1.Etiological surveillance for influenza-like illness cases in Jiangsu Province
SHI Chunlei ; DAI Qigang ; DONG Yanhui ; LIU Dongsheng ; ZHOU Shengnan
Journal of Preventive Medicine 2026;38(2):109-114
Objective:
To analyze the etiological surveillance results of influenza-like illness (ILI) cases in Jiangsu Province, and investigate the distribution characteristics of different influenza virus types, so as to provide the evidence for improving influenza prevention and control measures.
Methods:
Influenza laboratory testing data for sentinel surveillance of ILI cases in Jiangsu Province from 2019 to 2024 were collected through the China Influenza Surveillance Information System. The positive detection rate of influenza virus was calculated, and descriptive analysis was performed to characterize the distribution of different influenza virus types. Using the farthest neighbor linkage method, influenza virus positive detection rates clustering was analyzed by year and week. Clusters were defined based on inter-cluster distance, and the intensity of the positive detection rate was visualized through color gradients in the clustering heatmap.
Results:
From 2019 to 2024, a total of 183 878 ILI specimens were collected in Jiangsu Province. Among them, 20 059 specimens tested positive for influenza virus, corresponding to an overall positive detection rate of 10.91%, and an average annual positive detection rate of 10.89%. The primary circulating influenza virus types were influenza A H3N2 subtype, accounting for 40.92%, followed by influenza B Victoria linage at 34.00%, and influenza A H1N1 subtype at 24.80%. Influenza B Yamagata linage was not detected throughout the five-year period. Influenza A H3N2 subtype predominated during two distinct periods: from January to March 2019, and from June 2022 to December 2023. Influenz B Victoria linage was the dominant type from April 2019 to May 2022 and again from January to April 2024. Influenza A H1N1 subtype emerged as the primary type from May to December 2024. Year-based clustering analysis grouped the annual positive detection rates from 2019 to 2024 into three clusters. The closest cluster distance was observed between 2019 and 2024. The highest annual positive detection rate occurred in 2023. Both influenza A H3N2 and H1N1 subtype each formed a single cluster, with their peak positive detection rates also recorded in 2023. Influenza B Victoria lineage was separated into two clusters, with its highest positive detection rate occurring in 2020. Week-based clustering analysis revealed that influenza virus detection was concentrated in weeks 47 to 52 and weeks 1 to 15. More specifically, the positive detection rates for influenza A H3N2 subtype peaked during weeks 30 to 34 and weeks 42 to 52; for influenza A H1N1 subtype, during weeks 9 to 15 and weeks 51 to 52; and for influenza B Victoria lineage, during weeks 1 to 11 and weeks 50 to 52.
Conclusions
From 2019 to 2024, the average annual positive detection rate of influenza virus in Jiangsu Province remained relatively low. Influenza activity characterized by the alternating circulation of influenza A H1N1 subtype, influenza A H3N2 subtype, and influenza B Victoria linage. It is necessary to maintain the surveillance sensitivity for the influenza B Yamagata lineage.
2.Exploration of Training System for Visiting Physicians in Department of Rare Diseases
Jiayuan DAI ; Jing XIE ; Jingjing CHAI ; Yueying MAO ; Chunlei LI ; Yaping LIU ; Jin XU ; Min SHEN ; Shuyang ZHANG
JOURNAL OF RARE DISEASES 2026;5(1):112-116
The construction of a training system for visiting physicians in the department of rare diseases in China is an important measure to improve the overall diagnosis and treatment capacity for rare diseases and address the critical challenge of insufficient knowledge and skills among clinicians in practice. This article systematically describes the visiting physician training system established by the Department of Rare Diseases at Peking Union Medical College Hospital. It summarizes the training objectives and positioning, design logic, and learning modules of the system, aiming to provide a reference for the construction of the specialized talent team for rare diseases in China.
