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PubMed2026

Ablative radiotherapy in castration-resistant prostate cancer.

OBJECTIVE: To prove the oncological benefit of ablative radiotherapy in patients with up to five metastases from castration-resistant prostate cancer (CRPC) a single-centre randomised trial was initiated. PATIENTS AND METHODS: This monocentric, randomised, phase II clinical trial enrolled patients with up to five prostate-specific membrane antigen-positive bone or lymph node metastases developing prostate-specific antigen (PSA) progression during androgen deprivation (ADT) or ADT and androgen-receptor targeted therapy. Participants were randomised (2:1) to receive metastasis-directed therapy (MDT) or observation (OBS) without changing systemic therapy. The primary endpoint was the proportion of patients having PSA progression within 1 year, with statistical analyses conducted using intention-to-treat principles. Here, results of a planned interim analysis of the primary endpoint are reported. RESULTS: A total of 30 patients (12 in the observation arm and 18 in the MDT arm) were enrolled, PSA progression within 1 year occurred in 44% of the MDT group vs 75% in the OBS group (P = 0.14, not significant). The median time to PSA progression was significantly longer in the MDT arm (12.4 months) compared to the OBS arm (2.9 months, P = 0.03). The pre-defined criteria to discontinue the study were not met. Limitations include the single-centre design and small sample size at interim analysis. CONCLUSION: This pre-planned interim analysis of the primary endpoint did not meet the discontinuation criteria of the study protocol, suggesting that MDT in oligometastatic CRPC may extend the time to PSA progression without immediate change of systemic therapy. The continuation of the study in a multicentre setting is planned (Institutional funding by the TU Dresden, ClinicalTrials.gov identifier: NCT04141709).

Humans↗
PubMed2026

Proteomic and phosphoproteomic profiles of time-dependent dynamic changes in LPS-induced macrophage polarization.

The temporal proteomic and phosphoproteomic reprogramming during early M1 macrophage polarization (0-6 h) remains poorly understood. We performed time-resolved proteomic and phosphoproteomic analyses of LPS-stimulated RAW264.7 macrophages at seven time points within 6 h. Time-clustering of differentially expressed molecules revealed two patterns: initial change with partial recovery, and sustained dysregulation. Upregulated proteins and phosphorylation sites were enriched in the Rho GTPase signaling pathway, T-cell receptor signaling pathway, NF-κB cascade, osteoclast differentiation pathway, and antiviral immune pathway. Downregulated pathways were associated with cell cycle regulation, chromatin remodeling, RNA metabolism, and mRNA processing, indicating resource reallocation to prioritize acute inflammatory responses. Kinase-substrate network analysis confirmed the mitogen-activated protein kinase (MAPK), cyclin-dependent kinase (CDK), protein kinase B (AKT), and ribosomal S6 kinase (RSK) families as core upstream phosphorylation regulators. Integrated analysis revealed synergistic and antagonistic relationships between proteomic and phosphoproteomic changes. This study provides a temporal molecular atlas of M1 polarization, delineating inflammatory signaling dynamics and offering a basis for therapeutic target discovery in inflammatory diseases. SIGNIFICANCE: Macrophage M1 polarization is a central event in innate immune defense against pathogenic invasion, yet its dysregulation is a pivotal driver of the onset and progression of a broad spectrum of inflammation-associated disorders, spanning autoimmune diseases, infectious conditions and inflammatory bone diseases, making the dissection of its molecular regulatory mechanisms an urgent research priority in immunology and translational medicine. Dynamic molecular events within 0-6 h after LPS stimulation are critical for initiating and shaping M1 inflammatory activation, yet systematic time-resolved proteomic and phosphoproteomic profiling remains insufficient.In this study, we comprehensively characterized temporal proteome and phosphoproteome changes at seven consecutive time points during macrophage polarization, clarified two distinct dynamic molecular patterns, identified core signaling pathways and key kinase regulators involved in inflammatory reprogramming, and uncovered the leading role of post-translational phosphorylation modifications in initiating polarization. This work delineates the time-series molecular atlas of early macrophage activation, provides novel insights into the temporal regulatory mechanism of inflammatory signaling networks, and lays a solid experimental foundation for exploring new intervention targets and regulatory nodes in clinical translational research.

