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The impact of sex, age, and genetic ancestry on DNA methylation across tissues.

Understanding the consequences of individual DNA methylation variation is crucial for advancing our knowledge of human biology and disease, yet the collective impact of individual traits on DNA methylation and their downstream effects on gene expression across human tissues remains poorly understood. Here, we quantify the contributions of sex, age, genetic ancestry, and BMI on autosomal DNA methylation variation across nine human tissues and 424 individuals from the Genotype-Tissue Expression project. We show that genetic ancestry and age have a greater impact on DNA methylation compared with sex, with aging effects being more widespread but less pronounced. On average, <10% of the gene expression variation in sex, age, and ancestry is mediated by DNA methylation differences, with ancestry showing the largest proportion of mediation. We further show that ancestry-associated DNA methylation differences accumulate at CpG sites with extreme methylation states and are largely under genetic control. The female autosomal genome exhibits consistent hypermethylation across tissues at Polycomb-repressed regions. Ultimately, we show that age-related Polycomb target hypermethylation is observed across multiple tissues but not in the gonads. Our multi-individual, multitissue approach defines the key drivers of human DNA methylation variation in healthy conditions, establishing a baseline for the interpretation of DNA methylation changes in disease contexts.

Humans

Reliability-aware hierarchical learning for Chagas disease screening from 12-lead ECGs: tackling label uncertainty and class imbalance.

Objective.Chagas disease, a neglected tropical disease (NTD) with significant cardiovascular impact, remains underdiagnosed in resource-limited regions. Electrocardiogram (ECG) screening offers a low-cost tool for detecting cardiac involvement, yet algorithm development is challenged by label noise, data scarcity, and the latent nature of infection. This study proposes a robust ECG-based screening framework that explicitly addresses these constraints.Approach.We introduce aReliability-Aware Hierarchical Learningstrategy that calibrates supervision according to data provenance, prioritizing serology-confirmed labels over noisy self-reports. To mitigate data scarcity, we compare a specialized convolutional neural network (CNN) trained from scratch with a transfer learning approach based on a Spatio-Temporal ECG foundation Model (FM). Performance is evaluated across varying data scales, and the representation structure is analyzed to interpret model behavior.Main results.On the official hidden test set of the George B. Moody PhysioNet/Computing in Cardiology Challenge 2025, our approach achieved a Challenge Score of 0.163. We observe that while the specialized CNN performs competitively in data-rich regimes, the FM exhibits superior robustness in extreme low-resource settings. Furthermore, performance reaches a plateau imposed by underlying disease physiology. Bimodal score distributions suggest that models distinguish established cardiomyopathy from indeterminate infection, which remains electrophysiologically indistinguishable from healthy controls.Significance.These findings clarify both the potential and intrinsic limits of ECG-based AI screening for NTD-associated cardiac involvement. Reliability-aware supervision and data-efficient transfer learning provide a practical framework toward scalable and clinically meaningful ECG screening systems in resource-constrained environments.

Humans

Ten years on, still out of reach: barriers to PrEP access and retention in France according to frontline actors (QualiPrEP Study).

Pre-exposure prophylaxis (PrEP) for HIV has been available in France since 2014, and reimbursed since 2016, with general practitioners allowed to prescribe it since 2021. Despite these policy advances, uptake remains low among some of the most affected populations. This community-based qualitative study explored barriers to PrEP access and retention ten years into its implementation.Interviews were conducted with 28 PrEP frontline actors (healthcare professionals and community-based workers involved in promoting, prescribing, or supporting PrEP). The sample included one group discussion (n = 5), two triads (n = 6), two dyads (n = 4), and nine individual interviews (n = 13). Thematic analysis was inductive, with barriers classified across four main domains.Participants were mostly cisgender men, median age 48, born in France and abroad, and employed by NGOs in Paris. Thirteen barriers and four major themes emerged: (1) Internal psychosocial barriers: lack of knowledge, negative health-related reactions; HIV stigma; STI risk perception, taboos; (2) Internal pragmatic barriers: perceived limits of protection, usage and follow-up constraints; (3) External psychosocial barriers: limited physician knowledge and reluctance; (4) External pragmatic barriers: communication failures; structural constraints, lack of human and financial resources.Findings call for more targeted messaging, simplified care models and provider training. They highlight the need to address social and symbolic dimensions of PrEP, with insights from those supporting users to ensure more equitable implementation.

