Screening the hub genes and analyzing the mechanisms in discharged COVID-19 patients retesting positive through bioinformatics analysis.

BACKGROUND: After encountering COVID-19 patients who test positive again after discharge, our study analyzed the pathogenesis to further assess the risk and possibility of virus reactivation. METHODS: A separate microarray was acquired from the Gene Expression Omnibus (GEO), and its samples were divided into two groups: a "convalescent-RTP" group consisting of convalescent and "retesting positive" (RTP) patients (group CR) and a "healthy-RTP" group consisting of healthy control and RTP patients (group HR). The enrichment analysis was performed with R software, obtaining the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Subsequently, the protein-protein interaction (PPI) networks of each group were established, and the hub genes were discovered using the cytoHubba plugin. RESULTS: In this study, 6622 differentially expressed genes were identified in the group CR, among which RAB11B-AS1, DISP1, MICAL3, PSMG1, and DOCK4 were up-regulated genes, and ANAPC1, IGLV1-40, SORT1, PLPPR2, and ATP1A1-AS1 were down-regulated. 7335 genes were screened in the group HR, including the top 5 up-regulated genes ALKBH6, AMBRA1, MIR1249, TRAV18, and LRRC69, and the top 5 down-regulated genes FAM241B, AC018529.3, AL031963.3, AC006946.1, and FAM149B1. The GO and KEGG analysis of the two groups revealed a significant enrichment in immune response and apoptosis. In the PPI network constructed, group CR and group HR identified 10 genes, respectively, and TP53BP1, SNRPD1, and SNRPD2 were selected as hub genes. CONCLUSIONS: Using the messenger ribonucleic acid (mRNA) expression data from GSE166253, we found TP53BP1, SNRPD1, and SNRPD2 as hub genes in RTP patients, which is vital to the management and prognostic prediction of RTP patients.

The Diagnostic Accuracy of Xpert Xpress to SARS-CoV-2: A systematic review.

The SARS-CoV-2 infection rate, as well as mortality rate, is high. There is an urgent need for a high-throughput, accurate and reliable method of diagnosing COVID-19 pneumonia. We included references from databases, such as PubMed, Cochrane Library, Web of Science, and Embase, and extracted data. Then, MetaDisc and STATA were used to establish forest plots and funnel plots for meta-analysis. We collected 14 articles and performed a systematic review. The following results were obtained: sensitivity and specificity were 0.97 (0.96 to 0.98) and 0.97 (0.96 to 0.98) respectively; PLR and NLR were 24.51 (16.63-36.12) and 0.03 (0.01 to 0.10) respectively, DOR was 975.15 (430.11-2210.88), and AUC was 0.9926. When Xpress detects SARS-CoV-2 in different samples, the heterogeneity is small and the specificity and sensitivity are extremely high. We recommend the employment of Xpert Xpress analysis in rapid screening.

Occupational Burnout Among Frontline Health Professionals in a High-Risk Area During the COVID-19 Outbreak: A Structural Equation Model.

Background: The outbreak of coronavirus disease 2019 (COVID-19) resulted in a substantial workload and stress for frontline health professionals in high-risk areas. Little research has investigated the mechanism of occupational burnout among the frontline health professionals located in the center of the epidemic in Wuhan, China. Methods: A total of 199 frontline health professionals from Wuhan Jinyintan Hospital completed the cross-sectional survey. Mechanisms of occupational burnout (according to the Maslach Burnout Inventory-General Survey, MBI-GS) among the health professionals in Jinyintan Hospital during the COVID-19 outbreak were examined using a structural equation model (SEM). Results: The levels of the three burnout dimensions (emotional exhaustion, cynicism, and professional efficacy) were high at 34.2, 50.8, and 35.2%, respectively. Frontline health professionals in this stressful period reported significantly greater emotional exhaustion (p < 0.001) and job-related cynicism (p < 0.001), but no significant difference in professional efficacy (p = 0.449), when compared to employees in a large multinational company. The SEM results revealed that both acute stress symptoms and psychosomatic symptoms significantly predicted the emotional exhaustion and occupation cynicism dimensions of burnout. Conclusion: The study reveals the occupational burnout mechanism of frontline health professionals during the COVID-19 peak at the time of the outbreak. This study provides an important contribution to understanding the future psychological interventions necessary for frontline health professionals during an epidemic crisis.