3.Gene-predicted associations between 731 immune cell phenotypes and rheumatoid arthritis
Fengzhi LIU ; Yuna DONG ; Wenyi TIAN ; Chunlei WANG ; Xiaodong LIANG ; Lin BAO
Chinese Journal of Tissue Engineering Research 2026;30(5):1311-1319
BACKGROUND:Rheumatoid arthritis is widely prevalent worldwide,with its high incidence and universality that considerably affects patients' quality of life.Previous studies have focused on a few immune cells or cytokines,whereas this study comprehensively provides a more complete view of the immune mechanisms in rheumatoid arthritis.OBJECTIVE:To explore the causal relationship between 731 immune cell phenotypes and rheumatoid arthritis using the Mendelian randomization method,thereby providing evidence of causality.METHODS:The 731 immune cell phenotypes used in this study were sourced from the GWAScatalog database,jointly developed by the National Human Genome Research Institute(NHGRI)and the European Bioinformatics Institute(EBI).The rheumatoid arthritis data were from the Finngen database,developed by the Finnish Institute for Molecular Medicine(FIMM).The inverse variance weighting method was employed as the primary analytical approach.Additionally,multiple analytical methods,including MR-Egger,weighted mode,simple mode,and weighted median,were concurrently utilized to complement the final results.Sensitivity analyses(Cochran's Q test,MR-Egger regression,and MR-presso analysis)were also conducted to verify the stability and feasibility of the data.RESULTS AND CONCLUSION:(1)After excluding results through heterogeneity testing,the inverse variance weighting analysis indicated that 10 absolute cell counts,15 median fluorescence intensities of surface antigen levels,1 morphological characteristic,and 9 relative cell counts had a causal relationship with the occurrence of rheumatoid arthritis.(2)According to cell classification,this study found that seven types of B cells,seven types of classical dendritic cells,six types of mature T cells,four types of monocytes,three types of myeloid cells,three types of TBNK cells(lymphocyte subset T cells,B cells and natural killer cells),and five types of Tregs had a causal association with the occurrence of rheumatoid arthritis.(3)Through comprehensive bidirectional two-sample MR analysis,we demonstrated the complex causal relationships between multiple immune phenotypes and rheumatoid arthritis,highlighting the intricate interaction patterns between the immune system and rheumatoid arthritis.These results provide new biomarkers for the early screening and diagnosis of rheumatoid arthritis in China,and help to improve the diagnostic accuracy and sensitivity.
4.Gene-predicted associations between 731 immune cell phenotypes and rheumatoid arthritis
Fengzhi LIU ; Yuna DONG ; Wenyi TIAN ; Chunlei WANG ; Xiaodong LIANG ; Lin BAO
Chinese Journal of Tissue Engineering Research 2026;30(5):1311-1319
BACKGROUND:Rheumatoid arthritis is widely prevalent worldwide,with its high incidence and universality that considerably affects patients' quality of life.Previous studies have focused on a few immune cells or cytokines,whereas this study comprehensively provides a more complete view of the immune mechanisms in rheumatoid arthritis.OBJECTIVE:To explore the causal relationship between 731 immune cell phenotypes and rheumatoid arthritis using the Mendelian randomization method,thereby providing evidence of causality.METHODS:The 731 immune cell phenotypes used in this study were sourced from the GWAScatalog database,jointly developed by the National Human Genome Research Institute(NHGRI)and the European Bioinformatics Institute(EBI).The rheumatoid arthritis data were from the Finngen database,developed by the Finnish Institute for Molecular Medicine(FIMM).The inverse variance weighting method was employed as the primary analytical approach.Additionally,multiple analytical methods,including MR-Egger,weighted mode,simple mode,and weighted median,were concurrently utilized to complement the final results.Sensitivity analyses(Cochran's Q test,MR-Egger regression,and MR-presso analysis)were also conducted to verify the stability and feasibility of the data.RESULTS AND CONCLUSION:(1)After excluding results through heterogeneity testing,the inverse variance weighting analysis indicated that 10 absolute cell counts,15 median fluorescence intensities of surface antigen levels,1 morphological characteristic,and 9 relative cell counts had a causal relationship with the occurrence of rheumatoid arthritis.(2)According to cell classification,this study found that seven types of B cells,seven types of classical dendritic cells,six types of mature T cells,four types of monocytes,three types of myeloid cells,three types of TBNK cells(lymphocyte subset T cells,B cells and natural killer cells),and five types of Tregs had a causal association with the occurrence of rheumatoid arthritis.(3)Through comprehensive bidirectional two-sample MR analysis,we demonstrated the complex causal relationships between multiple immune phenotypes and rheumatoid arthritis,highlighting the intricate interaction patterns between the immune system and rheumatoid arthritis.These results provide new biomarkers for the early screening and diagnosis of rheumatoid arthritis in China,and help to improve the diagnostic accuracy and sensitivity.
