Hypercytokinemia is a critically fatal factor in COVID-19. However, underlying pathogenic mechanisms are unknown. Here we show that brinogen and leukotriene-A4 hydrolase (LTA4H), two of the most potent in ammatory contributors, are elevated by 67.7 and astonishing 227.7% in the plasma of patients infected by SARS-CoV-2 and admitted to intensive care unit in comparison with healthy control, respectively. Conversely, transferrin identi ed as a brinogen immobilizer in our recent work and Spink6 are down-regulated by 40.3 and 25.9%, respectively. Furthermore, we identify Spink6 as the rst endogenous inhibitor of LTA4H, a pro-in ammatory enzyme catalyzing nal and rating limited step in biosynthesis of leukotriene-B4 that is an extremely in ammatory mediator and a target to design superior anti-in ammatory drugs. Additionally, virus Spike protein is found to evoke LTA4H and brinogen expression in vivo. Collectively, these ndings identify the imbalance between in ammatory drivers and antagonists, which likely contributes to hypercytokinemia in COVID-19. Spink6 may have superior antiin ammatory function because it speci cally targets epoxide hydrolase of LTA4H to inhibit leukotriene-B4 biosynthesis without effecting LTA4H's aminopeptidase activity.

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Background: Since December 2019, acute respiratory disease (ARD) due to 2019 novel coronavirus (2019-nCoV) emerged in Wuhan city and rapidly spread throughout China. We sought to delineate the clinical characteristics of these cases. Methods:We extracted the data on 1,099 patients with laboratory-confirmed 2019-nCoV ARD from 552 hospitals in 31 provinces/provincial municipalities through January 29 th , 2020. Results:The median age was 47.0 years, and 41.90% were females. Only 1.18% of patients had a direct contact with wildlife, whereas 31.30% had been to Wuhan and 71.80% had contacted with people from Wuhan. Fever (87.9%) and cough (67.7%) were the most common symptoms. Diarrhea is uncommon. The median incubation period was 3.0 days (range, 0 to 24.0 days). On admission, ground-glass opacity was the typical radiological finding on chest computed tomography (50.00%).Significantly more severe cases were diagnosed by symptoms plus reverse-transcriptase polymerase-chain-reaction without abnormal radiological findings than non-severe cases (23.87% vs. 5.20%, P<0.001). Lymphopenia was observed in 82.1% of patients. 55 patients (5.00%) were . CC-BY-NC-ND 4.0 International license It is made available under a author/funder, who has granted medRxiv a license to display the preprint in perpetuity.is the (which was not peer-reviewed) The copyright holder for this preprint . https://doi.org/10. 1101 /2020 admitted to intensive care unit and 15 (1.36%) succumbed. Severe pneumonia was independently associated with either the admission to intensive care unit, mechanical ventilation, or death in multivariate competing-risk model (sub-distribution hazards ratio, 9.80; 95% confidence interval, 4.06 to 23.67). Conclusions:The 2019-nCoV epidemic spreads rapidly by human-to-human transmission. Normal radiologic findings are present among some patients with 2019-nCoV infection. The disease severity (including oxygen saturation, respiratory rate, blood leukocyte/lymphocyte count and chest X-ray/CT manifestations) predict poor clinical outcomes. Abstract: 249 words; main text: 2677 words : medRxiv preprint Clinical outcomesThe percentages of patients being admitted to the ICU, requiring invasive ventilation and death were 5.00%, 2.18% and 1.36%, respectively. This corresponded to 67 (6.10%) of patients having reached to the composite endpoint ( Table 3).Results of the univariate competing risk model are shown in Table E1 in Supplementary Appendix. Severe pneumonia cases (SDHR, 9.803; 95%CI, 4.06 to 23.67), leukocyte count greater than 4,000/mm 3 (SDHR, 4.01; 95%CI, 1.53 to 10.55) and interstitial abnormality on chest X-ray (SDHR, 4.31; 95%CI, 1.73 to 10.75) were associated with the composite endpoint (Fig. 2, see Table E2 in Supplementary Appendix). Sensitivity analyses are shown in Figure E2 in Supplementary Appendix. DiscussionThis study has shown that fever occurred in only 43.8% of patients with 2019-nCoV ARD on presentation but developed in 87.9% following hospitalization. Severe pneumonia occurred in 15.7% of cases. No radiolo...

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 development of artificial intelligence (AI)-based technologies in medicine is advancing rapidly, but real-world clinical implementation has not yet become a reality. Here we review some of the key practical issues surrounding the implementation of AI into existing clinical workflows, including data sharing and privacy, transparency of algorithms, data standardization, and interoperability across multiple platforms, and concern for patient safety. We summarize the current regulatory environment in the United States and highlight comparisons with other regions in the world, notably Europe and China.

Highlights d AI system that can diagnose COVID-19 pneumonia using CT scans d Prediction of progression to critical illness d Potential to improve performance of junior radiologists to the senior level d Can assist evaluation of drug treatment effects with CT quantification

BACKGROUND:The novel coronavirus disease 2019 (COVID-19) has become a global health emergency. The cumulative number of new confirmed cases and deaths are still increasing out of China. Independent predicted factors associated with fatal outcomes remain uncertain.RESEARCH QUESTION: The goal of the current study was to investigate the potential risk factors associated with fatal outcomes from COVID-19 through a multivariate Cox regression analysis and a nomogram model. STUDY DESIGN AND METHODS:A retrospective cohort of 1,590 hospitalized patients with COVID-19 throughout China was established. The prognostic effects of variables, including clinical features and laboratory findings, were analyzed by using Kaplan-Meier methods and a Cox proportional hazards model. A prognostic nomogram was formulated to predict the survival of patients with COVID-19. RESULTS:In this nationwide cohort, nonsurvivors included a higher incidence of elderly people and subjects with coexisting chronic illness, dyspnea, and laboratory abnormalities on admission compared with survivors. Multivariate Cox regression analysis showed that age $ 75 years (hazard ratio [HR], 7.86; 95% CI, 2. 44-25.35), age between 65 and 74 years (HR, 3.43; 95% CI, 1.24-9.5), coronary heart disease (HR, 4.28; 95% CI, 1.14-16.13), cerebrovascular disease (HR, 3.1; 95% CI, 1.07-8.94), dyspnea (HR, 3.96; 95% CI,, procalcitonin level > 0.5 ng/mL (HR, 8.72; 95% CI,, and aspartate aminotransferase level > 40 U/L (HR, 2.2; 95% CI, 1.1-6.73) were independent risk factors associated with fatal outcome. A nomogram was established based on the results of multivariate analysis. The internal bootstrap resampling approach suggested the nomogram has sufficient discriminatory power with a C-index of 0.91 (95% CI, 0.85-0.97). The calibration plots also showed good consistency between the prediction and the observation. INTERPRETATION:The proposed nomogram accurately predicted clinical outcomes of patients with COVID-19 based on individual characteristics. Earlier identification, more intensive surveillance, and appropriate therapy should be considered in patients at high risk.