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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 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

[Tafluprost/timolol fixed-dose combination versus tafluprost monotherapy for open-angle glaucoma and ocular hypertension: a multicenter, randomized, double-blind, parallel-group trial].

Objective: To evaluate the efficacy and safety of a preservative-free tafluprost/timolol maleate fixed-dose combination compared with preservative-free tafluprost monotherapy in Chinese patients with open-angle glaucoma (OAG) or ocular hypertension (OHT). Methods: This was a multicenter, randomized, double-blind, parallel-controlled clinical trial conducted across 25 centers, including the Eye & ENT Hospital of Fudan University, from January 2019 to November 2022. Patients diagnosed with OAG or OHT who required enhanced intraocular pressure (IOP) reduction after a 4-week washout period were enrolled. Participants were randomized 1∶1 to receive either tafluprost/timolol or tafluprost once daily for 3 months. The primary endpoint was the change from baseline in mean diurnal IOP (the average of measurements at 8:00, 10:00, and 16:00) at Month 3. Secondary endpoints included IOP changes at individual time points and the proportion of responders achieving predefined IOP reduction thresholds. Safety was assessed via the incidence of adverse events (AEs). Analysis of covariance (ANCOVA) using the Markov Chain Monte Carlo (MCMC) method was employed for the primary endpoint; superiority was established if the upper limit of the 95% confidence interval (CI) was<0 mmHg (1 mmHg=0.133 kPa). Continuous variables in secondary endpoints were compared using ANCOVA, and responder rates were analyzed using Fisher's exact test. Results: A total of 219 patients were enrolled (tafluprost/timolol group: n=110; tafluprost group: n=109). The primary efficacy analysis set included 215 patients (tafluprost/timolol: n=107; tafluprost: n=108). Baseline characteristics were well-balanced between the two groups. The majority of patients were male [127 (59.1%)] with a mean age of (44.80±15.71) years at screening. At Month 3, the mean diurnal IOP reduction from baseline was (6.56±3.44) mmHg in the tafluprost/timolol group and (5.36±2.94) mmHg in the tafluprost group. After adjusting for baseline IOP, the between-group difference was -1.312 mmHg (95%CI: -2.010 to -0.696); as the upper limit was<0 mmHg, tafluprost/timolol demonstrated superior IOP-lowering efficacy to tafluprost. Responder rates for IOP reductions of≥15%,≥25%, and≥30% were significantly higher in the tafluprost/timolol group (P=0.016, 0.028, and 0.032, respectively). The incidence of ocular AEs was 20.0% (22/110) in the tafluprost/timolol group and 26.6% (29/109) in the tafluprost group. The most common AE was conjunctival hyperemia, occurring in 2.7% (3/110) and 8.3% (9/109) of the groups, respectively. Conclusion: Compared with preservative-free tafluprost monotherapy, the tafluprost/timolol combination provides significantly greater IOP reduction in patients with OAG and OHT, while maintaining a favorable safety profile.

Humans↗

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

Chemoradiotherapy versus short-course radiotherapy for response-adapted organ preservation in early-stage and intermediate-stage rectal cancer (STAR-TREC): 12-month results of an international, multicentre, open-label, parallel-group, randomised, phase 2/3 trial.