5.Changing prevalence and antibiotic resistance profiles of carbapenem-resistant Enterobacterales in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Wenxiang JI ; Tong JIANG ; Jilu SHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yuanhong XU ; Ying HUANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yingchun XU ; Xiaojiang ZHANG ; 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 ; 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 ; Hong ZHANG ; Chun WANG ; Wenhui HUANG ; 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 2025;25(4):445-454
Objective To summarize the changing prevalence of carbapenem resistance in Enterobacterales based on the data of CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021 for improving antimicrobial treatment in clinical practice.Methods Antimicrobial susceptibility testing was performed using a commercial automated susceptibility testing system according to the unified CHINET protocol.The results were interpreted according to the breakpoints of the Clinical & Laboratory Standards Institute(CLSI)M100 31st ed in 2021.Results Over the seven-year period(2015-2021),the overall prevalence of carbapenem-resistant Enterobacterales(CRE)was 9.43%(62 342/661 235).The prevalence of CRE strains in Klebsiella pneumoniae,Citrobacter freundii,and Enterobacter cloacae was 22.38%,9.73%,and 8.47%,respectively.The prevalence of CRE strains in Escherichia coli was 1.99%.A few CRE strains were also identified in Salmonella and Shigella.The CRE strains were mainly isolated from respiratory specimens(44.23±2.80)%,followed by blood(20.88±3.40)%and urine(18.40±3.45)%.Intensive care units(ICUs)were the major source of the CRE strains(27.43±5.20)%.CRE strains were resistant to all the β-lactam antibiotics tested and most non-β-lactam antimicrobial agents.The CRE strains were relatively susceptible to tigecycline and polymyxins with low resistance rates.Conclusions The prevalence of CRE strains was increasing from 2015 to 2021.CRE strains were highly resistant to most of the antibacterial drugs used in clinical practice.Clinicians should prescribe antimicrobial agents rationally.Hospitals should strengthen antibiotic stewardship in key clinical settings such as ICUs,and take effective infection control measures to curb CRE outbreak and epidemic in hospitals.
6.Changing distribution and antibiotic resistance profiles of the respiratory bacterial isolates in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Ying FU ; Yunsong YU ; Jie LIN ; 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 ; 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 WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(4):431-444
Objective To characterize the changing species distribution and antibiotic resistance profiles of respiratory isolates in hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Methods Commercial automated antimicrobial susceptibility testing systems and disk diffusion method were used to test the susceptibility of respiratory bacterial isolates to antimicrobial agents following the standardized technical protocol established by the CHINET program.Results A total of 589 746 respiratory isolates were collected from 2015 to 2021.Overall,82.6%of the isolates were Gram-negative bacteria and 17.4%were Gram-positive bacteria.The bacterial isolates from outpatients and inpatients accounted for(6.0±0.9)%and(94.0±0.1)%,respectively.The top microorganisms were Klebsiella spp.,Acinetobacter spp.,Pseudomonas aeruginosa,Staphylococcus aureus,Haemophilus spp.,Stenotrophomonas maltophilia,Escherichia coli,and Streptococcus pneumoniae.Each microorganism was isolated from significantly more males than from females(P<0.05).The overall prevalence of methicillin-resistant S.aureus(MRSA)was 39.9%.The prevalence of penicillin-resistant S.pneumoniae was 1.4%.The prevalence of extended-spectrum β-lactamase(ESBL)-producing E.coli and K.pneumoniae was 67.8%and 41.3%,respectively.The overall prevalence of carbapenem-resistant E.coli,K.pneumoniae,Enterobacter cloacae,Pseudomonas aeruginosa,and Acinetobacter baumannii was 3.7%,20.8%,9.4%,29.8%,and 73.3%,respectively.The prevalence of β-lactamase was 96.1%in Moraxella catarrhalis and 60.0%in Haemophilus influenzae.The H.influenzae isolates from children(<18 years)showed significantly higher resistance rates to β-lactam antibiotics than the isolates from adults(P<0.05).Conclusions Gram-negative bacteria are still predominant in respiratory isolates associated with serious antibiotic resistance.Antimicrobial resistance surveillance should be strengthened in clinical practice to support accurate etiological diagnosis and appropriate antimicrobial therapy based on antimicrobial susceptibility testing results.
