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PubMed2026

Ecr positively regulates activity of the PhoQ/PhoP signalling system in Klebsiella pneumoniae.

BACKGROUND: The rising prevalence of polymyxin resistance in multidrug-resistant Klebsiella pneumoniae presents a critical situation with limited therapeutic options. METHODS: Methods Genomic sequencing of 15 clinical polymyxin-resistant K. pneumoniae strains with multidrug resistance revealed that MgrB inactivation, predominantly disrupted by insertion sequences (ISs) in the IS1, IS4, and IS5 families, was the leading cause of polymyxin resistance. Comparative transcriptomics of wild-type, ΔmgrB, and ΔmgrBΔphoP were performed to elucidate the MgrB-PhoPQ regulatory network. RESULTS: This study conducted a system-wide analysis of the regulatory network and identified a species-specific PhoPQ regulon in K. pneumoniae.Beyond the classical MgrB-PhoPQ-ArnBCADTEF pathway, we identified a previously unannotated PhoPQ-regulated gene, 144 bp LN739_RS09850, encoding an Ecr homologue from Enterobacter cloacae. This protein has been reported to confer colistin heteroresistance, with the underlying mechanism not yet functionally validated. This study revealed that overexpression of Ecr homologues decreased colistin susceptibility in both K. pneumoniae and E. cloacae, but this phenotype was abolished upon phoP deletion, confirming PhoP's essential role. Consistent with this dependency, comparative transcriptomics of Ecr-overexpressing K. pneumoniae vs. control revealed significant upregulation of mgrB, phoPQ, arnBCADTE, and pmrD. Two-hybrid bacterial assays further demonstrated direct Ecr-PhoQ interaction. Electrophoretic mobility shift assay confirmed that PhoP directly binds to the ecr promoter in vitro, and a β-galactosidase reporter assay demonstrated that PhoP enhanced ecr promoter activity, indicating that PhoP regulates ecr expression by directly controlling its transcription. CONCLUSION: Collectively, these findings suggest that PhoP may directly activate the transcription of Ecr, with Ecr feedback activating the PhoPQ system via interaction with PhoQ, leading to induction of the arn operon and consequent polymyxin resistance.

Klebsiella pneumoniae↗
PubMed2026

Role of omentin-1 in the global proteome of porcine pituitary cells: insights into proliferation- and apoptosis-related processes.

The anterior pituitary integrates endocrine regulation, cellular growth, and adaptive responses. Adipokines, secreted mainly by adipose tissue, act as hormonal signals linking metabolism, inflammation, appetite, and reproduction. They regulate hypothalamic-pituitary-ovarian axis by modulating hormone secretion and intracellular signaling. The presence of adipokine receptors in anterior pituitary suggests local metabolic-endocrine interactions. Omentin-1, predominantly expressed in visceral adipose tissue, participates in glucose metabolism and ovarian steroid regulation. Recent findings indicate that omentin-1 modulates tropic hormones, their receptors, and adipokine balance in anterior pituitary cells. We hypothesized that omentin-1 affects protein expression and signaling pathways involved in pituitary cell proliferation and apoptosis. This study examined its effects in anterior pituitary cells from Large White and Meishan pigs. Proteomic analysis identified 230 candidate differentially abundant proteins after omentin-1 treatment: 30 downregulated and 3 upregulated in Large White pigs, and 107 downregulated and 90 upregulated in Meishan pigs, associated with enriched 116 Gene Ontology terms. Key proteins were associated with cell cycle, DNA replication, gene expression, and posttranscriptional/posttranslational regulation. Responses differed between breeds. CDK5RAP2 and SIX1 were linked to proliferative control in Large White pigs, whereas AKT1S1 and RHOA were among the proteins associated with the broader proteomic response observed in Meishan pigs. Meishan pigs showed dynamic apoptotic protein regulation, including HTRA2, PARP2, and DFFA. Complementary in vitro experiments demonstrated that omentin-1 downregulated cyclins and caspase-3, upregulated BCL2, increased BCL2/BAX ratio, and modulated ERK1/2, AKT, AMPKα, and STAT3 phosphorylation. Together, these findings suggest that omentin-1 modulates proteomic networks and intracellular signaling associated with anterior pituitary cell function during the mid-luteal phase of the estrous cycle.

