1.Dynamic Monitoring and Correlation Analysis of General Body Indicators, Blood Glucose, and Blood Lipid in Obese Cynomolgus Monkeys
Yanye WEI ; Guo SHEN ; Pengfei ZHANG ; Songping SHI ; Jiahao HU ; Xuzhe ZHANG ; Huiyuan HUA ; Guanyang HUA ; Hongzheng LU ; Yong ZENG ; Feng JI ; Zhumei WEI
Laboratory Animal and Comparative Medicine 2025;45(1):30-36
ObjectiveThis study aims to investigate the dynamic changes in general body parameters, blood glucose, and blood lipid profiles in obese cynomolgus monkeys, exploring the correlations among these parameters and providing a reference for research on the obese cynomolgus monkey model. Methods30 normal male cynomolgus monkeys aged 5 - 17 years old (with body mass index < 35 kg/m² and glycated hemoglobin content < 4.50%) and 99 spontaneously obese male cynomolgus monkeys (with body mass index ≥35 kg/m² and glycated hemoglobin content < 4.50%) were selected. Over a period of three years, their abdominal circumference, skinfold thickness, body weight, body mass index, fasting blood glucose, glycated hemoglobin, and four blood lipid indicators were monitored. The correlations between each indicator were analyzed using repeated measurement ANOVA, simple linear regression, and multiple linear regression correlation analysis method. Results Compared to the control group, the obese group exhibited significantly higher levels of abdominal circumference, skinfold thickness, body weight, body mass index, and triglyceride (P<0.05). In the control group, skinfold thickness increased annually, while other indicators remained stable. Compared with the first year, the obese group showed significantly increased abdominal circumference, skinfold thickness, body weight, body mass index, triglyceride, and fasting blood glucose in the second year(P<0.05), with this increasing trend persisting in the third year (P<0.05). In the control group, the obesity incidence rates in the second and third years were 16.67% and 23.33%, respectively, while the prevalence of diabetes remained at 16.67%. In the obese group, the diabetes incidence rates were 29.29% and 44.44% in years 2 and 3, respectively. Among the 11-13 year age group, the incidence rates were 36.36% and 44.68%, while for the group older than 13 years, the rates were 28.13% and 51.35%. Correlation analysis revealed significant associations (P<0.05) between fasting blood glucose and age, abdominal circumference, skinfold thickness, body weight, and triglyceride in the diabetic monkeys. Conclusion Long-term obesity can lead to the increases in general physical indicators and fasting blood glucose levels in cynomolgus monkeys, and an increase in the incidence of diabetes. In diabetic cynomolgus monkeys caused by obesity, there is a high correlation between their fasting blood glucose and age, weight, abdominal circumference, skinfold thickness, and triglyceride levels, which is of some significance for predicting the occurrence of spontaneous diabetes.
2.Construction of dynamic online nomogram for spontaneous rupture of primary liver cancer
Yunfang DONG ; Peng CHEN ; Ziyan YIN ; Ji LIANG ; Wei SHI ; Feng LIU ; Manqin HU
Chinese Journal of Hepatobiliary Surgery 2025;31(1):23-28
Objective:To construct and evaluate the nomogram prediction model of spontaneous rupture of primary liver cancer (STRPLC), and make the web-based dynamic online nomogram.Methods:Clinical data of 346 patients with PLC treated in the Second Affiliated Hospital of Kunming Medical University were retrospectively analyzed, including 87 males and 15 females, aged 58.15±10.32 years. Single factor and multiple factor logistic regression analysis were used to screen the influencing factors of STRPLC, and the prediction model was constructed based on the nomogram. Receiver operating characteristic (ROC) curve, calibration curve and clinical decision analysis were used to evaluate the model. The web-based dynamic online nomogram was developed using the DynNom package in R4.3.1 software.Results:Multivariate logistic regression analysis showed that the independent risk factors for spontaneous rupture and hemorrhage of tumor were no history of systematic anti-tumor therapy, alpha-fetoprotein (AFP) level, tumor protrusion on liver surface, tumor length, invasion of major blood vessels, and moderate to large amount of ascites (all P<0.05). The area under the receiver operating characteristic curve (AUC) of the prediction model constructed by this nomogram is 0.913 (95% CI: 0.884-0.943), the best cutoff value is 0.254, with a sensitivity of 0.892, and a specificity of 0.803. The calibration curve shows a good agreement between the predicted probability and the actual probability. The decision curve of the model is above the two invalid lines of " none" and " all" in the horizontal range of 0.07-0.98, and the clinical net benefit of the model is >0. Then user-friendly web-based dynamic online nomogram is constructed. Conclusion:Large tumor size, superficial location, no history of systematic anti-tumor therapy, high AFP level, invasion of major blood vessels, and moderate to large amount of ascites are independent risk factors for STRPLC. The prediction model and dynamic online nanogram constructed by this method can effectively assess the risk of STRPLC.
