SEARCH · Search PubMed

Results for “Models, Molecular”

Search indexed PubMed citations on genomics, clinical trials, systematic reviews and public health. Explore titles, authors and supplied subject terms, then open the PubMed record.

SourceResearch connectionFrom yearThrough year

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

1,088 records · Page 19Linked to original sources

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

Effects of rumen fluid transplantation on longissimus dorsi muscle development in Xizang sheep: An association analysis based on transcriptomic and serum metabolomic profiles.

This study aimed to investigate the effects of rumen fluid transplantation (RFT) on the growth and development of the longissimus dorsi muscle in female Xizang sheep. After RFT, muscle lightness differed significantly between the two groups, with the LDC group showing significantly higher lightness than the LDT group. In contrast, no significant differences were observed between groups in other muscle phenotypic traits, including drip loss, pH, cooking loss, shear force, redness, and yellowness. Antioxidant-related indices (SOD, GSH-PX, MDA, CAT, and T-AOC) also showed no significant differences between groups. Histological analysis revealed that muscle fiber length, width, and density were significantly greater in the experimental group than in the control group. Transcriptomic analysis identified 515 differentially expressed genes (DEGs), of which 419 were downregulated. KEGG analysis indicated that genes involved in muscle development-related pathways, such as cell adhesion and the PI3K-Akt signaling pathway, were predominantly downregulated. Key serum metabolites (L-kynurenine, IPA, allantoin, and propionylcarnitine) showed highly significant positive correlations with muscle fiber growth indices. In contrast, metabolites such as l-carnitine, acetylcarnitine, and citrulline were negatively correlated with muscle fiber growth, but positively correlated with the expression of muscle structure-related genes (COL11A1 and EFNA5) and with meat lightness. Overall, this study provides new insights into the potential molecular basis by which RFT influences muscle growth and development. However, the mechanisms by which RFT affects muscle development and meat quality-related traits remain unclear and warrant further investigation.

Animals↗

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

Integrin-Linked Kinases 1, 4, and 5 participate in cell wall-mediated innate immunity to leaf and root pathogens.

The cell wall integrity (CWI) pathway is triggered by plasma membrane-localized receptors in plant cells and serves to orchestrate responses to cell wall damage by initiating compensatory changes under stressful environments. The essential role of CWI maintenance as part of plants' interactions with pests or pathogens and during growth is well known. Nevertheless, CWI pathways remain to be fully characterized. Here, we show that altered Integrin-Linked Kinase 1 (ILK1) expression causes widespread defects in the transcriptional program activated by the bacterial elicitor flg22, primarily in genes associated with cell wall integrity and immunity. These transcriptional deficiencies are recapitulated in mutant lines with altered ILK4 or ILK5 expression. Analysis of molecular and cellular defenses in ilk mutants revealed reduced callose accumulation in leaves treated with bacterial (elf18) and plant (pep1) elicitors and increased pathogen susceptibility. Histochemical analysis of cell-wall-associated staining across diverse cells and organs of ilk mutants revealed modified lignin-associated patterns in the root xylem and altered calcofluor staining patterns in the seed coat. All ilk mutants exhibited altered root morphology due to mechano-touch and high-NaCl stress. Based on these results, we propose that ILKs contribute to pathways connecting elicitor-triggered immune signaling with cell-wall-associated stress responses and that ILK-related defense functions may extend to the cotton root-nematode interaction, while the mechanism remains to be elucidated.

Cell Wall↗

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↗

PubMed2026

RPLP0 drives diffuse large B-cell lymphoma cell proliferation through reactive oxygen species-dependent AKT/mTOR activation and inhibition of stress-induced autophagy.

Diffuse large B-cell lymphoma (DLBCL) is a common, aggressive subtype of non-Hodgkin lymphoma with poor outcomes. Identifying the primary molecular causes of DLBCL remains key. The present study examined the function of ribosomal protein lateral stalk subunit P0 (RPLP0) in DLBCL pathogenesis. The Cancer Genome Atlas-DLBCL and GSE12453 datasets overlapping differentially expressed genes were identified. Hub genes were identified via protein-protein interaction network analysis. DLBCL cells were subjected to functional tests following RPLP0 overexpression or knockdown. Reverse transcription-quantitative PCR, western blotting, flow cytometry, transmission electron microscopy, colony formation assay and biochemical analysis were among the tests performed. N-acetylcysteine (NAC), rapamycin (RAPA) and 3-MA were among the medication therapies. In the DLBCL datasets, six ribosome-associated genes were differentially expressed. RPLP0 knockdown inhibited the proliferation of DLBCL cells and caused G2-phase arrest, without impacting apoptosis. Thioredoxin, heat shock protein family A member 1A and heat shock protein family B member 1 expression was downregulated by RPLP0 knockdown, which also increased the NAD+/NADH ratio, promoted reactive oxygen species (ROS) accumulation and caused mitochondrial membrane potential depolarization. Meanwhile, 3-MA reversed the effects of RPLP0 knockdown, which encouraged LC3-II accumulation, autophagy-related gene 5 (ATG5) overexpression and an increase in autophagic vesicles. Autophagy-related indicators were decreased, and AKT/mTOR phosphorylation was increased by RPLP0 overexpression, which RAPA inhibited. NAC therapy preserved the viability of RPLP0-silenced cells, restored p-AKT/p-mTOR levels and restored normal LC3 and ATG5 expression. These findings suggest that RPLP0 regulates stress-induced autophagy through ROS-dependent AKT/mTOR signaling and may represent a potential therapeutic target for DLBCL.