Lipopolysaccharides↗
PubMed2026

Transdermal 17β-Estradiol for the Treatment of COVID-19: Protocol of an Early Terminated Phase 2 Randomized Controlled Trial.

BACKGROUND: Early epidemiological studies suggested that pre- and postmenopausal women receiving estrogen therapy were less likely to develop severe disease or die from COVID-19 infection. Potential mechanisms include estrogen-mediated immunomodulation and 17β-estradiol-induced downregulation of angiotensin-converting enzyme type 2 (ACE2), the cellular receptor for SARS-CoV-2. OBJECTIVE: This study aimed to evaluate the feasibility, safety, and preliminary efficacy of transdermal 17β-estradiol as an adjunctive treatment for COVID-19 in men and postmenopausal women. METHODS: We designed and conducted a randomized controlled trial comparing 17β-estradiol transdermal gel plus standard care with standard care alone in adults with confirmed COVID-19. Initial ethics and funding approvals were obtained in March 2021. Owing to changes in the epidemiology of COVID-19 in Qatar and revisions to national quarantine policies, protocol amendments were required before recruitment commenced in February 2022. The treatment duration was reduced from 10 to 7 days due to changes in national quarantine guidelines. Recruitment and follow-up were conducted between February 2022 and June 2022. RESULTS: Recruitment was substantially lower than anticipated because widespread COVID-19 vaccination, declining disease severity, and revised national quarantine policies markedly reduced the number of eligible hospitalized patients. Consequently, the planned sample size was not achieved, and the study was terminated in June 2022. A total of 29 men with mild COVID-19 were enrolled, with 44.8% (n=13) randomized to standard care and 55.2% (n=16) to transdermal 17β-estradiol plus standard care. The intervention was well tolerated, with no adverse safety signals or thromboembolic events reported. CONCLUSIONS: Although the study was underpowered to assess efficacy because recruitment targets were not achieved, it showed that transdermal 17β-estradiol was well tolerated, with no major safety concerns among enrolled participants. The experience also provided important operational lessons for conducting clinical trials during rapidly evolving pandemics. Adequately powered studies are required to determine whether transdermal estrogen has therapeutic potential against COVID-19, other ACE2-mediated coronavirus infections, or potentially other severe viral illnesses.

Humans↗
PubMed2026

Naltrexone Is Superior to Placebo for Abstinence and Craving Reduction in Alcohol-Associated Cirrhosis: NAL-CI Trial.

