Objectives: Overweight and obesity have been related to a variety of adverse health outcomes. Understanding the overweight and obesity epidemic in Bangladesh, particularly among reproductive-aged women, is critical for monitoring and designing effective control measures. The purpose of this study was to determine the prevalence of overweight and obesity in reproductive-aged women and to identify the risk factors of overweight and obesity. Design: A total of 70,651 women were obtained from the five most recent and successive Bangladesh Demographic and Health Surveys (BDHS). The multilevel logistic regression model was used to explore the individual-and community-level factors of overweight and obesity. Setting: Five most recent nationally representative household surveys across all regions. Participants: Reproductive aged (15–49 years) non-pregnant women. Results: Approximately 35.2% (95% CI: 34.9-35.6%) of women were either overweight or obese in Bangladesh. At the individual- and community-level, higher age (aOR=5.79, 95% CI: 5.28–6.34), secondary or higher education (aOR=1.69 [1.60–1.78]), relatively wealthiest households (aOR=4.41 [4.10-4.74]), electronic media access (aOR=1.32 [1.26–1.37]), and community high literacy (aOR=1.10 [1.04–1.15]) of women were significantly positively associated with being overweight or obese. Whereas, rural residents (aOR=0.79 [0.76–0.82]), from larger-sized households (aOR=0.80 [0.73–0.87]) and have high community employment (aOR=0.92 [0.88–0.97]) were negatively associated with the probability of being overweight or obese. Conclusion: Individual- and community-level factors influenced the overweight and obesity of Bangladeshi reproductive-aged women. Interventions and a comprehensive public health plan aimed at identifying and addressing the growing burden of overweight and obesity should be a top focus.

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As coronavirus proliferation and death rates explode across the nation, the globe is on the verge of another health crisis, with daily doses of mental stress and depression among people of all ages. Our study was designed to investigate depression and stress among tertiary level students in Bangladesh during COVID-19 and to explore the influencing factors associated to them. We considered socio-demographics, educational information, financial information, life-style factors, and Depression Anxiety Stress Scale-21 etc. Univariate, bi-variate and binary logistic regression analysis was conducted. In our study, 32.6% (n=132) of the respondent were mentally depressed, and 44.9% (n=182) were stressed. Our analysis indicates that students aged more than 25 years, in a relationship, and those who ignored news were more likely to get depressed. Similarly, those who believed to have a hangout effect, ignored news, and spent more than 5 hours online daily were more likely to experience stress. On the other hand, students who are extrovert, participated in extra-curricular activities, did physical activities, meditated/prayed, solved problems, and studied more than 2 hours were less likely to get depressed. Likewise, students who were extrovert participated in physical activities and studied more than 2 hours were less likely to get stressed. We need to address students' mental health issues because of its long-lasting impact on current and future society, and make informed decisions to tackle depression and stress.

Leukemia cancer is one of the most leading detrimental cancer diseases in worldwide. A huge number of genes are responsible for cancer diseases. Therefore, it is necessary to identify the most informative genes of Leukemia cancer. The main objectives of this study are to: (i) identify the most informative genes using five feature selection techniques (FST) and (ii) adopt six classifiers to classify the cancer disease and compare them. Leukemia cancer data has been taken from Kent ridge biomedical data repository, USA. There are 7129 genes and 72 patients. Among them, 47 patients are cancer and 25 are control. We have used five FST as t-test; Wilcoxon sign rank sum (WCSRS) test, random forest (RF), Boruta and least absolute shrinkage and selection operator (LASSO). We have also used six classifiers as Adaboost (AB), classification and regression tree (CART), artificial neural network (ANN), random forest (RF), linear discriminant analysis (LDA) and naive Bayes (NB). The performances of these classifiers are evaluated by accuracy (ACC), sensitivity (SE), specificity (SP), positive predictive value (PPV), negative predictive value (NPV), and F-measure (FM). We used simulated dataset to check the validity of proposed method. The results indicate that the combination of LASSO based FST and NB classifier gives the highest classification accuracy of 99.95%. On the basis of the results, we can conclude that the combination of LASSO based FST and NB classifier predicts the leukemia cancer more accurately compare to any other combination of FST and classifiers utilized in this study.