1.Analysis of Changes on Volatile Components of Ligusticum sinense cv. Chaxiong Rhizome Before and After Wine Processing Based on Electronic Nose and HS-GC-MS
Wen ZHANG ; Peng ZHENG ; Jiangshan ZHANG ; Xiaolin XIAO ; Zaodan WU ; Li XIN ; Wenhui GONG ; Jinlian ZHANG
Chinese Journal of Experimental Traditional Medical Formulae 2025;31(2):173-181
ObjectiveBy comparing the composition and content of volatile components in raw products, wine-washed products and wine-fried products of Ligusticum sinense cv. Chaxiong rhizome(LSCR), to investigate the influence of wine processing on the volatile components of LSCR, in order to provide a basis for the development of quality standards for LSCR and its processed products. MethodsElectronic nose was used to identify the odors of LSCR, wine-washed and wine-fried LSCR, and their volatile components were detected by headspace gas chromatography-mass spectrometry(HS-GC-MS), and the relative mass fractions of these components were determined by peak area normalization method. Principal component analysis(PCA) and orthogonal partial least squares-discriminant analysis(OPLS-DA) were performed on the obtained sample data by SIMCA 14.1 software, and the differential components of LSCR, wine-washed and wine-fried LSCR were screened according to the variable importance in the projection(VIP) value>1. Pearson correlation analysis was used to explore the relationship between volatile differential flavor components and electronic nose sensors. ResultsElectronic nose detection results showed that there were significant differences in the odors of LSCR, wine-washed and wine-fried LSCR, mainly reflected in the sensors S2, S4, S5, S6, S11, S12, S13. And a total of 62 compounds were identified from LSCR and its wine-processed products, among which 46, 50 and 51 compounds were identified from LSCR, wine-fried and wine-washed LSCR, respectively. There were 21 differential components between the raw products and wine-fried products, of which 10 components were increased and 11 were decreased after processing. There were 20 differential components between the raw products and wine-washed products, of which 11 constituents increased and 9 decreased after processing. There were 17 differential components between the wine-wash products and wine-fried products. Compared with the wine-washed products, the contents of 13 components in the wine-fried products increased, and the contents of 4 components decreased. The increasing trend of the content of phthalides in the wine-washed products was more obvious than that in the wine-fried products, but the content of total volatile components was higher in the wine-fried products than the wine-washed products. Correlation analysis showed that there were different degrees of correlation between the 7 differential sensors of electronic nose and 24 differential volatile components, mainly phthalides and olefins. ConclusionThe odor and the content of volatile components in LSCR changed obviously after wine processing, and n-butylphthalide, Z-butylidenephthalide and E-ligustilide can be used as the candidate differential markers of volatile components in LSCR before and after wine processing.
2.Spatiotemporal distribution of newly diagnosed echinococcosis patients in Qinghai Province from 2016 to 2022
Xinlu CUI ; Xiao MA ; Na LIU ; Jia LIU ; Wen LEI ; Shusheng WU ; Xianglan QIN ; Chunhua GONG ; Xiaojin MO ; Shijie YANG ; Ting ZHANG ; Li CAO
Chinese Journal of Schistosomiasis Control 2024;36(5):474-480
Objective To investigate the spatiotemporal distribution characteristics and potential influencing factors of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022, so as to provide insights into the formulation of the echinococcosis control strategy in Qinghai Province. Methods The number of individuals screened for echinococcosis, number of newly diagnosed echinococcosis cases, number of registered dogs and number of stray dogs were captured from the annual reports of echinococcosis control program in Qinghai Province from 2016 to 2022, and the detection of newly diagnosed echinococcosis cases was calculated. The number of populations, precipitation, temperature, wind speed, sunshine hours, average altitude, number of year-end cattle stock, number of year-end sheep stock, gross domestic product (GDP) per capita, and number of village health centers in each county (district) of Qinghai Province were captured from the Qinghai Provincial Statistical Yearbook, and county-level electronic maps in Qinghai Province were downloaded from the National Platform for Common Geospatial Information Services. The software ArcGIS 10.8 was used to map the distribution of newly diagnosed echinococcosis cases in Qinghai Province, and the spatial autocorrelation analysis of newly diagnosed echinococcosis