1.Dynamics of eosinophil infiltration and microglia activation in brain tissues of mice infected with Angiostrongylus cantonensis
Fanna WEI ; Renjie ZHANG ; Yahong HU ; Xiaoyu QIN ; Yunhai GUO ; Xiaojin MO ; Yan LU ; Jiahui SUN ; Yan ZHOU ; Jiatian GUO ; Peng SONG ; Yanhong CHU ; Bin XU ; Ting ZHANG ; Yuchun CAI ; Muxin CHEN
Chinese Journal of Schistosomiasis Control 2025;37(2):163-175
Objective To investigate the changes in eosinophil counts and the activation of microglial cells in the brain tissues of mice at different stages of Angiostrongylus cantonensis infection, and to examine the role of microglia in regulating the progression of angiostrongyliasis and unravel the possible molecular mechanisms. Methods Fifty BALB/c mice were randomly divided into the control group and the 7-d, 14-d, 21-day and 25-d infection groups, of 10 mice in each group. All mice in infection groups were infected with 30 stage III A. cantonensis larvae by gavage, and animals in the control group was given an equal amount of physiological saline. Five mice were collected from each of infection groups on days 7, 14, 21 d and 25 d post-infection, and 5 mice were collected from the control group on the day of oral gavage. The general and focal functional impairment was scored using the Clark scoring method to assess the degree of mouse neurological impairment. Five mice from each of infection groups were sacrificed on days 7, 14, 21 d and 25 d post-infection, and 5 mice from the control group were sacrificed on the day of oral gavage. Mouse brain tissues were sampled, and the pathological changes of brain tissues were dynamically observed using hematoxylin and eosin (HE) staining. Immunofluorescence staining with eosinophilic cationic protein (ECP) and ionized calcium binding adaptor molecule 1 (Iba1) was used to assess the degree of eosinophil infiltration and the counts of microglial cells in mouse brain tissues in each group, and the morphological parameters of microglial cells (skeleton analysis and fractal analysis) were quantified by using Image J software to determine the morphological changes of microglial cells. In addition, the expression of M1 microglia markers Fcγ receptor III (Fcgr3), Fcγ receptor IIb (Fcgr2b) and CD86 antigen (Cd86), M2 microglia markers Arginase 1 (Arg1), macrophage mannose receptor C-type 1 (Mrc1), chitinase-like 3 (Chil3), and phagocytosis genes myeloid cell triggering receptor expressed on myeloid cells 2 (Trem2), CD68 antigen (Cd68), and apolipoprotein E (Apoe) was quantified using real-time quantitative reverse transcription PCR (RT-qPCR) assay in the mouse cerebral cortex of mice post-infection. Results A large number of A. cantonensis larvae were seen on the mouse meninges surface post-infection, and many neuronal nuclei were crumpled and deeply stained, with a large number of bleeding points in the meninges. The median Clark scores of mouse general functional impairment were 0 (interquartile range, 0), 0 (interquartile range, 0.5), 6 (interquartile range, 1.0), 14 (interquartile range, 8.5) points and 20 (interquartile range, 9.0) points in the control group and the 7-d, 14-d, 21-d and 25-d groups, respectively (H = 22.45, P < 0.01), and the median Clark scores of mouse focal functional impairment were 0 (interquartile range, 0), 2 (interquartile range, 2.5), 7 (interquartile range, 3.0), 18 (interquartile range, 5.0) points and 25 (interquartile range, 6.5) points in the control group and the 7-d, 14-d, 21-d and 25-d groups, respectively (H = 22.72, P < 0.01). The mean scores of mice general and focal functional impairment were all higher in the infection groups than in the control group (all P values < 0.05). Immunofluorescence staining showed a significant difference in the eosinophil counts in mouse brain tissues among the five groups (F = 40.05, P < 0.000 1), and the eosinophil counts were significantly higher in mouse brain tissues in the 14-d (3.08 ± 0.78) and 21-d infection groups (5.97 ± 1.37) than in the control group (1.00 ± 0.28) (both P values < 0.05). Semi-quantitative analysis of