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PubMed2026

Unravelling bioanalytical innovations, degradation processes, and impurity landscapes of VEGFR inhibitors.

From pre-formulation studies to clinical trials, VEGFR-targeted small-molecule tyrosine kinase inhibitors (TKIs) require rigorous analytical standards. Bioanalysis, stability-indicating studies, and impurity profiling are used to examine chromatographic advances for VEGFR-targeted TKIs like sunitinib, pazopanib, axitinib, sorafenib, cabozantinib, vandetanib, apatinib, lenvatinib, nintedanib, and regorafenib. An LC-MS/MS and UPLC-MS/MS routinely show sub ng/mL performance, as shown by LLOQs (0.2 ng/mL) for sunitinib and axitinib, 1 ng/mL for pazopanib, 5-7 ng/mL for sorafenib, 0.5-1.5 ng/mL for regorafenib metabolic products, and 0.1-0.5 ng/mL for lenvatinib. These approaches are used for pharmacokinetics and therapeutic drug monitoring due to their good correlation coefficient of 0.1-10,000 ng/mL, accuracy of 95%-108%, and precision of 15% RSD. UPLC-QTOF-MS/MS distinguishes degradants and metabolites during forced degradation studies, enabling structural elucidation following ICH M7 risk evaluation protocol. HPTLC/MLC offers fast, sensitive screenings, while RP-HPLC/DAD or HPLC-UV offer reliable, cost-effective routine quality-control solutions with LOD/LOQ in the μg/mL range and linearity of 10-240 μg/mL. This review lists the structures and CAS numbers of ten VEGFR-2 TKI degradants and metabolites, as well as pharmacopeial impurities in SMILES forms. It will be useful for future method development and regulatory applications. To ensure VEGFR-targeted TKI quality, safety, and therapeutic efficacy, LC-MS/MS for trace quantification and HRMS for structure elucidation provide a robust, future-oriented framework. To improve VEGFR-targeted TKI quality, safety, and regulatory compliance, analytical development should focus on HRMS-based impurity characterization, AI-assisted degradation prediction, green chromatography, and harmonized bioanalytical validation.

Humans↗
PubMed2026

Gastric carcinoma classification in the WHO 6th edition (2026): Updated framework and emerging entities.

The sixth edition of the WHO Classification of Digestive System Tumours (2026) represents an important step in the continuing evolution of gastric carcinoma classification. While preserving morphology as the foundation of diagnosis, it incorporates advances in molecular pathology, genotype-phenotype correlations, tumour evolution, and predictive biomarker assessment. This review summarizes the development of the WHO classification from the third edition (2000) to the sixth edition (2026) and highlights its relationship with other major classification systems, including those of Laurén, Nakamura, and the Japanese Gastric Carcinoma Association (JGCA). Major histological categories remain largely unchanged; however, several important conceptual and diagnostic refinements have been introduced. These include recognition of crawling-type adenocarcinoma as a distinctive variant of tubular adenocarcinoma, subclassification of poorly cohesive carcinoma into signet-ring cell and non-signet-ring cell subtypes, introduction of the concept of pure signet-ring cell carcinoma, and increased emphasis on tumour evolution. The sixth edition also expands and refines the spectrum of uncommon gastric carcinoma subtypes, including gastric carcinoma with lymphoid stroma, AFP-producing carcinoma, micropapillary adenocarcinoma, gastric adenocarcinoma of fundic-gland type, and gastric sarcomatoid carcinoma. Crucially, molecular subgroups originally proposed by The Cancer Genome Atlas (TCGA) and actionable biomarkers-including HER2 (ERBB2), Claudin 18.2, mismatch repair deficiency/microsatellite instability (dMMR/MSI), and programmed death-ligand 1 (PD-L1)-have transitioned from research-based categories into essential tools for precision oncology. Rather than providing exhaustive diagnostic criteria, this review offers a conceptual framework and encourages consultation of the original WHO text for full details. These advances illustrate the transition of gastric carcinoma classification from a predominantly morphology-based system toward an integrated histomolecular framework that more closely links pathological diagnosis with tumour biology, prognostication, and therapeutic stratification.

Crawling-type adenocarcinoma↗
PubMed2026

Matched targeted therapy use after broad genomic profiling in advanced Non-Small cell lung cancer.

