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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

Prevalence of Theileria luwenshuni in goats (Capra hircus) on Tarama Island, Okinawa, Japan.

Caprine theileriosis is an economically important tick-borne disease caused by various Theileria species, particularly Theileria lestoquardi, Theileria luwenshuni, and Theileria uilenbergi, in goats (Capra hircus). Goat farming plays an economically and culturally important role on Tarama Island, Okinawa, Japan. Because goats on the island are mainly managed under an extensive grazing system, tick infestation is common. However, Theileria infections have not previously been investigated in goats on Tarama Island. To address this, archived DNA samples prepared from blood collected from 44 goats on Tarama Island were screened using a universal PCR assay targeting 18S rRNA sequences of Theileria and Babesia species. Two DNA samples were positive, and sequencing analysis of the amplicons identified T. luwenshuni. To further investigate the epidemiology of T. luwenshuni on Tarama Island, blood samples were subsequently collected from 96 goats across 19 farms. From each blood sample, a thin blood smear was prepared and genomic DNA was extracted. Microscopic examination of Diff-Quik-stained smears detected intraerythrocytic Theileria-like organisms in 35 (36.5%) goats. In addition, screening of DNA samples using a newly developed T. luwenshuni-specific PCR assay detected 77 (80.2%) positive goats, and the subsequent sequencing analysis confirmed the PCR results. Given that T. luwenshuni can cause severe disease in small ruminants, our findings highlight the importance of managing T. luwenshuni infection in goats on Tarama Island.

Animals↗
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

Beyond predictive performance: A systematic review and critical methodological appraisal of AI/ML and conventional modelling strategies in breast, colorectal, and pancreatic Cancer.

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n = 907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n = 35), colorectal cancer (n = 21), and pancreatic cancer (n = 9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans↗
PubMed2026

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

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

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

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

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

Metagenome-scale modeling to assess microbiome metabolic complementarity for precision microbiota transplantation therapies.

Fecal microbiota transplantation (FMT) holds therapeutic promise beyond recurrent Clostridioides difficile infection, but clinical outcomes remain unpredictable and donor-selection strategies remain limited, in part because the role of donor‒recipient metabolic interactions in shaping the post-FMT community remains poorly understood. Here, we leverage metagenome-scale metabolic modeling to quantify metabolic niche complementarity between donor and recipient microbiomes and predict post-FMT community composition. Using MICOM-derived metabolic models, we show that donor genomes whose metabolic flux profiles are more dissimilar from the recipient community colonize at significantly higher rates in a murine FMT model. In a human IBS trial, the same metric predicted post-FMT community composition via leave-one-out cross-validation and captured known disease-associated alterations in short-chain fatty acid, sulfur, and gas metabolism. We then performed 2,548 in silico FMT simulations between IBS-D/M patients and donors from the OpenBiome biobank to evaluate personalized donor screening, identifying super-donors characterized by high taxonomic diversity, broad metabolic niche coverage, and community interaction networks dominated by cross-feeding rather than competition. Together, these results support metabolic niche complementarity as a potential determinant of post-FMT community composition and provide a mechanistic basis for evaluating donor-recipient metabolic compatibility. This framework offers a scalable approach for generating testable hypotheses for personalized donor selection.

Fecal Microbiota Transplantation↗
PubMed2026

Meta-analysis of source identification and apportionment in soil: A systematic review of analytical procedures, receptor modeling, and environmental applications.

Soil pollution poses significant risks to ecosystems and human health, necessitating accurate source identification and apportionment to guide mitigation strategies. This systematic review evaluates the application of Positive Matrix Factorization (PMF) and other receptor models in soil pollution studies, focusing on analytical procedures, tracer indicators, and environmental applications. This review aims to provide a comprehensive framework for conducting soil source apportionment studies, aiding policymakers in designing effective, region-specific environmental management strategies by compiling global trends and methodological insights. The study addresses sampling protocols, emphasizing representativeness and quality control. Data from 500 peer-reviewed publications highlight the dominance of research in China, Eastern Europe, and South Asia, with agricultural soils being the most frequently studied. Key findings reveal that traffic emissions (20.8 %) and industrial activities (19.4 %) are the primary global contributors to soil contamination, with regional variations such as coal combustion in cold climates and agricultural inputs in developing regions. Policy recommendations include stricter industrial regulations, sustainable agricultural practices, and targeted remediation efforts based on source-specific risks.

