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简介:AlthoughanassociationbetweenthegroupAbetahemolyticstreptococcusandrheumaticfeverhasbeenrecognizedformorethanhalfacentury,manyimportantissuesaboutthisrelationshipremainincompletelydefined.Theinitiatingpharyngealthroatinfectionand
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简介:AbstractBackground: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 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, heart rate, 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.
简介:TheZ10andZ37strainsofhemorrhagicfeverwithrenalsyndrome(HFRS)virusandtheMongoliangerbil(Merionsunguiculatus)kidneycellswereusedtopreparetheinactivatedbivalentvaccine.AphaseⅡclinicaltrialuseofthisvaccinewasmadein750Chinesevolunteers.Theresultsshowedthatthesidereactionratewas2.5%andthesero-conversionrateofneutralizingantibodiesagainstHantaanandSeoulvirusesintheinoculatedvolunteerswere87.6%and96.3%respectively.
简介:摘要1例8岁11月龄男性患儿,智力低下,语言落后,1岁9月龄癫痫首次发作,4岁诊断为儿童孤独症,体格检查有特殊面容,视频脑电图示广泛性棘慢波发放。染色体微阵列检测到8q22.2q22.3区域约2.34 Mb片段微缺失,8q22.2q22.3微缺失的病例非常罕见。
简介:AbstractBackground:The transmission and fatal risk of severe fever with thrombocytopenia syndrome (SFTS), an emerging infectious disease first discovered in China in 2009, still needed further quantification. This research aimed to analyze the SFTS clusters and assess the transmission and mortality risk for SFTS.Methods:Both epidemiological investigation and case reports regarding SFTS clusters in China during 2011-2021 were obtained from the Public Health Emergency Information Management System of the Chinese Center for Disease Control and Prevention Information System. The transmission risk was evaluated by using the secondary attack rate (SAR) and relative risk (RR). Mortality risk factors were analyzed using a logistic regression model.Results:There were 35 SFTS clusters during 2011-2021 involving 118 patients with a fatality rate of 22.0%. The number of clusters annually increased seasonally from April to September. The clusters mainly occurred in Anhui (16 clusters) and Shandong provinces (8 clusters). The SAR through contact with blood or bloody fluids was much higher than that through contact with non-bloody fluids (50.6% vs 3.0%; χ2 = 210.97, P < 0.05), with an RR of 16.61 [95% confidence interval (CI): 10.23-26.97]. There was a statistically significant difference in the SAR between exposure to the blood of a deceased person during burial preparation and exposure to the living patients’ blood (66.7% vs 34.5%; χ2= 6.40, P < 0.05), with an RR of 1.93 (95% CI: 1.11-3.37). The mortality risk factors were a long interval from onset to diagnosis [odds ratio (OR)= 1.385), 95% CI: 1.083-1.772, P= 0.009) and advanced age (OR: 1.095, 95% CI: 1.031-1.163, P= 0.01).Conclusions:The SFTS clusters showed a high mortality rate and resulted in a high SAR. Contact with a bleeding corpse was associated with a higher infection risk, compared with contacting the blood from living patients. It is important to promote early detection and appropriate case management of patients with SFTS, as well as improved handling of their corpses, to prevent further transmission and mortality.
简介:“NoticeaboutEnhancingSupervisionandAdministrationofAntibioticSailsinRetailDrugStoresforImprovingProperUse”issuedbyChinesegovernmentwouldbeimplementedon1^stJuly,thenceantibioticswouldbesoldonlywhenprescriptionsprescribedbydoctorsareavailable.Thispieceofnewsiscitedfromtheeditor'sremarksofanationalnewspaperHealthDaily(健康日报)datedJune14,2004.Foralongperiod