Corpus overview


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MeSH Disease

HGNC Genes

SARS-CoV-2 proteins

ProteinS (2227)

ProteinN (575)

NSP5 (418)

ComplexRdRp (253)

ProteinE (148)


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    COVID-19 MESHD in Wuhan: Immediate Psychological Impact on 5062 Health Workers

    Authors: Zhou Zhu; Shabei Xu; Hui Wang; Zheng Liu; Jianhong Wu; Guo Li; Jinfeng Miao; Chenyan Zhang; Yuan Yang; Wenzhe Sun; Suiqiang Zhu; Yebin Fan; Junbo Hu; Jihong Liu; Wei Wang

    doi:10.1101/2020.02.20.20025338 Date: 2020-02-23 Source: medRxiv

    BACKGROUND: The outbreak of COVID-19 MESHD has laid unprecedented psychological stress on health workers (HWs). We aimed to assess the immediate psychological impact on HWs at Tongji Hospital in Wuhan, China. METHODS: We conducted a single-center, cross-sectional survey of HWs via online questionnaires between February 8th and 10th, 2020. We evaluated stress, depression MESHD and anxiety MESHD by Impact of Event Scale-Revised (IES-R), Patient Health Questionnaire-9 (PHQ-9), and Generalized Anxiety Disorder MESHD 7-item (GAD-7), respectively. We also designed a questionnaire to assess the effect of psychological protective measures taken by Tongji Hospital. Multivariate logistic regression was used to identify predictors of acute stress MESHD, depression MESHD, and anxiety MESHD. RESULTS: We received 5062 completed questionnaires (response rate, 77.1 percent). 1509 (29.8 percent), 681 (13.5 percent) and 1218 (24.1 percent) HWs reported stress, depression MESHD and anxiety symptoms MESHD. Women (hazard ratio[HR], 1.31; P=0.032), years of working> 10 years (HR, 2.02; P<0.001), concomitant chronic diseases MESHD (HR, 1.51; P<0.001), history of mental disorders MESHD (HR, 3.27; P<0.001), and family members or relatives confirmed or suspected (HR, 1.23; P=0.030) were risk factors for stress, whereas care provided by hospital and department administrators(odds ratio [OR], 0.76; P=0.024) and full coverage of all departments with protective measures (OR, 0.69; P=0.004) were protective factors. CONCLUSIONS: Women and those who have more than 10 years of working, concomitant chronic diseases MESHD, history of mental disorders MESHD, and family members or relatives confirmed or suspected are susceptible to stress, depression MESHD and anxiety MESHD among HWs during the COVID-19 pandemic MESHD. Psychological protective measures implemented by the hospital could be helpful.

    Joint spatio-temporal analysis of multiple response types using the hierarchical generalized transformation model with application to coronavirus disease 2019 and social distancing

    Authors: Jonathan R. Bradley

    id:2002.09983v3 Date: 2020-02-23 Source: arXiv

    Social distancing can be described as an effort to maintain a physical distance between individuals and has become a necessary public health measure to combat cornoavirus disease MESHD 2019 ( COVID-19 MESHD). Social distancing is known to weaken incidences and deaths due to COVID-19 MESHD, however, there are detrimental economic and psychological effects. This motivates us to analyze incidences (and deaths) of COVID-19 MESHD along with a measure of the health of the US economy (i.e., the adjusted closing price of the Dow Jones Industrial), and a measure of the public interest in COVID-19 MESHD through Google Trends data. The model we implement is developed to be easily adapted to a data scientist's preferred method for continuous data, which is done to aid future analyses of this important dataset. This dataset consists of multiple response types (e.g., continuous-valued, count-valued, binomial counts). Thus, we introduce a reasonable easy-to-implement all-purpose method that "converts" a statistical model for continuous responses (the preferred model) into a Bayesian model for multi-response data sets. To do this, we transform the data such that the continuous-valued transformed data can be reasonably modeled using the preferred model and the transformation itself is treated as unknown. The implementation of our approach involves two steps. The first step produces posterior replicates of the transformed data using a latent conjugate multivariate (LCM) model. The second step involves generating values from the posterior distribution implied by the preferred model. We refer to our model as the hierarchical generalized transformation (HGT) model. In a simulation, we demonstrate the flexibility of the HGT model by incorporating two different preferred models: Bayesian additive regression trees ( BART HGNC) and the spatial mixed effects (spatio-temporal mixed effects) models.