INTRODUCTION: While broad genomic profiling is increasingly used in advanced NSCLC (aNSCLC), the impact of test results on subsequent guideline-concordant targeted therapy selection remains incompletely understood. METHODS: Using a merged dataset of two large, nationwide, patient-level databases, we identified patients who were diagnosed with aNSCLC 2017-2023, had potentially actionable genomic profiling findings, and initiated systemic therapy. Patients were categorized into actionability subgroups based on contemporaneous regulatory approvals and NCCN guideline recommendations. Within each subgroup, we assessed receipt of guideline-concordant targeted therapy within 24 months, including potential underuse (non-receipt of recommended treatment) and overuse (receipt of non-recommended treatment). RESULTS: Among 6620 patients (67.4% ≥65 years, 54.6% female, 68.9% White), guideline-concordant targeted therapy use varied substantially by actionability category: 2313 (89.6%) of 2582 patients with available 1st-line on-label options received them (10.4% underuse), while 212 (67.3%) of 315 patients with available later-line on-label options received them after 1st-line (32.7% underuse). Among 441 patients with available guideline-concordant off-label options, only 122 (27.7%) received them (72.3% underuse). Conversely, 238 (8.6%) of 3282 patients received matched but guideline-discordant off-label options, representing overuse of ineffective or unestablished therapies. Smoking history, squamous histology, and high PD-L1 expression were associated with lower targeted therapy receipt. CONCLUSIONS: In this cohort study of aNSCLC care, the guideline concordance of targeted therapy use varied by clinical actionability of molecular testing results. Underuse was more common in patients with later-line and off-label targeted therapy options. Patients with classical smoking-related risk profiles were substantially less likely to receive targeted therapy even when actionable alterations were identified.

Journal Article↗
PubMed2026

Therapeutic-drug-monitoring-based ATG Targeted Dosing Strategy in Unmanipulated Haploidentical Haematopoietic Stem Cell Transplantation: a randomized, multicenter, phase 3 clinical trial.

Anti-thymocyte globulin (ATG) has been a standard prophylaxis for graft-versus-host disease (GVHD). However, the pharmacokinetics of ATG in vivo vary significantly, and weight-based fixed dosing may not optimize efficacy while minimizing toxicity. We investigated the clinical results of a therapeutic-drug-monitoring (TDM)-based, dose-optimized ATG strategy versus weight-based fixed dosing in haploidentical haematopoietic stem cell transplantation (NCT05166967). Patients were randomly assigned in a 1:1 ratio to receive a targeted dose of ATG or a fixed dose of 10&#x202f;mg/kg. The primary endpoint was the 365-day graft-versus-host disease-free and relapse-free survival (GRFS). From January 1, 2022, to January 16, 2024, 204 patients were enrolled, with 102 patients in each group. The 365-day GRFS was higher in the targeted dose group (66.7%) than in the fixed dose group (50.0%; hazard ratio [HR], 0.666; 95% confidence interval [CI], 0.4456 to 0.9954; P&#x202f;=&#x202f;0.048). The cumulative incidence of moderate to severe chronic GVHD at day 365 was significantly lower in the targeted dose group (9.8%; 95% CI, 5.0 to 16.5) compared with the fixed dose group (22.5%; 95% CI, 15.0 to 31.1; P&#x202f;=&#x202f;0.026). Fewer grade 3-5 infections were reported in the targeted dose group (44.1%) than in the fixed dose group (70.6%; P&#x202f;<&#x202f;0.001). More patients in the targeted dose group achieved optimal ATG exposure (P&#x202f;=&#x202f;0.007) and superior CD4+ T-cell reconstitution (P&#x202f;=&#x202f;0.002). These findings support the clinical utility of a TDM-based individualized ATG dosing strategy that balances efficacy and toxicity for GVHD prophylaxis in allogeneic stem cell transplantation. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT05166967.

Humans↗
PubMed2026

A Phase II Feasibility Study Combining Pembrolizumab and Metformin in Patients with Metastatic Head and Neck Cancer.