cases was performed. In addition, the spacetime scan analyses of number of individuals screened for echinococcosis, number of newly diagnosed echinococcosis cases and geographical coordinates in Qinghai Province were performed with the software SaTScan 10.1.2, and the spatial stratified heterogeneity of the detection of newly diagnosed echinococcosis cases was investigated with the software GeoDetector. Results A total of 6 569 426 residents were screened for echinococcosis in Qinghai Province from 2016 to 2022, and 5 924 newly diagnosed echinococcosis cases were found. The detection of newly diagnosed echinococcosis cases appeared a tendency towards a decline over years from 2016 to 2022 (χ2 = 11.107, P < 0.01), with the highest detection in Guoluo Tibetan Autonomous Prefecture in 2017 (82.12/105). There were spatial clusters in the detection of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2018 (Moran’s I = 0.34 to 0.65, all Z values > 1.96, all P values < 0.05), and the distribution of newly diagnosed echinococcosis cases appeared random distribution from 2019 to 2022 (Moran’s I = −0.09 to 0.04, all Z values < 1.96, all P values > 0.05). Local spatial autocorrelation analysis showed high-high clusters and low-low clusters in the detection of new diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022, and space-time scan analysis showed that the first most likely cluster areas of newly diagnosed echinococcosis cases in Qinghai Province from 2016 to 2022 were mainly distributed in Yushu Tibetan Autonomous Prefecture and Guoluo Tibetan Autonomous Prefecture. GeoDetector-based analysis of the driving factors for the spatial stratified heterogeneity of detection of newly diagnosed echinococcosis cases in Qinghai Province showed that average altitude, number of village health centers, number of cattle and sheep stock, GDP per capita, annual average sunshine hours, and annual average temperature had a strong explanatory power for the spatial distribution of newly diagnosed echinococcosis cases, with q values of 0.630, 0.610, 0.600, 0.590, 0.588, 0.537 and 0.526, respectively. Conclusions The detection of newly diagnosed echinococcosis cases appeared a tendency towards a decline in Qinghai Province over years from 2016 to 2022, showing spatial clustering. Targeted control measures are required in cluster areas of newly diagnosed echinococcosis cases for further control of the disease.
3.Correlation analysis between eNOS gene single nucleotide polymorphism and systemic lupus erythematosus in Hainan
Xuan ZHANG ; Hui-Tao WU ; Qi ZHANG ; Gui-Ling LIN ; Xi-Yu YIN ; Wen-Lu XU ; Zhe WANG ; Zi-Man HE ; Ying LIU ; Long MI ; Yan-Ping ZHUANG ; Ai-Min GONG
Medical Journal of Chinese People's Liberation Army 2024;49(9):986-991
Objective To investigate the relationship between single nucleotide polymorphisms(SNPs)in the eNOS gene and genetic susceptibility to systemic lupus erythematosus(SLE)in Hainan.Methods Blood samples were collected from SLE patients(SLE group,n=214)and healthy controls(control group,n=214)from January 2020 to December 2022 at the First Affiliated Hospital of Hainan Medical College and Hainan Provincial People's Hospital.The bases of eNOS gene rs3918188,rs1799983 and rs1007311 loci in each group were detected by SNaPshot sequencing technology.Logistic regression was used to analyze the correlation between genotypes,alleles and gene models(dominant model,recessive model,and overdominant model)of the above 3 target loci of the eNOS gene and genetic susceptibility to SLE.Haplotype analysis was conducted using HaploView 4.2 software to investigate the relationship between haploid and genetic susceptibility to SLE at each site.Results The results of logistic regression analysis revealed that the CC genotype and the C allele at rs3918188 locus were risk factors for genetic susceptibility to SLE(CC vs.AA:OR=2.449,P<0.05;C vs.A:OR=2.133,P<0.001).In recessive model at rs3918188 locus,CC genotype carriers had an increased risk of SLE development compared with AA+AC genotype carriers(OR=2.774,P<0.001).In contrast,in overdominant model at this locus,AC genotype carriers had a decreased risk of SLE occurrence compared with AA+CC genotype carriers(OR=0.385,P<0.001).In addition,polymorphisms of rs1799983 and rs1007311 were not associated with susceptibility to SLE in genotype,allele type and the 3 genetic models(P>0.05).Haplotype analysis revealed a strong linkage disequilibrium between the rs1007311 and rs1799983 loci of the eNOS gene,but no significant correlation was found between haplotype and genetic susceptibility to SLE(P>0.05).Conclusion The CC genotype and C allele at rs3918188 locus of eNOS gene may be risk factors for SLE in Hainan,while the risk of SLE occurrence is reduced in carriers of AC genotype under the overdominant model.