    Two Things about COVID-19 MESHD Might Need Attention

    Authors: Xiaodong Jia; Chengliang Yin; Shanshan Lu; Yan Chen; Qingyan Liu; Junfan Bai; Yinying Lu

    id:10.20944/preprints202002.0315.v1 Date: 2020-02-23 Source: Preprints.org

    The spread of 2019 novel coronavirus disease MESHD ( COVID-19 MESHD) throughout the world has been a severe challenge for public health. The human angiotensin-converting enzyme 2 HGNC ( ACE2 HGNC) has a remarkably high affinity binding to severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). By the search for network database and re-analysis of pubic data, we found the level of ACE2 HGNC expression in adipose tissue was higher than that in lung tissue, which indicated the adipose tissue might be vulnerable to SARS-CoV-2 as well; the levels of ACE2 HGNC expressed by adipocytes and adipose progenitor cells were similar between non- obese MESHD individuals and obese MESHD individuals, but obese MESHD individuals have more adiposes so as to increase the number of ACE2 HGNC-expressing cells; the expression of ACE2 HGNC in tumor MESHD tissues posed by five different types of cancers MESHD increased significantly compared with that in adjacent tissues. Thus, we suggest that more attentions might be given to obese MESHD individuals and the five types of cancer MESHD patients during the outbreak of COVID-19 MESHD.

    Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 MESHD epidemic in China: a web-based cross-sectional survey

    Authors: Yeen Huang; Ning Zhao

    doi:10.1101/2020.02.19.20025395 Date: 2020-02-23 Source: medRxiv

    Background: China has been severely affected by COVID-19 MESHD (Corona Virus Disease MESHD 2019) since December, 2019. This study aimed to assess the population mental health burden during the epidemic, and to explore the potential influence factors. Methods: Using a web-based cross-sectional survey, we collected data from 7,236 self-selected volunteers assessed with demographic information, COVID-19 MESHD related knowledge, Generalized Anxiety Disorder MESHD-7 ( GAD HGNC-7), Center for Epidemiology Scale for Depression MESHD (CES-D), and Pittsburgh Sleep Quality Index (PSQI). Logistic regressions were used to identify influence factors associated with mental health problem. Results: Of the total sample analyzed, the overall prevalence of GAD HGNC, depressive symptoms MESHD, and sleep quality were 35.1%, 20.1%, and 18.2%, respectively. Young people reported a higher prevalence of GAD HGNC and depressive symptoms MESHD than older people (P<0.001). Compared with other occupational group, healthcare workers have the highest rate of poor sleep MESHD quality (P<0.001). Multivariate logistic regression showed that age (< 35 years) and times to focus on the COVID-19 MESHD ([≥] 3 hours per day) were associated with GAD HGNC, and healthcare workers were associated with poor sleep quality. Conclusions: Our study identified a major mental health burden of the public during COVID-19 MESHD epidemic in China. Young people, people who spent too much time on the epidemic, and healthcare workers were at high risk for mental illness MESHD. Continuous surveillance and monitoring of the psychological consequences for outbreaks should become routine as part of preparedness efforts worldwide.

    From Isolation to Coordination: How Can Telemedicine Help Combat the COVID-19 MESHD Outbreak?

    Authors: Yunkai Zhai; Yichuan Wang; Minhao Zhang; Jody Hoffer Gittell; Shuai Jiang; Baozhan Chen; Fangfang Cui; Xianying He; Jie Zhao; Xiaojun Wang

    doi:10.1101/2020.02.20.20025957 Date: 2020-02-23 Source: medRxiv

    The rapid spread of Coronavirus disease 2019 MESHD ( COVID-19 MESHD) presents China with a critical challenge. As normal capacity of the Chinese hospitals is exceeded, healthcare professionals struggling to manage this unprecedented crisis face the difficult question of how best to coordinate the medical resources used in highly separated locations. Responding rapidly to this crisis, the National Telemedicine Center of China (NTCC), located in Zhengzhou, Henan Province, has established the Emergency Telemedicine Consultation System (ETCS), a telemedicine-enabled outbreak alert and response network. ETCS is built upon a doctor-to-doctor (D2D) approach, in which health services can be accessed remotely through terminals across hospitals. The system architecture of ETCS comprises three major architectural layers: (1) telemedicine service platform layer, (2) telemedicine cloud layer, and (3) telemedicine service application layer. Our ETCS has demonstrated substantial benefits in terms of the effectiveness of consultations and remote patient monitoring, multidisciplinary care, and prevention education and training.

