IMPORTANCEEarly identification of patients with novel coronavirus disease 2019 (COVID-19) who may develop critical illness is of great importance and may aid in delivering proper treatment and optimizing use of resources.OBJECTIVE To develop and validate a clinical score at hospital admission for predicting which patients with COVID-19 will develop critical illness based on a nationwide cohort in China. DESIGN, SETTING, AND PARTICIPANTSCollaborating with the National Health Commission of China, we established a retrospective cohort of patients with COVID-19 from 575 hospitals in 31 provincial administrative regions as of January 31, 2020. Epidemiological, clinical, laboratory, and imaging variables ascertained at hospital admission were screened using Least Absolute Shrinkage and Selection Operator (LASSO) and logistic regression to construct a predictive risk score (COVID-GRAM). The score provides an estimate of the risk that a hospitalized patient with COVID-19 will develop critical illness. Accuracy of the score was measured by the area under the receiver operating characteristic curve (AUC). Data from 4 additional cohorts in China hospitalized with COVID-19 were used to validate the score. Data were analyzed between February 20, 2020 and March 17, 2020.MAIN OUTCOMES AND MEASURES Among patients with COVID-19 admitted to the hospital, critical illness was defined as the composite measure of admission to the intensive care unit, invasive ventilation, or death. RESULTSThe development cohort included 1590 patients. the mean (SD) age of patients in the cohort was 48.9 (15.7) years; 904 (57.3%) were men. The validation cohort included 710 patients with a mean (SD) age of 48.2 (15.2) years, and 382 (53.8%) were men and 172 (24.2%). From 72 potential predictors, 10 variables were independent predictive factors and were included in the risk score: chest radiographic abnormality (

The sudden deterioration of patients with novel coronavirus disease 2019 (COVID-19) into critical illness is of major concern. It is imperative to identify these patients early. We show that a deep learning-based survival model can predict the risk of COVID-19 patients developing critical illness based on clinical characteristics at admission. We develop this model using a cohort of 1590 patients from 575 medical centers, with internal validation performance of concordance index 0.894 We further validate the model on three separate cohorts from Wuhan, Hubei and Guangdong provinces consisting of 1393 patients with concordance indexes of 0.890, 0.852 and 0.967 respectively. This model is used to create an online calculation tool designed for patient triage at admission to identify patients at risk of severe illness, ensuring that patients at greatest risk of severe illness receive appropriate care as early as possible and allow for effective allocation of health resources.

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The aim of the present study was to investigate the role of anti-inflammation for MSCs transplantation in rat models of myocardial infarction. Rats with AMI induced by occlusion of the left coronary artery were randomized to MSCs transplantation group, MI group and sham operated group. The effects of MSCs transplantation on cardiac inflammation and left ventricular remodeling in non-infarcted zone were observed after 4 weeks of MI. We found that MSC transplantation (1) decreased protein production and gene expression of inflammation cytokines TNF-alpha, IL-1beta and IL-6, (2) inhibited deposition of type I and III collagen, as well as gene and protein expression of MMP-1 and TIMP-1, (3) attenuated LV cavitary dilation and transmural infarct thinning, thus prevent myocardial remodeling after myocardial infarction, and (4) increased EF, FS, LVESP and dp/dtmax (P < 0.01), decreased LVDd, LVEDV, LVEDP (P < 0.05). Anti-inflammation role for MSCs transplantation might partly account for the cardiac protective effect in ischemic heart disease.

The landmark report by de Bold et al. in 1981 signified the heart as one of the endocrine organs involved in fluid and salt balance (de Bold AJ, Borenstein HB, Veress AT, Sonnenberg H. 28: 89-94, 1981). Atrial natriuretic peptide (ANP) and brain natriuretic peptide (BNP) are secreted from cardiomyocytes in response to cardiac stretch as in the case of heart failure, whereas C-type natriuretic peptide (CNP) is secreted from endothelial and renal cells in response to cytokines and endothelium-dependent agonists, such as acetylcholine. Binding ANP or BNP to natriuretic peptide receptor A induces cyclic guanylyl monophosphate as second messenger in the target cells to mediate the following: natriuresis; water diuresis; increasing glomerular filtration rate; decreasing systemic sympathetic activities; plasma volume; cardiac output and blood pressure; and curbing mitoses of heart fibroblasts and hypertrophy of cardiovascular muscle cells. ANP, BNP, and CNP are cleared from the bloodstream by natriuretic peptide receptor C and degraded by an ectoenzyme called neprilysin (NEP). The plasma levels of BNP are typically>100 pg/ml in patients with congestive heart failure. Sacubitril/valsartan is an angiotensin receptor NEP inhibitor that prevents the clinical progression of surviving patients with heart failure more effectively than enalapril, an angiotensin-converting enzyme inhibitor. A thorough understanding of the renal and cardiovascular effects of natriuretic peptides is of major importance for first-year medical students to gain insight into the significance of plasma levels of BNP in patients with heart failure.