AKT/mTOR signaling pathway↗

PubMed2026

A Pilot Study: Developing a Lactating Dairy Goat Model to Study Staphylococcus aureus Mastitis in Women.

INTRODUCTION: Lactational mastitis is common in lactating women, with Staphylococcus aureus as the most commonly isolated agent associated with infectious lactational mastitis. Currently, there are no evidence-based guidelines for antimicrobial treatment due to barriers in obtaining pharmacokinetic data from lactating women. To overcome this barrier, a suitable large animal model is needed. Goats are an ideal translational model for human mastitis due to their anatomical and physiological similarity to humans. The objective of this pilot study was to assess if goats would develop clinical mastitis following intramammary inoculation with a clinical human isolate of S. aureus with the goal of establishing an alternative in vivo model for future research. The hypothesis was that the infected mammary gland half would show similar clinical signs to women with mastitis and demonstrate a similar local immune response when compared to the control mammary gland half. METHODS: One half of the mammary gland of two healthy lactating does was inoculated with a clinical human isolate of S. aureus. The other half of the mammary gland was sham inoculated with sterile buffered saline. Physical examinations, mammary gland assessments, and sterile milk samples were collected every 12 hours post inoculation. At 96 hours post inoculation, the goats were euthanized, and the mammary glands were examined for pathological changes. RESULTS: Goats did not develop systemic signs of disease following inoculation. Focal infected mammary gland changes included warmth, swelling, redness, discoloration, and reduced milk production; the other mammary gland half remained normal throughout the study period. S. aureus was enumerated from only the infected mammary gland half. The microscopic findings of the infected half showed neutrophilic inflammation and cell necrosis consistent with acute mastitis. DISCUSSION: This pilot study demonstrated lactating does can develop clinical signs like those observed in women. Goats have the potential to be a promising animal model to study infectious lactational mastitis.

Animals↗

PubMed2026

How the Social Context and Peer Helping Contribute to Better Alcohol Outcomes Among Sober Living House Residents: Mediation Analyses.

BACKGROUND: Sober Living Houses (SLHs) adopt a social model approach, which emphasizes peer helping. Although SLHs appear to be effective, little is known regarding why. This longitudinal study examined whether higher SLH social model adherence produces better resident outcomes by increasing resident helping. METHODS: Baselines were conducted with 205 residents entering 28 SLHs, with follow-ups through 6 months. Measures included 1-month perceived SLH social model adherence; 2-month help given to and received from SLH residents; and 6-month alcohol use and severity. Analyses were lagged, multivariate mediation models accounting for clustering within SLH. Separate models examined help given and received for each outcome, yielding four model tests. RESULTS: The hypothesized model was unsupported, with all four tests showing nonsignificant indirect effects. However, exploratory post-hoc tests showed significant indirect effects between higher 1-month resident helping and better 6-month alcohol outcomes via higher 1-month SLH social model adherence. Effects held across three of four model tests. CONCLUSIONS: Results suggest that residences adhering to social model principles do not achieve better outcomes by stimulating helping, but rather that more resident helping may foster a supportive SLH social environment, which itself drives better outcomes. Thus, residences might emphasize both resident helping and social model principles.

Sober living↗

PubMed2026

Predicting ACL injury risk in athletes: A systematic review of machine learning-based models.

