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1.
Chinese Journal of Zoonoses ; 36(5):372-376, 2020.
Article | WHO COVID | ID: covidwho-647937

ABSTRACT

The epidemiology characteristics of 2019 novel coronavirus diseases (COVID-19) cases in Hainan were collected and analyzed for providing next stage control and prevention strategy in next stage Spatial and temporal distribution, population characteristic, cluster, the interval between onset, visiting clinic, admitted were analyzed Local cases and severe cases were also included in the analysis Result showed that a total of 168 confirmed cases, including 36 severe cases and 5 fatal cases were reported Cases were mainly distributed in Haikou, Sanya etc tourism cities and counties The first case occurred in Jan 13th and the epidemic peak occurred in Jan 24th Since Feb 6th, onset of illness has declined The male-to-female ratio was 0 9:1 The median age was 51 years Cases older than 50 years accounted for 54 8% Retirees accounted for 36 9%, which was highest in all cases Since Feb, the proportion of local cases rose dramatically The period from onset to visiting clinic (OTV), from first visiting clinic to diagnosis (VTF), from onset to diagnosis (OTD) and from onset to be admitted (OTA) was longer in local cases than imported cases Median age and the percentage of underlying diseases of severe/extreme cases were higher than mild/ordinary cases OTV of severe/extreme cases was longer than mild/ordinary cases, while for VTF, the former was shorter than latter The epidemic was divided into three stages Most of cases in the first stage were imported cases, while in the second stage most of cases were local cases There were few cases in the third stages We should strengthen personal protection and health monitoring for people in service industry, isolate the close contacts, and carry out publicity and education to raise the awareness of medical treatment for people, especially for old people Clinical doctors should monitor the state of the patients older than 60 years and with underlying diseases We should step up epidemic monitoring prevention and control measure for people return from holiday and immigrant to consolidate the effects of prevention and control work

2.
Signal Transduct Target Ther ; 5(1): 89, 2020 06 10.
Article in English | MEDLINE | ID: covidwho-595441

ABSTRACT

Coronavirus infections of multiple origins have spread to date worldwide, causing severe respiratory diseases. Seven coronaviruses that infect humans have been identified: HCoV-229E, HCoV-OC43, HCoV-NL63, HCoV-HKU1, SARS-CoV, MERS-CoV, and SARS-CoV-2. Among them, SARS-CoV and MERS-CoV caused outbreaks in 2002 and 2012, respectively. SARS-CoV-2 (COVID-19) is the most recently discovered. It has created a severe worldwide outbreak beginning in late 2019, leading to date to over 4 million cases globally. Viruses are genetically simple, yet highly diverse. However, the recent outbreaks of SARS-CoV and MERS-CoV, and the ongoing outbreak of SARS-CoV-2, indicate that there remains a long way to go to identify and develop specific therapeutic treatments. Only after gaining a better understanding of their pathogenic mechanisms can we minimize viral pandemics. This paper mainly focuses on SARS-CoV, MERS-CoV, and SARS-CoV-2. Here, recent studies are summarized and reviewed, with a focus on virus-host interactions, vaccine-based and drug-targeted therapies, and the development of new approaches for clinical diagnosis and treatment.


Subject(s)
Antiviral Agents/therapeutic use , Betacoronavirus/drug effects , Coronavirus Infections/drug therapy , Host-Pathogen Interactions/drug effects , Pandemics , Pneumonia, Viral/drug therapy , Signal Transduction/drug effects , Betacoronavirus/genetics , Betacoronavirus/immunology , Betacoronavirus/pathogenicity , Coronavirus Infections/epidemiology , Coronavirus Infections/immunology , Coronavirus Infections/virology , Cytokines/antagonists & inhibitors , Cytokines/genetics , Cytokines/immunology , Gene Expression Regulation , Host-Pathogen Interactions/genetics , Host-Pathogen Interactions/immunology , Humans , Middle East Respiratory Syndrome Coronavirus/drug effects , Middle East Respiratory Syndrome Coronavirus/genetics , Middle East Respiratory Syndrome Coronavirus/immunology , Middle East Respiratory Syndrome Coronavirus/pathogenicity , Molecular Targeted Therapy/methods , Peptidyl-Dipeptidase A/genetics , Peptidyl-Dipeptidase A/immunology , Pneumonia, Viral/epidemiology , Pneumonia, Viral/immunology , Pneumonia, Viral/virology , SARS Virus/drug effects , SARS Virus/genetics , SARS Virus/immunology , SARS Virus/pathogenicity , Severe Acute Respiratory Syndrome/drug therapy , Severe Acute Respiratory Syndrome/epidemiology , Severe Acute Respiratory Syndrome/immunology , Severe Acute Respiratory Syndrome/virology , Signal Transduction/genetics , Signal Transduction/immunology , Spike Glycoprotein, Coronavirus/genetics , Spike Glycoprotein, Coronavirus/immunology , T-Lymphocytes/drug effects , T-Lymphocytes/immunology , T-Lymphocytes/virology
3.
Respir Res ; 21(1): 83, 2020 Apr 15.
Article in English | MEDLINE | ID: covidwho-60448

