Academic stress is prevalent among Chinese high school students and linked to adverse mental health outcomes, yet its interplay with rumination and resilience at the component level remains poorly understood from a cognitive transactional stress perspective.

This study investigated the interrelationships among academic stress, rumination and resilience at the subdimension level, examining both concurrent network structures and their temporal dynamics across three time points.

Three-wave longitudinal data were collected from 311 Chinese high school students (55.6% male; M_age = 16.21 years) in September 2024, January 2025 and April 2025.

Participants completed measures of rumination (symptom rumination, brooding, reflective pondering), resilience (goal focus, emotion regulation, positive cognition, family support, interpersonal assistance) and academic pressure (parental, self, teacher, social). Cross-sectional network analysis, network comparison tests, and cross-lagged panel network (CLPN) models examined network structure, temporal stability and prospective associations.

Cross-sectional networks revealed persistent negative associations between academic pressures and resilience resources. Symptom rumination consistently exhibited the highest strength centrality. Network structure remained globally invariant, though connectivity increased from T1 to T2. CLPN analyses revealed longitudinal associations between multiple academic pressure dimensions and both rumination and resilience dimensions across time, with rumination subdimensions (symptom rumination, brooding and reflective pondering) emerging as key bridge nodes connecting the two communities.

Academic stress, rumination and resilience operate as a dynamically interconnected network, highlighting theoretically informed pathways that could guide the development of future school-based interventions targeting academic pressure, cognitive regulation and resilience enhancement.

While the field of imaging transcriptomics is evolving rapidly, several methodological challenges persist in functional enrichment analysis. Here, we introduce BrainEnrich, an R package that integrates whole-brain gene expression profiles from the Allen Human Brain Atlas (AHBA) with in vivo imaging-derived phenotypes (IDPs). By offering a suite of flexible association methods, aggregation strategies, comprehensive lists of predefined gene sets, and both competitive and self-contained null models, the package enables researchers to examine the spatial coupling between molecular profiles and IDPs at both group and individual levels. A novel feature of BrainEnrich is its individual-level enrichment analysis, which mapped individual IDPs onto a molecular coordinate framework, capturing the molecular signature of individual IDPs and enabling a deeper exploration of inter-individual variability. Its statistical power was examined through extensive simulation studies based on linear regression with different combinations of test statistics and null models. The results suggest that with appropriate null models, this approach effectively controlled Type 1 error while retaining sensitivity to detect associations between molecular profiles and phenotypic data. Two case studies were performed to demonstrate the utility of the package. In the group-level enrichment analysis, the effect size map of case-control comparison in cortical thickness of major depressive disorder patients was associated with molecular pathways such as synaptic signaling, lipid regulation, and steroid hormone balance, providing candidate molecular annotations for group-level IDPs. A separate case study found that synaptic gene set scores showed nominal associations with multiple cognitive measures, demonstrating its utility for individual-level molecular annotations to explore associations with phenotypic variables. Collectively, BrainEnrich provides a flexible framework for integrating macro-level IDPs with micro-level transcriptomic profiles for molecular contextualization of IDPs.

Patients with functional esophageal disorders exhibit symptoms such as chest pain, heartburn, dysphagia, globus sensation, or reflux hypersensitivity in the absence of structural abnormalities. This is characterized by dysregulated gut-brain interactions and visceral hypersensitivity.

A systematic search of the MEDLINE, EMBASE, Web of Science and the Cochrane central register of controlled trials databases was performed to December 31, 2025. Relevant randomized controlled trials (RCT) reporting the effects of gut-brain neuromodulator (GBN) therapy on functional chest pain (FCP), functional heartburn (FH), reflux hypersensitivity (RH), functional dysphagia (FD), and globus were analyzed.

Among 2538 screened records, 29 RCTs were included. Neuromodulators provided symptom relief in 18-67% of patients with FCP, with 52-71% achieving ≥ 50% symptom reduction. Moreover, 46-80% of patients experienced a globus reduction of more than 50%. In contrast, the number of included studies for RH and FH was small, and the evidence was inconsistent. In healthy individuals, human experimental models indicated that central or local sensory-modulatory pathways can mitigate acid-induced hyperalgesia, thereby offering a promising therapeutic strategy for RH. Convincing evidence to support the use of GBN in the treatment of FD is lacking. However, its effects on normal human esophageal motility also provides strategies for drug selection, although these outcome indicators vary substantially.

