Stigma and its associated factors among patients with COVID-19 in Dhaka City: evidence from a cross-sectional investigation.

Background: Coronavirus disease 2019 (COVID-19) has become a significant disease pandemic. Dhaka City alone has contributed about one-third to the total COVID-19 cases in Bangladesh. Globally, patients with infectious diseases, including COVID-19, experience stigma. There was no quantitative estimate of stigma experienced by patients with COVID-19 in the country. Therefore, this study aimed to assess the prevalence of stigma and its associated factors among patients with COVID-19 in Dhaka. Methods: A cross-sectional study was conducted among 384 respondents aged 18 years or older who had been hospitalized or had stayed at home and were tested negative 15 days to 6 months before the day of data collection. Data collection was done through in-person and telephone interviews using a semi-structured survey questionnaire. A 15-item COVID-19-related stigma scale questionnaire was used to assess stigma. Binary logistic regression analysis was performed to identify the predictors of stigma. Results: More than half (53.1%) of the respondents experienced stigma when they were COVID-19 positive. Females were at a 3.24 times higher risk of experiencing stigma than their male counterparts. Respondents from the 60+ age group and 40-59 age group were 63.0% and 48.0% less likely to experience stigma than those from the 18-39 age group. Non-hospitalised patients had 1.67 times higher odds of facing stigma than those hospitalised. Conclusions: This study reported a high prevalence of stigma among the patients with COVID-19 in Dhaka City. The current evidence base of stigma experience among patients with COVID-19 offers a solid foundation for creating effective strategies and policies and designing appropriate interventions to counter stigma, which will improve the psychological well-being of patients with COVID-19 in Bangladesh.

Image-based effective feature generation for protein structural class and ligand binding prediction.

Proteins are the building blocks of all cells in both human and all living creatures of the world. Most of the work in the living organism is performed by proteins. Proteins are polymers of amino acid monomers which are biomolecules or macromolecules. The tertiary structure of protein represents the three-dimensional shape of a protein. The functions, classification and binding sites are governed by the protein's tertiary structure. If two protein structures are alike, then the two proteins can be of the same kind implying similar structural class and ligand binding properties. In this paper, we have used the protein tertiary structure to generate effective features for applications in structural similarity to detect structural class and ligand binding. Firstly, we have analyzed the effectiveness of a group of image-based features to predict the structural class of a protein. These features are derived from the image generated by the distance matrix of the tertiary structure of a given protein. They include local binary pattern (LBP) histogram, Gabor filtered LBP histogram, separate row multiplication matrix with uniform LBP histogram, neighbor block subtraction matrix with uniform LBP histogram and atom bond. Separate row multiplication matrix and neighbor block subtraction matrix filters, as well as atom bond, are our novels. The experiments were done on a standard benchmark dataset. We have demonstrated the effectiveness of these features over a large variety of supervised machine learning algorithms. Experiments suggest support vector machines is the best performing classifier on the selected dataset using the set of features. We believe the excellent performance of Hybrid LBP in terms of accuracy would motivate the researchers and practitioners to use it to identify protein structural class. To facilitate that, a classification model using Hybrid LBP is readily available for use at http://brl.uiu.ac.bd/PL/. Protein-ligand binding is accountable for managing the tasks of biological receptors that help to cure diseases and many more. Therefore, binding prediction between protein and ligand is important for understanding a protein's activity or to accelerate docking computations in virtual screening-based drug design. Protein-ligand binding prediction requires three-dimensional tertiary structure of the target protein to be searched for ligand binding. In this paper, we have proposed a supervised learning algorithm for predicting protein-ligand binding, which is a similarity-based clustering approach using the same set of features. Our algorithm works better than the most popular and widely used machine learning algorithms.