3.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.
4.Clinical study of intracranial hypotension targeted body posture combined with pharmacotherapy in the treatment of chronic subdural hematoma
Jiayu CHEN ; Zhe WANG ; Di ZANG ; Ruizhe ZHENG ; Xiangru YE ; Zengxin QI ; Zeyu XU ; Zhiqiang LI ; Chengfeng SUN ; Liangjun SHEN ; Luoping SHENG ; Fulin XU ; Ruyong YE ; Kaiyu ZHOU ; Weijun TANG ; Yueqing HU ; Dapeng SHI ; Yuquan WANG ; Xizhen WU ; Ying WANG ; Qilin ZHANG ; Feili LIU ; Guo YU ; Yiping LU ; Yirui SUN ; Ning ZHANG ; Feng HUANG ; Xialong GU ; Han ZHANG ; Jian DING ; Yongyan BI ; Haolan DU ; Jing ZHANG ; Hailong JI ; Ding DING ; Wei ZHANG ; Xuehai WU
Chinese Journal of Surgery 2025;63(3):212-218
Objective:To compare the efficacy of body posture combined with pharmacotherapy and pharmacotherapy alone in the treatment of chronic subdural hematoma(CSDH).Methods:Firstly, retrospective case series study was conducted. Thirty cases of CSDH that had received body posture combined with pharmacotherapy at Department of Neurosurgery, Huashan Hospital Affiliated to Fudan University from December 2016 to October 2020 were studied retrospectively. Twenty-seven patients were male, and 3 patients were female. The age of patients ( M(IQR)) was 66(16) years (range:28 to 84). Nineteen patients had unilateral hematoma, and 11 patients had bilateral hematoma. All patients received pharmacotherapy and body posture therapy that was to raise their lower limbs 20 to 30 cm with leg lift pad and get abdominal compressed with customized abdominal belt in supine position. Patients were required to maintain the body posture as much as possible, with the maximum to 16 to 18 hours per day. Patients with unilateral hematoma should tilt the head to the affected side and avoid tilting it to the opposite side. For patients with bilateral hematoma, there was no need for head lateralization. Patient were treated with oral dexamethasone and atorvastatin simultaneously. The preliminary efficacy of body posture combined with pharmacotherapy was determined by hematoma improvement rate which was analyzed by Clopper-Pearson method. Then, the multi-center, prospective, randomized controlled trial had carried out in 9 medical centers from August 2020 to November 2021. The stratified block randomization method was adopted. Patients were randomized in a ratio of 1∶1 to either receive pharmacotherapy alone(the control group) or body posture combined with pharmacotherapy(the experiment group) for 3 months and followed up for 6 months. Effective treatment was defined as complete absorption of hematoma, or the hematoma volume decreased by more than 10 ml and Markwalder grading scale score had improved by more than 1 point compared to the baseline. The efficacy rate and surgery conversion rate at 3 months and recurrence at 6 months were observed. Comparison between groups was performed with paired sample t test, Mann-Whitney U test, χ2 test, corrected χ2 test, or Fisher exact probability method. Logistic regression was used to compare the effective rate and operation rate between the two groups. Results:In the respective study, 30 patients completed follow-up 13 to 353 days after treatment. At the last follow-up, the incidence of almost complete absorption or significantly absorption of hematoma (hematoma volume was significantly reduced accompanied by symptom improvement) was 93.3%. The 95% CI for the incidence that analyzed by the Clopper-Pearson method was 77.9% to 99.2%. One hundred and six