Effects of school-level and area-level socio-economic factors on elementary school student COVID-19 infections: a population-based observational study.

OBJECTIVES: To estimate the variability of the cumulative incidence of SARS-CoV-2 infections among elementary school students attributable to individual schools and/or their geographic areas, and to ascertain whether socio-economic characteristics of school populations and/or geographic areas may be predictive of this variability. DESIGN: Population-based observational study of SARS-CoV-2 infections among elementary school children. SETTING: 3994 publicly funded elementary schools in 491 forward sortation areas (designated geographic unit based on first three characters of Canadian postal code), Ontario, Canada, September 2020 to April 2021. PARTICIPANTS: All students attending publicly funded elementary schools with a positive molecular test for SARS-CoV-2 reported by the Ontario Ministry of Education. MAIN OUTCOME MEASURES: Cumulative incidence of laboratory-confirmed elementary school student SARS-CoV-2 infections in Ontario, 2020-21 school year. RESULTS: A multilevel modelling approach was used to estimate the effects of socio-economic factors at the school and area levels on the cumulative incidence of elementary school student SARS-CoV-2 infections. At the school level (level 1), the proportion of the student body from low-income households was positively associated with cumulative incidence (ß=0.083, p<0.001). At the area level (level 2), all dimensions of marginalisation were significantly related to cumulative incidence. Ethnic concentration (ß=0.454, p<0.001), residential instability (ß=0.356, p<0.001) and material deprivation (ß=0.212, p<0.001) were positively related, while dependency (ß=-0.204, p<0.001) was negatively related. Area-related marginalisation variables explained 57.6% of area variability in cumulative incidence. School-related variables explained 1.2% of school variability in cumulative incidence. CONCLUSIONS: The socio-economic characteristics of the geographic area of schools were more important in accounting for the cumulative incidence of SARS-CoV-2 elementary school student infections than individual school characteristics. Schools in marginalised areas should be prioritised for infection prevention measures and education continuity and recovery plans.

Molecular docking approaches and its significance in assessing the antioxidant properties in different compounds.

In the last few years, the significance of antioxidant compounds and their properties has attracted great interest from the scientific community. The role of an antioxidant in managing & regulating oxidative stress and also in the protection of the human body from severe adverse effects due to excess release of free radicles or reactive oxygen species (ROS) is remarkable. From aiding protection & combating severe illnesses such as cancer, neurodegeneration, aging, and diabetes to being a vital part of the treatment of SARs-CoV-19 is of great importance. Therefore, the study of anti-oxidants is of great importance in human sustenance. Additionally, molecular docking techniques and their various mathematical features help in understanding the molecular interactions of anti-oxidants based on their lowest binding energy. The evaluation of the binding score between two constituent molecules will provide insight as to the binding process and also suggest possible novel therapeutic targets for the treatment of diseases. In this chapter, we will discuss the significance of molecular docking techniques in the study of antioxidant compounds.

A molecular dynamics simulation study of the ACE2 receptor with screened natural inhibitors to identify novel drug candidate against COVID-19.

BACKGROUND & OBJECTIVES: The massive outbreak of Novel Severe Acute Respiratory Syndrome Coronavirus (SARS-CoV-2) has turned out to be a serious global health issue worldwide. Currently, no drugs or vaccines are available for the treatment of COVID-19. The current computational study was attempted to identify a novel therapeutic inhibitor against novel SARS-CoV-2 using in silico drug discovery pipeline. METHODS: In the present study, the human angiotensin-converting enzyme 2 (ACE2) receptor was the target for the designing of drugs against the deadly virus. The 3D structure of the receptor was modeled & validated using a Swiss-model, Procheck & Errat server. A molecular docking study was performed between a group of natural & synthetic compounds having proven anti-viral activity with ACE2 receptor using Autodock tool 1.5.6. The molecular dynamics simulation study was performed using Desmond v 12 to evaluate the stability and interaction of the ACE2 receptor with a ligand. RESULTS: Based on the lowest binding energy, confirmation, and H-bond interaction, cinnamic acid (-5.20 kcal/mol), thymoquinone (-4.71 kcal/mol), and andrographolide (Kalmegh) (-4.00 kcal/mol) were screened out showing strong binding affinity to the active site of ACE2 receptor. MD simulations suggest that cinnamic acid, thymoquinone, and andrographolide (Kalmegh) could efficiently activate the biological pathway without changing the conformation in the binding site of the ACE2 receptor. The bioactivity and drug-likeness properties of compounds show their better pharmacological property and safer to use. INTERPRETATION & CONCLUSIONS: The study concludes the high potential of cinnamic acid, thymoquinone, and andrographolide against the SARS-CoV-2 ACE2 receptor protein. Thus, the molecular docking and MD simulation study will aid in understanding the molecular interaction between ligand and receptor binding site, thereby leading to novel therapeutic intervention.