共检索9条数据Total:9
2021-10-18
Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.; Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.; Key Laboratory of Shaanxi Province for Craniofacial Precision Medicine Research, College of Stomatology, Xi'an Jiaotong University, Xi'an, China.; Department of Statistics and Actuarial Science, University of Hong Kong, Hong Kong SAR, China.; Department of Biostatistics, MD Anderson Cancer Center, Houston, TX, United States.
Background: We provided a comprehensive evaluation of efficacy of available treatments for coronavirus disease 2019 (COVID-19). Methods: We searched for candidate COVID-19 studies in WHO COVID-19 Global Research Database up to August 19, 2021. Randomized controlled trials for suspected or confirmed COVID-19 patients published on peer-reviewed journals were included, regardless of demographic characteristics. Outcome measures included mortality, mechanical ventilation, hospital discharge and viral clearance. Bayesian network meta-analysis with fixed effects was conducted to estimate the effect sizes using posterior means and 95% equal-tailed credible intervals (CrIs). Odds ratio (OR) was used as the summary measure for treatment effect. Bayesian hierarchical models were used to estimate effect sizes of treatments grouped by the treatment classifications. Results: We identified 222 eligible studies with a total of 102,950 patients. Compared with the standard of care, imatinib
2021-07-28
Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing 210009, China; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing 210009, China. Electronic address: jinhui_hld@163.com.; Department of Infectious Disease Epidemiology, London School of Hygiene & Tropical Medicine, London, UK; Laboratory of Data Discovery for Health (D24H), Hong Kong Science Park, Hong Kong, SAR, China.
We aimed to estimate the coronavirus disease 2019 (COVID-19) vaccine acceptance rate and identify predictors associated with acceptance. To this end, we searched PubMed, Web of Science, Cochrane Library, and Embase databases until November 4, 2020. Meta-analyses were performed to estimate the rate with 95% confidence intervals (CI). Predictors were identified to be associated with vaccination intention based on the health belief model framework. Thirty-eight articles, with 81,173 individuals, were included. The pooled COVID-19 vaccine acceptance rate was 73.31% (95%CI: 70.52, 76.01). Studies using representative samples reported a rate of 73.16%. The pooled acceptance rate among the general population (81.65%) was higher than that among healthcare workers (65.65%). Gender, educational level, influenza vaccination history, and trust in the government were strong predictors of COVID-19 vaccination willingness. People who received an influenza vaccination in the last year were more
2021-05-04
School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.; School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.; School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.; School of Statistics and Mathematics, Zhejiang Gongshang University, Hangzhou 310018, China.; Modern Business Research Center, Zhejiang Gongshang University, Hangzhou 310018, China.
The outbreak of COVID-19 in late 2019 has had a huge impact on people's daily life. Many restaurant businesses have been greatly affected by it. Consumers' preferences for catering industry in China have changed, such as environmental hygiene, variety of dishes, and service methods. Therefore, the analysis of consumer preference differences and changes before and after the epidemic can not only provide emergency strategies for the catering industry but further improve the catering industry's ability to deal with public health emergencies. This paper takes five cities in China as representatives to explore the impact of COVID-19 on China's catering industry. Based on catering review data from August 2019 to April 2020, this paper first carries out Latent Dirichlet Allocation (LDA) topic analysis and SNOWNLP (A Python library for processing Chinese text) sentiment analysis. Then this paper compares the results of topic classification and sentiment analysis before and after the epidemic.
2021-03-31
Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA arestar1@jhmi.edu.; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.; Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.; Hornet, San Francisco, California, USA.; Department of Health Behavior and Society, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland, USA.
BACKGROUND: We characterised the impact of COVID-19 on the socioeconomic conditions, access to gender affirmation services and mental health outcomes in a sample of global transgender (trans) and non-binary populations. METHODS: Between 16 April 2020 and 3 August 2020, we conducted a cross-sectional survey with a global sample of trans and non-binary people (n=849) through an online social networking app. We conducted structural equational modelling procedures to determine direct, indirect and overall effects between poor mental health (ie, depression and anxiety) and latent variables across socioecological levels: social (ie, reduction in gender affirming services, socioeconomic loss impact) and environmental factors (ie, COVID-19 pandemic environment). RESULTS: Anxiety (45.82%) and depression (50.88%) in this sample were prevalent and directly linked to COVID-19 pandemic environment. Adjusted for gender identity, age, migrant status, region, education and level of socioeconomic
Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, School of Public Health, Southeast University, Nanjing, China.
