1.Cloning, subcellular localization and expression analysis of SmIAA7 gene from Salvia miltiorrhiza
Yu-ying HUANG ; Ying CHEN ; Bao-wei WANG ; Fan-yuan GUAN ; Yu-yan ZHENG ; Jing FAN ; Jin-ling WANG ; Xiu-hua HU ; Xiao-hui WANG
Acta Pharmaceutica Sinica 2025;60(2):514-525
The auxin/indole-3-acetic acid (Aux/IAA) gene family is an important regulator for plant growth hormone signaling, involved in plant growth, development, as well as response to environmental stresses. In the present study, we identified
2.A preliminary exploration of influenza-like illness surveillance and influenza vaccination in Jing’an District of Shanghai, 2017‒2023
Ruijue HUA ; Lixue LYU ; Biao XU ; Jin HUANG ; Ping YU
Shanghai Journal of Preventive Medicine 2025;37(4):313-318
ObjectiveTo understand the surveillance of influenza-like illness (ILI) and influenza vaccination status in Jing’an District, Shanghai, and to provide a basis for optimizing influenza prevention and control strategies. MethodsThe sentinel surveillance data for ILI and virological surveillance data of influenza viruses in Jing’an District were collected from the Chinese influenza surveillance information system, and data for influenza vaccination were collected from Shanghai immunization information system from September 2017 to August 2023. Epidemiological characteristics of ILI, influenza etiology, and the temporal and population distributions of influenza vaccination were analyzed using descriptive epidemiological methods. ResultsILI as a percentage of total visit surveillance units (ILI%) reported by sentinel hospital was increased in Jing’an District of Shanghai from September 2017 to August 2023 (F=18.841, P=0.012). The peak of the influenza cases mainly appeared in winter-spring, but there were two peaks in winter-spring and summer from September 2019 to August 2020, from September 2020 to August 2021, and from September 2021 to August 2022. In particular, there were two peaks in winter-spring from September 2022 to August 2023, with a rebound during the descending process. The average positive rate of ILI was 21.64% (2 421/11 189) during the 6 years. There was a peak in winter-spring during every year with the exception of the period from September 2020 to August 2021. The dominant strains were B/Yamagata and A/H1N1 in winter-spring from September 2017 to August 2018. The dominant strain was A/H1N1 in winter-spring from September 2018 to August 2019 and from September 2022 to August 2023. The dominant strain was B/Victoria in winter-spring from September 2019 to August 2020 and from September 2021 to August 2022. Different subtype strains occurred alternately, and the dominant strains were A/H1N1 and A/H3N2 in recent years. The influenza vaccination coverage was 2.94% from September 2017 to August 2023, and the vaccination coverage was highest in young children. The vaccination coverage for females was higher than that for males (χ2=546.963, P<0.001), and the vaccination coverage for registered residents was higher compared to that for migrants (χ2=123.141, P<0.001). ConclusionILI% exhibits an upward trend in Jing’an District of Shanghai, and the dominant strain is A subtype. The influenza vaccination coverage is still low, which is insufficient to have an impact on the spread of influenza. It is recommended that the surveillance of ILI and variations of influenza virus strains should be improved continuously, and effective steps should be taken to promote influenza vaccination.
