1.Current status and prospect of self-administered visual function testing tools for telemedicine
Qianyi PAN ; Xiaotong HAN ; Jiaqing ZHANG ; Lixia LUO
International Eye Science 2025;25(5):765-769
One of the significant hurdles in telemedicine, particularly in ophthalmology, is the absence of direct physical examination. This specialty depends extensively on specialized instruments that typically require proficient operators. Visual function tests are crucial for both outpatient and inpatient ophthalmic services, playing a vital role in screening, diagnosing, monitoring treatment effectiveness, and managing follow-ups for various eye conditions. The progress in mobile technology has paved the way for expanding these tests beyond traditional clinic settings, promoting the creation of patient-focused, straightforward, cost-effective, and efficient measurement tools. In light of the swift advancement of digital technologies, this article reviews the characteristics, and reliability of self-administered visual function tests tools, including visual acuity, refractive error assessment, visual field, contrast sensitivity, and color vision, along with other pertinent diagnostic tools that have been developed and validated for accuracy and repeatability through research, with a view to providing ophthalmologists and patients with scientific and practical references when selecting and using these tools, further promoting efficiency and efficacy of teleophthalmology.
2.Development and application on a full process disease diagnosis and treatment assistance system based on generative artificial intelligence.
Wanjie YANG ; Hao FU ; Xiangfei MENG ; Changsong LI ; Ce YU ; Xinting ZHAO ; Weifeng LI ; Wei ZHAO ; Qi WU ; Zheng CHEN ; Chao CUI ; Song GAO ; Zhen WAN ; Jing HAN ; Weikang ZHAO ; Dong HAN ; Zhongzhuo JIANG ; Weirong XING ; Mou YANG ; Xuan MIAO ; Haibai SUN ; Zhiheng XING ; Junquan ZHANG ; Lixia SHI ; Li ZHANG
Chinese Critical Care Medicine 2025;37(5):477-483
The rapid development of artificial intelligence (AI), especially generative AI (GenAI), has already brought, and will continue to bring, revolutionary changes to our daily production and life, as well as create new opportunities and challenges for diagnostic and therapeutic practices in the medical field. Haihe Hospital of Tianjin University collaborates with the National Supercomputer Center in Tianjin, Tianjin University, and other institutions to carry out research in areas such as smart healthcare, smart services, and smart management. We have conducted research and development of a full-process disease diagnosis and treatment assistance system based on GenAI in the field of smart healthcare. The development of this project is of great significance. The first goal is to upgrade and transform the hospital's information center, organically integrate it with existing information systems, and provide the necessary computing power storage support for intelligent services within the hospital. We have implemented the localized deployment of three models: Tianhe "Tianyuan", WiNGPT, and DeepSeek. The second is to create a digital avatar of the chief physician/chief physician's voice and image by integrating multimodal intelligent interaction technology. With generative intelligence as the core, this solution provides patients with a visual medical interaction solution. The third is to achieve deep adaptation between generative intelligence and the entire process of patient medical treatment. In this project, we have developed assistant tools such as intelligent inquiry, intelligent diagnosis and recognition, intelligent treatment plan generation, and intelligent assisted medical record generation to improve the safety, quality, and efficiency of the diagnosis and treatment process. This study introduces the content of a full-process disease diagnosis and treatment assistance system, aiming to provide references and insights for the digital transformation of the healthcare industry.
