboratory Medicine, Shanxian Central Hospital, Heze, China. + + + + Shi + Qianjin + Q + + Department of Radiation Oncology, Siyang Hospital, Suqian, China. + + + + Shen + Kang + K + + Department of Radiation Oncology, Siyang Hospital, Suqian, China. + + + + Qiu + Hao + H + + Department of Radiation Oncology, Siyang Hospital, Suqian, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + bone marrow suppression + cervical cancer + concurrent chemoradiotherapy + dose–volume histogram + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 31 + + + 2026 + 3 + 14 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 14 + + + epublish + + 42222384 + PMC13216769 + 10.3389/fonc.2026.1800585 + + + + + + 42222381 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + Hybrid handcrafted and deep feature fusion for automated acute myeloid leukemia classification using TCMA-Net on a class-balanced dataset. + + 1774702 + 1774702 + + 10.3389/fonc.2026.1774702 + + Acute myeloid leukemia (AML) is a life-threatening hematological malignancy that requires accurate and timely diagnosis for effective clinical management. However, conventional cytomorphological analysis is time-consuming, subjective, and highly dependent on expert interpretation. Existing artificial intelligence-based approaches are often limited by severe class imbalance and inadequate feature representation, which restrict their generalization capability. To address these challenges, this study proposes a robust and high-performance AML classification framework based on hybrid feature fusion and deep learning. The proposed approach integrates handcrafted features with deep representations extracted from DenseNet201 and MobileNetV2 to form the Hybrid AML Descriptor Network (HADNet), which is further combined with the Transformer-based Tri block Convolutional Multi head Attention Network (TCMA-Net) classifier to capture both local and global contextual information. In addition, a Synthetic Acquisition Artifact Augmentation (S3A) strategy is introduced to mitigate class imbalance by generating realistic variations in illumination and noise. Extensive experiments were conducted on the AML cytomorphology Ludwig Maximilian University (LMU) dataset, where the proposed framework achieved a testing accuracy of 99.20%, outperforming traditional machine learning models and deep learning baselines. The effectiveness of the approach is further validated through ablation studies and external evaluation on the acute lymphoblastic leukemia (ALL) dataset, demonstrating strong robustness and generalization across different data distributions. Overall, the results indicate that the integration of handcrafted and deep features significantly enhances classification performance, stability, and reliability. The proposed framework provides a promising solution for automated AML diagnosis and has the potential to support clinical decision-making systems. + Copyright © 2026 Alshehri, Mushtaq, Taha, Ijaz, Irfan, Altayar, Jalal, Abu-Alghayth, Al Shmrany and Eltayb Hassan. + + + + Alshehri + Osama M + OM + + Department of Clinical Laboratory Sciences, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia. + + + + Mushtaq + Zohaib + Z + + Department of Electrical, Electronics and Computer Systems, University of Sargodha, Sargodha, Pakistan. + + + + Taha + Muhammad + M + + Department of Electronics Engineering, Islamia University of Bahawalpur, Bahawalpur, Pakistan. + + + + Ijaz + Maham + M + + Department of Food Science and Technology, Islamia University of Bahawalpur, Bahawalpur, Pakistan. + + + + Irfan + Muhammad + M + + Electrical Engineering Department, College of Engineering, Najran University, Najran, Saudi Arabia. + + + + Altayar + Malik A + MA + + Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia. + + + + Jalal + Mohammed M + MM + + Department of Medical Laboratory Technology, Faculty of Applied Medical Sciences, University of Tabuk, Tabuk, Saudi Arabia. + + + + Abu-Alghayth + Mohammed H + MH + + Department of Medical Laboratory Sciences, College of Applied Medical Sciences, University of Bisha, Bisha, Saudi Arabia. + + + + Al Shmrany + Humood + H + + Department of Medical Laboratory, College of Applied Medical Sciences, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia. + + + + Eltayb Hassan + Elhashimi + E + + Department of Clinical Laboratory Science, College of Applied Medical Sciences, Najran University, Najran, Saudi Arabia. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + DenseNet201 + HADNet + MobileNetV2 + TCMA-Net + acute myeloid leukemia + convolutional neural network + handcrafted features + vision transformer + + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 8 + + + 2026 + 3 + 30 + + + 2026 + 4 + 6 + + + 2026 + 6 + 1 + 6 + 14 + + + epublish + + 42222381 + PMC13215842 + 10.3389/fonc.2026.1774702 + + + + + + 42222375 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2234-943X + + 16 + + 2026 + + + Frontiers in oncology + Front Oncol + + Predicting the efficacy of recombinant human thrombopoietin in treating cancer therapy-related thrombocytopenia: based on stacking ensemble methods. + + 1751214 + 1751214 + + 10.3389/fonc.2026.1751214 + + Cancer treatment-induced thrombocytopenia (CTIT) is a common adverse effect of cancer therapy. CTIT increases the risk of bleeding, prolongs hospital stays, raises medical costs, and can negatively impact anti-tumor treatment outcomes, potentially leading to patient death. Therefore, it is crucial to initiate platelet-boosting therapy in a timely manner based on the individual circumstances of patients experiencing CTIT. + Patients who developed cancer treatment-induced thrombocytopenia and received Rh-TPO treatment from January 2023 and December 2023 were obtained for establishing the dataset. With absolute platelets increase as the outcome variable, univariate analysis was performed to screen out statistically significant factors, and 18 clinical variables were selected as initial features. The least absolute shrinkage and selection operator (LASSO) regression analysis was then used to identify the most important features. Based on this, a stacking ensemble model was constructed using cross-validation with out-of-fold predictions to prevent information leakage, and the predictive performance of the model was evaluated. Finally, the SHapley Additive exPlanations (SHAP) algorithm was used to explain the model, and a visual analysis of the features was conducted. + A total of 400 inpatients who developed cancer treatment-induced thrombocytopenia and received Rh-TPO treatment were included, of which 280 inpatients were assigned to the training set and 1,20 to the testing set. After LASSO regression screening, the study identified 7 key features: ethnicity, height, baseline serum creatinine, pre-chemotherapy platelet count, follow-up days after chemotherapy, platelet count before Rh-TPO, and duration of Rh-TPO treatment. We compared the performance of different machine learning models and selected the best three models as base models. Combined with Linear Regression as the meta-learner, we built a stacking ensemble model using 3-fold cross-validation with out-of-fold predictions. The stacking ensemble model showed best prediction ability compared to independent models with R² of 0.77 (training) and 0.74 (testing), MAE of 6.39 (training) and 8.34 (testing), MSE of 62.17 (training) and 97.70 (testing), RMSE of 7.88 (training) and 9.88 (testing), and MAPE of 0.09 (training) and 0.12 (testing). SHAP analysis showed that pre-chemotherapy PLT value and the follow-up days after chemotherapy were the important features affecting the prediction results. + The predictive model developed in this study could be beneficial for accurately predicting the improvement in platelet count in patients with CTIT who use Rh-TPO, facilitating timely assistance for patients in avoiding the risks caused by a drop in platelet count in a timely manner. + Copyright © 2026 Hou, Huangfu, Guo, Gao, Lu, Li and Wang. + + + + Hou + Kun + K + + Department of Pharmacy, Peking University Cancer Hospital/Affiliated Cancer Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + The Laboratory for Inheritance and Development of Integrated Chinese (Mongolian) and Western Medicine in Anti-Tumor Therapy, Peking University Cancer Hospital/Affiliated Cancer Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + + Huangfu + Rui + R + + School of Pharmacy, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + + Guo + Zhijuan + Z + + Pathology Department, Peking University Cancer Hospital/Affiliated Cancer Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + + Gao + Yan + Y + + Department of Pharmacy, Peking University Cancer Hospital/Affiliated Cancer Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + + Lu + Haiwen + H + + Department of Medical Simulated Center, Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + + Li + Zhongwu + Z + + Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education), Department of Pathology, Peking University Cancer Hospital & Institute, Beijing, China. + + + + Wang + Zhenfei + Z + + The Laboratory for Inheritance and Development of Integrated Chinese (Mongolian) and Western Medicine in Anti-Tumor Therapy, Peking University Cancer Hospital/Affiliated Cancer Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Oncol + 101568867 + 2234-943X + + + cancer treatment-induced thrombocytopenia + machine learning + predictive models + recombinant human thrombopoietin + stacking ensemble mode + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 11 + 21 + + + 2026 + 4 + 20 + + + 2026 + 4 + 28 + + + 2026 + 6 + 1 + 6 + 14 + + + epublish + + 42222375 + PMC13218961 + 10.3389/fonc.2026.1751214 + + + + + + 42222353 + + 2026 + 06 + 01 + +
+ + 1432-9840 + + 29 + 3 + + 2026 + + + Ecosystems (New York, N.Y.) + Ecosystems + + Topography is a Major Determinant of Forest-Savanna Distributions in Mosaic Landscapes in Central Africa. + + 51 + 51 + + 10.1007/s10021-026-01070-2 + + Forests and savannas frequently co-occur as patches within tropical landscape mosaics, yet the mechanisms controlling their spatial configuration remain unclear. The presence of both vegetation states under similar climatic conditions is often attributed to fire-vegetation feedbacks, but could also reflect variation in overlooked external drivers. In Central Africa, forest-savanna mosaics become more common with increasing topographic roughness, but how well topographic heterogeneity explains the forest-savanna configuration within mosaic landscapes is unknown. Here we address this question and examine the role of individual topographic variables that may influence tree cover by, for instance, changing water availability and fire spread. We identify mosaic landscapes from remotely sensed tree cover data and derive topographic variables from a digital elevation model. We use these variables to develop machine learning algorithms predicting vegetation state within mosaic landscapes. Models achieved an average prediction accuracy of 0.75, with local elevation (relative to the surrounding 500 m or 5000 m) emerging as the strongest predictor of vegetation state. Both model accuracy and the role of topographic predictors varied strongly among landscapes, reflecting the diverse pathways by which topography can influence tree cover. Overall, our findings indicate that topographic heterogeneity is a major driver of forest-savanna mosaics in Central Africa. Mosaic landscapes are more deterministic than previously assumed, suggesting that their response to disturbances and climate change will be spatially heterogeneous, thereby reducing the likelihood of abrupt large-scale shifts between forest and savanna states. + The online version contains supplementary material available at 10.1007/s10021-026-01070-2. + © The Author(s) 2026. + + + + Zwaan + Aart + A + 0009-0001-1213-006X + + Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands. + https://ror.org/04pp8hn57 + grid.5477.1 + 0000 0000 9637 0671 + + + + Staal + Arie + A + + Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands. + https://ror.org/04pp8hn57 + grid.5477.1 + 0000 0000 9637 0671 + + + + Te Beest + Mariska + M + + Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands. + https://ror.org/04pp8hn57 + grid.5477.1 + 0000 0000 9637 0671 + + + Centre for African Conservation Ecology, Nelson Mandela University, Gqeberha, South Africa. + https://ror.org/03r1jm528 + grid.412139.c + 0000 0001 2191 3608 + + + + Rietkerk + Max + M + + Copernicus Institute of Sustainable Development, Utrecht University, Utrecht, The Netherlands. + https://ror.org/04pp8hn57 + grid.5477.1 + 0000 0000 9637 0671 + + + + eng + + Journal Article + + + 2026 + 05 + 27 + +
+ + United States + Ecosystems + 100955213 + 1432-9840 + + + alternative ecosystem states + bistability + ecotone + feedbacks + forest–savanna mosaics + tipping points + topography + tropical ecosystems + + Conflict of interestThe authors declare that they have no conflict of interest. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 28 + + + 2026 + 4 + 26 + + + 2026 + 6 + 1 + 6 + 14 + + + ppublish + + 42222353 + PMC13216132 + 10.1007/s10021-026-01070-2 + 1070 + + + + + + 42222348 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2950-3299 + + 34 + 2 + + 2026 + Jun + 18 + + + Molecular therapy. Oncology + Mol Ther Oncol + + Integrating multi-omics analysis and machine learning to refine molecular subtypes and prognostic assessment of lower-grade glioma. + + 201227 + 201227 + + 10.1016/j.omton.2026.201227 + + Lower-grade glioma (LGG) is a highly heterogeneous disease, making accurate prognosis prediction and the development of precise, personalized treatment plans for patients challenging. The multi-omics data from patients with LGG were analyzed using 10 clustering algorithms to identify subgroups at very high resolution. Ten machine learning algorithms were subsequently integrated to develop a robust artificial intelligence model (AIM). We identified two cancer subtypes (CSs) linked to prognosis through multi-omics clustering. After screening 46 hub genes, we integrated 10 machine learning algorithms into 117 combinations to select the AIM with the highest average C-index. The gradient boosting machine (GBM)-based AIM outperformed previous prognostic signatures in nearly all cohorts. Patients in the low-AIM group had a better prognosis and greater sensitivity to immunotherapy, whereas those in the high-AIM group had a poorer prognosis and lower immunotherapy sensitivity. However, MG132 showed promise as a potential therapeutic agent. In vitro studies confirmed the oncogenic role of the hub gene CSDC2 in LGG cells; its knockdown reduced the proliferation, invasion, and migration of these cells. In summary, the AIM we developed is highly valuable for predicting the prognosis of patients with LGG and identifying those who are sensitive to immunotherapy. + © 2026 The Authors. + + + + Jiang + Qian + Q + + Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + + Yang + Ai-Yu + AY + + Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + + Mo + Guan-Ling + GL + + Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + + Mo + Li-Gen + LG + + Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + + Deng + Teng + T + + Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + + Huang + Su-Ning + SN + + Department of Radiation Oncology, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + + Hou + Sheng-Tao + ST + + Brain Research Centre, Department of Neuroscience, School of Life Sciences, Southern University of Science and Technology, Shenzhen, Guangdong, China. + + + + Huang + Qian-Rong + QR + + Department of Neurosurgery, Guangxi Medical University Cancer Hospital, Nanning, Guangxi, China. + + + School of Pre-clinical Medicine, Guangxi Medical University, Nanning, Guangxi, China. + + + + eng + + Journal Article + + + 2026 + 05 + 07 + +
+ + United States + Mol Ther Oncol + 9918752083706676 + 2950-3299 + + + artificial intelligence + lower-grade glioma + machine learning + multi-omics + prognosis + + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 12 + 14 + + + 2026 + 4 + 22 + + + 2026 + 5 + 5 + + + 2026 + 6 + 1 + 6 + 13 + + + epublish + + 42222348 + PMC13217492 + 10.1016/j.omton.2026.201227 + S2950-3299(26)00103-7 + + + + + + 42222296 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1178-1998 + + 21 + + 2026 + + + Clinical interventions in aging + Clin Interv Aging + + A Preliminary Predictive Panel for Pre-Frailty Based on Serum Proteomic Biomarkers: A Two-Phase Cross-Sectional Study. + + 608726 + 608726 + + 10.2147/CIA.S608726 + + Prefrailty is associated with anomalies in protein metabolism; however, the serum proteomic signatures remain unclear. This study investigated protein profiles across different health statuses and evaluated their potential for the early identification of prefrailty. + Older adults were categorized as robust, prefrail, and frail. Untargeted proteomic screening in a discovery cohort (n = 30) was followed by parallel reaction monitoring (PRM) validation (n = 99). Multidimensional clinical parameters and differentially expressed proteins were integrated within machine learning pipelines to refine the search for characteristic features of prefrailty. + 166 proteins were found to be differentially expressed across frailty statuses, with 15 significantly prefrailty-associated proteins subsequently confirmed by PRM validation. These proteins were functionally enriched in pathways related to cell signaling, protein metabolism, immune regulation, and skeletal muscle function maintenance. A Random Forest model, further assembled from gait speed, skeletal muscle mass, E3-independent E2 ubiquitin-conjugating enzyme (UBE2O), Timed Up and Go test time, alpha-actinin-3 (ACTN3), and Mini-Mental State Examination score, exhibited the most robust performance for early frailty identification among multiple algorithms compared. + This exploratory study identified candidate serum protein biomarkers associated with prefrailty. Preliminary machine learning models incorporating UBE2O and ACTN3 suggested the feasibility of discriminating prefrailty from robust status, reflecting underlying proteomic heterogeneity among community-dwelling older adults. + © 2026 Ye et al. + + + + Ye + Yu + Y + + Medical School, Chinese PLA General Hospital, Beijing, People's Republic of China. + + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Liu + Jinwei + J + + Medical School, Chinese PLA General Hospital, Beijing, People's Republic of China. + + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Zhang + Zhen + Z + + Medical School, Chinese PLA General Hospital, Beijing, People's Republic of China. + + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Xu + Shuaixuan + S + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Chang + Chenghao + C + + Medical School, Chinese PLA General Hospital, Beijing, People's Republic of China. + + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Cao + Mengyu + M + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Zhang + Yongyi + Y + + Medical School, Chinese PLA General Hospital, Beijing, People's Republic of China. + + + Department of Anaesthesiology, No. 962 Hospital of the PLA Joint Logistic Support Force, Harbin, Heilongjiang, People's Republic of China. + + + + Wang + Fang + F + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Zhang + Nihui + N + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + Wang + Guibin + G + + State Key Laboratory of Medical Proteomics, Beijing Institute of Lifeomics, Beijing, People's Republic of China. + + + + Peng + Nan + N + + Medical School, Chinese PLA General Hospital, Beijing, People's Republic of China. + + + Department of Rehabilitation Medicine, second Medical Center of Chinese PLA General Hospital, Beijing, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + New Zealand + Clin Interv Aging + 101273480 + 1176-9092 + + + + 0 + Biomarkers + + + EC 2.3.2.23 + Ubiquitin-Conjugating Enzymes + + + IM + + + Humans + + + Biomarkers + blood + + + Female + + + Proteomics + methods + + + Cross-Sectional Studies + + + Aged + + + Male + + + Frailty + blood + diagnosis + + + Machine Learning + + + Aged, 80 and over + + + Geriatric Assessment + methods + + + Random Forest + + + Ubiquitin-Conjugating Enzymes + blood + + + + frailty + machine learning + older adults + pre-frailty + proteomics + + The authors report no conflicts of interest in this work. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 13 + + + 2026 + 5 + 15 + + + 2026 + 6 + 1 + 6 + 13 + + + epublish + + 42222296 + PMC13222000 + 10.2147/CIA.S608726 + 608726 + + + + + + 42222284 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-042X + + 17 + + 2026 + + + Frontiers in physiology + Front Physiol + + Interpretable multi-center machine learning model driven by facial image features for non-invasive early risk assessment of lung cancer. + + 1835790 + 1835790 + + 10.3389/fphys.2026.1835790 + + Early identification of lung cancer is critical for improving patient survival and prognosis. Conventional pathological biopsy is invasive, and computed tomography (CT) involves ionizing radiation risks. Facial imaging, as a non-invasive and accessible biological signal, is a promising novel approach for lung cancer auxiliary diagnosis. + To develop and validate a non-invasive lung cancer auxiliary diagnosis model based on facial image features, and explore the value of facial imaging in lung cancer early screening. + Multi-center patients with benign pulmonary nodules and lung cancer were enrolled in this study. Facial images were collected via the TFDA-1 Digital Tongue and Face Diagnosis Instrument, with 124 features extracted. Logistic regression was used for feature selection and simple correlation analysis of facial image features, based on which four machine learning models (XGBoost, LightGBM, SVM, GBDT) were constructed. Model training and optimization were performed using 10-fold cross-validation combined with grid search for hyperparameter tuning. Model performance was comprehensively evaluated using Accuracy, Precision, Sensitivity, Specificity, F1-Score, area under the curve (AUC), average precision (AP), and Brier Score; pairwise comparisons of AUCs among models were conducted using the DeLong test. Clinical utility was assessed using calibration curves and decision curves; model interpretability was analyzed via the SHapley Additive exPlanations (SHAP) method; and generalization capability was validated using an independent external validation cohort. + The XGBoost model achieved the best overall performance, with an AUC of 0.900 and accuracy of 0.807 in the internal test set, and an AUC of 0.906 and accuracy of 0.813 in the external validation set, demonstrating favorable generalization stability. DeLong test showed that XGBoost achieved the highest AUC in the internal test set without significant differences among models (all P ≥ 0.05). In external validation, its AUC was significantly higher than GBDT, LightGBM (P < 0.001) and SVM (P < 0.05). + We successfully constructed a non-invasive auxiliary screening model for lung cancer using facial image features. Facial imaging shows significant value in early lung cancer screening, providing a novel, accessible strategy to improve the popularization and availability of lung cancer early screening in clinical practice. + Copyright © 2026 Shi, Chun, Zhang, Xu, Chen, Jiao, Wang, Tu and Xu. + + + + Shi + Yulin + Y + + Teaching Experiment and Training Center, Academic Affairs Office, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + Shanghai Key Laboratory of Health Identification and Assessment, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Chun + Yi + Y + + Shanghai Key Laboratory of Health Identification and Assessment, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Zhang + Shuyi + S + + Shanghai Key Laboratory of Health Identification and Assessment, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Xu + Xiaoyan + X + + Shanghai Key Laboratory of Health Identification and Assessment, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Chen + Wenlian + W + + Longhua Hospital Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Jiao + Lijing + L + + Yueyang Hospital of Integrated Traditional Chinese and Western Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Wang + Lixin + L + + Shanghai Pulmonary Hospital, Shanghai, China. + + + + Tu + Liping + L + + Shanghai Key Laboratory of Health Identification and Assessment, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + School of Artificial Intelligence in Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Xu + Jiatuo + J + + Shanghai Key Laboratory of Health Identification and Assessment, School of Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + School of Artificial Intelligence in Traditional Chinese Medicine, Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + Seventh People's Hospital of Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Physiol + 101549006 + 1664-042X + + + benign pulmonary nodule + facial image feature + interpretable machine learning + lung cancer + multicenter study + non-invasive 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 21 + + + 2026 + 4 + 21 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 13 + + + epublish + + 42222284 + PMC13218986 + 10.3389/fphys.2026.1835790 + + + + + + 42222274 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2692-3114 + + 7 + + 2026 + + + Exploration of targeted anti-tumor therapy + Explor Target Antitumor Ther + + + ESR1 mutations in ER-positive breast cancer: from endocrine resistance to ctDNA-guided therapeutic interception. + + 1002375 + 1002375 + + 10.37349/etat.2026.1002375 + + Endocrine resistance in estrogen receptor-positive (ER+) breast cancer has undergone a fundamental reconceptualization over the past decade. The discovery that activating mutations in the ESR1 gene encoding ERα emerge under aromatase inhibitor (AI) selection pressure and drive ligand-independent receptor activation established a shift from empirical treatment sequencing to molecularly guided intervention. This review provides a synopsis of the structural biology underlying constitutive ER activation, the evolutionary dynamics of ESR1-mutant clones detectable through circulating tumor DNA (ctDNA), and the clinical evidence demonstrating that early molecular detection can trigger therapeutic switches that alter disease trajectory. The regulatory approval of elacestrant for ESR1-mutant disease and randomized trial data showing progression-free survival (PFS) benefit from ctDNA-guided endocrine switching (PADA-1, SERENA-6) position ESR1 genotyping as a dynamic biomarker with direct therapeutic implications. We examine the integration of oral selective ER degraders (SERDs) into treatment algorithms, the role of co-occurring alterations in the phosphatidylinositol 3-kinase/protein kinase B (PI3K/AKT) pathway, and emerging directions, including machine learning approaches to ctDNA kinetics and adaptive trial designs that treat clonal evolution as an actionable target. The convergence of structural mechanisms, liquid biopsy technology, and biomarker-driven drug development provides a framework for precision oncology in endocrine-resistant breast cancer. While these advances are substantial, important challenges remain, including the lack of mature overall survival (OS) data from interception trials, cost and accessibility barriers to serial ctDNA monitoring in diverse global healthcare settings, the unresolved question of optimal therapeutic sequencing in patients with concurrent ESR1 and PI3K pathway alterations, and the need to distinguish clinically actionable low-variant allele frequency (VAF) ESR1 calls from background noise in liquid biopsies. + © The Author(s) 2026. + + + + Martinez + Thais + T + + Herbert Wertheim College of Medicine, Florida International University, Miami, FL 33199, USA. + + + + Wegner + Samantha + S + + Campbell University School of Osteopathic Medicine, Lillington, NC 27546, USA. + + + + Bahmad + Hisham F + HF + 0000-0003-3799-2595 + + Department of Pathology and Laboratory Medicine, University of Miami Miller School of Medicine, Miami, FL 33136, USA. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 26 + +
+ + United States + Explor Target Antitumor Ther + 101770662 + 2692-3114 + + + ESR1 + breast cancer + ctDNA + endocrine resistance + estrogen receptor + review + + The authors declare that they have no conflicts of interest. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 28 + + + 2026 + 5 + 7 + + + 2026 + 6 + 1 + 6 + 13 + + + epublish + + 42222274 + PMC13220126 + 10.37349/etat.2026.1002375 + + + + + + 42222122 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + Artificial intelligence optimizes immune rejection prediction and management in heart transplantation: a structured narrative review. + + 1790244 + 1790244 + + 10.3389/fcvm.2026.1790244 + + Heart transplantation remains the definitive therapy for end-stage heart failure, yet long-term outcomes are limited by three core clinical bottlenecks in immune rejection management: imprecise preoperative donor-recipient matching, overreliance on invasive endomyocardial biopsy (EMB) for postoperative rejection surveillance, and high inter-observer variability in manual pathological diagnosis of rejection. Artificial intelligence (AI) has emerged as a promising tool to address these gaps, but the methodological quality and clinical translation readiness of supporting evidence have not been comprehensively synthesized. + This structured narrative review synthesized original research published between October 1, 2020, and October 1, 2025, identified via a targeted PubMed search and manual reference screening. Two independent reviewers performed study selection and data extraction, with discrepancies resolved by consensus. Common methodological limitations across included studies were synthesized qualitatively. + A total of 42 studies were included in the final qualitative synthesis. Preoperatively, 3D-Convolutional Neural Networks (3D-CNNs) enabled automated, accurate total cardiac volume (TCV) measurement for anatomical matching, while machine learning models identified non-linear synergistic risk factors for postoperative adverse events, outperforming traditional regression models. Postoperatively, AI models integrating non-invasive biomarkers (gene expression profiles, extracellular vesicles, donor-derived cell-free DNA) showed high diagnostic accuracy for rejection, with one single-center retrospective study estimating a 56.8% reduction in unnecessary EMB procedures (prospective clinical validation is still required). For pathological diagnosis, AI models improved the sensitivity of high-grade acute cellular rejection (ACR) detection from 39.5% to 74.4% compared with manual assessment, generative adversarial networks (GANs) addressed rare rejection sample scarcity with a rejection region detection AUROC of 98.84%, and explainable AI tools aligned model decisions with pathologists' judgment. The overall methodological quality of included studies was suboptimal, with most studies limited by single-center retrospective design, small sample size, and lack of independent external validation. + AI has demonstrated promising potential to optimize donor-recipient matching, enable non-invasive rejection surveillance, and standardize pathological diagnosis in heart transplantation. However, most current evidence comes from exploratory, single-center retrospective studies with important methodological limitations that restrict their immediate clinical translation. Future research should prioritize prospective, multi-center clinical validation, standardized biomarker and model reporting, and federated learning data ecosystems to translate AI innovations into routine clinical practice. + © 2026 Chen, Lai, Luo, Li and Wang. + + + + Chen + Kaixin + K + + Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. + + + + Lai + Junlin + J + + Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. + + + + Luo + Yijie + Y + + Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. + + + + Li + Chenghao + C + + Department of Cardiovascular Surgery, Zhongnan Hospital, Wuhan University, Wuhan, Hubei, China. + + + + Wang + Guohua + G + + Department of Cardiovascular Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + artificial intelligence + deep learning + donor-recipient matching + heart transplantation + immune rejection + machine learning + non-invasive monitoring + pathological 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 26 + + + 2026 + 4 + 16 + + + 2026 + 4 + 24 + + + 2026 + 6 + 1 + 6 + 11 + + + epublish + + 42222122 + PMC13216486 + 10.3389/fcvm.2026.1790244 + + + + + + 42222119 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + Development and validation of the C-reactive protein-triglyceride-glucose index for predicting short- and long-term mortality in critically ill patients with coronary artery disease: a multicenter cohort study. + + 1763569 + 1763569 + + 10.3389/fcvm.2026.1763569 + + The C-reactive protein-triglyceride glucose index (CTI) has been proposed as a novel biomarker of insulin resistance and inflammation, but its association with mortality in critically ill patients with coronary artery disease (CAD) remains unclear. This study aimed to evaluate the associations between the CTI and both short- and long-term all-cause mortality and to assess the predictive value of this index. + Patients with CAD were identified from the MIMIC-IV database and divided into internal training and testing cohorts, while an external validation cohort was derived from the eICU-CRD and Shenzhen Regional Health Information Platform (SRHIP) database. Based on the optimal CTI cut-off values, patients were grouped into three categories. The primary outcomes were short-term (30-day) and long-term (365-day) all-cause mortality. Associations between the CTI and mortality were examined using Kaplan-Meier curves, restricted cubic spline regression, and Cox proportional hazards models. Subgroup, mediation and sensitivity analyses tested result robustness. The CTI was further compared with other single predictors, and six machine learning (ML) models were built to assess its predictive performance. Finally, the SHapley Additive exPlanations (SHAP) analysis identified feature contributions, and a user-friendly web application was developed. + The primary cohort included 1,561 patients, and two external validation cohorts included 242 and 105 patients from the eICU-CRD and SRHIP databases. High CTI values were significantly associated with increased short- and long-term mortality, demonstrating a nonlinear dose-response relationship. The CTI exhibited particularly high predictive value for short-term outcomes. The incorporation of the CTI into ML models notably improved the predictive performance, and this improvement was confirmed in the external validation cohort. + The CTI was identified as an independent predictor of short- and long-term mortality in critically ill patients with CAD, with a particularly high predictive value for short-term risk stratification. Integrating the CTI into predictive models significantly increased the prognostic accuracy. + © 2026 Yang, Xia, Guo, Wu, Wu and Li. + + + + Yang + Jian + J + + Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China. + + + + Xia + Yilin + Y + + Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China. + + + + Guo + Weixi + W + + Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China. + + + + Wu + Liqun + L + + Shenzhen Health Development Research and Data Management Center, Shenzhen, China. + + + + Wu + Xusheng + X + + Shenzhen Health Development Research and Data Management Center, Shenzhen, China. + + + + Li + Bei + B + + Department of Biomedical Informatics, School of Life Sciences, Central South University, Changsha, China. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + c-reactive protein-triglyceride-glucose index + coronary artery disease + inflammation + insulin resistance + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 12 + 9 + + + 2026 + 4 + 11 + + + 2026 + 4 + 29 + + + 2026 + 6 + 1 + 6 + 11 + + + epublish + + 42222119 + PMC13216771 + 10.3389/fcvm.2026.1763569 + + + + + + 42222117 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2297-055X + + 13 + + 2026 + + + Frontiers in cardiovascular medicine + Front Cardiovasc Med + + A novel glycolipid composite index predicting cardiovascular disease in Chinese adults with abnormal glucose metabolism: a nationwide cohort study. + + 1791890 + 1791890 + + 10.3389/fcvm.2026.1791890 + + Cardiovascular disease (CVD) is the leading cause of mortality among individuals with abnormal glucose metabolism. Existing insulin resistance (IR) surrogate indexes show limited predictive capacity in Chinese populations and fail to capture comprehensive glycolipid metabolic dysregulation. We developed and validated TyG-GLM6, a composite index integrating six metabolic parameters (fasting glucose, triglycerides, HDL-C, LDL-C, Age, and BMI), and compared its predictive performance against nine conventional IR indexes. + This prospective cohort study analyzed 3,684 participants aged ≥45 years with abnormal glucose metabolism from the China Health and Retirement Longitudinal Study (2011-2020). Associations between TyG-GLM6 and incident CVD were evaluated using multivariate logistic regression and restricted cubic splines. Seven machine learning algorithms were implemented, with performance assessed via ROC curves and SHAP analysis. External validation was conducted in 2,105 participants from a tertiary hospital. + During 9-year follow-up, 824 (22.4%) participants developed CVD. After full adjustment including biochemical markers, TyG-GLM6 was the only index retaining independent predictive significance (OR: 1.04, 95% CI: 1.01-1.08, P = 0.028), while eGDR, TyG-WC, and CVAI were attenuated to non-significance. TyG-GLM6 exhibited a linear dose-response relationship with CVD risk (P for nonlinear = 0.768, P for overall < 0.001) and consistent performance across sex and age subgroups. Logistic regression achieved optimal performance (AUC: 0.587), with TyG-GLM6 among top predictors. External validation confirmed independent prediction (adjusted OR: 2.02, 95% CI: 1.84-2.20, P < 0.001). + TyG-GLM6 demonstrates superior independent predictive value for CVD in Chinese adults with abnormal glucose metabolism, outperforming conventional IR indexes in fully adjusted models. Its linear dose-response relationship, demographic robustness, and external validation support its utility for early risk stratification and personalized prevention strategies. Validation in ethnically diverse populations is warranted. + © 2026 Peng, Zhu, Xie, Yong, Ye, Gao and Shuai. + + + + Peng + Dan-Dan + DD + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + Zhu + Ping + P + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + Xie + Ji-Chen + JC + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + Yong + Wei + W + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + Ye + Kai + K + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + Gao + Jun-Jie + JJ + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + Shuai + Zhao-Xia + ZX + + Department of Clinical Laboratory, the Second Affiliated Hospital of Wannan Medical University, Wuhu, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Cardiovasc Med + 101653388 + 2297-055X + + + CHARLS + TyG-GLM6 + abnormal glucose metabolism + cardiovascular disease + insulin resistance + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 1 + 20 + + + 2026 + 4 + 15 + + + 2026 + 4 + 20 + + + 2026 + 6 + 1 + 6 + 11 + + + epublish + + 42222117 + PMC13215857 + 10.3389/fcvm.2026.1791890 + + + + + + 42222087 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-2392 + + 17 + + 2026 + + + Frontiers in endocrinology + Front Endocrinol (Lausanne) + + Development and external validation of an interpretable machine learning model for diagnosing coronary heart disease in patients with type 2 diabetes and MASLD. + + 1830594 + 1830594 + + 10.3389/fendo.2026.1830594 + + Patients with type 2 diabetes mellitus (T2DM) and metabolic dysfunction-associated steatotic liver disease (MASLD) face substantially elevated coronary heart disease (CHD) risk, yet no machine learning diagnostic models exist specifically for this population. This study aimed to develop and validate an interpretable machine learning model for identifying CHD in T2DM-MASLD patients. + Using data from 1,269 patients (development cohort) and 1,058 patients (external validation cohort) from two Chinese hospitals, we compared seven machine learning algorithms. Angiographically confirmed CHD served as the diagnostic endpoint. Nine features were selected by univariate analysis, LASSO regression, and the Boruta algorithm. The best-performing model was selected based on comprehensive evaluation of discrimination, calibration, and clinical utility. Model interpretability was assessed using SHapley Additive exPlanations (SHAP), and external validation was performed in an independent cohort. + Feature selection identified nine predictors: total cholesterol (TC), chest distress, apolipoprotein B (ApoB), male sex, triglycerides (TG), age, chest pain, red cell distribution width (RDW), and cardiac troponin (cTn). The XGBoost model achieved the best performance, with an AUC of 0.896 (95% CI, 0.862-0.930) in internal validation and 0.865 (95% CI, 0.837-0.893) in external validation, with excellent calibration (Brier score: 0.112). To facilitate clinical application, a freely accessible web-based calculator was developed for real-time individualized CHD risk prediction. + This is the first interpretable machine learning model externally validated for CHD diagnosis in T2DM-MASLD patients, demonstrating robust performance using nine routinely available clinical parameters. The model's interpretability through SHAP analysis enhances clinical trust and supports individualized risk communication between physicians and patients to guide decisions regarding coronary angiography. + Copyright © 2026 Deng, Feng, Li, Wei, Zhu and Lu. + + + + Deng + Chunxia + C + + Department of Endocrinology and Metabolism, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China. + + + + Feng + Ling + L + + Department of Endocrinology and Metabolism, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China. + + + + Li + Tingting + T + + Department of Respiratory and Critical Care Medicine, Guangxi Hospital of the First Affiliated Hospital, Sun Yat-sen University, Nanning, Guangxi, China. + + + + Wei + Suosu + S + + Clinical Research Center of Guangxi Academy of Medical Sciences, The People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China. + + + + Zhu + Huiming + H + + Department of Clinical Laboratory, Nantong Sixth People's Hospital Affiliated to Shanghai University, Nantong, Jiangsu, China. + + + + Lu + Jie + J + + Department of Endocrinology and Metabolism, Guangxi Academy of Medical Sciences and the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China. + + + + eng + + Journal Article + Validation Study + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Endocrinol (Lausanne) + 101555782 + 1664-2392 + + IM + + + Humans + + + Diabetes Mellitus, Type 2 + complications + + + Coronary Disease + diagnosis + etiology + epidemiology + + + Male + + + Female + + + Machine Learning + + + Middle Aged + + + Aged + + + Boosting Machine Learning Algorithms + + + Predictive Learning Models + + + China + epidemiology + + + + SHAP + XGBoost + coronary heart disease + external validation + machine learning + metabolic dysfunction-associated steatotic liver disease + type 2 diabetes mellitus + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 14 + + + 2026 + 4 + 24 + + + 2026 + 4 + 30 + + + 2026 + 6 + 1 + 6 + 11 + + + epublish + + 42222087 + PMC13218859 + 10.3389/fendo.2026.1830594 + + + + + + 42222083 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-2392 + + 17 + + 2026 + + + Frontiers in endocrinology + Front Endocrinol (Lausanne) + + Identification of BMI-related high-risk feature combinations for diabetes among young adults with normal baseline fasting plasma glucose using interpretable machine learning: a health check-up cohort study. + + 1850071 + 1850071 + + 