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The effect of monetary versus point-based rewards on effort-cost decision making in individuals at clinical high risk for psychosis.

OBJECTIVE: The dissemination of inexpensive computerized behavioral tasks indexing amotivation may enhance the assessment of clinical high risk (CHR) across settings. However, the impact of varying reward value in such tasks is unclear. If point-based rewards engage participants, this could improve the scalability of computerized assessments. We tested how point-based rewards versus money impacted effort-cost decision-making in CHR individuals. We further assessed how negative symptom severity and household income interacted with reward-type to impact behavior. METHODS: Participants completed the Effort Expenditure for Reward Task (EEfRT). Participants were randomly assigned to receive either money or points for their performance during the EEfRT. Data from a large sample of CHR (N = 233) individuals and healthy controls (HC; N = 157) were collected. RESULTS: Across diagnostic groups, we observed heightened effort expenditure when money was used as a reward (b = 0.13, p = 0.018). We did not find an interaction of CHR status (b = 0.07, p = 0.845) or negative symptoms (b = 0.01, p = 0.429) with reward-type. Within CHR individuals, heightened negative symptom severity was associated with reduced expended effort (b = -0.03, p = 0.016), regardless of reward type. In an exploratory analysis, we found that individuals in the money condition with relatively high household income expended less effort during high reward, high probability trials (b = -0.24, p = 0.046). CONCLUSIONS: Across CHR and HC individuals, individuals pursuing money expended greater effort. While we did not find a group by reward type interaction, CHR individuals with heightened negative symptom severity expended less effort across trials, replicating prior work. Present findings support further study of point-based rewards in tasks indexing amotivation.

Humans

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

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

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

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

Paediatric penile length: a systematic review and meta-analysis.

OBJECTIVE: To assess geographical variation in stretched penile length among prepubertal boys and evaluate temporal trends over the past two decades, as defining reference values for genital organ size remains crucial for early identification of development disorders. METHODS: The PubMed, Cochrane and Scopus databases (no deadlines for publishing were imposed) were searched according to the Preferred Reporting Items for Systematic Review and Meta-analyses statement. Five authors independently extracted individual participant data and assessed the risk of bias. Studies with quantitative penile lengths were included; those involving congenital malformations were excluded. The review protocol was prospectively registered in the International Prospective Register of Systematic Reviews (registration number CRD42022335643). RESULTS: A total of 55 studies from 2000 to 2024 were evaluated, including data from 31&#x2009;915 boys. Pooled mean stretched penile length estimates were 3.07&#x2009;cm (95% confidence interval [CI] 2.88-3.26&#x2009;cm) for the 1-week-old boys, 3.73&#x2009;cm (95% CI 3.53-3.93&#x2009;cm) for the 1-year-old boys, 4.69&#x2009;cm (95% CI 4.49-4.88&#x2009;cm) for the 2-5&#x2009;year-old boys, and 5.43&#x2009;cm (95% CI 5.20-5.66&#x2009;cm) for the 5-10&#x2009;year- old boys. When comparing data from the 2000s to the 2020s, stretched penile length decreased by 16.2% (from 3.46 to 2.90&#x2009;cm), 16.3% (from 4.17 to 3.49&#x2009;cm), 21.5% (from 5.31 to 4.17) and 26.2% (from 6.46 to 4.77&#x2009;cm) in the 1-week-old, 1-year-old, 2-5-year-old and 5-10-year-old boys, respectively. Subgroup analysis for those aged >2&#x2009;years showed significant variations by geographical region (P&#x2009;<&#x2009;0.001). CONCLUSIONS: The present study observed large variations in penile length across geographical regions and among prepubescent boys of different ages, while also suggesting a possible decline over the past two decades.

Humans

Development and Validation of a Predictive Model for Identification of Cognitive Impairment Risk in Older Adults with Subjective Cognitive Decline&#xff1a;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

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&#x2009;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)&#x2009;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

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&#xa0;&#x3bc;M, and intracellular concentrations and cell viability were monitored over 48&#xa0;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

Multiscale Modeling Primer: Focus on Chromatin and Epigenetics.

