1.Mapping of QTL associated with rice cooking quality and candidate gene analysis.
Qiaona LE ; Ziwen HUANG ; Ruohui DAI ; Sanfeng LI ; Mengjia LI ; Yuan FANG ; Yuexing WANG ; Yuchun RAO
Chinese Journal of Biotechnology 2024;40(1):122-136
Excavating the quantitative trait locus (QTL) associated with rice cooking quality, analyzing candidate genes, and improving cooking quality-associated traits of rice varieties by genetic breeding can effectively improve the taste of rice. In this study, we used the indica rice HZ, the japonica rice Nekken2 and 120 recombinant inbred lines (RILs) populations constructed from them as experimental materials to measure the gelatinization temperature (GT), gel consistency (GC) and amylose content (AC) of rice at the maturity stage. We combined the high-density genetic map for QTL mapping. A total of 26 QTLs associated with rice cooking quality (1 QTL associated with GT, 13 QTLs associated with GC, and 12 QTLs associated with AC) were detected, among which the highest likelihood of odd (LOD) value reached 30.24. The expression levels of candidate genes in the localization interval were analyzed by quantitative real-time polymerase chain reaction (qRT-PCR), and it was found that the expression levels of six genes were significantly different from that in parents. It was speculated that the high expression of LOC_Os04g20270 and LOC_Os11g40100 may greatly increase the GC of rice, while the high expression of LOC_Os01g04920 and LOC_Os02g17500 and the low expression of LOC_Os03g02650 and LOC_Os05g25840 may reduce the AC. The results lay a molecular foundation for the cultivation of new high-quality rice varieties, and provide important genetic resources for revealing the molecular regulation mechanism of rice cooking quality.
Quantitative Trait Loci
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Oryza/genetics*
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Plant Breeding
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Cooking
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Genetic Association Studies
2.Exploring Risk Factors for Primary Liver Cancer in Patients with Chronic Hepatitis C Based on Machine Learning Prediction Models
Rong YANG ; Bin FANG ; Lingling ZHENG ; Jinhua CHEN ; Wenjuan ZHOU
Cancer Research on Prevention and Treatment 2024;51(12):1015-1020
Objective To construct a risk prediction model for liver cancer in patients with chronic hepatitis C based on seven different machine learning algorithms and select the optimal model. Methods A total of 236 patients with chronic hepatitis C were selected as the research subjects. Patients were divided into a case group and a control group according to whether liver cancer occurs. Prediction models were constructed based on seven machine learning algorithms including classification and regression tree, random forest, gradient boosting decision tree, extreme gradient boosting (XGBoost), logistic regression, K-near neighbor, and support vector machine. The Shapley additive explanations (SHAP) algorithm was used to interpret the best prediction model. Results Among the seven models, the XGBoost model had the best comprehensive prediction performance (accuracy of 0.933, sensitivity of 0.775, specificity of 0.960, area under the ROC curve of 0.956, F1 score of 0.764). The SHAP algorithm suggested that AFP, age, AST, diabetes, BMI, PLT, ALT, liver cysts, FIB-4, and gender contributed to the model decision and are the risk factors for liver cancer in patients with chronic hepatitis C. Conclusion This study develops an interpretable machine learning model based on the XGBoost algorithm, which has a good reference value for individualized monitoring of liver cancer in patients with chronic hepatitis C.
3.Nursing effect of PDCA circulating nursing model based on health concept on patients with valvular heart disease
Liying XING ; Wenbin FANG ; Li LI
China Modern Doctor 2024;62(1):78-81
Objective To explore the nursing effect of PDCA circulation nursing model based on health concept on patients with valvular heart disease.Methods A total of 116 patients with valvular heart disease hospitalized in Jinhua People's Hospital from December 2018 to December 2021 were selected as study objects.According to random number table method,the included patients were divided into control group and observation group,with 58 cases in each group.The patients in control group received routine nursing intervention,while the patients in observation group received PDCA circulation nursing intervention.The quality of life,self-rating anxiety scale(SAS)score,self-rating depression scale(SDS)score,complications and nursing satisfaction of two groups were compared.Results After nursing,the scores of physical function,psychological function,social function and overall quality of life in two groups were significantly higher than those before nursing(P<0.05),and the scores of above indexes of patients in observation group were significantly higher than those in control group(P<0.05).After nursing,SAS and SDS scores of two groups were significantly lower than those before nursing(P<0.05),and SAS and SDS scores of observation group were significantly lower than those of control group(P<0.05).The complication rate of observation group was significantly lower than that of control group(χ2=4.668,P=0.003).There was no significant difference in nursing satisfaction between two groups(χ2=2.511,P=0.113).Conclusion Patients with valvular heart disease can improve their quality of life,relieve anxiety and depression,and reduce complications by using PDCA circulation nursing model based on health concept.