7.Multimodal Imaging Evaluation of Changes in Metabolic Microenvironment in the Brain of Neonatal Rats After Cerebral Hypoxia and Ischemia of Prematurity
Xiaozu ZHANG ; Haimo ZHANG ; Yijing WANG ; Tao JU ; Youcheng QIN ; Chang LIU ; Miao YU ; Chunlei ZHANG ; Xiaoli WANG
Chinese Journal of Medical Imaging 2025;33(5):501-506
Purpose Based on multimodal imaging combined with a variety of histological techniques,to visually evaluate the changes of rats brain metabolic microenvironment after cerebral hypoxia and ischemia of prematurity,and to discuss the effects of abnormal lactate metabolism in the brain on oligodendrocyte precursor cells,so as to provide a basis for the early diagnosis and treatment of premature white matter injury(PWMI).Materials and Methods A total of 36 SPF-grade healthy 3-day-old Sprague-Dawley neonatal rats were randomly assigned to the sham surgery(Sham)group and the model(PWMI)group using a random number table method,with 18 rats in each group.A neonatal rat PWMI model was established by hypoxia-ischemia method.Twenty-four hours after modeling,laser speckle imaging was used to monitor cerebral blood flow and blood oxygen changes.Multimodal imaging was used to observe the changes in brain tissue microstructure and metabolism after PWMI.HE staining was used to observe the morphological changes of nerve cells in the white matter of the brain.Enzyme-linked immunosorbent assay was used to detect the changes of lactate content and lactate dehydrogenase activity in the white matter region of the brain after PWMI in neonatal rats.PDGFR-α immunofluorescence staining was used to observe the dynamic changes of the number of oligodendrocyte precursor cells in the subependymal zone after PWMI in neonatal rats.Results Twenty-four hours after modeling,the multimodal imaging results showed that the T2WI and diffusion-weighted imaging on the injured side of the PWMI group showed high intensity,and the relative cerebral blood flow,relative oxygen saturation,relative apparent diffusion coefficient and amide proton transfer(APT)Lorentzian difference value were lower than those in the Sham group(t=29.466,23.522,59.006,54.778,10.263,all P<0.001),and the lactate content was higher than that in the Sham group(t=-7.521,P<0.001).The results of HE staining and enzyme-linked immunosorbent assay showed that the arrangement of nerve cells in the white matter area of the injured side of the brain in the PWMI group was loose and disordered.The lactate content and lactate dehydrogenase activity were higher than those in the Sham group(t=-6.079,-10.548,both P<0.001).The results of immunofluorescence staining showed that the number of PDGFR-α+cells in the subependymal zone of the damaged side of the PWMI group was higher than that of the Sham group at 24 hours after modeling,and lower than that in the Sham group at 11 days after modeling(t=-8.386,6.676,both P<0.001).The correlation analysis between the lactate content and APT Lorentzian difference value in the brain and the number of oligodendrocyte precursor cells in the brain 11 days after modeling showed that the number of oligodendrocyte precursor cells in the subependymal zone was positively correlated with the APT Lorentzian difference value(r=0.821,P=0.001 1),and negatively correlated with the lactate content in the brain(r=-0.880,P=0.000 2).Conclusion Multimodal imaging can monitor the early brain metabolism changes of PWMI in neonatal rats,especially the changes of lactate,and provide a visual basis for their early diagnosis.The level of lactate in the brain increases after cerebral hypoxia and ischemia in prematurity,and oligodendrocyte precursor cells increase transiently and then decrease,resulting in PWMI.