BACKGROUND AND AIMS: Alcohol use disorder (AUD) coexisting with cirrhosis carries high morbidity and mortality, with no approved pharmacotherapy for AUD. We evaluated the safety and efficacy of naltrexone, an opioid receptor antagonist, in patients with compensated alcohol-associated cirrhosis (AaC) and AUD. METHODS: One hundred patients with compensated AaC and DSM-5 AUD were randomised 1:1 to naltrexone (50&#x2009;mg/day) or placebo for 12&#x2009;weeks. The primary endpoint was point-prevalence abstinence at 12&#x2009;weeks, defined as no alcohol use in the four preceding weeks. Secondary endpoints included craving (Obsessive Compulsive Drinking Scale [OCDS]-Obsessive and Compulsive subscales), lapses, relapses, and hepatic safety. Standardised psychosocial support was provided to both arms. RESULTS: Baseline characteristics were well matched between groups (mean MELD 12.6 vs. 12.7; CTP score 5.9 vs. 6.2; age 42.9 vs. 44.3&#x2009;years). AUDIT and OCDS scores were comparable between groups. Abstinence at 12&#x2009;weeks was significantly higher with naltrexone: 64% (32/50) versus 22% (11/50), p&#x2009;<&#x2009;0.001; OR 10.86 (95% CI: 1.89-62.2). Naltrexone significantly reduced lapses at 3&#x2009;months (28% vs. 54%, p&#x2009;=&#x2009;0.008) and showed a trend toward fewer heavy-drinking relapses (12% vs. 28%, p&#x2009;=&#x2009;0.07). Maintenance of abstinence at 6&#x2009;months favoured naltrexone (22% vs. 8%, p&#x2009;=&#x2009;0.09). No patient developed hepatic decompensation attributable to study medication, and no AST/ALT elevation exceeding 5&#xd7; ULN was observed in either group. Mean craving scores were lower with naltrexone by week 12 than with placebo: OCDS-O score (6.63&#x2009;&#xb1;&#x2009;1.16 vs. 9.29&#x2009;&#xb1;&#x2009;1.78, p&#x2009;<&#x2009;0.01) and OCDS-C score (6.35&#x2009;&#xb1;&#x2009;1.23 vs. 9.02&#x2009;&#xb1;&#x2009;1.86, p&#x2009;<&#x2009;0.01). Adverse events were comparable between the groups. CONCLUSION: Naltrexone is safe and effective in patients with compensated alcohol-associated cirrhosis, achieving a threefold higher abstinence rate and significantly reducing craving compared with placebo. These findings support the use of naltrexone as a pharmacological option in patients with compensated AaC and AUD. TRIAL REGISTRATION: NCT04391764.

Humans↗
PubMed2026

Efficacy and safety of once-weekly semaglutide 2&#xb7;4 mg in Chinese adults with overweight or obesity (STEP 12): a randomised, double-blind, placebo-controlled, multicentre, phase 3b trial.

BACKGROUND: Semaglutide 2&#xb7;4 mg is a GLP-1 receptor agonist that reduces bodyweight, and provides other cardiometabolic benefits, among people with a BMI at least 30 kg/m2 or at least 27 kg/m2 and with weight-related comorbidities. This trial aimed to evaluate the efficacy, tolerability, and safety of semaglutide 2&#xb7;4 mg in adults from mainland China and Taiwan with overweight or obesity according to locally defined, BMI thresholds. METHODS: This completed randomised, double-blind, placebo-controlled, multicentre, two-armed, parallel-group, phase 3b trial (STEP 12) was conducted at 19 sites across mainland China and Taiwan. Adults with a BMI of 24-<28 kg/m2 and at least one weight-related comorbidity, or a BMI of 28-<30 kg/m2, with or without type 2 diabetes, were randomly assigned (2:1) to once-weekly subcutaneous semaglutide 2&#xb7;4 mg or placebo, plus lifestyle intervention, for 44 weeks. Randomisation was performed by the study sponsor using the Randomisation Trial Supplies Management System. Coprimary endpoints were percentage change in bodyweight and the proportion of participants achieving at least 5% bodyweight reduction. Safety was analysed descriptively in all participants who received the trial intervention. Missing data at week 44 were imputed with washout multiple imputation. This study is registered with ClinicalTrials.gov, NCT06041217, and is completed. FINDINGS: Between Sept 15, 2023, and May 7, 2025, of 254 screened participants, 161 (66&#xb7;5%) of 242 participants were randomly assigned to semaglutide 2&#xb7;4 mg and 81 (33&#xb7;5%) to placebo; 121 (50&#xb7;0%) participants were female, and 47 (19&#xb7;4%) participants had type 2 diabetes. Bodyweight reduction was greater with semaglutide versus placebo (-12&#xb7;1% [SE 0&#xb7;6] vs -2&#xb7;2% [0&#xb7;8]; estimated treatment difference -9&#xb7;9 percentage points [95% CI -11&#xb7;8 to -8&#xb7;0]; p<0&#xb7;0001), with a greater proportion of participants achieving at least 5% bodyweight reduction (80&#xb7;5% vs 24&#xb7;4%; odds ratio [OR] 14&#xb7;8 [95% CI 7&#xb7;4 to 29&#xb7;6]; p<0&#xb7;0001). Adverse events were reported in 141 (87&#xb7;6%) of 161 participants in the semaglutide 2&#xb7;4 mg group and 61 (75&#xb7;3%) of 81 participants in the placebo group, with gastrointestinal disorders being the most common. INTERPRETATION: Semaglutide 2&#xb7;4 mg provided a superior reduction in bodyweight versus placebo in Chinese adults with overweight or obesity. The safety profile was consistent with the known profile of semaglutide. FUNDING: Novo Nordisk A/S. TRANSLATION: For the Mandarin translation of the abstract see Supplementary Materials section.