4.Study on the protective mechanism of dapagliflozin on kidney in diabetic nephropathy rats
Yuyan YE ; Peng WANG ; Xia FANG ; Jing YANG
China Modern Doctor 2024;62(10):60-63,71
Objective To investigate the protective effect of dapagliflozin on kidney and the expression of AMP-activated protein kinase(AMPK)/mammalian target of rapamycin(mTOR)signaling pathway in diabetic nephropathy(DN)rats.Methods A total of 40 SPF Wistar male rats were randomly divided into normal group,model group,low-dose group and high-dose group,with 10 rats in each group.After the DN model was successfully prepared,the rats in normal group were given normal diet + normal saline by gavage,the rats in model group was given high sugar and high fat feed + normal saline by gavage,the rats in low-dose group was given high sugar and high fat feed+1mg/(kg·d)of dapagliflozin by gavage,the rats in high-dose group was given high sugar and high fat feed+10mg/(kg·d)of dapagliflozin by gavage.Rats in each group were continuously gavaged for 12 weeks.Renal function indexes,renal pathological changes,p-AMPK and p-mTOR protein expression,collagen type Ⅰ(COL Ⅰ),collagen type Ⅳ(COL Ⅳ)and fibronectin(FN)of all groups were compared.Results Blood urea nitrogen(BUN),serum creatinine(SCr),24h urinary protein quantity,p-mTOR protein expression,COL Ⅰ,COL Ⅳ and FN levels of rats in model group,low-dose group and high-dose group were significantly higher than those in normal group,and p-AMPK protein expression was significantly lower than that of normal group(P<0.05).BUN,SCr,24h urinary protein quantity,p-mTOR protein expression,COL Ⅰ,COL Ⅳ and FN levels of rats in low-dose group and high-dose group were significantly lower than those in model group,while p-AMPK protein expression was significantly higher than that in model group(P<0.05).BUN,SCr,24h urinary protein quantity,p-mTOR protein expression,COL Ⅰ,COL Ⅳ and FN levels in high-dose group were significantly lower than those in low-dose group,and p-AMPK protein expression was significantly higher than that in low-dose group(P<0.05).Conclusion Dapagliflozin has a good kidney protection effect on DN rats,and its mechanism may be related to the regulation of AMPK/mTOR signaling pathway.
5.Accuracy assessment of refractive status in patients implanted with extended depth of focus intraocular lens
Chunxia* YU ; Xiaoling* FANG ; Wenwen XUE ; Meng CHEN ; Shenyu BEN ; Jinhua TAO ; Yulan WANG
International Eye Science 2024;24(11):1821-1825
AIM: To evaluate the refractive status through computer refractometer and OPD-Scan III auto refractometer in cataract patients after extended depth of focus(EDOF)intraocular lens implantation.METHODS: Retrospective observational study. A total of 61 cases(76 eyes)that received phacomulsification and implanted with TECNIS® Symfony ZXR00 intraocular lens in Shanghai Eye Diseases Prevention & Treatment Center from May 2022 to May 2023 were collected. Measurements from the computer refractometer, OPD-Scan III auto refractometer, and subjective refraction, were taken from all patients on the same day postoperatively.RESULTS: There were statistical significant difference in sphere(S)and spherical equivalent(SE)readings from the computer refractometer and subjective refraction(all P<0.01), with mean differences of -0.67±0.37 D and -0.75±0.35 D, respectively, and the S and SE obtained from computer refractometer more incline to myopia than those from subjective refraction; there were statistical significant difference in computer refractometer and subjective refraction(P<0.01), with a relative small absolute difference(0.21±0.24 D). The S, cylinder(C)and SE of computer refractometer(S, C, SE)were positively correlated with subjective refraction(r=0.7994, 0.7929, and 0.8118, respectively, all P<0.01). Additionally, there were statistical significant differences in S, C and SE of OPD-Scan Ⅲ and subjective refraction(P<0.01), and the absolute differences of S(0.63±0.36 D), C(0.35±0.26 D)and SE(0.53±0.36 D)were small. Furthermore, the S, C and SE of OPD-Scan Ⅲ were positively correlated with subjective refraction(r=0.4410, 0.4982, 0.5224, all P<0.01).CONCLUSION: In patients who received implantation of EDOF lenses, the consistency of computer refractometer, OPD-Scan III auto refractometer and subjective refraction was good. The average difference of the S and SE obtained via computer refractometer was large, but both exhibited a myopic shift relative to those derived from subjective refraction, and the C values demonstrated minimal discrepancy. Furthermore, the differences between OPD-Scan III auto refractometer and subjective refraction were small, but the direction of the difference is unstable, sometimes it is myopic deviation, while sometimes it is hyperopic deviation.
6.Preliminary investigation of microarray-based analysis of DDX5 affecting head and neck squamous cell car-cinoma
Guoqi LIU ; Chunxia LIU ; Jingjing WANG ; Jinhua ZUO ; Fang WANG ; Jiaojiao SONG ; Donglin YU ; Xian-Grui MA ; Wenlong WANG
Journal of Practical Stomatology 2024;40(6):810-816
Objective:To investigate the expression and role of DEAD-box RNA helicases 5(DDX5 helicases)in head and neck squamous carcinoma(HNSCC).Methods:Tissue microarray microarray was used to assess relevant mRNA expression profile data,and R software was used to screen differential mRNAs(DEGs).The expression level of DDX5 was predicted using GEPIA 2,TCGA databases,and detected by immunohistochemistry,western blot and RT-qPCR in the HNSCC tissue and cell lines.Based on high-throughput sequencing data of DECs,differentially expressed miRNAs(DEMIs)relevant DDX5 competitive endogenous RNA network(ceRNA)was constructed.The software cytoscape was used to visualize the ceRNA network map and further screen the regulatory ax-is.Results:The results of microarray screening revealed that DDX5 expression in HNSCC was upregulated.Immunohistochemistry ver-ified that DDX5 was stronger expressed in the nuclei of squamous carcinoma cells.qPCR results suggested that significant expression of DDX5 mRNA at the tissue and cellular levels(P<0.05).Western blot results showed high expression of DDX5 protein in the tissues.The ceRNA network was constructed,from which the relevant HNSCC axis circRNA-039626-miR-222-5p-DDX5 was identified.Con-clusion:DDX5 is highly expressed in HNSCC,and the circRNA-039626-miR-222-5p-DDX5 axis may be a potential regulatory axis for the development of HNSCC.
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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