8.Distribution and resistance profiles of bacterial strains isolated from cerebrospinal fluid in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Juan MA ; Lixia ZHANG ; Yang YANG ; Fupin HU ; Demei ZHU ; Han SHEN ; Wanqing ZHOU ; Wenen LIU ; Yanming LI ; Yi XIE ; Mei KANG ; Dawen GUO ; Jinying ZHAO ; Zhidong HU ; Jin LI ; Shanmei WANG ; Yafei CHU ; Yunsong YU ; Jie LIN ; Yingchun XU ; Xiaojiang ZHANG ; Jihong LI ; Bin SHAN ; Yan DU ; Ping JI ; Fengbo ZHANG ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Xiaobo MA ; Yanping ZHENG ; Yuanhong XU ; Ying HUANG ; Yunzhuo CHU ; Sufei TIAN ; Hua YU ; Xiangning HUANG ; Sufang GUO ; Xuesong XU ; Chao YAN ; Fangfang HU ; Yan JIN ; Chunhong SHAO ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Fang DONG ; Zhiyong LÜ ; Lei ZHU ; Jinhua MENG ; Shuping ZHOU ; Yan ZHOU ; Chuanqing WANG ; Pan FU ; Yunjian HU ; Xiaoman AI ; Ziyong SUN ; Zhongju CHEN ; Hong ZHANG ; Chun WANG ; Yuxing NI ; Jingyong SUN ; Kaizhen WEN ; Yirong ZHANG ; Ruyi GUO ; Yan ZHU ; Jinju DUAN ; Jianbang KANG ; Xuefei HU ; Shifu WANG ; Yunsheng CHEN ; Qing MENG ; Yong ZHAO ; Ping GONG ; Ruizhong WANG ; Hua FANG ; Jilu SHEN ; Jiangshan LIU ; Hongqin GU ; Jiao FENG ; Shunhong XUE ; Bixia YU ; Wen HE ; Lin JIANG ; Longfeng LIAO ; Chunlei YUE ; Wenhui HUANG
Chinese Journal of Infection and Chemotherapy 2025;25(3):279-289
Objective To investigate the distribution and antimicrobial resistance profiles of common pathogens isolated from cerebrospinal fluid(CSF)in CHINET program from 2015 to 2021.Methods The bacterial strains isolated from CSF were identified in accordance with clinical microbiology practice standards.Antimicrobial susceptibility test was conducted using Kirby-Bauer method and automated systems per the unified CHINET protocol.Results A total of 14 014 bacterial strains were isolated from CSF samples from 2015 to 2021,including the strains isolated from inpatients(95.3%)and from outpatient and emergency care patients(4.7%).Overall,19.6%of the isolates were from children and 80.4%were from adults.Gram-positive and Gram-negative bacteria accounted for 68.0%and 32.0%,respectively.Coagulase negative Staphylococcus accounted for 73.0%of the total Gram-positive bacterial isolates.The prevalence of MRSA was 38.2%in children and 45.6%in adults.The prevalence of MRCNS was 67.6%in adults and 69.5%in children.A small number of vancomycin-resistant Enterococcus faecium(2.2%)and linezolid-resistant Enterococcus faecalis(3.1%)were isolated from adult patients.The resistance rates of Escherichia coli and Klebsiella pneumoniae to ceftriaxone were 52.2%and 76.4%in children,70.5%and 63.5%in adults.The prevalence of carbapenem-resistant E.coli and K.pneumoniae(CRKP)was 1.3%and 47.7%in children,6.4%and 47.9%in adults.The prevalence of carbapenem-resistant Acinetobacter baumannii(CRAB)and Pseudomonas aeruginosa(CRPA)was 74.0%and 37.1%in children,81.7%and 39.9%in adults.Conclusions The data derived from antimicrobial resistance surveillance are crucial for clinicians to make evidence-based decisions regarding antibiotic therapy.Attention should be paid to the Gram-negative bacteria,especially CRKP and CRAB in central nervous system(CNS)infections.Ongoing antimicrobial resistance surveillance is helpful for optimizing antibiotic use in CNS infections.