Animals↗
PubMed2026

Ageing effects on chemical, physical, mechanical, and morphological properties of clear aligners - a systematic review.

BACKGROUND: Clear aligner (CA) therapy has experienced rapid use over the past two decades to treat orthodontic malocclusions. However, evidence on CA material degradation in the oral environment remains limited and often focuses on single brands or isolated material properties. OBJECTIVES: To investigate CA ageing characteristics across different materials and brands and evaluate the chemical, physical, mechanical, and morphological changes following simulated or intraoral ageing. SEARCH METHODS: Five databases (PubMed, Web of Science, MEDLINE [Ovid], ProQuest, and Scopus) were searched to 18 March 2026, with no restrictions. ELIGIBILITY CRITERIA: Studies assessing CA properties after intraoral use or simulated ageing (thermocycling, cyclic loading, or liquid immersion) were included. DATA COLLECTION AND ANALYSIS: Study selection followed PRISMA 2020. RoB was assessed using QUIN for purely in vitro studies, JBI for cohort in vivo studies, and Cochrane RoB 2 for RCTs. Results were synthesised narratively and organised by property domain, as substantial methodological heterogeneity precluded formal meta-analysis. Where protocols were comparable, a simple pooled weighted mean was calculated and presented graphically. RESULTS: Ninety-five studies were included. RoB was low in eight studies, moderate in sixty-two, and high in twenty-five. Chemical composition remained largely stable during ageing, though some brands showed trace elemental release. Physical, mechanical, and morphological properties showed material-dependent deterioration. Pooled discolouration was greatest with coffee (weighted mean ΔE = 70.9), versus tea (ΔE = 18.4) and red wine (ΔE = 11.5), with Invisalign® consistently exceeding the clinically perceptible threshold. Force decay of 40-90% typically occurred within 48 h. Thermoplastic polyurethane (TPU)-based and directly printed aligners (DPAs) generally showed greater susceptibility than polyethylene terephthalate glycol-modified (PETG)-based aligners, though findings on hardness, roughness, and stiffness were inconsistent. CONCLUSIONS: CA materials undergo clinically relevant degradation during use, particularly in TPU-based and DPAs aligners. Clinicians may need to prioritise material-specific protocols, reinforce dietary and cleaning instructions, and consider force decay when determining aligner replacement intervals. PROSPERO number: CRD420251110248.

Humans↗
PubMed2026

Suppression of AAV-Delivered Transgene Expression Using Artificial MicroRNAs Delivered by an Alternative AAV Serotype.

Adeno-associated virus (AAV) gene transfer vectors mediate long-term expression in nondividing cells, an advantage for treating chronic disorders. However, current platforms lack a way to selectively shut down transgene expression if adverse effects arise. To create an "off switch," we hypothesized that incorporating unique artificial microRNA (amiRNA) target sequences into an AAV expression cassette would allow subsequent suppression of transgene expression using a second AAV vector encoding the cognate amiRNA. We introduced 22-nt sequences absent from human and mouse transcriptomes into the 3' untranslated region (UTR) of a therapeutic AAV cassette. To identify optimal amiRNAs, two tandem copies of each amiRNA were cloned into the 3'UTR of an mCherry reporter gene. In vitro assessment of six amiRNA/target pairs using a dual luciferase assay identified four amiRNAs that efficiently suppressed reporter expression. Cells cotransfected with target site 3 (TS3) and amiRNA-T3B showed the greatest reduction in luciferase activity (80%, p < 0.0001) and were selected for further study. The "off-switch" system was then evaluated using an AAV5 therapeutic vector expressing a recombinant humanized anti-IgE monoclonal antibody (AAV5-TBG-anti-IgE-TS3), designed for long-term suppression of allergen-induced reactions. Co-transfection of HEK293T cells with anti-IgE-TS3 and amiRNA-T3B significantly reduced anti-IgE mRNA and protein levels relative to a control amiRNA (p < 0.0001). In vivo testing in Balb/c mice (n = 5) involved intravenous administration of AAV5-anti-IgE-TS3 (3.2 &#xd7; 1010 gc), followed 4 weeks later by an AAVrh.10 amiRNA vector (AAVrh.10-TBG-amiRNA-T3B; 1 &#xd7; 1011 gc). Control mice receiving only the therapeutic vector expressed 18.4 &#xb1; 13.8 &#xb5;g/mL serum anti-IgE at 10 weeks. In contrast, mice receiving the amiRNA "off" vector showed marked suppression of anti-IgE (0.3 &#xb1; 0.15 &#xb5;g/mL, p < 0.0001). These findings provide proof-of-concept that AAV-delivered amiRNAs can selectively switch off transgene expression, offering a strategy to improve the safety of AAV-mediated gene therapies.