BACKGROUND: Early ACL injury risk identification in athletes is essential. This systematic review examines machine learning (ML) models for predicting ACL injuries, evaluating their methodological quality, performance, and reliability. METHOD: A comprehensive electronic search was conducted across PubMed, Scopus, Web of Science, and IEEE Xplore databases, supplemented by Google Scholar for grey literature, covering articles published between January 1, 2015, and August 30, 2025. Eligible studies were appraised using the Prediction Model Study Risk of Bias Assessment Tool (PROBAST) for methodological quality and risk of bias, and the Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD) guidelines for quality of evidence. RESULTS: Ten studies were included. PROBAST showed eight studies had moderate risk of bias and two low risk. TRIPOD found only two studies met quality criteria. ML models included logistic regression (n = 5), support vector machines (n = 4), k-nearest neighbor (n = 3), decision trees (n = 3), random forests (n = 5), neural networks (n = 2), linear discriminant analysis (n = 1), and pre-trained CNNs (n = 1). AUC ranged from 0.63 to 0.98. Accuracy (reported in six studies) ranged from 26% to 95%; however, these values should be interpreted with caution due to the absence of confidence intervals, lack of class imbalance handling, and limited external validation across studies. Tree-based ensemble methods such as random forest achieved competitive accuracy (74-86%), while SVM, a non-ensemble classifier, reported accuracy ranging from 71% to 95%; however, the highest values were obtained in studies with notably small sample sizes (n = 12 to n = 39), raising concerns about overfitting and generalizability. CONCLUSION: Current ML algorithms show promise for identifying athletes at high ACL injury risk and detecting relevant risk factors. Although study quality was generally satisfactory, future research should prioritize external validation and model interpretability to support clinical translation.

Humans↗

PubMed2026

Diagnostic performance of machine learning models for malignant and non-malignant pleural effusion: Systematic review and meta-analysis.

BACKGROUND: Accurately distinguishing malignant pleural effusion (MPE) from non-malignant pleural effusion is clinically important, but the generalisability and methodological quality of machine-learning (ML) models remain uncertain. METHODS: We searched eight databases to 23 April 2026. Diagnostic performance was pooled using random-effects and Reitsma bivariate models, and study quality was assessed using PROBAST+AI. RESULTS: Forty-two studies were included; 17 contributed to the AUC meta-analysis and 14 to the bivariate analysis. The pooled AUC was 0.90 (95 % CI 0.85-0.94; 95 % prediction interval 0.62-0.98), with sensitivity of 0.80 (95 % CI 0.77-0.83) and specificity of 0.87 (95 % CI 0.79-0.92). Only nine studies reported external, temporal or independent validation. Externally validated studies had a lower pooled AUC than studies without external validation (0.83 vs 0.92), with lower specificity observed in the two externally validated studies contributing sensitivity and specificity data. All 42 development assessments had high overall quality concerns, and all 42 model evaluations were judged at high risk of bias. CONCLUSIONS: ML models showed good apparent accuracy for distinguishing MPE from non-MPE, but the evidence was limited by substantial heterogeneity, high risk of bias and scarce external validation. The pooled estimates reflect the average performance of different selected models rather than the expected accuracy of a single clinical test. ML models should be regarded as adjuncts to existing diagnostic pathways until they are confirmed by rigorous multicentre prospective external validation and clinical-impact studies.

Humans↗

PubMed2026

Comprehensive multi-post-translational modifications profiling reveals age-associated remodeling in skeletal muscle.

Sarcopenia, characterized by the progressive loss of skeletal muscle mass and function, is a major hallmark of aging. Post-translational modifications (PTMs) play essential roles in regulating protein activity and cellular homeostasis; however, how multiple PTMs are remodeled during skeletal muscle aging remains incompletely characterized. Here, we performed comprehensive multi-layered proteomic profiling of skeletal muscle from young (3-month-old) and aged (24-month-old) mice, systematically quantifying the global proteome together with five major PTMs: acetylation, phosphorylation, N-glycosylation, O-glycosylation, and ubiquitination. In total, we identified 5 337 proteins and mapped thousands of PTM sites, generating an integrated atlas of age-associated proteomic and PTM remodeling in skeletal muscle. Pathway enrichment analyses revealed distinct modification-specific patterns: acetylation and phosphorylation were predominantly associated with metabolic and mitochondrial-related pathways; N-glycosylation was enriched in immune- and secretory pathway-related processes; O-glycosylation was associated with muscle contraction-related pathways; and ubiquitination was preferentially linked to cytoskeletal organization in muscle cells. Correlation analyses further uncovered diverse association patterns among different PTMs across protein- and modification-level datasets. Phosphorylation and ubiquitination exhibited consistent positive associations, whereas acetylation and ubiquitination showed both inverse and concordant co-variation patterns across subsets of proteins. Phosphorylation and O-glycosylation displayed heterogeneous association patterns across different proteins, and acetylation and phosphorylation demonstrated positive correlations with distinct age-associated directional changes across protein subsets. Together, these results provide a comprehensive, multi-dimensional view of age-associated remodeling of the skeletal muscle proteome and multiple PTM layers, offering a valuable resource for understanding molecular alterations accompanying muscle aging and sarcopenia.

Animals↗

PubMed2026

Nimodipine in animal models of demyelination relevant to multiple sclerosis: a systematic review.

