ABSTRACT
The novel coronavirus disease 2019 (COVID-19) infections have rapidly spread throughout the world, and the virus has acquired an ability to spread via aerosols even at long distances. Hand washing, face-masking, and social distancing are the primary preventive measures against infections. With mounting scientific evidence, World Health Organisation (WHO) declared COVID-19 an air-borne disease. This ensued the need to disinfect air to reduce the transmission. Ultraviolet C (UVC) comprising the light radiation of 200-280 nm range is a commonly used method for inactivation of pathogens. The heating, ventilation, and air conditioning (HVAC) systems are not beneficial in closed spaces due to poor or no ability to damage circulating viruses. Therefore, standard infection-prevention practices coupled with a strategy to reduce infectious viral load in air substantially might be helpful in reducing virus transmissibility. In this study, we implemented UV light-based strategies to combat COVID-19 and future pandemics. We tested various disinfection protocols by using UVC-based air purification systems and currently installed such a system in workspaces, rushed out places, hospitals and healthcare facilities for surface, air, and water disinfection. In this study, we designed a prototype device to test the dose of UVC required to inactivate SARS-CoV-2 in aerosols and demonstrate that the radiation rapidly destroys the virus in aerosols. The UVC treatment renders the virus non-infectious due to chemical modification of nucleic acid. We also demonstrate that UVC treatment alters the Spike protein conformation that may further affect the infectivity of the virus. We show by using a mathematical model based on the experimental data that UVC-based air disinfection strategy can substantially reduce the risk of virus transmission. The systematic treatment by UVC of air in the closed spaces via ventilation systems could be helpful in reducing the active viral load in the air.
ABSTRACT
BackgroundEmergence of new variant of SARS-CoV-2, namely omicron, has posed a global concern because of its high rate of transmissibility and mutations in its genome. Researchers worldwide are trying to understand the evolution and emergence of such variants to understand the mutational cascade events. MethodsWe have considered all omicron genomes (n = 302 genomes) available till 2nd December 2021 in the public repository of GISAID along with representatives of variants of concern (VOC), i.e., alpha, beta, gamma, delta, and omicron; variant of interest (VOI) mu and lambda; and variant under monitoring (VUM). Whole genome-based phylogeny and mutational analysis were performed to understand the evolution of SARS CoV-2 leading to emergence of omicron variant. ResultsWhole genome-based phylogeny depicted two phylogroups (PG-I and PG-II) forming variant specific clades except for gamma and VUM GH. Mutational analysis detected 18,261 mutations in the omicron variant, majority of which were non-synonymous mutations in spike (A67, T547K, D614G, H655Y, N679K, P681H, D796Y, N856K, Q954H), followed by RNA dependent RNA polymerase (rdrp) (A1892T, I189V, P314L, K38R, T492I, V57V), ORF6 (M19M) and nucleocapsid protein (RG203KR). ConclusionDelta and omicron have evolutionary diverged into distinct phylogroups and do not share a common ancestry. While, omicron shares common ancestry with VOI lambda and its evolution is mainly derived by the non-synonymous mutations.
ABSTRACT
COVID-19 has posed unforeseen circumstances and throttled major economies worldwide. India has witnessed two waves affecting around 31 million people representing 16% of the cases globally. To date, the epidemic waves have not been comprehensively investigated to understand pandemic progress in India. In the present study, we aim for a cross-sectional analysis since its first incidence up to 26th July 2021. We have performed the pan Indian evolutionary study using 20,086 high-quality complete genomes of SARS-CoV-2. Based on the number of cases reported and mutation rates, we could divide the Indian epidemic into seven different phases. First, three phases constituting the pre-first wave had a very less average mutation rate (<11), which increased in the first wave to 17 and then doubled in the second wave (~34). In accordance with the mutation rate, variants of concern (alpha, beta, gamma and delta) and interest (eta and kappa) also started appearing in the first wave (1.5% of the genomes), which dominated the second (~96% of genomes) and post-second wave (100% of genomes) phases. Whole genome-based phylogeny could demarcate the post-first wave isolates from previous ones by the point of diversification leading to incidences of VOCs and VOIs in India. Nation-wide mutational analysis depicted more than 0.5 million events with four major mutations in ~97% of the total 20,086 genomes in the study. These included two mutations in coding (spike (D614G) and NSP 12b (P314L) of RNA dependent RNA polymerase), one silent mutation (NSP3 F106F) and one extragenic mutation (5 UTR 241). Large scale genome-wide mutational analysis is crucial in expanding knowledge on evolution of deadly variants of SARS-CoV-2 and timely management of the pandemic.
ABSTRACT
Viruses are dependent on the host tRNA pool, and an optimum codon usage pattern (CUP) is the driving force in its evolution. Systematic analysis of CUP of the coding sequences (CDS) of representative major pangolin lineages A and B of SARS-CoV-2 indicate a single transmission event of a codon-optimized virus from its source into humans. Here, no direct congruence could be detected in CUP of all CDS of SARS-CoV-2 with the non-human natural SARS viruses further reiterating its novelty. Several CDS show similar CUP with bat or pangolin, while others have distinct CUP pointing towards a possible hybrid nature of the virus. At the same time, phylogenetic diversity suggests the role of even silent mutations in its success by adapting to host tRNA pool. However, genomes of SARS-CoV-2 from primary infections are required to investigate the origins amongst the competing natural or lab leak theories.
ABSTRACT
BackgroundThis study aims to analyze the dynamics of the published articles and preprints of Covid-19 related literature from different scientific databases and sharing platforms. MethodsThe PubMed, Elsevier, and Research Gate (RG) databases were under consideration in this study over a specific time. Analyses were carried out on the number of publications as (a) function of time (day), (b) journals and (c) authors. Doubling time of the number of publications was analyzed for PubMed "all articles" and Elsevier published articles. Analyzed databases were (1A) PubMed "all articles" (01/12/2019-12/06/2020) (1B) PubMed Review articles (01/12/2019-2/5/2020) and (1C) PubMed Clinical Trials (01/01/2020-30/06/2020) (2) Elsevier all publications (01/12/2019-25/05/2020) (3) RG (Article, Pre Print, Technical Report) (15/04/2020-30/4/2020). FindingsTotal publications in the observation period for PubMed, Elsevier, and RG were 23000, 5898 and 5393 respectively. The average number of publications/day for PubMed, Elsevier and RG were 70.0 {+/-}128.6, 77.6{+/-}125.3 and 255.6{+/-}205.8 respectively. PubMed shows an avalanche in the number of publication around May 10, number of publications jumped from 6.0{+/-}8.4/day to 282.5{+/-}110.3/day. The average doubling time for PubMed, Elsevier, and RG was 10.3{+/-}4 days, 20.6 days, and 2.3{+/-}2.0 days respectively. In PubMed average articles/journal was 5.2{+/-}10.3 and top 20 authors representing 935 articles are of Chinese descent. The average number of publications per author for PubMed, Elsevier, and RG was 1.2{+/-}1.4, 1.3{+/-}0.9, and 1.1{+/-}0.4 respectively. Subgroup analysis, PubMed review articles mean and median review time for each article were <0|17{+/-}17|77> and 13.9 days respectively; and reducing at a rate of-0.21 days (count)/day. InterpretationAlthough the disease has been known for around 6 months, the number of publications related to the Covid-19 until now is huge and growing very fast with time. It is essential to rationalize the publications scientifically by the researchers, authors, reviewers, and publishing houses. FundingNone