Evaluation of the validity and applicability of published prognostic prediction models for coronavirus disease 2019 (COVID-19) is essential, because determining the patients' prognosis at an early stage may reduce mortality. This study was aimed to utilize the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) to report the completeness of COVID-19-related prognostic models and appraise its effectiveness in clinical practice. A systematic search of the Web of Science and PubMed was performed for studies published until August 11, 2020. All models were assessed on model development, external validation of existing models, incremental values, and development and validation of the same model. TRIPOD was used to assess the completeness of included models, and the completeness of each item was also reported. In total, 52 publications were included, including 67 models. Age, disease history, lymphoma count, history of hypertension and
2021-05-26
Department of Mathematics and Physics, North China Electric Power University, Baoding, China.; Department of Epidemiology and Biostatistics, Fudan University, Shanghai, China.; Key Laboratory of Environmental Medicine Engineering, School of Public Health, Southeast University, Nanjing, China.; Department of Mathematics and Physics, North China Electric Power University, Baoding, China.; Department of Mathematics and Physics, North China Electric Power University, Baoding, China.; Department of Global Health, Peking University, Beijing, China.; Key Laboratory of Environmental Medicine Engineering, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, School of Public Health, Southeast University, Nanjing, China duwei@seu.edu.cn.
OBJECTIVE: We aim to explore and compare the effect of global travel restrictions and public health countermeasures in response to COVID-19 outbreak. DESIGN: A data-driven spatio-temporal modelling to simulate the spread of COVID-19 worldwide for 150 days since 1 January 2020 under different scenarios. SETTING: Worldwide. INTERVENTIONS: Travel restrictions and public health countermeasures. MAIN OUTCOME: The cumulative number of COVID-19 cases. RESULTS: The cumulative number of COVID-19 cases could reach more than 420 million around the world without any countermeasures taken. Under timely and intensive global interventions, 99.97% of infections could be avoided comparing with non-interventions. The scenario of carrying out domestic travel restriction and public health countermeasures in China only could contribute to a significant decrease of the cumulative number of infected cases worldwide. Without global travel restriction in the study setting, 98.62% of COVID-19 cases could be
2021-04-01
Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.; The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, Australia.; National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.; The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, Australia.; Department of Computer Center, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.; National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.; National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.; National Engineering Research Center for Big Data Technology and System, Services Computing Technology and System Lab, Cluster and Grid Computing Lab, School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, China.; The Australia-China Joint Research Centre for Energy Informatics and Demand Response Technologies, Centre for Distributed and High Performance Computing, School of Computer Science, The University of Sydney, Sydney, NSW, Australia.; Department of Laboratory Medicine, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) has become a global public health concern. Many inpatients with COVID-19 have shown clinical symptoms related to sepsis, which will aggravate the deterioration of patients' condition. We aim to diagnose Viral Sepsis Caused by SARS-CoV-2 by analyzing laboratory test data of patients with COVID-19 and establish an early predictive model for sepsis risk among patients with COVID-19. METHODS: This study retrospectively investigated laboratory test data of 2,453 patients with COVID-19 from electronic health records. Extreme gradient boosting (XGBoost) was employed to build four models with different feature subsets of a total of 69 collected indicators. Meanwhile, the explainable Shapley Additive ePlanation (SHAP) method was adopted to interpret predictive results and to analyze the feature importance of risk factors. FINDINGS: The model for classifying COVID-19 viral sepsis with seven coagulation function indicators achieved
2021-02-24
Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Suzhou Centre for Disease Control and Prevention, Suzhou, China.; Nanjing Centre for Disease Control and Prevention, Nanjing, China.; Huaian Centre for Disease Control and Prevention, Huaian, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; School of Public Health, Nanjing Medical University, Nanjing, China.; Department of Epidemiology and Health Statistics, School of Public Health, Southeast University, Nanjing, China.; Key Laboratory of Environmental Medicine Engineering, Ministry of Education, Nanjing, China.; Department of Acute Infectious Diseases Control and Prevention, Jiangsu Provincial Centre for Disease Control and Prevention, Nanjing, China.; NHC Key Laboratory of Enteric Pathogenic Microbiology, Nanjing, China.
