Shaheed Ziaur Rahman Medical College

Abstract Selecting an appropriate supportive response for a mental health question post is a key step toward scalable mental health question answering, yet it remains difficult due to semantic mismatch, noisy informal language, and the need to align supportive intent beyond surface lexical overlap. We propose MF-GAT, a novel multi view graph attention matching framework that constructs a Concept Interaction Graph (CIG) to explicitly encode post response concept alignments and their interaction structure. MF-GAT learns three complementary evidence streams, including local interaction features, multi view fusion features, and global context features, and applies view specific graph attention to propagate and reweight informative relational signals over the CIG. A gated fusion module then adaptively integrates the view representations into a unified matching vector for prediction. We evaluate MF-GAT both as a pair classification model and as a retrieval ranking model for selecting the best support from a candidate pool, reporting Accuracy and F1 together with standard ranking metrics including MRR and NDCG. On the MHQA benchmark, MF-GAT achieves 0.95 Accuracy and 0.85 F1, outperforming BERT (0.89, 0.67), CIG-GCN (0.92, 0.79), ARC-II (0.85, 0.60), and MatchPyramid (0.82, 0.59). These results show that novel multi view interaction graph modeling with attention based propagation improves both supportive response classification and practical retrieval quality for mental health support selection.

The increasing trend of the adoption of digital technology in the modern workplace has given rise to opportunities as well as challenges for mental health, necessitating the applicability of machine learning for mental health screening. Mental health data obtained from digital technology is high dimensional and heterogeneous in nature and is not effective for predictive models. This problem is tackled by introducing a hybrid model of Autoencoder and Light Gradient Boosting Machine (LightGBN) for mental health screening in a technologydominated workplace. The autoencoder is used for efficient extraction of latent vectors from survey forms, and nonlinear classification can be obtained by LightGBM. This method is tested for accuracy on the Mental Health in Tech Survey dataset by implementing Logistic Regression, Support Vector Machines (SVM), Random Forest (RF), and XGBoost Models as baseline models. Experimental results show that the proposed framework outperforms all baseline methods, achieving 96 percent accuracy, an F1-score of 0.960, and a ROC-AUC of 0.98. The results demonstrate that combining deep representation learning with gradientboosted ensembles enables accurate, reliable, and interpretable mental health screening in digitally mediated work settings.

Depression is a major health concern among the elderly, affecting their quality of life. The Geriatric Depression Scale - Short Form (GDS-15) is commonly used to screen for depression in older adults. This study aimed to translate, culturally adapt, and validate the GDS-15 into Bengali for use among elderly people in Bangladesh. The GDS-15 was translated into Bengali, followed by cultural adaptation. A cross-sectional study was conducted with 100 elderly participants aged 60 years, and above in Chittagong Medical College Hospital. Psychometric validation was performed using reliability testing, chi-square analysis, ANOVA, and principal component analysis (PCA). The Bengali version of the GDS-15 demonstrated strong construct validity, with significant correlations between the original and translated versions of the items (r = 0.9788, p < 0.001). However, reliability analysis showed a low Cronbach's alpha (0.4753), indicating suboptimal internal consistency. The PCA revealed multiple components, suggesting the multidimensionality of the scale in this population. The Bengali GDS-15 showed low internal consistency which warrants the need for further cultural refinement and psychometric validation before routine use among the population. However, the Bengali GDS-15 has the potential to serve as a useful screening tool for geriatric depression in Bangladesh with further improvements.

Background: High-dose letrozole (≥20 mg/day) is increasingly used for ovulation induction in anovulatory infertility, particularly in poor responders to standard doses. However, concerns remain regarding its short-term impact on ovarian reserve markers such as anti-Müllerian hormone (AMH) and antral follicle count (AFC). Methods: This non-randomized prospective comparative study was conducted at the Department of Obstetrics and Gynecology, 250 bedded Mohammad Ali Hospital, Bogura. From February 2025 to January 2026. A total of 50 anovulatory women undergoing ovulation induction were enrolled through consecutive sampling. Patients were non-randomly allocated to high-dose letrozole (≥20 mg/day for 5 days, n=25) or standard-dose letrozole (5 mg/day for 5 days, n=25). AMH and AFC were measured at baseline, 1 week, 1 month, 3 months, and 6 months post-cycle. Follicular response (follicles >14 mm) and cycle cancellation rates were recorded. Data were analyzed using SPSS version 26. Results: The study population had a mean age of 30.0 ± 3.3 years, with PCOS as the most common diagnosis (62%). High-dose letrozole produced significantly more follicles >14 mm than standard-dose (5.2 ± 1.4 vs. 2.8 ± 0.8, p<0.001). In the high-dose group, AMH declined transiently at 1 week (2.9±0.6 to 2.3±0.5 ng/mL, 21% reduction, p<0.001) and AFC declined at 1 month (13.5±2.8 to 11.2±2.5, 17% reduction, p<0.001). Both markers returned to baseline by 3 months and remained stable through 6 months. The standard-dose group showed no significant changes in any marker. Cycle cancellation rates were comparable (8% vs. 4%, p=1.00). Conclusion: High-dose letrozole increased follicular response but was associated with transient, reversible reductions in AMH and AFC, with recovery by 3 months. These outcomes suggest that there is no sustained short-term suppression of ovarian reserve markers in this cohort. Larger randomized studies are needed to assess outcomes, including ovulation, pregnancy, live birth, multiple pregnancy, and adverse events, before routine recommendations.

A rare case of primary amenorrhea caused by cervical hypoplasia and vaginal agenesis highlights the critical need for early diagnosis and timely surgical intervention to prevent severe complications such as hematometra, endometriosis, infections, infertility, and a staged multidisciplinary approach optimizes outcomes in these rare, complex presentations.