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SARS-CoV-2, an emerged strain of corona virus family became almost serious health concern worldwide. Despite vaccines availability, reports suggest the occurrence of SARS-CoV-2 infection even in a vaccinated population. With frequent evolution and expected multiple COVID-19 waves, improved preventive, diagnostic, and treatment measures are required. In recent times, phytochemicals have gained attention due to their therapeutic characteristics and are suggested as alternative and complementary treatments for infectious diseases. This present study aimed to identify potential inhibitors against reported protein targets of SARS-CoV-2. We computationally investigated potential SARS-CoV-2 protein targets from the literature and collected druggable phytochemicals from Indian Medicinal Plants, Phytochemistry and Therapeutics (IMPPAT) database. Further, we implemented a systematic workflow of molecular docking, dynamic simulations and generalized born surface area free-energy calculations (MM-GBSA). Extensive literature search and assessment of 1508 articles identifies 13 potential SARS-CoV-2 protein targets. We screened 501 druggable phytochemicals with proven biological activities. Analysis of 6513(501 *13) docked phytochemicals complex, 26 were efficient against SARS-CoV-2. Amongst, 4,8-dihydroxysesamin and arboreal from Gmelina arborea were ranked potential against most of the targets with binding energy ranging between − 10.7 to − 8.2 kcal/mol. Additionally, comparative docking with known drugs such as arbidol (−6.6 to −5.1 kcal/mol), favipiravir (−5.5 to −4.5 kcal/mol), hydroxychloroquine (−6.5 to −5.1 kcal/mol), and remedesivir (−8.0 to −5.3 kcal/mol) revealed equal/less affinity than 4,8-dihydroxysesamin and arboreal. Interestingly, the nucleocapsid target was found commonly inhibited by 4,8-dihydroxysesamin and arboreal. Molecular dynamic simulation and Molecular mechanics generalized born surface area (MM-GBSA)calculations reflect that both the compounds possess high inhibiting potential against SARS-CoV-2 including the recently emerged Omicron variant (B.1.1.529). Overall our study imparts the usage of phytochemicals as antiviral agents for SARS-CoV-2 infection. Additional in vitro and in vivo testing of these phytochemicals is required to confirm their potency. [ABSTRACT FROM AUTHOR]

Attempts have been made to use jamun (Syzygium cumini Linn) seed extract on cotton fabric to evaluate its antibacterial activity against some Gram positive and Gram negative bacterial strains. Different concentrations of jamun extract (125, 250, 500 and 1000 mg / mL) have been used and zone of inhibition (ZOI) is measured. The findings indicate that jamun seed has an antibacterial effect on bleached cotton fabric at different bacterial strain of Staphylococcus aureus (NCIM 2079) and Streptococcus agalactiae (NCIM 2401). The antibacterial potential of jamun seed powder is due to the rich bioactive compounds. High inhibition percentage is obtained for 1000 mg/mL seed extract and minimum for 125 mg/mL. Streptococcus agalactiae (77% strain) shows the maximum 24 mm ZOI and Staphylococcus aureus (55% strain) shows 18 mm ZOI. [ABSTRACT FROM AUTHOR]

Recently, in December 2019 the Coronavirus disease surprisingly influenced the lives of millions of people in the world with its swift spread. To support medical experts/doctors with the overpowering challenge of prediction of total cases in India, a machine-learning algorithm was developed. In this research article, the author describes the possibility of predicting the COVID-19 total, active cases, death and cured cases in India up to 25th June 2020 by applying linear regression and support vector machine. It is extremely tricky to manage the occurrence of corona virus since it is expanding exponentially day to day and is difficult to handle with a limited number of doctors and beds to treat the infected individuals with limited time. Hence, it is essential to develop a machine learning based computerized predicting model. The development effort in this article is based on publicly available data that is downloaded from KAGGLE to estimate the spread of the disease within a short period. We have calculated the RMSE, R2, MAE of LR and SVR models and concluded that the RMSE of linear regression is less than the SVR. Therefore, the LR will help doctors to forecast for the next few days., (© 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the Emerging Trends in Materials Science, Technology and Engineering.)