1.Epidemiological characteristics of sexually transmitted diseases in Yangzhou City from 2019 to 2023
XU Chun ; LI Jincheng ; YANG Wenbin ; JIANG Yan ; YANG Kejiao ; BU Chunhong
Journal of Preventive Medicine 2025;37(2):158-162
Objective:
o analyze the epidemic characteristics of five sexually transmitted diseases (STDs), including syphilis, gonorrhea, condyloma acuminatum, genital herpes and genital Chlamydia trachomatis infection in Yangzhou City, Jiangsu Province from 2019 to 2023, so as to provide the reference for the prevention and control strategies of STDs.
Methods:
Data of the onset time and diagnostic types of STDs cases in Yangzhou City from 2019 to 2023 were collected from the Infectious Disease Surveillance System of Chinese Disease Prevention and Control Information System. The temporal, regional and population characteristics of five types of STDs was analyzed using the descriptive epidemiological method.
Results:
A total of 10 895 cases of STDs were reported in Yangzhou City from 2019 to 2023, with an average annual reported incidence rate of 47.83/105. The average annual reported incidence rates of syphilis, gonorrhea, condyloma acuminatum, genital herpes and genital Chlamydia trachomatis infections were 41.11/105, 2.83/105, 2.59/105, 0.43/105 and 0.85/105, respectively. The reported incidence rate of STDs showed a decreasing trend from 2019 to 2023 (P<0.05), with an average annual growth rate of -3.44%. The reported incidence rates of syphilis and gonorrhea showed a decreasing trend (both P<0.05), with average annual growth rates of -4.26% and -6.47%, respectively. The reported incidence rate of genital Chlamydia trachomatis infection showed an increasing trend (P<0.05), with an average annual growth rate of 22.32%. Baoying County, Guangling District and Hanjiang District had the top three reported incidence rates of STDs, at 56.61/105, 55.61/105 and 46.50/105, respectively. The average annual reported incidence rate of STDs among males was higher than that among females (53.19/105 vs. 42.54/105, P<0.05). The STD cases were primarily people aged 50 years and above, with 6 641 cases accounting for 60.95%. The occupations of STD cases were mainly farmers, housekeepers and unemployed, with 4 670 and 3 273 cases accounting for 42.86% and 30.04%, respectively.
Conclusions
The overall reported incidence of STDs in Yangzhou City from 2019 to 2023 showed a downward trend, while the reported incidence of genital Chlamydia trachomatis infection showed an upward trend. The individuals aged 50 years and above, farmers, housekeepers and the unemployed were identified as high-risk groups for STDs.
2.Evaluation of the performance of the artificial intelligence - enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula
Jihua ZHOU ; Shaowen BAI ; Liang SHI ; Jianfeng ZHANG ; Chunhong DU ; Jing SONG ; Zongya ZHANG ; Jiaqi YAN ; Andong WU ; Yi DONG ; Kun YANG
Chinese Journal of Schistosomiasis Control 2025;37(1):55-60
Objective To evaluate the performance of the artificial intelligence (AI)-enabled snail identification system for recognition of Oncomelania hupensis robertsoni and Tricula in schistosomiasis-endemic areas of Yunnan Province. Methods Fifty O. hupensis robertsoni and 50 Tricula samples were collected from Yongbei Township, Yongsheng County, Lijiang City, a schistosomiasis-endemic area in Yunnan Province in May 2024. A total of 100 snail sample images were captured with smartphones, including front-view images of 25 O. hupensis robertsoni and 25 Tricula samples (upward shell opening) and back-view images of 25 O. hupensis robertsoni and 25 Tricula samples (downward shell opening). Snail samples were identified as O. hupensis robertsoni or Tricula by schistosomiasis control experts with a deputy senior professional title and above according to image quality and morphological characteristics. A standard dataset for snail image classification was created, and served as a gold standard for recognition of snail samples. A total of 100 snail sample images were recognized with the AI-enabled intelligent snail identification system based on a WeChat mini program in smartphones. Schistosomiasis control professionals were randomly sampled from stations of schistosomisis prevention and control and centers for disease control and prevention in 18 schistosomiasis-endemic counties (districts, cities) of Yunnan Province, for artificial identification of 100 snail sample images. All professionals are assigned to two groups according the median years of snail survey experiences, and the effect of years of snail survey experiences on O. hupensis robertsoni sample image recognition was evaluated. A receiver operating characteristic (ROC) curve was plotted, and the sensitivity, specificity, accuracy, Youden’s index and the area under the curve (AUC) of the AI-enabled intelligent snail identification system and artificial identification were calculated for recognition of snail sample images. The snail sample image recognition results