GBNs can alleviate FCP, improve globus, and to some extent regulate esophageal sensation. However, their effectiveness for FD, FH, and RH remains controversial. Accordingly, future well-designed and phenotype-specific RCTs are required to establish the role of GBNs in clinical management.

The C-reactive protein-triglyceride-glucose index (CTI) is a composite marker integrating systemic inflammation and insulin resistance. Although CTI has been associated with cardiovascular risk in general and cardiometabolic populations, its prognostic significance in patients hospitalised with acute decompensated heart failure (ADHF) remains unclear.

This single-centre retrospective cohort study included 1,494 patients hospitalised with ADHF between January 2020 and June 2024. CTI was calculated as 0.412 × ln[CRP (mg/L)] + ln[TG (mg/dL) × FPG (mg/dL)/2], and patients were stratified into tertiles. The endpoints were all-cause mortality, cardiovascular (CV) death, and major adverse cardiac and cerebrovascular events (MACCEs). Associations between CTI and outcomes were assessed using multivariable Cox regression and restricted cubic spline analyses. Incremental prognostic performance beyond a comprehensive clinical model was evaluated using optimism-corrected Harrell's C-index, continuous net reclassification improvement (NRI), integrated discrimination improvement (IDI), time-dependent receiver operating characteristic curves, and decision curve analysis.

During a median follow-up of 498 days, event rates increased progressively across CTI tertiles. In the fully adjusted model, each 1-standard deviation increase in CTI was independently associated with higher risks of all-cause mortality (hazard ratio [HR] 1.52, 95% confidence interval [CI] 1.29-1.80), CV death (HR 1.51, 95% CI 1.24-1.84), and MACCEs (HR 1.42, 95% CI 1.26-1.59). Patients in the highest CTI tertile had the greatest risk across all endpoints (all P for trend < 0.001). Restricted cubic spline analyses showed positive, approximately linear associations between CTI and all outcomes. Adding CTI to the clinical model yielded modest improvements in discrimination, with C-index increases from 0.665 to 0.672 for all-cause mortality, from 0.697 to 0.705 for CV death, and from 0.644 to 0.652 for MACCEs. Significant improvements in NRI and IDI were also observed for all outcomes.

Elevated CTI was independently associated with increased risks of all-cause mortality, CV death, and MACCEs in patients hospitalised with ADHF. Addition of CTI to a comprehensive clinical model provided modest but statistically significant incremental prognostic value beyond established clinical predictors and related metabolic or inflammatory markers evaluated in this study. CTI may serve as a simple adjunctive marker for metabolic-inflammatory risk profiling and refined risk stratification in ADHF, pending external validation.

Although maintenance dialysis (MD) affects only a small proportion of patients with tuberculosis (TB), individuals receiving dialysis are at increased risk of TB and may be more likely to experience unfavourable treatment outcomes. Evidence on this association remains limited in Brazil, particularly in high-burden settings characterized by geographic barriers and challenges in access to specialized healthcare services. We assessed the association between MD and unfavourable TB treatment outcomes in Amazonas, Brazil.

We conducted a retrospective population-based cohort study including all new TB cases reported in Amazonas, Brazil, between 2001 and 2023. Probabilistic record linkage was performed between the state TB surveillance system (SINAN-TB) and a dialysis registry to identify patients receiving MD before TB diagnosis. The primary exposure was MD prior to TB diagnosis. Unfavourable outcomes comprised death from any cause, loss to follow-up, or treatment failure. Multivariable logistic regression was used to estimate adjusted odds ratios (aORs) with 95% confidence intervals (CIs).

