COVID vaccine hesitancy among the tribal population and its determinants: A community-based study at berhampore block of Murshidabad District, West Bengal.

Background: On January 16, 2021, India rolled out the COVID vaccination drive. A successful and effective vaccination campaign requires much more than the availability of a safe and effective vaccine. This includes identifying vulnerable populations with lower vaccine confidence and identifying the drivers of vaccine hesitancy. Objective: This study aims to find out vaccine hesitancy among the tribal population regarding COVID-19 vaccination. Methods: It was an observational descriptive cross-sectional study, conducted at Manindranagar and Hatinagar gram panchayat of Berhampore Block of Murshidabad district, West Bengal, from June 2021-November 2021, among tribal people aged >18 years. A total of 198 tribal people were selected by applying the probability proportional to size sampling method. Participants were interviewed using predesigned, pretested, and semi-structured schedules. Potential predictors of hesitancy were investigated using the multivariate logistic regression model. Results: Vaccine hesitancy was present among 36.9% of the study participants. Fear of side effects (78.1%) was the most common reason of vaccine hesitancy. Only 30.8% of them received at least one dose of vaccine. Vaccine hesitancy was associated with decreased family income in the last 1 year (adjusted odds ratio [AOR] = 8.23), knowledge regarding vaccine (AOR = 0.41), adherence to COVID-appropriate behavior (AOR = 0.45), and trust on the local health-care worker (AOR = 0.32). Conclusion: Vaccine hesitancy among the tribal population is driven by a lack of knowledge and awareness. Their economic status, attitudes toward the health system, and accessibility factors may also play a major role in vaccine hesitancy. Extensive information, education, and communication activity, more involvement of health-care workers in the awareness campaign, and establishment of vaccination centers in tribal villages may be helpful.

Development of lab score system for predicting COVID-19 patient severity: A retrospective analysis.

AIM: To develop an accurate lab score based on in-hospital patients' potent clinical and biological parameters for predicting COVID-19 patient severity during hospital admission. METHODS: To conduct this retrospective analysis, a derivation cohort was constructed by including all the available biological and clinical parameters of 355 COVID positive patients (recovered = 285, deceased = 70), collected in November 2020-September 2021. For identifying potent biomarkers and clinical parameters to determine hospital admitted patient severity or mortality, the receiver operating characteristics (ROC) curve and Fischer's test analysis was performed. Relative risk regression was estimated to develop laboratory scores for each clinical and routine biological parameter. Lab score was further validated by ROC curve analysis of the validation cohort which was built with 50 COVID positive hospital patients, admitted during October 2021-January 2022. RESULTS: Sensitivity vs. 1-specificity ROC curve (>0.7 Area Under the Curve, 95% CI) and univariate analysis (p<0.0001) of the derivation cohort identified five routine biomarkers (neutrophil, lymphocytes, neutrophil: lymphocytes, WBC count, ferritin) and three clinical parameters (patient age, pre-existing comorbidities, admitted with pneumonia) for the novel lab score development. Depending on the relative risk (p values and 95% CI) these clinical parameters were scored and attributed to both the derivation cohort (n = 355) and the validation cohort (n = 50). ROC curve analysis estimated the Area Under the Curve (AUC) of the derivation and validation cohort which was 0.914 (0.883-0.945, 95% CI) and 0.873 (0.778-0.969, 95% CI) respectively. CONCLUSION: The development of proper lab scores, based on patients' clinical parameters and routine biomarkers, would help physicians to predict patient risk at the time of their hospital admission and may improve hospital-admitted COVID-19 patients' survivability.

Covid-19 Infection in India: A Comparative Analysis of the Second Wave with the First Wave.

Coronavirus disease 2019 (COVID-19) is considered as the most dreaded disease that has spread all over the world in the recent past. Despite its outbreak in December 2019-January 2020, a few continents and countries such as India started to experience a significant number of COVID-19-positive cases from March 2020. GISAID clade variation analysis in the period March 2020-February 2021 (period I) and March 2021-first week of April 2021 (period II) showed a rapid variation of SARS-CoV-2 in all continents and India over time. Studying the relationship of patient age or gender with viral clades in these two periods revealed that the population under 10 years of age was the least affected, whereas the 11-60-year-old population was the most affected, irrespective of patient gender and ethnicity. In the first wave, India registered quite a low number of COVID-19-positive cases/million people, but the scenario unexpectedly changed in the second wave, when even over 400,000 confirmed cases/day were reported. Lineage analysis in India showed the emergence of new SARS-CoV-2 variants, i.e., B.1.617.1 and B.1.617.2, during April-May 2021, which might be one of the key reasons for the sudden upsurge of confirmed cases/day. Furthermore, the emergence of the new variants contributed to the shift in infection spread by the G clade of SARS-CoV-2 from 46% in period II to 82.34% by the end of May 2021. Along with the management of the emergence of new variants, few factors viz., lockdown and vaccination were also accountable for controlling the upsurge of new COVID-19 cases throughout the country. Collectively, a comparative analysis of the scenario of the first wave with that of the second wave would suggest policymakers the way to prepare for better management of COVID-19 recurrence or its severity in India and other countries.

Synthetic and computational efforts towards the development of peptidomimetics and small-molecule SARS-CoV 3CLpro inhibitors.

Severe Acute Respiratory Syndrome (SARS) is a severe febrile respiratory disease caused by the beta genus of human coronavirus, known as SARS-CoV. Last year, 2019-n-CoV (COVID-19) was a global threat for everyone caused by the outbreak of SARS-CoV-2. 3CLpro, chymotrypsin-like protease, is a major cysteine protease that substantially contributes throughout the viral life cycle of SARS-CoV and SARS-CoV-2. It is a prospective target for the development of SARS-CoV inhibitors by applying a repurposing strategy. This review focuses on a detailed overview of the chemical synthesis and computational chemistry perspectives of peptidomimetic inhibitors (PIs) and small-molecule inhibitors (SMIs) targeting viral proteinase discovered from 2004 to 2020. The PIs and SMIs are one of the primary therapeutic inventions for SARS-CoV. The journey of different analogues towards the evolution of SARS-CoV 3CLpro inhibitors and complete synthetic preparation of nineteen derivatives of PIs and ten derivatives of SMIs and their computational chemistry perspectives were reviewed. From each class of derivatives, we have identified and highlighted the most compelling PIs and SMIs for SARS-CoV 3CLpro. The protein-ligand interaction of 29 inhibitors were also studied that involved with the 3CLpro inhibition, and the frequent amino acid residues of the protease were also analyzed that are responsible for the interactions with the inhibitors. This work will provide an initiative to encourage further research for the development of effective and drug-like 3CLpro inhibitors against coronaviruses in the near future.