A central challenge in modern biology is to understand how molecular interactions produce cellular and organismal functions across vast spatiotemporal scales. Nowhere is this challenge more apparent than in the study of chromatin, where meters of DNA compact into a micron-sized nucleus. How this polymer folds is a dynamic process, regulated by epigenetic modifications-chemical changes to DNA and histones that involve only a handful of atoms. These small changes cooperate to produce emergent, higher-order structures that define cellular identity and function. To explain this system, we must integrate static, high-resolution snapshots from techniques like cryo-EM with dynamic, lower-resolution data from microscopy and genomics. Multiscale computational models are essential tools that bridge these experimental gaps and reveal the mechanisms of emergent behavior. However, the communication divide between experimental biologists and quantitative modelers often hampers progress. This primer addresses that gap. It first introduces the fundamental biology of chromatin and epigenetics at an introductory level for non-biologists audiences. We then survey the landscape of computational approaches, from atomistic to systems-level models, and connect them to the experimental data that inform and validate them at an introductory level for non-computationalists. We argue that the next frontier will require us to build integrative models that can predict how molecular perturbations mechanistically alter cellular phenotypes, which will open a new era of chromatin-targeted therapeutics.

Chromatin Dynamics

Oil and gas well development and coccidioidomycosis risk in Kern County, CA, USA: a case-crossover study.

BACKGROUND: Coccidioidomycosis is an emerging fungal disease caused by inhaling Coccidioides spp spores. As spores reside in soil, activities that disturb soil and generate dust can aerosolise and transport the pathogen. The oil and gas industry has been extensively developed in some regions that are endemic for Coccidioides spp and has been associated with dust emissions. Although several adverse health outcomes have previously been associated with oil and gas development, its impact on coccidioidomycosis risk has not been investigated. We aimed to estimate the association between exposure to oil and gas well development (ie, wells in preproduction) and risk of coccidioidomycosis among residents living near new wells. METHODS: In this case-crossover study, we obtained information on reported coccidioidomycosis cases and oil and gas well development between 2007 and 2022 in Kern County, CA, USA. We then compared exposure to preproduction wells within 5 km of each individual's place of residence during both hazard (ie, the 49-139 days before case onset) and control periods using conditional logistic regression. FINDINGS: During the study period, 658&#x2009;108&#x2009;(72&#xb7;4%) of 909&#x2009;282 of Kern County residents lived within 5 km of at least one preproduction well, and 116&#x2009;020&#x2009;(12&#xb7;8%) lived within 5 km of 23 or more preproduction wells within a single 90-day period. We estimated that the odds of coccidiomycosis incidence were 12&#xb7;5% (95% CI 5&#xb7;8-19&#xb7;6) higher in the 90 days following exposure to at least one preproduction well within 5 km of an individual's place of residence and that the odds of infection increased by 0&#xb7;7% (0&#xb7;4-0&#xb7;9) for each additional preproduction well developed within this distance. INTERPRETATION: These findings support a previously-unrecognised association between the development of oil and gas wells and transmission of coccidioidomycosis, potentially driven by dust generation. Given the prevalence of oil and gas development in the study region, its impact on coccidioidomycosis incidence might be large. FUNDING: National Institutes of Health, National Science Foundation.

Journal Article

Efficacy of afoxolaner (NexGard&#xae;) for the treatment of myiasis caused by the zoonotic tumbu fly, Cordylobia anthropophaga, in domestic dogs under field conditions.