Among 58,611 TB cases included in the analysis, 112 (0.2%) had evidence of MD before TB diagnosis. MD was strongly associated with higher odds of unfavourable outcomes (aOR 5.45; 95% CI 3.60-8.24). Illicit drug use (aOR 2.18; 95% CI 1.97-2.40), HIV/AIDS (aOR 2.06; 95% CI 1.94-2.19), mental illness (aOR 1.53; 95% CI 1.27-1.83), alcohol use (aOR 1.38; 95% CI 1.29-1.48), and smoking (aOR 1.30; 95% CI 1.20-1.42) were also associated with increased odds of unfavourable outcomes. In contrast, directly observed therapy (DOT) (aOR 0.67; 95% CI 0.63-0.71), female sex (aOR 0.82; 95% CI 0.79-0.85), and diabetes mellitus (aOR 0.84; 95% CI 0.78-0.91) were associated with reduced odds.

Within a large population-based TB cohort, MD was a rare but clinically important exposure that was strongly associated with unfavourable treatment outcomes. The protective effect of DOT highlights a feasible intervention pathway. These findings support the integration of TB care into dialysis services and reinforce the need to prioritize this population for targeted TB screening and preventive strategies in high-burden settings.

Not applicable.

This study aimed to compare the predictive capabilities of BMI, WHR, Epicardial Adipose Tissue (EAT), and Visceral Adiposity Index (VAI) for MACE in individuals with diabetes mellitus. It further sought to explore whether increased EAT or visceral fat is a more significant factor for MACE than general obesity in this population. A prospective study was conducted involving 1071 individuals with diabetes mellitus (64% male, median age 58 years). EAT volume and spatial distribution were obtained by manually delineating cardiac magnetic resonance (CMR) images using CV142 software. MACE were defined as ischemic heart disease, hypertensive heart disease, heart failure, stroke, and cardiovascular disease (CVD)-related death. Baseline data, CMR parameters, and different obesity indices were compared among groups. The Kaplan-Meier (KM) curve was used to explore the relationship between different obesity indices and MACE in individuals with diabetes mellitus. The predictive performance of each index for MACE occurrence was compared using multi-model Cox multivariate regression analysis, and sensitivity analysis was performed. Over a median follow-up of 1508 days, 118 MACE occurred. The MACE and non-MACE groups differed in EAT volume, EAT distribution, and WHR. Among comorbidity subgroups, differences were observed in EAT volume, EAT distribution, BMI, WHR, and VAI. Kaplan-Meier analysis indicated that EAT distribution and WHR predicted MACE. In multivariable Cox regression, EAT location (EAT.LOC) predicted MACE in all three models and remained significant in the fully adjusted model (Model 3: HR: 1.76 → 1.85). In contrast, EAT volume, VAI, BMI, and WHR showed no predictive ability in any model. In within-model comparisons, EAT.LOC demonstrated superior predictive value (Model 3: ΔC-index = 0.016, NRI = 0.163, IDI = 0.112). Compared to traditional cardiovascular risk factor models, the model incorporating EAT.LOC also demonstrated favorable predictive value (ΔC-index = 0.013, NRI = 0.163, IDI = 0.07). Sensitivity analysis confirmed the robustness of these findings. EAT distribution, but not BMI, WHR, VAI, or EAT volume, independently predicts MACE risk in patients with diabetes mellitus. These findings indicate that EAT.LOC independently predicts MACE in diabetic patients and could be used for secondary risk stratification in this population. Nevertheless, the causal link between EAT.LOC and MACE remains to be established in future studies.

Patients hospitalized for acute exacerbation of chronic obstructive pulmonary disease (AECOPD) remain at risk of readmission and death after discharge. Opportunistic chest computed tomography (CT) body-composition metrics may provide additional prognostic information beyond conventional clinical scores.

To develop and validate a 90-day adverse-outcome prediction model for hospitalized AECOPD using admission clinical variables and opportunistic chest CT body-composition metrics.

A retrospective modelling cohort of 203 AECOPD admissions from the index centre was analysed. Admissions from 2021 to 2024 formed the development cohort (n = 152), and 2025 admissions formed the temporal-validation cohort (n = 51). An external-validation cohort from another centre included 103 admissions from records screened between 1 January 1 and 1 January 2025 after the model was locked. The primary outcome was 90-day readmission or death. LASSO was used for variable screening in the development cohort. A feature-count AUC plateau analysis and prespecified multialgorithm screening were used to lock the final model before validation. DECAF and BAP-65 were retained as comparator scores.