9.Changing antibiotic resistance profiles of the bacterial strains isolated from geriatric patients in hospitals across China:data from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Xiaoman AI ; Yunjian HU ; Chunyue GE ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Hui LI ; 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 ; 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 ; Wenhui HUANG ; 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 2025;25(3):290-302
Objective To investigate the antimicrobial resistance of clinical isolates from elderly patients(≥65 years)in major medical institutions across China.Methods Bacterial strains were isolated from elderly patients in 52 hospitals participating in the CHINET Antimicrobial Resistance Surveillance Program during the period from 2015 to 2021.Antimicrobial susceptibility test was carried out by disk diffusion method and automated systems according to the same CHINET protocol.The data were interpreted in accordance with the breakpoints recommended by the Clinical and Laboratory Standards Institute(CLSI)in 2021.Results A total of 514 715 nonduplicate clinical isolates were collected from elderly patients in 52 hospitals from January 1,2015 to December 31,2021.The number of isolates accounted for 34.3%of the total number of clinical isolates from all patients.Overall,21.8%of the 514 715 strains were gram-positive bacteria,and 78.2%were gram-negative bacteria.Majority(90.9%)of the strains were isolated from inpatients.About 42.9%of the strains were isolated from respiratory specimens,and 22.9%were isolated from urine.More than half(60.7%)of the strains were isolated from male patients,and 39.3%isolated from females.About 51.1%of the strains were isolated from patients aged 65-<75 years.The prevalence of methicillin-resistant strains(MRSA)was 38.8%in 32 190 strains of Staphylococcus aureus.No vancomycin-or linezolid-resistant strains were found.The resistance rate of E.faecalis to most antibiotics was significantly lower than that of Enterococcus faecium,but a few vancomycin-resistant strains(0.2%,1.5%)and linezolid-resistant strains(3.4%,0.3%)were found in E.faecalis and E.faecium.The prevalence of penicillin-susceptible S.pneumoniae(PSSP),penicillin-intermediate S.pneumoniae(PISP),and penicillin-resistant S.pneumoniae(PRSP)was 94.3%,4.0%,and 1.7%in nonmeningitis S.pneumoniae isolates.The resistance rates of Klebsiella spp.(Klebsiella pneumoniae 93.2%)to imipenem and meropenem were 20.9%and 22.3%,respectively.Other Enterobacterales species were highly sensitive to carbapenem antibiotics.Only 1.7%-7.8%of other Enterobacterales strains were resistant to carbapenems.The resistance rates of Acinetobacter spp.(Acinetobacter baumannii 90.6%)to imipenem and meropenem were 68.4%and 70.6%respectively,while 28.5%and 24.3%of P.aeruginosa strains were resistant to imipenem and meropenem,respectively.Conclusions The number of clinical isolates from elderly patients is increasing year by year,especially in the 65-<75 age group.Respiratory tract isolates were more prevalent in male elderly patients,and urinary tract isolates were more prevalent in female elderly patients.Klebsiella isolates were increasingly resistant to multiple antimicrobial agents,especially carbapenems.Antimicrobial resistance surveillance is helpful for accurate empirical antimicrobial therapy in elderly patients.
10.A Retrospective Study of Rescue Injuries and Agonal Injuries in 640 Death Cases
Xuanyi LI ; Guoli LV ; Wen YANG ; Chunlei WU ; Xiaoshan LIU ; Bin LUO ; Xinbiao LIAO ; Erwen HUANG
Journal of Sun Yat-sen University(Medical Sciences) 2025;46(1):81-87
[Objective]To clearly identify the difference between rescue injuries and agonal injuries and to avoid duplicate identifications and misidentifications.[Methods]Based on the forensic pathological data of 5 923 cases of death cause identification from 2013 to 2022 in Sun Yat-sen University Forensic Identification Center and Guangzhou Tianhe District Branch of Guangzhou Public Security Bureau,this study retrospectively studied the characteristics of rescue injuries and agonal injuries seen in cause of death identification and their influence on cause of death identification.[Results]Among all the 5 923 cases,640 cases were found to have rescue injuries or agonal injuries,and 624 cases received treatment,of which 609 cases were found to have rescue injuries(97.60%),44 cases were found to have agonal injuries,and 13 cases were found to have both types of injuries.Among the 640 cases,441 were male and 199 were female.The age of death was discontinuously distributed from 0 to 95 years old.The leading cause of death was disease,followed by mechanical injury and asphyxia.The main manifestations of rescue injuries were rib and sternum fractures,soft tissue injuries in the prechest area or face,and pericardial rupture.The most common injuries in agonal stage were falling after unconsciousness,inhalation of foreign body in respiratory tract or multiple violent injuries.Among the 640 cases,19 cases were repeatedly identified,including 15 cases of rescue injuries,6 cases of agonal injuries,and 2 cases of both types of injuries.Compared with the cases where neither type of injuries was detected,the repeated identification rate of treatment injuries and agonal injuries was significantly increased(χ2=4.04,P=0.044;χ2=43.49,P<0.001).Among the 640 cases,11 cases(1.72%)were misidentified as the initial injuries in the first identification,and 13 cases had combined rescue injuries or agonal injuries that were involved in death.[Conclusions]By elucidating the epidemiological characteristics of the two types of injuries,this study proved that the two types of injuries were associated with higher rates of repeated identification and misidentification,which provided a reference for reducing repeated identification and misidentification and improving the accuracy of cause of death identification.


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