Cordylobia anthropophaga is a major cause of furuncular myiasis in dogs and humans in sub-Saharan Africa, yet evidence for pharmacological control remains limited. Current management relies mainly on mechanical larval removal, and no controlled studies have assessed the efficacy of modern antiparasitic agents. This study evaluated the curative and preventive efficacy of a single dose of afoxolaner (NexGard&#xae;) in dogs with naturally acquired C. anthropophaga infestation under field conditions and explored a possible indirect protective effect in puppies after maternal treatment. A randomized, blinded, negative-controlled field study was conducted in Samburu County, Kenya, and included 104 naturally infested dogs allocated to a treated group (n&#x202f;=&#x202f;53) or an untreated control group (n&#x202f;=&#x202f;51). Clinical evaluations were performed on Days 0, 15 (&#xb1;2), 30 (&#xb1;2), and 45 (&#xb1;2). Efficacy was assessed based on the presence of active nodules and clinical scores. Additional observational data were collected from puppies born to or nursing from treated bitches. Compared to the control group, parasiticide efficacy was 94% on Day 15 and 100% on Day 30. The number of parasite-free dogs reached 86.3% on Day 15, 100% on Day 30, and 95.3% on Day 45. Treated dogs showed a significant clinical improvement from the first post-treatment assessment, whereas infestation persisted in controls (nodules: OR = 0.003, 95% CI 0.001-0.017; skin lesions: OR = 0.010, 95% CI 0.002-0.051; lymph node: OR = 0.013, 95% CI 0.002-0.074; body condition: OR = 0.073, 95% CI 0.027-0.195; all p&#x202f;<&#x202f;.001), with large effect sizes at the final visit for all outcomes (r&#x202f;=&#x202f;0.521-0.793). No C. anthropophaga infestations were detected in examined puppies from treated dams, including puppies exposed during pregnancy or lactation, up to 4 weeks after birth and up to 11 weeks of age, respectively. Afoxolaner appears effective to control canine cordylobiosis and may offer indirect protection to puppies, warranting further investigation.

Animals

Adaptive proteomic remodeling and eNOS upregulation in luminal endothelium and perivascular adipose tissue of patent saphenous vein grafts after CABG.

OBJECTIVE: Long-term patency of saphenous vein grafts (SVGs) remains a significant challenge in coronary artery bypass grafting (CABG). The biological factors underlying successful human grafts are poorly understood. We aimed to characterize the structural and molecular features associated with successful graft function. METHODS: Patent and occluded SVG and internal thoracic artery (ITA) grafts were obtained from explanted hearts of CABG patients undergoing heart transplantation for end-stage heart failure not attributable to graft failure, along with freshly harvested ITA and SVG controls. Samples underwent histomorphological analysis, immunohistochemistry (IHC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics. RESULTS: Patent ITA (ITA-P) showed minimal intimal hyperplasia with medial reinforcement, whereas patent SVGs (SVG-P) had organized, &#x3b1;-smooth muscle actin (&#x3b1;SMA)-positive myofibroblast-rich neointima. Endothelial nitric oxide synthase (eNOS) was markedly upregulated in patent grafts at two sites-the luminal endothelium and adventitial microvessels within perivascular adipose tissue (PVAT)-and lost at both sites in occluded SVG (SVG-O). Adventitial CD31-positive microvessels were significantly increased in patent grafts. Proteomically, ITA-P and SVG-P shared a largely common adaptive proteome enriched in translation, RNA processing, and extracellular matrix (ECM) organization, with shared upstream activation of NR4A3, EGFR, and STAT1, and conduit-specific signatures (IGF-1/RUNX2 in ITA-P; RETN/SRC/PTGES in SVG-P). PTGES was strongly expressed in the adventitia of SVG-P. CONCLUSIONS: Patent arterial and venous bypass grafts exhibited a shared adaptive phenotype characterized by dual-site upregulation of eNOS in both the luminal endothelium and the perivascular microvessels/PVAT. In SVG-P, PTGES was co-upregulated alongside eNOS, indicating a mechanistic link between the proteomic and IHC findings. These findings highlight the perivascular compartment as a site of adaptive, eNOS-associated changes in patent vein grafts.

Humans

PHACE syndrome: a systematic literature review and illustrative case report of a patient with severe cerebrovascular and neurodevelopmental sequelae.