The 90-day adverse outcome occurred in 66 of 152 development patients (43.4%), 18 of 51 temporal-validation patients (35.3%) and 35 of 103 external-validation patients (34.0%). LASSO retained prior AECOPD admissions, home oxygen before admission, diabetes mellitus, intermuscular adipose tissue area, long-term NIV before admission, heart rate and coronary artery disease. Feature-count analysis supported this seven-predictor set, and multialgorithm screening selected HistGradientBoosting for validation. In temporal validation, the locked model achieved AUC 0.80 (0.64-0.95), sensitivity 0.78 (0.59-0.94), specificity 0.88 (0.76-0.97) and Brier score 0.15 (0.09-0.21), with imperfect calibration (Hosmer-Lemeshow p < 0.001). In external validation, the locked model achieved AUC 0.77 (0.66-0.87), sensitivity 0.66 (0.49-0.80), specificity 0.79 (0.71-0.88) and Brier score 0.18 (0.15-0.21); the Hosmer-Lemeshow p value was 0.209.

A 90-day AECOPD prediction model combining clinical and opportunistic CT body-composition variables showed consistent discrimination across validation cohorts, but calibration remained a key implementation boundary. Formal multicentre validation and calibration updating are needed before routine clinical use.

Whether using both sodium-glucose cotransporter 2 inhibitors (SGLT2i) and glucagon-like peptide-1 receptor agonists (GLP-1RA) is more effective than using either alone in type 2 diabetes mellitus remains uncertain. We sought to assess cardiovascular and kidney outcomes of combined SGLT2i and GLP-1RA therapy versus monotherapy using network meta-analysis.

We identified randomized controlled trials (RCTs) of SGLT2i or GLP-1RA in patients with type 2 diabetes mellitus through a comprehensive search of MEDLINE, Embase, and the Cochrane Library until Aug. 15, 2025. We compared patients receiving SGLT2i, GLP-1RA, both SGLT2i and GLP-1RA, or neither medication in a systematic review and network meta-analysis. Primary outcomes were major adverse cardiovascular events (cardiovascular death, myocardial infarction, and stroke) and major adverse kidney events (decline in estimated glomerular filtration rate, kidney failure, and death due to kidney failure). Secondary outcomes included heart failure-related hospital admissions, serious adverse events, and hypoglycemia.

We included 12 RCTs with 99 683 participants. The risk of major adverse cardiovascular events did not differ significantly with combined SGLT2i and GLP-1RA therapy compared with SGLT2i (risk ratio [RR] 0.86, 95% confidence interval [CI] 0.74 to 1.01; very low certainty) or GLP-1RA alone (RR 0.95, 95% CI 0.81 to 1.12; very low certainty). Similarly, the risk of major adverse kidney events did not differ significantly with combined therapy from SGLT2i (RR 1.05, 95% CI 0.74 to 1.49; very low certainty) or GLP-1RA (RR 0.86, 95% CI 0.60 to 1.21; very low certainty). However, combined therapy was associated with a lower risk of heart failure-related hospital admission than SGLT2i (RR 0.72, 95% CI 0.52 to 0.99; very low certainty) or GLP-1RA (RR 0.62, 95% CI 0.45 to 0.86; very low certainty). Risks of serious adverse events or hypoglycemia did not differ significantly between combined therapy and monotherapy.

Compared with SGLT2i or GLP-1RA alone, combined therapy did not significantly reduce the risk of major adverse cardiovascular or kidney events but was associated with lower risks of heart failure-related hospital admission. Randomized controlled trials directly comparing combined therapy with monotherapy are needed to clarify the comparative effectiveness of combined therapy in patients with type 2 diabetes mellitus.

PROSPERO - CRD42024605727.