Humans

Facilitating Thought Progression via a Gamified Mobile Application for Depression: Possible Mediators of Outcomes.

Mobile health interventions represent a scalable and accessible alternative to traditional therapy, which often is out of reach due to high costs and societal stigma. Rumination is considered a key mechanism of emotional disorders and represents a potential treatment target for digital health interventions. The current study investigated the role of rumination as a mediator of the reduction in depression and anxiety reported after the use of a gamified mobile app based on the Facilitating Thought Progression (FTP) framework. One hundred-one adults with mild to moderate depression were randomized to the FTP intervention or a waitlist control group and completed weekly assessments of depression, anxiety, and rumination over 8 weeks. Multilevel structural equation modeling revealed that reduction in rumination significantly mediated decreases in depression and anxiety in the intervention group but not in the waitlist condition. These findings suggest that the FTP app targeted rumination and further highlights its role as a critical target for interventions for depression and anxiety.

Adult

Effect of a pharmacist-led mHealth app on adherence, quality of life, and glycaemic control in diabetes: A multicentre RCT.

AIMS: To evaluate whether CareAide&#xae;, a pharmacist-driven mHealth application, improves medication adherence, health-related quality of life (HRQoL), and glycaemic control in diabetes mellitus using structural equation modelling. METHODS: Pre-specified secondary analysis of the type 2 diabetes mellitus cohort from a 6-month multicentre open-label randomised controlled trial (N&#xa0;=&#xa0;663) across three Malaysian hospitals. Adherence was assessed by MMAS-8 (subjective) and Proportion of Days Covered (PDC; pharmacy-verified). HRQoL was measured by AQoL-6D and EQ-5D-5&#xa0;L. Structural equation modelling (SEM), Necessary Condition Analysis, and Importance-Performance Map Analysis (cIPMA) were applied. RESULTS: CareAide&#xae; produced large adherence gains (MMAS-8: 7.31 vs 5.55, d&#xa0;=&#xa0;1.64; PDC&#xa0;&#x2265;&#xa0;80%: 81.6% vs 33.0%; both p&#xa0;<&#xa0;0.001). Early 3-month adherence was the strongest predictor of sustained 6-month adherence in both models (&#x3b2; std&#xa0;=&#xa0;0.567 and 0.688; p&#xa0;<&#xa0;0.001). AQoL-6D utility improved significantly (0.669 vs 0.618; d&#xa0;=&#xa0;0.353, p&#xa0;<&#xa0;0.001), driven by coping (d&#xa0;=&#xa0;0.447) and relationships (d&#xa0;=&#xa0;0.254) domains. HRQoL did not mediate adherence; gains were a direct independent benefit. The intervention effect on HbA1c was not statistically significant in the PDC-based SEM model (&#x3b2;&#xa0;=&#xa0;&#xa0;-&#xa0;0.333, p&#xa0;=&#xa0;0.065); a group difference was, however, supported by baseline-adjusted ANCOVA (&#x3b2;&#xa0;=&#xa0;&#xa0;-&#xa0;0.41%, p&#xa0;=&#xa0;0.002), and the complete-case comparison was non-significant (p&#xa0;=&#xa0;0.153), so glycaemic findings warrant cautious interpretation. cIPMA identified the intervention as the primary optimisation target. CONCLUSIONS: CareAide&#xae; significantly improves medication adherence and psychosocial quality of life. Evidence for glycaemic benefit came from baseline-adjusted analysis (ANCOVA), though findings should be interpreted with caution given incomplete HbA1c data at one site. The first three months are the most critical period for pharmacist support. In this dataset, PDC appeared more sensitive than MMAS-8 to the HbA1c signal within 6&#xa0;months, but this finding requires confirmation in longer studies with more complete HbA1c data. TRIAL REGISTRATION: ClinicalTrials.gov NCT06068309.

Aged

The Effectiveness of Passive Half-Time Interventions on Simulated Second-Half Performance in Elite Youth Soccer Players.