BACKGROUND: Total mesorectal excision (TME) is the standard treatment for most early-stage and intermediate-stage rectal cancer but can cause substantial perioperative morbidity, functional impairment, and reduced quality of life. We assessed whether long-course chemoradiotherapy (LCCRT) or short-course radiotherapy (SCRT) could increase organ preservation and reduce surgery, toxicity, and quality-of-life harms without compromising oncological outcomes. METHODS: STAR-TREC is an international, multicentre, open-label, parallel-group, randomised, phase 2/3 trial in five European countries. Eligible patients were aged 16 years or older in the UK or aged 18 years or older elsewhere, had an Eastern Cooperative Oncology Group (ECOG) performance status of 0-1, and rectal adenocarcinoma (≤40 mm staged as mrT1-T3bN0). In phase 2, participants were randomly assigned (1:1:1) to LCCRT-based organ preservation (LCCRT-OP; 50 Gy in 25 fractions plus oral capecitabine 825 mg/m2 twice daily), SCRT-based organ preservation (SCRT-OP; 25 Gy in five fractions), or primary TME. Phase 2 assessed feasibility, with recruitment at months 12 and 24 as the primary endpoint and feasibility thresholds of four or more and six or more randomisations per month, respectively. Phase 3 adopted a partially randomised patient-preference design, allowing participants to choose either organ preservation or TME. Participants that chose organ preservation were randomly assigned (1:1) to receive LCCRT-OP or SCRT-OP using centralised, computer-generated assignment, with stratification by country and MRI T category (≤T3a vs T3b) using minimisation. The phase 3 primary endpoint was organ-preservation 30 months after treatment initiation, defined as absence of TME, stoma, or local recurrence, which was assessed in the modified intention-to-treat population, which included participants in phase 2 and phase 3. After a planned interim analysis of unmasked phase 2 data, the trial steering committee and independent data monitoring committee recommended reporting a 12-month, modified intention-to-treat analysis of implementation outcomes for participants recruited before Aug 8, 2023. This study is registered with ISRCTN (14240288) and is closed. FINDINGS: Between June 14, 2017, and April 8, 2024, 503 participants were enrolled at 37 sites. Phase 2 enrolled 120 participants, with recruitment rates of three and six participants per month at months 12 and 24, respectively. Overall, 12-month TME-free survival was 60% (47 of 78 participants). After phase 3 recruitment ended, interim analysis of unmasked phase 2 data showed an early TME-free survival benefit with LCCRT versus SCRT (12-month median TME-free survival not reached [95% CI not reached-not reached] vs 7·6 months [95% CI 6·4-not reached]; hazard ratio [HR] 3·7 [95% CI 1·7-8·0]; posterior probability of superiority >99·5%). The trial steering committee and independent data monitoring committee therefore recommended expanded analysis of 426 participants recruited before Aug 8, 2023: 120 from phase 2 and 306 from phase 3. 17 participants withdrew before treatment, leaving 409 in the modified intention-to-treat population: 163 allocated to LCCRT, 168 to SCRT, and 78 to primary TME. 116 (28%) participants were female and 293 (72%) were male. Among participants who opted for organ preservation, 12-month TME-free survival was 78·5% (95% CI 72·4-85·1) with LCCRT and 60·6% (53·6-68·4) with SCRT (HR 1·90 [95% CI 1·29-2·81]). The most common grade 3-4 serious adverse events were gastrointestinal disorders (four [2%] with LCCRT vs six [4%] with SCRT vs six [8%] with TME) and procedural complications (three [2%] with LCCRT vs five [3%] with SCRT vs five [6%] with TME). One participant allocated to primary TME died after an anastomotic leak. INTERPRETATION: These early results support a response-adapted organ-preservation approach, with LCCRT appearing more effective than SCRT at 12 months. Organ-preservation might also reduce treatment-related toxicity compared with primary TME. Longer follow-up is needed for the prespecified 30-month endpoint and definitive functional and oncological outcomes. FUNDING: Cancer Research UK, Stand Up to Cancer, Dutch Cancer Society, Danish Cancer Society, Kom Op Tegen Kanker, Cancerfonden, ALF Region Stockholm, RCC Region Stockholm.

Humans↗

PubMed2026

Ivonescimab plus chemotherapy versus placebo plus chemotherapy in patients with advanced EGFR-mutated non-small-cell lung cancer after disease progression on EGFR tyrosine kinase inhibitor therapy (HARMONi): a multicentre, randomised, double-blind, phase 3 trial.

