Transfer Function Model for COVID-19 Deaths in USA Using Case Counts as Input Series.

This paper presents a transfer function time series forecast model for COVID-19 deaths using reported COVID-19 case positivity counts as the input series. We have used deaths and case counts data reported by the Center for Disease Control for the USA from July 24 to December 31, 2021. To demonstrate the effectiveness of the proposed transfer function methodology, we have compared some summary results of forecast errors of the fitted transfer function model to those of an adequate autoregressive integrated moving average model and observed that the transfer function model achieved better forecast results than the autoregressive integrated moving average model. Additionally, separate autoregressive integrated moving average models for COVID-19 cases and deaths are also reported.

Synthesis of indole-based oxadiazoles and their interaction with bacterial peptidoglycan and SARS-CoV-2 main protease: In vitro, molecular docking and in silico ADME/Tox study

In the present study, Indole-based-oxadiazole (1A-17A) compounds were successfully synthesized. The structures of all synthesized compounds were fully characterized by different sophisticated spectroscopic techniques such 1H NMR, 13C NMR, EI-MS and HREI-MS. Further, the synthesized compounds were explored to investigate their broad-spectrum antibacterial and antibiofilm potential against multidrug resistant Pseudomonas aeruginosa (MDR-PA) and methicillin resistant Staphylococcus aureus (MRSA). The compounds possessed a broad spectrum of antibacterial activity having MIC values of values 1-8 mg/ml against the tested microorganisms. Compound A6 and A7 shows maximum antibacterial activity against MDR-PA, whereas A6, A7 and A11 shows highest activity against MRSA. Furthermore, antibiofilm assay shows that A6, A7 and A11 showed maximum inhibition of biofilm formation and it was found that at 4 mg/ml;A6, A7 and A11 inhibit MRSA biofilm formation by 81.1, 77.5 and 75.9%, respectively;whereas in case of P. aeruginosa;A6 and A7 showed maximum biofilm inhibition and inhibit biofilm formation by 81.5 and 73.7%, respectively. Molecular docking study showed that compounds A6, A7, A8, A10, and A11 had high binding affinity to bacterial peptidoglycan, indicating their potential inhibitory activity against tested bacteria, whereas A6 and A11 were found to be the most effective inhibitors of SARS CoV-2 main protease (3CLpro), with a binding affinity of −7.78 kcal/mol. Furthermore, SwissADME and pkCSM-pharmacokinetics online tools was applied to calculate the ADME/Tox profile of the synthesized compounds and the toxicity of these chemicals was found to be low. The Lipinski, Veber, Ghose, and Consensus LogP criteria were also used to predict drug-likeness levels of the compounds. Our findings imply that the synthesized compounds could be a useful for the preventing and treating biofilm-related microbial infection as well as SARS-CoV2 infections.

A molecular modeling approach to identify effective antiviral phytochemicals against the main protease of SARS-CoV-2.

The main protease of SARS-CoV-2 is one of the important targets to design and develop antiviral drugs. In this study, we have selected 40 antiviral phytochemicals to find out the best candidates which can act as potent inhibitors against the main protease. Molecular docking is performed using AutoDock Vina and GOLD suite to determine the binding affinities and interactions between the phytochemicals and the main protease. The selected candidates strongly interact with the key Cys145 and His41 residues. To validate the docking interactions, 100 ns molecular dynamics (MD) simulations on the five top-ranked inhibitors including hypericin, cyanidin 3-glucoside, baicalin, glabridin, and α-ketoamide-11r are performed. Principal component analysis (PCA) on the MD simulation discloses that baicalin, cyanidin 3-glucoside, and α-ketoamide-11r have structural similarity with the apo-form of the main protease. These findings are also strongly supported by root-mean-square deviation (RMSD), root-mean-square fluctuation (RMSF), radius of gyration (Rg), and solvent accessible surface area (SASA) investigations. PCA is also used to find out the quantitative structure-activity relationship (QSAR) for pattern recognition of the best ligands. Multiple linear regression (MLR) of QSAR reveals the R2 value of 0.842 for the training set and 0.753 for the test set. Our proposed MLR model can predict the favorable binding energy compared with the binding energy detected from molecular docking. ADMET analysis demonstrates that these candidates appear to be safer inhibitors. Our comprehensive computational and statistical analysis show that these selected phytochemicals can be used as potential inhibitors against the SARS-CoV-2.Communicated by Ramaswamy H. Sarma.

Evolution of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) as coronavirus disease 2019 (COVID-19) pandemic: A global health emergency.

According to data compiled by researchers at Johns Hopkins University in Baltimore, Maryland, more than two and half million cases of coronavirus disease 2019 (COVID-19), caused by a newly discovered virus named severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), have been confirmed on April 20, 2020 (Nature, 2020b). Since the emergence of this infectious disease in Asia (Wuhan, China) late last year, it has been subsequently span to every continent of the world except Antarctica (Rodríguez-Morales et al., 2020). Along with a foothold in every country, the current disease pandemic is disrupting practically every aspect of life all over the world. As the outbreak are continuing to evolve, several research activities have been conducted for better understanding the origin, functions, treatments, and preventions of this novel coronavirus. This review will be a summa of the key features of novel coronavirus (nCoV), the virus causing disease 2019 and the present epidemic situation worldwide up to April 20, 2020. It is expected that this record will play an important role to take more preventive measures for overcoming the challenges faced during this current pandemic.