The half-time period in soccer provides a potentially important window to implement short-duration interventions aimed at maintaining second-half performance. However, passive rest has been associated with a decline in subsequent physical and technical performance. While re-warm-up strategies are well studied, little is known about the efficacy of technology-based, passive recovery modalities, device-supported interventions that require minimal active movement or physical exertion from the athlete, during half-time intervals. This study examined whether percussive therapy, electrical muscle stimulation, and pneumatic compression can mitigate second-half performance decline in male adolescent soccer players. Forty-three academy-level players (17.5 &#xb1; 0.6 years) completed a simulated soccer protocol including the Loughborough Soccer Pass Test (LSPT), repeated 20-meter sprints, and completed Total Quality of Recovery (TQR) assessments before and after half-time. Participants were randomized into one of four intervention groups during the 15-minute half-time interval: Passive Rest (CON), Percussive Therapy (Theragun Pro; TG), EMS (PowerDot; PD), and Pneumatic Compression (RecoveryAir; COMP). Linear mixed-effects models assessed Time &#xd7; Condition interactions for performance and recovery outcomes. Passive half-time rest led to significant deterioration in technical skill, sprint performance, and perceived recovery (p < .05) for the control group. TG and PD significantly improved technical performance (LSPT scores) compared to the control group (d = 0.65 and 0.72, respectively; p < .01). Furthermore, TG and COMP were effective at maintaining 20-meter sprint times (d = 0.58 and 0.49; p < .01), whereas the control group experienced significant slowing. Perceived recovery (TQR) scores significantly declined in the CON group from First Half to Second Half (16.5 &#xb1; 2.2 to 11.1 &#xb1; 2.3. However, this decline was significantly attenuated in all intervention groups: TG (17.5 &#xb1; 1.9 to 14.2 &#xb1; 1.9), PD (17.3 &#xb1; 1.9 to 15.1 &#xb1; 1.9), and COMP (17.6 &#xb1; 1.9 to 15.9 &#xb1; 2.0). Short-duration passive interventions during half-time can mitigate performance decline in adolescent soccer players. TG and EMS appear most effective for preserving technical skills, while COMP may support perceived recovery. These findings highlight practical strategies for optimizing in-game performance and inform evidence-based half-time protocols.

Humans

The Role of Artificial Intelligence Combined With Digital Cholangioscopy for Indeterminant and Malignant Biliary Strictures: A Systematic Review and Meta-analysis.

BACKGROUND: Current endoscopic retrograde cholangiopancreatography (ERCP) and cholangioscopic-based diagnostic sampling for indeterminant biliary strictures remain suboptimal. Artificial intelligence (AI)-based algorithms by means of computer vision in machine learning have been applied to cholangioscopy in an effort to improve diagnostic yield. The aim of this study was to perform a systematic review and meta-analysis to evaluate the diagnostic performance of AI-based diagnostic performance of AI-associated cholangioscopic diagnosis of indeterminant or malignant biliary strictures. METHODS: Individualized searches were developed in accordance with PRISMA and MOOSE guidelines, and meta-analysis according to Cochrane Diagnostic Test Accuracy working group methodology. A bivariate model was used to compute pooled sensitivity and specificity, likelihood ratio, diagnostic odds ratio, and summary receiver operating characteristics curve (SROC). RESULTS: Five studies (n=675 lesions; a total of 2,685,674 cholangioscopic images) were included. All but one study analyzed a deep learning AI-based system using a convoluted neural network (CNN) with an average image processing speed of 30 to 60 frames per second. The pooled sensitivity and specificity were 95% (95% CI: 85-98) and 88% (95% CI: 76-94), with a diagnostic accuracy (SROC) of 97% (95% CI: 95-98). Sensitivity analysis of CNN studies (4 studies, 538 patients) demonstrated a pooled sensitivity, specificity, and accuracy (SROC) of 95% (95% CI: 82-99), 88% (95% CI: 72-95), and 97% (95% CI: 95-98), respectively. CONCLUSIONS: Artificial intelligence-based machine learning of cholangioscopy images appears to be a promising modality for the diagnosis of indeterminant and malignant biliary strictures.

Humans

Integrated single-cell and bulk transcriptomic analysis identifies a novel senescent fibroblast subtype associated with poor prognosis in acral melanoma.