PURPOSE: Survival for recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC) remains low with <20% immunotherapy response. Metformin increases tumor-infiltrating CD8+ T and natural killer (NK) cells, which harbor PD-1. In this phase II clinical trial (NCT04414540), we combined metformin and pembrolizumab to evaluate the overall response rate (ORR) in R/M HNSCC and assess NK-cell activity. PATIENTS AND METHODS: Eligible patients were randomized 1:1 into two arms: (i) metformin extended-release (ER) dose escalation to 2,000 mg over 14 days followed by combination with pembrolizumab 200 mg every 3 weeks or (ii) pembrolizumab 200 mg every 3 weeks followed by combination with metformin ER 2,000 mg daily. The primary endpoint was ORR per RECIST 1.1. Nineteen evaluable patients were planned to estimate the proportion of approximately 32% ORR. Safety was evaluated according to Common Terminology Criteria for Adverse Events v5.0. The distribution, activation, and cytotoxic function of NK cells were analyzed via flow cytometry. RESULTS: Twenty-one patients were enrolled; 76% were male, 52% were smokers, and the median age was 64 years. Ten patients had oropharyngeal tumors, of which nine were p16+. Eighteen patients were evaluable for response, including four complete and five partial responses for an ORR of 50% [95% confidence interval (29-71)]. Combination therapy was well tolerated with no unexpected adverse events (AE). Five grade 3 AEs occurred: nausea, diarrhea, fatigue, and weight loss. Metformin led to increased peripheral NK-cell maturation and cytotoxic ability. CONCLUSIONS: The combination of metformin and pembrolizumab was well tolerated with mild gastrointestinal AEs and promising activity, warranting further investigation in a randomized trial.

Humans↗
PubMed2026

Penpulimab and Gemcitabine With or Without Anlotinib in Metastatic Nasopharyngeal Carcinoma: A Randomized, Open-Label, Multicenter Phase 2 Study.

This prospective exploratory phase 2 study employed a three-cohort, two-phase design to evaluate the potential of anlotinib as a substitute for cisplatin in gemcitabine-penpulimab combinations for metastatic nasopharyngeal carcinoma (NPC) patients who were previously treated with cisplatin-based chemoradiotherapy. Patients enrolled in the study were randomized in a 1:1:1 ratio during the lead-in phase to one of three treatment arms: gemcitabine, cisplatin, penpulimab, and anlotinib (GP-PA, n&#x2009;=&#x2009;8); gemcitabine, cisplatin, and penpulimab (GP-P, n&#x2009;=&#x2009;6); or gemcitabine, penpulimab, and anlotinib (GAP, n&#x2009;=&#x2009;6). The expansion phase enriched the optimal cohort, with stratification based on PD-L1 expression. The primary endpoints were safety and objective response rate (ORR), while the secondary endpoints included duration of response, disease control&#xa0;rate (DCR), progression-free survival (PFS), and overall survival (OS). In the lead-in phase, grade &#x2265;&#x2009;3 treatment-emergent&#xa0;adverse events (TEAEs) occurred in 87.5% (GP-PA), 100% (GP-P), and 66.7% (GAP) patients, predominantly hematologic toxicities. ORR/DCR were 62.5%/87.5% (GP-PA), 83.3%/100% (GP-P), and 100%/100% (GAP). At median 20.2-month follow-up, median PFS/OS were 4.1/18.4&#x2009;months for GP-PA and not reached for GP-P/GAP. In the expansion phase, a total of 14 patients&#xa0;received GAP, with an ORR of 93.3% and grade &#x2265;&#x2009;3 TEAEs in 71.4% of patients. At data cut-off point, the median PFS had not been reached, and the 12-month PFS and OS rates were 53.8% and 78.6%, respectively. The GAP regimen demonstrated a favorable safety and efficacy profile, compared to the GP-PA and GP-P regimens in patients with metastatic NPC. These findings suggest that substituting cisplatin with anlotinib may offer a viable therapeutic strategy for this patient population.

Humans↗
PubMed2026

Phase IIB, Randomized, Double-Blind, Placebo-Controlled Clinical Trial of Intravenous Defibrotide for the Prevention and Treatment of Respiratory Distress and Cytokine Release Syndrome in COVID-19.