Artificial Intelligence
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Humans
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Delivery of Health Care
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Generative Artificial Intelligence
3.Pathogenic bacteria distribution,drug resistance changes and risk factors of death in patients with acute myeloid leukemia complicated with bloodstream infection
Xiaojuan JI ; Hao HAN ; Lixia ZHANG
Tianjin Medical Journal 2024;52(2):167-171
Objective To explore the distribution and drug resistance changes of pathogenic bacteria in adult acute myeloid leukemia(AML)with bloodstream infection,and to analyze risk factors of death of patients.Methods Changes of detection rate of pathogenic bacteria and drug resistance rate of main pathogenic bacteria of 85 patients with AML and bloodstream infection 30 months before confirmed diagnosis(pathogenic bacteria detected from January 2017 to June 2019)and 30 months after diagnosis(from July 2019 to December 2021)were compared.According to the prognosis at 6 months after bloodstream infection,patients were divided into the death group(33 cases)and the survival group(52 cases).Logistic regression analysis was used to analyze risk factors of death in patients with AML complicated with bloodstream infection.Results A total of 98 strains of pathogenic bacteria were detected in 85 patients with AML complicated with bloodstream infection,mainly gram-negative bacteria(65/98,66.33%),followed by Gram-positive bacteria(29/98,29.59%)and fungi(4/98,4.08%).The proportion of fungi(all were candida)detected in the last 30 months was more than that in the first 30 months(P<0.05).There were no significant differences in proportions of gram-negative bacteria and gram-positive bacteria and drug resistance rates of Escherichia coli and Staphylococcus aureus between the late 30 months and the first 30 months(P>0.05).Logistic regression analysis showed that the history of antibiotic use within 1 month before confirmed diagnosis and septic shock were independent risk factors for death in patients with AML complicated with bloodstream infection(P<0.05).Conclusion The main pathogens of adults with AML combined with bloodstream infection are gram-negative bacteria.However,candida infection rate has increased in recent years,and patients with antibiotic use before bloodstream infection and complicated with septic shock are prone to poor prognosis.
4.Stubborn Gout: Psychological and Behavioral Factors Affecting Physical Diseases
Jiarui LI ; Lixia CHEN ; Tao LI ; Yinan JIANG ; Shangzhu ZHANG ; Xi WANG ; Xulei CUI ; Han WANG ; Xiaoqing LI ; Jing WEI
Medical Journal of Peking Union Medical College Hospital 2024;15(5):1204-1210
A middle-aged male came to Peking Union Medical College Hospital for treatment because of "pain for 10+ years, aggravated with emotional instability for 5 years". The patient's pain had a huge impact on life, with poor results even after repeated diagnosis and treatment in other hospitals. After multi-disciplinary discussion, it had been clarified that the pain was mainly caused by gout. The disease was heavily influenced by psychosocial factors. Therefore, the patient fits the diagnosis of "Psychological and Behavioral Factors Affecting Physical Diseases". The multi-disciplinary comprehensive management of the patient was carried out to identify and treat psychological factors affecting other medical conditions. After this mental treatment was performed, the patient's conditions significantly improved. The diagnosis and treatment of this patient demonstrates the importance of the multi-disciplinary treatment team for somatic symptoms (disorders).
5.Establishment and application of measurement range of main blood quality indicators in provincial blood stations
Zixuan ZHANG ; Ying CHANG ; Xiaotong ZHANG ; Qingming WANG ; Yuan ZHANG ; Yue LIU ; Qinghua TIAN ; Ka LI ; Guorong LI ; Lixia CHEN ; Junhua SUN ; Yu KANG ; Pingchen HAN ; Xinyu ZHAO ; Song LI
Chinese Journal of Blood Transfusion 2024;37(8):918-926
Objective To obtain the monitoring measurement range of quality indicators of red blood cells,plasma and derivatives and leukocyte-reduced apheresis platelets provided by blood stations in Hebei province,explore the distribution of monitoring values and the change of monitoring level,so as to further strengthen the homogenization construction of quality control laboratories in blood stations in Hebei.Methods In 2023,the sampling data of 12 blood stations in Hebei from 2015 to 2022 were collected,scatter plots were made and the range markers were set,and the"mean±SD"line was taken as the upper limit and lower limit of the measurement range.In 2024,the monitoring values in 2023 were added,and the changes of two measurement ranges were compared to analyze the stability and overall level.Results Comparison of the measurement range from 2015 to 2022 and the measurement range from 2015 to 2023 showed that the standard deviation of the content of deleukocyte suspension of red blood cells-hemoglobin,washed erythrocyte-hemoglobin,washed erythrocyte-su-pernatant protein,cryoprecipitate coagulation factor-FⅧ,fresh frozen plasma-FⅧ,leukocyte-reduced apheresis platelets-leukocyte residue and leukocyte-reduced apheresis platelet-red