Objective: The SARS-CoV-2-infected disease (COVID-19) outbreak is a major threat to human beings. Previous studies mainly focused on Wuhan and typical symptoms. We analysed 74 confirmed COVID-19 cases with GI symptoms in the Zhejiang province to determine epidemiological, clinical and virological characteristics. Design: COVID-19 hospital patients were admitted in the Zhejiang province from 17 January 2020 to 8 February 2020. Epidemiological, demographic, clinical, laboratory, management and outcome data of patients with GI symptoms were analysed using multivariate analysis for risk of severe/critical type. Bioinformatics were used to analyse features of SARS-CoV-2 from Zhejiang province. Results: Among enrolled 651 patients, 74 (11.4%) presented with at least one GI symptom (nausea, vomiting or diarrhoea), average age of 46.14 years, 4-day incubation period and 10.8% had pre-existing liver disease. Of patients with COVID-19 with GI symptoms, 17 (22.97%) and 23 (31.08%) had severe/critical types and family clustering, respectively, significantly higher than those without GI symptoms, 47 (8.14%) and 118 (20.45%). Of patients with COVID-19 with GI symptoms, 29 (39.19%), 23 (31.08%), 8 (10.81%) and 16 (21.62%) had significantly higher rates of fever >38.5°C, fatigue, shortness of breath and headache, respectively. Low-dose glucocorticoids and antibiotics were administered to 14.86% and 41.89% of patients, respectively. Sputum production and increased lactate dehydrogenase/glucose levels were risk factors for severe/critical type. Bioinformatics showed sequence mutation of SARS-CoV-2 with m⁶A methylation and changed binding capacity with ACE2. Conclusion: We report COVID-19 cases with GI symptoms with novel features outside Wuhan. Attention to patients with COVID-19 with non-classic symptoms should increase to protect health providers.

Background: An outbreak of coronavirus disease 2019 (COVID-19) is becoming a public health emergency. Data are limited on the duration and host factors related to viral shedding.Methods: In this retrospective study, risk factors associated with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA shedding were evaluated in a cohort of 113 symptomatic patients from 2 hospitals outside Wuhan.Results: The median (interquartile range) duration of SARS-CoV-2 RNA detection was 17 (13–22) days as measured from illness onset. When comparing patients with early (<15 days) and late (≥15 days after illness onset) viral RNA clearance, prolonged SARS-CoV-2 RNA shedding was associated with male sex (P = .009), old age (P = .033), concomitant hypertension (P = .009), delayed admission to hospital after illness onset (P = .001), severe illness at admission (P = .049), invasive mechanical ventilation (P = .006), and corticosteroid treatment (P = .025). Patients with longer SARS-CoV-2 RNA shedding duration had slower recovery of body temperature (P < .001) and focal absorption on radiograph images (P < .001) than patients with early SARS-CoV-2 RNA clearance. Male sex (OR, 3.24; 95% CI, 1.31–8.02), delayed hospital admission (OR, 1.30; 95% CI, 1.10–1.54), and invasive mechanical ventilation (OR, 9.88; 95% CI, 1.11–88.02) were independent risk factors for prolonged SARS-CoV-2 RNA shedding.Conclusions: Male sex, delayed admission to hospital after illness onset, and invasive mechanical ventilation were associated with prolonged SARS-CoV-2 RNA shedding. Hospital admission and general treatments should be started as soon as possible in symptomatic COVID-19 patients, especially male patients.

The lifecycle of the Ebola virus in host cells The lifecycle of the Ebola virus in host cells

In January 2015, there was an outbreak of avian-origin influenza A (H7N9) virus in Zhejiang Province, China. A 45-year-old man was admitted to the First Affiliated Hospital of Zhejiang University with a high fever that had lasted 7 days, chills, and a cough with yellow sputum. Laboratory testing confirmed infection with the H7N9 virus, likely obtained from contact with poultry at a local live poultry market. A large dense shadow was apparent in the patient's left lung at the time of admission. Treatment with oseltamivir (75mg twice daily) did not improve the patient's condition. The decision was made to try using convalescent plasma to treat the infection. Convalescent plasma was administered 3 days after the patient was admitted to the hospital and led to a marked improvement. To our knowledge, this is the first report of the successful use of convalescent plasma to treat a case of H7N9 infection in China. These results suggest that the combination of convalescent plasma and antiviral drugs may be effective for the treatment of avian-origin H7N9 infection.

Objectives: To appraise effective predictors for infection in patients with decompensated cirrhosis (DC) by using XGBoost algorithm in a retrospective case-control study.Methods: Clinical data were retrospectively collected from 6,648 patients with DC admitted to five tertiary hospitals. Indicators with significant differences were determined by univariate analysis and least absolute contraction and selection operator (LASSO) regression. Further multi-tree extreme gradient boosting (XGBoost) machine learning-based model was used to rank importance of features selected from LASSO and subsequently constructed infection risk prediction model with simple-tree XGBoost model. Finally, the simple-tree XGBoost model is compared with the traditional logical regression (LR) model. Performances of models were evaluated by area under the receiver operating characteristic curve (AUROC), sensitivity, and specificity.Results: Six features, including total bilirubin, blood sodium, albumin, prothrombin activity, white blood cell count, and neutrophils to lymphocytes ratio were selected as predictors for infection in patients with DC. Simple-tree XGBoost model conducted by these features can predict infection risk accurately with an AUROC of 0.971, sensitivity of 0.915, and specificity of 0.900 in training set. The performance of simple-tree XGBoost model is better than that of traditional LR model in training set, internal verification set, and external feature set (P < 0.001).Conclusions: The simple-tree XGBoost predictive model developed based on a minimal amount of clinical data available to DC patients with restricted medical resources could help primary healthcare practitioners promptly identify potential infection.

The present study evaluated the potential combined effects of NAFLD and MetS on the development of osteoporosis. The relationship between NAFLD and MetS and osteoporosis was assessed in 938 postmenopausal female participants. Moderate and severe NAFLDs were combined as significant NAFLD (SNAFLD). All the subjects were divided into 4 subgroups based on the status of SNAFLD and MetS. Relative excess risk of interaction (RERI), attributable proportion (AP) of interaction, and synergy index (SI) were used to investigate the additive interaction of those two factors. NAFLD, SNAFLD, and MetS were independent factors for osteoporosis with the adjustment of age and other confounders. The incidence of osteoporosis in MetS (+) SNAFLD (+) group was significantly higher than that in other three groups. RERI was 2.556 (95% CI = 0.475–4.636), AP was 0.454 (95% CI = 0.201–0.706), and SI was 2.231 (95% CI = 1.124 to 4.428), indicating the significant combined interaction of SNAFLD and MetS on the development of osteoporosis. SNAFLD and MetS are independent risk factors for osteoporosis in postmenopausal females, respectively. Moreover, SNAFLD and MetS have an additive effect on the development of osteoporosis.