Eleven Routine Clinical Features Predict COVID-19 Severity

Severity prediction of COVID-19 remains one of the major clinical challenges for the ongoing pandemic. Here, we have recruited a 144 COVID-19 patient cohort consisting of training, validation, and internal test sets, longitudinally recorded 124 routine clinical and laboratory parameters, and built a machine learning model to predict the disease progression based on measurements from the first 12 days since the disease onset when no patient became severe. A panel of 11 routine clinical factors, including oxygenation index, basophil counts, aspartate aminotransferase, gender, magnesium, gamma glutamyl transpeptidase, platelet counts, activated partial thromboplastin time, oxygen saturation, body temperature and days after symptom onset, constructed a classifier for COVID-19 severity prediction, achieving accuracy of over 94%. Validation of the model in an independent cohort containing 25 patients achieved accuracy of 80%. The overall sensitivity, specificity, PPV and NPV were 0.70, 0.99, 0.93 and 0.93, respectively. Our model captured predictive dynamics of LDH and CK while their levels were in the normal range. This study presents a practical model for timely severity prediction and surveillance for COVID-19, which is freely available at webserver https://guomics.shinyapps.io/covidAI/.

Gut microbiota may underlie the predisposition of healthy individuals to COVID-19

The COVID-19 pandemic is spreading globally with high disparity in the susceptibility of the disease severity. Identification of the key underlying factors for this disparity is highly warranted. Here we describe constructing a proteomic risk score based on 20 blood proteomic biomarkers which predict the progression to severe COVID-19. We demonstrate that in our own cohort of 990 individuals without infection, this proteomic risk score is positively associated with proinflammatory cytokines mainly among older, but not younger, individuals. We further discovered that a core set of gut microbiota could accurately predict the above proteomic biomarkers among 301 individuals using a machine learning model, and that these gut microbiota features are highly correlated with proinflammatory cytokines in another set of 366 individuals. Fecal metabolomic analysis suggested potential amino acid-related pathways linking gut microbiota to inflammation. This study suggests that gut microbiota may underlie the predisposition of normal individuals to severe COVID-19.

Effect of nitric oxide on urotensin-Ⅱ expression in pulmonary arterioles of rats chronically exposed to hypoxia-hypercapnia / 中国病理生理杂志

AIM: To investigate the roles of nitric oxide/L-arginine(NO/L-Arg) pathway and urotensin-Ⅱ(UⅡ) in the development of pulmonary hypertension induced by chronic hypoxia-hypercapnia in rats.METHODS: Forty male Sprague-Dawley rats were randomly divided into four groups(n=10): normal control group(A),hypoxia-hypercapnia+saline group(B),hypoxia-hypercapnia+L-Arg liposome group(C) and hypoxia-hypercapnia+N-nitro-L-arginine methyl ester(L-NAME) group(D).Contents of UⅡ,UⅡ mRNA and receptor of UⅡ(UT) mRNA in pulmonary arterioles were measured with immunohistochemistry analysis and in situ hybridization,respectively.Change of small pulmonary vascular microstructure was also investigated.RESULTS:(1) The mean pulmonary artery pressure(mPAP) and the weight ratio of right ventricle to left ventricle plus septum [RV/(LV+S)] in B and D groups were all higher than those in A group(respectively,P