of AI-enabled intelligent snail identification system and artificial identification were compared with the gold standard, and the internal consistency of artificial identification results was evaluated with the Cronbach’s coefficient alpha. Results A total of 54 schistosomiasis control professionals were sampled for artificial identification of snail sample image recognition, with a response rate of 100% (54/54), and the accuracy, sensitivity, specificity, Youden’s index, and AUC of artificial identification were 90%, 86%, 94%, 0.80 and 0.90 for recognition of snail sample images, respectively. The overall Cronbach’s coefficient alpha of artificial identification was 0.768 for recognition of snail sample images, and the Cronbach’s coefficient alpha was 0.916 for recognition of O. hupensis robertsoni snail sample images and 0.925 for recognition of Tricula snail sample images. The overall accuracy of artificial identification was 90% for recognition of snail sample images, and there was no significant difference in the accuracy of artificial identification for recognition of O. hupensis robertsoni (86%) and Tricula snail sample images (94%) (χ2 = 1.778, P > 0.05). There was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (88%) and downward shell openings (92%) (χ2 = 0.444, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less (75%) and more than 6 years (90%) (χ2 = 7.792, P < 0.05). The accuracy, sensitivity, specificity and AUC of the AI-enabled intelligent snail identification system were 88%, 100%, 76% and 0.88 for recognition of O. hupensis robertsoni snail sample images, and there was no significant difference in the accuracy of recognition of O. hupensis robertsoni snail sample images between the AI-enabled intelligent snail identification system and artificial identification (χ2 = 0.204, P > 0.05). In addition, there was no significant difference in the accuracy of artificial identification for recognition of snail sample images with upward (90%) and downward shell openings (86%) (χ2 = 0.379, P > 0.05), and there was a significant difference in the accuracy of artificial identification for recognition of snail sample images between schistosomiasis control professionals with snail survey experiences of 6 years and less and more than 6 years (χ2 = 5.604, Padjusted < 0.025). Conclusions The accuracy of recognition of snail sample images is comparable between the AI-enabled intelligent snail identification system and artificial identification by schistosomiasis control professionals, and the AI-enabled intelligent snail identification system is feasible for recognition of O. hupensis robertsoni and Tricula in Yunnan Province.
3.Severe COVID-19 and inactivated vaccine in diabetic patients with SARS-CoV-2 infection.
Yaling YANG ; Feng WEI ; Duoduo QU ; Xinyue XU ; Chenwei WU ; Lihua ZHOU ; Jia LIU ; Qin ZHU ; Chunhong WANG ; Weili YAN ; Xiaolong ZHAO
Chinese Medical Journal 2025;138(10):1257-1259
4.Corticotropin releasing factor receptor 2 involves in pain sensitization and anxiety of chronic migraine mice
Luhong ZOU ; Chunhong YAN ; Lingzhi WU ; Xuejuan ZHANG ; Jiang BIAN
Chinese Journal of Neuromedicine 2024;23(2):131-139
Objective:To explore the role of corticotrophin releasing factor receptor 2 (CRFR2) in regulating pain sensitization and anxiety and its mechanism in chronic migraine mice.Methods:Forty-eight C57BL/6J mice were randomly divided into control group, model group, NBI35965 group and K41498 group ( n=12); chronic migraine models in the later 3 groups were established by intraperitoneally administrating 10 mg/kg nitroglycerin on the 1 st, 3 rd, 5 th, 7 th and 9 th d; mice in the NBI35965 group and K41498 group were injected with 100 nL NBI35965 or K41498 solution into the bilateral trigeminal nucleus caudalis on the 2 nd, 4 th, 6 th and 8 th d, and mice in the control group were injected with same volume of normal saline. Von frey fiber was used to detect the orbitofrontal mechanical pain threshold 2 h after intraperitoneal injection on the 1 st, 3 rd, 5 th, 7 th and 9 th d, and at 11 a.m. on the 10 th d. Elevated plus maze was used to detect the anxiety-like behaviors at 11 a.m. on the 11 th d. Western blotting was performed to detect the protein expressions of corticotrophin releasing factor (CRF), corticotrophin releasing factor receptor 1 (CRFR1), CRFR2 in the trigeminal nucleus caudalis. Real-time quantitative PCR (RT-qPCR) was used to detect the CRFR1 and CRFR2 mRNA expressions in the trigeminal nucleus caudalis. Immunofluorescent staining was used to detect the protein expressions of calcitonin gene-related peptide (CGRP), immediate-early gene c-fos, glial fibrillary acidic protein (GFAP) and ionized calcium-binding adapter molecule 1 (Iba-1) in the trigeminal nucleus caudalis. Results:Compared with the control group, the model group, NBI35965 group and K41498 group had significantly decreased orbitofrontal mechanical pain thresholds 3, 5, 7, 9, and 10 d after intraperitoneal injection ( P<0.05); compared