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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 = 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

Isometric Exercises for Tendinopathies: A Systematic Literature Review.

PURPOSE: Musculoskeletal disorders are among the most common reasons for medical consultation, with tendinopathies accounting for up to 30% of such presentations. Although exercise remains the cornerstone of management, the most effective modality continues to be debated. Eccentric exercise has long been the mainstay, but the role of isometric exercise in pain modulation and functional recovery remains unclear. This systematic review aims to evaluate the current evidence on the efficacy of isometric exercises in the management of tendinopathies across various anatomic sites. METHODS: A systematic search was conducted in PubMed, MEDLINE, Embase, and the Cochrane Library from inception to August 1, 2025. Eligible studies included randomized controlled trials and prospective or retrospective cohort studies assessing isometric exercise interventions for tendinopathy, with or without comparator groups. Case series and case reports were excluded. Data extracted included participant demographic characteristics, site of tendinopathy, symptom duration, treatment duration, adherence, and outcome measures such as Victorian Institute of Sports Assessment (A, P, or G), visual analog scale, Patient-Specific Functional Scale, 36 item short form health survey (SF-36), and EuroQol 5-Dimension questionnaires. Imaging outcomes (ultrasound or magnetic resonance imaging) were recorded where available. Methodological quality and risk of bias were assessed using the Cochrane Risk of Bias tool in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. FINDINGS: The search identified 304 articles, of which 13 randomized controlled trials met the inclusion criteria, encompassing 336 participants (40% female). Tendinopathy sites included the patellar (n = 4), Achilles (n = 3), lateral elbow (n = 2), rotator cuff (n = 2), wrist extensor (n = 1), and gluteal (n = 1) tendons. Median symptom duration was 23 months (interquartile range, 3-51 months). Intervention periods ranged from a single 45-minute session to 4-month exercise programs. Treatment adherence was high initially (nearly 100%) but declined over time, with 70% of participants in the isometric and 58% in the control (isotonic) groups completing ≥80% of sessions. Pain reduction and functional improvement were statistically significant in only 3 studies. Imaging-based outcomes were inconsistently reported, and study heterogeneity precluded meta-analysis. Overall methodological quality was rated as good in 3 studies and poor in 10. IMPLICATIONS: Current evidence provides limited support for the superiority of isometric exercise in tendinopathy management. Although isometric regimens appear tolerable and may confer short-term analgesic benefits, their long-term efficacy relative to eccentric or isotonic protocols remains uncertain. Future research should prioritize standardized outcome measures, longer follow-up, and uniform diagnostic criteria to strengthen the evidence base for exercise prescription in tendinopathies.

Humans↗

PubMed2026

Effects of continuous isomaltulose-containing gummy intake on interstitial glucose and salivary hormones during an 18-hole golf round: a randomized, double-blind controlled pilot study.

BACKGROUND: Golf is a prolonged, moderate-intensity sport requiring sustained physiological stability to manage cumulative stress and maintain performance. Although carbohydrate intake is commonly used to reduce fatigue, rapidly absorbed sugar-induced rapid blood glucose fluctuations may induce volatile arousal and latent metabolic stress. Isomaltulose, a slow-digesting disaccharide, provides a steadier glucose supply compared with sucrose. This exploratory pilot study examined the effects of isomaltulose intake on physiological stress markers, glycemic dynamics, and subjective responses during a competitive 18-hole golf round. METHODS: Twenty-three male collegiate golfers were randomized to either the isomaltulose group (ISO; n = 12) or the sucrose group (CON; n = 11) in a double-blind controlled trial. Participants consumed gummies containing isomaltulose or sucrose immediately after each hole (12.1 g carbohydrate per hole; total carbohydrate intake: 217.5 g). Primary outcomes were salivary stress markers [cortisol, testosterone, and dehydroepiandrosterone sulfate (DHEAS)] levels. Secondary outcomes included interstitial glucose concentration measured via continuous glucose monitoring, subjective assessments (i.e. sleepiness, relaxation, and concentration), and golf performance (18-hole score). Between-group comparisons at each time point were conducted using planned Welch's t-tests. RESULTS: No significant between-group differences were observed for 18-hole score (p = 0.38) or mean interstitial glucose concentration (p = 0.20). However, exploratory analyses revealed distinct hormonal variations; salivary DHEAS and testosterone levels were higher in the ISO group during the latter half of the round (p < 0.05), whereas both declined in the CON group. Regarding glycemic variability, the ISO group demonstrated a more stable glucose profile with a medium effect size for lower standard deviation (ISO: 14.7 ± 1.9 vs. CON: 16.7 ± 4.6 mg/dL; d = 0.58), although this difference was not significant. Conversely, subjective outcomes diverged; the CON group reported significantly greater subjective arousal (wakefulness and relaxation) (p < 0.01) relative to the ISO group. CONCLUSIONS: In conclusion, continuous intake of isomaltulose-containing gummies during an 18-hole golf round was associated with differences in selected physiological markers, including DHEAS and testosterone concentrations. However, these findings were not accompanied by improvements in objective golf performance outcomes compared with sucrose-containing gummies. Isomaltulose may influence glycemic dynamics and hormonal responses during prolonged golf play; however, the practical significance of these effects remains exploratory. Further studies with larger sample sizes and appropriate repeated-measures frameworks are needed to determine whether such physiological changes translate into meaningful performance or recovery benefits.

