Disability and depression among stroke survivors attending rehabilitation facilities at three designated tertiary care hospitals in Bangladesh: A cross-sectional study.

Background: Poststroke depression (PSD) is a highly prevalent and serious mental health condition affecting a significant proportion of stroke survivors worldwide. While its exact causes remain under investigation, managing PSD presents a significant challenge.

Aim: This study aimed to evaluate the prevalence and predictors of depression among Bangladeshi stroke victims.

The Role of Laparoscopy in Managing Impalpable Undescended Testes in a Tertiary Hospital.

One of the most prevalent congenital defects in boys is undescended testes. Ultrasound is commonly employed to locate the testis in cases of impalpable testes, which frequently yields false-negative results. Laparoscopy has gained widespread acceptance recently for managing impalpable testes.

Understanding mental health challenges and associated risk factors of post-natural disasters in Bangladesh: a systematic review.

Background And Objectives: Natural disasters are harmful occurrences caused by the Earth's geological and meteorological processes. Bangladesh is recognized as one of the country's most vulnerable to natural disasters. Therefore, the people of Bangladesh remain at high risk of natural disasters.

Food Safety Knowledge, Attitudes, Practices, and Associated Factors Among Adult Consumers in Bangladesh: Findings From a Nationwide Cross-Sectional Survey.

Aim: This study aimed to evaluate the food safety knowledge, attitudes, and practices (KAP) of Bangladeshi consumers and investigate their associations with various demographic factors.

Methods: This national cross-sectional study utilized a face-to-face survey using a structured questionnaire to recruit 1400 adult consumers between September 2022 and November 2022. Multiple linear regression analysis was employed to determine the factors associated with the food safety KAP.

Optimizing coastal groundwater quality predictions: A novel data mining framework with cross-validation, bootstrapping, and entropy analysis.

Investigating the potential of novel data mining algorithms (DMAs) for modeling groundwater quality in coastal areas is an important requirement for groundwater resource management, especially in the coastal region of Bangladesh where groundwater is highly contaminated. In this work, the applicability of DMA, including Gaussian Process Regression (GPR), Bayesian Ridge Regression (BRR) and Artificial Neural Network (ANN), for predicting groundwater quality in coastal areas was investigated. The optuna-based optimized hyperparameter is proposed to improve the accuracy of the models, including optuna-GPR and optuna-BRR as benchmark models.