microglia immunofluorescence showed a significant difference in the counts of microglial cells among the five groups (F = 17.66, P < 0.000 1), and higher Iba1 levels were detected in mouse brain tissues in 14-d (5.75 ± 1.28), 21-d (6.23 ± 1.89) and 25-d infection groups (3.70 ± 1.30) than in the control group (1.00 ± 0.30) (all P values < 0.05). Skeleton and fractal analyses showed that the branch length [(162.04 ± 34.10) μm vs. (395.37 ± 64.11) μm; t = 5.566, P < 0.05] and fractal dimension of microglial cells (1.30 ± 0.01 vs. 1.41 ± 0.03; t = 5.266, P < 0.05) were reduced in mouse brain tissues in the 21-d infection group relative to the control group. In addition, there were significant differences among the 5 groups in terms of M1 and M2 microglia markers Fcgr3 (F = 48.34, P < 0.05), Fcgr2b (F = 55.46, P < 0.05), Cd86 (F = 24.44, P < 0.05), Arg1 (F = 31.18, P < 0.05), Mrc1 (F = 15.42, P < 0.05) and Chil3 (F = 24.41, P < 0.05), as well as phagocytosis markers Trem2 (F = 21.19, P < 0.05), Cd68 (F = 43.95, P < 0.05) and Apoe (F = 7.12, P < 0.05) in mice brain tissues. Conclusions A. cantonensis infections may induce severe pathological injuries in mouse brain tissues that are characterized by massive eosinophil infiltration and persistent activation of microglia cells, thereby resulting in progressive deterioration of neurological functions.
2.Comparison of glucose fluctuation between metformin combined with acarbose or sitagliptin in Chinese patients with type 2 diabetes: A multicenter, randomized, active-controlled, open-label, parallel design clinical trial.
Xiaoling CAI ; Suiyuan HU ; Chu LIN ; Jing WU ; Junfen WANG ; Zhufeng WANG ; Xiaomei ZHANG ; Xirui WANG ; Fengmei XU ; Ling CHEN ; Wenjia YANG ; Lin NIE ; Linong JI
Chinese Medical Journal 2025;138(9):1116-1125
BACKGROUND:
Alpha-glucosidase inhibitors or dipeptidyl peptidase-4 inhibitors are both hypoglycemia agents that specifically impact on postprandial hyperglycemia. We compared the effects of acarbose and sitagliptin add on to metformin on time in range (TIR) and glycemic variability (GV) in Chinese patients with type 2 diabetes mellitus through continuous glucose monitoring (CGM).
METHODS:
This study was a randomized, open-label, active-con-trolled, parallel-group trial conducted at 15 centers in China from January 2020 to August 2022. We recruited patients with type 2 diabetes aged 18-65 years with body mass index (BMI) within 19-40 kg/m 2 and hemoglobin A1c (HbA1c) between 6.5% and 9.0%. Eligible patients were randomized to receive either metformin combined with acarbose 100 mg three times daily or metformin combined with sitagliptin 100 mg once daily for 28 days. After the first 14-day treatment period, patients wore CGM and entered another 14-day treatment period. The primary outcome was the level of TIR after treatment between groups. We also performed time series decomposition, dimensionality reduction, and clustering using the CGM data.
RESULTS:
A total of 701 participants received either acarbose or sitagliptin treatment in combination with metformin. There was no statistically significant difference in TIR between the two groups. Time below range (TBR) and coefficient of variation (CV) levels in acarbose users were significantly lower than those in sitagliptin users. Median (25th percentile, 75th percentile) of TBR below target level <3.9 mmol/L (TBR 3.9 ): Acarbose: 0.45% (0, 2.13%) vs . Sitagliptin: 0.78% (0, 3.12%), P = 0.042; Median (25th percentile, 75th percentile) of TBR below target level <3.0 mmol/L (TBR 3.0 ): Acarbose: 0 (0, 0.22%) vs . Sitagliptin: 0 (0, 0.63%), P = 0.033; CV: Acarbose: 22.44 ± 5.08% vs . Sitagliptin: 23.96 ± 5.19%, P <0.001. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV (group with small wave, moderate wave and big wave). No significant difference was found in the complexity of glucose time series index (CGI) between acarbose users and sitagliptin users. By using time series analysis and clustering, we distinguished three groups of patients with representative metabolism characteristics, especially in GV.