with model group, the K41498 group had significantly increased orbitofrontal mechanical pain thresholds 7, 9, and 10 d after intraperitoneal injection ( P<0.05). Compared with control group, the model group, NBI35965 group and K41498 group had significantly decreased entries and shorter time in opened arms ( P<0.05); compared with the model group, the K41498 group had significantly increased entries and shorter time in opened arms ( P<0.05). Compared with the control group, the model group, NBI35965 group and K41498 group had significantly higher CRF and CRFR2 protein expressions in the trigeminal nucleus caudalis ( P<0.05); compared with the model group, the K41498 group had statistically lower CRF protein expression in the trigeminal nucleus caudalis ( P<0.05). Compared with the control group, the model group, NBI35965 group and K41498 group had significantly higher CRFR2 mRNA expression in the trigeminal nucleus caudalis ( P<0.05). Compard with the control group, the model group, NBI35965 group and K41498 group had significantly increased CGRP, c-fos, Iba-1 and GFAP protein expressions in the trigeminal nucleus caudalis ( P<0.05); compared with the model group, the K41498 group had significantly decreased CGRP and c-fos protein expressions in the trigeminal nucleus caudalis ( P<0.05). Conclusion:CRFR2 can alter the orbitofrontal pain sensitization and anxiety-like behaviors in chronic migraine mice by regulating neuronal activation and CGRP release in the trigeminal nucleus caudalis.
5.Research and thinking on ophthalmologist training in African countries assisted by the Belt and Road Initiative
Wei SONG ; Chunhong YAN ; Shuangshuang SUN ; Sile YU ; Xingru HE
International Eye Science 2024;24(10):1676-1680
AIM:To investigate the challenges and needs of individual ophthalmologists participated in the training and their countries in the prevention and treatment of blindness and visual impairment, sum up the training effects, and discuss how to promote the development of eye health in the Belt and Road countries in the future.METHODS:A total of 48 ophthalmologists from 6 countries, including Kenya, Zambia, Nigeria, South Africa, Malawi, Botswana between August 1 and August 30, 2022, were trained and anonymous questionnaire survey was conducted. The questionnaire consists of 15 questions covering three aspects: challenges and strategies related to vision impairment and blindness in the country, training satisfaction, and recommendations for establishing optometric centers in Africa.RESULTS: A total of 48 questionnaires were distributed and 47 valid questionnaires were collected. The ophthalmologists hold the view that the biggest challenge of their countries in prevention and treatment of ocular diseases was high nursing costs, accounting for 36.17%, the biggest challenge faced by ophthalmologists was low wages, accounting for 29.79%. Building more eye specialist hospitals(38.30%)and providing more training opportunities(65.96%)can effectively help the countries and the ophthalmologists. The organizational satisfaction with the training courses reached 98%, the content and the lecturers' satisfaction were 100%.CONCLUSION:There are urgent needs to build more ophthalmic hospitals and provide more professional training opportunities to solve the difficulties in the prevention and treatment of eye diseases of the countries and the ophthalmologists. This training program has high satisfaction and good feedback.
6.Progress of interruption of schistosomiasis transmission and prospects in Yunnan Province
Yun ZHANG ; Lifang WANG ; Xiguang FENG ; Mingshou WU ; Meifen SHEN ; Hua JIANG ; Jing SONG ; Jiayu SUN ; Chunqiong CHEN ; Jiaqi YAN ; Zongya ZHANG ; Jihua ZHOU ; Yi DONG ; Chunhong DU
Chinese Journal of Schistosomiasis Control 2024;36(4):422-427
Schistosomiasis was once hyper-endemic in Yunnan Province. Following concerted efforts for over 70 years, remarkable achievements have been made for schistosomiasis control in the province. In 2004, the Mid- and Long-term Plan for Schistosomiasis Prevention and Control in Yunnan Province was initiated in Yunnan Province, and the target for transmission control of schistosomiasis was achieved in the province in 2009. Following the subsequent implementation of the Outline for Key Projects in Integrated Schistosomiasis Control Program (2009—2015) and the 13th Five - year Plan for Schistosomiasis Control in Yunnan Province, no acute schistosomiasis had been identified in Yunnan Province for successive 12 years, and no local Schistosoma japonicum infections had been detected in humans, animals or Oncomelania hupensis snails for successive 6 years in the province by the end of 2020. The transmission of schistosomiasis was interrupted in Yunnan Province in 2020. This review summarizes the history of schistosomiasis, changes in schistosomiasis prevalence and progress of schistosomiasis control in Yunnan Province, and proposes the future priorities for schistosomiasis control in the province.