blood cell concentration decreased from 8.132 to 7.993,6.252 to 6.104,0.273 to 0.267,57.506 to 56.276,0.920 to 0.892,0.653 to 0.644 and 2.653 to 2.603,respectively.The narrowing of the standard deviation range of the above items led to more concentrated monitoring values and reduced disper-sion.Comparison of the measurement range from 2015 to 2022 and the measurement range from 2015 to 2023 showed that the mean value of leukocyte residue of the deleukocyte suspension of red blood cells,hemoglobin content of the wash eryth-rocyte,protein content of supernatant of the wash erythrocyte,hemolysis rate of the wash erythrocyte,FⅧ content of the cryoprecipitate coagulation factor,plasma protein content of the fresh frozen plasma,FⅧ content of the fresh frozen plasma,platelet content of the leukocyte-reduced apheresis platelets changed from 0.362 to 0.476,44.915 to 44.861,0.280 to 0.283,0.137 to 0.142,133.989 to 133.271,60.262 to 60.208,1.301 to 1.277 and 3.036 to 3.033,respectively,and was closer to the national standard line,which reflects an increase in the number of unqualified monitoring values or values close to the national standard line in 2023.The long-term qualified rate of coagulation items was low,and no improvement has been ob-served.The stability of biochemical items has been enhanced but overall deviation has occurred,with the average value close to the national standard line.The possibility of subsequent testing failure has increased.The counting items showed no obvi-ous common characteristics.Conclusion The use of"mean±SD"in the analysis can visually display the distribution of mo-nitoring values of different items in Hebei,forming an indicator measurement range covering the past nine years.It shows the characteristics of each item,and provides reference for subsequent quality control laboratory data analysis of each blood sta-tions to takes active measures to improve the monitoring level.
6.The experience on the construction of the cluster prevention and control system for COVID-19 infection in designated hospitals during the period of "Category B infectious disease treated as Category A"
Wanjie YANG ; Xianduo LIU ; Ximo WANG ; Weiguo XU ; Lei ZHANG ; Qiang FU ; Jiming YANG ; Jing QIAN ; Fuyu ZHANG ; Li TIAN ; Wenlong ZHANG ; Yu ZHANG ; Zheng CHEN ; Shifeng SHAO ; Xiang WANG ; Li GENG ; Yi REN ; Ying WANG ; Lixia SHI ; Zhen WAN ; Yi XIE ; Yuanyuan LIU ; Weili YU ; Jing HAN ; Li LIU ; Huan ZHU ; Zijiang YU ; Hongyang LIU ; Shimei WANG
Chinese Critical Care Medicine 2024;36(2):195-201
The COVID-19 epidemic has spread to the whole world for three years and has had a serious impact on human life, health and economic activities. China's epidemic prevention and control has gone through the following stages: emergency unconventional stage, emergency normalization stage, and the transitional stage from the emergency normalization to the "Category B infectious disease treated as Category B" normalization, and achieved a major and decisive victory. The designated hospitals for prevention and control of COVID-19 epidemic in Tianjin has successfully completed its tasks in all stages of epidemic prevention and control, and has accumulated valuable experience. This article summarizes the experience of constructing a hospital infection prevention and control system during the "Category B infectious disease treated as Category A" period in designated hospital. The experience is summarized as the "Cluster" hospital infection prevention and control system, namely "three rings" outside, middle and inside, "three districts" of green, orange and red, "three things" before, during and after the event, "two-day pre-purification" and "two-director system", and "one zone" management. In emergency situations, we adopt a simplified version of the cluster hospital infection prevention and control system. In emergency situations, a simplified version of the "Cluster" hospital infection prevention and control system can be adopted. This system has the following characteristics: firstly, the system emphasizes the characteristics of "cluster" and the overall management of key measures to avoid any shortcomings. The second, it emphasizes the transformation of infection control concepts to maximize the safety of medical services through infection control. The third, it emphasizes the optimization of the process. The prevention and control measures should be comprehensive and focused, while also preventing excessive use. The measures emphasize the use of the least resources to achieve the best infection control effect. The fourth, it emphasizes the quality control work of infection control, pays attention to the importance of the process, and advocates the concept of "system slimming, process fattening". Fifthly, it emphasizes that the future development depends on artificial intelligence, in order to improve the quality and efficiency of prevention and control to the greatest extent. Sixth, hospitals need to strengthen continuous training and retraining. We utilize diverse training methods, including artificial intelligence, to ensure that infection control policies and procedures are simple. We have established an evaluation and feedback mechanism to ensure that medical personnel are in an emergency state at all times.