Humans↗

PubMed2026

The Impact of Baseline Negative Emotions on Postoperative Quality of Life in Adolescent Idiopathic Scoliosis Patients: A 2-Year Follow-Up Study.

OBJECTIVE: Adolescent idiopathic scoliosis (AIS) is a three-dimensional spinal deformity that develops during puberty without a clear etiology. Beyond physical manifestations, AIS severely impacts adolescents' psychological and social well-being, leading to anxiety, depression, and low self-esteem. While advancements in surgical techniques have enhanced objective outcomes, existing studies on AIS have primarily focused on objective indices, with limited attention to the long-term impact of preoperative negative emotions on patient-reported subjective quality of life. METHODS: This was a retrospective cohort study. A total of 112 eligible AIS patients who underwent posterior spinal correction surgery between April and August 2023 were enrolled. Inclusion criteria included confirmed AIS, completion of 2-year follow-up, and informed consent; exclusion criteria included missing imaging/questionnaire data, comorbid psychiatric/neurological diseases, or prior spinal surgery. Patients were grouped using the Hospital Anxiety and Depression Scale (HADS) administered on admission. Quality of life was assessed preoperatively and 2 years postoperatively using the Scoliosis Research Society-22 (SRS-22, evaluating self-image, mental health, pain, function, treatment satisfaction) and Short Form 36 Health Survey (SF-36, assessing 8 physical and mental health dimensions). Statistical analysis was performed via SPSS, using independent t-tests, paired t-tests, Mann-Whitney U test, and chi-square test. p < 0.05 was considered significant. RESULTS: There were no significant differences in baseline characteristics (age, gender, BMI, surgical parameters, scoliosis type, preoperative/postoperative Cobb angles) between the two groups (all p > 0.05). Preoperatively, SRS-22 and SF-36 scores showed no inter-group differences (all p > 0.05). Postoperatively, the Negative Emotion Group had significantly lower scores in SRS-22 mental health (3.9 ± 0.3 vs. 4.5 ± 0.2) and treatment satisfaction (4.0 ± 0.3 vs. 4.6 ± 0.7), as well as SF-36 general health (68.6 ± 6.4 vs. 79.7 ± 13.3), role-emotional (61.3 ± 9.3 vs. 70.8 ± 9.7), and mental health (61.8 ± 14.3 vs. 68.9 ± 10.7) (all p < 0.05); no inter-group differences were observed in physical function-related dimensions. Both groups showed significant improvements in physical function-related dimensions postoperatively. The Non-Negative Emotion Group also exhibited significant improvements in SRS-22 self-image/pain and SF-36 bodily pain (all p < 0.05), while the Negative Emotion Group showed no significant improvements in these dimensions. CONCLUSIONS: Preoperative anxiety and depression do not affect the recovery of physical function in AIS patients after spinal correction surgery but significantly impede improvements in subjective quality of life dimensions, including mental health and treatment satisfaction. These findings highlight the need to integrate psychological assessment and targeted interventions into the perioperative management of AIS. Such a patient-centered approach will help optimize both physical and psychological outcomes, ultimately achieving comprehensive rehabilitation for AIS adolescents.

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-α-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

Once-weekly IcoSema versus once-daily insulin glargine U100 in type 2 diabetes management (COMBINE 4): an open-label, multicentre, treat-to-target, randomised, phase 3b trial.

