Background: Young adults, including medical students, are increasingly exposed to unhealthy lifestyle behaviors and early metabolic abnormalities that may predispose them to long-term cardiovascular disease. This constitutes a major public health challenge in South East Asia now-a days. Methods: A cross-sectional study was conducted among undergraduate medical students at Noakhali Medical College, Bangladesh, between October 2024 and June 2025. Behavioral, anthropometric, clinical, and biochemical data were collected using standardized protocols. Lifestyle factors included diet, physical activity, sedentary behavior, sleep duration, and stress. Anthropometric measurements comprised body mass index (BMI) and waist circumference, while biochemical assessments included 75-gm oral glucose tolerance test (OGTT), glycated hemoglobin (HbA1c) and lipid profile. Descriptive statistics and bivariate analyses using Chi-square tests were performed. Results: A total of 135 students (age: 21.0±1.9 years; 69.6% female) were included. The prevalence of overweight/obesity (BMI ≥25 kg/m²) was 28.9%, and similar numbers had central obesity. More than half of participants exhibited prediabetes (51.9%), while 64.4% had dyslipidemia and 13.3% met criteria for metabolic syndrome. Hypertension was present in 4.4% of students. Overweight/obesity was significantly associated with hypertension (p = 0.002) and dyslipidemia (p = 0.033), but not with sex, physical inactivity, sedentary behavior, dietary intake, glucose intolerance, or short sleep duration. Conclusion: Undergraduate medical students in Bangladesh demonstrate a high burden of cardiometabolic risk factors, including excess body weight, early dysglycemia, and dyslipidemia. These findings underscore the need for early, targeted preventive and health promotion strategies within medical education to mitigate future cardiometabolic disease risk in this critical population.

Background: Colorectal cancer (CRC) is the second commonest cause of cancer-related death in the United Kingdom and the third commonest cancer worldwide. Preoperative chemoradiotherapy is widely used to down-stage rectal tumours and improve surgical outcomes; however, its long-term molecular consequences on normal rectal tissue remain poorly understood. Objective: To identify molecular changes in gene and protein expression in normal rectal tissue of CRC patients who received preoperative chemoradiotherapy compared to those who underwent surgery alone. Methods: Normal rectal muscle-layer tissue samples were collected from 14 patients (chemoradiotherapy group, n=7; surgery-only control group, n=7). RNA was extracted and used to synthesise cDNA for RT-qPCR analysis of 15 target genes involved in autophagy, oxidative stress, neuro-axonal transport, angiogenesis, and tissue elasticity. Protein expression of GPX3 was assessed by western blotting. Statistical analysis was performed using the Mann-Whitney U test. Results: Variable changes in gene expression were observed between the two groups, most notably in ATG7, CAT, PRCKD, and GPX4. None of these differences reached statistical significance (p > 0.05), although CAT showed a trend (p=0.073). GPX3 gene expression demonstrated a statistically significant difference (p=0.024), but western blotting revealed no corresponding significant change in GPX3 protein expression (p=0.80). Conclusion: Preoperative chemoradiotherapy appears to alter gene expression in the oxidative stress pathway in normal rectal tissue, though these changes were not statistically significant due to the small sample size. Larger studies with quantitative protein analysis are warranted to confirm these findings.

The readmission of patients into hospitals has been considered a measure of the quality of care delivered, the quality of discharge planning, and continuity of care. In Saudi Arabia, different digital health initiatives and advancements in electronic health record systems offer a timely opportunity to synthesize evidence on different artificial intelligence-based models for predicting hospital readmission risk and identify key design features of the model that are clinically relevant and useful. In this article, we have synthesized evidence on different artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes using different machine learning and deep learning techniques and different explainable artificial intelligence techniques for predicting short-term hospital readmission risk and healthcare utilization outcomes within a 7-30-day window using peer-reviewed articles published between 2020 and 2025. The evidence on different artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes using different machine learning and deep learning techniques and different explainable artificial intelligence techniques is discussed in detail with special emphasis on its applicability to hospital systems in Saudi Arabia. The gradient boosting and tree-based ensemble techniques are found to be consistently performing well for structured electronic health record-based predictive models. The representation learning-based techniques are found to be performing well for structured electronic health record-based predictive models. The outcome definition heterogeneity, class imbalance, temporal leakage, and lack of external validation are some of the limitations identified in different studies on artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes. For hospital systems in Saudi Arabia, different factors need to be considered for developing artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes. The methodological blueprint for hospital systems in Saudi Arabia is proposed using different artificial intelligence-based models for predicting hospital readmission risk and healthcare utilization outcomes based on different machine learning and deep learning techniques and different explainable artificial intelligence techniques using the PRISMA 2020 protocol and TRIPOD+AI protocol for systematic reviews and reporting of prediction model performance.