7.Prediction of potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest and maximum entropy models
Zongya ZHANG ; Chunhong DU ; Yun ZHANG ; Hongqiong WANG ; Jing SONG ; Jihua ZHOU ; Lifang WANG ; Jiayu SUN ; Meifen SHEN ; Chunqiong CHEN ; Hua JIANG ; Jiaqi YAN ; Xiguang FENG ; Wenya WANG ; Peijun QIAN ; Jingbo XUE ; Shizhu LI ; Yi DONG
Chinese Journal of Schistosomiasis Control 2024;36(6):562-571
Objective To predict the potential geographic distribution of Oncomelania hupensis in Yunnan Province using random forest (RF) and maximum entropy (MaxEnt) models, so as to provide insights into O. hupensis surveillance and control in Yunnan Province. Methods The O. hupensis snail survey data in Yunnan Province from 2015 to 2016 were collected and converted into O. hupensis snail distribution site data. Data of 22 environmental variables in Yunnan Province were collected, including twelve climate variables (annual potential evapotranspiration, annual mean ground surface temperature, annual precipitation, annual mean air pressure, annual mean relative humidity, annual sunshine duration, annual mean air temperature, annual mean wind speed, ≥ 0 ℃ annual accumulated temperature, ≥ 10 ℃ annual accumulated temperature, aridity and index of moisture), eight geographical variables (normalized difference vegetation index, landform type, land use type, altitude, soil type, soil textureclay content, soil texture-sand content and soil texture-silt content) and two population and economic variables (gross domestic product and population). Variables were screened with Pearson correlation test and variance inflation factor (VIF) test. The RF and MaxEnt models and the ensemble model were created using the biomod2 package of the software R 4.2.1, and the potential distribution of O. hupensis snails after 2016 was predicted in Yunnan Province. The predictive effects of models were evaluated through cross-validation and independent tests, and the area under the receiver operating characteristic curve (AUC), true skill statistics (TSS) and Kappa statistics were used for model evaluation. In addition, the importance of environmental variables was analyzed, the contribution of environmental variables output by the models with AUC values of > 0.950 and TSS values of > 0.850 were selected for normalization processing, and the importance percentage of environmental variables was obtained to analyze the importance of environmental variables. Results Data of 148 O. hupensis snail distribution sites and 15 environmental variables were included in training sets of RF and MaxEnt models, and both RF and MaxEnt models had high predictive performance, with both mean AUC values of > 0.900 and all mean TSS values and Kappa values of > 0.800, and significant differences in the AUC (t = 19.862, P < 0.05), TSS (t = 10.140, P < 0.05) and Kappa values (t = 10.237, P < 0.05) between two models. The AUC, TSS and Kappa values of the ensemble model were 0.996, 0.954 and 0.920, respectively. Independent data verification showed that the AUC, TSS and Kappa values of the RF model and the ensemble model were all 1, which still showed high performance in unknown data modeling, and the MaxEnt model showed poor performance, with TSS and Kappa values of 0 for 24%(24/100) of the modeling results. The modeling results of 79 RF models, 38 MaxEnt models and their ensemble models with AUC values of > 0.950 and TSS values of > 0.850 were included in the evaluation of importance of environmental variables. The importance of annual sunshine duration (SSD) was 32.989%, 37.847% and 46.315% in the RF model, the MaxEnt model and their ensemble model, while the importance of annual mean relative humidity (RHU) was 30.947%, 15.921% and 28.121%, respectively. Important environment variables were concentrated in modeling results of the RF model, dispersed in modeling results of the MaxEnt model, and most concentrated in modeling results of the ensemble model. The potential distribution of O. hupensis snails after 2016 was predicted to be relatively concentrated in Yunnan Province by the RF model and relatively large by the MaxEnt model, and the distribution of O. hupensis snails predicted by the ensemble model was mostly the joint distribution of O. hupensis snails predicted by RF and MaxEnt models. Conclusions Both RF and MaxEnt models are effective to predict the potential distribution of O. hupensis snails in Yunnan Province, which facilitates targeted O. hupensis snail control.
