Highlights COVID -19 cases now confirmed in multiple countries. assessed the prevalence of comorbidities in infected patients. comorbidities are risk factors for severe patients compare with Non-severe.J o u r n a l P r e -p r o o f 2 help the health sector guide vulnerable populations and assess the risk of deterioration.Background: An outbreak of Novel Coronavirus in Wuhan, China, the epidemic is more widespread than initially estimated, with cases now confirmed in multiple countries. Aims:The aim of the meta-analysis was to assess the prevalence of comorbidities in the COVID-19 infection patients and the risk of underlying diseases in severe patients compared to non-severe patients. Methods:A literature search was conducted using the databases PubMed, EMBASE, and Web of sciences until February 25, 2020. Risk ratio (OR) and 95% confidence intervals (CIs) were pooled using random-effects models.Results: Eight studies were included in the meta-analysis, including 46248 infected patients. The result showed the most prevalent clinical symptom was fever ( 91±3, 95% CI 86-97% ), followed by cough (67±7, 95% CI 59-76%), fatigue ( 51±0, 95% CI 34-68% ) and dyspnea ( 30±4, 95% CI 21-40%). The most prevalent comorbidity were hypertension (17±7, 95% CI 14-22%) and diabetes ( 8±6, 95% CI 6-11% ), followed by cardiovascular diseases ( 5±4, 95% CI 4-7% ) and respiratory system disease( 2±0, 95% CI 1-3% ). Compared with the Non-severe patient, the pooled odds ratio of hypertension, respiratory system disease, cardiovascular disease in severe patients were (OR 2.36, 95% CI: 1.46-3.83) ,(OR 2.46, 95% CI: 1.76-3.44) and (OR 3.42, 95% CI: 1.88-6.22)respectively. Conclusion:We assessed the prevalence of comorbidities in the COVID-19 infection patients and found underlying disease, including hypertension, respiratory system disease and cardiovascular, may be a risk factor for severe patients compared with Non-severe patients.
Background Controlled attenuation parameter (CAP) is a non-invasive method for diagnosing hepatic steatosis. Despite good diagnostic performance, clinical application of CAP is limited due to the influences of covariates. Here, a systematic review on the performance of CAP in the diagnosis and staging of hepatic steatosis in NAFLD patients was performed. Methods The sensitivity, specificity, diagnostic odds ratio (DOR) and area under receiver operating characteristics (AUROC) curves of the pooled data for CAP in diagnosing and staging the mild (Stage 1), moderate (Stage 2) and severe (Stage 3) steatosis in NAFLD patients were assessed. The clinical utility of CAP was evaluated by Fagan plot. Heterogeneity was explored using subgroup analysis. Results Nine studies involving 1297 patients with liver biopsy-proven NAFLD were analyzed. The pooled sensitivity of CAP in detecting mild hepatic steatosis was 87% with a specificity of 91% and a DOR of 84.35. The pooled sensitivity of CAP in detecting moderate hepatic steatosis was 85% with a specificity of 74% and a DOR of 21.28. For severe steatosis, the pooled sensitivity was 76% with a specificity of 58% and a DOR of 4.70. The mean AUROC value for CAP in the diagnosis of mild, moderate, and severe steatosis was 0.96, 0.82 and 0.70, respectively. A subgroup analysis indicated that variation in the geographic regions, cutoffs, age and body mass index (BMI) could be the potential sources of heterogeneity in the diagnosis of moderate to severe steatosis. Conclusions CAP should be cautiously considered as a non-invasive substitute for liver biopsy in clinical practice. Electronic supplementary material The online version of this article (10.1186/s12876-019-0961-9) contains supplementary material, which is available to authorized users.
Hepatocellular carcinoma (HCC) is a highly malignant tumor found in the bile duct epithelial cells, and the second most common tumor of the liver.However, the pivotal roles of most molecules of tumorigenesis in HCC are still unclear. Hence, it is essential to detect the tumorigenic mechanism and develop novel prognostic biomarkers for clinical application. The data of HCC mRNA-seq and clinical information from The Cancer Genome Atlas (TCGA) database were analyzed by weighted gene co-expression network analysis (WGCNA). Co-expression modules and clinical traits were constructed by the Pearson correlation analysis, interesting modules were selected and gene ontology and pathway enrichment analysis were performed. Intramodule analysis and protein-protein interaction construction of selected modules were conducted to screen hub genes. In addition, upstream transcription factors and microRNAs of hub genes were predicted by miRecords and NetworkAnalyst database. Afterward, a high connectivity degree of hub genes from two networks was picked out to perform the differential expression validation in the Gene Expression Profiling Interactive Analysis database and Human Protein Atlas database and survival analysis in Kaplan-Meier plotter online tool. By utilizing WGCNA, several hub genes that regulate the mechanism of tumorigenesis in HCC were identified, which was associated with clinical traits including the pathological stage, histological grade, and liver function. Surprisingly, ZWINT, CENPA, RACGAP1, PLK1, NCAPG, OIP5, CDCA8, PRC1, and CDK1 were identified statistically as hub genes in the blue module, which were closely implicated in pathological T stage and histologic grade of HCC. Moreover, these genes also were strongly associated with the HCC cell growth and division. Network and survival analyses found that nine hub genes may be considered theoretically as indicators to predict the prognosis of patients with HCC or clinical treatment target, it will be necessary for basic experiments and large-scale cohort studies to validate further. K E Y W O R D Shepatocellular carcinoma, hub gene, weighted gene co-expression network analysis
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