    Early Phylogenetic Estimate Of The Effective Reproduction Number Of 2019-nCoV

    Authors: Alessia Lai; Annalisa Bergna; Carla Acciarri; Massimo Galli; Gianguglielmo Zehender

    doi:10.1101/2020.02.19.20024851 Date: 2020-02-23 Source: medRxiv

    To reconstruct the evolutionary dynamics of the 2019 novel coronavirus, 52 2019-nCOV genomes available on 04 February 2020 at GISAID were analysed. The two models used to estimate the reproduction number (coalescent-based exponential growth and a birth-death MESHD skyline method) indicated an estimated mean evolutionary rate of 7.8 x 10-4 subs/site/year (range 1.1x10-4-15x10-4). The estimated R value was 2.6 (range 2.1-5.1), and increased from 0.8 to 2.4 in December 2019. The estimated mean doubling time of the epidemic was between 3.6 and 4.1 days. This study proves the usefulness of phylogeny in supporting the surveillance of emerging new infections even as the epidemic is growing.

    Clinical characteristics of 50404 patients with 2019-nCoV infection MESHD

    Authors: Pengfei Sun; Shuyan Qie; Zongjan Liu; Jizhen Ren; Jianing Jianing Xi

    doi:10.1101/2020.02.18.20024539 Date: 2020-02-23 Source: medRxiv

    Objective: We aim to summarize reliable evidences of evidence-based medicine for the treatment and prevention of the 2019 novel coronavirus (2019-nCoV) by analyzing all the published studies on the clinical characteristics of patients with 2019-nCoV. Methods: PubMed, Cochrane Library, Embase, and other databases were searched. Several studies on the clinical characteristics of 2019-nCoV infection MESHD were collected for Meta-analysis. Results: Ten studies were included in Meta-analysis, including a total number of 50466 patients with 2019-nCoV infection MESHD. Meta-analysis shows that, among these patients, the incidence of fever MESHD was 89.1%, the incidence of cough MESHD was 72.2%, and the incidence of muscle soreness MESHD or fatigue MESHD was 42.5%. The incidence of acute respiratory distress syndrome MESHD ( ARDS MESHD) was 14.8%, the incidence of abnormal chest computer MESHD tomography (CT) was 96.6%, the percentage of severe cases in all infected MESHD cases was 18.1%, and the case fatality rate of patients with 2019-nCoV infection MESHD was 4.3%. Conclusion: Fever MESHD and cough MESHD are the most common symptoms in patients with 2019-nCoV infection MESHD, and most of these patients have abnormal chest CT examination. Several people have muscle soreness MESHD or fatigue MESHD as well as ARDS MESHD. Diarrhea MESHD, hemoptysis, headache MESHD, sore throat, shock, and other symptoms only occur in a small number of patients. The case fatality rate of patients with 2019-nCoV infection MESHD is lower than that of Severe Acute Respiratory Syndrome MESHD (SARS) and Middle East Respiratory Syndrome MESHD ( MERS MESHD).

    Pharmacoinformatics and Molecular Dynamic Simulation Studies Reveal Potential Inhibitors of SARS-CoV-2 Main Protease PROTEIN 3CLpro PROTEIN

    Authors: Mubarak A. Alamri; Muhammad Tahir ul Qamar; Safar M. Alqahtani

    id:10.20944/preprints202002.0308.v1 Date: 2020-02-23 Source: Preprints.org

    The SARS-CoV-2 was confirmed to cause the regional outbreak of coronavirus disease 2019 MESHD ( COVID-19 MESHD) in Wuhan, China. The 3C-like protease ( 3CLpro PROTEIN), an essential enzyme for viral replication, is a valid target to compacts SARS-CoV MESHD and MERS-CoV. In this research, an integrated library consisting of 1000 compounds from Asinex Focused Covalent (AFCL) library and 16 FDA-approved protease inhibitors were screened against SARS-CoV-2 3CLpro PROTEIN. Top compounds with significant docking scores and making stable interactions with catalytic dyad residues were obtained. The screening results in identification of compound 621 from AFCL library as well as Paritaprevir and Simeprevir from FDA-approved protease inhibitors as potential inhibitors of SARS-CoV-2 3CLpro PROTEIN. The mechanism and dynamic stability of binding between the identified compounds and SARS-CoV-2 3CLpro PROTEIN were characterized using 50 nanoseconds (ns) molecular dynamic (MD) simulation approach. The identified compounds are potential inhibitors worthy of further development as SARS-CoV-2 3CLpro PROTEIN inhibitors/drugs. Importantly, the identified FDA-approved therapeutics could be ready for clinical trials to treat infected MESHD patients and help to curb the COVID-19 MESHD.

