1.Immune microenvironment regulates bone regeneration
Hu YANG ; Yu ZHENG ; Chengming JIA ; Tong WANG ; Guangfei ZHANG ; Yaoyao JI
Chinese Journal of Tissue Engineering Research 2026;30(3):701-710
BACKGROUND:The local immune microenvironment plays an important regulatory role in the process of bone formation,and the immune system is intricately linked to the skeletal system.OBJECTIVE:To systematically review the promotion of bone regeneration from three aspects:immune cell regulation of microenvironment,regulation of immune response by small extracellular vesicles,and induction of immune response by bone biomaterials,and to elucidate the immune regulatory mechanisms involved in bone regeneration.METHODS:Relevant literature was retrieved from PubMed,CNKI,WanFang Database,and VIP Database,using the search terms of"osteoimmunology,immune microenvironment,small extracellular vesicles,bone regeneration,bone tissue repair,biomaterials,and tissue engineering"in English and Chinese.Repeat and irrelevant literature was screened and removed,and 92 articles that met the criteria were selected for intensive reading and review.RESULTS AND CONCLUSION:Multiple immune cells and bone cells are in the same microenvironment,and immune cells can regulate the differentiation and activity of bone cells,collectively forming an immune microenvironment that affects bone regeneration.Neutrophils can significantly reduce local inflammatory responses in the early stages of bone injury,creating a favorable microenvironment for bone regeneration.M1 macrophages can clear foreign bodies and reduce early inflammatory responses,while M2 macrophages can promote the expression of osteogenic markers and factors,playing an important role in the repair process of bone injury.B cells and T cells can directly or indirectly affect the generation and activity of osteoblasts and osteoclasts,regulate bone metabolism,and promote bone regeneration.Extracellular vesicles of small cells regulate the local immune microenvironment through paracrine secretion,promoting bone formation and angiogenesis at the site of bone injury.The metal ions,surface hydrophilicity,porosity,pore size,surface morphology,and surface roughness on the surface of biomaterials can directly regulate local immune responses,and have anti-inflammatory,angiogenic,and osteogenic effects,thereby accelerating bone regeneration.
2.Immune microenvironment regulates bone regeneration
Hu YANG ; Yu ZHENG ; Chengming JIA ; Tong WANG ; Guangfei ZHANG ; Yaoyao JI
Chinese Journal of Tissue Engineering Research 2026;30(3):701-710
BACKGROUND:The local immune microenvironment plays an important regulatory role in the process of bone formation,and the immune system is intricately linked to the skeletal system.OBJECTIVE:To systematically review the promotion of bone regeneration from three aspects:immune cell regulation of microenvironment,regulation of immune response by small extracellular vesicles,and induction of immune response by bone biomaterials,and to elucidate the immune regulatory mechanisms involved in bone regeneration.METHODS:Relevant literature was retrieved from PubMed,CNKI,WanFang Database,and VIP Database,using the search terms of"osteoimmunology,immune microenvironment,small extracellular vesicles,bone regeneration,bone tissue repair,biomaterials,and tissue engineering"in English and Chinese.Repeat and irrelevant literature was screened and removed,and 92 articles that met the criteria were selected for intensive reading and review.RESULTS AND CONCLUSION:Multiple immune cells and bone cells are in the same microenvironment,and immune cells can regulate the differentiation and activity of bone cells,collectively forming an immune microenvironment that affects bone regeneration.Neutrophils can significantly reduce local inflammatory responses in the early stages of bone injury,creating a favorable microenvironment for bone regeneration.M1 macrophages can clear foreign bodies and reduce early inflammatory responses,while M2 macrophages can promote the expression of osteogenic markers and factors,playing an important role in the repair process of bone injury.B cells and T cells can directly or indirectly affect the generation and activity of osteoblasts and osteoclasts,regulate bone metabolism,and promote bone regeneration.Extracellular vesicles of small cells regulate the local immune microenvironment through paracrine secretion,promoting bone formation and angiogenesis at the site of bone injury.The metal ions,surface hydrophilicity,porosity,pore size,surface morphology,and surface roughness on the surface of biomaterials can directly regulate local immune responses,and have anti-inflammatory,angiogenic,and osteogenic effects,thereby accelerating bone regeneration.
