1.Quantitative detection on different HBsAg levels by chemiluminescence immunoassay and time-resolved immunofluorescence assay
Xiaoyu FU ; Feiyuan WU ; Gang CHEN ; Yanling XIE ; Guohua DENG ; Shaojun GAN ; Deming TAN
Chinese Journal of Infection Control 2017;16(3):258-262
Objective To evaluate the accuracy and feasibility of time-resolved immunofluorometric assay (TRI FA) for detection of HBsAg based on Abbott automated chemiluminescence immunoassay(CMIA),so as to carry out this project in primary hospitals,and provide reference for individual antiviral strategy and prediction of therapeutic effect.Methods Serum of 157 patients infected with hepatitis B virus were detected with CMIA and TRIFA,specimens with HBsAg titers exceeding the detection limit were firstly diluted,then performed quantitative analysis.HBsAg levels were divided into 4 groups:≤100 IU/mL,101-1 000 IU/mL,1 001-20 000 IU/mL,and > 20 000 IU/mL,quantitative correlation between two methods was analyzed.Results The linear regression equation of two methods was Y=2.323X-896.3,correlation coefficent r=0.943,P<0.001.CMIA was as a reference,4 groups were divided for analysis,results showed that when detected specimens was at low concentration of HBsAg,TRIFA value was low compared with CMIA method,while detected specimens was at high concentration of HB sAg,CMIA value was high,two reagents had good consistency in the detection of different concentrations of HBsAg(both P<0.05),when concentration was at 1 001-20 000 IU/mL,consistency was the best.Conclusion The accuracy of two reagents in the quantitative detection of HBsAg is similar,and the best correlation of detection value is 1 000-20 000 IU/mL.TRIFA assay has wide application for its low-cost and easy to be operated,which is especially suitable for primary hospitals.
2.Epidemiological characteristics and influencing factors of scoliosis in primary and secondary school students in Guangdong Province
LI Meng, QU Yabin, SUN Yi, GAN Ping, SHEN Shaojun
Chinese Journal of School Health 2022;43(2):292-295
Objective:
To investigate the epidemiological characteristics and associated factors of scoliosis in primary and secondary school students in Guangdong, and to provide guidance for scoliosis control.
Methods:
Using a stratified cluster random sampling method, a total of 38 649 students aged 9-18 were selected from 132 primary and secondary schools in the Pearl River Delta and non Pearl River Delta cities for scoliosis screening and related associated factors questionnaire survey from September to October 2020.
Results:
A total of 1 440 students were detected with scoliosis, with a detection rate of 3.73%. The detection rate of girls was 4.90%, which was higher than that of boys at 2.66%( χ 2=386.89, P <0.01). The detection rate in the Pearl River Delta region was 4.09%, which was higher than the non Pearl River Delta region at 3.38%( χ 2=13.22, P <0.01). The detection rate in urban areas was 4.51%, which was higher than counties at 2.79%( χ 2=78.70, P <0.01). The detection rate increased with the increase of the school period, high school (5.94%)>junior high school (4.50%)>elementary school (1.35%)( χ 2=386.89, P <0.01). Multivariate Logistic regression analysis showed that region, urbanicity, gender, educational stage, exercise, using electronic mobile devices, nutritional status are the influencing factors for scoliosis ( OR=0.41-3.78, P <0.05).
Conclusion
The detection rate of scoliosis in primary and secondary school students in Guangdong Province varies by gender, urbanicity and educational stages. Female students, as well as junior and senior high school students should be paid more attention.
3.Feasibility analysis of quantitative detection on serum HBeAg/HBeAb by time-resolved immunofluorescence assay.
Xiaoyu FU ; Feiyuan WU ; Gang CHEN ; Yanling XIE ; Guohua DENG ; Shaojun GAN ; Lei FU
Journal of Central South University(Medical Sciences) 2016;41(8):852-855
OBJECTIVE:
To determine whether time-resolved immunofluorescence assay (TRIFA) shares the similar accuracy and specificity with automatic chemiluminescence immunoassay (CMIA) in analyzing HBeAg levels in hepatitis B.
METHODS:
A total of 157 serum samples were collected from outpatients with infection of HBV in Xiangya Hospital, Central South University. CMIA and TRIFA were used to analyze HBeAg quantitation and HBeAg/HBeAb qualitative detection, respectively.
RESULTS:
The linear regression equation for the two methods was Y=0.72779X-4.0551 (r=0.712, P<0.001). Compared with the CMIA, the sensitivity and specificity in detection of HBeAg by TRIFA were 89.89% and 100%, respectively, and the coincidence rate of HBeAg was 94.27% by two assays. Similarly, the sensitivity and specificity in detection of HBeAb by TRIFA were 100% and 95.45%, respectively. The coincidence rate was 97.45% by two assays.