CONCLUSIONS:
Acarbose had slight advantages over sitagliptin in improving GV and reducing the risk of hypoglycemia. Time series analysis of CGM data may predict GV and the risk of hypoglycemia.
TRIAL REGISTRATION
Chinese Clinical Trial Registry: ChiCTR2000039424.
Humans
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Metformin/therapeutic use*
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Sitagliptin Phosphate/therapeutic use*
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Acarbose/therapeutic use*
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Diabetes Mellitus, Type 2/blood*
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Middle Aged
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Male
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Female
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Adult
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Blood Glucose/drug effects*
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Hypoglycemic Agents/therapeutic use*
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Aged
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Glycated Hemoglobin/metabolism*
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Adolescent
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Young Adult
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China
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East Asian People
3.USP20 as a super-enhancer-regulated gene drives T-ALL progression via HIF1A deubiquitination.
Ling XU ; Zimu ZHANG ; Juanjuan YU ; Tongting JI ; Jia CHENG ; Xiaodong FEI ; Xinran CHU ; Yanfang TAO ; Yan XU ; Pengju YANG ; Wenyuan LIU ; Gen LI ; Yongping ZHANG ; Yan LI ; Fenli ZHANG ; Ying YANG ; Bi ZHOU ; Yumeng WU ; Zhongling WEI ; Yanling CHEN ; Jianwei WANG ; Di WU ; Xiaolu LI ; Yang YANG ; Guanghui QIAN ; Hongli YIN ; Shuiyan WU ; Shuqi ZHANG ; Dan LIU ; Jun-Jie FAN ; Lei SHI ; Xiaodong WANG ; Shaoyan HU ; Jun LU ; Jian PAN
Acta Pharmaceutica Sinica B 2025;15(9):4751-4771
T-cell acute lymphoblastic leukemia (T-ALL) is a highly aggressive hematologic malignancy with a poor prognosis, despite advancements in treatment. Many patients struggle with relapse or refractory disease. Investigating the role of the super-enhancer (SE) regulated gene ubiquitin-specific protease 20 (USP20) in T-ALL could enhance targeted therapies and improve clinical outcomes. Analysis of histone H3 lysine 27 acetylation (H3K27ac) chromatin immunoprecipitation sequencing (ChIP-seq) data from six T-ALL cell lines and seven pediatric samples identified USP20 as an SE-regulated driver gene. Utilizing the Cancer Cell Line Encyclopedia (CCLE) and BloodSpot databases, it was found that USP20 is specifically highly expressed in T-ALL. Knocking down USP20 with short hairpin RNA (shRNA) increased apoptosis and inhibited proliferation in T-ALL cells. In vivo studies showed that USP20 knockdown reduced tumor growth and improved survival. The USP20 inhibitor GSK2643943A demonstrated similar anti-tumor effects. Mass spectrometry, RNA-Seq, and immunoprecipitation revealed that USP20 interacted with hypoxia-inducible factor 1 subunit alpha (HIF1A) and stabilized it by deubiquitination. Cleavage under targets and tagmentation (CUT&Tag) results indicated that USP20 co-localized with HIF1A, jointly modulating target genes in T-ALL. This study identifies USP20 as a therapeutic target in T-ALL and suggests GSK2643943A as a potential treatment strategy.