Dependovirus↗
PubMed2026

Candidate biomarkers for early Giardia duodenalis infection revealed by time-resolved secretome proteomics.

Giardia duodenalis is a zoonotic protozoan parasite that causes giardiasis in humans and other mammals. Early diagnosis remains challenging because current diagnostic methods, including microscopy and enzyme-linked immunosorbent assays (ELISAs), primarily detect established infections. Consequently, a critical diagnostic gap exists during the early stage of infection within the first 2-48&#xa0;h following exposure. To address this limitation, we characterized the proteins released by in vitro-cultured G. duodenalis trophozoites under serum-free conditions and evaluated their potential as early diagnostic biomarkers. Proteomic analysis of culture supernatants collected during early trophozoite incubation identified 31,773 peptides corresponding to 2504 quantifiable proteins. Temporal profiling showed distinct secretion patterns, including proteins that peaked during the early stage, progressively accumulated over time, or remained persistently abundant throughout the incubation period. Based on their secretion characteristics and predicted immunogenic properties, five candidate biomarkers were selected for further evaluation. Polyclonal antibodies raised against selected candidates successfully detected the corresponding proteins in serum-free culture supernatants, providing preliminary evidence for their potential utility as early-stage diagnostic targets. These findings identify stage-associated candidate proteins that may serve as a resource for future early giardiasis diagnostic development, provide a valuable resource for investigating host-parasite interactions, and establish a foundation for future diagnostic assay development. However, further validation in clinical and biological samples is required to confirm their diagnostic applicability. SIGNIFICANCE: Giardiasis, caused by Giardia duodenalis, is a major diarrheal disease worldwide. Although enzyme-linked immunosorbent assays (ELISAs) provide rapid detection, their diagnostic utility is limited by the lack of biomarkers capable of identifying infection during its earliest stages, creating a critical gap in the detection of active infection within 2-48&#xa0;h following exposure. Using data-independent acquisition proteomics, this study provides a time-resolved characterization of proteins released by G. duodenalis trophozoites into serum-free culture supernatants. Our findings reveal temporal secretion dynamics of protein secretion and identify candidate biomarkers with potential utility for the development of early-stage diagnostic assays pending rigorous biological and clinical validation. In addition, this proteomic resource provides a foundation for investigating host-parasite interactions and may facilitate the development of future point-of-care diagnostic strategies.

Giardiasis↗
PubMed2026

Premenarche risk factors for future dysmenorrhoea: a prospective cohort study.