3.Pre-operative risk assessment of hepatocellular carcinoma recurrence in liver transplant recipients by non-invasive detection of pre-existing genetic lesions
Suqin YANG ; Sunbin LING ; Jianhua LI ; Yan WANG ; Jiapei WANG ; Qiwei HUANG ; Fanming LIU ; Yiqi ZHUANG ; Yingyu ZHENG ; Rui WANG ; Zhe YANG ; Xiaoping ZHENG ; Kai WANG ; Zhikun LIU ; Jun CHEN ; Jianguo WANG ; Haiyang XIE ; Lin ZHOU ; Leiming CHEN ; Guoqiang CAO ; Dandan CHEN ; Junfang JI ; Bin ZHAO ; Chao JIANG ; Di LU ; Xuyong WEI ; Hangjin JIANG ; Qiaonan SHAN ; Hengbo SHI ; Yong-Zhen XU ; Shusen ZHENG ; Zhengxin WANG ; Shengda LIN ; Xiao XU
Clinical and Molecular Hepatology 2026;32(2):884-903
Background/Aims:
Liver transplantation (LT) following total hepatectomy is a life-saving treatment for hepatocellular carcinoma (HCC). The HCC recurrence after LT hinders the effectiveness of the procedure. The objective of this study is to develop a pre-operative risk stratification model based on a liquid biopsy.
Methods:
We conducted a comprehensive multi-omics study of 260 HCC patients from three centers, including clinical data, low-coverage whole-genome sequencing of cell-free DNA (cfDNA) from plasma, as well as whole-exome, single-nucleus RNA, and spatial transcriptomics from matched tumor and non-tumor tissues.
Results:
We identified cfDNA-derived copy number alteration (CNA) signatures associated with post-transplant recurrence. By integrating cfDNA-derived CNA profiles with single-cell transcriptomic data, we traced recurrence-associated cfDNA to a distinct subpopulation of malignant cells within the primary tumor. These cells were embedded in a pro-metastatic microenvironment of specialized endothelial subtypes and cancer-associated fibroblasts. Notably, most recurrence-associated lesions were detectable in cfDNA prior to liver transplantation (LT). Building on these insights, we developed the ZJU Criteria based on CNA fragments and tumor markers, a pre-LT risk prediction tool that integrates conventional clinical factors with cfDNA-derived CNA signatures, and validated it using internal and independent external cohorts.
Conclusion
Our findings suggest that post-transplant recurrence commonly originates from advanced subclones that emerge late during tumor evolution. The ZJU Criteria provides an accurate, non-invasive strategy that significantly improves pre-LT risk stratification and clinical decision-making for patients with HCC.