8.Surveillance of bacterial resistance in tertiary hospitals across China:results of CHINET Antimicrobial Resistance Surveillance Program in 2022
Yan GUO ; Fupin HU ; Demei ZHU ; Fu WANG ; Xiaofei JIANG ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Yuling XIAO ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Jingyong SUN ; Qing CHEN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yunmin XU ; Sufang GUO ; Yanyan WANG ; Lianhua WEI ; Keke LI ; Hong ZHANG ; Fen PAN ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Wei LI ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Qian SUN ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanqing ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Wenhui HUANG ; Juan LI ; Quangui SHI ; Juan YANG ; Abulimiti REZIWAGULI ; Lili HUANG ; Xuejun SHAO ; Xiaoyan REN ; Dong LI ; Qun ZHANG ; Xue CHEN ; Rihai LI ; Jieli XU ; Kaijie GAO ; Lu XU ; Lin LIN ; Zhuo ZHANG ; Jianlong LIU ; Min FU ; Yinghui GUO ; Wenchao ZHANG ; Zengguo WANG ; Kai JIA ; Yun XIA ; Shan SUN ; Huimin YANG ; Yan MIAO ; Mingming ZHOU ; Shihai ZHANG ; Hongjuan LIU ; Nan CHEN ; Chan LI ; Jilu SHEN ; Wanqi MEN ; Peng WANG ; Xiaowei ZHANG ; Yanyan LIU ; Yong AN
Chinese Journal of Infection and Chemotherapy 2024;24(3):277-286
Objective To monitor the susceptibility of clinical isolates to antimicrobial agents in tertiary hospitals in major regions of China in 2022.Methods Clinical isolates from 58 hospitals in China were tested for antimicrobial susceptibility using a unified protocol based on disc diffusion method or automated testing systems.Results were interpreted using the 2022 Clinical &Laboratory Standards Institute(CLSI)breakpoints.Results A total of 318 013 clinical isolates were collected from January 1,2022 to December 31,2022,of which 29.5%were gram-positive and 70.5%were gram-negative.The prevalence of methicillin-resistant strains in Staphylococcus aureus,Staphylococcus epidermidis and other coagulase-negative Staphylococcus species(excluding Staphylococcus pseudintermedius and Staphylococcus schleiferi)was 28.3%,76.7%and 77.9%,respectively.Overall,94.0%of MRSA strains were susceptible to trimethoprim-sulfamethoxazole and 90.8%of MRSE strains were susceptible to rifampicin.No vancomycin-resistant strains were found.Enterococcus faecalis showed significantly lower resistance rates to most antimicrobial agents tested than Enterococcus faecium.A few vancomycin-resistant strains were identified in both E.faecalis and E.faecium.The prevalence of penicillin-susceptible Streptococcus pneumoniae was 94.2%in the isolates from children and 95.7%in the isolates from adults.The resistance rate to carbapenems was lower than 13.1%in most Enterobacterales species except for Klebsiella,21.7%-23.1%of which were resistant to carbapenems.Most Enterobacterales isolates were highly susceptible to tigecycline,colistin and polymyxin B,with resistance rates ranging from 0.1%to 13.3%.The prevalence of meropenem-resistant strains decreased from 23.5%in 2019 to 18.0%in 2022 in Pseudomonas aeruginosa,and decreased from 79.0%in 2019 to 72.5%in 2022 in Acinetobacter baumannii.Conclusions The resistance of clinical isolates to the commonly used antimicrobial agents is still increasing in tertiary hospitals.However,the prevalence of important carbapenem-resistant organisms such as carbapenem-resistant K.pneumoniae,P.aeruginosa,and A.baumannii showed a downward trend in recent years.This finding suggests that the strategy of combining antimicrobial resistance surveillance with multidisciplinary concerted action works well in curbing the spread of resistant bacteria.
9.Changing distribution and resistance profiles of common pathogens isolated from urine in the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yanming LI ; Mingxiang ZOU ; Wen'en LIU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):287-299
Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8%(62 219/261 893),and gram-negative bacteria 76.2%(199 674/261 893).The most common species were E.coli(46.7%),E.faecium(10.4%),K.pneumoniae(9.8%),E.faecalis(8.7%),P.mirabilis(3.5%),P.aeruginosa(3.4%),SS.agalactiae(2.6%),and E.cloacae(2.1%).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2%,52.8%and 37.0%,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7%,18.5%,16.4%,and 40.3%,respectively.Lower than 10%of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90%of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was<2%.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.
10.Changing resistance profiles of Enterococcus in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Na CHEN ; Ping JI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Wen'en LIU ; Yanming LI ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):300-308
Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7%)and Enterococcus faecalis(41.5%).The strains were mainly isolated from urinary tract specimens(46.9%±2.6%),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6%).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1%and 1.3%,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7%in 2015 to 3.4%in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3%.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.


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