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Background A previous study identified students taking Bangladeshi university entrance tests as a vulnerable group at a higher risk of suicidal behaviours before the COVID-19 pandemic. However, the impact of the pandemic on the magnitude of these behaviours among this population remains unexplored. Aims This study aimed to investigate the prevalence of suicidal ideation and associated factors among Bangladeshi university entrance test takers following the pandemic. In addition, an approach based on Geographic Information System (GIS) data was used to visualise the distribution of suicidal ideation across the country. Methods A cross-sectional approach was used to collect data among participants taking the entrance test at Jahangirnagar University in September 2022. Using SPSS, data were analysed with chi-squared tests and binary regression, and ArcGIS was used to map the results across the nation. Results The study revealed a prevalence of 14.4% for past-year suicidal ideation, with 7.4% and 7.2% reporting suicide plans and attempts, respectively. Notably, repeat test-takers exhibited a higher prevalence of suicidal behaviours. Significant risk factors for suicidal ideation included urban residence, smoking, drug use, COVID-19 infection and deaths among close relations, depression, anxiety and burnout. The GIS-based distribution indicated significant variation in the prevalence of suicidal ideation across different districts, with higher rates observed in economically and infrastructurally deprived areas. Conclusions Urgent measures are needed to address the high prevalence of suicidal behaviours among students taking university entrance tests students in Bangladesh, particularly in light of the COVID-19 pandemic. Enhanced mental health support, targeted prevention efforts and improved resources in economically disadvantaged regions are crucial to safeguard the well-being of these students.

Skin diseases are frequent and quite perennial in the world, and in some cases, these lead to cancer. These are curable if detected earlier and treated appropriately. An automated image-based detection system consisting of four main modules: image enhancement, region of interest segmentation, feature extraction, and detection can facilitate early identification of these diseases. Diverse image-based methods incorporating machine learning techniques are developed to diagnose different types of skin diseases. This article focuses on the review of the tools and techniques used in the diagnosis of 28 common skin diseases. Furthermore, it has discussed the available image databases and the evaluation metrics for the performance analysis of various diagnosis systems. This is vital for figuring out the implementation framework as well as the efficacy of the diagnosis methods for the neophyte. Based on the performance accuracy, the state-of-the-art method for the diagnosis of a particular disease is figured out. It also highlights challenges and shows future research directions.

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