3.Prediction of Pulmonary Nodule Progression Based on Multi-modal Data Fusion of CCNet-DGNN Model
Lehua YU ; Yehui PENG ; Wei YANG ; Xinghua XIANG ; Rui LIU ; Xiongjun ZHAO ; Maolan AYIDANA ; Yue LI ; Wenyuan XU ; Min JIN ; Shaoliang PENG ; Baojin HUA
Chinese Journal of Experimental Traditional Medical Formulae 2025;31(24):135-143
ObjectiveThis study aims to develop and validate a novel multimodal predictive model, termed criss-cross network(CCNet)-directed graph neural network(DGNN)(CGN), for accurate assessment of pulmonary nodule progression in high-risk individuals for lung cancer, by integrating longitudinal chest computed tomography(CT) imaging with both traditional Chinese and western clinical evaluation data. MethodsA cohort of 4 432 patients with pulmonary nodules was retrospectively analyzed. A twin CCNet was employed to extract spatiotemporal representations from paired sequential CT scans. Structured clinical assessment and imaging-derived features were encoded via a multilayer perceptron, and a similarity-based alignment strategy was adopted to harmonize multimodal imaging features across temporal dimensions. Subsequently, a DGNN was constructed to integrate heterogeneous features, where nodes represented modality-specific embeddings and edges denoted inter-modal information flow. Finally, model optimization was performed using a joint loss function combining cross-entropy and cosine similarity loss, facilitating robust classification of nodule progression status. ResultsThe proposed CGN model demonstrated superior predictive performance on the held-out test set, achieving an area under the receiver operating characteristic curve(AUC) of 0.830, accuracy of 0.843, sensitivity of 0.657, specificity of 0.712, Cohen's Kappa of 0.417, and F1 score of 0.544. Compared with unimodal baselines, the CGN model yielded a 36%-48% relative improvement in AUC. Ablation studies revealed a 2%-22% increase in AUC when compared to simplified architectures lacking key components, substantiating the efficacy of the proposed multimodal fusion strategy and modular design. Incorporation of traditional Chinese medicine (TCM)-specific symptomatology led to an additional 5% improvement in AUC, underscoring the complementary value of integrating TCM and western clinical data. Through gradient-weighted activation mapping visualization analysis, it was found that the model's attention predominantly focused on nodule regions and effectively captured dynamic associations between clinical data and imaging-derived features. ConclusionThe CGN model, by synergistically combining cross-attention encoding with directed graph-based feature integration, enables effective alignment and fusion of heterogeneous multimodal data. The incorporation of both TCM and western clinical information facilitates complementary feature enrichment, thereby enhancing predictive accuracy for pulmonary nodule progression. This approach holds significant potential for supporting intelligent risk stratification and personalized surveillance strategies in lung cancer prevention.
4.Design and development of medical device products with oocyte collection collector as an example
Ying ZHANG ; Hua YIN ; Jin MA ; Chunyong YU
International Journal of Biomedical Engineering 2024;47(5):513-516
With the increasingly strict regulation of the entire life cycle of medical devices in China, design and development are key processes that affect the quality of medical device products. It is urgent to standardize the design and development process of various medical device enterprises to further reduce the clinical application risks of products. The design and development process of the oocyte collection device was taken as an example, and the design and development activities according to the requirements of quality management system were carried out. The planning, input, output, review, verification, and confirmation process of the product design and development were also analyzed. The designed oocyte collection device meets the design input requirements and can meet the expected clinical use. To carry out design and development activities in accordance with the requirements of the quality management system, and to implement risk management throughout the entire design and development process, in order to design products that meet clinical low risk and are safe and reliable.