CONCLUSION
TRIFA has similar accuracy, sensitivity, and specificity with CMIA in quantitative detection of HBeAg, and their coincidence rate in detection of HBeAg/HBeAb is high.
Feasibility Studies
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Fluorescent Antibody Technique
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Hepatitis B
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Hepatitis B Antibodies
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Hepatitis B e Antigens
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Hepatitis B virus
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Humans
4. Epidemiological analysis on 1 052 cases of COVID-19 in epidemic clusters
Hong GAN ; Yi ZHANG ; Min YUAN ; Xiaoyan WU ; Zhirong LIU ; Meng LIU ; Jiabing WU ; Shaojun XU ; Lei GONG ; Honglyu XU ; Fangbiao TAO
Chinese Journal of Epidemiology 2020;41(5):E027-E027
Objective To understand the epidemiological characteristics of the cases of COVID-19 epidemic clusters, and explore the influence of family factors and social factors such as group activities on the spread of the disease. Methods The data of cases of COVID-19 epidemic clusters from 19 January, 2020 to 25 February, 2020 were collected from the official platforms of 36 cities in 6 provinces in China. Descriptive statistical methods, χ 2 test and curve fitting were used to analyze the epidemiological characteristics of the clustered cases. Results By 25 February, 2020, the data of 1 052 cases in 366 epidemic clusters were collected. In these clustered cases, 86.9%(914/1 050) occurred in families. Among the 1 046 cases with gender information, 513 were males (49.0%) and 533 were females (51.0%). The cases were mainly young adults between 18 and 59 years old, accounting for 68.5% (711/1 038). In the 366 epidemic clusters , the clusters in which the first confirmed cases with the history of sojourn in Wuhan or Hubei accounted for 47.0%(172/366). From 19 January to 3 February, 2020, the first confirmed cases with Wuhan or Hubei sojourn history accounted for 66.5%. From 4 to 25 February, the first confirmed cases who had Wuhan or Hubei sojourn history accounted for only 18.2%. The median of interval between the first generation case onset and the second generation case onset was 5 (2-8) days. The median of onset- diagnosis interval of the initial cases was 6 (3-9) days, and the median of onset-diagnosis interval of the secondary cases was 5 (3-8) days. Conclusions Epidemic clusters of COVID-19 were common in many cities outside Wuhan and Hubei. Close contact in family was one of the main causes for the spread of household transmission of the virus. After 4 February, the epidemic clusters were mainly caused by the first generation or second generation cases in local areas, and the time for diagnosis became shorter.
5.Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients (version 2024)
Yao LU ; Yang LI ; Leiying ZHANG ; Hao TANG ; Huidan JING ; Yaoli WANG ; Xiangzhi JIA ; Li BA ; Maohong BIAN ; Dan CAI ; Hui CAI ; Xiaohong CAI ; Zhanshan ZHA ; Bingyu CHEN ; Daqing CHEN ; Feng CHEN ; Guoan CHEN ; Haiming CHEN ; Jing CHEN ; Min CHEN ; Qing CHEN ; Shu CHEN ; Xi CHEN ; Jinfeng CHENG ; Xiaoling CHU ; Hongwang CUI ; Xin CUI ; Zhen DA ; Ying DAI ; Surong DENG ; Weiqun DONG ; Weimin FAN ; Ke FENG ; Danhui FU ; Yongshui FU ; Qi FU ; Xuemei FU ; Jia GAN ; Xinyu GAN ; Wei GAO ; Huaizheng GONG ; Rong GUI ; Geng GUO ; Ning HAN ; Yiwen HAO ; Wubing HE ; Qiang HONG ; Ruiqin HOU ; Wei HOU ; Jie HU ; Peiyang HU ; Xi HU ; Xiaoyu HU ; Guangbin HUANG ; Jie HUANG ; Xiangyan HUANG ; Yuanshuai HUANG ; Shouyong HUN ; Xuebing JIANG ; Ping JIN ; Dong LAI ; Aiping LE ; Hongmei LI ; Bijuan LI ; Cuiying LI ; Daihong LI ; Haihong LI ; He LI ; Hui LI ; Jianping LI ; Ning LI ; Xiying LI ; Xiangmin LI ; Xiaofei LI ; Xiaojuan LI ; Zhiqiang LI ; Zhongjun LI ; Zunyan LI ; Huaqin LIANG ; Xiaohua LIANG ; Dongfa LIAO ; Qun LIAO ; Yan LIAO ; Jiajin LIN ; Chunxia LIU ; Fenghua LIU ; Peixian LIU ; Tiemei LIU ; Xiaoxin LIU ; Zhiwei LIU ; Zhongdi LIU ; Hua LU ; Jianfeng LUAN ; Jianjun LUO ; Qun LUO ; Dingfeng LYU ; Qi LYU ; Xianping LYU ; Aijun MA ; Liqiang MA ; Shuxuan MA ; Xainjun MA ; Xiaogang MA ; Xiaoli MA ; Guoqing MAO ; Shijie MU ; Shaolin NIE ; Shujuan OUYANG ; Xilin OUYANG ; Chunqiu PAN ; Jian PAN ; Xiaohua PAN ; Lei PENG ; Tao PENG ; Baohua QIAN ; Shu QIAO ; Li QIN ; Ying REN ; Zhaoqi REN ; Ruiming RONG ; Changshan SU ; Mingwei SUN ; Wenwu SUN ; Zhenwei SUN ; Haiping TANG ; Xiaofeng TANG ; Changjiu TANG ; Cuihua TAO ; Zhibin TIAN ; Juan WANG ; Baoyan WANG ; Chunyan WANG ; Gefei WANG ; Haiyan WANG ; Hongjie WANG ; Peng WANG ; Pengli WANG ; Qiushi WANG ; Xiaoning WANG ; Xinhua WANG ; Xuefeng WANG ; Yong WANG ; Yongjun WANG ; Yuanjie WANG ; Zhihua WANG ; Shaojun WEI ; Yaming WEI ; Jianbo WEN ; Jun WEN ; Jiang WU ; Jufeng WU ; Aijun XIA ; Fei XIA ; Rong XIA ; Jue XIE ; Yanchao XING ; Yan XIONG ; Feng XU ; Yongzhu XU ; Yongan XU ; Yonghe YAN ; Beizhan YAN ; Jiang YANG ; Jiangcun YANG ; Jun YANG ; Xinwen YANG ; Yongyi YANG ; Chunyan YAO ; Mingliang YE ; Changlin YIN ; Ming YIN ; Wen YIN ; Lianling YU ; Shuhong YU ; Zebo YU ; Yigang YU ; Anyong YU ; Hong YUAN ; Yi YUAN ; Chan ZHANG ; Jinjun ZHANG ; Jun ZHANG ; Kai ZHANG ; Leibing ZHANG ; Quan ZHANG ; Rongjiang ZHANG ; Sanming ZHANG ; Shengji ZHANG ; Shuo ZHANG ; Wei ZHANG ; Weidong ZHANG ; Xi ZHANG ; Xingwen ZHANG ; Guixi ZHANG ; Xiaojun ZHANG ; Guoqing ZHAO ; Jianpeng ZHAO ; Shuming ZHAO ; Beibei ZHENG ; Shangen ZHENG ; Huayou ZHOU ; Jicheng ZHOU ; Lihong ZHOU ; Mou ZHOU ; Xiaoyu ZHOU ; Xuelian ZHOU ; Yuan ZHOU ; Zheng ZHOU ; Zuhuang ZHOU ; Haiyan ZHU ; Peiyuan ZHU ; Changju ZHU ; Lili ZHU ; Zhengguo WANG ; Jianxin JIANG ; Deqing WANG ; Jiongcai LAN ; Quanli WANG ; Yang YU ; Lianyang ZHANG ; Aiqing WEN
Chinese Journal of Trauma 2024;40(10):865-881
Patients with severe trauma require an extremely timely treatment and transfusion plays an irreplaceable role in the emergency treatment of such patients. An increasing number of evidence-based medicinal evidences and clinical practices suggest that patients with severe traumatic bleeding benefit from early transfusion of low-titer group O whole blood or hemostatic resuscitation with red blood cells, plasma and platelet of a balanced ratio. However, the current domestic mode of blood supply cannot fully meet the requirements of timely and effective blood transfusion for emergency treatment of patients with severe trauma in clinical practice. In order to solve the key problems in blood supply and blood transfusion strategies for emergency treatment of severe trauma, Branch of Clinical Transfusion Medicine of Chinese Medical Association, Group for Trauma Emergency Care and Multiple Injuries of Trauma Branch of Chinese Medical Association, Young Scholar Group of Disaster Medicine Branch of Chinese Medical Association organized domestic experts of blood transfusion medicine and trauma treatment to jointly formulate Chinese expert consensus on blood support mode and blood transfusion strategies for emergency treatment of severe trauma patients ( version 2024). Based on the evidence-based medical evidence and Delphi method of expert consultation and voting, 10 recommendations were put forward from two aspects of blood support mode and transfusion strategies, aiming to provide a reference for transfusion resuscitation in the emergency treatment of severe trauma and further improve the success rate of treatment of patients with severe trauma.