SARS-CoV-2 RNA-dependent RNA polymerase as a target for high-throughput drug screening.

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 has necessitated rapid development of drug screening tools. RNA-dependent RNA polymerase (RdRp) is a promising target due to its essential functions in replication and transcription of viral genome. To date, through minimal RNA synthesizing machinery established from cryo-electron microscopy structural data, there has been development of high-throughput screening assays for directly screening inhibitors that target the SARS-CoV-2 RdRp. Here, we analyze and present verified techniques that could be used to discover potential anti-RdRp agents or repurposing of approved drugs to target the SARS-CoV-2 RdRp. In addition, we highlight the characteristics and application value of cell-free or cell-based assays in drug discovery.

Systematic Mendelian randomization study of the effect of gut microbiome and plasma metabolome on severe COVID-19 (preprint)

Background COVID-19 could develop severe respiratory symptoms in certain infected patients, especially in the patients with immune disorders. Gut microbiome and plasma metabolome act important immunological modulators in the human body and could contribute to the immune responses impacting the progression of COVID-19.Methods Based on two-sample Mendelian randomization framework, the causal effects of 131 microbiota in genus or species level and 452 plasma metabolites on severe COVID-19 are estimated. Single nucleotide polymorphisms (SNPs) strongly associated with the abundance of intestinal bacteria in gut and the concentration of metabolites in plasma have been utilized as the instrument variables to infer whether they are causal factors of severe COVID-19. In addition, mediation analysis is conducted to find the potential link between the microbiota and metabolite which identified by polygenic Mendelian randomization analysis, while colocalization analysis has been performed to validate the causal relationships which identified by cis-Mendelian randomization analysis.Results Mendelian randomization support 13 microbiota and 53 metabolites, which are significantly causal association with severe COVID-19. Mediation analysis find 11 mediated relations, such as myo-inositol, 2-stearoylglycerophosphocholine and alpha-glutamyltyrosine, which appeared to mediate the association of Howardella and Ruminiclostridium 6 with severe COVID-19 respectively, while Butyrivibrio and Ruminococcus gnavus appeared to mediate the association of myo-inositol and N-acetylalanine respectively. Ruminococcus torques abundance was colocalized with severe COVID-19 (PP.H4 = 0.77) and the colon expression of permeability related protein RASIP1 (PP.H4 = 0.95).Conclusions Our study results highlight the causal relationships of gut microbiome and plasma metabolome for severe COVID-19, which have the promise to be served as clinical biomarkers for risk stratification and prognostication, and novel basis to unravel the pathophysiological mechanisms of severe COVID-19.

SARS-CoV-2 RNA-dependent RNA polymerase as a target for high-throughput drug screening

The ongoing COVID-19 pandemic caused by the SARS-CoV-2 has necessitated rapid development of drug screening tools. RNA-dependent RNA polymerase (RdRp) is a promising target due to its essential functions in replication and transcription of viral genome. To date, through minimal RNA synthesizing machinery established from cryo-electron microscopy structural data, there has been development of high-throughput screening assays for directly screening inhibitors that target the SARS-CoV-2 RdRp. Here, we analyze and present verified techniques that could be used to discover potential anti-RdRp agents or repurposing of approved drugs to target the SARS-CoV-2 RdRp. In addition, we highlight the characteristics and application value of cell-free or cell-based assays in drug discovery.

A Survey on Deep Learning in COVID-19 Diagnosis.

According to the World Health Organization statistics, as of 25 October 2022, there have been 625,248,843 confirmed cases of COVID-19, including 65,622,281 deaths worldwide. The spread and severity of COVID-19 are alarming. The economy and life of countries worldwide have been greatly affected. The rapid and accurate diagnosis of COVID-19 directly affects the spread of the virus and the degree of harm. Currently, the classification of chest X-ray or CT images based on artificial intelligence is an important method for COVID-19 diagnosis. It can assist doctors in making judgments and reduce the misdiagnosis rate. The convolutional neural network (CNN) is very popular in computer vision applications, such as applied to biological image segmentation, traffic sign recognition, face recognition, and other fields. It is one of the most widely used machine learning methods. This paper mainly introduces the latest deep learning methods and techniques for diagnosing COVID-19 using chest X-ray or CT images based on the convolutional neural network. It reviews the technology of CNN at various stages, such as rectified linear units, batch normalization, data augmentation, dropout, and so on. Several well-performing network architectures are explained in detail, such as AlexNet, ResNet, DenseNet, VGG, GoogleNet, etc. We analyzed and discussed the existing CNN automatic COVID-19 diagnosis systems from sensitivity, accuracy, precision, specificity, and F1 score. The systems use chest X-ray or CT images as datasets. Overall, CNN has essential value in COVID-19 diagnosis. All of them have good performance in the existing experiments. If expanding the datasets, adding GPU acceleration and data preprocessing techniques, and expanding the types of medical images, the performance of CNN will be further improved. This paper wishes to make contributions to future research.

Investigation of a family cluster of COVID-19 in a district of Shanghai in 2021

Objective: To analyze the epidemiological characteristics of a family cluster of COVID-19 in a district of Shanghai, and to provide a scientific basis for prevention and control of cluster epidemic of COVID-19.

Exosomal Vaccine Loading T Cell Epitope Peptides of SARS-CoV-2 Induces Robust CD8+ T Cell Response in HLA-A Transgenic Mice.

