ObjectiveTo investigate the clinical significance of co-infection with hepatitis B virus (HBV) and Epstein-Barr virus (EBV) in HBV-related liver diseases such as chronic hepatitis B (CHB), liver cirrhosis, and hepatocellular carcinoma (HCC). MethodsA retrospective analysis was performed for the clinical data of 487 patients with HBV infection who were diagnosed in Zhongnan Hospital of Wuhan University from May 2016 to August 2018, among whom 194 (39.8%) had co-infection with HBV and EBV. The patients were divided into groups according to the copy number of EBV DNA (＞400 IU/ml), Child-Pugh class (Child-Pugh class A, B, and C), and progression of liver disease (CHB, liver cirrhosis, and HCC), and related indices were compared between groups. The t-test was used for comparison of normally distributed continuous data between two groups; an analysis of variance was used for comparison between multiple groups, and the least significant difference t-test was used for further comparison between two groups. The Kruskal-Wallis H test was used for comparison of non-normally distributed continuous data between groups, and the Dunn-Bonferroni test was used for further comparison between two groups. The chi-square test was used for comparison of categorical data between groups. ResultsThe patients with CHB had a significantly higher copy number of HBV-DNA than those with liver cirrhosis or HCC (t=2.417 and 3.258, P=0.017 and 0.001), while the patients with HCC tended to have a higher copy number of EBV DNA than those with CHB or liver cirrhosis, but there was no significant difference between the three groups (F=1.161, P=0.315). After adjustment for liver function based on Child-Pugh class, the HCC patients with Child-Pugh class A liver function had a significantly higher copy number of EBV DNA than the CHB patients and the patients with liver cirrhosis (t=2.062 and 2.615, P=0.041 and 0.010)， the liver cirrhosis patients with Child-Pugh class C liver function had a significantly higher copy number of EBV DNA than the CHB patients (t=2.647,P=0.012). ALT/AST, globulin, and lymphocyte percentage were specific clinical indices for co-infection with HBV and EBV. ConclusionThere is an increase in EBV load in HCC patients, and both EBV and HBV are involved in the progression of liver diseases. Dynamic quantification of EBV DNA in patients with HBV infection has a certain significance in early intervention of the progression of liver diseases.

Objective: The purpose of our study was to investigate the predictive abilities of clinical and computed tomography (CT)features for outcome prediction in patients with coronavirus disease (COVID-19). Materials and Methods: The clinical and CT data of 238 patients with laboratory-confirmed COVID-19 in our two hospitalswere retrospectively analyzed. One hundred sixty-six patients (103 males; age 43.8 ± 12.3 years) were allocated in thetraining cohort and 72 patients (38 males; age 45.1 ± 15.8 years) from another independent hospital were assigned in thevalidation cohort. The primary composite endpoint was admission to an intensive care unit, use of mechanical ventilation, ordeath. Univariate and multivariate Cox proportional hazard analyses were performed to identify independent predictors. Anomogram was constructed based on the combination of clinical and CT features, and its prognostic performance wasexternally tested in the validation group. The predictive value of the combined model was compared with models built on theclinical and radiological attributes alone. Results: Overall, 35 infected patients (21.1%) in the training cohort and 10 patients (13.9%) in the validation cohortexperienced adverse outcomes. Underlying comorbidity (hazard ratio [HR], 3.35; 95% confidence interval [CI], 1.67–6.71;p < 0.001), lymphocyte count (HR, 0.12; 95% CI, 0.04–0.38; p < 0.001) and crazy-paving sign (HR, 2.15; 95% CI, 1.03–4.48;p = 0.042) were the independent factors. The nomogram displayed a concordance index (C-index) of 0.82 (95% CI, 0.76–0.88),and its prognostic value was confirmed in the validation cohort with a C-index of 0.89 (95% CI, 0.82–0.96). The combinedmodel provided the best performance over the clinical or radiological model (p < 0.050). Conclusion Underlying comorbidity, lymphocyte count and crazy-paving sign were independent predictors of adverseoutcomes. The prognostic nomogram based on the combination of clinical and CT features could be a useful tool for predictingadverse outcomes of patients with COVID-19.