BACKGROUND: In Bangladesh, the rapid spread of COVID-19 virus created a panic situation among the citizen. Specially, the psycho-emotional disorder of the university students is increasing which has not been adequately investigated. AIM: Relating this, the study aimed to assess the psycho-emotional changes of the university students through investigating their level of depression and anxiety during panic and post-panic period of COVID-19 pandemic in Bangladesh. METHOD: A cross-sectional online survey was conducted among the university students in Bangladesh from April to July 2020 (panic period, n = 170) and then from August to November 2020 (post-panic period, n = 170). The Patient Health Questionnaire (PHQ)-9 and Generalized Anxiety Disorder-7 questionnaires were used to assess the depression and anxiety levels of the students, respectively. Various univariate and multivariable statistical techniques were used to analyze the data. RESULTS: The proportion of depression symptoms was 49.4% and 52.4% during the panic period and post-panic period, respectively. Anxiety symptoms were experienced by 38.2% of students during the panic period, and the percentage was nearly identical in the post-panic interval. In post-panic period, depression levels found a slight increase. Urban students reported significantly (p < 0.05) higher levels of depression and anxiety than their counterparts. Female students also exhibited significantly more anxiety symptoms (p < 0.002) than male. Depression symptoms significantly varied by family types and student’s place of residence. CONCLUSIONS: This study compared the depression and anxiety symptoms of the university students during two periods of the COVID-19 pandemic. Although the difference was small, it remains an issue for the university students in Bangladesh due to the disruption to their academic lives.

Background: Low birth weight is recognized as a pivotal risk factor affecting child survival and growth. Although Bangladesh has made commendable progress in public health, an infant mortality rate of 38 per 1000 live births and a 16% prevalence of low birth weight remain significant concerns compared to other developing countries. This situation poses a significant challenge for the formulation of future health policies in Bangladesh. As a result, this study aims to identify potential risk factors contributing to low birth weight and infant mortality among children in Bangladesh.Methods: The data is extracted from the 2014 Bangladesh Demographic and Health Survey. The response variables are infant mortality and low birth weight. In the bivariate analysis, Log‐rank tests and Chi‐square tests of independence were conducted. Cox proportional hazards and binary logistic regression models were utilized to determine the impact of risk factors on infant mortality and low birth weight.Results: This study identified several significant factors associated with children's low birth weight, including wealth index, parental education, birth order, twin births, mother's body mass index, and child sex. Additionally, wealth index, parental education, twin status, media exposure, birth order, antenatal care visits, prenatal care assistance, and low birth weight were identified as potential risk factors for infant mortality in Bangladesh.Conclusion: This study revealed that maternal and child characteristics, along with knowledge about child health care during pregnancy, can potentially reduce the risk of low birth weight and infant mortality among children in Bangladesh. To improve child health and survival, policymakers should prioritize community‐based health education programs, and encourage parents to seek healthcare information from institutional medical facilities during pregnancy and after birth.

Temporal gene expression data contain ample information to characterize gene function and are now widely used in bio-medical research. A dense temporal gene expression usually shows various patterns in expression levels under different biological conditions. The existing literature investigates the gene trajectory using the mean function. However, temporal gene expression curves usually show a strong degree of heterogeneity under multiple conditions. As a result, rates of change for gene expressions may be different in non-central locations and a mean function model may not capture the non-central location of the gene expression distribution. Further, the mean regression model depends on the normality assumptions of the error terms of the model, which may be impractical when analyzing gene expression data. In this research, a linear quantile mixed model is used to find the trajectory of gene expression data. This method enables the changes in gene expression over time to be studied by estimating a family of quantile functions. A statistical test is proposed to test the similarity between two different gene expressions based on estimated parameters using a quantile model. Then, the performance of the proposed test statistic is examined using extensive simulation studies. Simulation studies demonstrate the good statistical performance of this proposed test statistic and show that this method is robust against normal error assumptions. As an illustration, the proposed method is applied to analyze a dataset of 18 genes in P. aeruginosa, expressed in 24 biological conditions. Furthermore, a minimum Mahalanobis distance is used to find the clustering tree for gene expressions.

Background and Aims: Diabetes mellitus, characterized by high blood glucose, is an overwhelming public health concern globally, including in Bangladesh. The implication of this trend may pose a significant challenge to the health systems due to the lack of awareness and improper management of this chronic disease. To formulate strategies for public health planning, this study aims to explore the potential risk factors for elevated blood glucose levels among Bangladeshi individuals using advanced statistical methods and a nationally representative data set.Methods: This study utilized data from the 2017−18 Bangladesh Demographic and Health Survey and included 11,863 individuals. A nonparametric Kruskal−Wallis test assessed the significant association between fasting plasma glucose levels and various risk factors. Additionally, a robust quantile regression model was applied to examine the net effects of each risk factor at different quantiles of the distribution.Results: The prevalence of diabetes is 8.1% among individuals in the study population, with variations observed across different administrative divisions in the country. Respondents from the Dhaka division respondents had a higher likelihood (24.1%) of having elevated plasma glucose and the Rangpur division had a lower risk (10.3%) of developing diabetes disease. This study identified several potential risk factors associated with elevated blood glucose levels, including hypertensive disease, overweight and obese body mass index, higher economic status, reduced physical activities, and older age, significantly contributing to develop diabetes mellitus.Conclusion: This study recommends promoting healthy lifestyles, increased physical activity, effective hypertension management, obesity reduction, and nationwide screening programs to control diabetes and noncommunicable diseases in Bangladesh. These preventive measures are crucial for reducing the existing prevalence of diabetes and working toward achieving the Sustainable Development Goals by 2030.

Childhood vaccination is crucial for safeguarding children against potentially life-threatening diseases. This study aims to investigate the significant fa

Type 2 diabetes mellitus (T2DM) is a global health concern driven by factors such as obesity, sedentary behavior, and poor diet. This study uses data from the 2017-18 Bangladesh Demographic and Health Survey (BDHS) to analyze regional and individual predictors of diabetes and predi-abetes. Employing a Bayesian multinomial mixed-effects model, we account for regional varia-bility and individual factors like age, gender, BMI, residence, wealth, education, employment, and hypertension. Our results indicate significant regional differences and associations between de-mographic and health-related factors with diabetes risk. Younger individuals and those with higher BMI are more likely to be diabetic, while hypertension significantly increases diabetes risk. We applied machine learning (ML) models, including logistic regression, decision trees, k-nearest neighbor, linear discriminant analysis, and random forest, to classify diabetic status using these predictors, assessing their accuracy through 10-fold cross-validation. Logistic regression and linear discriminant analysis demonstrated robust performance across various response distributions. Simulation studies further examined the impact of different response distributions on model performance, revealing significant differences in classification accuracies. This approach of esti-mating parameters with a Bayesian model, applying ML for prediction, and conducting simulation studies to explore various scenarios highlights the importance of integrating these methodologies for effective diabetes prediction, providing insights for public health strategies to mitigate T2DM's impact.