10.3389/fendo.2026.1850071 + + Body mass index (BMI) is an easily obtainable indicator for diabetes risk screening, but its residual risk value among young adults with normal fasting plasma glucose (FPG) remains insufficiently understood. This cohort study investigated the association between BMI and incident diabetes, its nonlinear risk pattern, and BMI-related risk structures among young adults with normal baseline FPG. Data were obtained from the Rich Healthcare Group health check-up database in China. Participants aged <40 years without diabetes at baseline, with complete BMI data and at least one follow-up visit, were included; those with baseline FPG <5.6 mmol/L were defined as the primary analytic population. Cox regression and restricted cubic spline analysis were used to examine the association between BMI and incident diabetes. Four machine learning models were compared, with logistic regression selected as the primary interpretable model and XGBoost used as an exploratory nonlinear model. SHapley Additive exPlanations were applied to interpret model-derived variable contributions. A total of 103,693 participants were included, and 266 incident diabetes events occurred during a median follow-up of 2.99 years. BMI was independently associated with incident diabetes in the multivariable Cox model (HR = 1.284, 95% CI: 1.250-1.319; P <0.001). Restricted cubic spline analysis showed a significant nonlinear association, with risk increasing more steeply beyond approximately 28 kg/m². In the validation set, logistic regression and XGBoost achieved ROC-AUC values of 0.812 and 0.817, respectively; however, their low PR-AUC values indicated limited ability to identify true positive cases under the very low event rate. SHAP analysis identified BMI as the most influential predictor in the exploratory XGBoost model and suggested possible model-derived joint contribution patterns involving triglycerides and systolic blood pressure, but formal Cox-based interaction testing did not confirm statistically significant multiplicative interactions. These findings suggest that BMI-related diabetes risk among normoglycemic young adults is nonlinear and embedded within a broader metabolic risk structure. Combining conventional regression with interpretable machine learning may support earlier identification and refined risk stratification of young adults at increased diabetes risk before fasting glucose becomes abnormal. + Copyright © 2026 Xu, Zhang and Zhang. + + + + Xu + Zhen + Z + + Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Zhang + Ying + Y + + Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + Zhang + Huachun + H + + Nursing Department, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Endocrinol (Lausanne) + 101555782 + 1664-2392 + + + + 0 + Blood Glucose + + + IM + + + Humans + + + Body Mass Index + + + Male + + + Female + + + Blood Glucose + analysis + + + Machine Learning + + + Fasting + blood + + + China + epidemiology + + + Risk Factors + + + Adult + + + Follow-Up Studies + + + Young Adult + + + Cohort Studies + + + Boosting Machine Learning Algorithms + + + Incidence + + + Diabetes Mellitus, Type 2 + epidemiology + blood + diagnosis + + + + SHAP + body mass index + diabetes + interpretable machine learning + normal fasting plasma glucose + young adults + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 4 + 8 + + + 2026 + 4 + 26 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 11 + + + epublish + + 42222083 + PMC13218894 + 10.3389/fendo.2026.1850071 + + + + + + 42222071 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-2392 + + 17 + + 2026 + + + Frontiers in endocrinology + Front Endocrinol (Lausanne) + + Lipid metabolism-based machine learning models for predicting large for gestational age in non-diabetic pregnancies. + + 1758008 + 1758008 + + 10.3389/fendo.2026.1758008 + + The majority of LGA occurs in normoglycemic pregnancies, yet prediction models integrating lipid metabolism for this population are lacking. This study aimed to develop and validate machine learning models based on lipid metabolic profiles to predict LGA risk in non-diabetic pregnant women. + We retrospectively analyzed 1,968 singleton pregnancies without diabetes. Lipid profiles were assessed at early pregnancy (11-14 weeks) and mid-pregnancy (20-24 weeks). The dataset was randomly divided into training (n = 1,574) and independent test sets (n = 394) using stratified sampling. Four machine learning algorithms-logistic regression (LR), random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost)-were developed using nested 5×5 cross-validation to prevent overfitting and ensure unbiased performance estimation. Model performance was evaluated using area under the receiver operating characteristic curve (AUC), and feature importance was assessed using SHAP analysis. + Hyperparameter optimization demonstrated stable convergence across outer folds (coefficient of variation < 1.5% for all models). On the independent test set, XGBoost achieved the highest performance with an AUC of 0.776 (95% CI: 0.730-0.822), significantly outperforming LR (AUC 0.688, P < 0.001). SHAP analysis identified pre-pregnancy BMI (mean SHAP value: 1.48), gestational weight gain (0.93), and mid-pregnancy triglycerides (0.73) as the strongest predictors. Lipid metabolism-related parameters collectively accounted for approximately 40% of the model's predictive capacity. + Machine learning models incorporating lipid metabolic profiles demonstrated superior performance in predicting LGA among non-diabetic pregnancies. This approach enables early risk stratification and may guide personalized prenatal management. + Copyright © 2026 Liu, Xu, Mi and Yan. + + + + Liu + Wanqing + W + + Department of Obstetrics and Gynecology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Xu + Ya + Y + + Department of Obstetrics and Gynecology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Mi + Chen + C + + Department of Obstetrics and Gynecology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Yan + Shanling + S + + Department of Ultrasound, Deyang People's Hospital, Deyang, Sichuan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Endocrinol (Lausanne) + 101555782 + 1664-2392 + + IM + + + Humans + + + Female + + + Pregnancy + + + Lipid Metabolism + physiology + + + Infant, Large for Gestational Age + + + Predictive Learning Models + + + Retrospective Studies + + + Machine Learning + + + Adult + + + Boosting Machine Learning Algorithms + + + Prediction Algorithms + + + Random Forest + + + Classification Algorithms + + + Gestational Age + + + + large for gestational age + lipid metabolism + machine learning + non-diabetic pregnancy + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 12 + 1 + + + 2026 + 4 + 19 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 11 + + + epublish + + 42222071 + PMC13218891 + 10.3389/fendo.2026.1758008 + + + + + + 42222052 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2359-7232 + + 14 + 1 + + 2026 + Jan-Mar + + + Discoveries (Craiova, Romania) + Discoveries (Craiova) + + Responsible Innovation in AI-Driven Teledentistry: Ethical, Legal, and Economic Considerations for the Globalization Era. + + e224 + e224 + + 10.15190/d.2026.3 + + Teledentistry has evolved from asynchronous communication to real-time consultations, with adoption accelerating during and post-COVID. Concurrently, AI has been leveraged to enhance diagnostic accuracy, efficiency, and accessibility using machine learning, GANs, and connected devices. This scoping review, conducted using Arksey and O'Malley framework and Joanna Briggs Institute guidance, explores the ethical, legal, and economic considerations of AI-driven teledentistry. Following a PRISMA-ScR compliant screening process by two independent reviewers, 137 studies published between 2018 and 2025 were included. Unlike previous reviews that have primarily focused on clinical applications or the general use of AI in healthcare, this review addresses the ethical, legal, and economic considerations of AI in teledentistry in a single paper. It underscores the importance of explainable AI, explores cross-border regulatory challenges, and discusses possible cost models for adoption in smaller practices. This review indicates that AI-powered teledentistry could enhance diagnostic accuracy, facilitate early detection, improve monitoring, and increase accessibility. Nonetheless, these advantages come with critical concerns including data privacy, potential biases, patient autonomy, accountability, and cost. Addressing these issues through governance, oversight, transparency, and economic viability is essential. AI-enabled teledentistry has the potential to transform dental care delivery, but its integration must be approached with careful consideration of its associated challenges. + Copyright © 2026, Kaushik et al., Applied Systems and Discoveries Journals. + + + + Kaushik + Richa + R + + School of Family Medicine and Public Health Sciences, Wayne State University, Detroit, USA. + + + + Rapaka + Ravindra + R + + University of Connecticut, Storrs, CT, USA. + + + + eng + + Journal Article + Review + + + 2026 + 03 + 31 + +
+ + Romania + Discoveries (Craiova) + 101665061 + 2359-7232 + + + Artificial Intelligence (AI) + Data Privacy + Economic Impact. + Ethical Considerations + Healthcare Accessibility + Teledentistry + + Conflict of interests: The authors have no conflicts of interest to disclose + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 12 + 17 + + + 2026 + 3 + 28 + + + 2026 + 3 + 31 + + + 2026 + 6 + 1 + 6 + 10 + + + epublish + + 42222052 + PMC13222122 + 10.15190/d.2026.3 + 349 + + + + + + 42221978 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1177-9322 + + 20 + + 2026 + + + Bioinformatics and biology insights + Bioinform Biol Insights + + A Novel Bioinformatics Pipeline and a Machine-Learning Approach for Antimicrobial Resistance Phenotypic Prediction. + + 11779322261453756 + 11779322261453756 + + 10.1177/11779322261453756 + + Overuse of antimicrobial drugs is known to cause an increase in bacterial resistance among surviving pathogens, reducing the effectiveness of future treatments. Publicly available sequencing collections, such as the National Center for Biotechnology Information Sequence Read Archive (SRA), allow for global investigation of antimicrobial resistance across pathogens. In this study, we developed a pipeline to process 10 803 globally sourced unique bacterial isolates from publicly available SRA datasets (9 pathogens, 4 antibiotics), representing sequencing data generated by multiple laboratories using diverse sequencing platforms and protocols. The pipeline extracted SRA metadata to determine read layout and length, applied quality control, trimming, and decontamination. Preprocessed isolates were mapped reads to an antimicrobial resistance gene class and to a strain-level genome reference library constructed from complete genomes on the SRA submitted between 2009 and 2020. Three classifiers-L1-penalized logistic regression, random forest, and extreme gradient boosting-were trained on the resulting feature matrices, and their outputs were combined in a majority-vote ensemble. Internal training resulted in 83.8% balanced accuracy on average, and external testing yielded 80.2%. Variable importance analyses identified known resistance gene classes and strain markers such as Acinetobacter baumannii LAC-4 and Campylobacter jejuni 81-176 and NCTC13255, confirming biological relevance. This work demonstrates a scalable approach for antimicrobial resistance prediction using heterogeneous sequencing data. + © The Author(s) 2026. + + + + Visser + Owen + O + 0009-0001-4877-0560 + + Department of Biostatistics, University of Florida, Gainesville, USA. + + + + Agboli + Victor + V + 0009-0006-5597-8315 + + Department of Biostatistics, University of Florida, Gainesville, USA. + + + + Datta + Somnath + S + + Department of Biostatistics, University of Florida, Gainesville, USA. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + United States + Bioinform Biol Insights + 101467187 + 1177-9322 + + + antimicrobial resistance prediction + bioinformatics pipeline + phenotypic prediction + + The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 10 + 21 + + + 2026 + 5 + 10 + + + 2026 + 6 + 1 + 6 + 10 + + + epublish + + 42221978 + PMC13219803 + 10.1177/11779322261453756 + 10.1177_11779322261453756 + + + + + + 42221892 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-462X + + 17 + + 2026 + + + Frontiers in plant science + Front Plant Sci + + Tea tree recognition based on multi-source satellite data across Southeast China. + + 1801301 + 1801301 + + 10.3389/fpls.2026.1801301 + + The tea plant (Camellia Sinensis), as the world's most popular non-alcoholic beverage, underscores the importance of precise and timely spatial data for industry insights and sustainability. Yet, the accurate delineation of spectrally similar vegetation types, notably tea trees, continues to elude conventional methods. This study innovatively integrates Sentinel-1 radar data with Sentinel-2 imagery to effectively overcome optical observation limitations imposed by Yunnan's cloudy climate, particularly during non-growing seasons (e.g., February). By systematically analyzing annual phenological dynamics, we quantitatively identified April as the optimal temporal window for discriminating tea trees from spectrally similar vegetation, such as rubber and natural forests. Furthermore, optimizing the model through a feature selection process to eliminate redundant features significantly enhanced the overall classification accuracy from 87.1% to 89.1%. This study could assistant monitoring the crop dynamics and timely respond to cultivation activity. + Copyright © 2026 Cong, Zhao, Qiu and Zhao. + + + + Cong + Nan + N + + Lhasa Plateau Ecosystem Research Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. + + + College of Resources and Environmental Sciences, China Agricultural University, Beijing, China. + + + + Zhao + Rongrong + R + + Lhasa Plateau Ecosystem Research Station, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, China. + + + College of Resources and Environmental Sciences, China Agricultural University, Beijing, China. + + + + Qiu + Yuxin + Y + + College of Resources and Environmental Sciences, China Agricultural University, Beijing, China. + + + + Zhao + Chuang + C + + College of Resources and Environmental Sciences, China Agricultural University, Beijing, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Plant Sci + 101568200 + 1664-462X + + + classification accuracy + crop distribution + machine learning + remote sensing + tea trees + + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 2 + 2 + + + 2026 + 4 + 8 + + + 2026 + 4 + 20 + + + 2026 + 6 + 1 + 6 + 9 + + + epublish + + 42221892 + PMC13216473 + 10.3389/fpls.2026.1801301 + + + + + + 42221889 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-462X + + 17 + + 2026 + + + Frontiers in plant science + Front Plant Sci + + Advancing anthracnose resistance in dry beans through the transition from traditional to computational breeding efforts. + + 1809294 + 1809294 + + 10.3389/fpls.2026.1809294 + + Anthracnose, caused by Colletotrichum lindemuthianum, is a major threat to dry beans (Phaseolus vulgaris), causing significant yield losses worldwide. Despite considerable progress in breeding resistant varieties using traditional methods such as phenotypic selection and crossbreeding, the ongoing challenges posed by the pathogen's genetic diversity and environmental variability call for more sustainable solutions. Traditional breeding methods have made notable advancements, but with the increasing pressure of climate change and evolving disease dynamics, there is a growing need to complement these approaches with modern computational tools. The integration of genomics, phenomics, and bioinformatics has introduced new possibilities in disease resistance breeding. Techniques such as high-throughput sequencing, genome-wide association studies (GWAS), and marker-assisted selection have accelerated the identification of resistance genes, while machine learning and multi-omics approaches provide a deeper understanding of host-pathogen-environment interactions. Therefore, this review aims to provide a comprehensive synthesis of the historical development of anthracnose resistance breeding, highlighting the role of traditional methods and the transition toward computational strategies. It emphasizes how combining both approaches can enhance the development of durable, high-yielding, anthracnose-resistant dry beans, offering more effective solutions to global production challenges. + Copyright © 2026 Patel, Ahlawat, Lorenc, Vazin, Maleki and Yoosefzadeh Najafabadi. + + + + Patel + Aashvi + A + + Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada. + + + + Ahlawat + Sajal + S + + Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada. + + + + Lorenc + Philip + P + + Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada. + + + + Vazin + Maryam + M + + Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada. + + + + Maleki + Masoud + M + + Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada. + + + + Yoosefzadeh Najafabadi + Mohsen + M + + Department of Plant Agriculture, University of Guelph, Guelph, ON, Canada. + + + Department of Bioinformatics, University of Guelph, Guelph, ON, Canada. + + + + eng + + Journal Article + Review + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Plant Sci + 101568200 + 1664-462X + + + climate change + disease resistance + genomics + machine learning + marker-assisted selection + multi-omics + phenotyping + + 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 MY 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 11 + + + 2026 + 3 + 28 + + + 2026 + 4 + 1 + + + 2026 + 6 + 1 + 6 + 9 + + + epublish + + 42221889 + PMC13220701 + 10.3389/fpls.2026.1809294 + + + + + + 42221886 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-462X + + 17 + + 2026 + + + Frontiers in plant science + Front Plant Sci + + Monitoring plant moisture content and optimizing irrigation prescriptions based on UAV multimodal data. + + 1827521 + 1827521 + + 10.3389/fpls.2026.1827521 + + With the continuous advancement of smart agriculture, multi-modal remote sensing based on unmanned aerial vehicles (UAVs) offers new technical approaches for monitoring and managing crop moisture in fields. However, significant challenges remain in developing high-precision field-scale crop Plant Moisture Content (PMC) prediction models and translating them into actionable irrigation strategies. + This study focuses on winter wheat, employing field experiments with PMC and water use efficiency (WUE) as indicators of crop water status. Vegetation indices (VIs) derived from UAV data were used to construct a leaf area index (LAI) inversion model. Crop Height was extracted from oblique photogrammetry point cloud data. By combining the Penman-Monteith equation with dual crop coefficients, an improved evapotranspiration (ET) model was developed, utilizing multispectral data from UAVs, thermal infrared data, point cloud-derived plant height, and LAI inversion results. Further utilizing VIs, temperature indices (TIs), and machine learning algorithms (Random Forest Regression (RFR), Back Propagation Neural Network (BPNN), Partial Least Squares Regression (PLSR), and Support Vector Regression (SVR), we established PMC prediction models for winter wheat at different growth stages. These models, integrated with WUE, form the basis for an irrigation scheduling optimization framework at the field scale. + Results indicate that VIs, the difference between canopy temperature and air temperature (ΔT), Crop Water Stress Index (CWSI), and ET exhibit varying correlations with PMC during three critical growth stages of winter wheat, with ET showing the highest correlation during the jointing and heading stages (absolute correlation coefficient |r| ≥ 0.639). Compared to PMC prediction models constructed with different combinations of VIs, ET, VIs+ET, and VIs+TIs, the model employing the RFR algorithm with multimodal inputs (VIS+TIs+ET) demonstrated the best performance. The model's predictive accuracy gradually improved across all growth stages, peaking during the grain-filling stage, with the coefficient of determination(R2) of 0.900 and a normalized root mean square error (nRMSE) of 2.688%. Optimal WUE varied across growth stages under different irrigation treatments. The highest values were achieved at the jointing stage under treatment W3 (PMC = 81.8%), and at the heading and grain-filling stages under treatment W1 (PMC = 76.8% and 64.0%, respectively). + The study suggests that stage-specific irrigation scheduling based on PMC thresholds can improve overall water use efficiency. This study shows that integrating multi-modal UAV data with machine learning and an improved ET model enables high-precision PMC monitoring, supporting data-driven irrigation scheduling in precision agriculture. + Copyright © 2026 Fu, Hou, Wang, Chen, Cheng, Li and Duan. + + + + Fu + Wanna + W + + Heilongjiang Provincial Hydraulic Research Institute, Harbin, China. + + + Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, China. + + + + Hou + Xinyue + X + + Heilongjiang Provincial Hydraulic Research Institute, Harbin, China. + + + + Wang + Dawei + D + + Heilongjiang Provincial Hydraulic Research Institute, Harbin, China. + + + + Chen + Zhen + Z + + Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, China. + + + + Cheng + Qian + Q + + Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, China. + + + + Li + Yafeng + Y + + Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, China. + + + + Duan + Fuyi + F + + Institute of Farmland Irrigation, Chinese Academy of Agricultural Sciences, Xinxiang, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Plant Sci + 101568200 + 1664-462X + + + modified evapotranspiration + plant moisture content + precision irrigation + unmanned aerial vehicle + water use efficiency + + 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 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 3 + 10 + + + 2026 + 4 + 30 + + + 2026 + 5 + 1 + + + 2026 + 6 + 1 + 6 + 9 + + + epublish + + 42221886 + PMC13219351 + 10.3389/fpls.2026.1827521 + + + + + + 42221843 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1178-7074 + + 19 + + 2026 + + + International journal of general medicine + Int J Gen Med + + IL18 Works Like a Two-Side Coin in Acute Pancreatitis. + + 606433 + 606433 + + 10.2147/IJGM.S606433 + + Acute pancreatitis is a common disease with limited supportive treatments. Finding effective biomarkers is of great significance for early diagnosis and therapy, as well as to achieve better prognosis. + The core genes of AP were identified through bioinformatics and machine learning. The expression, clinical features, biological function and immunological effects of the characteristic gene were also evaluated. AP murine models were constructed to verify the results in vivo. Finally, Mendelian randomization studies were performed to determine the causal relationship between IL-18 and AP through genome-wide association studies. + A total of 100 core genes were obtained, and IL18 was identified as the characteristic gene for AP. The expression of IL18 was increased in AP (p<0.001) with an AUC value of 0.917. And some immune responses were inhibited when IL18 is highly expressed. In addition, the OR for IL18 and AP was 0.908 (95% CI = 0.843-0.978, p=0.011) via inverse variance weighting (IVW). + Elevated IL18 can be used to predict the clinical prognosis and immune responses in AP. Meanwhile, MR suggests that an increase in IL18 indicates a low risk of AP, implying that the course of AP often presents a self-limiting feature in clinic. + © 2026 Zhou et al. + + + + Zhou + Kena + K + 0000-0002-1681-8830 + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + Liu + Leheng + L + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + Bao + Jingpiao + J + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + Wang + Chuanyang + C + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + Wang + Xingpeng + X + 0009-0005-2725-6350 + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + Jiang + Weiliang + W + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + Wan + Rong + R + + Department of Gastroenterology, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, People's Republic of China. + + + Shanghai Key Laboratory of Pancreatic Disease, Institute of Pancreatic Disease, Shanghai Jiao Tong University School of Medicine, Shanghai, 201620, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + New Zealand + Int J Gen Med + 101515487 + 1178-7074 + + + IL18 + acute pancreatitis + bioinformatics + immune responses + machine learning + + The authors report no conflicts of interest in this work. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 2 + + + 2026 + 5 + 17 + + + 2026 + 6 + 1 + 6 + 8 + + + epublish + + 42221843 + PMC13220902 + 10.2147/IJGM.S606433 + 606433 + + + + + + 42221829 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2589-0042 + + 29 + 6 + + 2026 + Jun + 19 + + + iScience + iScience + + Development, validation, and implementation of the antibody-secreting cell maturity index: Universal prediction of human plasma cell maturity. + + 116050 + 116050 + + 10.1016/j.isci.2026.116050 + + Defining the maturity of long-lived antibody-secreting cells (ASCs) is important for vaccine optimization and research into autoimmune diseases, but current assessment of plasma cell maturity is limited. We developed a universal, robust method to define plasma cell maturity using a meta-analysis of public, human cytometry data, with harmonized expression of ASC maturity markers across samples. Vaccination or infection samples were selected to train a random forest-based machine learning prediction model (performance of r = 0.866). After evaluation (performance of r = 0.616), the final model, named the antibody-secreting cell maturity index (ASC-ME), was applied to compartments with long-term ASC survival niches, namely bone marrow and gut. Both compartments showed increased ASC maturity, biologically validating the ASC-ME model. Example analyses highlight the broad spectrum for model application, e.g., in vaccine research, clinical trials or in ASC-related autoimmune diseases. Overall, our ASC-ME model, accessible via an online platform, offers a robust approach in evaluating ASC maturity. + © 2026 The Author(s). + + + + Steinmetz + Tobit D + TD + + University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9700RB Groningen, the Netherlands. + + + + Yang + Guiyou + G + + University of Groningen, University Medical Center Groningen, Department of Epidemiology, 9700RB Groningen, the Netherlands. + + + + de Nooijer + Tessa + T + + University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9700RB Groningen, the Netherlands. + + + + van der Meer + Nisha + N + + University of Groningen, University Medical Center Groningen, Department of Oncology, 9700RB Groningen, the Netherlands. + + + + Karmi + Naomi + N + + University of Groningen, University Medical Center Groningen, Department of Gastroenterology and Hepatology, 9700RB Groningen, the Netherlands. + + + + Wilbrink + Rick + R + + University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9700RB Groningen, the Netherlands. + + + + Bootsma + Hendrika + H + + University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9700RB Groningen, the Netherlands. + + + + Verstappen + Gwenny M + GM + + University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9700RB Groningen, the Netherlands. + + + + Kroese + Frans G M + FGM + + University of Groningen, University Medical Center Groningen, Department of Rheumatology and Clinical Immunology, 9700RB Groningen, the Netherlands. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
+ + United States + iScience + 101724038 + 2589-0042 + + + Artificial intelligence + Components of the immune system + + The authors declare no competing interests. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 6 + 27 + + + 2025 + 12 + 16 + + + 2026 + 5 + 5 + + + 2026 + 6 + 1 + 6 + 8 + + + epublish + + 42221829 + PMC13217880 + 10.1016/j.isci.2026.116050 + S2589-0042(26)01425-2 + + + + + + 42221812 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2589-0042 + + 29 + 6 + + 2026 + Jun + 19 + + + iScience + iScience + + Integration of single-cell regulon atlas and bulk RNA-seq for individualized prognostic prediction in stomach adenocarcinoma. + + 116100 + 116100 + + 10.1016/j.isci.2026.116100 + + Transcriptional regulators reflect cellular heterogeneity and are key for prognostic modeling. Given the poor prognosis of stomach adenocarcinoma (STAD), regulator-derived signatures are vital for risk stratification. Using multi-stage scRNA-seq data, we delineated the transcriptional regulatory landscape of STAD and identified Helicobacter pylori-associated epithelial heterogeneity in intestinal metaplasia. We then developed a 23-regulator machine learning-based STAD prognostic signature (SPS) from malignant epithelial cells to predict overall survival (OS). Patients with high-SPS exhibited significantly worse OS than patients with low-SPS (hazard ratio [HR] = 1.50, 95% CI: 1.09-2.09, log rank p = 7.11 × 10-3). Notably, SPS outperformed other established STAD prognosis models across various independent datasets and remained an independent prognostic factor after adjusting for clinical and pathologic factors. Moreover, integrating SPS with tumor stage and age showed superior accuracy to stage alone. Collectively, our study establishes a robust regulator-based prognostic signature, holding potential to facilitate precision prognostication in STAD. + © 2026 The Author(s). + + + + Zhang + Aiping + A + + Department of Gastroenterology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China. + + + + Li + Zhe + Z + + Department of Epidemiology, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, Jiangsu, China. + + + + Jiang + Xin + X + + Department of Gastroenterology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China. + + + + Li + Yaoyao + Y + + Department of Gastroenterology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China. + + + + Zhang + Min + M + + Department of Gastroenterology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China. + + + + Ding + Yanbing + Y + + Department of Gastroenterology, The Affiliated Hospital of Yangzhou University, Yangzhou University, Yangzhou, Jiangsu, China. + + + + eng + + Journal Article + + + 2026 + 05 + 22 + +
+ + United States + iScience + 101724038 + 2589-0042 + + + Health sciences + Medicine + Oncology + + The authors declare no competing interests. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 9 + 9 + + + 2026 + 1 + 26 + + + 2026 + 5 + 8 + + + 2026 + 6 + 1 + 6 + 8 + + + epublish + + 42221812 + PMC13217890 + 10.1016/j.isci.2026.116100 + S2589-0042(26)01475-6 + + + + + + 42221810 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2212-277X + + 15 + 1 + + 2026 + + + Perspectives on medical education + Perspect Med Educ + + Teaching Personalized Doctor-Patient Communication with AI: PerTRAIN - a Prototype for Interpersonally Responsive Virtual Patients in Medical Education. + + 465 + 474 + 465-474 + + 10.5334/pme.2379 + + Personalized medicine requires physicians to adapt clinical communication and decision-making to patients' individual motives, emotions, and interpersonal behaviors. However, training these skills remains challenging, as established simulation-based formats-particularly actor-based simulations-are resource-intensive and difficult to scale. Consequently, there is a need for scalable and flexible training approaches that allow repeated practice across diverse patient personalities and clinical contexts. The PerTRAIN (Personalization Training in Medicine) project addresses this need by leveraging large language models (LLMs) to simulate virtual patients with dynamically adapting personality expression at scale. Grounded in Contemporary Integrative Interpersonal Theory (CIIT), patient behavior is modelled along the dimensions of agency and communion and updated in response to the medical trainee's behavior, enabling systematic variation and dynamic adaptation of interpersonal behavior within medical scenarios. This allows trainees to learn how a patients personality shapes communication, and to practice adaptive, patient-centered communication strategies and appropriate clinical decision-making. For the personalization trainings we developed clinical cases in which personality expression substantially influences behavior in doctor-patient interactions, that represent common encounters in primary care, and that align with national guidelines for patient-centered care. An initial chat-based implementation enables structured interactions, dynamic personality adaptation, and iterative refinement. The system is designed as a complementary tool to existing simulation formats, offering a scalable, low-threshold environment for repeated practice and reflection. A first application in curricular teaching is scheduled for 2026. Future extensions of the framework are discussed, such as large-scale empirical validation, modelling long-term interpersonal trajectories, and the extension of interactions to multimodal formats. + Copyright: © 2026 The Author(s). + + + + Junga + Anna + A + 0000-0002-4165-9114 + + Institute of Education and Student Affairs, University of Münster, Münster, Germany. + + + + Hätscher + Ole + O + 0009-0009-1410-4023 + + Department of Psychology, University of Münster, Münster, Germany. + + + + Dabel + Jennifer + J + 0009-0001-6458-9702 + + Institute of Education and Student Affairs, University of Münster, Münster, Germany. + + + + Mado + Gabriyel + G + 0009-0003-4103-2113 + + Institute of General Practice and Family Medicine, University of Münster, Germany. + + + + Ajani + Alberta + A + 0000-0001-5917-9904 + + Institute of General Practice and Family Medicine, University of Münster, Germany. + + + + Pielage + Leon + L + 0009-0003-0933-6424 + + Institute for Geoinformatics & Institute for Computer Science, University of Münster, Münster, Germany. + + + + Kockwelp + Pascal + P + 0009-0001-9118-0929 + + Institute for Geoinformatics & Institute for Computer Science, University of Münster, Münster, Germany. + + + + Breil + Simon M + SM + 0000-0001-5583-3884 + + Department of Psychology, University of Münster, Münster, Germany. + + + Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Germany. + + + + Baur + Helena + H + 0009-0004-7263-8131 + + Institute of Education and Student Affairs, University of Münster, Münster, Germany. + + + + Siebenbrock + Jan + J + 0009-0003-1441-0940 + + Institute of Education and Student Affairs, University of Münster, Münster, Germany. + + + + Risse + Benjamin + B + 0000-0001-5691-4029 + + Institute for Geoinformatics & Institute for Computer Science, University of Münster, Münster, Germany. + + + + Grammer + Tanja + T + 0000-0002-0694-7233 + + University of Münster, Germany. + + + + Marschall + Bernhard + B + 0000-0002-1354-8687 + + University of Münster, Germany. + + + + Back + Mitja D + MD + 0000-0003-2186-1558 + + Department of Psychology, University of Münster, Münster, Germany. + + + Joint Institute for Individualisation in a Changing Environment (JICE), University of Münster and Bielefeld University, Germany. + + + + eng + + Journal Article + + + 2026 + 05 + 29 + +
+ + Netherlands + Perspect Med Educ + 101590643 + 2212-2761 + + IM + + + Humans + + + Physician-Patient Relations + + + Communication + + + Education, Medical + methods + trends + + + Large Language Models + + + Precision Medicine + methods + + + Artificial Intelligence + trends + + + Patient Simulation + + + The authors have no competing interests to declare. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 1 + 5 + + + 2026 + 2 + 3 + + + 2026 + 6 + 1 + 6 + 8 + + + epublish + + 42221810 + PMC13220740 + 10.5334/pme.2379 + + + + + + 42221770 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-861X + + 13 + + 2026 + + + Frontiers in nutrition + Front Nutr + + Integrating sarcopenia into ICU-acquired weakness risk stratification: a machine learning-based prediction model for critical care. + + 1823112 + 1823112 + + 10.3389/fnut.2026.1823112 + + Sarcopenia is closely associated with weakness in elderly and various chronic disease populations. However, its specific role as a predisposing factor for ICU-acquired weakness (ICU-AW) in critically ill patients remains unclear. This study aimed to investigate whether sarcopenia is a predictor of ICU-AW and to develop a machine learning-based prediction model integrating sarcopenia for risk stratification. + A retrospective analysis was conducted on data from a prospectively maintained database. A total of 1,324 critically ill patients were enrolled and randomly divided into a training set (n = 927) and a validation set (n = 397) in a 7:3 ratio. Sarcopenia was assessed by measuring the skeletal muscle area at the third lumbar vertebra (L3) level using computed tomography. Least absolute shrinkage and selection operator (LASSO) regression and the Boruta algorithm were employed to identify key predictors. 10 machine learning models were developed and their predictive performance was compared. SHAP (SHapley Additive exPlanations) analysis was used to quantify the feature importance of each predictor in the optimal model. + Six predictors were identified by intersecting the results from LASSO regression and the Boruta algorithm in the training set: age, APACHE II score, sarcopenia, sepsis, mechanical ventilation, and lactic acid. Among the 10 machine learning models developed, the XGBoost model exhibited the best overall predictive performance in the training set, achieving an area under the curve (AUC) of 0.838 (95% confidence interval [CI]: 0.807-0.868) and the lowest Brier score of 0.137. Decision curve analysis and clinical impact curves confirmed its stable clinical predictive value. SHAP analysis revealed that sarcopenia ranked as the third most important predictor, following APACHE II and age. In the validation set, the XGBoost model maintained excellent discriminative ability and predictive performance, with an AUC of 0.843 (95% CI: 0.804-0.882) and good calibration, demonstrating a positive net benefit across a threshold probability range of 9%-92%. + Sarcopenia is an important predictor of ICU-AW in critically ill patients. Integrating sarcopenia into the XGBoost model effectively identifies high-risk patients, providing a valuable tool for early risk stratification in intensive care settings. + Copyright © 2026 Zheng, Liu, Zhang, Zhang, Yang, Chen and Ye. + + + + Zheng + Peng + P + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + Liu + Xinwei + X + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + Zhang + Chunxia + C + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + Zhang + Wei + W + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + Yang + Sheng + S + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + Chen + Yong + Y + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + Ye + Yong + Y + + Department of Intensive Care Unit, Clinical Oncology School of Fujian Medical University, Fujian Cancer Hospital, NHC Key Laboratory of Cancer Metabolism, Fuzhou, Fujian, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Nutr + 101642264 + 2296-861X + + + ICU-acquired weakness + XGBoost + machine learning + risk stratification + sarcopenia + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 3 + 4 + + + 2026 + 4 + 22 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 7 + + + epublish + + 42221770 + PMC13215805 + 10.3389/fnut.2026.1823112 + + + + + + 42221642 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-2565 + + 14 + + 2026 + + + Frontiers in public health + Front Public Health + + The impact of low-carbon city pilot policies on urban energy intensity. + + 1806792 + 1806792 + + 10.3389/fpubh.2026.1806792 + + Urban energy intensity measures the energy required per unit of economic output and is closely linked to energy-related emissions that shape air quality and climate-related health risks. Using panel data from 273 prefecture-level Chinese cities (2006-2021), we estimate the causal impact of China's Low-Carbon City Pilot Scheme (LCCPS) on urban energy intensity. We apply a partially linear double/debiased machine learning framework with city and year fixed effects, enabling flexible adjustment for high-dimensional confounders. The LCCPS lowers urban energy intensity by about 0.11 units, and the result is robust to alternative specifications. Channel analyses suggest that pilot designation strengthens fiscal support intensity and stimulates enterprise green innovation, which together contribute to reduced energy intensity. Effects are strongest in large and coastal cities and in resource-scarce regions, but weaker in small and medium-sized cities, inland areas, and resource-rich regions. By reducing the energy required for economic activity, low-carbon pilots may also generate public-health co-benefits through cleaner urban environments. + Copyright © 2026 Qian, Hu, Guo, Liu and Hu. + + + + Qian + Yuren + Y + + School of Humanities and Social Sciences, Jiangsu University of Science and Technology, Zhenjiang, China. + + + + Hu + Jiahan + J + + College of Engineering, University of Perpetual Help System Laguna, Laguna, Philippines. + + + + Guo + Bingnan + B + + School of Humanities and Social Sciences, Jiangsu University of Science and Technology, Zhenjiang, China. + + + + Liu + Tangfa + T + + School of Economics and Management, Gannan University of Science and Technology, Ganzhou, China. + + + + Hu + Hao + H + + School of Economics, Shanghai University, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Public Health + 101616579 + 2296-2565 + + + + 7440-44-0 + Carbon + + + IM + + + Cities + + + China + + + Carbon + analysis + + + Humans + + + Air Pollution + prevention & control + + + + causal inference + fiscal support + green technological innovation + low-carbon city pilot policy + urban energy intensity + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 8 + + + 2026 + 3 + 27 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 6 + + + epublish + + 42221642 + PMC13218860 + 10.3389/fpubh.2026.1806792 + + + + + + 42221580 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2235-2988 + + 16 + + 2026 + + + Frontiers in cellular and infection microbiology + Front Cell Infect Microbiol + + Identification and evaluation of gut microbiome as non-invasive biomarkers for early lung adenocarcinoma from a multi-center study. + + 1813261 + 1813261 + + 10.3389/fcimb.2026.1813261 + + This study aims to characterize the GM in LUAD patients and develop and validate a GM-based diagnostic model for LUAD. + In this prospective, randomized, multi-center study, the GM was characterized, and an LUAD classifier was developed using a training cohort of 175 early-stage LUAD patients and 107 healthy controls. The model was further validated in a test cohort, two independent external cohorts from Jiangsu and Hainan, and an advanced LUAD cohort. Additional ML models were also developed and compared to assess their predictive performance. + LUAD patients exhibited reduced microbial diversity and significantly altered microbial composition compared to healthy controls. The phylum Verrucomicrobia and 13 genera, including Enterococcus and Akkermansia, were more abundant in the LUAD group, while 5 phyla, such as Fusobacteria and Cyanobacteria, and 17 genera, including Lactobacillus and Weissella, were enriched in the control group. Using random forest (RF), eight operational taxonomic units were identified as the optimal subset, achieving an area under the curve (AUC) of 0.998 in the training cohort and maintaining high accuracy in the test cohort (AUC = 96.9%). The model also demonstrated robust performance in two independent cohorts from Jiangsu (AUC = 97.6%) and Hainan (AUC = 82.9%), with strong diagnostic potential for advanced LUAD. Among five common models, the RF model exhibited the highest diagnostic accuracy. + This study provides a comprehensive characterization of the gut microbiome in LUAD and develops a diagnostic model based on microbial