Case report: histopathology and molecular pathology analysis on enteric tissue of a COVID-19 patient.

AIMS: Patients with COVID-19 can also have enteric symptoms. Here we analyzed the histopathology of intestinal detachment tissue from a patient with COVID-19. METHODS: The enteric tissue was examined by hematoxylin & eosin stain, PAS (Periodic acid-Schiff) staining, Gram staining, Ziehl-Neelsen stain and Grocott's Methenamine Silver (GMS) Stain. The distribution of CD3, CD4, CK20 and CD68, cytomegalovirus (CMV) and Herpes Simplex Virus (HSV) antigen were determined by immunohistochemistry. In situ hybridization (ISH) of SARS-CoV-2 and Epstein-Barr virus-encoded small RNA (EBER) were also performed. RESULTS: We observed mucosal epithelium shedding, intestinal mucosal erosion, focal inflammatory necrosis with hemorrhage, massive neutrophil infiltration, macrophage proliferation accompanied by minor lymphocyte infiltration. Fungal spores and gram positive cocci but not mycobacteria tuberculosis were identified. Immunohistochemistry staining showed abundant CD68+ macrophages but few lymphocytes infiltration. HSV, CMV and EBV were negative. ISH of SARS-CoV-2 RNA showed positive signal which mostly overlapped with CD68 positivity. CONCLUSIONS: The in situ detection of SARS-CoV-2 RNA in intestinal macrophages implicates a possible route for gastrointestinal infection. Further study is needed to further characterize the susceptibility of enteric cells to SARS-CoV-2 infection.

Prediction of COVID-19 Patients at High Risk of Progression to Severe Disease.

In order to develop a novel scoring model for the prediction of coronavirus disease-19 (COVID-19) patients at high risk of severe disease, we retrospectively studied 419 patients from five hospitals in Shanghai, Hubei, and Jiangsu Provinces from January 22 to March 30, 2020. Multivariate Cox regression and orthogonal projections to latent structures discriminant analysis (OPLS-DA) were both used to identify high-risk factors for disease severity in COVID-19 patients. The prediction model was developed based on four high-risk factors. Multivariate analysis showed that comorbidity [hazard ratio (HR) 3.17, 95% confidence interval (CI) 1.96-5.11], albumin (ALB) level (HR 3.67, 95% CI 1.91-7.02), C-reactive protein (CRP) level (HR 3.16, 95% CI 1.68-5.96), and age ≥60 years (HR 2.31, 95% CI 1.43-3.73) were independent risk factors for disease severity in COVID-19 patients. OPLS-DA identified that the top five influencing parameters for COVID-19 severity were CRP, ALB, age ≥60 years, comorbidity, and lactate dehydrogenase (LDH) level. When incorporating the above four factors, the nomogram had a good concordance index of 0.86 (95% CI 0.83-0.89) and had an optimal agreement between the predictive nomogram and the actual observation with a slope of 0.95 (R2 = 0.89) in the 7-day prediction and 0.96 (R2 = 0.92) in the 14-day prediction after 1,000 bootstrap sampling. The area under the receiver operating characteristic curve of the COVID-19-American Association for Clinical Chemistry (AACC) model was 0.85 (95% CI 0.81-0.90). According to the probability of severity, the model divided the patients into three groups: low risk, intermediate risk, and high risk. The COVID-19-AACC model is an effective method for clinicians to screen patients at high risk of severe disease.