INTRODUCTION: Endothelial dysfunction is key in COVID-19 pathogenesis. This randomized, double-blind phase IIb trial investigated continuous intravenous infusion of defibrotide in patients hospitalized with SARS-CoV-2 infection and respiratory failure. METHODS: One-hundred and fifty patients were randomized (2:1) to defibrotide or placebo, stratified by disease severity (WHO COVID-19 severity scale 4/5 vs. 6). The primary endpoint was clinical improvement time (days from first improvement through Day 30). RESULTS: Median clinical improvement time was not significantly different with defibrotide versus placebo (15.0 [IQR: 0-24] vs. 20.0 [IQR: 9-25] days; p&#x2009;=&#x2009;0.10). Day-30 (23.0% vs. 22.0%) and Day-60 (26.0% vs. 22.0%) mortality, reduction in mean fraction of inspired oxygen during treatment, and median duration of hospitalization did not differ with defibrotide versus placebo. Defibrotide demonstrated favorable safety, with no differences versus placebo in serious adverse events (34.0% vs. 36.0%), hypotension (16.0% vs. 12.0%), or hemorrhage (13.0% vs. 8.0%). Exploratory pre-specified biomarker analyses showed greater early d-dimer reduction and lymphocyte recovery with defibrotide, although these results require validation. CONCLUSION: Continuous intravenous infusion of defibrotide was safe but did not improve clinical outcomes in severe COVID-19. Further analyses will explore mechanistic actions and pharmacokinetics of defibrotide and the pathophysiology of endothelial dysfunction in COVID-19. TRIAL REGISTRATION: EudraCT identifier: 2020-001409-21. CLINICALTRIALS: gov identifier: NCT04348383.

Adult↗
PubMed2026

A Randomized Phase II Study of Combination Atezolizumab and Varlilumab (CDX-1127) with or without Cobimetinib in Previously Treated Unresectable Biliary Tract Cancer.

PURPOSE: The addition of MEK inhibition (MEKi) to programmed cell death ligand 1 (PD-L1) blockade improves progression-free survival (PFS) in patients with advanced biliary tract cancer. Although MEK inhibitors may increase tumor cell immunogenicity, they can impair T-cell priming/effector function, limiting combination efficacy. We hypothesized that the addition of a CD27 agonist could restore T-cell function and enhance antitumor immunity in this combination. PATIENTS AND METHODS: We conducted a randomized, phase II trial evaluating atezolizumab (840 mg, intravenously, days 1 and 15) in combination with the CD27 costimulatory monoclonal antibody [CDX-1127/varlilumab (3 mg/kg, intravenously, days 1 and 15)], with/without the addition of an MEK inhibitor [cobimetinib (60 mg, orally, daily, days 1-21, off days 22-28)] in unresectable biliary tract cancer following at least one metastatic therapy. Overall response rate (ORR) and PFS were coprimary endpoints. Treatment-related changes in CD8+ tumor-infiltrating lymphocytes (TIL) were the primary correlative outcomes. RESULTS: The trial was closed early following interim preplanned ORR analysis. At closure, 57 patients had been enrolled [n = 29 in the cobimetinib + atezolizumab + varlilumab (CAV) arm; n = 28 in the atezolizumab + varlilumab (AV) arm]. A majority (67%) had intrahepatic cholangiocarcinoma, and 32% were immunotherapy experienced. Both regimens were well tolerated without new safety signals. Objective responses were rare [0% (CAV); 3.8% (AV)]. The median PFS (mPFS) was 2.40 (CAV) and 1.84 (AV) months [hazard ratio (HR), 0.67; 95% confidence interval (CI), 0.38-1.18]. Among immunotherapy-experienced patients, the mPFS was 3.62 (CAV) and 1.84 (AV) months (HR, 0.54; 95% CI, 0.18-1.62). Treatment with CAV increased intratumoral CD8+ T-cell density compared with treatment with AV. CONCLUSIONS: The combinations of atezolizumab and varlilumab with/without cobimetinib were safe, but neither meaningfully improved outcomes in biliary tract cancer treated in the later lines. Correlative tissue studies validated preclinical work that MEKi increases CD8+ TILs.

Humans↗
PubMed2026

Statistical test to compare the linkage model and the admixture model based on central limit results.