biomarkers, which is validated across regionally diverse cohorts, highlighting its potential as a reliable and non-invasive screening tool for LUAD. + Copyright © 2026 Zhang, Wu, Shen, Peng, Li, Zheng, Jia, Liu and Liu. + + + + Zhang + Weidong + W + + Anesthesia and Operation Centre, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + Department of Thoracic Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + + Wu + Shurui + S + + Department of Thoracic Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + + Shen + Leilei + L + + Department of Thoracic Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + Department of Thoracic Surgery, Hainan Hospital of Chinese PLA General Hospital, Hainan, China. + + + + Peng + Peng + P + + Department of Anesthesiology, The Second Affiliated Hospital of Soochow University, Suzhou, Jiangsu, China. + + + + Li + Rong + R + + Department of Health Medicine, The Second Medical Centre & National Clinical Research Centre for Geriatric Diseases of Chinese PLA General Hospital, Beijing, China. + + + + Zheng + Qian + Q + + Department of Thoracic Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + + Jia + Xiaodong + X + + Department of Oncology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + + Liu + Qingyan + Q + + Department of Oncology, The Fifth Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + + Liu + Yang + Y + + Department of Thoracic Surgery, The First Medical Centre of Chinese PLA General Hospital, Beijing, China. + + + + eng + + Journal Article + Multicenter Study + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Cell Infect Microbiol + 101585359 + 2235-2988 + + + + 0 + RNA, Ribosomal, 16S + + + 0 + Biomarkers + + + IM + + + Humans + + + Gastrointestinal Microbiome + + + Prospective Studies + + + Female + + + Male + + + Adenocarcinoma of Lung + diagnosis + microbiology + + + Middle Aged + + + Bacteria + classification + genetics + isolation & purification + + + Aged + + + Lung Neoplasms + diagnosis + microbiology + + + RNA, Ribosomal, 16S + genetics + + + Biomarkers + + + China + + + + dignostic assessment + gastrointestinal microbiome + lung adenocarcinoma (LUAD) of lung + machine learning (ML) + random forest + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 2 + 18 + + + 2026 + 4 + 21 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 5 + + + epublish + + 42221580 + PMC13216471 + 10.3389/fcimb.2026.1813261 + + + + + + 42221574 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2632-1297 + + 8 + 3 + + 2026 + + + Brain communications + Brain Commun + + Early-life blood pressure and midlife brain and cognitive health: tests in two birth cohorts. + + fcag172 + fcag172 + + 10.1093/braincomms/fcag172 + + Elevated blood pressure (BP) in midlife is a well-established risk factor for impaired brain health and cognitive ability in old age. We hypothesized that exposure to elevated BP within the first five decades of life may contribute to this risk through impacts on brain health and/or cognitive ability evident by midlife. Participants (n = 893) were selected from the Dunedin Multidisciplinary Health and Development Study (The Dunedin Study). Exposures were systolic (SBP) and diastolic (DBP) blood pressures measured at ages 7, 11, 18, 26, 32, 38 and 45. Cumulative early-life exposure to blood pressure was also quantified as the area under the curve (AUC). Brain health was assessed at age 45 via imaging measures comprising BrainAGE (difference between chronological age and age predicted from machine-learning models of brain-imaging data), white matter hyperintensity (WMH) burden, and retinal arteriolar calibres (RAC)-a proxy for cerebral small vessel remodelling. Cognitive ability (assessed using IQ) was also measured at age 45, with replication of cognitive findings tested in a larger contemporary cohort, the 1970 British Cohort Study. We found limited evidence for any association between BP in the first four decades of life and brain health or cognitive ability at age 45. Most associations instead emerged for BP from early-midlife onwards. Midlife BP was associated with older BrainAGE (Beta for DBP at age 45 = 0.11 [0.04, 0.19]; P = 0.003) and higher WMH burden (Beta = 0.09 [0.02, 0.17]; P = 0.019). Effect estimates for SBP were similar. For cognitive ability, DBP at ages 38 and 45 showed modest associations with age 45 IQ, which became null after accounting for childhood IQ. These findings were broadly replicated in the 1970 British Cohort Study for age 47 IQ. Only RAC in The Dunedin Study were found to associate with BP from childhood (Beta for age 7 DBP = -0.09 [-0.16, -0.03]; P = 0.006), and the magnitude of these estimates increased during midlife (Beta at age 45 = -0.37 [-0.45, -0.30]; P < 0.001). We found little evidence for any association between BP prior to age 40 and BrainAGE, WMH volume, or cognitive ability in midlife. However, cumulative exposure to elevated BP from childhood was associated with reduced RAC, suggesting a potential link between BP and adverse cerebral small vessel remodelling from childhood. + © The Author(s) 2026. Published by Oxford University Press on behalf of the Guarantors of Brain. + + + + Mugoba + Mayibongwe + M + 0009-0001-8056-7677 + + Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London WC1E 7HB, UK. + + + + Houts + Renate M + RM + + Departments of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708, USA. + + + + Knodt + Annchen R + AR + + Departments of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708, USA. + + + + Theodore + Reremoana F + RF + + Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin 9016, New Zealand. + + + + Poulton + Richie + R + + Dunedin Multidisciplinary Health and Development Research Unit, University of Otago, Dunedin 9016, New Zealand. + + + + Hariri + Ahmad R + AR + + Departments of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708, USA. + + + + Caspi + Avshalom + A + 0000-0003-0082-4600 + + Departments of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708, USA. + + + MRC Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London SE5 8AF, UK. + + + + Moffitt + Terrie E + TE + + Departments of Psychology and Neuroscience, and Psychiatry and Behavioral Sciences, Duke University, Durham, NC 27708, USA. + + + MRC Social, Genetic, and Developmental Psychiatry Centre, Institute of Psychology, Psychiatry, and Neuroscience, King's College London, London SE5 8AF, UK. + + + + Chiesa + Scott T + ST + 0000-0003-4323-2189 + + Department of Population Science and Experimental Medicine, Institute of Cardiovascular Science, University College London, London WC1E 7HB, UK. + + + + eng + + Journal Article + + + 2026 + 05 + 13 + +
+ + England + Brain Commun + 101755125 + 2632-1297 + + + BCS70 + Dunedin + blood pressure + brain health + cognition + + The authors report no competing interests. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 9 + 16 + + + 2026 + 4 + 3 + + + 2026 + 5 + 12 + + + 2026 + 6 + 1 + 6 + 5 + + + epublish + + 42221574 + PMC13218384 + 10.1093/braincomms/fcag172 + fcag172 + + + + + + 42221554 + + 2026 + 06 + 01 + +
+ + 2694-1899 + + 5 + 1 + + 2026 + + + Data science in science + Data Sci Sci + + Neurodatascience: Past, Present, and Future. + 2619222 + 10.1080/26941899.2026.2619222 + + The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing recommendations on how to foster the necessary team science approach to continue the advancement of research at the intersection of neuroscience and data science, which we call neurodatascience, toward a sustainable future. + + + + Cooper + Keiland W + KW + 0000-0002-0358-9645 + + Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA. + + + Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, USA. + + + + Shahbaba + Babak + B + 0000-0002-8102-1609 + + Department of Statistics, University of California, Irvine, California, USA. + + + + Fortin + Norbert J + NJ + 0000-0002-6793-6984 + + Department of Neurobiology and Behavior, University of California, Irvine, Irvine, California, USA. + + + Center for the Neurobiology of Learning and Memory, University of California, Irvine, Irvine, California, USA. + + + + eng + + Journal Article + + + 2026 + 04 + 17 + +
+ + United States + Data Sci Sci + 9918557182406676 + 2694-1899 + + + Brain activity + big Data + computational neuroscience + deep learning + dimensionality reduction + machine learning + neural decoding + signal processing + + Disclosure Statement No potential conflict of interest was reported by the authors. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 6 + 1 + 6 + 5 + + + ppublish + + 42221554 + PMC13218777 + 10.1080/26941899.2026.2619222 + 2619222 + + + + + + 42221490 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-302X + + 17 + + 2026 + + + Frontiers in microbiology + Front Microbiol + + Clinical efficacy and gut microbiota profiling by 16S rRNA sequencing in children with Henoch-Schönlein purpura treated with integrated Chinese and Western medicine. + + 1697699 + 1697699 + + 10.3389/fmicb.2026.1697699 + + Henoch-Schönlein purpura (HSP) is the most common systemic vasculitis in children. Recent studies have suggested that gut microbiota dysbiosis may contribute to its pathogenesis, but microbial alterations and treatment-related changes remain unclear. + A total of 142 children were initially enrolled, including 103 HSP patients and 39 healthy controls. After excluding 30 patients due to incomplete follow-up, 112 participants were included in the final clinical analysis: 39 healthy controls, 38 HSP patients treated with integrated Chinese and Western medicine (integrated group), and 35 treated with Western medicine (WM group). Both patient groups received an identical Western medicine regimen; the integrated group additionally received Compound Tuizi Decoction, an oral herbal formulation prescribed according to the TCM syndrome pattern of "blood-heat with stasis-toxin." For microbiota analysis, fecal samples were collected at baseline and after 4 weeks of treatment in the integrated group, at baseline in the WM group, and once in healthy controls. A total of 78 stool samples passed quality control and were included in microbiota analyses. All samples were sequenced in a single Illumina NovaSeq 6000 run to minimize batch effects. Gut microbiota was analyzed using 16S rRNA gene sequencing, alpha and beta diversity metrics, differential abundance analysis, functional prediction (KEGG), and machine learning models. Multivariate analyses (PERMANOVA) adjusting for age, sex, and baseline diversity were performed to isolate treatment-related effects. + At baseline, HSP patients exhibited reduced microbial diversity compared to healthy controls (Shannon index: INT-BL, 4.12 ± 0.67, p = 0.041; WM-BL, 4.42 ± 0.69, p = 0.032; vs. HC, 5.28 ± 0.84), along with increased relative abundances of Bacteroidota and Proteobacteria. After treatment, both groups showed clinical improvement. Although short-term clinical outcomes were comparable, the integrated group demonstrated significant restoration of microbiota composition and diversity toward a healthy profile. Notably, Blautia and Faecalibacterium increased more substantially in the integrated group. Random forest analysis identified discriminatory genera including Lachnoclostridium, Ruminococcus, Eubacterium, and Blautia. ROC analysis revealed that the combined marker panel achieved high classification accuracy for INT-BL vs. HC (LOOCV AUC = 0.865 [0.686-0.992]), with Ruminococcus torques group showing the highest individual discriminatory power (AUC = 0.917 under the 70/30 split), and strong discrimination for WM-BL vs. INT-AT (AUC = 0.883), while INT-AT vs. HC (AUC = 0.556) and WM-BL vs. HC (AUC = 0.548) showed weaker performance, suggesting partial microbiota restoration after integrated treatment. Functional prediction indicated reduced polyketide sugar unit biosynthesis in the integrated group compared to WM (FDR = 0.0058), and decreased histidine metabolism in the integrated group after treatment compared to controls (FDR = 0.0215). + Pediatric HSP is associated with gut microbiota dysbiosis. Both treatment approaches were associated with partial microbiota restoration, with more pronounced compositional and functional changes observed in the integrated group. Given the add-on design of this study, the microbiota changes observed in the integrated group can be largely attributed to the herbal intervention. These findings suggest that microbiota modulation may play a role in the treatment of pediatric vasculitis, though the clinical significance of these changes needs to be confirmed in larger randomized controlled trials. + Copyright © 2026 Xu, Zhang, Chen, Liu and Wang. + + + + Xu + Kefei + K + + Department of Pediatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China. + + + Hubei University of Chinese Medicine, Wuhan, Hubei, China. + + + Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China. + + + Hubei Shizhen Laboratory, Wuhan, Hubei, China. + + + Hubei Provincial Key Laboratory of Research and Application of Liver and Kidney in Traditional Chinese Medicine, Wuhan, China. + + + + Zhang + Jun + J + + Taihe Hospital, Affiliated Hospital of Hubei University of Medicine, Shiyan, China. + + + + Chen + Xuelian + X + + Department of Pediatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China. + + + Hubei University of Chinese Medicine, Wuhan, Hubei, China. + + + Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China. + + + Hubei Shizhen Laboratory, Wuhan, Hubei, China. + + + Hubei Provincial Key Laboratory of Research and Application of Liver and Kidney in Traditional Chinese Medicine, Wuhan, China. + + + + Liu + Xiaoying + X + + Department of Pediatrics, Hubei Provincial Hospital of Traditional Chinese Medicine, Wuhan, China. + + + Hubei University of Chinese Medicine, Wuhan, Hubei, China. + + + Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China. + + + Hubei Shizhen Laboratory, Wuhan, Hubei, China. + + + Hubei Provincial Key Laboratory of Research and Application of Liver and Kidney in Traditional Chinese Medicine, Wuhan, China. + + + + Wang + Linqun + L + + Hubei University of Chinese Medicine, Wuhan, Hubei, China. + + + Affiliated Hospital of Hubei University of Chinese Medicine, Wuhan, China. + + + Hubei Shizhen Laboratory, Wuhan, Hubei, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Microbiol + 101548977 + 1664-302X + + + 16S rRNA sequencing + IgA vasculitis + functional prediction + gut microbiota + integrated Chinese and Western medicine + microbial diversity + pediatric vasculitis + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 9 + 2 + + + 2026 + 3 + 30 + + + 2026 + 4 + 14 + + + 2026 + 6 + 1 + 6 + 4 + + + epublish + + 42221490 + PMC13215996 + 10.3389/fmicb.2026.1697699 + + + + + + 42221434 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1179-1411 + + 18 + + 2026 + + + International journal of women's health + Int J Womens Health + + Machine Learning Models for Predicting Liver Metastasis at Diagnosis and Overall Survival in Ovarian Cancer: A SEER-Based Study. + + 597887 + 597887 + + 10.2147/IJWH.S597887 + + Ovarian cancer (OC) is one of the most common gynecological tumors, and liver metastases are the most common metastatic sites of OC. However, there is a lack of machine learning (ML) models that can predict the diagnosis and prognosis of liver metastases in OC patients. Therefore, this study aims to develop an effective predictive model. + This was a retrospective study based on the Surveillance, Epidemiology, and End Results (SEER) database. Patients with OC diagnosed from 2010 to 2020 were extracted from the SEER database. The dataset was partitioned into a training cohort (60%) and a validation cohort (40%). The primary endpoints of this study were the liver metastases at diagnosis in OC patients and the 12-, 36-, and 60-month overall survival (OS) of OC liver metastases patients. After feature selection via Boruta algorithm, 9 ML diagnostic models and 5 prognostic models were constructed. In the diagnostic model, area under the curve (AUC), accuracy, kappa, sensitivity, specificity, positive predictive value, negative predictive value evaluated model reliability. AUC and Brier score were used for prognostic models. + Of 27,065 OC patients, 1053 had liver metastases at diagnosis. It was observed that histological type, T stage, grade, age, N stage, CA125, laterality, and race were associated with liver metastasis at diagnosis in OC patients. Histological type, chemotherapy, surgery, radiotherapy, lung metastasis, bone metastasis, age, tumor grade, and marital status were associated with OS in patients with OC liver metastasis. In the training cohort, KNN had the highest AUC value (0.863). In the validation cohort, Ridge had the highest AUC (0.758). Among the 9 diagnostic models, Ridge regression stands out the most, with AUC (0.758), sensitivity (0.929), and negative predictive value (0.994) were the highest. For 12-month OS, RSF model had highest AUC (0.876) in both training and validation groups. Among 5 prognostic models, RSF showed best comprehensive performance. The stage T was the most discriminative features for diagnosing liver metastasis. For OS at 12, 36, and 60 months, the most discriminative prognostic features were chemotherapy, histological type, and age, respectively. Surgery and chemotherapy were associated with improved OS. + The Ridge regression and RSF had favorable predictive performance in the diagnostic and prognostic models, respectively, compared with the other tested models; this may further help clinicians identify patients with liver metastasis at the time of OC diagnosis and select appropriate treatment options. + © 2026 Li et al. + + + + Li + Chao + C + + Department of Obstetrics and Gynecology, Wuhan Fourth Hospital, Wuhan, Hubei, 430000, People's Republic of China. + + + + Huang + Lihong + L + + Department of Obstetrics and Gynecology, Wuhan Fourth Hospital, Wuhan, Hubei, 430000, People's Republic of China. + + + + Jiang + Rui + R + + Department of Gastrointestinal Surgery, Wuhan Fourth Hospital, Wuhan, Hubei, 430000, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 26 + +
+ + New Zealand + Int J Womens Health + 101531698 + 1179-1411 + + + diagnosis + liver metastases + machine learning + ovarian cancer + prognosis + + The authors declare that they have no competing interests. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 1 + 21 + + + 2026 + 5 + 4 + + + 2026 + 6 + 1 + 6 + 4 + + + epublish + + 42221434 + PMC13222004 + 10.2147/IJWH.S597887 + 597887 + + + + + + 42221399 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 3050-9157 + + 13 + 3 + + 2026 + May-Jun + + + AACE endocrinology and diabetes + AACE Endocrinol Diabetes + + Harnessing Microbiome Therapy to Treat Metabolic Syndrome. + + 463 + 472 + 463-472 + + 10.1016/j.aed.2025.11.015 + + Metabolic syndrome, a global health crisis marked by insulin resistance, obesity, and dyslipidemia, necessitates novel therapeutic approaches beyond conventional symptom management. Emerging research highlights the gut microbiome as a pivotal modulator of metabolic health, with dysbiosis-characterized by reduced microbial diversity and proinflammatory shifts-implicated in disease pathogenesis. This review synthesizes evidence from preclinical and clinical studies on microbiome-targeted therapies, including fecal microbiota transplantation, designer probiotics, and synbiotics, which aim to restore microbial balance and ameliorate metabolic dysfunction. + This review synthesizes evidence from preclinical and clinical studies on microbiome-targeted therapies, including fecal microbiota transplantation, designer probiotics, and synbiotics, which aim to restore microbial balance and ameliorate metabolic dysfunction. + Fecal microbiota transplantation transfers beneficial microbiota to enhance insulin sensitivity, while probiotics and synbiotics modulate inflammation, strengthen gut barrier integrity, and stimulate metabolic regulators like glucagon-like peptide-1 and short-chain fatty acids. Mechanistically, these therapies mitigate systemic inflammation, improve glucose/lipid homeostasis, and reduce intestinal permeability linked to endotoxin translocation. Clinical trials report improved glycemic control, lipid profiles, and weight management, underscoring their multitargeted potential. However, challenges such as donor variability, lack of standardized protocols, and long-term safety concerns hinder widespread application. Personalized approaches, informed by machine learning and microbial biomarkers, alongside innovations in Clustered Regularly Interspaced Short Palindromic Repeats-based engineering and encapsulation technologies, may address these limitations. + Despite promising outcomes, rigorous large-scale trials and interdisciplinary collaboration are essential to validate efficacy, optimize delivery, and ensure ethical compliance. In conclusion, microbiome therapies represent a paradigm shift in treating metabolic syndrome by targeting root causes, yet translating preclinical success into clinical practice demands further innovation and evidence-based standardization. + © 2026 The Author(s). + + + + Al Jnainati + Mustafa + M + + University of Bologna, Bologna, Italy. + + + + Govindarajan + Aishwarya + A + + Chettinad Hospital and Research Institute, Kelambakkam, Tamil Nadu, India. + + + + Tyagi + Srishti + S + + Venkateshwara Institute of Medical Sciences, Gajraula, India. + + + + Iltaf + Muhammad + M + + Medical Teaching Institution, Ayub Teaching Hospital/Ayub Medical College, Abbottabad, Pakistan. + + + + Al Jnainati + Jana + J + + University of Milano-Bicocca, Italy. + + + + Ayoub + Mohammad + M + + University of Roma Camillus, Rome, Italy. + + + + Shadab + Hooria Aiman + HA + + Ayub Medical College, Abbotabad, Pakistan. + + + + eng + + Journal Article + Review + + + 2026 + 02 + 05 + +