Diabetic foot ulcers are among the most challenging complications of diabetes. Diabetes heightens patients' risk of severe complications, including amputations and death, while also driving up healthcare system costs. Given the significance of this problem, the present study conducted a systematic review of studies that used Classical Statistical and Machine Learning Approaches to identify factors influencing the development of diabetic foot ulcers in individuals with diabetes. A thorough literature search was conducted in PubMed, Scopus and Web of Science, covering their inception through 7 September 2025. This systematic review adhered to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMAs) guidelines. Data were analysed narratively using content analysis. Eligible studies used predictive methods to identify factors associated with the development of diabetic foot ulcers. A total of 4396 articles were screened, and 66 studies were selected for full-text review after application of the inclusion and exclusion criteria. The review of these studies identified 95 factors associated with predicting diabetic foot ulcers, among which neuropathy, diabetes duration, age, body mass index and peripheral vascular disease were the most frequently reported. In addition, among the predictive models used in the studies, logistic and Cox regression models were the most useful for predicting factors associated with diabetic foot ulcers. This study identifies key predictive factors for diabetic foot ulcers, enabling healthcare systems to target high-risk patients through early screening. Proactive identification of vulnerable diabetic patients can prevent severe complications, such as amputation.

Problem-solving is essential for the self-management of type 2 diabetes but remains challenging for underserved individuals. Although mobile health (mHealth) interventions can improve diabetes self-management, few focus on problem-solving.

This study evaluates the efficacy of Mobile Diabetes Detective (MoDD), a fully automated web-based intervention with SMS text messaging that provides problem-solving support tailored to self-monitoring data, for improving glycemic control among medically underserved adults with type 2 diabetes.

This open-label, 1:1 cluster-randomized controlled trial was conducted in 2013-2018. Participants were adults with type 2 diabetes (glycated hemoglobin [HbA1c] >7.5%) receiving care at 8 Federally Qualified Health Centers serving medically underserved communities in the New York metropolitan area. The centers served as clusters and were randomized using computer-generated allocation. Recruitment and study sessions were conducted either in person or in a hybrid format. The intervention arm used MoDD for 12 months, whereas the control arm received standard diabetes education and routine care. The primary outcome was the change in HbA1c from baseline to 12 months, recorded from medical chart data. We hypothesized greater improvement in the intervention arm than in the control arm. Secondary outcomes included psychosocial measures. Outcomes were compared between groups using intention-to-treat analyses. This report presents the final analysis of the outcomes.

This trial randomized 248 participants (intervention arm: n=126; control arm: n=122); 219 were included in the final analysis (intervention arm: n=111; control arm: n=108). Participants were predominantly female (147/219, 67.1%) and ethnically and racially diverse (112/219, 51.1%, Hispanic and 92/219, 42%, African American), with a mean baseline HbA1c of 9.9%. Overall, of the 111 participants, 44 (39.6%) engaged with MoDD at least once weekly in the first 30 days, and 22 (19.8%) engaged at least once weekly in the first 90 days. HbA1c did not differ significantly between groups at baseline (intervention: 9.81%, 95% CI 9.42%-10.20%; control: 9.95%, 95% CI 9.55%-10.34%; difference=0.14%, P=.63) or at 12 months (intervention: 9.36%, 95% CI 8.95%-9.78%; control: 9.58%, 95% CI 9.15%-10.01%; difference=-0.22%, P=.47). Both groups demonstrated reductions in HbA1c from baseline to 3 months. Sustained within-group improvement at 12 months was observed in the intervention group but not in the control group. No intervention-related adverse events were reported.

This study evaluated the impact of a mobile intervention for problem-solving in diabetes. MoDD is innovative because it operates autonomously and tailors support to individuals' self-monitoring data. Although there was no significant between-group difference in HbA1c, the intervention group showed sustained within-group improvement at 12 months. These findings highlight the potential long-term benefits of autonomous mHealth interventions for problem-solving. The study observed an increase in diabetes distress, possibly reflecting heightened awareness of uncontrolled blood glucose levels. If implemented in clinical practice, MoDD could complement diabetes education and help improve glycemic control.

ClinicalTrials.gov NCT02021591; https://clinicaltrials.gov/ct2/show/NCT02021591.