Remote work and the COVID-19 pandemic: An artificial intelligence-based topic modeling and a future agenda.

As remote work has become more common than ever throughout the COVID-19 pandemic, it has drawn special attention from scholars. However, the outcome has been significantly sporadic and fragmented. In our systematic review, we use artificial intelligence-based machine learning tools to examine the relevant extant literature in terms of its dominant topics, diversity, and dynamics. Our results identify-eight research themes: (1) Effect on employees at a personal level, (2) Effect on employees' careers, (3) Family life and gender roles, (4) Health, well-being, and safety, (5) Labor market dynamics, (6) Economic implications, (7) Remote work management, (8) Organizational remote work strategies. With further content analysis, we structure the sporadic research into three overarching categories. Finally, for each category, we offer a detailed agenda for further research.

In-silico elucidation reveals potential phytochemicals against angiotensin-converting enzyme 2 (ACE-2) receptor to fight coronavirus disease 2019 (COVID-19).

The coronavirus (SARS-CoV-2) pandemic is rapidly advancing and spreading worldwide, which poses an urgent need to develop anti-SARS-CoV-2 agents. A human receptor, namely, angiotensin-converting enzyme 2 (ACE-2), supports the SARS-CoV-2 entry, therefore, serves as a target for intervention via drug. In the current study, bioinformatic approaches were employed to screen potent bioactive compounds that might be ACE-2 receptor inhibitors. The employment of a docking study using ACE receptor protein with a ready-to-dock database of phytochemicals via MOE software revealed five compounds as potent molecules. Among them, astragaloside exhibited the highest binding affinity -21.8 kcal/mol and stable interactions within the active site of the ACE-2 receptor. Similarly, the phytochemicals such as pterocaryanin B, isoastragaloside II, and astraisoflavan glucoside followed by oleuropein showed a stronger binding affinity. We hypothesize these compounds as potential lead candidates for the development of anti- COVID-19 target-specific drugs.

Immunoinformatics and Molecular Docking Studies Predicted Potential Multiepitope-Based Peptide Vaccine and Novel Compounds against Novel SARS-CoV-2 through Virtual Screening.

BACKGROUND: Coronaviruses (CoVs) are enveloped positive-strand RNA viruses which have club-like spikes at the surface with a unique replication process. Coronaviruses are categorized as major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Nowadays, a new strain of coronaviruses is identified and named as SARS-CoV-2. Multiple cases of SARS-CoV-2 attacks are being reported all over the world. SARS-CoV-2 showed high death rate; however, no specific treatment is available against SARS-CoV-2. METHODS: In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against SARS-CoV-2 for the development of the coronavirus vaccine. Cytotoxic T-lymphocyte and B-cell epitopes were predicted for SARS-CoV-2 coronavirus protein. Multiple sequence alignment of three genomes (SARS-CoV, MERS-CoV, and SARS-CoV-2) was used to conserved binding domain analysis. RESULTS: The docking complexes of 4 CTL epitopes with antigenic sites were analyzed followed by binding affinity and binding interaction analyses of top-ranked predicted peptides with MHC-I HLA molecule. The molecular docking (Food and Drug Regulatory Authority library) was performed, and four compounds exhibiting least binding energy were identified. The designed epitopes lead to the molecular docking against MHC-I, and interactional analyses of the selected docked complexes were investigated. In conclusion, four CTL epitopes (GTDLEGNFY, TVNVLAWLY, GSVGFNIDY, and QTFSVLACY) and four FDA-scrutinized compounds exhibited potential targets as peptide vaccines and potential biomolecules against deadly SARS-CoV-2, respectively. A multiepitope vaccine was also designed from different epitopes of coronavirus proteins joined by linkers and led by an adjuvant. CONCLUSION: Our investigations predicted epitopes and the reported molecules that may have the potential to inhibit the SARS-CoV-2 virus. These findings can be a step towards the development of a peptide-based vaccine or natural compound drug target against SARS-CoV-2.

Determine the Potential Epitope Based Peptide Vaccine Against Novel SARS-CoV-2 Targeting Structural Proteins Using Immunoinformatics Approaches.

Coronaviruses (CoVs) belong to the Coronaviridae-family. The genus Beta-coronaviruses, are enveloped positive strand RNA viruses with club-like spikes at the surface with a unique replication process and a large RNA genome (∼25 kb). CoVs are known as one of the major pathogenic viruses causing a variety of diseases in birds and mammals including humans (lethal respiratory dysfunctions). Recently, a new strain of coronavirus has been identified and named as SARS-CoV-2. A large number of COVID-19 (disease caused by SARS-CoV-2) cases are being diagnosed all over the World especially in China (Wuhan). COVID-19 showed high mortality rate exponentially, however, not even a single effective cure is being introduced yet against COVID-19. In the current study, immunoinformatics approaches were employed to predict the antigenic epitopes against COVID-19 for the development of a coronavirus peptide vaccine. Cytotoxic T-lymphocyte (CTL) and B-cell epitopes were predicted for SARS-CoV-2 coronavirus structural proteins (Spikes, Membrane, Envelope, and Nucleocapsid). The docking complexes of the top 10 epitopes having antigenic sites were analyzed led by binding affinity and binding interactional analyses of top ranked predicted peptides with the MHC-I HLA molecule. The predicted peptides may have potential to be used as peptide vaccine against COVID-19.