BACKGROUND: PHACE syndrome is a rare neurocutaneous disorder defined by the association of large segmental infantile hemangiomas of the head and neck with malformations of the posterior fossa, cerebral and cervical arteries, heart, eyes, and ventral midline structures. Although facial hemangiomas are often the presenting feature, the cerebrovascular, neurodevelopmental, and airway manifestations are responsible for the greatest long-term morbidity. METHODS: A systematic literature review was conducted following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, searching PubMed, Web of Science, EMBASE, and PsycINFO. After removal of duplicates and screening of 308 records, five studies meeting the inclusion criteria were retained for qualitative synthesis. We additionally present the case of a now 12-year-old girl with PHACE syndrome characterized by a left V1-distribution facial hemangioma, ocular abnormalities, multiple cerebrovascular venous and arterial malformations, neonatal intraventricular hemorrhage with hydrocephalus, and a subsequently diagnosed dural arteriovenous fistula requiring repeated embolization. RESULTS: The five included studies collectively describe epidemiology and early supportive care needs, long-term health outcomes and quality of life into adulthood, airway hemangioma prevalence and management, and the clinical spectrum of infantile hemangiomas with minimal or arrested growth (IH-MAG) as a cutaneous marker of PHACE syndrome. Across studies, cerebrovascular arteriopathy (72-91%) and facial hemangioma residua (>&#x2009;90%) were the most consistent findings, while progressive arteriopathy, headaches, learning differences, and airway involvement emerged as the principal sources of long-term morbidity. The reported case illustrates an unusually severe cerebrovascular phenotype, including neonatal hemorrhagic hydrocephalus, dural venous sinus thrombosis, and a late dural arteriovenous fistula, culminating in ataxic cerebral palsy and mild intellectual disability. CONCLUSIONS: PHACE syndrome requires a multidisciplinary, lifelong follow-up strategy. The presented case underscores that cerebrovascular complications may evolve over years to decades after the initial diagnosis, reinforcing the need for long-term neuroradiological surveillance even after apparent clinical stability.

Humans

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&#x2009;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

Review: The African turquoise killifish as a model for the integrative physiology of vertebrate aging.

With increasing emphasis on extending healthy lifespan, aging research requires vertebrate models that permit efficient mechanistic investigation and intervention testing within practical time and cost constraints. The African turquoise killifish (Nothobranchius furzeri) has attracted growing attention because it combines an exceptionally short life cycle with an intact vertebrate physiological context and an expanding genetic toolkit, enabling relatively rapid evaluation of candidate aging interventions and mechanistic analysis across molecular, tissue, and organismal levels. This review assesses N. furzeri from an integrative-physiology perspective, focusing on germline-soma interactions, gut microbiota-host crosstalk, nutrient sensing and metabolic remodeling, temperature responsiveness, and AMPK-mTOR-linked programs. It also examines expanding genome-engineering and reporter approaches that support mechanistic and tissue-resolved investigation of these physiological processes. Building on recent reviews of killifish biology, disease modeling, regeneration, and the hallmarks of aging, we synthesize evidence across major intervention domains, distinguish established phenotypic effects from incompletely resolved mechanisms, and highlight functional endpoints, methodological standardization, and the appropriate interpretation of the model's translational relevance. Together, these features position N. furzeri as a strategically useful vertebrate platform for rapid mechanistic testing, intervention evaluation, and prioritization of aging-related pathways. Future progress will require improved methodological standardization, tissue-resolved causal studies, and question-driven cross-species validation where appropriate.

Animals

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&#xa0;=&#xa0;5), support vector machines (n&#xa0;=&#xa0;4), k-nearest neighbor (n&#xa0;=&#xa0;3), decision trees (n&#xa0;=&#xa0;3), random forests (n&#xa0;=&#xa0;5), neural networks (n&#xa0;=&#xa0;2), linear discriminant analysis (n&#xa0;=&#xa0;1), and pre-trained CNNs (n&#xa0;=&#xa0;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&#xa0;=&#xa0;12 to n&#xa0;=&#xa0;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