4.The role of NLRP3 signaling pathway in allergic rhinoconjunctivitis
Yubo GONG ; Xiaohua GUO ; Wen-Jun LU ; Yuanchao LI ; Changyu QIU ; Yuanyuan SHI ; Liping XIA ; Lin SHI ; Wei WU ; Ling LUO
The Journal of Practical Medicine 2024;40(14):1922-1927
Objective The objective of this study was to establish a mouse model of allergic rhinoconjunctivitis and investigate the role of the NLRP3 signaling pathway in allergic rhinoconjunctivitis.Methods Thirty-three female C57 mice(SPF)were randomLy divided into 3 groups:the control group,the experimental group,and the NLRP3-/-group.On days 0,4,7,14,and 21,the experimental group and NLRP3-/-group received a 0.2 mL intraperitoneal injection of medicine containing OVA(100 μg)and adjuvant Al(OH)3(4 mg),respectively.After an interval of 3 days,each eye and nose were dosed with 10 μL of 5%OVA for five consecutive days a week to induce allergic symptoms.During sensitization and excitation stages,the control group was replaced with an equiva-lent amount of PBS.Ocular and nasal symptoms were observed and scored.The levels of OVA-specific IgE,IL-4,IL-17,and IL-18 in serum were measured using ELISA,while changes in palpebral conjunctiva and nasal mucosa were assessed by hematoxylin-eosin staining.The expression of NLRP3 mRNA in conjunctival tissue and nasal mucosa was determined using real-time PCR analysis.Statistical analysis was performed using SPSS17.0 software with P<0.05 considered as statistically significant difference.Results The experimental group and NLRP3-/-group exhibited induced nasal and ocular allergic symptoms.In the experimental group,the duration of nasal allergy symptoms was(10.500±1.080)days,while the duration of eye allergy symptoms was(20.300±2.058)days.In the NLRP3-/-group,the duration of nasal allergy symptoms was(13.400±1.955)days,and for eye allergy symp-toms it was(20.900±2.132)days.The duration of nasal allergies in the NLRP3-/-group significantly exceeded that in the experimental group(P<0.05),whereas there were no significant differences observed in eye allergy durations between these two groups(P>0.05).Levels of OVA-specific IgE,IL-4,and IL-17 were significantly higher in both the experimental and NLRP3-/-groups compared to those in the control group(P<0.05).Additionally,serum IL-18 content increased significantly in the experimental group when compared with both control and NLRP3-/-groups(P<0.05).Conjunctival tissue lesions as well as nasal mucosa damage were evident in both experimental and NLRP3-/-groups.mRNA expression levels of NLRP3 within conjunctival tissue and nasal mucosa from the experimental group showed a significant increase when compared to those from both control and NLRP3-/-groups(P<0.05).Conclusion Allergic rhinoconjunctivitis pathogenesis is influenced by various factors;however,the involvement of NLPR3 signaling pathway promotes its development.
5.Efficacy evaluation of extending or switching to tenofovir amibufenamide in patients with chronic hepatitis B: a phase Ⅲ randomized controlled study
Zhihong LIU ; Qinglong JIN ; Yuexin ZHANG ; Guozhong GONG ; Guicheng WU ; Lvfeng YAO ; Xiaofeng WEN ; Zhiliang GAO ; Yan HUANG ; Daokun YANG ; Enqiang CHEN ; Qing MAO ; Shide LIN ; Jia SHANG ; Huanyu GONG ; Lihua ZHONG ; Huafa YIN ; Fengmei WANG ; Peng HU ; Xiaoqing ZHANG ; Qunjie GAO ; Chaonan JIN ; Chuan LI ; Junqi NIU ; Jinlin HOU
Chinese Journal of Hepatology 2024;32(10):883-892
Objective:In chronic hepatitis B (CHB) patients with previous 96-week treatment with tenofovir amibufenamide (TMF) or tenofovir disoproxil fumarate (TDF), we investigated the efficacy of sequential TMF treatment from 96 to 144 weeks.Methods:Enrolled subjects who were previously assigned (2:1) to receive either 25 mg TMF or 300 mg TDF with matching placebo for 96 weeks received extended or switched TMF treatment for 48 weeks. Efficacy was evaluated based on virological, serological, biological parameters, and fibrosis staging. Statistical analysis was performed using the McNemar test, t-test, or Log-Rank test according to the data. Results:593 subjects from the initial TMF group and 287 subjects from the TDF group were included at week 144, with the proportions of HBV DNA<20 IU/ml at week 144 being 86.2% and 83.3%, respectively, and 78.1% and 73.8% in patients with baseline HBV DNA levels ≥8 log10 IU/ml. Resistance to tenofovir was not detected in both groups. For HBeAg loss and seroconversion rates, both groups showed a further increase from week 96 to 144 and the 3-year cumulative rates of HBeAg loss were about 35% in each group. However, HBsAg levels were less affected during 96 to 144 weeks. For patients switched from TDF to TMF, a substantial further increase in the alanine aminotransferase (ALT) normalization rate was observed (11.4%), along with improved FIB-4 scores.Conclusion:After 144 weeks of TMF treatment, CHB patients achieved high rates of virological, serological, and biochemical responses, as well as improved liver fibrosis outcomes. Also, switching to TMF resulted in significant benefits in ALT normalization rates (NCT03903796).
