Identification of Key Genes Related to Immune Cells in Patients with COVID-19 Via Integrated Bioinformatics-Based Analysis.

COVID-19 has spread all over the world which poses a serious threat to social economic development and public health. Despite enormous progress has been made in the prevention and treatment of COVID-19, the specific mechanism and biomarker related to disease severity or prognosis have not been clarified yet. Our study intended to further explore the diagnostic markers of COVID-19 and their relationship with serum immunology by bioinformatics analysis. The datasets about COVID-19 were downloaded from the Gene Expression Omnibus (GEO) dataset. The differentially expressed genes (DEGs) were selected via the limma package. Then, weighted gene co-expression network analysis (WGCNA) was conducted to identify the critical module associated with the clinic status. The intersection DEGs were processed for further enrichment analysis. The final diagnostic genes for COVID-19 were selected and verified through special bioinformatics algorithms. There were significant DEGs between the normal and COVID-19 patients. These genes were mainly enriched in cell cycle, complement and coagulation cascade, extracellular matrix (ECM) receptor interaction, and the P53 signaling pathway. As much as 357 common intersected DEGs were selected in the end. These DEGs were enriched in organelle fission, mitotic cell cycle phase transition, DNA helicase activity, cell cycle, cellular senescence, and P53 signaling pathway. Our study also identified CDC25A, PDCD6, and YWAHE were potential diagnostic markers of COVID-19 with the AUC (area under curve), 0.958 (95% CI 0.920-0.988), 0.941(95% CI 0.892-0.980), and 0.929 (95% CI 0.880-0.971). Moreover, CDC25A, PDCD6, and YWAHE were correlated with plasma cells, macrophages M0, T cells CD4 memory resting, T cells CD8, dendritic cells, and NK cells. Our study discovered that CDC25A, PDCD6, and YWAHE can be used as diagnostic markers for COVID-19. Moreover, these biomarkers were also closely associated with immune cell infiltration, which plays a pivotal role in the diagnosis and progression of COVID-19.

Comparison of laboratory parameters, clinical symptoms and clinical outcomes of COVID-19 and influenza in pediatric patients: A systematic review and meta-analysis.

BACKGROUND: New and more severe clinical manifestations associated with the coronavirus disease 2019 (COVID-19) are emerging constantly in the pediatric age group. Patients in this age group are also primary carriers of the influenza virus and are at a higher risk of developing severe infection. However, studies comparing influenza and COVID-19 to show which condition causes a more severe form of disease amongst the pediatric age group are scarce. AIM: To compare the laboratory results, clinical symptoms and clinical outcomes in pediatric patients with COVID-19 and influenza. METHODS: A systematic and comprehensive search was carried out in databases and search engines, including EMBASE, Cochrane, MEDLINE, ScienceDirect and Google Scholar from 1964 until January 2022. A meta-analysis was carried out using a random-effects model and pooled odds ratio (OR) or standardized mean difference (SMD) and 95%CI. RESULTS: A total of 16 studies satisfied the inclusion criteria. Pediatric COVID-19 patients had a significantly reduced risk of cough (pooled OR = 0.16; 95%CI: 0.09 to 0.27), fever (pooled OR = 0.23; 95%CI: 0.12 to 0.43), and dyspnea (pooled OR = 0.54; 95%CI: 0.33 to 0.88) compared to influenza patients. Furthermore, total hemoglobin levels (pooled SMD = 1.22; 95%CI: 0.29 to 2.14) in COVID-19 patients were significantly higher as compared to pediatric influenza patients. There was no significant difference in symptoms such as sore throat, white blood cell count, platelets, neutrophil and lymphocytes levels, and outcomes like mortality, intensive care unit admission, mechanical ventilation or length of hospital stay. CONCLUSION: COVID-19 is associated with a significantly lower rate of clinical symptoms and abnormal laboratory indexes compared to influenza in the pediatric age group. However, further longitudinal studies of the outcomes between influenza and COVID-19 pediatric patients are needed.

Data Mining on COVID-19 Vaccines: Side Effects.

COVID-19 is a pandemic disease affecting billions of people worldwide. Taking vaccines is a most effective approach to gain fully control. Thanks to the coordinated efforts from all over the world, several brands of vaccines targeting COVID-19 have passed through clinical trials and been brought to the public. Growing numbers of people are taking vaccines and share their feedback on social media, mostly on Twitter. In this study, we used Twitter data to analyze the side effects on each individual and quantify these side effects in a brand-wise and country-wise manner. Based on Twitter data, we found that the United States has the largest number of people getting vaccinated, Pfizer is the most widely used vaccine brand around the world and the most frequent side effect is cold. From our analysis, the side effects of vaccines are under controllable and are acceptable, and everyone can join the vaccinated camping without hesitation.

Demystifying COVID-19 publications: institutions, journals, concepts, and topics.

OBJECTIVE: We analyzed the COVID-19 Open Research Dataset (CORD-19) to understand leading research institutions, collaborations among institutions, major publication venues, key research concepts, and topics covered by pandemic-related research. METHODS: We conducted a descriptive analysis of authors' institutions and relationships, automatic content extraction of key words and phrases from titles and abstracts, and topic modeling and evolution. Data visualization techniques were applied to present the results of the analysis. RESULTS: We found that leading research institutions on COVID-19 included the Chinese Academy of Sciences, the US National Institutes of Health, and the University of California. Research studies mostly involved collaboration among different institutions at national and international levels. In addition to bioRxiv, major publication venues included journals such as The BMJ, PLOS One, Journal of Virology, and The Lancet. Key research concepts included the coronavirus, acute respiratory impairments, health care, and social distancing. The ten most popular topics were identified through topic modeling and included human metapneumovirus and livestock, clinical outcomes of severe patients, and risk factors for higher mortality rate. CONCLUSION: Data analytics is a powerful approach for quickly processing and understanding large-scale datasets like CORD-19. This approach could help medical librarians, researchers, and the public understand important characteristics of COVID-19 research and could be applied to the analysis of other large datasets.

Investigation and precautions of SARS-CoV-2 infection among healthcare workers in Emergency Center

Objective: To analyze the causes of SARS-CoV-2 nosocomial infection among healthcare workers (HCWs) and explore the effective precaution strategies in Emergency Center.