4.Effect of luteinising hormone levels on the outcome of in vitro fertilization and embryo transfer in an infertile population with normal ovarian reserve
Qi ZHENG ; Xun SU ; Jing YANG ; Dongmei JI
Acta Universitatis Medicinalis Anhui 2026;61(5):888-893
ObjectiveTo investigate the effects of luteinizing hormone (LH) levels on in vitro fertilization and embryo transfer (IVF-ET) outcomes in an infertile population with normal ovarian reserve (NOR) undergoing controlled ovarian hyperstimulation (COH) with gonadotropin-releasing hormone antagonist protocol, and to explore the reasonable level intervals of LH during the follicular phase. MethodsA retrospective analysis was conducted on 911 NOR infertility patients undergoing IVF-ET treated with gonadotropin-releasing hormone antagonist protocol. Patients were grouped based on baseline LH (bLH) levels, LH levels on the day of antagonist addition, and LH levels on the day of human chorionic gonadotropin (hCG) injection. According to bLH levels, patients were divided into Group A2 (n=391) and Group B2 (n=391). Based on LH levels on the day of antagonist addition, patients were divided into Group A3 (n=600) and Group B3 (n=311). According to LH levels on the day of hCG injection, patients were classified into Group A4 (n=225), Group B4 (n=227), Group C4 (n=231), and Group D4 (n=228). The clinical characteristics, COH outcomes, and pregnancy outcomes of patients in each group were compared. Results① The bLH level could be used as a predictor of assisted reproduction outcomes in an infertile population with NOR, and going for assisted reproduction at bLH≥4.51 IU/L was more favorable for the development of pregnancy outcomes; ② Addition of antagonist at LH≥5 IU/L on the day of antagonist addition improved pregnancy outcome ;③ On the day of hCG injection, there was no statistically significant difference in pregnancy outcomes among the four groups; ④ Analyses using generalized linear models revealed that basal follicle-stimulating hormone (bFSH), basal estradiol (bE2), Gn days, Gn volume and 2PN affected the number of good quality embryos. ConclusionIn an infertile population with NOR using gonadotropin-releasing hormone antagonist, bLH and gonadotropin-releasing hormone antagonist addition day LH levels may influence IVF outcomes. LH levels should be controlled at 5 IU/L and above on antagonist addition days, and bLH levels should be controlled at 4.51 IU/L and above.
5.Action mechanism of Gegenmaqi prescription in treatment of periarthritis of shoulder combined with type 2 diabetes based on TCMSP database
Tong WANG ; Yu ZHENG ; Chengming JIA ; Hu YANG ; Guangfei ZHANG ; Yaoyao JI
Chinese Journal of Tissue Engineering Research 2025;29(35):7669-7678
BACKGROUND:Gegenmaqi prescription has a good effect on periarthritis of shoulder combined with type 2 diabetes and has a good application prospect,but the specific mechanism is not clear.OBJECTIVE:To explore the action mechanism of Gegenmaqi prescription on periarthritis of shoulder and type 2 diabetes by network pharmacology,molecular docking,and molecular dynamics.METHODS:The active components and protein targets of Gegenmaqi prescription were retrieved from the Traditional Chinese Medicine System Pharmacology database and analysis platform,referred to as TCMSP jointly established by the Shanghai Institute of Materia Medica,Chinese Academy of Sciences and the Institute of Chinese Materia Medica,and China Academy of Chinese Medical Sciences in 2013.Genecards created by Professor Doron Lancet's team at the Weizmann Institute of Science in Israel in 1997,Drugbank created by scientists at the University of Alberta in Canada in 2006,and the OMIM database established by Dr.Victor A.McKusick's team at Johns Hopkins University in the United States in 1966 were used to search the disease protein targets of periarthritis of shoulder and type 2 diabetes,and the intersection targets were obtained based on the WeChat online tool.The protein-protein interaction network was constructed based on the STRING database created in 2000 by Peer Bork's team at the European Bioinformatics Institute(EMBL),and the protein-protein interaction relationship was analyzed.The core targets were screened according to the degree value.The intersection targets were subjected to GO and KEGG enrichment analyses.Finally,molecular docking and molecular dynamics simulation were used to verify the binding of key components to key targets.RESULTS AND CONCLUSION:(1)One hundred and forty-two active ingredients of Gegenmaqi prescription were obtained,including 65 intersections between component targets and disease targets,5 key active ingredients(β-sitosterol,stigmasterol,kaempferol,quercetin,and formononetin),and 5 key targets(AKT1,tumor necrosis factor,interleukin-10,JUN,and TP53).(2)GO function enrichment included 508 items,390 biological process items,77 molecular function items and 41 cell component items.KEGG pathway analysis showed 146 pathways,mainly involving advanced glycation end products receptor signaling pathway,lipid and atherosclerosis signaling pathway,tumor necrosis factor signaling pathway,and interleukin-17 signaling pathway.(3)Molecular docking showed that the key components and key targets had good binding activity.Molecular dynamics simulation showed that β-sitosterol had stable interactions with AKT1,tumor necrosis factor and interleukin 10.(4)Gegenmaqi prescription has been comprehensively studied,and the material basis of its pharmacological effect has been primarily clarified.It is predicted that Gegenmaqi prescription can treat periarthritis of shoulder combined with type 2 diabetes through multi-components,multi-targets,and multi-pathways to exert anti-inflammatory and regulate insulin secretion.
