Background As an emergent and fulminant infectious disease, Corona Virus Disease 2019 (COVID-19) has caused a worldwide pandemic. The early identification and timely treatment of severe patients are crucial to reducing the mortality of COVID-19. This study aimed to investigate the clinical characteristics and early predictors for severe COVID-19, and to establish a prediction model for the identification and triage of severe patients. Methods All confirmed patients with COVID-19 admitted by the Second Affiliated Hospital of Air Force Medical University were enrolled in this retrospective non-interventional study. The patients were divided into a mild group and a severe group, and the clinical data were compared between the two groups. Univariate and multivariate analysis were used to identify the independent early predictors for severe COVID-19, and the prediction model was constructed by multivariate logistic regression analysis. Receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the prediction model and each early predictor. Results A total of 40 patients were enrolled in this study, of whom 19 were mild and 21 were severe. The proportions of patients with venerable age (≥60 years old), comorbidities, and hypertension in severe patients were higher than that of the mild ( P < 0.05). The duration of fever and respiratory symptoms, and the interval from illness onset to viral clearance were longer in severe patients ( P < 0.05). Most patients received at least one form of oxygen treatments, while severe patients required more mechanical ventilation ( P < 0.05). Univariate and multivariate analysis showed that venerable age, hypertension, lymphopenia, hypoalbuminemia and elevated neutrophil lymphocyte ratio (NLR) were the independent high-risk factors for severe COVID-19. ROC curves demonstrated significant predictive value of age, lymphocyte count, albumin and NLR for severe COVID-19. The sensitivity and specificity of the newly constructed prediction model for predicting severe COVID-19 was 90.5% and 84.2%, respectively, and whose positive predictive value, negative predictive value and crude agreement were all over 85%. Conclusions The severe COVID-19 risk model might help clinicians quickly identify severe patients at an early stage and timely take optimal therapeutic schedule for them.

No abstract

No abstract

In this study, 130 Staphylococcus aureus isolates from samples associated with pork production were tested for prevalence of 18 staphylococcal enterotoxin (SE) genes. Approximately 94.6% (123/130) of isolates from different stages of pork production harbored one or more SE genes forming 37 different enterotoxin gene profiles. Seb was present in 60.0% of the S. aureus isolates, the highest among the genes tested. The genes, sed, sej, seo, sep, ser, and seu, were not found. The five classical SE genes (including sea, seb, sec, sed, see) had lower prevalence than the egc gene cluster (seg, sei, sem, sen, seo, or seu). Notably, ∼6.9% (9/130) isolates harbored five SE genes. Classical SE genes were relatively higher in raw meat isolates than swine farm isolates, suggesting that raw meat isolates have a greater potential for classical staphylococcal food poisoning. Incomplete egc clusters were mainly distributed in swine farm isolates, and some of them coexisted with other classical SE genes (seb, sec), showing that swine farms could be potential sources of enterogenic S. aureus of food safety concern. Characterizing the distributions of enterotoxin genes among S. aureus may provide epidemiological information for the benefit of public health and food safety.