Complement and complement regulatory proteins are upregulated in lungs of COVID-19 patients.

We explored the pathological changes and the activation of local complement system in COVID-19 pneumonia. Lung paraffin sections of COVID-19 infected patients were analyzed by HE (hematoxylin-eosin) staining. The deposition of complement C3, the deposition of C3b/iC3b/C3d and C5b-9, and the expression of complement regulatory proteins, CD59, CD46 and CD55 were detected by immunohistochemistry. In COVID-19 patients' lung tissues, fibrin exudation, mixed with erythrocyte, alveolar macrophage and shed pneumocyte are usually observed in the alveoli. The formation of an "alveolar emboli" structure may contribute to thrombosis and consolidation in lung tissue. In addition, we also found that compared to normal tissue, the lung tissues of COVID-19 patients displayed the hyper-activation of complement that is represented by extensive deposition of C3, C3b/iC3b/C3d and C5b-9, and the increased expression level of complement regulatory proteins CD55, and especially CD59 but not CD46. The thrombosis and consolidation in lung tissues may contribute to the pathogenesis of COVID-19. The increased expression of CD55 and CD59 may reflect a feedback of self-protection on the complement hyper-activation. Further, the increased C3 deposition and the strongly activated complement system in lung tissues may suggest the rationale of complement-targeted therapeutics in conquering COVID-19.

A Large-Scale Clinical Validation Study Using nCapp Cloud Plus Terminal by Frontline Doctors for the Rapid Diagnosis of COVID-19 and COVID-19 pneumonia in China

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.

Clinical predictors of COVID-19 disease progression and death: Analysis of 214 hospitalised patients from Wuhan, China.

INTRODUCTION: COVID-19 has spread rapidly worldwide and has been declared a pandemic. OBJECTIVES: To delineate clinical features of COVID-19 patients with different severities and prognoses and clarify the risk factors for disease progression and death at an early stage. METHODS: Medical history, laboratory findings, treatment and outcome data from 214 hospitalised patients with COVID-19 pneumonia admitted to Eastern Campus of Renmin Hospital, Wuhan University in China were collected from 30 January 2020 to 20 February 2020, and risk factors associated with clinical deterioration and death were analysed. The final date of follow-up was 21 March 2020. RESULTS: Age, comorbidities, higher neutrophil cell counts, lower lymphocyte counts and subsets, impairment of liver, renal, heart, coagulation systems, systematic inflammation and clinical scores at admission were significantly associated with disease severity. Ten (16.1%) moderate and 45 (47.9%) severe patients experienced deterioration after admission, and median time from illness onset to clinical deterioration was 14.7 (IQR 11.3-18.5) and 14.5 days (IQR 11.8-20.0), respectively. Multivariate analysis showed increased Hazards Ratio of disease progression associated with older age, lymphocyte count <1.1 × 109/L, blood urea nitrogen (BUN)> 9.5 mmol/L, lactate dehydrogenase >250 U/L and procalcitonin >0.1 ng/mL at admission. These factors were also associated with the risk of death except for BUN. Prediction models in terms of nomogram for clinical deterioration and death were established to illustrate the probability. CONCLUSIONS: These findings provide insights for early detection and management of patients at risk of disease progression or even death, especially older patients and those with comorbidities.

Chinese experts’ consensus on the Internet of Things-aided diagnosis and treatment of coronavirus disease 2019 (COVID-19)

The aim is to diagnose COVID-19 earlier and to improve its treatment by applying medical technology, the “COVID-19 Intelligent Diagnosis and Treatment Assistant Program (nCapp)” based on the Internet of Things. Terminal eight functions can be implemented in real-time online communication with the “cloud” through the page selection key. According to existing data, questionnaires, and check results, the diagnosis is automatically generated as confirmed, suspected, or suspicious of 2019 novel coronavirus (2019-nCoV) infection. It classifies patients into mild, moderate, severe or critical pneumonia. nCapp can also establish an online COVID-19 real-time update database, and it updates the model of diagnosis in real time based on the latest real-world case data to improve diagnostic accuracy. Additionally, nCapp can guide treatment. Front-line physicians, experts, and managers are linked to perform consultation and prevention. nCapp also contributes to the long-term follow-up of patients with COVID-19. The ultimate goal is to enable different levels of COVID-19 diagnosis and treatment among different doctors from different hospitals to upgrade to the national and international through the intelligent assistance of the nCapp system. In this way, we can block disease transmission, avoid physician infection, and epidemic prevention and control as soon as possible.

Corticosteroid, oseltamivir and delayed admission are independent risk factors for prolonged viral shedding in patients with Coronavirus Disease 2019.

INTRODUCTION: Coronavirus Disease 2019 (COVID-19) has spread worldwide, and it has reached to more than 14.5 million cases. Although Hubei province is the epicenter of China, little is known about epidemiological and clinical features of COVID-19 in other areas in Hubei province around Wuhan. In addition, the virological data, particularly the factors associated with viral shedding of COVID-19 has not been well described. OBJECTIVE: To describe the epidemiological and clinical features of patients with COVID-19 in Tianmen city, and identify risk factors associated with prolonged viral shedding of COVID-19. METHODS: Inpatients with COVID-19 admitted before February 9, 2020 were included. Characteristics were compared between patients with early and late viral RNA shedding. Multivariate cox regression model was used to investigate variables associated with prolonged viral shedding. RESULTS: One hundred and eighty-three patients were included. About 8.2% patients were categorized as critical degree of severity. All patients received antiviral therapy, with arbidol and interferon being the commonest. About 38.3% and 16.9% patients were treated with corticosteroid and immunoglobulin, respectively. Time from onset to admission (HR = 0.829, P < 0.001), and administration of corticosteroid (HR = 0.496, P = 0.002), arbidol (HR = 2.605, P = 0.008) and oseltamivir (HR = 0.416, P < 0.001) were independently associated with duration of viral shedding. CONCLUSION: Symptoms of patients from Tianmen are relatively mild. Treatment should be started as early as possible, but corticosteroid and oseltamivir should be initiated with caution. In addition, clinical trials on arbidol should be conducted to demonstrate its effectiveness.