Purpose: Current vaccines for the SARS-CoV-2 virus mainly induce neutralizing antibodies but overlook the T cell responses. This study aims to generate an exosomal vaccine carrying T cell epitope peptides of SARS-CoV-2 for the induction of CD8+ T cell response. Methods: Thirty-one peptides presented by HLA-A0201 molecule were conjugated to the DMPE-PEG-NHS molecules, and mixed with DSPE-PEG to form the peptide-PEG-lipid micelles, then fused with exosomes to generate the exosomal vaccine, followed by purification using size-exclusion chromatography and validation by Western blotting, liquid nuclear magnetic resonance (NMR) test and transmission electron microscopy. Furthermore, the exosomal vaccine was mixed with Poly (I:C) adjuvant and subcutaneously administered for three times into the hybrid mice of HLA-A0201/DR1 transgenic mice with wild-type mice. Then, the epitope-specific T cell responses were detected by ex vivo ELISPOT assay and intracellular cytokine staining. Results: The exosomal vaccine was purified from the Peak 2 fraction of FPLC and injected into the hybrid mice for three times. The IFN-γ spot forming units and the frequencies of IFN-γ+/CD8+ T cells were 10-82-fold and 13-65-fold, respectively, higher in the exosomal vaccine group compared to the Poly (I:C) control group, without visible organ toxicity. In comparison with the peptides cocktail vaccine generated in our recent work, the exosomal vaccine induced significantly stronger T cell response. Conclusion: Exosomal vaccine loading T cell epitope peptides of SARS-CoV-2 virus was initially generated without pre-modification for both peptides and exosomes, and elicited robust CD8+ T cell response in HLA-A transgenic mice.

Epidemic characteristics of local cases infected with SARS-CoV-2 in Yangpu District of Shanghai, China

Objective: To analyze the incidence and epidemic characteristics of local cases infected with SARS-CoV-2 in Yangpu District of Shanghai, China, and provide scientific evidence for the prevention and control of coronavirus disease-19 (COVID-19).

Mental health conditions among the general population, healthcare workers and quarantined population during the coronavirus disease 2019 (COVID-19) pandemic.

This study sought to assess the differences in mental health conditions among the general population, quarantined population and healthcare workers during the COVID-19 outbreak in China. An online rapid assessment captured depressive and anxiety symptoms, and sleep quality data. A total of 2689 participants (n=374 general population, n=403 healthcare workers, n=1912 quarantined population) were included in the final statistical analysis. The proportion of individuals with mild and/or serious depression and anxiety were higher in the general population when compared to the quarantined population and healthcare workers (58.6% vs. 25.1%vs. 48.6%, P<0.001; 41.2% vs. 18.5% vs. 35.7%, P<0.001). The prevalence of sleep disturbance was higher among healthcare workers than the general population and quarantined population (29.8% vs. 24.1% vs. 22.7%, P=0.013). Logistic regression analysis showed that, perceived effect on daily life was associated with depression, anxiety and sleep disturbance in the general population, quarantined population and the healthcare workers. The general population had a greater risk of developing psychological problems. The healthcare workers suffered the poorest sleep quality. Future research must further explorethe targeted measures for the general population and healthcare workers while combating COVID-19.

Associations between insecurity and stress among Chinese university students: The mediating effects of hope and self-efficacy.

BACKGROUND: In the context of the pandemic, exploration on the association between insecurity and stress among university students is limited. The current study aims to investigate the parallel mediation role of hope and self-efficacy in the relationship between insecurity and stress among university students during the COVID-19 pandemic. METHODS: We employed a cross-sectional research design in a university by distributing questionnaires online. 5286 participants were recruited (mean age = 19.65; SD = 1.13). Items were from the Security Questionnaire, Depression Anxiety and Stress Scale-21, and the Positive Psychology Capital (Psycap) Questionnaire (PPQ). Parallel mediation analysis was performed using PROCESS macro in SPSS. RESULTS: The results indicate that insecurity predicted students' stress positively and that students with high-level perceived insecurity are more likely to perceive stress. Moreover, hope and self-efficacy mediated the relationship between insecurity and stress, indicating that hope and self-efficacy could buffer the negative effects of insecurity on stress. LIMITATIONS: This study examines the mediating model between insecurity and stress among Chinese university students. The generalizability of the findings in other regions remains to be explored. Additionally, the roles of other positive self-beliefs including optimism and resilience in relieving stress can be further explored in future research. CONCLUSIONS: This research provides direct evidence of insecurity effects on stress among university students, enriching relevant theories in the field of stress. Moreover, this research suggests that enhancing positive self-beliefs such as hope, and self-efficacy helps to relieve students' stress during COVID-19.

Serological immunochromatographic approach in diagnosis with SARS-CoV-2 infected COVID-19 patients (preprint)

An outbreak of new coronavirus SARS-CoV-2 was occurred in Wuhan, China and rapidly spread to other cities and nations. The standard diagnostic approach that widely adopted in the clinic is nuclear acid detection by real-time RT-PCR. However, the false-negative rate of the technique is unneglectable and serological methods are urgently warranted. Here, we presented the colloidal gold-based immunochromatographic (ICG) strip targeting viral IgM or IgG antibody and compared it with real-time RT-PCR. The sensitivity of ICG assay with IgM and IgG combinatorial detection in nuclear acid confirmed cases were 11.1%, 92.9% and 96.8% at the early stage (1-7 days after onset), intermediate stage (8-14 days after onset), and late-stage (more than 15 days), respectively. The ICG detection capacity in nuclear acid-negative suspected cases was 43.6%. In addition, the consistencies of whole blood samples with plasma were 100% and 97.1% in IgM and IgG strips, respectively. In conclusion, serological ICG strip assay in detecting SARS-CoV-2 infection is both sensitive and consistent, which is considered as an excellent supplementary approach in clinical application.