+ + United States + AACE Endocrinol Diabetes + 9919052034906676 + 3050-9157 + + + dysbiosis + fecal microbiota transplantation + metabolic syndrome + microbiome + preventive medicine + probiotic + + The authors have no conflicts of interest to disclose. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 7 + 7 + + + 2025 + 10 + 14 + + + 2025 + 11 + 10 + + + 2026 + 6 + 1 + 6 + 3 + + + epublish + + 42221399 + PMC13221947 + 10.1016/j.aed.2025.11.015 + S3050-9157(26)00020-2 + + + + + + 42221314 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1664-0640 + + 17 + + 2026 + + + Frontiers in psychiatry + Front Psychiatry + + A hierarchical machine learning model for predicting self-harm and suicidal behaviour in hospitalised patients with schizophrenia using clinical history and nursing observations. + + 1760380 + 1760380 + + 10.3389/fpsyt.2026.1760380 + + This study aimed to develop and evaluate a two-layered machine learning framework that combines admission clinical information with longitudinal nursing observations to identify schizophrenia inpatients at high risk of self-harm or suicidal acts. + We retrospectively reviewed the records of 477 patients with schizophrenia hospitalised in Liaoning Province between July 2021 and July 2024. According to whether at least one self-injurious or suicidal episode was documented during the index admission, 159 individuals were assigned to a high-risk group and 318 to a non-high-risk group. At admission, 18 baseline variables (including age, sex, history of self-harm, hopelessness/depression, and educational attainment) were extracted from electronic medical records, and 39 nurse-rated behavioural items were scored weekly using the Psychiatric Patient Nursing Observation Scale. Static and dynamic feature sets were used to train six classifiers [regularized logistic regression (LR), support vector machine (SVM), extreme gradient boosting, random forest, multi-layer perceptron, and K-nearest neighbours]. The best static model (regularized LR) and the best dynamic model (SVM) were combined through probability-level weighted fusion to generate a hierarchical risk score. + Multivariable analysis of admission features showed that previous self-harm [odds ratio (OR) = 4.323], hopelessness/depression (OR = 3.090), younger age (OR = 0.938), and higher educational level (OR = 1.357) were independent predictors of self-harm/suicidal behaviour. Among dynamic indicators, negative self-evaluation (OR = 2.303), self-reported depression (OR = 1.812), insomnia (OR = 1.768), talking to oneself (OR = 1.733), crying (OR = 1.700), and reduced conversation with others (OR = 1.422) remained significant. The optimised static LR model achieved an area under the curve (AUC) of 0.7564, and the dynamic SVM model reached an AUC of 0.8531. Their fusion further improved performance (AUC = 0.9048; sensitivity 0.8542; specificity 0.7789; accuracy 0.8042). This hierarchical model outperformed the best flat combined-feature model (SVM; AUC = 0.9022) in sensitivity (0.8542 vs. 0.6667), indicating a more clinically appropriate detection of high-risk patients. + A hierarchical machine learning approach that integrates baseline clinical history with repeated nursing assessments can effectively flag schizophrenia inpatients at high risk for self-harm and suicidal behaviour, supporting timely and individualised preventive strategies in psychiatric wards. + Copyright © 2026 Liang, Meng, Duan, Yang, Zhu, Wang, Sun, Wang, Liu, Sun, Hu, Shao, Ren, Shao and Zhang. + + + + Liang + Chen + C + + Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China. + + + Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China. + + + University of Chinese Academy of Sciences, Beijing, China. + + + + Meng + Xianfeng + X + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Duan + Ying + Y + + Liaoning Maternal and Child Health Hospital, Shenyang, Liaoning, China. + + + + Yang + Wei + W + + Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China. + + + Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China. + + + University of Chinese Academy of Sciences, Beijing, China. + + + + Zhu + Gang + G + + Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China. + + + + Wang + Jinhuan + J + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Sun + Ying + Y + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Wang + Mingtao + M + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Liu + Miao + M + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Sun + Chenhui + C + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Hu + Kunyuan + K + + Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang, China. + + + Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang, China. + + + University of Chinese Academy of Sciences, Beijing, China. + + + + Shao + Wei + W + + Shengjing Hospital of China Medical University, Shenyang, Liaoning, China. + + + + Ren + Jintao + J + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + Shao + Xiaojun + X + + Department of Psychiatry, The First Affiliated Hospital of China Medical University, Shenyang, Liaoning, China. + + + + Zhang + Yang + Y + + Liaoning Provincial Mental Health Center, Tieling, Liaoning, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Psychiatry + 101545006 + 1664-0640 + + + clinical history + hierarchical model + machine learning + nursing observations + risk prediction + schizophrenia + self-harm + suicidal behaviour + + 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 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 12 + 4 + + + 2026 + 4 + 21 + + + 2026 + 4 + 24 + + + 2026 + 6 + 1 + 6 + 3 + + + epublish + + 42221314 + PMC13219302 + 10.3389/fpsyt.2026.1760380 + + + + + + 42221295 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1179-1594 + + 19 + + 2026 + + + Risk management and healthcare policy + Risk Manag Healthc Policy + + Machine Learning-Based Early Prediction of Lower Extremity Deep Vein Thrombosis in the ICU: A Multicenter Study. + + 603569 + 603569 + + 10.2147/RMHP.S603569 + + Deep vein thrombosis (DVT) is a prevalent and life-threatening complication in the Intensive Care Unit (ICU). Traditional risk tools often lack specificity for ICU acquired DVT. This study aimed to develop and externally validate an interpretable machine learning (ML) model for accurate DVT risk prediction in critically ill patients. + This multicenter retrospective study included 2000 patients from two centers, partitioned into training, internal testing, and external validation sets. LASSO regression and the Boruta algorithm identified robust predictors. Eight ML algorithms were trained and evaluated using the Area Under the Curve (AUC), calibration plots, and Decision Curve Analysis (DCA). The SHapley Additive exPlanations (SHAP) framework was utilized for model interpretability. + The Random Forest (RF) model outperformed other ML algorithms and traditional tools (e.g. Caprini score). It achieved AUCs of 0.869 (95% CI: 0.822-0.916) in training, 0.850 (95% CI: 0.801-0.900) in internal testing, and 0.831 (95% CI: 0.771-0.888) in external validation. SHAP analysis identified six dominant predictors: immobilization duration, D-dimer, femoral vein catheterization, APACHE II score, malignancy, and age. The RF model successfully captured non-linear interactions, particularly exponential risk increases from prolonged immobilization and elevated D-dimer. DCA demonstrated a higher net clinical benefit than default strategies. + Integrating six readily available clinical variables, the RF model offers a robust and interpretable tool for DVT risk stratification, outperforming traditional scores. To facilitate real-world clinical application, an accessible web-based calculator is being developed to guide early, personalized thromboprophylaxis in the ICU. + © 2026 Li et al. + + + + Li + Yang + Y + + Department of Intensive Care Medicine, Taixing People's Hospital, Taixing, Jiangsu, People's Republic of China. + + + + Xu + Ling + L + + Department of Intensive Care Medicine, Taixing People's Hospital, Taixing, Jiangsu, People's Republic of China. + + + + Chen + Yunfeng + Y + + Department of Ultrasound, Nanjing Gaochun People's Hospital, Nanjing, Jiangsu, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 25 + +
+ + England + Risk Manag Healthc Policy + 101566264 + 1179-1594 + + + deep vein thrombosis + intensive care units + machine learning + random forest + risk prediction + + The authors declare that they have no competing interests in this work. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2026 + 3 + 13 + + + 2026 + 5 + 14 + + + 2026 + 6 + 1 + 6 + 2 + + + epublish + + 42221295 + PMC13221438 + 10.2147/RMHP.S603569 + 603569 + + + + + + 42221294 + + 2026 + 06 + 01 + +
+ + 3005-0693 + + + 2026 + Mar + 24 + + + Nature health + Nat Health + + Mapping the local effectiveness of mass drug administration for malaria using transportability methods. + 10.1038/s44360-026-00094-1 + + Numerous trials have evaluated the effectiveness of mass drug administration (MDA) in rapidly reducing malaria transmission, but it is unknown whether the estimated effects generalize to other populations eligible for MDA. A recent cluster randomized trial in Senegal found that MDA reduced malaria incidence by 55% in areas that routinely deploy seasonal malaria chemoprevention (SMC). Here, we used transportability models with machine learning to generalize trial effects to 116 non-trial communes where SMC is the standard of care. Accounting for differences in weather, vegetation and population density between trial and non-trial areas, we estimated considerable reductions in incidence (ranging from 36% to 65%) in 74 non-trial communes, with larger decreases in areas having higher precipitation, denser vegetation and lower temperatures. We found that MDA was not effective in the postintervention year in non-trial communes, supporting the notion that MDA's effects are short-lived. Our approach offers a scalable framework for generalizing trial findings to target environmentally mediated infectious disease interventions. + + + + Roh + Michelle E + ME + 0000-0002-6268-0801 + + Institute for Global Health Sciences, University of California, San Francisco (UCSF), San Francisco, CA, USA. + + + Department of Obstetrics and Gynecology, Oregon Health and Science University, Portland, OR, USA. + + + + Tong + Yanwei + Y + 0000-0002-4532-168X + + Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. + + + + Heitmann + Gabriella Barratt + GB + 0000-0002-4212-0954 + + Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. + + + + Jia + Junran + J + 0009-0007-5410-885X + + Department of Statistics, Stanford University, Stanford, CA, USA. + + + + Ba + El-Hadji Konko Ciré + EKC + + Université Iba Der Thiam de Thiès, Thiès, Senegal. + + + + Ndiaye + Jean Louis + JL + + Université Iba Der Thiam de Thiès, Thiès, Senegal. + + + Deceased: Jean Louis Ndiaye. + + + + Fogelson + Ari + A + + Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. + + + + Milligan + Paul + P + + Department of Infectious Disease Epidemiology, London School of Hygiene and Tropical Medicine, London, UK. + + + + Seck + Amadou + A + + Université Iba Der Thiam de Thiès, Thiès, Senegal. + + + + Diallo + Abdoulaye + A + + Université Iba Der Thiam de Thiès, Thiès, Senegal. + + + + Lo + Aminata Colle + AC + + Université Iba Der Thiam de Thiès, Thiès, Senegal. + + + + Baiocchi + Michael + M + + Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. + + + + Gosling + Roly + R + + Department of Disease Control, London School of Hygiene and Tropical Medicine, London, UK. + + + + Bennett + Adam + A + + Institute for Global Health Sciences, University of California, San Francisco (UCSF), San Francisco, CA, USA. + + + PATH, Seattle, WA, USA. + + + + Hsiang + Michelle S + MS + 0000-0003-1217-2742 + + Institute for Global Health Sciences, University of California, San Francisco (UCSF), San Francisco, CA, USA. + + + Department of Epidemiology and Biostatistics, UCSF, San Francisco, CA, USA. + + + Department of Pediatrics, UCSF, San Francisco, CA, USA. + + + Chan Zuckerberg Biohub, San Francisco, CA, USA. + + + + Benjamin-Chung + Jade + J + 0000-0003-3631-3132 + + Department of Epidemiology and Population Health, Stanford University, Stanford, CA, USA. + + + Chan Zuckerberg Biohub, San Francisco, CA, USA. + + + + eng + + Journal Article + + + 2026 + 03 + 24 + +
+ + United States + Nat Health + 9919250716706676 + 3005-0693 + + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 6 + 2 + + + aheadofprint + + 42221294 + PMC13218659 + 10.1038/s44360-026-00094-1 + + + + + + 42221257 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 0975-7651 + + 17 + 5 + + 2026 + May + + + Indian journal of surgical oncology + Indian J Surg Oncol + + Multiparametric MRI Radiomics for the Prediction of Histologic Grade in Bladder Cancer. + + 1057 + 1064 + 1057-1064 + + 10.1007/s13193-025-02397-3 + + Histological grade holds great clinical significance in the management and prognosis of bladder cancer; therefore, timely and accurate prediction through non-invasive techniques such as MRI may improve health outcomes. Our objective was to create an MRI-based radiomics model that can predict the histological grade of cancer pre-operatively. In a prospective study, we gathered data from 45 bladder cancer patients who had an mpMRI scan from December 2018 to December 2022 prior to their operative procedure. Radiomics features were extracted from T2-weighted (T2W), diffusion-weighted imaging (DWI), and dynamic DCE-MRI-enhanced (DCE) MR images obtained from a 1.5 T MRI scanner. A standard 5-point VI-RADS scoring system was also assessed for each scan. The variable clustering algorithm was applied to these features, and all cluster features were univariably assessed using receiver operating characteristic (ROC) curves. Multiple predictive models were created and cross-validated based on multivariable analysis to minimize overfitting and predict the grade of the tumor. Among 45 eligible patients, 28 (62.2%) patients had high-grade tumors and the rest 17 (37.8%) were low grade. In the adjusted analysis, only DCE-MRI based (Gray Level Co-occurrence Matrix (Gray Level Co-occurrence Matrix (GLCM))-inverse variance (OR = 1.42, p = 0.028), Major Axis Length (OR = 1.04, p = 0.03)), and age (OR = 1.08, p = 0.039) were associated with the high-grade bladder cancer. Our radiomics models comprising DCE-MRI-based parameters, a T2W parameter, and age yielded the highest performance for predicting grades of bladder cancer (AUC = 0.91; 95% CI 0.82-1.00). These models demonstrated reasonably high predictive performance in bootstrap validation analysis as well. An mpMRI radiomics approach based on MRI has the potential to serve as a non-invasive imaging tool for preoperative grading of bladder cancer. + The online version contains supplementary material available at 10.1007/s13193-025-02397-3. + © The Author(s), under exclusive licence to Indian Association of Surgical Oncology 2025. 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. + + + + Selvaraju + Anup + A + + Department of Radiodiagnosis and Interventional Radiology, All India, Institute of Medical Sciences, New Delhi, India. + https://ror.org/02dwcqs71 + grid.413618.9 + 0000 0004 1767 6103 + + + + Dwivedi + Durgesh Kumar + DK + + Department of Radiodiagnosis, King George Medical University, Lucknow, India. + https://ror.org/00gvw6327 + grid.411275.4 + 0000 0004 0645 6578 + + + + Patbamniya + Naveen Kumar + NK + + Department of Urology, Gandhi Medical College, Bhopal, India. + https://ror.org/04npss011 + grid.415285.f + + + + Kumar + Manoj + M + + Department of Urology, All India Institute of Medical Sciences, New Delhi, India. + https://ror.org/02dwcqs71 + grid.413618.9 + 0000 0004 1767 6103 + + + + Seth + Amlesh + A + + Department of Urology, All India Institute of Medical Sciences, New Delhi, India. + https://ror.org/02dwcqs71 + grid.413618.9 + 0000 0004 1767 6103 + + + + Dwivedi + Alok Kumar + AK + + Division of Biostatistics & Epidemiology, Department of Molecular and Translational Medicine, Paul L. Foster School of Medicine, Texas Tech University Health Sciences Center, El Paso, TX USA. + https://ror.org/033ztpr93 + grid.416992.1 + 0000 0001 2179 3554 + + + Biostatistics and Epidemiology Consulting Lab, Office of Research, Texas Tech University Health Sciences Center, El Paso, TX USA. + https://ror.org/033ztpr93 + grid.416992.1 + 0000 0001 2179 3554 + + + + Joshi + Rakesh Chandra + RC + + Amity Centre for Artificial Intelligence, Amity University, Noida, UP India. + https://ror.org/02n9z0v62 + grid.444644.2 + 0000 0004 1805 0217 + + + + Kaushal + Seema + S + + Department of Pathology, All India Institute of Medical Sciences, New Delhi, India. + https://ror.org/02dwcqs71 + grid.413618.9 + 0000 0004 1767 6103 + + + + Das + Chandan J + CJ + 0000-0001-6505-5940 + + Department of Radiodiagnosis and Interventional Radiology, All India, Institute of Medical Sciences, New Delhi, India. + https://ror.org/02dwcqs71 + grid.413618.9 + 0000 0004 1767 6103 + + + + eng + + Journal Article + + + 2025 + 08 + 06 + +
+ + India + Indian J Surg Oncol + 101532448 + 0975-7651 + + + Machine learning + Magnetic resonance imaging + Oncology + Radiomics + Urinary bladder cancer + + Conflict of InterestThe authors declare no competing interests. + + + + + 2026 + 6 + 1 + 12 + 39 + + + 2026 + 6 + 1 + 12 + 38 + + + 2025 + 4 + 2 + + + 2025 + 7 + 23 + + + 2027 + 5 + 1 + + + 2026 + 6 + 1 + 6 + 2 + + + ppublish + + 42221257 + PMC13216390 + 10.1007/s13193-025-02397-3 + 2397 + + + + + + 42221222 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 1178-7031 + + 19 + + 2026 + + + Journal of inflammation research + J Inflamm Res + + Characterization and Clinical Diagnostic Potential of IHRDEGs in Renal Interstitial Fibrosis: An Integrative Data Analysis and Model Construction Study. + + 551642 + 551642 + + 10.2147/JIR.S551642 + + Renal interstitial fibrosis (RIF) is a critical pathological process in the progression of chronic kidney disease (CKD). This study aimed to identify and validate inflammation- and hypoxia-related differentially expressed genes (IHRDEGs) associated with RIF and to construct a robust diagnostic model with potential clinical applications. + Three public GEO datasets (GSE22459, GSE76882, GSE53605) comprising 76 RIF and 142 control samples were integrated following batch correction and normalization. Differentially expressed IHRDEGs were screened and analyzed using GO and KEGG pathway enrichment. A diagnostic model was constructed using logistic regression and optimized through SVM and LASSO algorithms. Immune infiltration was evaluated using ssGSEA, and consensus clustering was used to define molecular subtypes. Experimental validation was conducted in a rat model of RIF using RT-qPCR, Western blotting, and immunohistochemistry. + A total of five hub IHRDEGs (EDN1, HLA-G, MYC, HIF1A, and TLR2) were identified and incorporated into a diagnostic model that demonstrated strong predictive ability (AUC 0.7-0.9; sensitivity and specificity > 70-90%). These genes were significantly correlated with immune cell infiltration patterns. Subtype analysis revealed two distinct molecular clusters of RIF with different immunopathological features. Co-expression and regulatory interaction analyses further elucidated the involvement of hub genes in fibrotic mechanisms. Experimental validation confirmed the upregulation of hub genes at both mRNA and protein levels in the RIF model. + This study uncovers the diagnostic and mechanistic significance of inflammation- and hypoxia-related genes in RIF. The five identified hub genes may serve as promising biomarkers and therapeutic targets. These findings provide novel insights into the immune-hypoxia interplay in renal fibrosis and offer a potential framework for early diagnosis and targeted treatment of CKD-related fibrosis. + © 2026 Zhang et al. + + + + Zhang + Jie + J + + School of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, People's Republic of China. + + + Department of Nephrology, Affiliated Hospital of Gansu University of Chinese Medicine, Lanzhou, Gansu, 730020, People's Republic of China. + + + + Dang + Xinyu + X + + School of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, People's Republic of China. + + + + Dai + Enlai + E + + School of Traditional Chinese and Western Medicine, Gansu University of Chinese Medicine, Lanzhou, Gansu, 730000, People's Republic of China. + + + + eng + + Journal Article + + + 2026 + 05 + 23 + +