BACKGROUND: Acral melanoma (AM) exhibits significant intratumoral heterogeneity, but its tumor microenvironment (TME) and immune regulation remain unclear. This study aims to dissect TME heterogeneity and establish a prognostic model based on key cell subpopulations. METHODS: We collected AM single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA). Unsupervised clustering, CellChat, and Scissor analysis were performed to characterize cellular heterogeneity, cell-cell communication, and prognosis-related cell subpopulations. Kaplan-Meier analysis was used to assess the prognostic value of key genes, which were further validated by multiplex immunohistochemistry (mIHC). RESULTS: In AM, Mel_C2, C7, and C9 with high SEMA6A and KIT expression were strongly linked to poor prognosis. We further identified a senescent fibroblast subpopulation (sCAF_CDKN2A) characterized by high fibroblast senescence signature (FSS) scores. Integrating Scissor analysis of fibroblast subtypes with bulk prognostic data, we identified COL3A1, VCAN, and KIT as prognosis-associated genes upregulated in poor-outcome-related fibroblast subsets. Cell-cell communication analysis revealed that sCAF_CDKN2A engages in an immunosuppressive network, interacting with regulatory T cells (Tregs) via MIF signaling and receiving signals from exhausted CD8+ T cells through PPIA-BSG interactions. Using transcription factor expression patterns from these fibroblast subtypes, we constructed a prognostic model that effectively stratified patients into distinct risk groups with significant differences in overall survival (OS). mIHC confirmed significantly higher protein levels of SEMA6A and COL3A1 in tumor tissues compared to matched normal tissues. CONCLUSIONS: We established a novel prognostic model for AM and identified sCAF_CDKN2A as an immunosuppressive senescent fibroblast subpopulation driving poor prognosis.

Acral melanoma

Robotic assistance in total hip arthroplasty: a systematic review and meta-analysis of leg length, cup orientation, and early outcomes.

This review examined whether robotic assistance alters postoperative leg-length discrepancy (LLD), acetabular cup orientation, or early hip-specific outcomes relative to conventional total hip arthroplasty (THA). We searched PubMed and Web of Science through May 2026 for comparative English-language reports. Study eligibility, data extraction, and methodological appraisal were undertaken independently by two reviewers. Mean differences (MDs) and 95% confidence intervals (CIs) were calculated in Review Manager 5.4. Model selection was based on the target estimand and anticipated clinical and methodological diversity; leave-one-out and alternative-model sensitivity analyses were undertaken for heterogeneous outcomes. The protocol is registered with PROSPERO (CRD420261454043). The review included seven studies and 968 participants. Compared with conventional THA, robot-assisted THA yielded a smaller postoperative LLD (MD = -2.02, 95% CI -3.46 to -0.58; P = 0.006) and a higher Harris Hip Score (MD = 2.96, 95% CI 1.12 to 4.80; P = 0.002). Mean cup anteversion was lower in the robotic group (MD = -1.52, 95% CI -2.29 to -0.76; P < 0.0001), whereas cup inclination did not differ (MD = -0.71, 95% CI -3.26 to 1.83; P = 0.58). The robotic group also had higher Forgotten Joint Score (MD = 14.68, 95% CI 5.02 to 24.33; P = 0.003) and Oxford Hip Score values (MD = 2.61, 95% CI 0.71 to 4.51; P = 0.007). Robotic assistance was linked to a modest improvement in leg-length restoration and to higher scores on several early functional measures. The limited number of studies, predominance of nonrandomized designs, and marked heterogeneity in some analyses temper the certainty of these findings.

Humans

"Out of sync and overlooked" - Relationship between social jetlag and anxiety in adolescents: Systematic review and meta-analysis.

Anxiety is the most prevalent mental health difficulty in adolescence, a period characterised by a shift towards an eveningness chronotype that is not aligned with societal demands (i.e., school start times). Experiencing "social jetlag" (SJL), a discrepancy in weekday-weekend sleep timing, is proposed to be associated with increased anxiety. A PRISMA-compliant systematic review and meta-analysis was conducted to investigate the relationship between SJL and anxiety in adolescents (age range: 12-18 years). Systematic searches were conducted in PsycINFO, Web of Science, Embase, PubMed, MEDLINE, and ProQuest Dissertations & Theses Global on 14th November 2024 to retrieve empirical studies analysing the relationship between SJL and anxiety in 12-18-year-olds. A multi-level random-effect meta-analysis was conducted in R to estimate the magnitude of the association between SJL and anxiety. After screening 2,138 records, 18 studies were included in the systematic review, with 12 included in the meta-analysis (235,526 participants in total) and six in a narrative review. A small association was found between increased SJL and more severe anxiety (Fisher's z&#x202f;=&#x202f;0.0614, 95% CI [0.0268, 0.0961], p&#x202f;=&#x202f;0.0011). These findings highlight the importance of addressing behavioural strategies targeting healthy regular sleep as a tool to improve mental health in adolescence.