To understand the characteristics and influencing factors related to cluster infections in Jiangsu Province, China, we investigated case reports to explore transmission dynamics and influencing factors of scales of cluster infection. The effectiveness of interventions was assessed by changes in the time-dependent reproductive number (Rt). From 25th January to 29th February, Jiangsu Province reported a total of 134 clusters involving 617 cases. Household clusters accounted for 79.85% of the total. The time interval from onset to report of index cases was 8 days, which was longer than that of secondary cases (4 days) (χ2 = 22.763, P < 0.001) and had a relationship with the number of secondary cases (the correlation coefficient (r) = 0.193, P = 0.040). The average interval from onset to report was different between family cluster cases (4 days) and community cluster cases (7 days) (χ2 = 28.072, P < 0.001). The average time interval from onset to isolation of patients with secondary
2021-06-30
Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Laboratory for CryoEM Structural Biology, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan.; Laboratory for CryoEM Structural Biology, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan.; Department of Viral Infections, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Department of Immunoparasitology, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Institute for Advanced Co-Creation Studies, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Department Oncogene Research, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Department Oncogene Research, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Laboratory for Supramolecular Crystallography, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan.; Laboratory of Virus Control, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Laboratory of Virus Control, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan.; Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Department of Dermatology, Graduate school of Medicine, Osaka University, Osaka 565-0871, Japan.; Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Hyogo 650-0047, Japan.; Drug Discovery Research Center, HuLA immune, Inc., Osaka 565-0871, Japan.; Drug Discovery Research Center, HuLA immune, Inc., Osaka 565-0871, Japan.; Drug Discovery Research Center, HuLA immune, Inc., Osaka 565-0871, Japan.; Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan.; Department of Respiratory Medicine, Kobe City Medical Center General Hospital, Hyogo 650-0047, Japan.; Department of Rheumatology, Kobe City Medical Center General Hospital, Hyogo 650-0047, Japan.; Department of Clinical Research, Osaka Minami Medical Center, Kawachinagano, Osaka 586-8521, Japan.; Institute for Advanced Co-Creation Studies, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan.; Department of Immunoparasitology, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan.; Department of Health Development and Medicine, Graduate school of Medicine, Osaka University, Osaka 565-0871, Japan.; Laboratory of Virus Control, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan.; Laboratory for Supramolecular Crystallography, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan.; Laboratory for CryoEM Structural Biology, Institute for Protein Research, Osaka University, Osaka 565-0871, Japan.; Department Oncogene Research, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan.; Department of Genome Informatics, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan.; Department of Viral Infections, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan.; Department of Immunochemistry, Research Institute for Microbial Diseases, Osaka University, Osaka 565-0871, Japan; Laboratory of Immunochemistry, World Premier International Immunology Frontier Research Centre, Osaka University, Osaka 565-0871, Japan; Center for Infectious Disease Education and Research, Osaka University, Osaka 565-0871, Japan. Electronic address: arase@biken.osaka-u.ac.jp.
Antibodies against the receptor-binding domain (RBD) of the SARS-CoV-2 spike protein prevent SARS-CoV-2 infection. However, the effects of antibodies against other spike protein domains are largely unknown. Here, we screened a series of anti-spike monoclonal antibodies from coronavirus disease 2019 (COVID-19) patients and found that some of antibodies against the N-terminal domain (NTD) induced the open conformation of RBD and thus enhanced the binding capacity of the spike protein to ACE2 and infectivity of SARS-CoV-2. Mutational analysis revealed that all of the infectivity-enhancing antibodies recognized a specific site on the NTD. Structural analysis demonstrated that all infectivity-enhancing antibodies bound to NTD in a similar manner. The antibodies against this infectivity-enhancing site were detected at high levels in severe patients. Moreover, we identified antibodies against the infectivity-enhancing site in uninfected donors, albeit at a lower frequency. These findings