BACKGROUND: Stepwise treatment intensification is recommended for managing type 2 diabetes, including insulin initiation when non-insulin glucose-lowering medications are insufficient. Guidelines recommend combining a GLP-1 receptor agonist with basal insulin to improve glycaemic efficacy while reducing weight gain and hypoglycaemia risk. COMBINE 4 evaluated the efficacy and safety of IcoSema, a once-weekly combination therapy of basal insulin icodec and semaglutide (a GLP-1-receptor agonist) versus insulin glargine U100 (glargine U100) in people with type 2 diabetes on oral glucose-lowering medications. METHODS: COMBINE 4 was a 40-week, randomised, open-label, treat-to-target, phase 3b trial conducted across 97 sites in nine countries. Adults (aged ≥18 years) with type 2 diabetes (HbA1c ≥8·0%) receiving oral glucose-lowering medications were randomly allocated in a 1:1 ratio without stratification to IcoSema or once-daily glargine U100. The titration target was 3·9-5·0 mmol/L (70-90 mg/dL). The primary endpoint was change in HbA1c and the secondary confirmatory endpoint was change in bodyweight, both from baseline to week 40, evaluated in all randomly allocated participants. Adverse events were recorded during weeks 0-45. This trial is registered with ClinicalTrials.gov (NCT06269107) and is complete. FINDINGS: Of 653 individuals screened between Feb 15 and Aug 6, 2024, 151 did not meet screening criteria and 17 withdrew before initiating treatment; 243 were randomised to IcoSema and 242 to glargine U100. Of the 485 randomly allocated participants, 286 (59%) were male and 199 (41%) were female, and median age was 58 years (range 26-82). For HbA1c, from baseline (9·57% for IcoSema and 9·50% for glargine U100), mean change to week 40 was greater with IcoSema versus glargine U100 (-3·32 vs -2·44 percentage points; estimated treatment difference [ETD] -0·88 percentage points [95% CI -1·12 to -0·63]), confirming superiority of IcoSema (p<0·001). From baseline to week 40, mean bodyweight decreased with IcoSema and increased with glargine U100 (-0·79 vs 3·81 kg; ETD -4·61 kg [95% CI -5·46 to -3·75]), confirming superiority of IcoSema (p<0·001). Rate of combined clinically significant (blood glucose <3·0 mmol/L [<54 mg/dL], confirmed with a blood glucose meter) or severe hypoglycaemia (severe cognitive impairment requiring external assistance for recovery) was statistically significantly lower with IcoSema versus glargine U100 (0·29 vs 0·59 episodes per person-year of exposure; estimated rate ratio 0·56 [95% CI 0·32 to 0·97]; p=0·04). Gastrointestinal disorders were the most frequently reported adverse events with IcoSema. INTERPRETATION: Once-weekly IcoSema demonstrated superior HbA1c reduction and bodyweight change, with lower rates of clinically significant or severe hypoglycaemia, versus glargine U100, suggesting that IcoSema might be an effective once-weekly treatment option for insulin-naive individuals with type 2 diabetes inadequately controlled on oral glucose-lowering medications. FUNDING: Novo Nordisk.

Humans↗

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 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

Snow carps as an emerging model for polyploid genome evolution.

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Editorial↗

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 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 = 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 = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 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

Corrigendum to "The impact of diverse ancestry on polygenic risk score-based prediction models for psychiatric and neurodevelopmental disorders" [Psychiatry Research 2026 Oct:364:117322].

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Published Erratum↗

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↗

PubMed2026

Rational design of high-productivity perfusion processes for CHO Cells: From growth inhibitory strategies to model-driven optimization.

While perfusion culture for Chinese hamster ovary (CHO) cells offers advantages such as continuous operation and flexibility, it suffers from product loss through cell bleeding and difficulties in reaching high productivity due to sustained rapid cell growth. Growth inhibitory strategies are widely used to enhance productivity in fed‑batch processes; however, their practical implementation and comparative effectiveness in perfusion processes remain insufficiently explored. Meanwhile, process development often relies on costly trial‑and‑error approaches. Here, we systematically compared three growth inhibitory strategies in perfusion culture-low cell‑specific perfusion rate (CSPR), sodium butyrate, and mild hypothermia-with respect to cell growth, metabolism, productivity, and product quality. Genome‑scale metabolic flux sampling analysis revealed that low‑CSPR and sodium butyrate induce a convergent up‑regulation of energy metabolism, correlating with greater gains in specific productivity (qp). Building on this insight, we developed a growth‑kinetic model for the combined low‑CSPR + butyrate strategy, incorporating parameter uncertainty. This model‑guided framework enabled the rational design of two distinct high‑productivity perfusion processes: a sustained mode that achieved robust long‑term stability alongside substantial productivity gains, and a high‑intensity mode that pushed qp and daily volumetric titer to their maxima, with increases of up to 108.94% and 190.36%, respectively, in a model CHO cell line with a moderate baseline productivity. Our study provides a proof‑of‑concept framework for perfusion intensification, from strategy selection to rational process design.

Animals↗

PubMed2026

Risk prediction models for blood transfusion in patients undergoing total hip and knee arthroplasty: a systematic review and meta-analysis.

OBJECTIVE: To systematically review and evaluate published risk prediction models for perioperative blood transfusion in patients undergoing total hip or knee arthroplasty (THA/TKA). METHODS: We systematically searched PubMed, Web of Science, the Cochrane Library, and Embase from inception to May 31, 2025. Two researchers independently screened the literature, extracted data, and assessed the risk of bias and applicability using the Prediction model Risk Of Bias Assessment Tool (PROBAST). The area under the receiver operating characteristic curve (AUC) values were pooled via a meta-analysis using Stata 18.0. RESULTS: d Fourteen studies containing 36 prediction models were included. The incidence of blood transfusion among THA/TKA patients ranged from 3.2% to 30.8%. Preoperative hemoglobin (Hb) level, tranexamic acid (TXA) use, operative duration, intraoperative blood loss, and age were the most frequently incorporated predictors. Model sensitivity ranged from 58% to 94.5%, and specificity ranged from 71.3% to 94%. Meta-analysis showed that the pooled AUC value of the 13 validated models was 0.87 (95% CI: 0.85-0.90), suggesting good discriminatory performance. All models were rated as having a high risk of bias. The applicability of four studies was rated as unclear. CONCLUSION: Although the included studies demonstrated promising discriminative ability of prediction models for blood transfusion in THA/TKA, all were assessed as having a high risk of bias using the PROBAST tool. Therefore, future research should prioritize the development of models with larger sample sizes, rigorous study designs, and multicenter external validation.

Humans↗