patients were enrolled in the multicenter study. Fifty-five patients underwent body posture combined with pharmacotherapy. The age was 74(17) years (range:26 to 92). Thirty-nine patients were males and 16 were females. Fifty-one patients underwent pharmacotherapy alone. The age was 69(12) years (range:48 to 84). Thirty-seven patients were males and 14 were females. The length of body posture recorded in diary card was (15.7±2.3) hours(range:7.6 to 19.3 hours). The efficacy rate in the body posture combined with pharmacotherapy group and pharmacotherapy alone group were 83.6% (46/55) and 56.9% (29/51), respectively at 3 months. The result of the logistic regression analysis showed that the efficacy of body posture combined with pharmacotherapy group was better than that of pharmacotherapy alone group ( OR=3.88,95% CI:1.57 to 9.58, P=0.003). Surgery rate in the body posture combined with pharmacotherapy group and pharmacotherapy alone group were 5.5% (3/55) and 21.6% (11/51) respectively. The result of Logistic regression showed that the pharmacotherapy alone group was more likely to be converted to surgery ( OR=0.21,95% CI:0.05 to 0.80, P=0.023). At the 6 months, no recurrence of cases was found in the body posture combined with pharmacotherapy group. However, the recurrence rate of pharmacotherapy alone group was 6.3% (3/48), there was no significant difference between the two groups ( P>0.05). Conclusion:The effect of body posture combined with pharmacotherapy for chronic subdural hematoma is better than that of pharmacotherapy alone.
5.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.
6.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.
7.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.
8.Creation and Exploration of the"Organized Fill-in-the-Blank Format"Disci-pline Construction Model for Forensic Medicine in the New Era
Zhi-Wen WEI ; Hong-Xing WANG ; Jun-Hong SUN ; Hao-Liang FAN ; Hong-Liang SU ; Le-Le WANG ; Wen-Ting HE ; Zhe CHEN ; Jie ZHANG ; Xiang-Jie GUO ; Ji LI ; Geng-Qian ZHANG ; Xin-Hua LIANG ; Jiang-Wei YAN ; Qiang-Qiang ZHANG ; Cai-Rong GAO ; Ying-Yuan WANG ; Hong-Wei WANG ; Jun XIE ; Bo-Feng ZHU ; Ke-Ming YUN
Journal of Forensic Medicine 2025;41(1):25-29
Forensic medicine has been designated as a first-level discipline,presenting new opportunities and challenges for the development of forensic medicine.Since the 1980s,the establishment of foren-sic medicine discipline and the cultivation of high-level forensic talents have become hot topics in the development of forensic medicine in China.Since the 13th Five-Year Plan,the forensic team of Shanxi Medical University has been aiming at the forefront,proposing the development goals of"Five First-class"and the discipline development path"Six Major Achievements".It has selected benchmark disci-plines,identified gaps in disciplinary development,unified thoughts,formulated completion timelines,concentrated superior resources,assigned tasks to individuals,and created an"Organized Fill-in-the-Blank Format"forensic medicine discipline construction model with the characteristics of the new era.The construction model of forensic medicine has achieved good results in the goals,discipline frame-work,scientific research,talent cultivation,discipline team and platform construction,forming a rela-tively complete discipline construction and management system,and accumulating valuable experience for the construction of first-level discipline and high-level talent cultivation of forensic medicine.