Soil Pollutants↗
PubMed2026

Risk Factors and Predictive Model for Postoperative High Myopia in Children Undergoing Congenital Cataract Surgery With Intraocular Lens Implantation.

PURPOSE: To identify risk factors associated with the development of high myopia following congenital cataract surgery and to establish a robust predictive model. DESIGN: Retrospective clinical cohort study. SUBJECTS: This retrospective study included 106 pediatric patients who underwent congenital cataract surgery with primary IOL implantation (mean follow-up 8.19 years). The model was externally validated in an independent cohort of 72 patients with a mean follow-up of 7.83 years. METHODS: Preoperative and postoperative ocular biometric parameters were collected. Risk factors for postoperative high myopia were analyzed using Cox proportional hazards regression, which served as the basis for model construction. The predictive performance of the model was rigorously evaluated for discrimination and calibration. Discriminative ability was quantified using Harrell's C-index and the area under the receiver operating characteristic curve (AUC). Model calibration was assessed via calibration plots by comparing predicted probabilities with actual observed outcomes. Internal validation was performed using a bootstrapping method (500 iterations) to ensure model stability and adjust for potential overfitting. RESULTS: An initial postoperative refraction of <+0.75D, and a higher IOL Power to Axial length Ratio (IOL/AL ratio) were identified as significant risk factors for the development of postoperative high myopia. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. The predictive model demonstrated robust performance, achieving a C-index of 0.711 (internal validation C-index: 0.713). The area under the receiver operating characteristic curve (AUC) values for predicting high myopia at 5 and 10 years were 0.858 and 0.745, respectively. Furthermore, calibration curves demonstrated excellent agreement between the predicted and observed outcomes throughout the follow-up period. In external validation, the model achieved a C-index of 0.825, 5-year AUC of 0.833, and 10-year AUC of 0.713. CONCLUSIONS: Our analysis established that initial postoperative refraction <+0.75D, and an elevated IOL/AL ratio are key determinants of high myopia risk following surgery. Shorter preoperative axial length was associated with a greater magnitude of postoperative myopic shift. This predictive framework provides clinicians with a practical tool to optimize preoperative IOL selection and identify high-risk infants who require vigilant myopia prevention and balanced amblyopia management.

Humans↗
PubMed2026

Development and validation of a comprehensive prognostic model for 28-day ICU mortality in non-traumatic subarachnoid hemorrhage: an analysis based on the MIMIC-IV database.

BACKGROUND: Due to the complex pathophysiology of non-traumatic subarachnoid hemorrhage (SAH), accurate risk prediction remains a challenge. Our aim is to develop and validate a comprehensive prognostic model that integrates demographic characteristics, vital signs, laboratory parameters, and more, to provide clinical decision-making support in real-world practice. METHODS: We conducted a retrospective cohort study of 785 Non-traumatic subarachnoid hemorrhage patients. The cohort was randomly divided into a training set (n&#xa0;=&#xa0;549) and a validation set (n&#xa0;=&#xa0;236). Feature selection was performed using LASSO regression, followed by backward stepwise Cox regression for optimization. A nomogram was constructed based on independent predictive factors, and model performance was assessed using discrimination, calibration, and decision curve analysis. To prevent immortal-time bias, all predictors were anchored to a fixed early (first-24-hour) measurement window, treatment variables were modelled as binary indicators rather than cumulative exposures, and a five-model sensitivity analysis with baseline-severity adjustment was performed. RESULTS: The development of our model followed a systematic approach: first, 15 potential predictive factors were selected via LASSO regression, which were then refined to 12 independent predictors using backward stepwise Cox regression. The final predictive factors included: Ventilation, AHT, Nimodipine 60&#xa0;mg, Age, SAPS.II, Input amount, Calcium total, Platelet count, White blood cells, Anion gap, pH, and Chloride. The integrated model demonstrated excellent predictive ability for 7-day, 14-day, and 21-day mortality in both the training set (AUC: 0.972, 0.934, 0.898) and the validation set (AUC: 0.968, 0.948, 0.911). Calibration curves and decision curve analysis confirmed the model's reliability and clinical utility across different time points. We constructed a nomogram for individualized risk prediction. Univariate Kaplan-Meier survival analysis demonstrated significant stratification of survival outcomes by each predictor, while restricted cubic spline analysis revealed non-linear relationships between continuous variables and mortality risk. Random survival forest analysis identified the top three predictive factors (Nimodipine 60&#xa0;mg, Ventilation, AHT) and compared them with our full 12-variable model, confirming superior performance of the integrated model at all time points. At the 28-day primary endpoint, the model achieved a time-dependent AUC of 0.898 (training) and 0.904 (validation); after restricting predictors to the early baseline window, the leakage-controlled model retained good discrimination (validation C-index 0.803). CONCLUSIONS: Our ICU 28-day mortality prognosis model demonstrated robust performance in predicting ICU 28-day mortality in non-traumatic subarachnoid hemorrhage. The model, through the nomogram, provides individualized risk assessment, aiding clinical decision-making and patient stratification.