5.Factors of prognosis of patients with acute myocardial infarction complicated with cardiogenic shock undergoing primary percutaneous coronary intervention under the support of mechanical devices
Ming-Hua LUO ; Yu-Shan CHEN ; He WANG ; Huai-Min GUAN ; Jin-Hong XIE ; Cheng-Jie QIU ; Yong-Hua ZONG ; Sha-Sha SHANG ; Yun-Wei WANG
Chinese Journal of Interventional Cardiology 2024;32(4):197-202
Objective To investigate the factors influencing prognosis in patients with acute myocardial infarction complicated with cardiogenic shock undergoing primary percutaneous coronary intervention(PPCI).Methods Patients with acute myocardial infarction complicated with cardiogenic shock who underwent PPCI at our hospital between January 2015 and December 2019 were enrolled.Clinical baseline characteristics,coronary angiography and PCI-related parameters,and mechanical support information were collected.The patients were followed up for one year and divided into survival and death groups based on their survival status within one year.Differences in various factors between the two groups were compared.Results A total of 40 patients were enrolled,including 26 in the survival group and 14 in the death group.There were no differences in baseline data,diagnosis,risk factors,and comorbidities between the two groups.The survival group had a lower heart rate and higher blood pressure trend at admission compared to the death group.Myocardial enzymes were significantly lower in the survival group compared to the death group(median CK peak:496.00(198.25,2 830.00)U/L vs.3 040.00(405.75,5 626.53)U/L,P=0.003;median CK-MB peak:52.65(31.75,219.50)U/L vs.306.00(27.25,489.63)U/L,P=0.006).When comparing coronary angiography and PCI-related indicators between the two groups,the survival group had a higher rate of complete revascularization compared to the control group(53.85%vs.21.43%,P=0.048).The survival group had a higher proportion of extracorporeal membrane oxygenation(ECMO)combined with intra-aortic balloon pump(IABP)support compared to the control group[38.46%vs.7.14%,P=0.034].Conclusions Survival in patients with acute myocardial infarction complicated with cardiogenic shock undergoing PPCI is associated with lower level of myocardial enzymes,ECMO combined with IABP support and complete revascularization.
6.Study on the safety and efficacy of novel portable extracorporeal membrane oxygenation in animal experiments in vivo
Meng-En ZHAI ; Jian-Chao LUO ; Lin-He LU ; Yu-Chao REN ; Ping JIN ; Zhen-Hua LIU ; Jian YANG ; Zhen-Xiao JIN ; Jin-Cheng LIU ; Yang LIU
Chinese Journal of Interventional Cardiology 2024;32(8):447-450
Objective To verify the safety and efficacy of a new portable extracorporeal membrane oxygenation(ECMO)system(Xijing Advanced Life Support System JC-Ⅲ)in large animals.Methods A total of 10 healthy small fat-tail sheep underwent veno-arterial extracorporeal membrane oxygenation(VA-ECMO)support by carotid arterial-jugular catheterization to evaluate the performance of the JC-Ⅲ ECMO system.Systemic anticoagulation was achieved by continuous infusion of heparin.Active coagulation time(ACT)was recorded every 2 hours during the experiment,and the ACT was maintained between 200-250 s.Centrifugal pump speed is set at 3 000-3 500 r/min.The changes of hemoglobin,blood cell counts,hematocrit,liver and kidney function were monitored before and 24 h after ECMO initiation,respectively.After the experiment,the pump and oxygenator were dissected to probe the thrombosis.Results The success rate of VA-ECMO operation was 100%,and there was no hemolysis,pump thrombosis and oxygenator thrombosis after 24 h of ECMO.Before and after the operation,there were no significant changes in indicators such as hemoglobin content,white blood cell counts,platelet counts,alanine aminotransferase concentration,aspartate aminotransferase concentration,urea,creatinine,high-sensitivity troponin Ⅰ,and N-terminal pro-brain natriuretic peptide(all P>0.05).Conclusions This in vivo study confirms that Xijing Advanced Life support System JC-Ⅲ is safe and effective.
7.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.
8.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.
9.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.