4.Application of IgG antibody combination of wild strain and epidemic strain of COVID-19 in identifying epidemic Omicron BA.5 strain infection
Jinjin CHU ; Hua TIAN ; Chuchu LI ; Zhifeng LI ; Chen DONG ; Xiaoxiao KONG ; Jiefu PENG ; Ke XU ; Jianli HU ; Changjun BAO ; Liguo ZHU
Chinese Journal of Preventive Medicine 2024;58(9):1354-1359
Objective:To explore the application of COVID-19-specific IgG antibody in identifying epidemic Omicron BA.5 strain infection.Method:Omicron BF.7/BA.5 naturally infected population, healthy population vaccinated with the COVID-19 vaccine, and Omicron BF.7/BA.5 breakthrough cases were enrolled into this study. The serum WT-S-IgG and BA.5-S-IgG were detected by indirect ELISA, and the serum-specific IgG antibody levels of different populations were compared. The application value of the two antibody titers and the ratio of the two antibodies in identifying Omicron BA.5 epidemic strain infection were explored by the ROC curve, aiming to provide technical support for pathogen diagnosis.Results:The antibody titers of WT-S-IgG and BA.5-S-IgG in the breakthrough cases were higher than those in the naturally infected population and the healthy population ( P<0.05). The area under the curve (AUC) of WT-S-IgG and BA.5-S-IgG in identifying epidemic Omicron BA.5 strain infection was 0.947 and 0.961, respectively. The AUC of BA.5-S-IgG and WT-S-IgG antibody titer ratio was 0.873. When the antibody titer ratio was 0.855, the sensitivity and specificity were 80.00% and 90.00%, respectively. According to the interval since the last infection, the AUC of the ratio of BA.5-S-IgG to WT-S-IgG antibody titer to identify the infection of epidemic strains less than 30 days and more than 30 days was 0.887 and 0.863, respectively, and the sensitivity and specificity were both above 80%. Conclusion:Both BA.5-S-IgG and WT-S-IgG, as well as the combination of these two antibodies, are of high value in the identification of epidemic strains.
5.Application of IgG antibody combination of wild strain and epidemic strain of COVID-19 in identifying epidemic Omicron BA.5 strain infection
Jinjin CHU ; Hua TIAN ; Chuchu LI ; Zhifeng LI ; Chen DONG ; Xiaoxiao KONG ; Jiefu PENG ; Ke XU ; Jianli HU ; Changjun BAO ; Liguo ZHU
Chinese Journal of Preventive Medicine 2024;58(9):1354-1359
Objective:To explore the application of COVID-19-specific IgG antibody in identifying epidemic Omicron BA.5 strain infection.Method:Omicron BF.7/BA.5 naturally infected population, healthy population vaccinated with the COVID-19 vaccine, and Omicron BF.7/BA.5 breakthrough cases were enrolled into this study. The serum WT-S-IgG and BA.5-S-IgG were detected by indirect ELISA, and the serum-specific IgG antibody levels of different populations were compared. The application value of the two antibody titers and the ratio of the two antibodies in identifying Omicron BA.5 epidemic strain infection were explored by the ROC curve, aiming to provide technical support for pathogen diagnosis.Results:The antibody titers of WT-S-IgG and BA.5-S-IgG in the breakthrough cases were higher than those in the naturally infected population and the healthy population ( P<0.05). The area under the curve (AUC) of WT-S-IgG and BA.5-S-IgG in identifying epidemic Omicron BA.5 strain infection was 0.947 and 0.961, respectively. The AUC of BA.5-S-IgG and WT-S-IgG antibody titer ratio was 0.873. When the antibody titer ratio was 0.855, the sensitivity and specificity were 80.00% and 90.00%, respectively. According to the interval since the last infection, the AUC of the ratio of BA.5-S-IgG to WT-S-IgG antibody titer to identify the infection of epidemic strains less than 30 days and more than 30 days was 0.887 and 0.863, respectively, and the sensitivity and specificity were both above 80%. Conclusion:Both BA.5-S-IgG and WT-S-IgG, as well as the combination of these two antibodies, are of high value in the identification of epidemic strains.