ABSTRACT

BACKGROUND: The outbreak of coronavirus disease 2019 (COVID-19) caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in China has been declared a public health emergency of international concern. The cardiac injury is a common condition among the hospitalized patients with COVID-19. However, whether N terminal pro B type natriuretic peptide (NT-proBNP) predicted outcome of severe COVID-19 patients was unknown. METHODS: The study initially enrolled 102 patients with severe COVID-19 from a continuous sample. After screening out the ineligible cases, 54 patients were analyzed in this study. The primary outcome was in-hospital death defined as the case fatality rate. Research information and following-up data were obtained from their medical records. RESULTS: The best cut-off value of NT-proBNP for predicting in-hospital death was 88.64 pg/mL with the sensitivity for 100% and the specificity for 66.67%. Patients with high NT-proBNP values (> 88.64 pg/mL) had a significantly increased risk of death during the days of following-up compared with those with low values (≤88.64 pg/mL). After adjustment for potential risk factors, NT-proBNP was independently correlated with in-hospital death. CONCLUSION: NT-proBNP might be an independent risk factor for in-hospital death in patients with severe COVID-19. TRIAL REGISTRATION: ClinicalTrials, NCT04292964. Registered 03 March 2020.


Subject(s)
Coronavirus Infections , Hospital Mortality , Natriuretic Peptide, Brain/analysis , Pandemics , Peptide Fragments/analysis , Pneumonia, Viral , Adult , Aged , Betacoronavirus , Clinical Laboratory Techniques , Coronavirus Infections/diagnosis , Coronavirus Infections/mortality , Female , Humans , Male , Middle Aged , Mortality , Pneumonia, Viral/diagnosis , Pneumonia, Viral/mortality , Predictive Value of Tests , Prognosis , Reference Values , Retrospective Studies , Risk Factors
4.
Clin Infect Dis ; 2020 Mar 28.
Article in English | MEDLINE | ID: covidwho-17886

ABSTRACT

BACKGROUND: The novel coronavirus SARS-CoV-2 is a newly emerging virus. The antibody response in infected patient remains largely unknown, and the clinical values of antibody testing have not been fully demonstrated. METHODS: A total of 173 patients with SARS-CoV-2 infection were enrolled. Their serial plasma samples (n=535) collected during the hospitalization were tested for total antibodies (Ab), IgM and IgG against SARS-CoV-2. The dynamics of antibodies with the disease progress was analyzed. RESULTS: Among 173 patients, the seroconversion rate for Ab, IgM and IgG was 93.1%, 82.7% and 64.7%, respectively. The reason for the negative antibody findings in 12 patients might due to the lack of blood samples at the later stage of illness. The median seroconversion time for Ab, IgM and then IgG were day-11, day-12 and day-14, separately. The presence of antibodies was <40% among patients within 1-week since onset, and rapidly increased to 100.0% (Ab), 94.3% (IgM) and 79.8% (IgG) since day-15 after onset. In contrast, RNA detectability decreased from 66.7% (58/87) in samples collected before day-7 to 45.5% (25/55) during day 15-39. Combining RNA and antibody detections significantly improved the sensitivity of pathogenic diagnosis for COVID-19 (p<0.001), even in early phase of 1-week since onset (p=0.007). Moreover, a higher titer of Ab was independently associated with a worse clinical classification (p=0.006). CONCLUSIONS: The antibody detection offers vital clinical information during the course of SARS-CoV-2 infection. The findings provide strong empirical support for the routine application of serological testing in the diagnosis and management of COVID-19 patients.

5.
Chin Med J (Engl) ; 133(5): 583-589, 2020 Mar 05.
Article in English | MEDLINE | ID: covidwho-10177

ABSTRACT

BACKGROUND: Fever is the most common chief complaint of emergency patients. Early identification of patients at an increasing risk of death may avert adverse outcomes. The aim of this study was to establish an early prediction model of fatal adverse prognosis of fever patients by extracting key indicators using big data technology. METHODS: A retrospective study of patients' data was conducted using the Emergency Rescue Database of Chinese People's Liberation Army General Hospital. Patients were divided into the fatal adverse prognosis group and the good prognosis group. The commonly used clinical indicators were compared. Recursive feature elimination (RFE) method was used to determine the optimal number of the included variables. In the training model, logistic regression, random forest, adaboost and bagging were selected. We also collected the emergency room data from December 2018 to December 2019 with the same inclusion and exclusion criterion. The performance of the model was evaluated by accuracy, F1-score, precision, sensitivity and the areas under receiver operator characteristic curves (ROC-AUC). RESULTS: The accuracy of logistic regression, decision tree, adaboost and bagging was 0.951, 0.928, 0.924, and 0.924, F1-scores were 0.938, 0.933, 0.930, and 0.930, the precision was 0.943, 0.938, 0.937, and 0.937, ROC-AUC were 0.808, 0.738, 0.736, and 0.885, respectively. ROC-AUC of ten-fold cross-validation in logistic and bagging models were 0.80 and 0.87, respectively. The top six coefficients and odds ratio (OR) values of the variables in the Logistic regression were cardiac troponin T (CTnT) (coefficient=0.346, OR = 1.413), temperature (T) (coefficient=0.235, OR = 1.265), respiratory rate (RR) (coefficient= -0.206,OR = 0.814), serum kalium (K) (coefficient=0.137, OR = 1.146), pulse oxygen saturation (SPO2) (coefficient= -0.101, OR = 0.904), and albumin (ALB) (coefficient= -0.043, OR = 0.958). The weights of the top six variables in the bagging model were: CTnT, RR, lactate dehydrogenase, serum amylase, heartrate, and systolic blood pressure. CONCLUSIONS: The main clinical indicators of concern included CTnT, RR, SPO2, T, ALB and K. The bagging model and logistic regression model had better diagnostic performance comprehesively. Those may be conducive to the early identification of critical patients with fever by physicians.

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