    Effectiveness of intervention strategies for Coronavirus Disease 2019 MESHD and an estimation of its peak time

    Authors: Jinhua Pan; Ye Yao; Zhixi Liu; Mengyi Li; Ying Wang; Weizhen Dong; Haidong Kan; Weibing Wang

    doi:10.1101/2020.02.19.20025387 Date: 2020-02-23 Source: medRxiv

    Background: Since its first cases occurrence in Wuhan, China, the Coronavirus Disease 2019 MESHD ( COVID-19 MESHD) has been spreading rapidly to other provinces and neighboring countries. A series of intervention strategies have been implemented, but didn't stop its spread. Methods: Two mathematical models have been developed to simulate the current epidemic situation in the city of Wuhan and in other parts of China. Special considerations were given to the mobility of people for the estimation and forecast the number of asymptomatic infections, symptomatic infections, and the infections of super-spreading events (Isse). Findings: The basic reproductive number (R0) was calculated for the period between 18 January 2020 and 16 February 2020: R0 declined from 5.75 to 1.69 in Wuhan and from 6.22 to 1.67 in the entire country (not including the Wuhan area). At the same time, Wuhan is estimated to observe a peak in the number of confirmed cases around 6 February 2020. The number of infected individuals in the entire country (not including the Wuhan area) peaked around February 3. The results also show that the peak of new asymptomatic cases per day in Wuhan occurred on February 6, and the peak of new symptomatic infections have occurred on February 3. Concurrently, while the number of confirmed cases nationwide would continue to decline, the number of real-time COVID-19 MESHD inpatients in Wuhan has reached a peak of 13,030 on February 14 before it decreases. The model further shows that the COVID-19 MESHD cases will gradually wane by the end of April 2020, both in Wuhan and the other parts of China. The number of confirmed cases would reach the single digit on March 27 in Wuhan and March 19 in the entire country. The five cities with top risk index in China with the exclusion of Wuhan are: Huanggang, Xiaogan, Jingzhou, Chongqing, and Xiangyang city. Interpretations: Although the national peak time has been reached, a significant proportion of asymptomatic patients and the infections of super-spreading events (Isse) still exist in the population, indicating the potential difficulty for the prevention and control of the disease. As the Return-to-Work tide is approaching and upgrading, further measures (e.g., escalatory quarantine, mask wearing when going out, and sit apart when taking vehicles) will be particularly crucial to stop the COVID-19 MESHD in other cities outside of Wuhan.

    Novel Coronavirus 2019 ( Covid-19 MESHD) epidemic scale estimation: topological network-based infection dynamic model

    Authors: Keke Tang; Yining Huang; Meilian Chen

    doi:10.1101/2020.02.20.20023572 Date: 2020-02-23 Source: medRxiv

    Backgrounds: An ongoing outbreak of novel coronavirus pneumonia MESHD ( Covid-19 MESHD) hit Wuhan and hundreds of cities, 29 territories globally. We present a method for scale estimation in dynamic while most of the researchers used static parameters. Methods: We use historical data and the SEIR model for important parameters assumption. And according to the timeline, we use dynamic parameters for infection topology network building. Also, the migration data is used for the Non-Wuhan area estimation which can be cross-validated for the Wuhan model. All data are from the public. Results: The estimated number of infections is 61,596 (95%CI: 58,344.02-64,847.98) by 25 Jan in Wuhan. And the estimation number of the imported cases from Wuhan of Guangzhou was 170 (95%CI: 161.27-179.26), infection scale in Guangzhou is 315 (95%CI: 109.20-520.79), while the imported cases are 168 and the scale of the infection is 339 published by the authority. Conclusions: Using dynamic network models and dynamic parameters for different time periods is an effective way of infection scale modeling.

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MeSH Disease
HGNC Genes
SARS-CoV-2 Proteins


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