2021-08-26

Department of Emergency Medicine, The First Affiliated Hospital of Anhui Medical University, 218 jixi road, shushan district, Hefei, Anhui, China.; Department of Emergency Medicine, The First Affiliated Hospital of Anhui Medical University, 218 jixi road, shushan district, Hefei, Anhui, China.; Department of Emergency Medicine, The First Affiliated Hospital of Anhui Medical University, 218 jixi road, shushan district, Hefei, Anhui, China.; Department of Emergency Medicine, The First Affiliated Hospital of Anhui Medical University, 218 jixi road, shushan district, Hefei, Anhui, China.; Department of Emergency Medicine, The First Affiliated Hospital of Anhui Medical University, 218 jixi road, shushan district, Hefei, Anhui, China. zhanghong20070703@163.com.

BACKGROUND: Covid-19 became a global pandemic in 2019. Studies have shown that coronavirus can cause neurological symptoms, but clinical studies on its neurological symptoms are limited. In this meta-analysis, we aimed to summarize the various neurological manifestations that occurred in COVID-19 patients and calculate the incidence of various neurological manifestations. At the same time, we further explored the mechanism of nervous system injury and prognosis in COVID-19 patients in combination with their nervous system manifestations. This study provides a reference for early clinical identification of COVID-19 nervous system injury in the future, so as to achieve early treatment and reduce neurological sequelae. METHODS: We systematically searched all published English literature related to the neurological manifestations of COVID-19 from January 1, 2020, to April 30, 2021, in Pubmed, Embase, and Cochrane Library. The keywords used were COVID-19 and terminology related to the

2021-08-16

School of Medicine, Peking Union Medical College (PUMC), PUMC & Chinese Academy of Medical Sciences, Beijing, China.; Department of Gastroenterology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing, China.; Department of Infectious Diseases, Peking Union Medical College Hospital, Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, China.; School of Medicine, Tsinghua University, Beijing, China.; Department of Respiratory and Critical Care Medicine, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing, China.; Department of Pharmacy, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing, China.; Department of Gastroenterology, Peking Union Medical College Hospital, Peking Union Medical College & Chinese Academy of Medical Science, Beijing, China.

Patients with inflammatory bowel disease, psoriasis or other rheumatic diseases treated with corticosteroids, immunomodulators and biologics might face additional risk during COVID-19 epidemic due to their immunocompromised status. However, there was still no unanimous opinion on the use of these therapy during COVID-19 epidemic. Current studies suggested that systemic corticosteroids might increase the risk of hospitalization, as well as risks of ventilation, ICU, and death among patients with immune-mediated inflammatory diseases. Anti-TNF agent was associated with lower rate of hospitalization, as well as lower risks of ventilation, ICU, and death. No significant changes in rates of hospitalization, ventilation, ICU and mortality were observed in patients treated with immunomodulators or biologics apart from anti-TNF agents. The underlying mechanism of these results might be related to pathway of antiviral immune response and cytokine storm induced by SARS-COV-2 infection. Decision

2021-10-03

School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.; Institute for Vaccine Safety, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.; School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.; School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.; School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.; School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China.; Institute for Vaccine Safety, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.; Department of International Health, Johns Hopkins Bloomberg School of Public Health, Baltimore, USA.; School of Public Health, Key Laboratory of Public Health Safety, Fudan University, Ministry of Education, Shanghai, China. yhj@fudan.edu.cn.; Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, China. yhj@fudan.edu.cn.; Department of Infectious Diseases, Huashan Hospital, Fudan University, Shanghai, China. yhj@fudan.edu.cn.