Adult↗
PubMed2026

Long-term safety of oral orforglipron in Japanese participants with type 2 diabetes (ACHIEVE-J): a multicentre, randomised, open-label, parallel-group phase 3 trial.

BACKGROUND: Orforglipron, an oral GLP-1 receptor agonist, requires further evaluation in east Asian populations with type 2 diabetes, given this group's distinct pathophysiological characteristics. This study aimed to assess orforglipron as add-on treatment to diet and exercise alone or to oral antihyperglycaemic medications in Japanese participants with type 2 diabetes. METHODS: This multicentre, randomised, open-label phase 3 study was conducted in 40 medical research centres and hospitals in Japan. Adults with type 2 diabetes and elevated glucose levels managing their condition with diet and exercise alone or with one or two oral antihyperglycaemic medications were assigned (1:1:1) via computer-generated random sequence to receive once-daily oral orforglipron (3 mg, 12 mg, or 36 mg). Randomisation was stratified by background therapy, baseline HbA1c (&#x2264;8&#xb7;5% or >8&#xb7;5%), and metformin use (yes vs no; applied only to &#x3b1;-glucosidase inhibitors, thiazolidinedione, and glinides). Investigators, participants, and site staff were not masked to treatment. The primary endpoint was safety for 52 weeks, assessed in all randomly assigned participants who received at least one dose of orforglipron. This study is registered with ClinicalTrials.gov, NCT06010004 (ACHIEVE-J). FINDINGS: Between Sept 28, 2023, and June 5, 2025, 450 participants were screened and 401 were randomly assigned to three groups (3 mg, n=132; 12 mg, n=135; and 36 mg, n=134). 352 (88%) completed study treatment. 339 participants (85%, 95% CI 80&#xb7;7-87&#xb7;8) had at least one treatment-emergent adverse event (TEAE), more frequently in the 36-mg group (118 [88%, 95% CI 81&#xb7;5-92&#xb7;5]) than in the 3-mg (107 [81%, 73&#xb7;5-86&#xb7;8]) and 12-mg (114 [84%, 77&#xb7;4-89&#xb7;6]) groups. Most TEAEs were of mild (267 [67%, 61&#xb7;8-71&#xb7;0]) or moderate (63 [16%, 12&#xb7;5-19&#xb7;6]) severity. Discontinuations due to an adverse event occurred in 19 of 134 participants (14%, 95% CI 9&#xb7;3-21&#xb7;1) in the 36-mg group compared with seven of 132 (5%, 2&#xb7;6-10&#xb7;5) in the 3-mg group and 11 of 135 (8%, 4&#xb7;6-14&#xb7;0) in the 12-mg group. Across treatment groups, gastrointestinal symptoms were the most common TEAEs leading to study treatment discontinuation (3 mg: 5 [3&#xb7;8%, 1&#xb7;6-8&#xb7;6]; 12 mg: 8 [5&#xb7;9%, 3&#xb7;0-11&#xb7;3]; and 36 mg: 11 [8&#xb7;2%, 4&#xb7;7-14&#xb7;1]). Level 2 (blood glucose <54 mg/dL) hypoglycaemia events occurred in three of 135 participants in the 12-mg group (2%, 0&#xb7;8-6&#xb7;3) and in three of 134 in the 36-mg group (2%, 0&#xb7;8-6&#xb7;4) groups. No level 3 (severe) hypoglycaemia events occurred. Outcomes were generally similar across background therapies. INTERPRETATION: Treatment with orforglipron in combination with diet and exercise alone or one or two oral antihyperglycaemic medications for 52 weeks demonstrated an acceptable safety profile in Japanese adults with type 2 diabetes. FUNDING: Eli Lilly. TRANSLATION: For the Japanese translation of the abstract see Supplementary Materials section.