In the Admixture Model, the probability that an individual carries a certain allele at a specific marker depends on the allele frequencies in K ancestral populations and the proportion of the individual's genome originating from these populations. The markers are assumed to be independent. The Linkage Model is a Hidden Markov Model that extends the Admixture Model by incorporating linkage between neighboring loci. We prove consistency and asymptotic normality of maximum likelihood estimators for the ancestry of individuals in the Linkage Model, complementing earlier results by (Pfaff et al., 2004; Pfaffelhuber and Rohde, 2022; Heinzel, 2025) for the Admixture Model. These results are used to prove that a statistical test that allows for model selection between the Admixture Model and the Linkage Model is an asymptotic level-&#x3b1;-test. Finally, we demonstrate the practical relevance of our results by applying the test to real-world data from The 1000 Genomes Project Consortium (2015).

Genetic Linkage↗
PubMed2026

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks↗
PubMed2026

The landscape of pruning for large language models: A systematic review and unified taxonomy.

Confronting the inherent tension between the exceptional capabilities and the immense computational costs of Large Language Models (LLMs), pruning has become a crucial technique for achieving efficient deployment. However, a systematic analytical framework dedicated specifically to LLM pruning remains absent. In this paper, we aim to bridge this gap. We first elucidate the theoretical foundations that underpin the effectiveness of pruning, namely overparameterization and redundancy, and then propose a multidimensional taxonomy that organizes existing approaches along the axes of granularity, timing, and criteria. Building upon this unified perspective, we further analyze performance recovery mechanisms and the broader evaluation ecosystem, while also exploring forward-looking challenges such as interpretability, automation, and hardware-algorithm co-design. Through this comprehensive synthesis, we seek to provide an integrated and coherent analytical lens for advancing both research and practice in LLM pruning.

Large Language Models↗
PubMed2025

An integrated multiscale air quality modelling framework for industrial park pollution: Linking local emissions to regional transport.

Capturing the spatiotemporal distribution of pollutants in industrial parks remains challenging for regional air quality models because of their coarse resolution (3 km), resulting in uncertainties in local emission quantification. To address this, we developed the Integrated Multiscale Air Quality Modelling System for Industry (IAQMS-Industry), coupling the regional Nested Air Quality Prediction Modelling System (NAQPMS) with a city-scale chemical transport model. This framework integrates point-source locations and Gaussian plume dispersion to simulate particulate matter with a diameter smaller than 2.5 micrometres (PM2.5) at 100 m resolution. Applied to the Beijing Yi Zhuang and Tangshan industrial parks and evaluated against observations. The coupled model achieved a normalized mean bias (NMB) ranging from 3.1 % to 6.2 %, improving upon NAQPMS (-16.9 % to -7.7 %). Spatial analysis revealed that coarse regional grids underestimated the PM2.5&#x200b; concentrations at industrial sites by smoothing gradients, whereas IAQMS-Industry successfully resolved spatial patterns. Industrial point emissions accounted for 22.9 %-26.4 % of PM2.5 in the coupled model, which was significantly greater than the regional model estimates of 1.6 %-13.7 %. These findings indicate that regional models overestimate pollutant dispersion processes in industrial parks while underestimating local industrial impacts. By explicitly resolving point-source dynamics and linking them to regional transport, IAQMS-Industry provides a robust tool for designing targeted emission controls in industrial cities and balancing local air quality improvements with minimized regional pollution outflow. This study underscores the necessity of multiscale modelling for accurate source apportionment and informed environmental governance in industrial zones.

Air Pollution↗
PubMed2026

Penalized Cumulative Probability Model for a Continuous Outcome Subject to Detection Limits.