BACKGROUND: Dysmenorrhoea, or pain during menstruation, is common in adolescence and is often dismissed or left untreated. Dysmenorrhoea can interfere with daily functioning and can lead to other chronic pain conditions; however, little is known about the risk factors for dysmenorrhoea. We aimed to characterise premenarche risk factors for the presence and severity of future dysmenorrhoea. METHODS: In this prospective cohort study, we obtained data for female adolescents from the population-based Adolescent Brain Cognitive Development Study (USA) who were premenarchal at baseline (age 9-10 years) and had both reached menarche and completed the Menstrual Cycle Survey at 3-year follow-up (age 12-13 years). Parents or guardians provided sociodemographic information and completed the Child Behavior Checklist, the Sleep Disturbance Scale for Children, and the Pubertal Development Scale, which captured data on non-painful somatic symptoms, attention problems, anxiety, depression, sleep disturbances, and pubertal development at baseline. Our primary objective was to analyse associations between dysmenorrhoea at 3-year follow-up (status and severity) with select symptom domains (sleep problems, attention problems, somatic symptoms, anxious or depressive symptoms, and baseline pain status) at baseline. We also investigated associations between dysmenorrhoea and participant characteristics (pubertal status, race or ethnicity, and income-to-needs ratio) that underlie social determinants of health. Differences by race were tested using Fisher exact tests. Differences by ethnicity and baseline pain status were tested using &#x3c7;2 tests. Differences in continuous variables were assessed using ANOVA. Wilcoxon-Rank Sum tests were used in analyses of sleep problems, attention problems, and somatic symptoms, and ANOVA was used for pubertal status and income-to-needs ratio. Multinomial logistic regression was used to test associations with dysmenorrhoea severity, and linear regression was used to test associations with dysmenorrhoea status and menstrual pain interference. FINDINGS: 2254 female adolescents were included in this study. 1299 (57&#xb7;6%) participants developed dysmenorrhoea at age 12-13 years, and 247 (19&#xb7;0% of those with dysmenorrhoea) reported severe dysmenorrhoea. Non-painful somatic symptoms were prospectively associated with future dysmenorrhoea (odds ratio [OR] 1&#xb7;17 [95% CI 1&#xb7;04-1&#xb7;32]; p=0&#xb7;0070), whereas anxiety or depression, sleep disturbances, and attention problems were not. Sleep disturbances were prospectively associated with menstrual pain interference (&#x3b2; coefficient 0&#xb7;16 [95% CI 0&#xb7;01-0&#xb7;31]). Advanced pubertal status at ages 9-10 years was prospectively associated with risk of dysmenorrhoea 3 years later (OR 1&#xb7;79 [95% CI 1&#xb7;47-2&#xb7;17]; p<0&#xb7;0001), as was lower income-to-needs ratio (0&#xb7;96 [0&#xb7;93-1&#xb7;00]; p=0&#xb7;031). Black (1&#xb7;38 [1&#xb7;05-1&#xb7;83]; p=0&#xb7;024) and Hispanic (1&#xb7;34 [1&#xb7;03-1&#xb7;68]; p=0&#xb7;010) young females were at a significantly greater risk of experiencing dysmenorrhoea than were White and non-Hispanic young females, respectively. INTERPRETATION: Sociodemographic characteristics and clinical symptoms present before menarche might help to identify at-risk individuals for dysmenorrhoea before pain becomes a lifelong issue. FUNDING: The National Institute of Nursing Research, the National Institute of Diabetes and Digestive and Kidney Diseases, and the Eunice Kennedy Shriver National Institute for Child Health and Human Development.

Humans↗
PubMed2026

Human iPSC-EV-loaded nanofiber stent coatings accelerate vascular repair by enhancing EGFR/HIF-1&#x3b1; signaling and suppressing ROCK1-mediated remodeling.

