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.
It has been demonstrated that noncoding RNAs have significant physiological and pathological roles. Modulation of noncoding RNAs may offer therapeutic approaches as per recent findings. Small RNAs, mostly long noncoding RNAs, siRNA, and microRNAs make up noncoding RNAs. Inhibiting or promoting protein breakdown by binding to 3’ untranslated regions of target mRNA, microRNAs post-transcriptionally control the pattern of gene expression. Contrarily, long non-coding RNAs perform a wider range of tasks, including serving as molecular scaffolding, decoys, and epigenetic regulators. This article provides instances of long noncoding RNAs and microRNAs that may be a biomarker of CVD (cardiovascular disease). In this paper we highlight various RNA-based vaccine formulation strategies designed to target these biomarkers—that are either currently in the research pipeline or are in the global pharmaceutical market—along with the physiological hurdles that need to be overcome.
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.
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