Humans↗
PubMed2026

Effects of phytosterols supplementation on hepatic lipid metabolism and metabolic outcomes in obese rodent models: a systematic review and meta-analysis.

This study aimed to synthesize and quantitatively assess the available evidence on the effects of phytosterol supplementation on hepatic lipid metabolism and obesity-related metabolic outcomes in obese rodent models, integrating biochemical, histological, and molecular evidence. A systematic search was conducted in electronic databases (PubMed, EMBASE, and Web of Science). Data on study design, population, intervention, outcomes, and risk of bias were extracted and analyzed. A quantitative meta-analysis was performed. Meta-analysis showed reductions in body weight, serum triglycerides, total cholesterol, LDL-C, VLDL-C, glucose, liver weight, hepatic cholesterol, hepatic triglycerides, and nonalcoholic fatty liver disease activity score. No significant changes were observed for adiposity index, HDL-C, insulin, or hepatic expression of PPAR&#x3b1;, FAS, and SREBP1c. Conversely, CPT1A expression was significantly increased following PS supplementation. Subgroup analyses indicated that the beneficial effects on lipid and hepatic outcomes were generally consistent across rodent species (mice, rats, and hamsters), obesity induction models, and routes of administration, although the magnitude of responses varied between strains, with C57BL/6 mice showing more pronounced metabolic improvements. Additional analyses suggested that treatment duration and phytosterol composition may modulate specific outcomes, whereas dose-response meta-regression identified dose-dependent associations for serum and hepatic cholesterol, and PPAR&#x3b1; expression in dietary supplementation studies. Overall, the available preclinical evidence suggests that phytosterol supplementation may improve several metabolic and hepatic outcomes in rodent models of obesity. However, the substantial heterogeneity across studies highlights the need for standardized experimental protocols and future clinical studies before these findings can be translated to human health.

Animals↗
PubMed2026

Glucocorticoids and placental 11&#x3b2;HSD2 - A systematic review of human studies and animal models.

CONTEXT: Elevated prenatal glucocorticoid (GC) exposure is linked to adverse offspring outcomes. The placental enzyme 11&#x3b2;-hydroxysteroid-dehydrogenase-type-2 (11&#x3b2;HSD2) protects the fetus by converting maternal derived cortisol to inactive cortisone. Although in vitro studies suggest GC mediated upregulation of 11&#x3b2;HSD2, in vivo evidence remains inconclusive. METHODS: PubMed, Embase, and PsycInfo were searched in October 2024 for human and mammalian animal studies on endogenous or exogenous GCs during pregnancy and associations with placental 11&#x3b2;HSD2 (mRNA, protein, activity, gene methylation). Narrative synthesis was conducted due to heterogeneity precluding meta-analysis. RESULTS: Eighteen studies (eight human, ten animal populations) met inclusion criteria. Exogenous GC exposure was associated with modifications in placental 11&#x3b2;HSD2 expression in animal models, with effects varying by substance, timing, and species. Dexamethasone trended towards increased expression in rodents, whereas betamethasone increased expression in non-human primates but not rodents. Human studies on endogenous GCs showed inconsistent associations with 11&#x3b2;HSD2 changes. In asthmatic pregnancies, moderate inhaled GC-use maintained enzyme activity compared to untreated patients. No convincing sex-specific trend emerged. CONCLUSIONS: GC exposure alters placental 11&#x3b2;HSD2 in a substance- and species-specific way; translational relevance remains limited based on current literature. Future studies should employ technological advances and include GC-sensitive biomarkers to clarify mechanisms of maternal-fetal stress transmission.

Female↗