1.Progress on antisense oligonucleotide in the field of antibacterial therapy
Jia LI ; Xiao-lu HAN ; Shi-yu SONG ; Jin-tao LIN ; Zhi-qiang TANG ; Zeng-ming WANG ; Liang XU ; Ai-ping ZHENG
Acta Pharmaceutica Sinica 2025;60(2):337-347
With the widespread use of antibiotics, drug-resistant bacterial infections have become a significant threat to human health. Finding new antibacterial strategies that can effectively control drug-resistant bacterial infections has become an urgent task. Unlike small molecule drugs that target bacterial proteins, antisense oligonucleotide (ASO) can target genes related to bacterial resistance, pathogenesis, growth, reproduction and biofilm formation. By regulating the expression of these genes, ASO can inhibit or kill bacteria, providing a novel approach for the development of antibacterial drugs. To overcome the challenge of delivering antisense oligonucleotide into bacterial cells, various drug delivery systems have been applied in this field, including cell-penetrating peptides, lipid nanoparticles and inorganic nanoparticles, which have injected new momentum into the development of antisense oligonucleotide in the antibacterial realm. This review summarizes the current development of small nucleic acid drugs, the antibacterial mechanisms, targets, sequences and delivery vectors of antisense oligonucleotide, providing a reference for the research and development of antisense oligonucleotide in the treatment of bacterial infections.
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.Construction of a predictive diagnostic model for pulmonary aspergillosis using GM test combined with serum albumin
Yunxia ZHAI ; Ping XU ; Jing ZHAO ; Jing XUE ; Fanghua LI ; Jin LI
International Journal of Laboratory Medicine 2024;45(21):2566-2571,2576
Objective To evaluate the biochemical indicators,nutritional status,and immune levels of pa-tients with pulmonary aspergillosis(PA)and other pulmonary diseases,and to construct a predictive model for PA so as to improve the diagnostic efficacy of clinical PA.Methods A total of 40 PA patients and 39 pa-tients with other pulmonary diseases who were hospitalized in the hospital from January 2020 to August 2022 were retrospectively analyzed.The expression trends and differences of serum 1,3-β-D Glucan(G test),galac-tomannan test(GM test),biochemical indexes,blood routine indexes and immune cell subsets were analyzed and compared.The receiver operating characteristic(ROC)curve and binary Logistic regression analysis were used to construct the predictive model for PA by the combination of clinical indicators.Results Serum GM test,G test,albumin,hemoglobin,hematocrit,lymphocytes,B lymphocytes,CD44 T lymphocytes and CD4/CD8 ratio displayed significant differences between PA patients and patients with other lung disease(P<0.05).The levels of GM test in alveolar lavage fluid of PA patients were significantly higher than that in the serum,and the differences were statistically significant(P<0.05).The ROC curve analysis showed that the GM test,as an independent predictor of PA,had good predictive accuracy[0.85<area under the curve(AUC)<0.95].Besides,albumin,natural killev cells,CD4+T lymphocytes and CD4/CD8 ratio had general predictive efficacy(0.70<AUC<0.85).The prediction efficacy of G test and B lymphocytes was poor(AUC<0.70).The Logistic regression analysis showed that the combination of GM test and serum albumin could construct the optimal prediction model,and the prediction formula of the combined model was as fol-lows:Logit(P)=17.781× GM-0.131×albumin+1.394.The prediction accuracy of the combined model was 0.924(95%CI:0.865-0.982),the sensitivity was 87.5%,the specificity was 81.2%,and the cut off value was 17.781×GM-0.131×albumin-1.735.Conclusion This study retrospectively analyzed the differences in various clinical indicators between patients with PA and patients with other pulmonary diseases,and then screen the key clinical indicators as candidate predictors which displayed significantly different ex-pression between the two groups.The optimal prediction model for the diagnosis of PA is constructed by the combination of GM test and serum albumin through ROC curve and Logistic regression analysis.This model may significantly improve the diagnostic efficiency of PA in clinical,and provide the reference for the early di-agnosis and effective treatment of PA patients.
