COVID-19 Progression: A County-Level Analysis of Vaccination and Case Fatality in Mississippi, USA.

The COVID-19 pandemic has created a severe upheaval in the U.S., with a particular burden on the state of Mississippi, which already has an exhausted healthcare burden. The main objectives of this study are: (1) to analyze the county-level COVID-19 cases, deaths, and vaccine distribution and (2) to determine the correlation between various social determinants of health (SDOH) and COVID-19 vaccination coverage. We analyzed COVID-19-associated data and county-level SDOH factors in 82 counties of Mississippi. The cumulative COVID-19 and socio-demographic data variables were grouped into feature and target variables. The statistical and exploratory data analysis (EDA) was conducted using Python 3.8.5. The correlation between the target and feature variables was performed by Pearson Correlation analysis. The heat Map Correlation Matrix was visually presented to illustrate the correlation between each pair of features and each target variable. Results indicated that people of Asian descent had the highest vaccination coverage of 77% fully vaccinated compared to 52%, 46%, 42% and 25% for African Americans, Whites, Hispanics, and American Indians/Alaska Natives, respectively. The county-level vaccination rate was significantly higher among the minority populations than the White population. It was observed that COVID-19 cases and deaths were positively correlated with per capita income and negatively correlated with the percentage of persons without a high school diploma (age 25+). This study strongly demonstrates that different SDOH factors influence the outcome of the COVID-19 vaccination rate, which also affects the total number of COVID-19 cases and deaths. Vaccine promotion should be given to all populations regardless of race and ethnicity to achieve uniform acceptance. Therefore, statewide policy recommendations focusing on specific community needs should help achieve health equity in COVID-19 vaccination management.

Current Status in the Design and Development of Agonists and Antagonists of Adenosine A3 Receptor as Potential Therapeutic Agents.

Adenosine receptors (ARs) belongs to the family of G-protein coupled receptors (GPCR) that are responsible for the modulation of a wide variety of physiological functions. The ARs are also implicated in many diseases such as cancer, arthritis, cardiovascular and renal diseases. The adenosine A3 receptor (A3AR) has emerged as a potential drug target for the progress of new and effective therapeutic agents for the treatment of various pathological conditions. This receptor's involvement in many diseases and its validity as a target has been established by many studies. Both agonists and antagonists of A3AR have been extensively investigated in the last decade with the goal of developing novel drugs for treating diseases related to immune disorders, inflammation, cancer, and others. In this review, we shall focus on the medicinal chemistry of A3AR ligands, exploring the diverse chemical classes that have been projected as future leading drug candidates. Also, the recent advances in the therapeuetic applications of A3AR ligands are highlighted.

Is clay-polycation adsorbent future of the greener society? In silico modeling approach with comprehensive virtual screening.

Presence of organic pollutants in the wastewater and aquatic environment is one of the serious concerns worldwide. Superior adsorption of organic pollutants on modified clays with organocations is well approved nowadays. Among hybrid materials, clay-polyelectrolyte nanocomposites (CPN) are one of the specifically designed materials for the efficient adsorption of diverse organic pollutants. Due to higher surface area of the clay mineral coupled with a polymer coating, they have an explicit affinity for the organic pollutants. In this background, we have developed statistically significant and mechanistically interpretable quantitative structure-property relationship (QSPR) model for adsorption coefficient of diverse organic pollutants to the protonated montmorillonite-poly-4-vinylpyridine-co-styrene (Mt-HPVPcoS), a hybrid CPN. Further, the model was employed to predict the logkd value of ∼0.9 million chemicals from five diverse databases spanning from existing and experimental pharmaceuticals, natural and synthetic chemicals and dyes with unknown logkd value for the mentioned CPN. The reliability of predicted data is checked with two layers confidence screening i.e. the applicability domain study followed by prediction quality check by 'Prediction Reliability Indicator'. Thus, prediction of each compound can be used for data gap filling by environmental regulatory authorities as well as industries. Followed by, maximum common substructure-based (MCS) algorithm is employed for individual database to extract the important structural scaffold for higher logkd to the mentioned CPN.

Single or mixture halogenated chemicals? Risk assessment and developmental toxicity prediction on zebrafish embryos based on weighted descriptors approach.

Halogenated chemicals including perfluoroalkyl substances (PFASs) represent an emerging class of endocrine-disrupting pollutants for human populations across the globe. Distress related to their environmental fate and toxicity has initiated several research projects, but the amount of experimental data available for these pollutants is limited. The objective of this study is to assess the toxicity of potentially "safer" alternatives, in relation to their existing counterparts. Developmental toxicity data on zebrafish (Danio rerio) embryos of single and tertiary halogenated mixtures were modeled employing quantitative structure-toxicity relationship (QSTR) tool. The computed models are then employed for toxicity prediction of theoretically generated binary and tertiary mixtures (which have no experimental data) to check their possible threshold and mode of toxicity for future risk assessment. Further, for toxicity screening, we have prepared a huge external dataset consists of single (24), binary (276) and tertiary (2024) mixtures of PFASs. It was accomplished by combination method and predicted through developed models for interpretation of toxicity threats for individuals and mixtures along with identification of diverse range and combination of toxicity thresholds. We found that chemicals in mixtures displayed concentration addition of individual chemical suggesting a similar mode of toxic action and non-interaction of chemicals. Not only that, mixtures of halogenated compounds including PFASs showed less toxicity than their single counterparts and the obtained toxicity trend is: Single chemical > Binary mixture > Tertiary mixture.