ABSTRACT
Background The outbreak of coronavirus disease 2019 (COVID-19) has become a global pandemic acute infectious disease, especially with the features of possible asymptomatic carriers and high contagiousness. Currently, it is difficult to quickly identify asymptomatic cases or COVID-19 patients with pneumonia due to limited access to reverse transcription-polymerase chain reaction (RT-PCR) nucleic acid tests and CT scans. Goal This study aimed to develop a scientific and rigorous clinical diagnostic tool for the rapid prediction of COVID-19 cases based on a COVID-19 clinical case database in China, and to assist doctors to efficiently and precisely diagnose asymptomatic COVID-19 patients and cases who had a false-negative RT-PCR test result. Methods With online consent, and the approval of the ethics committee of Zhongshan Hospital Fudan University (NCT04275947, B2020-032R) to ensure that patient privacy is protected, clinical information has been uploaded in real-time through the New Coronavirus Intelligent Auto-diagnostic Assistant Application of cloud plus terminal (nCapp) by doctors from different cities (Wuhan, Shanghai, Harbin, Dalian, Wuxi, Qingdao, Rizhao, and Bengbu) during the COVID-19 outbreak in China. By quality control and data anonymization on the platform, a total of 3,249 cases from COVID-19 high-risk groups were collected. The effects of different diagnostic factors were ranked based on the results from a single factor analysis, with 0.05 as the significance level for factor inclusion and 0.1 as the significance level for factor exclusion. Independent variables were selected by the step-forward multivariate logistic regression analysis to obtain the probability model. Findings We applied the statistical method of a multivariate regression model to the training dataset (1,624 cases) and developed a prediction model for COVID-19 with 9 clinical indicators that are accessible. The area under the receiver operating characteristic (ROC) curve (AUC) for the model was 0.88 (95% CI: 0.86, 0.89) in the training dataset and 0.84 (95% CI: 0.82, 0.86) in the validation dataset (1,625 cases). Discussion With the assistance of nCapp, a mobile-based diagnostic tool developed from a large database that we collected from COVID-19 high-risk groups in China, frontline doctors can rapidly identify asymptomatic patients and avoid misdiagnoses of cases with false-negative RT-PCR results.
ABSTRACT
Coagulopathy is associated with both inflammation and infection, including infection with the novel SARS-CoV-2 (COVID-19). Endothelial cells (ECs) fine tune hemostasis via cAMP-mediated secretion of von Willebrand factor (vWF), which promote the process of clot formation. The e xchange p rotein directly a ctivated by c AMP (EPAC) is a ubiquitously expressed intracellular cAMP receptor that plays a key role in stabilizing ECs and suppressing inflammation. To assess whether EPAC could regulate vWF release during inflammation, we utilized our EPAC1 -null mouse model and revealed an increased secretion of vWF in endotoxemic mice in the absence of the EPAC1 gene. Pharmacological inhibition of EPAC1 in vitro mimicked the EPAC1 −/− phenotype. EPAC1 regulated TNFα-triggered vWF secretion from human umbilical vein endothelial cells (HUVECs) in a phosphoinositide 3-kinases (PI3K)/endothelial nitric oxide synthase (eNOS)-dependent manner. Furthermore, EPAC1 activation reduced inflammation-triggered vWF release, both in vivo and in vitro . Our data delineate a novel regulatory role of EPAC1 in vWF secretion and shed light on potential development of new strategies to controlling thrombosis during inflammation. Key Point PI3K/eNOS pathway-mediated, inflammation-triggered vWF secretion is the target of the pharmacological manipulation of the cAMP-EPAC system.