Adolescent

Pregnancy diet based on ancestral patterns increases growth in subcortical fetal brain regions.

Evidence on the biological basis for maternal nutrition effects on fetal and newborn neurodevelopment remains limited. This randomized controlled trial in Ecuador tested a maternal dietary pattern-derived from empirical studies of nutrition in human evolution and adapted locally-on offspring growth and brain development. Pregnant women (n = 215) in their first trimester were randomized to: 1) control (n = 104); or 2) Mikhuna ("nourish" in Kichwa) intervention (n = 111). The intervention, from 12 wk gestation to birth, consisted of a weekly food delivery (8 eggs, 500 g fish, and a variety of sustainably sourced fruits and vegetables) and a behavior change communication strategy encouraging diet diversity and limiting highly processed foods. Longitudinal data collection occurred at 12 wk, 21 wk, 35 wk gestation, and 2 wk postpartum, and included ultrasound imaging of fetal bone and brain parameters, maternal dietary intakes, anthropometry, socioeconomic and demographic variables, and other biomarkers. At close of intervention, a significantly higher percentage of women met the minimum dietary diversity threshold in Mikhuna (74.5%) vs. control groups (55.8%) (P = 0.004). Generalized linear regression models showed significant differences in Mikhuna compared to control for: corpus callosum length 0.19 cm (95% CI [0.02, 0.35]), gangliothalamic ovoid height 0.15 cm (95% CI [0.03 to 0.26]), and femur length -0.10 cm (95% CI [-0.19, -0.02]) from 21 wk to 35 wk; and corpus callosum Z 0.56 (95% CI [0.03, 1.09]) and femur length Z -0.21 (95% CI [-0.42, 0.00]) at 35 wk. The Mikhuna intervention increased the growth of subcortical fetal brain structures, which have established roles in motor control, cognition, and signal transmission.

Female

Innovations in microbial physical mutagenesis for food fermentation: An overview from traditional to emerging technologies.

Microbial strains serve as an important factor affecting fermentation efficiency and product quality. To obtain superior strains, mutation breeding is a classic strategy. Compared to chemical mutagenesis, physical mutagenesis directly induces genomic changes, providing notable advantages such as the elimination of chemical residues and environmental sustainability, hence rendering it a favored method for enhancing food-grade microorganisms. Conventional physical mutagenesis mostly depends on UV, rays, high pressure, or space radiation. As physical technologies advance, emerging methods such as ion implantation, plasma, microwave, ultrasound, and pulsed light are widely utilized for genetic modification. Mutagenesis technologies are progressively transitioning from single-effect to multi-effect synergy. Recent evaluations indicate that emerging technologies can enhance microbial mutation efficiency at the application level relative to established technologies. Nonetheless, the systematic clarification and comparative analysis at the mechanistic level remain inadequate, hindering intuitive comprehension of the qualities and distinctions across techniques. Furthermore, physical mutagenesis encounters several significant obstacles, such as cellular damage, limited rates of advantageous mutations, and laborious screening processes. This review carefully elucidates the mechanisms and properties of physical mutagenesis technology and delineates the distinctions among approaches through comparative analysis. Simultaneously, solutions for optimizing mutagenesis are presented to tackle the principal challenges mentioned above. This review aims to offer a theoretical foundation and practical guidance for the enhanced application of physical mutagenesis technologies in microbial breeding.

Mutagenesis

Non-destructive prediction of lead content in oilseed rape leaves by fluorescence hyperspectral technology based on neural network.

Based on fluorescence hyperspectral imaging (FHSI), this study targeted rapid, non-destructive quantification of lead (Pb) content in oilseed rape leaves treated with varying silicon (Si) concentrations, acquiring fluorescence spectra over the 484.43-1001.61&#xa0;nm wavelength range. To optimize spectral data quality, preprocessing methods (Savitzky-Golay smoothing, first derivative, detrending) were comprehensively compared. Characteristic wavelengths were then selected via interval variable iterative shrinkage, which effectively compressed data dimensionality and reduced computational load. A hybrid SE-CL1DA model, fusing a 1D convolutional neural network, a long short-term memory network and SE attention mechanism was constructed, with Bayesian optimization tuning hyperparameters to boost stability. The BO-SE-CL1DA outperformed both traditional machine learning and insufficiently optimized deep learning model (Rp2=0.9609, RMSE&#xa0;=&#xa0;0.0377&#xa0;mg/kg, RPD&#xa0;=&#xa0;5.1736), thus enabling accurate Pb estimation, supporting Si-regulated heavy metal stress management and facilitating agricultural contamination monitoring.