7.Mechanism and clinical management of Bruton's tyrosine kinase inhibitor-mediated bleeding
Song LIXIA ; Kang HONGYANG ; Han GUOJIANG ; Liu JIE ; Fan LING ; Tong CHANGQING
Chinese Journal of Clinical Oncology 2024;51(14):737-741
Bruton's tyrosine kinase(BTK)inhibitors are novel drugs targeted for the treatment of B-cell lymphoma.BTK inhibitors have pro-duced strong curative effects,especially for mantle cell lymphoma(MCL),chronic lymphocytic leukemia/small lymphocytic lymphoma(CLL/SSL),and Waldenstr?m's macroglobulinemia(WM).However,the adverse effect of bleeding has gradually been noted with the wide-spread use of BTK inhibitors in clinical practice.Bleeding events are caused by the off-target effects of BTK inhibitors,which affect platelet function through multiple signaling pathways during use.Bleeding affects patient treatment and threatens their quality of life.As such,the clinical management of bleeding should be strengthened.This paper provides a review of the mechanisms of action and clinical manage-ment of bleeding caused by BTK inhibitors.
8.Analysis of mask fit testing based on two-dimensional photographic measurement of facial shape
Jing HAN ; Wanjie YANG ; Bo KANG ; Lixia SHI ; Jingbo JIA ; Xiang WANG ; Weili YU
Chinese Journal of Integrated Traditional and Western Medicine in Intensive and Critical Care 2024;31(3):324-328
Objective To investigate the pass rates of fit tests for various brands of medical protective masks and to explore methods for quickly matching these masks based on their head and face dimensions.Methods A total of 202 medical staff from designated hospitals in Tianjin were selected as subjects.Quantitative fit tests were conducted on 5 brands of masks(A,B,C,D,and E)using an aerosol condensation nucleus counter.Two-dimensional photographic measurement was used to obtain the face length and width of the subjects,categorizing them into face types#1 to#10.The pass rates of masks across different face zones,brands,and face types were compared.Results A total of 202 testers participated in this study.According to the guidelines,face type#1 was the most common[43.6%(88/202)],followed by face type#3[18.2%(37/202)].The majority of subjects were categorized as face types#1,#2,#3,and#4,totaling 176 subjects(87.1%).A total of 914 tests were conducted,with 678 passes,resulting in an overall mask pass rate of 74.18%.The pass rates of masks A,B,and C were significantly higher than those of masks D and E[87.03%(161/185),85.57%,(166/194),82.02%(146/178)vs.62.98%(114/181),51.70%(91/176),all P<0.05].The pass rate of adjustable head-mounted masks was significantly higher than that of non-adjustable masks[79.54%(587/738)vs.51.70%(91/176),P<0.05].The fit factor(FF)for mask B in face types#1 to#5 was significantly higher than that in face types#6 to#10[200(163,200)vs.132(86,200),P<0.05].Conclusions Two-dimensional photographic measurement can quickly obtain facial information of the subjects and match the corresponding masks.Hospitals can match masks with higher test pass rates according to the proportion of face types among medical staff.When selecting masks,preference should be given to adjustable head-mounted masks.
9.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.
10.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.

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