Aged↗
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↗
PubMed2026

Revealing the Shared Genetic Architecture of Metabolic Dysfunction-Associated Steatotic Liver Disease-Related Traits Through Genomic Structural Equation Modeling.

Although individual traits related to metabolic dysfunction-associated steatotic liver disease (MASLD) have been investigated through large-scale genome-wide association studies (GWASs), the shared genetic susceptibility across these traits remains unclear. We therefore conducted a multivariate GWAS of key MASLD-related traits to elucidate their common genetic architecture. We applied genomic structural equation modeling to model a latent genetic factor (MASLD-F) underlying genetically correlated MASLD-related traits, leveraging their GWAS-derived genetic correlations. We then performed functional annotations, including fine-mapping, transcriptome-wide association study, and cell- and tissue-type-specific enrichment analyses, and conducted Mendelian randomization analyses to identify modifiable risk factors. Our multivariate MASLD-F GWAS identified 50 independent variants across 48 genomic loci. Transcriptomic imputation identified several MASLD-F-associated genes, including ARNTL, NPC1, BTBD10, VDAC2, TSKU, SFMBT1, and ABHD17C. We observed significant enrichment of MASLD-F-related genetic signals predominantly in brain tissues, pancreatic islets, and the adrenal gland. Additionally, six modifiable risk factors and four modifiable protective factors for MASLD-F were identified. These findings reveal a complex shared genetic architecture underlying MASLD components, thereby expanding our understanding of disease pathogenesis and providing novel insights for precision medicine and public health interventions.

Humans↗
PubMed2026

Evaluation of a cornea-specialized large language model for diagnostic and management accuracy in complex corneal cases.

PURPOSE: To evaluate whether a cornea-specialized large language model (LLM) enhanced with retrieval-augmented generation (RAG) improves clinicians' diagnostic and management accuracy in complex corneal cases compared to a general-purpose GPT-4o model and unaided clinician performance. METHODS: This prospective, randomized, masked evaluation study involved three cornea trainees who each independently reviewed 39 real-world corneal cases under three experimental conditions: unaided, GPT-4o-assisted, and assisted by a cornea-specialized GPT-4o model. The cornea-specialized model was constructed by embedding over 200 publicly available Wikipedia articles into GPT-4o's RAG framework. Participants provided open-ended diagnoses and selected the next-step management options (multiple choice). They were allowed up to three GPT-4o queries per case, and the AI-assisted arms were randomized to minimize bias. Accuracy for both tasks was compared against expert reference standards using McNemar's test. RESULTS: Diagnostic accuracy was 48.7%, 20.5%, and 38.5% unaided, improving to 69.2%, 46.2%, and 59.0% with general GPT-4o (p<0.04). The cornea-specialized GPT-4o further improved accuracy to 71.8%, 48.7%, and 74.4%, with improvements over unaided performance for all clinicians (p<0.01). For next-step decisions, unaided accuracy was 76.9%, 87.2%, and 59.0%. With the specialized model, Ophthalmologist 3 improved to 71.8% (p<0.05), Ophthalmologist 1 remained high at 82.1%, and Ophthalmologist 2 declined to 64.1% (p<0.05). CONCLUSIONS: A cornea-specialized LLM enhanced with RAG improved diagnostic accuracy in complex corneal cases, particularly among clinicians with lower baseline performance. Effects on management accuracy were inconsistent. Future studies should explore the use of open-ended management tasks and examine whether smaller, curated retrieval corpora yield better model performance.

Humans↗