Plant Leaves

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

BACKGROUND: Predictive modelling for cancer risk, treatment-related complications, and survival is central to precision oncology. Conventional logistic regression (LR) and Cox proportional hazards (CoxPH) regression remain widely used but are limited when modelling nonlinear interactions, high-dimensional imaging features, and multimodal clinical-metabolic predictors. Artificial intelligence (AI) and machine learning (ML) methods offer expanded capability through automated feature extraction, ensemble learning, and flexible survival modelling, but the evidence on when AI/ML adds value over conventional models across cancer sites and predictive tasks remains fragmented. OBJECTIVE: To systematically evaluate the methodological performance, validation strategies, and translational limitations of AI/ML models compared with conventional statistical models in published predictive-modelling studies for breast, colorectal, or pancreatic cancer. METHODS: PubMed, Scopus, and Web of Science were searched for studies published between January 2019 and March 2025. Two reviewers independently conducted title-and-abstract screening, full-text eligibility assessment, and PROBAST risk-of-bias assessment. Sixty-five studies (n&#xa0;=&#xa0;907,567 participants) were narratively synthesised by cancer site, predictive task, model family, comparator, validation strategy, predictor modality, and calibration or explainability reporting. RESULTS: The 65 studies comprised breast cancer (n&#xa0;=&#xa0;35), colorectal cancer (n&#xa0;=&#xa0;21), and pancreatic cancer (n&#xa0;=&#xa0;9). AI/ML superiority over LR and CoxPH was task- and data-dependent. CNN- and U-Net-based models predominated in imaging and body-composition tasks, tree-based ensembles consistently outperformed LR for tabular perioperative complication prediction, and CoxPH remained competitive, and in the largest pancreatic risk study, superior to XGBoost (C-index 0.802 vs 0.723) in well-structured datasets. PROBAST analysis-domain risk was moderate in 54 of 65 studies (83%), driven by limited external validation, sparse calibration reporting (11/65), and few decision-curve analyses (7/65). CONCLUSION: AI/ML adds the most methodological value in imaging-derived feature extraction and nonlinear perioperative prediction, while conventional regression remains preferable in large, structured datasets with linear predictors. Clinical translation requires standardised body-composition definitions, external validation, calibration assessment, decision-curve analysis, and explainability, in line with TRIPOD+AI and CLAIM standards.

Humans

Comparative Effectiveness of Pharmacogenomics for Treatment of Depression.

PURPOSE/BACKGROUND: Pharmacogenomics (PGx), or the use of genetic information to assess drug-gene interactions, is an important step toward precision medicine. It is unclear if clinician use of PGx yields better outcomes for their patients. This study compared the effectiveness of combinatorial PGx-guided plus guideline-informed treatment (PGx+GIT) with guideline-informed treatment (GIT) alone to improve well-being in individuals with major depressive disorder. METHODS/PROCEDURES: Eligible participants (N=201) were randomized to PGx+GIT or GIT alone. PGx was measured with the proprietary GeneSight combinatorial test. PGx+GIT participant clinicians received test results within 2 business days to inform decisions about medication changes. Participants completed the World Health Organization Well-Being Index (WHO-5), Patient Health Questionnaire (PHQ-9), and PROMIS Profile physical functioning and social roles and activity domains every 2 weeks for 2 months and then every 2 months for the remaining 10 months. Monthly medication changes operationalized as necessary clinical adjustments were tracked with the medication recommendation tracking form. FINDINGS/RESULTS: Both groups improved average well-being over the 12-month study period (model-based change in WHO-5 per log (week) [95% CI]: 4.1 [3.3, 5.0] PGx+GIT and 4.8 [4.0, 5.5] GIT). PGx+GIT did not result in superior improvement in well-being (model-based difference [95% CI]: -0.6 [-1.8, 0.5], P =0.270), or any secondary outcomes. The effect of randomized treatment on well-being was not moderated by depression severity, number of previous failed medications for major depressive disorder, or presence of a comorbid condition. IMPLICATIONS/CONCLUSIONS: These data suggest PGx+GIT was not superior to GIT alone, possibly due to a ceiling effect of GIT, or PGx did not yield better results.