BACKGROUND: Ivonescimab has shown clinical efficacy in non-small-cell lung cancer (NSCLC). We aimed to assess the efficacy and safety of ivonescimab plus chemotherapy versus placebo plus chemotherapy in patients with advanced EGFR-mutated NSCLC whose disease progressed after third-generation EGFR tyrosine kinase inhibitor (TKI) therapy. METHODS: HARMONi is a randomised, placebo-controlled, double-blind, phase 3 trial done at 114 cancer centres and hospitals across Asia, Europe, and North America. Eligible patients were aged at least 18 years (upper limit: 75 years in Asia) with stage IIIB/IIIC or IV non-squamous EGFR-mutated NSCLC, disease progression after treatment with a third-generation EGFR-TKI, and an Eastern Cooperative Oncology Group performance status score of 0 or 1. Patients were randomly assigned (1:1) via a centralised interactive voice response system or interactive web response system to receive ivonescimab (20 mg/kg) or placebo plus pemetrexed (500 mg/m2) and carboplatin (target area under the curve 5 mg/mL per min) intravenously every 3 weeks. Randomisation was stratified by brain metastases status at enrolment and geographical region. The primary endpoints were progression-free survival by blinded independent radiology review committee and overall survival in the intention-to-treat population. Safety was assessed in patients who received at least one dose of trial treatment. This study is registered with ClinicalTrials.gov (NCT06396065), has completed enrolment, and is ongoing for treatment and follow-up. FINDINGS: From Jan 25, 2022, to Oct 1, 2024, 660 individuals were screened for eligibility; of these, 438 were enrolled and randomly assigned to receive ivonescimab plus chemotherapy or placebo plus chemotherapy (219 per group). Of enrolled patients, 257 (59%) were female and 181 (41%) were male; 306 (70%) reported race as Asian, and 105 (24%) as White. At a median follow-up of 22·3 months (95% CI 21·5-23·0), 275 progression or death events had occurred in 345 patients (129 events among 172 patients in the ivonescimab plus chemotherapy group and 146 events among 173 patients in the placebo plus chemotherapy group). Median progression-free survival was 6·8 months (95% CI 5·7-7·1) in the ivonescimab plus chemotherapy group versus 4·4 months (4·1-5·5) in the placebo plus chemotherapy group (hazard ratio [HR] 0·52; 95% CI 0·41-0·66; p<0·0001). At a median follow-up of 29·7 months (95% CI 27·7-31·0), 262 deaths occurred in 438 patients (122 in the ivonescimab plus chemotherapy group and 140 in the placebo plus chemotherapy group). Median overall survival was 16·8 months (14·3-19·0) in the ivonescimab plus chemotherapy group versus 14·0 months (12·8-15·7) in the placebo plus chemotherapy group (HR 0·79; 0·62-1·01). The most common grade 3-4 treatment-related adverse events in the ivonescimab plus chemotherapy versus the placebo plus chemotherapy group were decreased neutrophil count (42 [19%] of 218 vs 36 [17%] of 218), decreased white blood cell count (28 [13%] vs 24 [11%]), decreased platelet count (27 [12%] vs 14 [6%]), and anaemia (22 [10%] vs 27 [12%]). Serious treatment-related adverse events occurred in 61 (28%) patients in the ivonescimab plus chemotherapy group and 33 (15%) patients in the placebo plus chemotherapy group. Treatment-related adverse events led to death in four patients (disease progression, multiple organ dysfunction syndrome, and hepatic failure, each in one patient; gastrointestinal haemorrhage and pulmonary embolism in one patient) in the ivonescimab plus chemotherapy group and five patients (pneumonitis, myocardial infarction, cerebrovascular accident, cognitive disorder, and embolic stroke, each in one patient) in the placebo plus chemotherapy group. INTERPRETATION: Ivonescimab plus chemotherapy showed a clinically meaningful and statistically significant progression-free survival benefit in patients with EGFR-mutated NSCLC after progression on EGFR-TKI therapy. The clinical benefit and lack of new safety signals of ivonescimab with chemotherapy support the potential for the combination as a new treatment option in this patient population. FUNDING: Summit Therapeutics.

Humans↗

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

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↗

PubMed2026

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline：A Longitudinal Study.

BACKGROUND: Subjective cognitive decline (SCD) is a transitional state between objective cognitive impairment and cognitively intact mental status, providing a critical window for implementing preventive interventions to delay objective cognitive decline. AIMS: We aimed to develop a predictive model for SCD progression in older adults with mild cognitive impairment (MCI). This model will facilitate the identification of risk factors and establishment of targeted interventions for community-based SCD management. METHODS: Data from the China Health and Retirement Longitudinal Study (CHARLS) was utilized in this study, extracting 18 indicators. Potential predictors selected through univariate Cox regression and LASSO regression analyses were sequentially incorporated into a multivariable Cox regression model. A nomogram was constructed to establish a predictive model. Model validation encompassed Area Under Curve (AUC) metrics for discriminative capacity, complemented by quantitative assessments using calibration curve analysis for precision verification and decision curve analysis (DCA) for clinical utility evaluation. RESULTS: A total of 1099 older adults with SCD were included in the final analysis, of whom 114 (10.3%) developed MCI. Multivariable Cox regression identified residence, marital status, educational level, social participation, gait speed, and baseline cognitive function. The model demonstrated time-dependent AUC values of 0.885, 0.830, 0.839, and 0.836 in the training set when evaluating discriminative capacity at 2-, 4-, 7-, and 9-year, respectively. The predictive model showed excellent predictive ability according to AUC, calibration curve, and DCA. CONCLUSIONS: A predictive model was created to estimate the risk of developing MCI in older individuals with SCD, offering clinician-actionable intervention benchmarks for preventive care.