6.Pharmacokinetics and bioequivalence study of teriflunomide tablets in healthy Chinese subjects
Li-Li LIN ; Yan JIANG ; Qin ZHANG ; Hui-Ling QIN ; Qian ZHANG ; Yang XU ; Wei LIANG ; Lin-Ying MENG ; Zhao-Xing CHU ; Wei HU
The Chinese Journal of Clinical Pharmacology 2024;40(3):425-429
Objective To compare the pharmacokinetic profiles of the two teriflunomide tablets in healthy Chinese subjects under fasting and fed conditions and to evaluate their bioequivalence and safety.Methods A randomized,open,single-dose,parallel trial design was used to enroll 31 and 32 healthy Chinese male subjects in the fasting and fed groups,who were randomized to a single oral dose of 14 mg of either reference or test preparation of teriflunomide tablets.The plasma concentrations of teriflunomide were determined using liquid chromatography-tandem mass spectrometry method,and Phoenix WinNonlin 8.1 software was used to calculate pharmacokinetic parameters and perform bioequivalence analysis.Results Subjects received a single oral dose of the reference and test formulations of teriflunomide.The main pharmacokinetic parameters of teriflunomide in the fasting group were as follows:Cmax were(2.14±0.27)and(2.27±0.33)μg·mL-1,AUC0-72h were(105.70±11.20)and(107.72±11.77)μg·mL-1·h,tmax was 1.49 and 0.99 h;the main pharmacokinetic parameters of teriflunomide in the fed group were as follows:Cmaxwere(1.83±0.17)and(1.75±0.22)μg·mL-1,AUC0-72h were(102.66±9.18)and(101.57±13.01)μg·mL-1·h,tmax was 4.01 and 4.99 h.The 90%confidence intervals for the geometric means of Cmax and AUC0-72h for reference and test preparations in the fasting and fed groups were in the range of 80%to 125%.Conclusion The pharmacokinetic characteristics of the 2 formulations were similar under fasting and fed administration conditions,with good bioequivalence and safety;Postprandial administration may delay the time to peak of the drug.
7.Distribution and resistance surveillance of common pathogens of nosocomial infections in 10 teaching hospitals in China from 2020 to 2021
Shuguang LI ; Binghuai LU ; Yunzhuo CHU ; Rong ZHANG ; Ji ZENG ; Danhong SU ; Chao ZHUO ; Yan JIN ; Xiuli XU ; Kang LIAO ; Zhidong HU ; Hui WANG
Chinese Journal of Laboratory Medicine 2024;47(6):619-628
Objective:To investigate the spectrum and antimicrobial resistance of major pathogens causing nosocomial infections in China during 2020-2021.Methods:A total of 1 311 non-duplicated nosocomial pathogens causing bloodstream infections (BSI, n=670), hospital-acquired pneumonia (HAP, n=394) and intra-abdominal infections (IAI, n=297) were collected from 10 teaching hospitals across China. The minimum inhibitory concentrations (MICs) of clinical common strains were determined using agar dilution or broth microdilution method. Interpretation of reults followed the CLSI M100-Ed33 criteria, with data analysis conducted using WHONET-5.6 software. The Chi-square test was used to compare rates. Results:The most prevalent pathogens causing BSI were Escherichia coli (21.2%, 142/670), Klebsiella pneumoniae (14.9%, 100/670) and Staphylococcus aureus (11.5%, 77/670); the most prevalent pathogens causing HAP were K. pneumoniae (27.7%, 109/394), Acinetobacter baumanii (22.1%, 87/394) and Pseudomonas aeruginosa (18.3%, 72/394). IN IAI, E. coli (24.3%, 60/247), Enterococcus faecium and K. pneumoniae (both 14.6%, 36/247) were dominated. All S. aureus strains were susceptible to tigecycline, linezolid, daptomycin and glycopeptides. Rates of methicillin-resistant S. aureus (MRSA) and coagulase-negative Staphylococcus (MRCNS) were 36.5% (42/115) and 74.5% (38/51), respectively. The rate of vancomycin-resistant E. faecium and E. faecalis was 3.3% (3/90) and 1.9% (1/53), respectively. The prevalence of extended-spectrum β-lactamase (ESBL) was 23.7% (58/245) in