Impact on health and provision of healthcare services during the COVID-19 lockdown in India: A multicentre cross-sectional study

Introduction The COVID-19 pandemic resulted in a national lockdown in India from midnight on 25 March 2020, with conditional relaxation by phases and zones from 20 April. We evaluated the impact of the lockdown in terms of healthcare provisions, physical health, mental health and social well-being within a multicentre cross-sectional study in India. Methods The SMART India study is an ongoing house-to-house survey conducted across 20 regions including 11 states and 1 union territory in India to study diabetes and its complications in the community. During the lockdown, we developed an online questionnaire and delivered it in English and seven popular Indian languages (Hindi, Tamil, Marathi, Telegu, Kannada, Bengali, Malayalam) to random samples of SMART-India participants in two rounds from 5 May 2020 to 24 May 2020. We used multivariable logistic regression to evaluate the overall impact on health and healthcare provision in phases 3 and 4 of lockdown in red and non-red zones and their interactions. Results A total of 2003 participants completed this multicentre survey. The bivariate relationships between the outcomes and lockdown showed significant negative associations. In the multivariable analyses, the interactions between the red zones and lockdown showed that all five dimensions of healthcare provision were negatively affected (non-affordability: OR 1.917 (95% CI 1.126 to 3.264), non-accessibility: OR 2.458 (95% CI 1.549 to 3.902), inadequacy: OR 3.015 (95% CI 1.616 to 5.625), inappropriateness: OR 2.225 (95% CI 1.200 to 4.126) and discontinuity of care: OR 6.756 (95% CI 3.79 to 12.042)) and associated depression and social loneliness. Conclusion The impact of COVID-19 pandemic and lockdown on health and healthcare was negative. The exaggeration of income inequality during lockdown can be expected to extend the negative impacts beyond the lockdown

A Review on Innovations in Soil Remediation Techniques Using Machine Learning

The discharge of wastewater into the ecosystem has an impact on fish and human health, therefore the toxins needs to be removed. It is sustainable to remove pollutants from wastewater by utilizing biochar made from lignocellulosic biomass (LCB) that has undergone thermal degradation. Because of it's large surface area, hollow structure, the oxygen groupings, and relatively low cost, bio Char is now known as a change rival in catalytic processes. Biochar was used in conjunction with a number of cutting-edge, creative technologies to treat wastewater efficiently. Details collected soil sampling, such as facts about the toxins current, the nature of the soil, its surrounding circumstances, and the efficacy of various rehabilitation methods, can be used to training machine learning methods. Through data analysis, machine learning models are able to spot relationships and trends which human beings might miss, which improves the accuracy of projections regarding the results of soil cleanup. The review paper outlines the challenges facing biochar-based enzymes using immediate and new technologies, along with emphasizes the application of algorithmic learning in pollution removal. Limitations and likelihoods for additional investigation are examined

Bivariate Genome-Wide Association Analyses Identified Genes with Pleiotropic Effects for Femoral Neck Bone Geometry and Age at Menarche

10.1371/journal.pone.0060362PLoS ONE84e6036

Adolescents\u27 Behaviors, Fitness, and Knowledge Related to Active Living before and during the COVID-19 Pandemic: A Repeated Cross-Sectional Analysis

BACKGROUND: Nearly all schools in the United States experienced shutdown followed by phased reopening during the COVID-19 pandemic, thereby limiting students\u27 opportunities for physical activity (PA). This study aimed to examine adolescents\u27 PA at school (PAS) and PA out-of-school (PAO), screen-based sedentary behaviors (SbSB), health-related fitness, and knowledge understanding about PA and fitness before and during the pandemic. METHODS: Three rounds of data were collected: Time 1 pre-pandemic (January 2020; = 405), Time 2 schools partially reopened (February 2021; = 412), and Time 3 schools fully reopened (March 2021; = 450). Adolescents completed the Youth Activity Profile, the 20 m Progressive Aerobic Cardiovascular Endurance Run (PACER) test and Plank test, and a written test, to measure the behaviors (T1, T2, T3), fitness (T2-T3), and knowledge (T1, T2, T3), respectively. RESULTS: Inferential statistical analyses revealed a significant time effect for the behaviors and fitness. From T1 to T2 PAO decreased but PAS increased; whereas SbSB decreased at T3 compared to T1 and T2. Health-related fitness improved from T2 to T3. Further, the change patterns for SbSB varied by grade, and those for knowledge understanding varied by gender. CONCLUSION: The findings confirm the disruptive impact of the COVID-19 pandemic on adolescents\u27 active living but varied by school grade and gender. The favorable changes from T2 to T3 observed for fitness and other constructs may be partially attributable to an interrupted fitness education intervention. The findings may guide the design and evaluation of future interventions addressing the physical inactivity pandemic during public health crises (e.g., COVID-19)