4.Estimation model for exposure of intravenous busulfan in patients receiving autologous hematopoietic stem cell transplantation
Jin-Wen LI ; Yan XU ; Xiao-Dan WANG ; Ying-Xi LIAO ; Shuai HE ; Shan XU ; Ping ZHANG ; Wen-Juan MIAO
Chinese Pharmacological Bulletin 2024;40(6):1193-1198
Aim To establish limited sampling strategy to esti-mate area under the drug concentration versus time curve(AUC0-t)of lymphoma patients treated with autologous stem cell transplantation(ASCT)who had busulfan intravenous infu-sion.Methods Twelve lymphoma patients treated with ASCT received a conditioning regimen containing busulfan 105 mg·m-2,Ⅳ infusion for 3 h.Blood samples were obtained 1 h after the start of the first dose of the busulfan infusion,at 5 min,1 h,2 h,4 h,6 h and 18 h after the end of the drug administration.LC-MS/MS was used to determine the busulfan serum concentra-tion.After obtaining the clinical pharmacokinetic parameters of busulfan by traditional pharmacokinetic method,multiple linear stepwise regression analysis was used to establish the AUC0-t es-timation model of busulfan based on limited sampling method.The model was further verified by Jackknife and Bootstrap meth-od.Bland-Altman plots were used to evaluate the consistency between the limited sampling method and the classical pharma-cokinetic method.Results The multiple linear regression equa-tion analysis of C60min,C180min and C300min was obtained by the limited sampling method.The regression equation was AUC0-t=295.003C60min+233.050C180min+273.163C300min-1202.713,r2=0.995,MPE=-0.87%,RMSE=2.40%.Conclusion The limited sampling model with three-point estimation can be used to estimate the AUC0-t of busulfan exposure in lymphoma patients with ASCT to provide reference for clinical application of busulfan.
5.Mechanism of Kechuanting granules in suppressing IL-33/ILC2s and pathogenic T cells to intervene in allergic airway inflammation
Nan-Ting ZOU ; Zhao WU ; Xiao-Dong YAN ; Chun-Fei ZHANG ; Hao-Hong ZHANG ; Qing-Yan MO ; Ming-Qian JU ; Jin-Zhu XU ; Chun-Ping WAN
Chinese Pharmacological Bulletin 2024;40(7):1350-1357
Aim To investigate the mechanisms of Ke-chuanting granules(KCT)inhibiting the IL-33/ILC2s pathway and pathogenic T cells to intervene in allergic airway inflammation.Methods Network pharmacolo-gy was utilized to analyze the potential targets and mechanisms of KCT-treated asthma.Allergic asthma models were induced in mice using OVA.Lung histo-pathology was conducted to observe injury changes.ELISA and quantitative PCR were utilized to measure key inflammatory factors and their mRNA expression levels in Th2-type asthma.Western blot was used to detect the phosphorylation levels of relevant proteins in the MAPK pathway.Flow cytometry was performed to evaluate the proportions of ILC2s,Th1,Th 17,Th2 and Treg cells.Results Network pharmacology iden-tified 227 main active components and 143 key targets of KCT in treating asthma,primarily enriched in signa-ling pathways such as MAPK and IL-17.Further vali-dation experiments demonstrated that KCT significantly alleviated lung inflammatory injury in asthmatic mice,reduced the number of B cells,production of I L-4,TNF-α and TGF-β,downregulated JNK phosphoryla-tion levels in lung tissue,as well as mRNA levels of Il-33,Bcl11b,Rorα,Tcf-7,Jun,Mapk3 and Mapk14.KCT intervention reduced the numbers of ILC2s and Th 17 cells in lungs and spleens of mice,and inhibited Th2 cell infiltration in lungs.Conclusions KCT ex-hibits therapeutic effects on allergic airway inflamma-tion in asthma,closely associated with the inhibition of the IL-33/ILC2s pathway,pathogenic T cell subsets,and JNK-MAPK signaling pathway.
6.Research progress on neuroinflammation-related biomarkers in cognitive impairment diseases
Xiao-Yu SONG ; Jie ZHANG ; Xu WANG ; Jian-Ping DUAN ; Jin-Beng DING
Chinese Pharmacological Bulletin 2024;40(12):2218-2223
Diagnosis and treatment of cognitive impairment is one of the difficult problems in the diagnosis and treatment of brain diseases.Any factor that causes functional and structural abnormalities in cerebral cortex can lead to cognitive impair-ment,which has a high incidence,a large number of patients and a heavy burden of disease.Because of the complexity of its pathogenesis,no effective preventive and curative measures have yet been taken.In recent years,many studies reveal that neu-roinflammation is involved in the pathogenesis of neurological diseases related to cognitive impairment,which has become a re-search hotspot in this field.This article provides a summary of the role of neuroinflammation-related biomarkers in the patho-genesis of cognitive impairment and their regulatory mecha-nisms,with a view to providing a further theoretical basis for the prevention and treatment of cognitive impairment.
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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