High prevalence of anxiety, depression, and stress among remote learning students during the COVID-19 pandemic: Evidence from a meta-analysis

The coronavirus disease 2019 (COVID-19) pandemic has influenced all aspects significantly, and an estimated 1.5 billion students across the globe have been forced to keep up with online courses at home. Many recent empirical studies reported the prevalence of mental health problems among students caused by remote learning during the COVID-19 pandemic, but a few studies aggregated these results. Therefore, to strengthen statistical power, the article aimed to examine the prevalence of anxiety, depression, and stress among remote learning students during the COVID-19 pandemic via a meta-analysis. A total of 36 original articles have been selected from five academic databases, including Web of Science, PubMed, Scopus, EBSCO, and Google Scholar, covering 78,674 participants in 19 nations, and yielding 60 effect sizes (22 for anxiety, 17 for depression, and 21 for stress) based on the random effects model via Comprehensive Meta-Analysis (CMA) software. The results showed that the prevalence of anxiety, depression, and stress among remote learning students during the COVID-19 pandemic was as high as 58, 50, and 71%, respectively. Besides, the moderator analysis found that (1) the prevalence of anxiety and depression among students in higher education was significantly higher than that of students in elementary education. (2) an increasing number of medical students and students in emergency remote learning context suffered from mental stress than their non-medical and traditional distance learning counterparts. Thus, the COVID-19 pandemic triggers concerns related to physical health and mental disorders, especially for remote online learning students. The current situation should be brought to the forefront by educators to develop psychological interventions for relieving students' anxiety, depression, and stress during the pandemic period.

Single point mutations can potentially enhance infectivity of SARS-CoV-2 revealed by in silico affinity maturation and SPR assay† † Electronic supplementary information (ESI) available: ESI 1 and ESI file 2. See DOI: 10.1039/d1ra00426c

The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to the cell receptor and affect the potency of vaccines and antibodies. Here we used an in silico approach to predict how mutations on RBD affect its binding affinity to hACE2 (human angiotensin-converting enzyme2). The effect of all single point mutations on the interface was predicted. SPR assay results show that 6 out of 9 selected mutations can strengthen binding affinity. Our prediction has reasonable agreement with the previous deep mutational scan results and recently reported mutants. Our work demonstrated the in silico method as a powerful tool to forecast more powerful virus mutants, which will significantly benefit the development of broadly neutralizing vaccine and antibody. The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates viral cell attachment and serves as a promising target for therapeutics development.

Single point mutations can potentially enhance infectivity of SARS-CoV-2 revealed by in silico affinity maturation and SPR assay (preprint)

The RBD (receptor binding domain) of the SARS-CoV-2 virus S (spike) protein mediates the viral cell attachment and serves as a promising target for therapeutics development. Mutations on the S-RBD may alter its affinity to cell receptor and affect the potency of vaccines and antibodies. Here we used an in-silico approach to predict how mutations on RBD affect its binding affinity to hACE2 (human angiotensin-converting enzyme2). The effect of all single point mutations on the interface was predicted. SPR assay result shows that 6 out of 9 selected mutations can strengthen binding affinity. Our prediction has reasonable agreement with the previous deep mutational scan results and recently reported mutants. Our work demonstrated in silico method as a powerful tool to forecast more powerful virus mutants, which will significantly benefit for the development of broadly neutralizing vaccine and antibody.

Computational Prediction of Mutational Effects on the SARS-CoV-2 Binding by Relative Free Energy Calculations (preprint)

The ability of coronaviruses to infect humans is invariably associated with their binding strengths to human receptor proteins. Both SARS-CoV-2, initially named 2019-nCoV, and SARS-CoV were reported to utilize angiotensin-converting enzyme 2 (ACE2) as an entry receptor in human cells. To better understand the interplay between SARS-CoV-2 and ACE2, we performed computational alanine scanning mutagenesis on the “hotspot” residues at protein-protein interfaces using relative free energy calculations. Our data suggest that the mutations in SARS-CoV-2 lead to a greater binding affinity relative to SARS-CoV. In addition, our free energy calculations provide insight into the infectious ability of viruses on a physical basis, and also provide useful information for the design of antiviral drugs.