Mixed-type outcome data occur when the outcome variable's distribution is a mixture of both continuous and discrete ordinal variables. Such mixed-type outcomes are common in biomedical, psychological, and the health sciences, particularly for variables having either a detection or quantitation limit. When interest lies in identifying a combination of genomic features associated with a mixed-type outcome, any method used would require a variable selection strategy for high-dimensional data. Unfortunately, few variable selection methods exist for modeling a mixed-type outcome when the covariate space is high dimensional. This study develops a high-dimensional penalized cumulative probability model (CPM), to allow for the identification of genomic features associated with mixed-type outcome of interest. We demonstrated how such model may be estimated using the iterative penalization procedure-the generalized monotone incremental forward stagewise (GMIFS) algorithm. The Model-X knockoffs procedure was combined with the estimation algorithm to control the false discovery rates (FDR) when performing variable selection. Through extensive simulation studies, our penalized CPM was shown to outperform alternative methods in terms of controlled variable selection performance by achieving high statistical power with the FDR being controlled at the target level. We demonstrate the utility of our method by applying it to predict estimated glomeruli filtration rate (eGFR) in kidney transplant recipients at 24&#x2009;months post-transplant using baseline gene expression data as predictors. Our CPM model identified five genes associated with this mixed-type outcome which have important links to renal disease, which may provide prognostic guidance for kidney transplantation recipients.

Models, Statistical↗
PubMed2026

Construction of precision clinical-proteomics risk model based on machine learning for predicting heart failure in type II diabetes mellitus.

BACKGROUND AND AIMS: Heart failure (HF) is a severe complication in type 2 diabetes mellitus (T2DM), but current risk stratification scores have limited predictive accuracy. We aimed to develop novel prediction tools integrating clinical variables with proteomics to improve risk stratification of hospitalization for HF in T2DM. METHODS AND RESULTS: In this study, we included 2111 UK Biobank participants with T2DM but no prior HF, and profiled 2920 proteins to predict 10-year incident HF hospitalization. Participants were randomly divided into training (70%), tuning (10%), and validation (20%) sets.Three prediction models were developed: a Clinical model based on demographic characteristics, comorbidities, medication use, and laboratory indices; a Protein model based on 40 proteins selected by the Light Gradient Boosting Machine (LGBM); and the Clinical OMics and Protein ASSessment for Heart Failure (COMPASS-HF) model, which integrated both clinical variables and the LGBM-selected proteins. Models were evaluated for area under the curve (AUC), sensitivity, and specificity. During follow-up, 168 participants (7.96%) developed incident HF. The COMPASS-HF model showed better discrimination than the Clinical model, with an AUC of 0.897 (95% CI: 0.850-0.945) versus 0.790 (95% CI: 0.723-0.856). It also demonstrated higher sensitivity (0.882; 95% CI: 0.725-0.967) and consistent performance in subgroups. COMPASS-HF effectively stratified risk of hospitalization for HF, with cumulative incidence rates of 31.9% in the high-risk group and 1.2% in the low-risk group. CONCLUSIONS: By combining clinical and proteomic variables, we developed a high-performance HF prediction model for T2DM, enabling precise risk stratification and informing early intervention strategies.

Humans↗
PubMed2026

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n&#xa0;=&#xa0;907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed2026

Integrative modeling of the genome structure and dynamics in fission yeast.

Genome organization in the nucleus is highly structured and dynamic. Recent advances in genomic technology have enabled the measurement of genome-wide architecture and locus-specific motion, yielding contact maps and live-cell trajectories. However, these outcomes are derived from different modalities and are not directly comparable, with their quantitative integration being a key challenge. Here we establish a genome-wide live-cell imaging platform in fission yeast Schizosaccharomyces pombe, tracking 131 chromosomal loci, along with the spindle pole body (SPB) and nucleolus, to construct a quantitative map of locus dynamics. By integrating these dynamics with contact data through polymer modeling of Hi-C data, we build a physics-based "digital twin" of the S. pombe genome consistent with the spatiotemporal dynamics of interphase chromatin. We validate it against genome-wide mobility patterns and known architectural features, including centromere and telomere clustering. The model also identifies distinct dynamical regimes: centromere- and telomere-proximal loci relax within [Formula: see text]150 s, whereas the remaining loci relax within [Formula: see text]70 s. We measure semiperiodic dynamics of SPB motion, including a characteristic peak near 225 s and [Formula: see text] fluctuations. We use the model with SPB-directed forcing to show how these low-frequency components propagate through the genome to drive genome-wide chromatin displacements. Together, this predictive physics-based modeling framework integrates genome structure and dynamics to reveal how nuclear mechanical driving forces shape chromosome motion, linking mechanically driven chromatin responses to genome maintenance and regulation.

Schizosaccharomyces↗