10.Changing resistance profiles of Enterobacter isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shaozhen YAN ; Ziyong SUN ; Zhongju CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Fengbo ZHANG ; Ping JI ; Zhidong HU ; Jin LI ; Sufang GUO ; Han SHEN ; Wanqing ZHOU ; Yingchun XU ; Xiaojiang ZHANG ; Xuesong XU ; Chao YAN ; Chuanqing WANG ; Pan FU ; Wei JIA ; Gang LI ; Yuanhong XU ; Ying HUANG ; Dawen GUO ; Jinying ZHAO ; Wen'en LIU ; Yanming LI ; Hua YU ; Xiangning HUANG ; Bin SHAN ; Yan DU ; Shanmei WANG ; Yafei CHU ; Yuxing NI ; Jingyong SUN ; Yunsong YU ; Jie LIN ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Yan JIN ; Chunhong SHAO ; Jihong LI ; Lixia ZHANG ; Juan MA ; Yunzhuo CHU ; Sufei TIAN ; Jinju DUAN ; Jianbang KANG ; Ruizhong WANG ; Hua FANG ; Fangfang HU ; Yunjian HU ; Xiaoman AI ; Fang DONG ; Zhiyong LÜ ; Hong ZHANG ; Chun WANG ; Yong ZHAO ; Ping GONG ; Lei ZHU ; Jinhua MENG ; Xiaobo MA ; Yanping ZHENG ; Jinsong WU ; Yuemei LU ; Ruyi GUO ; Yan ZHU ; Kaizhen WEN ; Yirong ZHANG ; Chunlei YUE ; Jiangshan LIU ; Wenhui HUANG ; Shunhong XUE ; Xuefei HU ; Hongqin GU ; Jiao FENG ; Shuping ZHOU ; Yan ZHOU ; Yunsheng CHEN ; Qing MENG ; Bixia YU ; Jilu SHEN ; Rui DOU ; Shifu WANG ; Wen HE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(3):309-317
Objective To examine the changing antimicrobial resistance profile of Enterobacter spp.isolates in 53 hospitals across China from 2015 t0 2021.Methods The clinical isolates of Enterobacter spp.were collected from 53 hospitals across China during 2015-2021 and tested for antimicrobial susceptibility using Kirby-Bauer method or automated testing systems according to the CHINET unified protocol.The results were interpreted according to the breakpoints issued by the Clinical & Laboratory Standards Institute(CLSI)in 2021(M100 31st edition)and analyzed with WHONET 5.6 software.Results A total of 37 966 Enterobacter strains were isolated from 2015 to 2021.The proportion of Enterobacter isolates among all clinical isolates showed a fluctuating trend over the 7-year period,overall 2.5%in all clinical isolates amd 5.7%in Enterobacterale strains.The most frequently isolated Enterobacter species was Enterobacter cloacae,accounting for 93.7%(35 571/37 966).The strains were mainly isolated from respiratory specimens(44.4±4.6)%,followed by secretions/pus(16.4±2.3)%and urine(16.0±0.9)%.The strains from respiratory samples decreased slightly,while those from sterile body fluids increased over the 7-year period.The Enterobacter strains were mainly isolated from inpatients(92.9%),and only(7.1±0.8)%of the strains were isolated from outpatients and emergency patients.The patients in surgical wards contributed the highest number of isolates(24.4±2.9)%compared to the inpatients in any other departement.Overall,≤ 7.9%of the E.cloacae strains were resistant to amikacin,tigecycline,polymyxin B,imipenem or meropenem,while ≤5.6%of the Enterobacter asburiae strains were resistant to these antimicrobial agents.E.asburiae showed higher resistance rate to polymyxin B than E.cloacae(19.7%vs 3.9%).Overall,≤8.1%of the Enterobacter gergoviae strains were resistant to tigecycline,amikacin,meropenem,or imipenem,while 10.5%of these strains were resistant to polycolistin B.The overall prevalence of carbapenem-resistant Enterobacter was 10.0%over the 7-year period,but showing an upward trend.The resistance profiles of Enterobacter isolates varied with the department from which they were isolated and whether the patient is an adult or a child.The prevalence of carbapenem-resistant E.cloacae was the highest in the E.cloacae isolates from ICU patients.Conclusions The results of the CHINET Antimicrobial Resistance Surveillance Program indicate that the proportion of Enterobacter strains in all clinical isolates fluctuates slightly over the 7-year period from 2015 to 2021.The Enterobacter strains showed increasing resistance to multiple antimicrobial drugs,especially carbapenems over the 7-year period.

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