Exploration and validation of related hub gene expression during SARS-CoV-2 infection of human bronchial organoids.

BACKGROUND: In the novel coronavirus pandemic, the high infection rate and high mortality have seriously affected people's health and social order. To better explore the infection mechanism and treatment, the three-dimensional structure of human bronchus has been employed in a better in-depth study on severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). METHODS: We downloaded a separate microarray from the Integrated Gene Expression System (GEO) on a human bronchial organoids sample to identify differentially expressed genes (DEGS) and analyzed it with R software. After processing with R software, Gene Ontology (GO) and Kyoto PBMCs of Genes and Genomes (KEGG) were analyzed, while a protein-protein interaction (PPI) network was constructed to show the interactions and influence relationships between these differential genes. Finally, the selected highly connected genes, which are called hub genes, were verified in CytoHubba plug-in. RESULTS: In this study, a total of 966 differentially expressed genes, including 490 upregulated genes and 476 downregulated genes were used. Analysis of GO and KEGG revealed that these differentially expressed genes were significantly enriched in pathways related to immune response and cytokines. We construct protein-protein interaction network and identify 10 hub genes, including IL6, MMP9, IL1B, CXCL8, ICAM1, FGF2, EGF, CXCL10, CCL2, CCL5, CXCL1, and FN1. Finally, with the help of GSE150728, we verified that CXCl1, CXCL8, CXCL10, CCL5, EGF differently expressed before and after SARS-CoV-2 infection in clinical patients. CONCLUSIONS: In this study, we used mRNA expression data from GSE150819 to preliminarily confirm the feasibility of hBO as an in vitro model to further study the pathogenesis and potential treatment of COVID-19. Moreover, based on the mRNA differentiated expression of this model, we found that CXCL8, CXCL10, and EGF are hub genes in the process of SARS-COV-2 infection, and we emphasized their key roles in SARS-CoV-2 infection. And we also suggested that further study of these hub genes may be beneficial to treatment, prognostic prediction of COVID-19.

Characteristics of Viral Shedding in Respiratory Samples and Specific Antibodies Production in 564 COVID-19 Patients.

Positive nucleic acid (NA) results have been found in recovered and discharged COVID-19 patients, but the proportion is unclear. This study was designed to analyze the recurrent positive rate of NA results after consecutively negative results, and the relationship between the specific antibody production and positive NA rate. According to Strengthening the Reporting of Observational Studies in Epidemiology guidelines, data of inpatients in Sino-French New City Branch of Tongji Hospital between Jan. 28 and Mar. 6, 2020 were collected. A total of 564 COVID-19 patients over 14 years old who received the examinations of NA and antibodies against SARS-CoV-2 were included. Days of viral shedding and specific antibodies were recorded and assessed. Among NA tests in respiratory samples (throat swabs, nasopharyngeal swabs, sputum and flushing fluid in alveoli), the patients with all-negative NA results accounted for 17.20%, those with single-positive results for 46.63%, and those with multiple-positive results for 36.17% respectively. Besides, the recurrent positive NA results after consecutively negative results appeared in 66 patients (11.70%). For multiple-positive patients, median viral shedding duration was 20 days (range: 1 to 57 days). Of the 205 patients who received 2 or more antibody tests, 141 (68.78%) had decreased IgG and IgM concentrations. IgM decreased to normal range in 24 patients, with a median of 44 days from symptom onset. Viral shedding duration was not significantly correlated with gender, age, disease severity, changes in pulmonary imaging, and antibody concentration. It is concluded that antibody level and antibody change had no significant correlation with the positive rate of NA tests and the conversion rate after continuous negative NA tests. In order to reduce the recurrent positive proportion after discharge, 3 or more consecutive negative NA test results with test interval more than 24 h every time are suggested for the discharge or release from quarantine.