Analysis of the potential relationship between COVID-19 and Behcet's disease using transcriptome data.

To investigate the potential role of COVID-19 in relation to Behcet's disease (BD) and to search for relevant biomarkers. We used a bioinformatics approach to download transcriptomic data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and PBMCs of BD patients, screened the common differential genes between COVID-19 and BD, performed gene ontology (GO) and pathway analysis, and constructed the protein-protein interaction (PPI) network, screened the hub genes and performed co-expression analysis. In addition, we constructed the genes-transcription factors (TFs)-miRNAs network, the genes-diseases network and the genes-drugs network to gain insight into the interactions between the 2 diseases. We used the RNA-seq dataset from the GEO database (GSE152418, GSE198533). We used cross-analysis to obtain 461 up-regulated common differential genes and 509 down-regulated common differential genes, mapped the PPI network, and used Cytohubba to identify the 15 most strongly associated genes as hub genes (ACTB, BRCA1, RHOA, CCNB1, ASPM, CCNA2, TOP2A, PCNA, AURKA, KIF20A, MAD2L1, MCM4, BUB1, RFC4, and CENPE). We screened for statistically significant hub genes and found that ACTB was in low expression of both BD and COVID-19, and ASPM, CCNA2, CCNB1, and CENPE were in low expression of BD and high expression of COVID-19. GO analysis and pathway analysis was then performed to obtain common pathways and biological response processes, which suggested a common association between BD and COVID-19. The genes-TFs-miRNAs network, genes-diseases network and genes-drugs network also play important roles in the interaction between the 2 diseases. Interaction between COVID-19 and BD exists. ACTB, ASPM, CCNA2, CCNB1, and CENPE as potential biomarkers for 2 diseases.

Analysis of the potential relationship between COVID-19 and Behcet's disease using transcriptome data

To investigate the potential role of COVID-19 in relation to Behcet's disease (BD) and to search for relevant biomarkers. We used a bioinformatics approach to download transcriptomic data from peripheral blood mononuclear cells (PBMCs) of COVID-19 patients and PBMCs of BD patients, screened the common differential genes between COVID-19 and BD, performed gene ontology (GO) and pathway analysis, and constructed the protein-protein interaction (PPI) network, screened the hub genes and performed co-expression analysis. In addition, we constructed the genes-transcription factors (TFs)-miRNAs network, the genes-diseases network and the genes-drugs network to gain insight into the interactions between the 2 diseases. We used the RNA-seq dataset from the GEO database (GSE152418, GSE198533). We used cross-analysis to obtain 461 up-regulated common differential genes and 509 down-regulated common differential genes, mapped the PPI network, and used Cytohubba to identify the 15 most strongly associated genes as hub genes (ACTB, BRCA1, RHOA, CCNB1, ASPM, CCNA2, TOP2A, PCNA, AURKA, KIF20A, MAD2L1, MCM4, BUB1, RFC4, and CENPE). We screened for statistically significant hub genes and found that ACTB was in low expression of both BD and COVID-19, and ASPM, CCNA2, CCNB1, and CENPE were in low expression of BD and high expression of COVID-19. GO analysis and pathway analysis was then performed to obtain common pathways and biological response processes, which suggested a common association between BD and COVID-19. The genes-TFs-miRNAs network, genes-diseases network and genes-drugs network also play important roles in the interaction between the 2 diseases. Interaction between COVID-19 and BD exists. ACTB, ASPM, CCNA2, CCNB1, and CENPE as potential biomarkers for 2 diseases.

Prevalence of depression and its association with quality of life in patients after pacemaker implantation during the COVID-19 pandemic: A network analysis.

Background: This study was designed to investigate the prevalence and predictors of depression in patients after pacemaker implantation during the COVID-19 pandemic in addition to identifying specific depressive symptoms associated with quality of life (QOL) using network analysis (NA). Methods: This cross-sectional, observational study was conducted in China between July 1, 2021, and May 17, 2022. Descriptive analysis was used to calculate depression prevalence. Univariate analyses were used to compare differences in demographic and clinical characteristics between depressed and non-depressed patients following pacemaker implantation. Binary logistic regression analysis was used to assess factors independently associated with depression. Network analysis "expected influence," and flow function indexes were used to identify symptoms central to the depression network of the sample and depressive symptoms that were directly associated with QOL, respectively. Network stability was examined using a case-dropping bootstrap procedure. Results: In total, 206 patients implanted with a pacemaker met the study entry criteria and completed the assessment. The overall prevalence of depression (PHQ-9 total score ≥ 5) was 39.92% [95% confidence interval (CI) = 29.37-42.47%]. A binary logistic regression analysis revealed that patients with depression were more likely to report a poor health status (p = 0.031), severe anxiety symptoms (p < 0.001), and fatigue (p < 0.001). In the network model for depression, "Sad mood," "Poor Energy," and "Guilt" were the most influential symptoms. "Fatigue" had the strongest negative association with QOL, followed by "Sad mood" and "Appetite". Conclusion: Depression is common among patients having undergone pacemaker implantation during the COVID-19 pandemic. Anxiety, central symptoms of depression (i.e., "Sad mood", "Poor Energy", and "Guilt") and depressive symptoms linked to QOL (i.e., "Sad mood", "Appetite", and "Fatigue") identified in this study are promising targets for interventions and preventive measures for depression in patients who have undergone pacemaker implants.

Universal Health Insurance Coverage and the Economic Burden of Disease in Eastern China: A Pooled Cross-Sectional Analysis From the National Health Service Survey in Jiangsu Province.

China has achieved universal social health insurance coverage, but it is unclear whether this has alleviated the economic burden of disease for individuals. This was investigated in the present study by analyzing National Health Service Survey (2008-2018) data from Jiangsu province. Ordinary least squares and binary multivariate logistic regression of pooled cross-sectional data were carried out to evaluate the effect of universal health insurance coverage and other socioeconomic factors on the economic burden of disease. Total health expenses (THE) first increased and then decreased during the survey period while out-of-pocket health expenses (OOP) decreased except for urban residents, for whom OOP increased after 2013. Household catastrophic health expenditure (HCHE) was stable between 2008 and 2013 but increased after 2013. Social health insurance had a significant positive effect on the annual THE and OOP and a negative effect on HCHE, however, universal health insurance coverage could alleviated THE and the economic burden of disease on individuals (OOP) while it was insufficient to protect against the economic risk of diseases (HCHE), with greater benefits for urban as compared to rural residents. Other socioeconomic factors including age, marital status, education, income, and health status also influenced the economic burden of disease.