1.STUDY ON THE FERMENTATION CONDITION PRODUCING 2-KETO-LGLUCONIC ACID BY USING MIXED CULTURE OF MICROORGANISM
Wen-Chu LIN ; Qing YE ; Chun-Hong QIAO ; Guang-Lin YIN ;
Microbiology 1992;0(04):-
Vitamin C precusor-2-keto-L-gulonic acid can be prepared directly by mixed culture of Ghiconobacter oxy-dans SCB329 and Guconobacter subaxydans SCB110. To obtained its high yield, firstly, the proportion of the two micro- organisms, the ingredients of medium and the initial pH were optimized in shake flaskd, then L9 (34) orthogonal experiment confirmed that urea, C. S. L had high degree statistical meaning. Based on these data, an optimized fermentation media was obta ined: D-Sorbitol 9g, C. S.L1.5g, Urea1.5g, KH2PO40.1g, CaCO30.2g. By-product can be inhabited to the greatest extent and the yield increases by 20%.
2.Characteristics and stability of surveillance data on respiratory syndrome, during the Shanghai World Expo in Pudong New District
Xiao-Xi WANG ; Wei-Zhong YANG ; Qiao SUN ; Zhong-Jie LI ; Ding-Lun ZHOU ; Chu-Chu YE ; Ya-Jia LAN
Chinese Journal of Epidemiology 2012;33(6):562-566
Objective To reveal the characteristics and stability of the system through the analyzing the surveillance data of respiratory-feverous syndrome via the syndromic surveillance system which was established during the Shanghai World Expo in Pudong New District and provide references for the development and operation optimization on this Mass Gatherings Surveillance Systems.Methods Data used was from the surveillance data of respiratory-feverous syndrome collected from Pudong New District Syndromic Surveillance System,through May 1 to October 31,2010.On the basis of description of data characteristics,correlation analyses were conducted,when compared to the surveillance data of respiratory-feverous syndrome and Pudong influenza-like illness (ILI) used as reference.Comparison of variances on the surveillance data and the report lag time of the earlier and later surveillance periods were also carried out to evaluate the quality and stability of data.Results Reports on the respiratory-feverous syndrome showed a peak in late September with day-of-week effects and holiday effects.Correlation between respiratory-feverous syndrome and ILI was the strongest in the same day (r=0.596,P<0.05).In the earlier surveillance period from 2010-05-01 to 2010-07-31,the correlation between respiratory-feverous syndrome and ILI was not obvious (r=-0.058,P>0.05) ; however,the two-time series showed consistent trend with the correlation coefficient as 0.798 (P<0.05),in the later period from 2010-08-01 to 2010-10-31.In addition,variability of the surveillance data on respiratory-feverous syndrome was less in the later period than in the earlier one,with quality of the report on relatcd data better in the later period.Analyses on the correlations of reference sequence,variability and quality of report indicated that the stability of the later surveillance period was better than the earlier one.Conclusion Only with the operation of syndromic surveillance system for a certain period of time,could data in the system maintain stability.Surveillance data showed both day-of-week effects and holiday effects,suggesting that there was a need to choose early warning models with short baseline data.
3.Viral etiology of 363 elderly people with influenza-like illness and severe acute respiratory infections in Pudong New Area, Shanghai
Chu-Chu YE ; Wei-Ping ZHU ; Yuan-Ping WANG ; Zou CHEN ; Yi-Fei FU ; Qiao SUN ; Gen-Ming ZHAO
Shanghai Journal of Preventive Medicine 2016;28(11):772-776
Objective To understand the epidemiological characteristics of common respiratory viruses among influenza-like illness (ILI) and severe acute respiratory infections (SARI) in Pudong New Area, Shanghai , so as to help estimate the disease burden and conduct the valuable control strategies . Methods Respiratory specimen ( throat swab or sputum ) from cases older than 65 years old with ILI/SARI were collected from outpatient and inpatient settings in four sentinel hospitals in Pudong New Area . Each specimen was tested by multiplex PCR for eight target viral etiologies , including influenza virus , human rhinoviruses ( HRV ) , human para-influenza virus ( PIV ) , adenoviruses ( ADV ) , respiratory syncytial virus ( RSV ) , human metapneumovirus ( hMPV ) , human coronavirus ( hCoV ) and human bocavirus ( hBoV) .Chi-square tests or Fisher's Exact Test were used to compare and analyze . Results From January 1st, 2014 to June 30st, 2016, a total of 363 elderly cases with ILI/SARI were enrolled, with 202 (55.65%) male and a median age of 70 years old.142(39.12%) patients were detected posi-tive for any one of the eight viruses.Influenza was the predominant virus (20.94%, 76/363), with the positive proportion of ( 29 .83%) among ILI cases and ( 12 .09%) among SARI cases .The Influenza infection presented two seasonal peaks in winter ( December to February ) and summer ( July to September ) . Conclusion Influenza is identified as the leading viral pathogen both among ILI and SARI cases older than 65 years old, and two seasonal epidemic peaks areobserved in Shanghai .Influenza vacci-nation strategy should be advocated for the elderly population in Shanghai .
4.Preliminary application on China Infectious Diseases Automated-alert and Response System (CIDARS), between 2008 and 2010
Wei-Zhong YANG ; Zhong-Jie LI ; Sheng-Jie LAI ; Lian-Mei JIN ; Hong-Long ZHANG ; Chu-Chu YE ; Dan ZHAO ; Qiao SUN ; Wei LV ; Jia-Qi MA ; Jin-Feng WANG ; Ya-Jia LAN
Chinese Journal of Epidemiology 2011;32(5):431-435
Objective To analyze the results of application on China Infectious Diseases Automated-alert and Response System(CIDARS)and for further improving the system. Methods Amount of signal, proportion of signal responded, time to signal response, manner of signal verification and the outcome of each signal in CIDARS were descriptively analyzed from July 1,2008to June 30, 2010. Results A total of 533 829 signals were generated nationwide on 28 kinds of infectious diseases in the system. 97.13% of the signals had been responded and the median time to response was 1.1 hours. Among them, 2472 signals were generated by the fixed-value detection method which involved 9 kinds of diseases after the preliminary verification, field investigation and laboratory tests. 2202 signals were excluded, and finally 246 cholera cases, 15 plague cases and 9H5N1 cases as well as 39 outbreaks of cholera were confirmed. 531 357 signals were generated by the other method - the 'moving percentile method' which involved 19 kinds of diseases. The average amount of signal per county per week was 1.65, with 6603 signals(1.24%)preliminarily verified as suspected outbreaks and 1594 outbreaks were finally confirmed by further field investigation. For diseases in CIDARS, the proportion of signals related to suspected outbreaks to all triggered signals showed a positive correlation with the proportion of cases related to outbreaks of all the reported cases (r=0.963, P<0.01). Conclusion The signals of CIDARS were responded timely, and the signal could act as a clue for potential outbreaks, which helped enhancing the ability on outbreaks detection for local public health departments.
5.Construction of theoretical framework, item pool and rating scale words of
Xuan ZHONG ; Chu-Qiao YE ; Yan-Ying YE ; Ning TIAN
Chinese Acupuncture & Moxibustion 2021;41(12):1355-1359
Literature investigation and expert consultation were adopted to construct the theoretical framework and item pool of
Moxibustion
;
Thermosensing
6.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.