K. pneumonia and 60.5% (130/215) in E. coli.The rate of carbapenem-resistant K. pneumonia and E. coli was 29.8% (73/245) and 4.2% (9/215), respectively; the percentage of tigecycline-resistant K. pneumonia and E. coli was 1.6% (4/245) and 0, respectively; the rate of colistin-resistant K. pneumonia and E. coli was 1.6% (4/245) and 2.8% (6/215), respectively; the percentage of ceftazidime/avibactam-resistant K. pneumonia and E. coli was 2.0% (5/245) and 2.3% (5/215), respectively. The rate of carbapenem-resistant A. baumanii and P. aeruginosa was 76.7% (125/163) and 28.4% (33/116), respectively. A. baumanii showed low susceptibility to most antimicrobial agents except colistin (98.8%, 161/163) and tigecycline (89.6%, 146/163). Colistin, amikacin and ceftazidime/avibactam demonstrated high antibacterial activity against P. aeruginosa with susceptility rates of 99.1% (115/116), 94.0% (109/116) and 83.6% (97/116), respectively. Conclusions:The major pathogens of nosocomial infections were K. pneumonia, E. coli, A. baumanii, P. aeruginosa and S. aureus. Nosocomial Gram-negative pathogens exhibited high susceptibilities to tigecycline, colistin and ceftazidime/avibactam. Antimicrobial resistance in A. baumannii remains a significant challenge. The increasing prevalence of carbapenem-resistant Enterobacterales underscores the urgency of antibiotics rational applications and hospital infection controls.
8.Analysis and summary of clinical characteristics of 289 patients with paroxysmal nocturnal hemoglobinuria in Zhejiang Province
Gaixiang XU ; Weimei JIN ; Baodong YE ; Songfu JIANG ; Chao HU ; Xin HUANG ; Bingshou XIE ; Huifang JIANG ; Lili CHEN ; Rongxin YAO ; Ying LU ; Linjie LI ; Jin ZHANG ; Guifang OUYANG ; Yongwei HONG ; Hongwei KONG ; Zhejun QIU ; Wenji LUO ; Binbin CHU ; Huiqi ZHANG ; Hui ZENG ; Xiujie ZHOU ; Pengfei SHI ; Ying XU ; Jie JIN ; Hongyan TONG
Chinese Journal of Hematology 2024;45(6):549-555
Objective:To further improve the understanding of paroxysmal nocturnal hemoglobinuria (PNH), we retrospectively analyzed and summarized the clinical characteristics, treatment status, and survival status of patients with PNH in Zhejiang Province.Methods:This study included 289 patients with PNH who visited 20 hospitals in Zhejiang Province. Their clinical characteristics, comorbidity, laboratory test results, and medications were analyzed and summarized.Results:Among the 289 patients with PNH, 148 males and 141 females, with a median onset age of 45 (16-87) years and a peak onset age of 20-49 years (57.8% ). The median lactic dehydrogenase (LDH) level was 1 142 (604-1 925) U/L. Classified by type, 70.9% (166/234) were classical, 24.4% (57/234) were PNH/bone marrow failure (BMF), and 4.7% (11/234) were subclinical. The main clinical manifestations included fatigue or weakness (80.8%, 235/289), dizziness (73.4%, 212/289), darkened urine color (66.2%, 179/272), and jaundice (46.2%, 126/270). Common comorbidities were hemoglobinuria (58.7% ), renal dysfunction (17.6% ), and thrombosis (15.0% ). Moreover, 82.3% of the patients received glucocorticoid therapy, 70.9% required blood transfusion, 30.7% used immunosuppressive agents, 13.8% received anticoagulant therapy, and 6.3% received allogeneic hematopoietic stem cell transplantation. The 10-year overall survival (OS) rate was 84.4% (95% CI 78.0% -91.3% ) . Conclusion:Patients with PNH are more common in young and middle-aged people, with a similar incidence rate between men and women. Common clinical manifestations include fatigue, hemoglobinuria, jaundice, renal dysfunction, and recurrent thrombosis. The 10-year OS of this group is similar to reports from other centers in China.
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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