+ + New Zealand + J Inflamm Res + 101512684 + 1178-7031 + + + bioinformatics analysis + biomarker discovery + fibrotic progression + hypoxia signaling + immune cell infiltration + machine learning model + + The authors report no conflicts of interest in this work. + + + + + 2026 + 6 + 1 + 12 + 40 + + + 2026 + 6 + 1 + 12 + 39 + + + 2025 + 7 + 18 + + + 2026 + 4 + 27 + + + 2026 + 6 + 1 + 6 + 2 + + + epublish + + 42221222 + PMC13217453 + 10.2147/JIR.S551642 + 551642 + + + + + + 42221119 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Application of machine learning models for predicting risk factors of acute exacerbations in chronic obstructive pulmonary disease. + + 1804544 + 1804544 + + 10.3389/fmed.2026.1804544 + + Chronic obstructive pulmonary disease (COPD) is a chronic respiratory disease characterized by persistent respiratory symptoms and progressive airflow limitation. Acute exacerbations of COPD (AECOPD) are significant causes of hospitalization and death among COPD patients. This study aims to identify risk factors for AECOPD exacerbations and develop a highly accurate and interpretable predictive model using various statistical and machine learning methods. + We retrospectively analyzed data from 2,102 COPD patients admitted between 1 January 2019 and 31 December 2024. The primary outcome was AECOPD severity, defined as the need for treatment escalation. Initial feature selection was performed using LASSO regression to identify potential risk factors. To validate the model's effectiveness and explore its superior predictive performance, the dataset was partitioned by time period and proportion: The first 70% of observations in chronological order were used as the training set, with the remaining 30% as the test set. Multiple machine learning algorithms were then employed for model construction and comparison. To enhance model interpretability, we utilized SHapley Additive exPlanations (SHAP) to illustrate the contribution of each variable to the prediction outcomes. + Among the six machine learning models, the extreme gradient boosting (XGBoost) model demonstrated the optimal predictive performance, achieving an area under the receiver operating characteristic curve (AUC) of 0.960 (95% confidence interval (CI): 0.940-0.980) in the training set and 0.824 (95% CI: 0.804-0.844) in the test set. In the test set, the evaluation metrics were as follows: accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) were 0.805, 0.65, 0.872, 0.669, and 0.859, respectively. SHAP analysis revealed that creatinine (CREA), neutrophil percentage (NEU%), D-dimer, brain natriuretic peptide (BNP), white blood cell count (WBC), and hypertension (HTN) were important factors influencing the model output. + The XGBoost model developed in this study demonstrates robust performance in predicting AECOPD risk using routinely collected clinical and laboratory data. The integration of SHAP analysis enhances model transparency, supporting its potential utility in clinical risk stratification and early intervention. + Copyright © 2026 Kuang, Min, Deng, Zhao, Sun and Hong. + + + + Kuang + Dapeng + D + + Department of Emergency and Critical Care, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + Min + Jie + J + + Clinical Research Center for Mental Disorders, Shanghai Pudong New Area Mental Health Center, School of Medicine, Tongji University, Shanghai, China. + + + + Deng + Huibiao + H + + Department of Emergency and Critical Care, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + Zhao + Yajun + Y + + Department of Health Management Centre, Zhongshan Hospital, Fudan University, Shanghai, China. + + + + Sun + Yangyang + Y + + Henan Cancer Hospital, Zhengzhou, Henan, China. + + + + Hong + Jiang + J + + Department of Emergency and Critical Care, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + XGBoost + acute exacerbations of COPD (AECOPD) + chronic obstructive pulmonary disease (COPD) + machine learning + risk factors + + 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 + 2 + 5 + + + 2026 + 3 + 10 + + + 2026 + 3 + 25 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 1 + + + epublish + + 42221119 + PMC13216505 + 10.3389/fmed.2026.1804544 + + + + + + 42221116 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Predicting poor response to anti-osteoporosis therapy: a machine learning model integrating clinical and novel biomarker data. + + 1786209 + 1786209 + + 10.3389/fmed.2026.1786209 + + This study was conducted to develop and validate a prediction model integrating clinical characteristics and novel biomarkers. The goal was to identify patients at high risk for a poor response to standard anti-osteoporosis therapy prior to treatment initiation, thereby supporting personalized therapeutic decision-making. + A retrospective analysis was performed on 543 patients with primary osteoporosis admitted between January 2021 and December 2024. All patients received 12 months of standard treatment. Participants were randomly allocated to a training set (n = 380) and a validation set (n = 163) in a 7:3 ratio. In the training set, univariate analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate logistic regression were used to determine independent predictors. Three machine learning models-Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbors (KNN)-were then constructed. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), calibration curves, and decision curve analysis (DCA). SHapley Additive exPlanations (SHAP) values were used to interpret the optimal model. + Baseline characteristics were comparable between the training and validation sets (P > 0.05). Eight independent predictors of poor treatment response were identified: comorbid diabetes, history of fragility fracture, glucocorticoid use for ≥ 6 months, femoral neck bone mineral density T-score, and serum levels of osteocalcin, procollagen type I N-terminal propeptide, β-CrossLaps of type I collagen (β-CTX), and 25-Hydroxyvitamin D. Among the models, the RF algorithm demonstrated superior performance, with an AUC of 0.856 (95% CI: 0.808-0.905) in the training set and 0.825 (95% CI: 0.718-0.933) in the validation set. The model was well-calibrated, and DCA indicated a high net benefit. SHAP analysis confirmed serum β-CTX as the most significant predictive variable. + A predictive model integrating multi-dimensional factors was successfully developed and validated for assessing osteoporosis treatment efficacy. The RF-based model exhibited robust predictive performance and clinical utility. It shows potential for pre-therapeutic identification of high-risk patients, facilitating precision management in osteoporosis. + Copyright © 2026 Bi, Zhang, Zhang and Li. + + + + Bi + Yannan + Y + + Department of Orthopedics, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + Zhang + Maolin + M + + Department of Orthopedics, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + Zhang + Weiqiong + W + + Department of Orthopedics, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + Li + Jiahong + J + + Department of Orthopedics, The Fourth Affiliated Hospital, Guangzhou Medical University, Guangzhou, Guangdong, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + biomarkers + machine learning + osteoporosis + prediction model + treatment response 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 + 1 + 12 + + + 2026 + 4 + 1 + + + 2026 + 4 + 9 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 1 + + + epublish + + 42221116 + PMC13216482 + 10.3389/fmed.2026.1786209 + + + + + + 42221109 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + A machine learning-based classification model for interstitial lung disease in rheumatoid arthritis. + + 1807594 + 1807594 + + 10.3389/fmed.2026.1807594 + + This study aimed to develop and validate a preliminary classification and diagnostic model for rheumatoid arthritis-associated interstitial lung disease (RA-ILD) using routine, readily available clinical and laboratory parameters. Multiple machine learning algorithms were employed to construct a practical risk assessment tool suitable for use in primary hospital settings. + Clinical data were retrospectively collected. Patients were divided into RA and RA-ILD groups. After preprocessing, the cohort was randomly divided into training and validation sets. Variables demonstrating a trend toward significance on univariate analysis were subjected to LASSO regression, and feature variables were ultimately identified. Five machine learning models were constructed: CatBoost, logistic regression, support vector machine, decision tree, and random forest. Model performance was assessed on the validation set using accuracy, precision, recall, F1 score, and the area under the receiver operating characteristic (ROC) curve (AUC). The SHapley Additive exPlanations (SHAP) framework was used to identify key features and quantify their contributions to the optimal predictive model. + A total of 410 patients with RA were enrolled, among whom 100 (24.39%) were diagnosed with RA-ILD. 23 variables with a trend toward significance on univariate analysis were subjected to LASSO regression. Finally, seven features were selected for model construction: age, smoking history, LYMPH, LDH, RF, CA125, and CA199. Based on these features, five machine learning models were established for RA-ILD classification. In the validation set, the CatBoost model achieved the highest AUC of 0.784 (95% CI: 0.656-0.885) and the lowest Brier score of 0.158, demonstrating robust overall performance. The decision tree (DT) model exhibited comparable discriminatory ability, with an AUC of 0.783 (95% CI: 0.661-0.818), and attained the highest recall (0.653) and F1-score (0.603) across all models, reflecting strong classification efficacy. + Among the five evaluated models, CatBoost and DT models showed comparable and favorable overall performance for RA-ILD classification. SHAP analysis based on the CatBoost model identified CA199, CA125, and age as the most important contributors to model prediction. Both models hold promise for RA-ILD risk stratification in clinical practice, although further external validation is warranted. + Copyright © 2026 Li, Wang, He, Wang, Xu, Yang and Feng. + + + + Li + Mingyao + M + + Department of Rheumatology and Immunology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Wang + Qiaoli + Q + + Health Management Center, Deyang People's Hospital, Deyang, Sichuan, China. + + + + He + Junfeng + J + + Department of Rheumatology and Immunology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Wang + Xia + X + + Department of Rheumatology and Immunology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Xu + Yangyang + Y + + Department of Rheumatology and Immunology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Yang + Liwei + L + + Department of Rheumatology and Immunology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + Feng + Lin + L + + Department of Rheumatology and Immunology, Deyang People's Hospital, Deyang, Sichuan, China. + + + + eng + + Clinical Trial + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + CatBoost + interstitial lung disease + machine learning + rheumatoid arthritis + risk classification 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 + 2 + 9 + + + 2026 + 4 + 6 + + + 2026 + 4 + 16 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 1 + + + epublish + + 42221109 + PMC13215908 + 10.3389/fmed.2026.1807594 + + + + + + 42221107 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Machine learning model for predicting hypotension following continuous renal replacement therapy initiation in end-stage kidney disease patients: a SHAP-interpretable approach. + + 1807513 + 1807513 + + 10.3389/fmed.2026.1807513 + + Early prediction of intradialytic hypotension (IDH) after starting continuous renal replacement therapy (CRRT) is critical for timely intervention. However, effective models for predicting the risk of IDH in patients with end-stage kidney disease (ESKD) undergoing CRRT are currently lacking. Therefore, the aim of this study was to develop a machine learning (ML)-based predictive model to facilitate the early identification of high-risk patients and to support clinical decision-making. + Adult patients with ESKD who underwent CRRT were enrolled in this study and randomly divided into training (70%) and testing sets (30%). IDH was defined as a reduction in systolic blood pressure (SBP) ≥ 20 mmHg from baseline within 6 h after CRRT initiation; supplementary definitions were also applied, including a decrease in SBP ≥ 30 mmHg or in mean arterial pressure (MAP) ≥ 10 mmHg from baseline. Demographic characteristics, medication use, laboratory parameters, and treatment-related variables were collected. Multiple ML algorithms-including gradient boosting machine (GBM), extreme gradient boosting (XGBoost), decision tree (DT), support vector machine (SVM), random forest (RF), and logistic regression (LR)-were used to develop predictive models. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC) and other performance metrics. Shapley additive explanations (SHAP) was applied to quantify the contribution of each feature to the model predictions. + Overall, 1,103 patients were included. Within the dataset used in this study, the SVM model consistently outperformed the other compared algorithms across all definitions of IDH, with an AUC of 0.805, indicating good calibration and clinical utility. The optimized simplified model retained stable predictive capacity (AUC = 0.809). SHAP analysis revealed that SBP was the most important feature for predicting IDH. + The results of this study demonstrate the effectiveness of several ML algorithms in predicting the risk of IDH following the initiation of CRRT in patients with ESKD. The SVM model yielded the most favorable predictive performance in our comparative analysis. SBP was identified as a key predictor of IDH. The proposed model can assist in the clinical identification of high-risk patients and facilitate timely interventions. + Copyright © 2026 Qiu, Qiu, Tao, Fu, Bai and Cao. + + + + Qiu + Shuang + S + + State Key Laboratory of Frigid Zone Cardiovascular Disease, Cardiovascular Research Institute and Department of General Medicine, General Hospital of Northern Theater Command, Shenyang, China. + + + + Qiu + Dongxia + D + + Department of Blood Purification, General Hospital of Northern Theater Command, Shenyang, China. + + + + Tao + Yongyuan + Y + + Department of Blood Purification, General Hospital of Northern Theater Command, Shenyang, China. + + + + Fu + Panpan + P + + Department of Blood Purification, General Hospital of Northern Theater Command, Shenyang, China. + + + + Bai + Jiuxu + J + + Department of Blood Purification, General Hospital of Northern Theater Command, Shenyang, China. + + + + Cao + Ning + N + + Department of Blood Purification, General Hospital of Northern Theater Command, Shenyang, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + continuous renal replacement therapy + end-stage kidney disease + intradialytic hypotension + 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 + 2 + 9 + + + 2026 + 4 + 26 + + + 2026 + 4 + 28 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 1 + + + epublish + + 42221107 + PMC13219051 + 10.3389/fmed.2026.1807513 + + + + + + 42221102 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + LDAR outperforms other albumin-derived indices in predicting 28-day ICU mortality in critically ill myocardial infarction patients: a two-cohort study. + + 1801925 + 1801925 + + 10.3389/fmed.2026.1801925 + + Early risk stratification is crucial for improving outcomes in critically ill patients with acute myocardial infarction (AMI). Albumin-derived composite indices hold promise as convenient and effective predictive tools, but their relative efficacy and clinical utility remain unclear. + This two-cohort retrospective analysis utilized a derivation cohort from the MIMIC-IV public database and an external validation cohort from the ICU of Guizhou Medical University Affiliated Hospital. Six albumin-derived composite indices were evaluated. Statistical analyses employed Cox proportional hazards regression models to assess their association with mortality. Predictive performance was compared using the area under the receiver operating characteristic curve (AUC) and Delong's test. A multivariate risk prediction model was developed based on key prognostic variables selected by multiple machine learning algorithms. + The study included 4,850 critically ill AMI patients (4,210 in the derivation cohort, 640 in the validation cohort). Multivariable-adjusted analysis identified the red cell distribution width to Albumin Ratio (RAR), Urea nitrogen to Albumin Ratio (UAR), and Lactate Dehydrogenase to Albumin Ratio (LDAR) as independent predictors of 28-day ICU mortality. Among these, LDAR demonstrated the strongest predictive ability, with an AUC of 0.702 in the derivation cohort, a finding robustly validated externally (AUC = 0.703). Subgroup analysis indicated consistent predictive value across most populations but revealed a significant interaction with hyperlipidemia. Incorporating LDAR into traditional critical illness scores (e.g., APACHE II, SOFA) significantly improved their predictive discrimination (all Delong's test p < 0.05). A comprehensive model integrating 7 key variables (including LDAR, urea nitrogen, and lactate) selected by machine learning showed good and robust discriminative performance in both internal and external validation (AUCs of 0.767 and 0.735, respectively), significantly outperforming five traditional risk scores (all Delong's test p < 0.05). + Among the six albumin-derived composite indices, LDAR offers the best independent and incremental predictive value for 28-day ICU mortality in critically ill AMI patients. Its interaction with hyperlipidemia suggests potential for targeted risk stratification. The machine learning model incorporating LDAR and other variables demonstrates robust performance, providing a promising tool for the early clinical identification of high-risk patients. + Copyright © 2026 Meng, Ou, Mao, Liao, Wu, Zhang, Qian and Yang. + + + + Meng + Xiongwei + X + + Department of Cardiac Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China. + + + + Ou + Yi + Y + + Department of Cardiac Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China. + + + + Mao + Jialin + J + + Department of Cardiac Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China. + + + + Liao + Hongsheng + H + + Department of Cardiac Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China. + + + + Wu + Junhong + J + + Center for Translational Medicine, Guizhou Medical University, Guiyang, China. + + + + Zhang + Lin + L + + Center for Translational Medicine, Guizhou Medical University, Guiyang, China. + + + + Qian + Xingkai + X + + Center for Translational Medicine, Guizhou Medical University, Guiyang, China. + + + + Yang + Siyuan + S + + Department of Cardiac Surgery, Affiliated Hospital of Guizhou Medical University, Guiyang, China. + + + + eng + + Journal Article + + + 2026 + 05 + 15 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + acute myocardial infarction + albumin + correlation analysis + inflammatory nutrition composite index + 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 + 2 + 6 + + + 2026 + 4 + 20 + + + 2026 + 4 + 24 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 1 + + + epublish + + 42221102 + PMC13219332 + 10.3389/fmed.2026.1801925 + + + + + + 42221077 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-858X + + 13 + + 2026 + + + Frontiers in medicine + Front Med (Lausanne) + + Explainable machine learning identifies knee morphology thresholds for arthroscopic medial meniscus posterior root tear: a retrospective cohort study. + + 1819067 + 1819067 + + 10.3389/fmed.2026.1819067 + + Medial meniscus posterior root tear (MMPRT) is clinically important because disruption of posterior root function compromises hoop tension and load sharing, accelerating medial-compartment degeneration. Early or subtle posterior pathology may be underrecognized on MRI, highlighting the potential value of morphology-based risk awareness during MRI interpretation. + We retrospectively analyzed 608 patients who underwent arthroscopic surgery for knee joint injuries, including 281 patients with arthroscopically confirmed MMPRT and 327 controls without MMPRT. Demographic, clinical, and MRI-based morphologic parameters were compared in the training set. Variables identified from training-set comparisons and clinical/biomechanical relevance were used to develop 10 machine learning models, including CatBoost, Decision Tree, GBM, LightGBM, LASSO, Naive Bayes, Neural Network, Random Forest, Support Vector Machine, and XGBoost. Models were evaluated with 10-fold cross-validation and an independent testing set. Explainability was assessed using SHapley Additive exPlanations (SHAP), including global importance and dependence plots. + Training-set multivariable analysis identified older age, greater medial tibial slope (MTS), and deeper medial tibial plateau depth (MTPD) as independent factors associated with MMPRT. GBM achieved the highest AUC in the independent testing set and was selected for SHAP-based interpretation. SHAP analysis ranked age as the dominant contributor, followed by MTS and MTPD. Dependence plots suggested non-linear, threshold-like patterns in model contribution. Age showed an apparent transition from negative to positive SHAP contributions around midlife, MTS showed a threshold-like increase between approximately 6° and 8°, whereas MTPD shifted toward positive SHAP contributions around approximately 2.2-2.5 mm. + Age was the dominant model contributor, and MTS and MTPD were independently associated with MMPRT, exhibiting non-linear patterns in SHAP-based model interpretation. These findings may help raise suspicion for MMPRT in symptomatic patients undergoing MRI, particularly when age-related and tibial plateau morphologic risk patterns are present. + Copyright © 2026 Zhang, Guo, Li, Zheng, Yu, Chen, Li, Liao, Chen and He. + + + + Zhang + Minyuan + M + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Guo + Fengyuan + F + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Li + Yanlin + Y + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Zheng + Jiali + J + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Yu + Yang + Y + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Chen + Miao + M + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Li + Runze + R + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Liao + Yatong + Y + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + Chen + Qianzeng + Q + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + He + Chuan + C + + Department of Sports Medicine, The First Affiliated Hospital of Kunming Medical University, Kunming, China. + + + + eng + + Journal Article + + + 2026 + 05 + 14 + +