6.Analysis on risk factors of adverse events after non-sedated esophagogastroduo-denoscopy
Shuyue YANG ; Sifan LIU ; Xu JI ; Mengran ZHAO ; Zheng ZHANG ; Peng LI
Journal of Capital Medical University 2025;46(4):676-681
Objective To investigate the risk factor for adverse events(AEs)after non-sedated esophagogastroduodenoscopy(EGD).Methods The data on clinical manifestations,adverse events after non-sedated EGD and common risk factors were collected and retrospectively analyzed with statistical methods in patients who underwent non-sedated EGD from May 2018 to June 2019.These patients were divided into AEs group and non-AEs group.Results Of 2 384 patients,57.67%(1 375/2 384)presented with nausea,12.79%(305/2 384)vomiting,and 5.79%(138/2 384)presented with pharyngalgia.Multivariate Logistic regression analysis was performed.Advanced age(≥65 years old)(OR=0.683,95%CI:0.506-0.921)was protective factors for nausea after non-sedated EGD.Hypertension(OR=1.361,95%CI:1.026-1.806),overweight(OR=1.399,95%CI:1.154-1.695),obesity(OR=2.594,95%CI:1.760-3.823)and inspection duration>15 min(OR=3.107,95%CI:2.296-4.206)were independent risk factors for nausea after non-sedated EGD.Advanced age(OR=0.393,95%CI:0.221-0.699)and imported equipment(OR=0.697,95%CI:0.546-0.890)were protective factors for vomiting after non-sedated EGD.Moreover,inspection duration>15 min(OR=1.641,95%CI:1.008-2.699)was independent risk factors for vomiting after non-sedated EGD.There was no difference in success rate of non-sedated EGD between two groups(P<0.05).Conclusion Hypertension,overweight and obesity were independent risk factors for nausea after non-sedated EGD.The advanced age and imported equipment were protective factors for vomiting after non-sedated EGD.In addition,inspection duration over 15 min is a risk factor for AEs such as nausea and vomiting after nonsedative EGD.Whether AEs occurred or not is non-related to success rate of non-sedated EGD.
7.Research on the role of gastric microbiome in the progression of gastric cancer
Sifan LIU ; Shuyue YANG ; Xu JI ; Zheng ZHANG ; Peng LI
Journal of Capital Medical University 2025;46(4):682-687
Gastric cancer is a common clinical tumor,and its incidence and mortality rates rank among the top of malignant tumors.Helicobacter pylori(Hp)is an important carcinogenic factor for gastric cancer.Studies have found that in addition to Hp,other microorganisms in the stomach also play a key role in the progression of gastric cancer.The composition and diversity of the gastric microbiota vary significantly under different gastric disease conditions,which may affect the progression of gastric cancer.Hp can induce gastric epithelial inflammation and oxidative stress through various virulence factors,thereby promoting the occurrence of gastric cancer.Non-Hp microorganisms can be involved in the process of gastric cancer through pathways such as metabolic changes and immune regulation.In recent years,with the development of high-throughput sequencing technology,the complexity of gastric microbiota has gradually been revealed,which provides new insights for the early warning and intervention of gastric cancer.This article comprehensively review the role of gastric microbiota in the progression of gastric cancer.
