Mental Health Issues in the COVID-19 Pandemic and Responses in Bangladesh : View Point of Media Reporting

Background: The negative impact of COVID-19 on mental health has been reported by media throughout the world, although this role is not well-understood in low-and middle income countries (LMIC). We examined the reporting of mental health issues during the COVID-19 pandemic in Bangladesh and initiatives undertaken to support mental health reported from the viewpoint of media. Methods: We reviewed articles published in 10 local newspapers, including seven Bangla and three English newspapers, during the first year of the COVID-19 pandemic. News topics were identified through discussions among the team members, with searches across online newspapers and portals. Data extrapolated from newspapers were documented in an Excel spreadsheet. A mixed-method approach was used following a framework analysis for analyzing data. Recurring issues and commonly emerging topics were generated from the data. Descriptive statistics were applied for analyzing quantitative data. Results: Between March 2020 and March 2021, we have identified 201 reports on mental health issues including 45 reports (22.4%) focused on stress due to the associated financial crisis, unemployment and loneliness, 50 reports (24.9%) of 80 apparent suicides linked to family issues, disharmony in conjugal relationships, harassment, sexual violence, emotional breakdown, financial crisis, and stigma due to COVID-19.There were 77 reports (38.3%) concerning domestic violence during the pandemic. Twenty-nine reports (14.4%) referenced actions taken by different organizations to address mental health issues in response to the pandemic in Bangladesh. Conclusion: News coverage has the scope to highlight important issues that can emerge as a consequence of the COVID-pandemic, such as mental health, in a low resource setting. Capacity building of the media on the way to report mental health issues during emergency situations could be a useful strategy for more credible reporting on mental health issues during the COVID-19 pandemic for raising awareness of the public and policymakers about the negative consequences on mental health of the COVID-19 pandemic in Bangladesh. Adopting policies to support essential mental health care and promoting the local organizations to take timely public health measures will be imperative for averting the negative consequences of mental health due to the COVID-19 pandemic in Bangladesh

Novel and accurate mathematical simulation of various models for accurate prediction of surface tension parameters through ionic liquids

Ionic Liquids (ILs) as a novel class of liquid solvent simultaneously carry the positive characteristics of both molten salts and organic liquids. Remarkable positive properties of ILs have such as low vapor pressure and excellent permittivity have encouraged the motivation of researchers to use them in various applications over the last decade. Surface tension is an important physicochemical property of ILs, which its experimental-based measurement has been done by various researchers. Despite great precision, some major shortcomings such as high cost and health related problems caused the researchers to develop mathematical models based on artificial intelligence (AI) approach to predict surface tension theoretically. In this research, the surface tension of two novel ILs (bis [(trifluoromethyl) sulfonyl] imide and 1,3-nonylimidazolium bis [(trifluoromethyl) sulfonyl] imide) were predicted using three predictive models. The available dataset contains 45 input features, which is relatively high in dimension. We decided to use AdaBoost with different base models, including Gaussian Process Regression (GPR), support vector regression (SVR), and decision tree (DT). Also, for feature selection and hyper-parameter tuning, a genetic algorithm (GA) search is used. The final R2 -score for boosted DT, boosted GPR, and boosted SVR is 0.849, 0.981, and 0.944, respectively. Also, with the MAPE metric, boosted GPR has an error rate of 1.73E-02, boosted SVR has an error rate of 2.35E-02, and it is 3.36E-02 for boosted DT. So, the ADABOOST-GPR model was considered as the primary model for the research

α-glucosidase inhibitors isolated from Mimosa pudica L.

The aim of the study was to isolate digestive enzymes inhibitors from Mimosa pudica through a bioassay-guided fractionation approach. Repeated silica gel and sephadex LH 20 column chromatographies of bioactive fractions afforded stigmasterol, quercetin and avicularin as digestive enzymes inhibitors whose IC50 values as compared to acarbose (351.02 ± 1.46 μg mL−1) were found to be as 91.08 ± 1.54, 75.16 ± 0.92 and 481.7 ± 0.703 μg mL−1, respectively. In conclusion, M. pudica could be a good and safe source of digestive enzymes inhibitors for the management of diabetes in future