BACKGROUND: Multiple sclerosis (MS) is the most common inflammatory neurodegenerative disease in which axonal injury, neuronal death, and demyelination occur. Treatment for MS relapses remains limited, which alleviates acute loss of function but has no impact on long-term disability. This study aimed to perform a systematic review of the effects of nimodipine on experimental demyelination models, including experimental autoimmune encephalomyelitis (EAE) and Cuprizone models in rodents. METHODS: This study was conducted following the PRISMA statement. A systematic search was performed in PubMed, Scopus, the Cochrane Library, and Google Scholar. The primary outcome was EAE clinical disease severity (peak clinical score and/or cumulative disease burden). Secondary outcomes included relapse activity (when reported), histological myelin outcomes, oligodendrocyte lineage markers, neuroaxonal injury markers, and inflammatory readouts. Risk of bias was assessed using the SYRCLE tool. RESULTS: Out of 4660 results, 5 studies were included in the systematic review (four EAE studies and one cuprizone model). Nimodipine was administered using heterogeneous regimens (oral, intravenous, intraperitoneal, subcutaneous, or osmotic pump delivery; 1-30 mg/kg/day). The included studies reported the variable effects of nimodipine on relapse-related outcomes, myelination, inflammatory processes, and neuroprotection in the EAE model of MS. Across EAE studies, nimodipine generally reduced clinical disease severity or cumulative burden, although relapse-related outcomes were inconsistent. CONCLUSIONS: Preclinical evidence suggests that nimodipine may attenuate disease severity and demyelination and may promote repair-related processes in rodent models relevant to MS. However, to evaluate the clinical applicability of nimodipine in MS patients, well-powered, transparently reported preclinical replication and early-phase clinical studies are required before clinical translation.

Animals↗

PubMed2026

Modelling peak microbial pollution events caused by combined sewer overflows in a source-to-sea system.

Predicting peak microbial pollution events in downstream coastal bathing waters caused by combined sewer overflows (CSOs) is essential for protecting public health. In urban areas, wastewater effluents, CSOs, and surface runoff can contribute to elevated microorganism loads to downstream waters. These pressures are likely to be intensified by growing population density and more frequent heavy rainfalls due to climate change. This study developed a process-based model to simulate Escherichia coli (E. coli) emissions, transport, and fate from the initial sources to coastal beaches. A three-year retrospective simulation (2017-2019) shows that E. coli concentrations in CSO discharges varied widely across the catchment (4.6 - 7.3 (log10 CFU 100 ml-1)). 99th percentile E. coli concentrations (4.0 (log10 CFU 100 ml-1)) at the inland water outlet were dominated by local CSO emissions, whereas 90th percentile E. coli concentrations (3.6 (log10 CFU 100 ml-1)) reflected cumulative upstream contributions from both CSO and effluent emissions. With the simulation accuracy of 89%, the model reliably reproduced the E. coli dynamics on the downstream beach and showed strong performance in representing peak concentrations based on Complementary Cumulative Distribution Function (CCDF) analysis. The process-based model enables quantitative tracking of source contributions and identification of pollution hotspots, providing support for mitigation measures. The study lays down a source-to-sea modelling framework for representing pollution transport across the aquatic continuum and provides a transferable tool for microbial pollution forecasting and climate adaptation planning.

Climate projection↗

PubMed2026

Genomic characterization and pathogenicity of ruminant Listeria monocytogenes isolates in a murine oral infection model.

Listeria monocytogenes is a major foodborne pathogen; its ruminant isolates display zoonotic characteristics, causing similar clinical signs in humans, including abortion and encephalitis. However, data on whole genome sequencing and pathogenicity of ruminant L. monocytogenes isolates remain sparse. This study aimed to analyze the genotypic characteristics of L. monocytogenes isolates from ruminants with listeriosis. Furthermore, we assessed the in vivo pathogenicity of four ruminant L. monocytogenes isolates, characterized via whole-genome sequencing-based genetic clustering, in orogastrically inoculated mice. The isolate LM18 (serotype 1/2b, ST224, SL6178) had the lowest lethal dose compared to the other three isolates including previous hypervirulence type (serotype 4b, ST1, SL1) and caused secondary bacteremia in lungs, with sustained bacterial loads in the spleen and liver. Genomic (listeria pathogenicity island -1 and -3) and virulence gene (actA and llsX) mutation analyses associated with virulence suggested from well-recognized studies could not elucidate the virulence of the isolates. SSI-1, which only exists in the isolate LM18 (serotype 1/2b, ST224, SL6178), may help L. monocytogenes survive in the gastrointestinal environment, thereby affecting its virulence. Further research should investigate the role of SSI-1 in the pathogenicity of L. monocytogenes. Moreover, additional studies utilizing larger datasets of ruminant isolates are required to validate our genotypic characterization and to obtain a comprehensive picture of further genotypic differences crucial for L. monocytogenes pathogenicity.

Animals↗