BACKGROUND: The rapid process of research and development and lack of follow-up time post-vaccination aroused great public concern about the safety profile of COVID-19 vaccine candidates. To provide comprehensive overview of the safety profile of COVID-19 vaccines by using meta-analysis technique. METHODS: English-language articles and results posted on PubMed, Embase, Web of Science, PMC, official regulatory websites, and post-authorization safety surveillance data were searched through June 12, 2021. Publications disclosing safety data of COVID-19 candidate vaccines in humans were included. A meta-analysis of proportions was performed to estimate the pooled incidence and the pooled rate ratio (RR) of safety outcomes of COVID-19 vaccines using different platforms. RESULTS: A total of 87 publications with safety data from clinical trials and post-authorization studies of 19 COVID-19 vaccines on 6 different platforms were included. The pooled rates of local and systemic reactions were

2021-07-28

School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.; Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom.; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China.; Department of Radiology, Wuhan Pulmonary Hospital, Wuhan 430030, China.; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China.; Department of Radiology, Wuhan Pulmonary Hospital, Wuhan 430030, China.; HUST-HW Joint Innovation Lab, Wuhan 430074, China.; HUST-HW Joint Innovation Lab, Wuhan 430074, China.; HUST-HW Joint Innovation Lab, Wuhan 430074, China.; Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430030, China. Electronic address: chenweiwei_tjh@163.com.; School of Electronic Information and Communications, Huazhong University of Science and Technology, Wuhan 430074, China. Electronic address: xbai@hust.edu.cn.; Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, United Kingdom.

As COVID-19 is highly infectious, many patients can simultaneously flood into hospitals for diagnosis and treatment, which has greatly challenged public medical systems. Treatment priority is often determined by the symptom severity based on first assessment. However, clinical observation suggests that some patients with mild symptoms may quickly deteriorate. Hence, it is crucial to identify patient early deterioration to optimize treatment strategy. To this end, we develop an early-warning system with deep learning techniques to predict COVID-19 malignant progression. Our method leverages CT scans and the clinical data of outpatients and achieves an AUC of 0.920 in the single-center study. We also propose a domain adaptation approach to improve the generalization of our model and achieve an average AUC of 0.874 in the multicenter study. Moreover, our model automatically identifies crucial indicators that contribute to the malignant progression, including Troponin, Brain natriuretic

2021-04-27

National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; Clinical Trial Unit, First Affiliated Hospital of Sun Yat-Sen University, Guangzhou Province, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China.; China Standard Medical Information Research Center, Shenzhen, People's Republic of China.; China Standard Medical Information Research Center, Shenzhen, People's Republic of China.; China Standard Medical Information Research Center, Shenzhen, People's Republic of China.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China; Central China Subcenter of the National Center for Cardiovascular Diseases, Zhengzhou, People's Republic of China. Electronic address: xi.li@fwoxford.org.; National Clinical Research Center for Cardiovascular Diseases, State Key, Laboratory of Cardiovascular Disease, Fuwai Hospital, National Center for, Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union, Medical College, Beijing, People's Republic of China; Central China Subcenter of the National Center for Cardiovascular Diseases, Zhengzhou, People's Republic of China; Fuwai Hospital, Chinese Academy of Medical Sciences, Shenzhen, People's Republic of China. Electronic address: jing.li@fwoxford.org.

BACKGROUND: Qingfei Paidu Tang (QPT), a formula of traditional Chinese medicine, which was suggested to be able to ease symptoms in patients with Coronavirus Disease 2019 (COVID-19), has been recommended by clinical guidelines and widely used to treat COVID-19 in China. However, whether it decreases mortality remains unknown. PURPOSE: We aimed to explore the association between QPT use and in-hospital mortality among patients hospitalized for COVID-19. STUDY DESIGN: A retrospective study based on a real-world database was conducted. METHODS: We identified patients consecutively hospitalized with COVID-19 in 15 hospitals from a national retrospective registry in China, from January through May 2020. Data on patients' characteristics, treatments, and outcomes were extracted from the electronic medical records. The association of QPT use with COVID-19 related mortality was evaluated using Cox proportional hazards models based on propensity score analysis. RESULTS: Of the 8939 patients