ABSTRACT
Because of the present pandemic researchers are seeking for phytocandidates that can inhibit or stop SARS-CoV-2. The main protease (Mpro) of SARS-CoV-2 and spike glycoprotein (S) are both suppressed by bioactive compounds found in plants that work by docking them together. The Mpro proteins 6LU7 (complex with an inhibitor N3) and 5C3N (space group C2221) were employed in docking research. PyRx and AutoDock Vina software were used as docking engine. 22 identified phytoconstituents were selected from IMPPAT, a manually curated database, on the basis of their antiviral effects. Docking studies showed that phytoconstituents β-amyrin (-8.4 kcal/mol), withaferin A (-8.3 kcal/mol), oleanolic acid (-7.8 kcal/mol), and patentiflorin A (-8.1 kcal/mol) had the best results against 5C3N Mpro protein whereas kuwanon L (-7.1 kcal/mol), β-amyrin (-6.9 kcal/mol), oleanolic acid (-6.8 kcal/mol), cucurbitacin D (-6.5 kcal/mol), and quercetin (-6.5 kcal/mol) against 6LU7 Mpro protein. All the compounds were examined for their ADMET characteristics using SwissDock. Present research reports that the phytoconstituents along with docking score will be helpful for future drug development against Covid-19. © 2022 Industrial Chemistry Research Institute. All rights reserved.
ABSTRACT
With the periodic rise and fall of COVID-19 and countries being inflicted by its waves, an efficient, economic, and effortless diagnosis procedure for the virus has been the utmost need of the hour. Amongst the infected subjects, the asymptomatic ones need not be entirely free of symptoms caused by the virus. They might not show any observable symptoms like the symptomatic subjects, but they may differ from uninfected ones in the way they cough. These differences in the coughing sounds are minute and indiscernible to the human ear, however, these can be captured using machine learning models. In this paper, we present a deep learning approach to analyze the acoustic dataset provided in Track 1 of the DiCOVA 2021 Challenge containing cough sound recordings belonging to both COVID-19 positive and negative examples. To perform the classification we propose a ConvNet model. It achieved an AUC score percentage of 72.23 on a blind test set provided in the challenge for an unbiased evaluation of the models. Moreover, the ConvNet model incorporated with Data Augmentation further increased the AUC score percentage from 72.23 to 87.07. It also outperformed the DiCOVA 2021 Challenge's baseline model by 23% thus, claiming the top position on the DiCOVA 2021 Challenge leaderboard. This paper proposes the use of Mel Frequency Cepstral Coefficients as the input features to the proposed model. © 2021 IEEE.
ABSTRACT
A novel threat to mankind by novel coronavirus infection occurred in December 2019. According to the World Health Organization (WHO) Situation Report-141, 7,039,918 confirmed cases and 404,396 death cases were observed till 9 June 2020 in the different regions of world. Therefore, this article aims to summarize and share the update on the present status of the outbreak and possible treatment options. The present review focuses on latest statistics, diagnostic and preventive measures under study and the future planning of the researchers to discover an effective cure for this threat to the mankind. For carrying out this review, literature searches were performed on Clinicaltrials.gov, official website of WHO,Centers for Disease Control and Prevention, PubMed, Google scholars, etc. Data from these searches was collected and evaluated for getting the available literature on COVID-19 outbreak and drugs under study. The details of history, virology, epidemiology, possible therapeutic options, associated risk factors and preventive measures related to COVID-19 are compiled here in this review. Along with this, some ongoing clinical trials have also been included in this review in order to conclude the efforts of researchers towards controlling this outbreak. The trajectory and severity of this outbreak can't be predicted at present, but immediate actions are required to be taken in order to develop and implement an effective treatment against the global threat. © 2020 This is an Open Access article licensed under a Creative Commons license: Attribution 4.0 International (CC-BY). Published by Oriental Scientific Publishing Company.
ABSTRACT
Introduction: A novel threat to mankind occurred in December 2019 which was an outbreak of infection caused by a novel coronavirus (SARS-CoV-2 or 2019-nCoV). The infection was first developed in Wuhan, China, and has affected more than 200 countries around the world till now. Objective: The present study aims to assess the knowledge related to coronavirus disease (COVID-19), risk perception and preventive behaviours among the Pharmacy students in a part of India approximately 3 months after the onset of this outbreak in India. Methods: This survey was conducted from 2nd to 5th of September 2020 with Indian Pharmacy students (1st to 4th year). The knowledge, self-reported preventive behaviours and risk perceptions of COVID-19 were assessed using an online questionnaire. A total of 21 questions were there in the questionnaire in which 14 questions were about knowledge related to COVID-19, 4 items regarding preventive behaviours and 3 about risk perception. Results: A total of 268 participants completed the questionnaire. The participants were under the age group of 15-30 years. A high level of disease-related knowledge was found in the participants (77.66%). On an average 96.1% of participants were practising preventive behaviours. The aggregate score of items in risk perception section was found to be in the moderate range i.e., 5.38 out of 8. A significant negative correlation was obtained between risk perception and preventive behaviours. Conclusion: The trajectory and severity of this outbreak are very high, therefore, effective treatment against this global threat is required to be developed as early as possible. In the present study, a high level of disease-related knowledge and preventive behaviours were observed among the participants with a moderate level of risk perception. © IJCRR.