Arterial disease management is shifting from antiproliferative drug-eluting stents toward approaches that restore endothelial function and modulate smooth muscle cell (SMC) behavior. Stem cell-derived extracellular vesicles (EVs) carry miRNAs that promote endothelial proliferation and migration while restraining aberrant SMC growth and inflammation. Here, human induced pluripotent stem cell (iPSC)-derived EVs were collected by ultracentrifugation and incorporated into 50:50 poly (lactic-co-glycolic acid) (PLGA 503) core-shell nanofibrous membranes, which were fabricated as stent coatings for sustained release to overcome rapid clearance and poor tissue retention. EVs derived from three independent iPSC lines all enhanced tube formation in human umbilical vein endothelial cells (HUVECs) under hypoxic and serum-starved conditions and revealed a trend toward reduced platelet-derived growth factor-BB (PDGF-BB)-induced smooth muscle cell (SMC) migration. The fabricated core-shell nanofibers enabled sustained EV release, maintaining therapeutic efficacy for 28 days. Small RNA sequencing (NGS) analysis demonstrated that EVs from these independent iPSC lines shared miR-148a-3p and members of the miR-92 family, which collectively accounted for more than 75% of the reads within the 25 top-expressed miRNA set. In vitro, iPSC-EVs enhanced HUVEC proliferation and survival signaling by downregulating the negative regulators ERRFI1 and VHL, which are specific targets of miR-148a-3p and the miR-92 family, thereby activating the EGFR and HIF-1&#x3b1; axes and driving downstream ERK1/2 and VEGF expression under hypoxic and serum starvation stress conditions. Concurrently, iPSC-EVs prevented PDGF-BB-induced SMC phenotypic switching by downregulating ROCK1, a target of miR-148a-3p, thereby inhibiting downstream AKT and ERK signaling and preserving contractile markers while suppressing the synthetic phenotype. In vivo, the iPSC-EV-functionalized scaffolds significantly accelerated re-endothelialization and inhibited neointimal hyperplasia, evidenced by the upregulation of angiogenic factors (VEGF, CD31) and the concurrent suppression of pathological remodeling markers (&#x3b1;-SMA, MMPs) and inflammatory cytokines (IL-6, TGF-&#x3b2;1). Therefore, iPSC-EVs enriched with specific miRNAs and delivered via PLGA 503 core-shell nanofibers promote endothelial repair while suppressing SMC overgrowth, providing a promising strategy for vascular healing.

Core-shell nanofibers↗
PubMed2026

[Pathogenicity analysis and prenatal genetic counseling for five Chinese pedigrees harboring a hemizygous c.-32C>G variant of FGF13 gene].

OBJECTIVE: To explore the pathogenicity and prenatal counseling strategies for five Chinese pedigrees harboring a hemizygous c.-32C>G (NM_001139500.2) variant of fibroblast growth factor 13 (FGF13) gene. METHODS: Five Chinese pedigrees found to carry a hemizygous c.-32C>G variant of the FGF13 gene at the Prenatal Diagnosis Center of Henan Provincial People's Hospital between January 2024 and January 2025 were selected as study subjects. The pedigrees had undergone prenatal diagnosis for a family history of genetic disorders, abnormal fetal ultrasound findings, or advanced maternal age. A retrospective analysis was carried out, wherein clinical data for all members of the pedigrees were obtained through the medical records system and outpatient visit system. Peripheral blood samples were collected from all pedigree members, and amniotic fluid samples were obtained from the probands. Following extraction of genomic DNA, prenatal diagnosis was performed using chromosomal microarray analysis (CMA) and trio whole-exome sequencing (trio-WES). Sanger sequencing was used to determine the carrier status for the candidate variant, and Mini-Mental State Examination (MMSE) was used to assess the cognitive function of hemizygous individuals carrying the FGF13 gene c.-32C>G variant. Pathogenicity of candidate variant was assessed based on guidelines from the American College of Medical Genetics and Genomics (ACMG). This study was approved by the Medical Ethics Committee of the hospital (Ethics No.: 2021-171). RESULTS: CMA and trio-WES revealed no pathogenic variants in all probands, whilst trio-WES and Sanger sequencing had identified 11 male individuals carrying a hemizygous c.-32C>G variant of the FGF13 gene from the five pedigrees, which included six adult males, a young boy, and four fetuses. One fetus had undergone termination of pregnancy due to hydrocephalus, one was born pre-term at 34+1 weeks of gestation owing to maternal hypertension, and other two were delivered at full term. Follow-up of the survived males revealed no phenotypic manifestations related to language or intellectual impairment. Among these, three adult males underwent the MMSE assessment, all of whom showed normal cognitive function. Search of the gnomAD database suggested the carrier frequency of FGF13 c.-32C>G variant in the East Asian population to be 0.125%, with 11 hemizygous males documented. Three male patients harboring the variant showed severe intellectual disability. Both in vitro and in vivo studies suggested that it could reduce the translation levels of FGF13 protein. Based on the ACMG guidelines, it was classified as variant of uncertain significance (BS4+PS3_Supporting). CONCLUSION: There is insufficient evidence to classify the FGF13 c.-32C>G as a pathogenic variant in clinical practice, and its presence should not be considered an indication for pregnancy termination due to major birth defects.

Adult↗
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↗