+ + Switzerland + Front Med (Lausanne) + 101648047 + 2296-858X + + + MRI + machine learning + medial meniscus posterior root tear + medial tibial plateau depth + medial tibial slope + + 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 + 2 + 27 + + + 2026 + 4 + 28 + + + 2026 + 4 + 30 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 1 + + + epublish + + 42221077 + PMC13216056 + 10.3389/fmed.2026.1819067 + + + + + + 42220987 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2296-2360 + + 14 + + 2026 + + + Frontiers in pediatrics + Front Pediatr + + Prediction of atelectasis in Mycoplasma pneumoniae pneumonia using a SHapley Additive exPlanations-interpretable machine learning model. + + 1747948 + 1747948 + + 10.3389/fped.2026.1747948 + + This study aimed to evaluate the performance of three machine learning models-K-nearest neighbors (KNN), support vector machine (SVM), and neural network (NN)-for predicting the risk of atelectasis in children with Mycoplasma pneumoniae pneumonia (MPP). The study also incorporated SHAP (SHapley Additive exPlanations) analysis to interpret model predictions. + Based on the clinical data of 508 pediatric patients, we performed feature selection and developed KNN, SVM, and NN models. Model performance was compared on an independent validation set, and SHAP values were employed to elucidate the predictive logic of the models. + On the validation set, the neural network (NN) model demonstrated the best overall performance, with an AUC of 0.89 and an accuracy of 0.82. The KNN model showed comparable performance (AUC = 0.88), while the SVM model achieved the highest specificity (0.87). The SHAP analysis consistently identified neutrophil percentage (NEU.pct), serum amyloid A (SAA), and C-reactive protein (CRP) as the most critical variables influencing the predictions. + This study demonstrates the effectiveness of different machine learning models in predicting the risk of atelectasis in MPP. The neural network, in particular, exhibited superior performance owing to its powerful non-linear modeling capabilities. These interpretable models provide clinicians with a diverse set of tools to accommodate various clinical priorities, such as overall accuracy or high specificity, thereby facilitating the early identification and stratified management of high-risk children. + © 2026 Sun, Wang, Li and Wang. + + + + Sun + Jia + J + + Department of Pediatrics, Wuhan Wuchang Hospital, Wuhan, China. + + + + Wang + Tengfei + T + + School of Transportation and Logistics Engineering, Wuhan University of Technology, Wuhan, China. + + + + Li + Mengsi + M + + Department of Pediatrics, Wuhan Wuchang Hospital, Wuhan, China. + + + + Wang + Mian + M + + Department of Pediatrics, Wuhan Wuchang Hospital, Wuhan, China. + + + + eng + + Journal Article + + + 2026 + 05 + 11 + +
+ + Switzerland + Front Pediatr + 101615492 + 2296-2360 + + + Mycoplasma pneumoniae pneumonia + SHAP + atelectasis + neural network + 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. + + + + + 2025 + 11 + 17 + + + 2026 + 2 + 3 + + + 2026 + 2 + 27 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 6 + 0 + + + epublish + + 42220987 + PMC13220724 + 10.3389/fped.2026.1747948 + + + + + + 42220920 + + 2026 + 06 + 01 + + + 2026 + 06 + 01 + +
+ + 2008-126X + + 16 + 5 + + 2025 + Sep + + + Basic and clinical neuroscience + Basic Clin Neurosci + + Better Than Maximum Likelihood Estimation of Model-based and Model-free Learning Styles. + + 891 + 912 + 891-912 + + 10.32598/bcn.2024.5883.1 + + Various decision-making systems collaborate to shape human behavior. Goal-directed and habitual systems are the two primary systems studied by reinforcement learning (RL), with model-based (MB) and model-free (MF) learning styles, respectively. Human behavior can be viewed as a combination of these two decision-making paradigms, achieved by the weighted sum of the action values of these two styles within an RL framework. The weighting parameter is often assessed using the maximum likelihood (ML) or maximum a posteriori (MAP) estimation method. + In this study, we employ RL agents that use a combination of MB and MF decision-making to perform the well-known Daw two-stage task. ML and MAP methods yield less reliable estimates of the weighting parameter, often exhibiting a large bias toward extreme values. We propose the knearest neighbor as an alternative nonparametric estimate to improve the estimation error, where we devise a set of 20 features extracted from the behavior of the RL agent. Simulated experiments examine the proposed method. + Our method reduces the bias and variance of the estimation error, as demonstrated by the obtained results. Human behavior data from previous studies are also investigated. The proposed method enables the prediction of indices such as age, gender, IQ, dwell time of gaze, and psychiatric disorder indices, which are not captured by the traditional method. + In brief, the proposed method increases the reliability of the estimated parameters and enhances the applicability of RL paradigms in clinical trials. + Copyright© 2025 Iranian Neuroscience Society. + + + + Yazdani + Sadjad + S + 0000-0001-9068-0123 + + Department of Machine Intelligence and Robotics, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. + + + + Vahabie + Abdol-Hossein + AH + 0000-0003-1603-8866 + + Department of Machine Intelligence and Robotics, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. + + + + Nadjar-Araabi + Babak + B + 0000-0002-5283-263X + + Department of Machine Intelligence and Robotics, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. + + + + Nili Ahmadabadi + Majid + M + 0000-0002-6370-6057 + + Department of Machine Intelligence and Robotics, School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran. + + + + eng + + Journal Article + + + 2025 + 09 + 01 + +
+ + Iran + Basic Clin Neurosci + 101575211 + 2008-126X + + + Behavioral observation analysis + Behavioral parameter estimation + Maximum a posteriori (MAP) + Maximum likelihood (ML) + Model-based (MB)and model-free (MF) combined learning + Modeling different styles of learning + k-Nearest neighbors + + + + + + 2023 + 9 + 30 + + + 2024 + 7 + 28 + + + 2024 + 10 + 6 + + + 2026 + 6 + 1 + 8 + 5 + + + 2026 + 6 + 1 + 8 + 4 + + + 2026 + 6 + 1 + 5 + 59 + + + ppublish + + 42220920 + PMC13220689 + 10.32598/bcn.2024.5883.1 + BCN-16-891 + + + + \ No newline at end of file diff --git a/log_esecuzione.txt b/log_esecuzione.txt new file mode 100644 index 0000000000000000000000000000000000000000..b9b31238365416322d646daa7ab23e0612275bd0 GIT binary patch literal 17472 zcmeI3Yf~FX7KZ!tR_%Y7@(0IGmaDkft|BP{!e-YtUWDWILP{$n85Dz1BxDSg%1=-7 zK7G{8=rWQ(5*M#)YNXLjPxraL=k)2;pMT7|SvT*N+?2lUbIwI>&OLR{^qJA;td7Y2 zrgPK1b{lR>SK;99zN_dw(&vdgc4c?!>bm~yrd`3+blAJA?voDMM6LV0QPlN?zgyLt zk^52Cz50q=&K>Gm)t$S)yT6IzK~!hN!Cj-eChAYc$BFoeB-vP^T!$m4HU-J>R1zua zZbRR7$&SxR@~rFqqJA}WL~gewizkxE&t5j0zISwRpnlL|-%pa3Dc97! zDfdpFyjRs2b&bKjV|_L*r61{j(~n@x@M2 z_wJ#n3Y7frZu@|eAbn)BCb=Q~122Vh*?rZ0&~<2&{6=%H_3Ts@TGKm4$?t>DL$rK)@>M+>~~d9Yo31aB<+2?0IF#TN|pQc_|{_;?d#sw&E~73-Y7Q@ zK2$HK>hGDG(J?2>URIy8t#eNI@k+zbNA8XK3SPVBcBHMB^29aqf2wgJ_p7vkbRMVj z*=vL)jXSGxmes?Q&Ij(f^s%F>8J#b5wWE=tZd&I9-2*#8^ey+VD~`0F9u^cO!btDL z&%Ue?Jo@Nm%BW}lZX#riyvtjoHKeGQHPi+3;r ztjQ+!+*?^TXx`9uO*(IQ$?QoZaJ;T3=4E@j#$rBt3#qB~3%4q}Sr<<8os}k5g+A;0 z>z9={kNwq~6w(ALWbB^}VjPJ31rxN>A2h4?NrRb_s6d6K07oq{Ey}o~A|jDN!Cr zkUepL4_Y+9?w53uLnI?N+z`LeYV4<=0$ygm#T)+<&mr1Rd#~WLCW|p#&Z~7n9@-GP zK33~Utqb}^E>hM(bZz)IOv>1r#nOErtFWKp$3QM&9K*XvvZ)9~j#?uU6AgKZ(%>b$ z;Ma%x9qGF4c?9vAy4&>a4bMPi{63w-2VNE>&qGzw6-?Y0e-Aw7V@co{e!|!*x?k}q z2X>w6*H!T(BP>~Pi=xZ$D@(rgS-V#1=;Pap3duyB|U+^g4&dI=LczWMVt+Tll>8ALADN` z3=GQBd*s8Z0dmlT(Z_1Lvnfh%E zfvn@>b2DM3cu+w!1kQ|hGFLJZ?3)(Ip)9}0hblKpYgRNMEqp2|(U?ADP1cN-R)oSQ z@|nEoJ<*%NKh0}GCQQbLH=(!crBCs8vITLp?$H_DTi;nBbKc{9UZboD-?NGWYx*1< z8Hdr|nKW@GDzFS@9wzsO?gVX+V+e6G=$dT!+)J-LYPQP< zoT>S&AxMWlK8Xfu4wefs>W;>5YJ8p+MbRf;%lfQ$@s(4x*zoUwX16Qfc8{<)+>lhD zld24UfmCB2E27>k1MZk{9;ubJTv43k?t$Jai03qo(Q9ZM=Hm22E)eb~X&~y<)gLXW zMHZ#2K*jCK?asXU3za;Se+DEwUX`+L5BSHm-x8RJrdO|F68iP>JbniRn5f3U)`9yR2)g zhFY$Zj0LeS!DZ?|%(^Vb^y1NGim^TC6POoU_f1-H@J^xx>mA8Cyv^RJ2Qe$eGfk&< zpQr`?fU}?c)Am)b{(111R6Sf)L}I4;wR(8-)x%X)gf}nihJ&kK3uCW(Q4=R4COgAd z$S0x6szd#%UF}(9XO2RQW0i%hgiM4?spzY#HVSzPNE2$&!=c6?{?l`4YUUN?NBF(1 z=uzFWnE+Yzx}G-F226m?AcyXJc~nl34EZ;!S$yVwMqihVKw+z}kP)2?nfXjkCdrva zJQGaja9iGXsxGeY?3~NKo_u-R+Hlw51 z^}TpoUTi31_6l`jy#d)XOCmGJ&n)hflh0@lk0mlEK9ZeHv zeuyn&-?XK+MHFUU%>KdwQ;dfwy6k_X7hrrS*nvpWPNk>G7reMf;`}^}C{92ijAyqu)Hg zaPM`#((_e4xvLmB8b9O0z$dcMFC7CX#p56hocHtR(J*jP7JaiA$cVOgZ9Evrde_zR z!2bJ&Clitkmv1}|WW;e|AiL78f`OCM*ZrRhTi$f;^SrmJB9aHb7uJ#Qk@bUt zS&xr9ntK}-((Z2Of!F0{To_1{{<8Bx+j%ny9tUBdt)q;V2eM!8rZYhs^XfPl&%(ET zO@Lhu{A0=1*qFN?>!gB1&D-{pSpA<>#8BPGL19=c7_K+t0Ee|&-#gLoQlH%D*!!%ANFZUtM(AH0H>W_G(xO8X)HCimp^ zvKRcyVtoJIZow+BsO#-^8^q~w_?YvNy8mvstD$8t 90 else "") + logger.info("Richiesta HTTP: tentativo=%s/%s, url=%s", attempt + 1, max_retries, preview) print(f" -> GET {preview} (tentativo {attempt + 1}/{max_retries})") response = requests.get(url, headers=headers, timeout=15) if response.status_code == 200: + logger.info("Richiesta HTTP completata con successo: status=200") return response.json() if response_format == "json" else response.text if response.status_code == 429: # Priorità all'header Retry-After: il server conosce il proprio # stato meglio di qualsiasi euristica locale di backoff. retry_after = float(response.headers.get("Retry-After", wait_time)) + logger.warning("Rate limit HTTP 429: attesa=%ss", retry_after) print(f" -> 429 Rate limit. Attendo {retry_after:.0f}s...") time.sleep(retry_after) elif response.status_code in (500, 502, 503, 504): + logger.warning("Errore server HTTP %s: attesa=%ss", response.status_code, wait_time) print(f" -> {response.status_code} Errore server. Attendo {wait_time:.0f}s...") time.sleep(wait_time) else: # Errori 4xx (escluso 429): il problema è nella richiesta stessa, # non nel server, quindi un nuovo tentativo non porterebbe a risultati diversi. + logger.error("Errore HTTP non recuperabile: status=%s, risposta=%s", response.status_code, response.text[:120]) print(f" -> Errore {response.status_code}: {response.text[:120]}") return None except (requests.exceptions.Timeout, requests.exceptions.ConnectionError) as exc: + logger.warning("Errore di rete %s: attesa=%ss", type(exc).__name__, wait_time) print(f" -> Errore di rete ({type(exc).__name__}). Attendo {wait_time:.0f}s...") time.sleep(wait_time) + logger.error("Endpoint non raggiungibile dopo %s tentativi", max_retries) print(" [FALLIMENTO] Impossibile raggiungere l'endpoint dopo tutti i tentativi.") return None @@ -262,7 +272,8 @@ def save_openalex_json(results: List[Dict], output_dir: Path) -> Path: filepath = output_dir / f"openalex_{timestamp}.json" with open(filepath, "w", encoding="utf-8") as fh: json.dump(results, fh, ensure_ascii=False, indent=2) - print(f"\n[SALVATAGGIO] OpenAlex JSON → {filepath} ({len(results)} record)") + logger.info("Salvataggio OpenAlex JSON: file=%s, record=%s", filepath, len(results)) + print(f"\n[SALVATAGGIO] OpenAlex JSON -> {filepath} ({len(results)} record)") return filepath @@ -298,7 +309,8 @@ def save_pubmed_xml(articles_xml: List[str], output_dir: Path) -> Path: tree = ET.ElementTree(root) ET.indent(tree, space=" ") # Indentazione leggibile (Python >= 3.9) tree.write(filepath, encoding="unicode", xml_declaration=True) - print(f"[SALVATAGGIO] PubMed XML → {filepath} ({len(articles_xml)} record)") + logger.info("Salvataggio PubMed XML: file=%s, record=%s", filepath, len(articles_xml)) + print(f"[SALVATAGGIO] PubMed XML -> {filepath} ({len(articles_xml)} record)") return filepath @@ -335,7 +347,8 @@ def extract_openalex_data( Il file JSON viene salvato su disco come effetto collaterale. """ sep = "=" * 70 - print(f"\n{sep}\nESTRAZIONE OPENALEX — query: '{query}'\n{sep}") + logger.info("Avvio estrazione OpenAlex: query=%s, max_results=%s", query, max_results) + print(f"\n{sep}\nESTRAZIONE OPENALEX - query: '{query}'\n{sep}") all_results: List[Dict] = [] cursor: str = "*" # Il cursore "*" indica l'inizio della sequenza paginata @@ -353,6 +366,13 @@ def extract_openalex_data( results: List[Dict] = data["results"] all_results.extend(results) total_available = data.get("meta", {}).get("count", "?") + logger.info( + "OpenAlex pagina %s: estratti=%s, totale_accumulato=%s, disponibili=%s", + page_num, + len(results), + len(all_results), + total_available, + ) print( f" -> Estratti {len(results)} works " f"(totale: {len(all_results)} / {total_available} disponibili)" @@ -370,8 +390,10 @@ def extract_openalex_data( if all_results: save_openalex_json(all_results, output_dir) else: + logger.warning("OpenAlex non ha restituito dati per query=%s", query) print(" [ATTENZIONE] Nessun dato estratto. Nessun file salvato.") + logger.info("Estrazione OpenAlex completata: record=%s", len(all_results)) return all_results @@ -410,7 +432,8 @@ def extract_pubmed_data( Il file XML viene salvato su disco come effetto collaterale. """ sep = "=" * 70 - print(f"\n{sep}\nESTRAZIONE PUBMED — query: '{query}'\n{sep}") + logger.info("Avvio estrazione PubMed: query=%s, max_results=%s", query, max_results) + print(f"\n{sep}\nESTRAZIONE PUBMED - query: '{query}'\n{sep}") all_results: List[Dict] = [] raw_xml_list: List[str] = [] # Usato solo per il salvataggio finale su disco @@ -459,6 +482,13 @@ def extract_pubmed_data( batch_parsed = [parse_pubmed_xml_node(art) for art in articles] all_results.extend(batch_parsed) + logger.info( + "PubMed pagina %s: estratti=%s, totale_accumulato=%s, disponibili=%s", + page_num, + len(batch_parsed), + len(all_results), + total_available, + ) print( f" -> Estratti {len(batch_parsed)} articoli XML " @@ -488,8 +518,10 @@ def extract_pubmed_data( # max_results record, coerentemente con la lista restituita in memoria. save_pubmed_xml(raw_xml_list[:max_results], output_dir) else: + logger.warning("PubMed non ha restituito dati per query=%s", query) print(" [ATTENZIONE] Nessun dato estratto. Nessun file salvato.") + logger.info("Estrazione PubMed completata: record=%s", len(all_results[:max_results])) return all_results[:max_results] @@ -528,6 +560,7 @@ def extract_data( """ output_path = Path(output_dir) normalized = source.lower().strip() + logger.info("Dispatch API: source=%s, query=%s, output_dir=%s", normalized, query, output_path) dispatch: Dict[str, object] = { "openalex": lambda q: extract_openalex_data(q, output_dir=output_path), @@ -556,4 +589,4 @@ def extract_data( for src in ("openalex", "pubmed"): records = extract_data(query_test, source=src) - print(f"\n→ {src}: {len(records)} record estratti.\n") + print(f"\n-> {src}: {len(records)} record estratti.\n") diff --git a/www/services/file_extractor.py b/www/services/file_extractor.py index d07cfb40f..b22f44cf1 100644 --- a/www/services/file_extractor.py +++ b/www/services/file_extractor.py @@ -2,6 +2,7 @@ from __future__ import annotations +import logging import os import tempfile import zipfile @@ -13,6 +14,7 @@ SUPPORTED_EXTENSIONS = {".csv", ".xlsx", ".xls", ".txt", ".ciw", ".bib"} +logger = logging.getLogger(__name__) def _annotate_records(records: list[dict], file_extension: str, source: str) -> list[dict]: @@ -111,6 +113,7 @@ def _extract_zip_file(file_path: str, source_upper: str) -> list[dict]: ValueError: Propagata dall'estrazione annidata per contenuti non supportati. """ + logger.info("[%s] Estrazione archivio ZIP: %s", source_upper, file_path) all_records: list[dict] = [] with zipfile.ZipFile(file_path, "r") as archive: with tempfile.TemporaryDirectory() as tmp_dir: @@ -121,6 +124,7 @@ def _extract_zip_file(file_path: str, source_upper: str) -> list[dict]: nested_ext = os.path.splitext(filename)[1].lower() if nested_ext in SUPPORTED_EXTENSIONS: all_records.extend(extract_from_file(nested_path, source_upper)) + logger.info("[%s] Archivio ZIP elaborato: %s record", source_upper, len(all_records)) return all_records @@ -154,17 +158,19 @@ def extract_from_file(file_path: str, source: str) -> list[dict]: source_upper = source.upper().strip() file_extension = os.path.splitext(file_path)[1].lower() + logger.info("[%s] Avvio lettura file: %s", source_upper, file_path) if file_extension == ".zip": return _extract_zip_file(file_path, source_upper) if file_extension == ".bib": - print(f"[{source_upper}] Lettura file BibTeX: {file_path}") + logger.info("[%s] Lettura file BibTeX", source_upper) records = _read_bibtex_file(file_path) + logger.info("[%s] Record estratti da BibTeX: %s", source_upper, len(records)) return _annotate_records(records, file_extension, source_upper) if file_extension in {".txt", ".ciw"}: - print(f"[{source_upper}] Lettura file testuale: {file_path}") + logger.info("[%s] Lettura file testuale %s", source_upper, file_extension) # Gli export testuali usano convenzioni di tag specifiche per sorgente, # quindi vengono inviati ai parser dedicati prima dell'annotazione comune. @@ -181,19 +187,21 @@ def extract_from_file(file_path: str, source: str) -> list[dict]: f"WEB_OF_SCIENCE e COCHRANE. Ricevuto: {source_upper}" ) + logger.info("[%s] Record estratti da file testuale: %s", source_upper, len(records)) return _annotate_records(records, file_extension, source_upper) if file_extension in {".csv", ".xlsx", ".xls"}: - print(f"[{source_upper}] Lettura file tabellare {file_extension}: {file_path}") + logger.info("[%s] Lettura file tabellare %s", source_upper, file_extension) try: records = _read_tabular_file(file_path, file_extension, source_upper) except pd.errors.EmptyDataError: - print(f"[ERRORE] Il file '{file_path}' e' vuoto.") + logger.error("Il file '%s' e' vuoto.", file_path) return [] except Exception as exc: - print(f"[ERRORE] Impossibile leggere il file tabellare: {exc}") + logger.exception("Impossibile leggere il file tabellare: %s", exc) return [] + logger.info("[%s] Record estratti da file tabellare: %s", source_upper, len(records)) return _annotate_records(records, file_extension, source_upper) raise ValueError( diff --git a/www/services/parsers.py b/www/services/parsers.py index 6e29101f4..434d03216 100644 --- a/www/services/parsers.py +++ b/www/services/parsers.py @@ -8,9 +8,12 @@ campi e dei tipi resta responsabilita' di "standardizer.py". """ +import logging import re import xml.etree.ElementTree as ET +logger = logging.getLogger(__name__) + # PARSER WEB OF SCIENCE def parse_wos_data(datapath: str) -> list[dict]: @@ -34,6 +37,7 @@ def parse_wos_data(datapath: str) -> list[dict]: elem_data = [] data = {} current_key = None + logger.info("Parsing Web of Science: %s", datapath) with open(datapath, 'r', encoding='utf-8') as file: lines = file.readlines() @@ -72,6 +76,7 @@ def parse_wos_data(datapath: str) -> list[dict]: data[key] = [value] current_key = key + logger.info("Parsing Web of Science completato: %s record", len(elem_data)) return elem_data @@ -96,6 +101,7 @@ def parse_cochrane_data(datapath: str) -> list[dict]: data = [] current_record = {} current_key = None + logger.info("Parsing Cochrane: %s", datapath) with open(datapath, 'r', encoding='utf-8') as file: lines = file.readlines() @@ -142,6 +148,7 @@ def parse_cochrane_data(datapath: str) -> list[dict]: current_record['AB'] = current_record['AB'][22:].strip() data.append(current_record) + logger.info("Parsing Cochrane completato: %s record", len(data)) return data @@ -161,6 +168,7 @@ def parse_pubmed_medline_text(text: str) -> list[dict]: records = [] current_record = {} current_key = None + logger.info("Parsing PubMed MEDLINE: %s righe", len(text.splitlines())) for line in text.splitlines(): if not line.strip(): @@ -197,6 +205,7 @@ def parse_pubmed_medline_text(text: str) -> list[dict]: if current_record: records.append(current_record) + logger.info("Parsing PubMed MEDLINE completato: %s record", len(records)) return records diff --git a/www/services/standardizer.py b/www/services/standardizer.py index 66dcb822f..e9da3529b 100644 --- a/www/services/standardizer.py +++ b/www/services/standardizer.py @@ -11,12 +11,15 @@ from __future__ import annotations import ast +import logging import re from typing import Any import pandas as pd from . import format_functions as ff from .validation import validate_dataframe_contract, validate_record_contract +logger = logging.getLogger(__name__) + # Schema Target e contratti di tipo COLUMN_TYPE_CONTRACTS: dict[str, type] = { "DB": str, @@ -616,11 +619,26 @@ def convert2df( Notes: I record non rappresentati da dizionari vengono ignorati. Il dispatch sceglie tra ricaricamento standardizzato, mapping OpenAlex e formatter legacy in base ai metadati disponibili. """ + input_count = len(raw_records or []) source = source.upper().strip() records: list[dict[str, Any]] = [] + skipped_records = 0 + standardized_records = 0 + openalex_records = 0 + legacy_records = 0 + + logger.info( + "Avvio standardizzazione: source=%s, file_type=%s, record_input=%s, validate=%s, csv_export=%s", + source, + file_type, + input_count, + validate, + for_csv_export, + ) for raw_record in raw_records or []: if not isinstance(raw_record, dict): + skipped_records += 1 continue record_source = str(raw_record.get("_bibliometrix_source", source)).upper().strip() @@ -629,18 +647,31 @@ def convert2df( # Il dispatch preserva i record gia' standardizzati ed evita passaggi inutili attraverso formatter progettati per dati grezzi. if _looks_standardized(raw_record): record = transform_standardized_record(raw_record, default_db=record_source) + standardized_records += 1 elif record_source == "OPENALEX": record = transform_openalex_record(raw_record) + openalex_records += 1 else: record = transform_with_format_functions(raw_record, record_source, effective_file_type) + legacy_records += 1 if validate: validate_record_contract(record, COLUMN_TYPE_CONTRACTS) records.append(serialize_for_csv(record) if for_csv_export else record) + logger.info( + "Record trasformati: validi=%s, saltati=%s, gia_standardizzati=%s, openalex=%s, formatter_legacy=%s", + len(records), + skipped_records, + standardized_records, + openalex_records, + legacy_records, + ) + column_order = list(COLUMN_TYPE_CONTRACTS) if not records: + logger.warning("Nessun record valido da standardizzare; restituito DataFrame vuoto") return pd.DataFrame(columns=column_order) df = pd.DataFrame(records) @@ -653,5 +684,7 @@ def convert2df( df = df[column_order] if validate and not for_csv_export: validate_dataframe_contract(df, COLUMN_TYPE_CONTRACTS) + logger.info("Validazione DataFrame completata") + logger.info("DataFrame standardizzato pronto: righe=%s, colonne=%s", len(df), len(df.columns)) return df diff --git a/www/services/validation.py b/www/services/validation.py index c99166925..f8e3cf826 100644 --- a/www/services/validation.py +++ b/www/services/validation.py @@ -6,9 +6,12 @@ """ from __future__ import annotations +import logging from typing import Any import pandas as pd +logger = logging.getLogger(__name__) + class ValidationError(Exception): """Eccezione sollevata quando l'output standardizzato viola il contratto ETL.""" @@ -74,6 +77,7 @@ def validate_record_contract(record: dict[str, Any], contracts: dict[str, type]) ) if errors: + logger.error("Validazione record fallita: %s errori", len(errors)) raise ValidationError("\n".join(errors)) @@ -113,4 +117,5 @@ def validate_dataframe_contract(df: pd.DataFrame, contracts: dict[str, type]) -> ) if errors: + logger.error("Validazione DataFrame fallita: %s errori", len(errors)) raise ValidationError("\n".join(errors[:25]))