An in-depth annotation of the newly discovered coronavirus (2019-nCoV) genome has revealed differences between 2019-nCoV and severe acute respiratory syndrome (SARS) or SARS-like coronaviruses. A systematic comparison identified 380 amino acid substitutions between these coronaviruses, which may have caused functional and pathogenic divergence of 2019-nCoV.

Aims: This cross-sectional study is to investigate the presence of Epstein-Barr virus (EBV) in colonic mucosa of inflammatory bowel disease (IBD) patients and its correlation with the clinical disease activities and therapeutic regimens. Methods: Subjects undergoing colonoscopy for screening of polyps were recruited as control. EBV DNA load was analyzed by means of quantitative real-time polymerase chain reaction and EBV-encoded RNAs were tested by in situ hybridization in intestinal mucosa of IBD patients. EBV infection was defined as positive with either method. Clinical disease activity was assessed using the Mayo Clinic Score for ulcerative colitis and Crohn’s disease activity index for CD. Results: EBV was detectable in 33 out of 99 IBD patients (33.3%). In controls, EBV prevalence was 7.5% (3/40). We found a significant correlation between EBV prevalence and clinical disease activities (mild [10.71%, 3/28] versus moderate [32.73%, 18/55], severe [75.00%, n = 12/16], p < 0.001). However, no significant difference was found in EBV prevalence between patients who received immunosuppressive therapy and those who did not. Conclusions: EBV infection is common in colonic mucosa of IBD patients. There is a significant correlation between EBV infection and clinical disease activities of IBD. However, prospective studies are still needed to explore the exact role of EBV in IBD.

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Background and Aims A deep convolutional neural network (CNN) was used to achieve fast and accurate artificial intelligence (AI)‐assisted diagnosis of early gastric cancer (GC) and other gastric lesions based on endoscopic images. Methods A CNN‐based diagnostic system based on a ResNet34 residual network structure and a DeepLabv3 structure was constructed and trained using 21,217 gastroendoscopic images of five gastric conditions, peptic ulcer (PU), early gastric cancer (EGC) and high‐grade intraepithelial neoplasia (HGIN), advanced gastric cancer (AGC), gastric submucosal tumors (SMTs), and normal gastric mucosa without lesions. The trained CNN was evaluated using a test dataset of 1091 images. The accuracy, sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the CNN were calculated. The CNN diagnosis was compared with those of 10 endoscopists with over 8 years of experience in endoscopic diagnosis. Results The diagnostic specificity and PPV of the CNN were higher than that of the endoscopists for the EGC and HGIN images (specificity: 91.2% vs. 86.7%, by 4.5%, 95% CI 2.8–7.2%; PPV: 55.4% vs. 41.7%, by 13.7%, 95% CI 11.2–16.8%) and the diagnostic accuracy of the CNN was close to those of the endoscopists for the lesion‐free, EGC and HGIN, PU, AGC, and SMTs images. The CNN had image recognition time of 42 s for all the test set images. Conclusion The constructed CNN system could be used as a rapid auxiliary diagnostic instrument to detect EGC and HGIN, as well as other gastric lesions, to reduce the workload of endoscopists.