8.Changing resistance profiles of Haemophilus influenzae and Moraxella catarrhalis isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Hui FAN ; Chunhong SHAO ; Jia WANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Yunsheng CHEN ; Qing MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Wenqi SONG ; Kaizhen WEN ; Yirong ZHANG ; Chuanqing WANG ; Pan FU ; Chao ZHUO ; Danhong SU ; Jiangwei KE ; Shuping ZHOU ; Hua ZHANG ; Fangfang HU ; Mei KANG ; Chao HE ; Hua YU ; Xiangning HUANG ; Yingchun XU ; Xiaojiang ZHANG ; Wenen LIU ; Yanming LI ; Lei ZHU ; Jinhua MENG ; Shifu WANG ; Bin SHAN ; Yan DU ; Wei JIA ; Gang LI ; Jiao FENG ; Ping GONG ; Miao SONG ; Lianhua WEI ; Xin WANG ; Ruizhong WANG ; Hua FANG ; Sufang GUO ; Yanyan WANG ; Dawen GUO ; Jinying ZHAO ; Lixia ZHANG ; Juan MA ; Han SHEN ; Wanqing ZHOU ; Ruyi GUO ; Yan ZHU ; Jinsong WU ; Yuemei LU ; Yuxing NI ; Jingrong SUN ; Xiaobo MA ; Yanqing ZHENG ; Yunsong YU ; Jie LIN ; Ziyong SUN ; Zhongju CHEN ; Zhidong HU ; Jin LI ; Fengbo ZHANG ; Ping JI ; Yunjian HU ; Xiaoman AI ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Xuesong XU ; Chao YAN ; Yi LI ; Shanmei WANG ; Hongqin GU ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Jihong LI ; Bixia YU ; Cunshan KOU ; Jilu SHEN ; Wenhui HUANG ; Xiuli YANG ; Likang ZHU ; Lin JIANG ; Wen HE ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2025;25(1):30-38
Objective To investigate the distribution and antimicrobial resistance profiles of clinically isolated Haemophilus influenzae and Moraxella catarrhalis in hospitals across China from 2015 to 2021,and provide evidence for rational use of antimicrobial agents.Methods Data of H.influenzae and M.catarrhalis strains isolated from 2015 to 2021 in CHINET program were collected for analysis,and antimicrobial susceptibility testing was performed by disc diffusion method or automated systems according to the uniform protocol of CHINET.The results were interpreted according to the CLSI breakpoints in 2022.Beta-lactamases was detected by using nitrocefin disk.Results From 2015 to 2021,a total of 43 642 strains of Haemophilus species were isolated,accounting for 2.91%of the total clinical isolates and 4.07%of Gram-negative bacteria in CHINET program.Among the 40 437 strains of H.influenzae,66.89%were isolated from children and 33.11%were isolated from adults.More than 90%of the H.influenzae strains were isolated from respiratory tract specimens.The prevalence of β-lactamase was 53.79%in H.influenzae strains.The H.influenzae strains isolated from children showed higher resistance rate than the strains isolated from adults.Overall,779 strains of H.influenzae did not produce β-lactamase but were resistant to ampicillin(BLNAR).Beta-lactamase-producing strains showed significantly higher resistance rates to these antimicrobial agents than the β-lactamase-nonproducing strains.Of the 16 191 M.catarrhalis strains,80.06%were isolated from children and 19.94%isolated from adults.M.catarrhalis strains were mostly susceptible to both amoxicillin-clavulanic acid and cefuroxime,evidenced by resistance rate lower than 2.0%.Conclusions The emergence of antibiotic-resistant H.influenzae due to β-lactamase production poses a challenge for clinical anti-infective treatment.Therefore,it is very important to implement antibiotic resistance surveillance for H.influenzae and guide rational antibiotic use.All local clinical microbiology laboratories should actively improve antibiotic susceptibility testing and strengthen antibiotic resistance surveillance for H.influenzae.
9.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.
10.Surveillance of antimicrobial resistance in clinical isolates of Escherichia coli:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shanmei WANG ; Bing MA ; Yi LI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Aimin WANG ; 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 ; 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 ; Wenhui HUANG ; 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 2025;25(1):39-47
Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)%.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0%vs 29.5%).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7%vs 7.8%,0.8%vs 0.1%,both P<0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)%with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9%and 1.8%,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1%).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10%.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P<0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.

Result Analysis
Print
Save
E-mail