Nigeria accounts for 28.3% of global maternal deaths, with a maternal mortality ratio of 1,047 per 100,000 live births, the highest nationally estimated figure globally. This study assessed reproductive health (RH) service preparedness across 143 primary health care (PHC) facilities in Edo State, Nigeria, constructed a validated nine-item RH Composite Preparedness Score (CPS), and identified structural determinants of preparedness deficits using multivariate regression. A census-based cross-sectional facility survey was conducted across all 18 Local Government Areas of Edo State (23–25 September 2020) using the SOML Integrated Supportive Supervision checklist, aligned with the WHO Service Availability and Readiness Assessment framework. Ordinary Least Squares and binary logistic regression were applied alongside chi-square and one-way ANOVA tests to examine geographic variation and structural predictors. The mean RH CPS was 4.92/9 (SD = 1.31), indicating that facilities lacked over half of essential RH items on average. Partograph availability was critically deficient at 17.5% and maternal death audit forms at 14.7%, representing gaps of 82.5% and 85.3% respectively. No significant inter-district variation was detected, indicating systemic rather than geographically localized failure. OLS regression was non-significant (R² = 0.031; p = .743), demonstrating that general infrastructure variables do not explain RH preparedness deficits. Among 14 secondary hospitals, partographs and maternal death audit forms were each absent in 57.1% of facilities. These findings indicate that RH preparedness failures are programmatically rather than infrastructure-determined, and are addressable through targeted procurement, competency-based training, and mandatory accountability mechanisms integrated into existing supervision systems.

This study presents a robust machine learning framework to enhance stroke risk prediction and support global health objectives to reduce stroke-related morbidity and mortality. Using the Kaggle Stroke Prediction Dataset, the research integrates advanced data preprocessing, exploratory data analysis, and five machine learning algorithms: Logistic Regression, Random Forest, Gradient Boosting, XGBoost, and LightGBM. A stacked ensemble model serves as the core methodology, achieving superior predictive performance (accuracy 97%, F1-score 0.97, AUC-ROC 0.99) compared with individual models and prior works. Bayesian Optimization ensures optimal hyperparameter selection, while explainability methods such as SHAP and LIME bolster model interpretability to meet clinical transparency demands. The methodology aligns with the NZ Ngā Tikanga Paihere Framework, embedding principles of data privacy, cultural sensitivity, and fairness. Addressing challenges such as data imbalance and computational scalability via SMOTE and distributed computing, the model demonstrates robust performance, validated through bootstrapping and cross-validation. This research advances ethical, accurate, and actionable AI-driven stroke prediction for healthcare applications.

Purpose: This systematic review’s aim is to evaluate the role of autonomic markers in predicting supraventricular tachycardia (SVT) recurrence after catheter ablation. Understanding these markers can help to identify patients at higher risk of recurrence and improve post-procedural management. Methodology: A full literature review has been conducted using 12 electronic databases, such as PubMed, ScienceDirect, Springer, MDPI and Oxford Academic. It has covered studies in English from 1999 to 2025. The PRISMA 2020 guidelines for systematic reviews have been followed in the review. Studies have been chosen using the PICO framework: adult patients with SVT who undergo catheter ablation (Population), evaluation of autonomic markers like heart rate variability (HRV), baroreflex sensitivity (BRS), skin sympathetic nerve activity (SKNA), and P-wave alternans (Intervention), comparison with normal or baseline values (Comparator), and recurrence or non-recurrence of SVT as the outcomes (Outcome). Both qualitative and quantitative data were extracted, including statistical measures such as odds ratios, hazard ratios and predictive accuracy metrics. Findings: HRV is the most studied and widely applied autonomic marker. Early post-ablation HRV changes correlate with recurrence risk in AF studies, but evidence in SVT is limited and largely hypothetical. BRS, SKNA and PWA have showed potentiality but have been underexplored in SVT. Clinical and procedural factors, such as accessory pathway location and ablation strategy, is influencing the recurrence rates. In Bangladesh, HRV has been measured in tertiary centers, while other markers are not routinely applied due to resource constraints. Conclusion: Autonomic markers have promising potential to predict SVT recurrence, but no robust SVT-specific studies are there. Future research should focus on multicenter prospective studies with standardized measurement protocols and integration of clinical factors to develop reliable predictive models.

Men play a critical role in the transmission and control of sexually transmitted infections, yet they are often overlooked in public health discussions and interventions. Sexually transmitted infections (STI) is on the increase among male in rural communities. Possible contributing factors include multiple sexual partnerships, inconsistent condom use, alcohol and drug use, and delayed health-seeking behavior. Exploring these factors will provide valuable insight into designing more inclusive and effective STI prevention programs targeting men. This study is aim to evaluate the Rising trend of sexually transmitted infections among male in Urban areas of South-South Nigeria. This was a cross-sectional study involving 250 women. A well-structured questionnaire was administered to participants. The study lasted for a period of 3 months. Statistical analysis was done using SPSS version 25.0 and p < 0.05 was significant. The results revealed that most of the participants affected were between 23 to 37 years of age, 76% had secondary education, 68% were single, 72% farming, 72% are aware of STI, 72% have no knowledge about STI, 72% have multiple girl friends, 80% do not use condom, 80% has contacted STI, 80% treated STI with herbal, 76% do not treat their girl friend alongside, and 80% had complications from STI treatment.