Humans

Analysis of the Relationship between Early Clinical Factors and Glasgow Outcome Scale in Patients With Traumatic Brain Injury.

OBJECTIVE: This study aimed to evaluate the association between early clinical factors and the Glasgow outcome scale (GOS) in patients with traumatic brain injury (TBI). METHODS: We conducted a retrospective analysis of 98 TBI patients who underwent emergency surgery between January 2021 and January 2024. Based on GOS scores at 6 months post-surgery, patients were classified into a favorable outcome group (GOS&#xa0;&#x2265;&#xa0;4, defined as moderate disability or good recovery,&#xa0;n = 58) and an unfavorable outcome group (GOS < 4, i.e., death, persistent vegetative state, or severe disability,&#xa0;n = 40). Baseline and early clinical parameters were compared between groups. Statistically significant variables from univariate analysis were entered into a multivariate logistic regression model to identify independent prognostic factors. RESULTS: Significant intergroup differences were observed in age, time from injury to surgery, bleeding site, midline shift, Glasgow coma scale (GCS) score at admission, blood glucose level, and D-dimer level (all p < 0.05). Multivariate analysis confirmed that age, time from injury to surgery, GCS score, blood glucose, and D-dimer level were independent predictors of GOS (all p < 0.05). CONCLUSION: Early clinical factors, including age, time to surgery, GCS score, blood glucose, and D-dimer level, independently influence GOS in TBI patients. Time from injury to surgery&#xa0;emerged as a potentially modifiable factor in this cohort, suggesting that minimizing delays may improve outcomes.

Humans

Association between cumulative social disadvantage, as measured by the social determinants of health score, and epilepsy: a cross-sectional study.

BACKGROUND: Social determinants of health (SDoH) shape access to care, health behaviors, and long-term outcomes, yet their cumulative relationship with epilepsy has not been well quantified. This study examined whether a composite SDoH score was associated with epilepsy in adults. METHODS: This cross-sectional study used data from the National Health and Nutrition Examination Survey 2013-2018. The SDoH score ranged from 0 to 8 and summarized eight unfavorable social conditions. Epilepsy was identified using medication-based ascertainment. Survey-weighted logistic regression models were applied to evaluate the association between SDoH score and epilepsy. Restricted cubic spline, subgroup, sensitivity, and receiver operating characteristic analyses were also performed. RESULTS: A total of 13,119 participants were included, of whom 114 had epilepsy. Participants with epilepsy had a higher mean SDoH score than those without epilepsy (3.41&#xa0;&#xb1;&#xa0;0.24 vs. 2.35&#xa0;&#xb1;&#xa0;0.06, P&#xa0;<&#xa0;0.001). In the fully adjusted model, each 1-point increase in SDoH score was associated with 31% higher odds of epilepsy (OR 1.31, 95% CI 1.16-1.48). Compared with the low-score group (0-2), the adjusted odds ratios were 2.09 (95% CI 1.06-4.15) for scores of 3-5 and 2.67 (95% CI 1.34-5.33) for scores of 6-8. Spline analysis showed a significant overall association without evidence of nonlinearity. Adding SDoH components to demographic variables improved model discrimination (AUC 0.731 vs. 0.589, P for difference <0.001). CONCLUSION: Greater cumulative social disadvantage, as reflected by the SDoH score, was associated with higher odds of epilepsy.

Humans

Modelling the effects of biological intervention in a dynamical gene network.

Cellular response to environmental and internal signals can be modeled by dynamical gene regulatory networks (GRN). In the literature, three main classes of gene network models can be distinguished: (1) non-quantitative (or data-based) models which do not describe the probability distribution of gene expressions; (2) quantitative models which fully describe the probability distribution of all genes co-expression; and (3) mechanistic models which allow for a causal interpretation of gene interactions. We propose two rigorous frameworks to model gene alteration in a dynamical GRN, depending on whether the network model is quantitative or mechanistic. We explain how these models can be used for design of experiment, or, if additional alteration data are available, for validation purposes or to improve the parameter estimation of the original model. We apply these methods to the Gaussian graphical model, which is quantitative but non-mechanistic, and to mechanistic models of Bayesian networks and penalized linear regression.

Gene Regulatory Networks