Humans↗

PubMed2026

Diagnostic performance of machine learning models versus established risk stratification for intracranial aneurysm rupture: a systematic review and bivariate meta-analysis.

BACKGROUND: Machine learning (ML) models have been proposed to improve the discrimination of intracranial aneurysm rupture status beyond established clinical risk stratification tools. However, reported performance is heterogeneous and the relative contribution of model architecture and feature dominance remains unclear. METHODS: We performed a Preferred Reporting Items for Systematic Reviews and Meta-Analyses-diagnostic test accuracy systematic review and diagnostic meta-analysis of studies evaluating ML models for intracranial aneurysm rupture discrimination. PubMed, Embase and CENTRAL were searched to February 2026. Sensitivity and specificity were pooled using a bivariate random-effects model, with summary receiver operating characteristic curves generated across training, internal testing and external validation datasets. Models were compared with regression-based approaches and Population, Hypertension, Age, Size of aneurysm, Earlier subarachnoid haemorrhage, Site of aneurysm (PHASES) scores. Subgroup and meta-regression analyses explored associations between algorithm family and feature domain. RESULTS: Sixty-two retrospective cohorts (29 709 patients 209 models) met the inclusion criteria. In training datasets, pooled sensitivity and specificity for ML were 0.81 (95% CI 0.75 to 0.85) and 0.83 (0.80-0.86), with an area under the curve (AUC) of 0.878, exceeding PHASES (AUC 0.667). In testing datasets, ML retained higher discrimination (AUC 0.837) than regression models (0.806) and PHASES (0.646). In external validation, sensitivity was preserved (0.82), but specificity declined (0.66). Deep learning demonstrated the highest AUCs (training and testing). Incorporation of haemodynamic or radiomic features improved pooled discrimination relative to morphology alone. Evidence of small-study effects and mostly unclear Prediction Model Risk Of Bias Assessment Tool ratings were observed. CONCLUSIONS: ML approaches demonstrate higher pooled discrimination for aneurysm rupture status than conventional risk scores in retrospective datasets, but reduced external validation specificity and heterogeneity limit confidence for clinical translation. Prospective, externally validated, calibrated models are required before integration into routine cerebrovascular risk stratification.

Humans↗

PubMed2026

Validated UPLC-MS/MS quantification and intracellular PK-PD Modeling of periplocin-related cardiac glycosides in H/R-injured H9c2 cells.

Reliable intracellular quantification is essential for characterizing the target-site disposition and exposure-response relationships of bioactive natural products. In this study, an ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) method was developed and validated for the simultaneous determination of periplocin and four related cardiac glycoside metabolites in H9c2 cell lysates. Acceptable linearity, precision, recovery, and stability were achieved for intracellular quantification. Cells were treated with each compound at 50 μM, and intracellular concentrations and cell viability were monitored over 48 h. In hypoxia/reoxygenation (H/R) -injured cells, the time to maximum intracellular concentration was shortened for all five compounds, indicating altered cellular disposition under injury conditions. Cell viability was improved by all compounds during the observation period. Pharmacokinetic-pharmacodynamic (PK-PD) integration was performed using a sigmoid Emax model, and acceptable model fits were obtained, with Akaike information criterion (AIC) values ranging from 79.22 to 130.46. Low apparent EC50 values were estimated under this single-dose design, whereas the estimated Ke0 values suggested delayed equilibration with the effect compartment. These findings indicate that sustained cytoprotective responses can be produced by periplocin and related metabolic markers in injured cardiomyocytes. This intracellular bioanalytical strategy provides a quantitative approach for linking cellular exposure to pharmacodynamic response and may support further evaluation of periplocin-related cardiac glycosides.

Tandem Mass Spectrometry↗