9.Distribution and drug resistance of carbapenem-resistant gram-negative bacilli isolated from environment of ICU
Chunyan LI ; Jing ZHANG ; Liang TIAN ; Yilun ZHOU ; Bin WANG ; Mei FENG ; Yuan LI ; Shan WANG ; Wei JI
Chinese Journal of Nosocomiology 2025;35(17):2675-2680
OBJECTIVE To explore the isolation rates,drug resistance and molecular epidemiological characteristics of carbapenem-resistant gram-negative bacilli(CRGNB)isolated from intensive care units(ICU)of a tertiary hos-pital so as to provide bases for prevention and control of the nosocomial infections caused by CRGNB.METHODS The environmental surfaces that were high frequently contacted by the patients with CRGNB infections[carbapen-em-resistant Klebsiella pneumoniae(CRKP),carbapenem-resistant Acinetobacter baumannii(CRAB),carbap-enem-resistant Pseudomonas aeruginosa(CRPA)]and their hands were randomly sampled from the ICU of a ter-tiary three-A hospital from Apr.2024 to Aug.2024.Multilocus sequence typing(MLST)and detection of drug re-sistance genes were performed by means of complete genome sequencing technique and bioinformatics,and the ho-mology between the CRGNB strains isolated from the patients and the strains isolated from their surrounding was observed.RESULTS Totally 30(7.85%)strains of CRGNB were isolated,23(6.02%)of which were CRKP,7(1.83%)were CRAB,and no strain of CRPA was detected.The molecular subtyping showed that ST 11(93.33%)was dominant among the CRKP strains,and ST2(69.23%)was dominant among the CRAB strains.The phylogenetic analysis indicated that there were clonal transmission tendencies of CRKP-ST11 and CRAB-ST2.The analysis of drug resistance genes showed that the CRAB strains mainly carried ant(3")-lla(100%),blaOXA-23(92.31%)and amvA(92.31%);blaOXA-23 and blaOXA-66 were the major carbapenems resistance genes;the CRKP strains mainly carried the drug resistance genes emrDh,rmtB1,fosA and kdeA(all were 96.67%),followed by the carbapenems resistance gene blaKPC-2(90.00%).CONCLUSIONS ST11 is the predomi-nant molecular subtype for CRGNB among the CRKP strains isolated from the ICU,anf ST2 predominant among the CRAB strains;the carrying rates of drug resistance genes are high.There is risk of clonal transmission.It is necessary to strengthen the monitoring and take comprehensive infection control measures so as to reduce the incidence of nosocomial infections.
10.Construction and practice of digital medical laboratory management system: taking National Clinical Research Center for Aging and Medicine (HuaShan) as an example
Feng JI ; Jianping MAO ; Di HOU ; Wei LIU ; Huaizhou YOU ; Jing CHEN
Chinese Journal of Medical Science Research Management 2025;38(4):340-346
Objective:To address the inefficiency, safety hazards, and resource wastage in traditional medical laboratory management, this study proposes a digital laboratory information management system (LIMS) based on Total Quality Management (TQM) principles. The LIMS has been implemented at the National Center for Geriatric Medicine (Huashan) affiliated with Fudan University.Methods:Centered on the principles of " all-staff participation, whole-process control, and comprehensive management", a multidimensional management framework was developed to integrate personnel, equipment, reagents, and safety protocols. The system incorporated IoT, digital twin, and artificial intelligence (AI) technologies to achieve end-to-end digital control. A layered architecture (physical layer, data layer, model layer, etc.) was designed to integrate functional modules such as full lifecycle equipment management, dual-authentication for hazardous chemicals, and intelligent resource scheduling. A 3D digital twin model was deployed to visualize real-time laboratory operations.Results:Post-implementation, equipment reservation frequency and usage duration at the National Center increased by 114% and 124%, respectively, with no safety incidents reported. Equipment sharing utilization reached 85%, and reagent expiration waste decreased by 30%.Conclusions:The system transforms laboratory management from experience-driven to data-driven by breaking data silos and optimizing collaboration mechanisms. It provides a replicable technical pathway and practical insights for the healthcare industry′s digital transformation. However, further improvements are needed in mobile support and system scalability.

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