6.Safety profile of tenofovir amibufenamide therapy extension or switching in patients with chronic hepatitis B: a phase Ⅲ multicenter, randomized controlled trial
Zhihong LIU ; Qinglong JIN ; Yuexin ZHANG ; Guozhong GONG ; Guicheng WU ; Lvfeng YAO ; Xiaofeng WEN ; Zhiliang GAO ; Yan HUANG ; Daokun YANG ; Enqiang CHEN ; Qing MAO ; Shide LIN ; Jia SHANG ; Huanyu GONG ; Lihua ZHONG ; Huafa YIN ; Fengmei WANG ; Peng HU ; Xiaoqing ZHANG ; Qunjie GAO ; Peng XIA ; Chuan LI ; Junqi NIU ; Jinlin HOU
Chinese Journal of Hepatology 2024;32(10):893-903
Objective:In chronic hepatitis B (CHB) patients with previous 96-week treatment with tenofovir amibufenamide (TMF) or tenofovir disoproxil fumarate (TDF), we investigated the safety profile of sequential TMF treatment from 96 to 144 weeks.Methods:Enrolled subjects that previously assigned (2:1) to receive either 25 mg TMF or 300 mg TDF with matching placebo for 96 weeks received extending or switching TMF treatment for 48 weeks. Safety profiles of kidney, bone, metabolism, body weight, and others were evaluated.Results:666 subjects from the initial TMF group and 336 subjects from TDF group with at least one dose of assigned treatment were included at week 144. The overall safety profile was favorable in each group and generally similar between extended or switched TMF treatments from week 96 to 144. In subjects switching from TDF to TMF, the non-indexed estimated glomerular filtration rate (by non-indexed CKD-EPI formula) and creatinine clearance (by Cockcroft-Gault formula) were both increased, which were (2.31±8.33) ml/min and (4.24±13.94) ml/min, respectively. These changes were also higher than those in subjects with extending TMF treatment [(0.91±8.06) ml/min and (1.30±13.94) ml/min]. Meanwhile, switching to TMF also led to an increase of the bone mineral density (BMD) by 0.75% in hip and 1.41% in spine. On the other side, a slight change in TC/HDL ratio by 0.16 (IQR: 0.00, 0.43) and an increase in body mass index (BMI) by (0.54±0.98) kg/m 2 were oberved with patients switched to TMF, which were significantly higher than that in TMF group. Conclusion:CHB patients receiving 144 weeks of TMF treatment showed favorable safety profile. After switching to TMF, the bone and renal safety was significantly improved in TDF group, though experienceing change in metabolic parameters and weight gain (NCT03903796).
7.Formulation and Analysis on the Standard of Construction of Medication Safety Culture
Wenjing HOU ; Su SHEN ; Aiping WEN ; Jin LU ; Jiancun ZHEN ; Wei ZHANG ; Dan MEI ; Zhicheng GONG ; Yubo WU ; Qunhong SHEN ; Weiyi FENG ; Ling TAN ; Yanhua ZHANG ; Fang LIU ; Xiaole ZHANG
Herald of Medicine 2024;43(7):1079-1083
The construction of a medication safety culture is important for medication safety management and rational drug use.The construction of medication safety culture standards is formulated based on relevant national policies and regulations,accreditation standards for hospitals,expert opinions,the current situation,and the development trend of the healthcare industry.With scientificity,general applicability,instructive guidance,and practicality,they standardized basic requirements,management processes,and improvement of the construction of medication safety culture.To facilitate understanding and the implementation of the standards,we describe the process of standards formulation and explain the key points of the standards.
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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