1.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
2.Autophagy reduces bacterial translocation by regulating intestinal mucosal oxidative stress.
Xing LU ; Chengfen YIN ; Yaxiao SU ; Xinjing GAO ; Fengmei WANG ; Lei XU
Chinese Critical Care Medicine 2025;37(2):153-159
OBJECTIVE:
To investigate the mechanism of autophagy in regulating bacterial translocation in intestinal infection caused by hypervirulent Klebsiella pneumonia (hvKp) and explore the method of reducing translocation infection of intestinal bacteria.
METHODS:
Fifty C57BL/6J mice were divided into gavage group (n = 40) and control group (CO group, n = 10). The gavage group was orally administered with 200 μL/d of hvKp (colony count of 109 CFU/mL) continuously for 5 days to establish a hvKp intestinal infection model. CO group was given an equal amount of normal saline. After the experiment, the mice were anesthetized with lsofluraneand euthanized with cervical dislocation under anesthesia. Peripheral venous blood of mice was collected to detect bacterial translocation by 16S rDNA sequencing, then divided into translocation group (BT+ group) and non-translocation group (BT- group). Hematoxylin-eosin (HE) staining was used to evaluate intestinal morphology. The ultrastructural changes of intestinal tissues were observed by electron microscope. The levels of intestinal oxidative stress indicators such as superoxide dismutase (SOD), malondialdehyde (MDA) and glutathione peroxidase (GPx) were measured. Translocation was detected by in situ hybridization. The expression of tight junction protein microtubule-associated protein 1 light chain 3-II (LC3-II) and autophagy protein Beclin-1 were measured by Western blotting. The mRNA expression of tight junction proteins ZO-1 and Claudin-2 were detected by reverse transcription-polymerase chain reaction (RT-PCR). The expression of autophagy protein and tight junction protein were observed by immunofluorescence.
RESULTS:
Two out of 40 mice in the gavage group died after developing aspiration pneumonia. All mice in the CO group survived. The 16S rDNA sequencing results showed that no bacteria were detected in the peripheral blood of the CO group, but bacteria were detected in the peripheral blood of 18 mice in the gavage group, with a bacterial translocation rate of 47.4%. The BT- and BT+ groups showed intestinal mucosal tissue damage, with severe damage in the BT+ group. Compared with the CO group, the level of MDA in the BT- and BT+ groups were significantly increased, while the activities of SOD and GPx were significantly decreased. Compared with the BT- group, the MDA level in the BT+ group further increased, while the SOD and GPx activities further decreased [MDA (mmol/mg): 2.98±0.11 vs. 2.48±0.11, SOD (U/mg): 62.40±5.45 vs. 73.40±4.08, GPx (U/mg): 254.72±10.80 vs. 303.55±8.57, all P < 0.01]. The results of in situ hybridization detection showed that after continuous gastric lavage for 5 days, displaced hvKp was detected in the intestinal mucosal lamina propria and liver tissue of the BT+ group. Compared with the CO group, the protein expressions of LC3-II and Beclin-1 in the BT- and BT+ groups were significantly increased. The protein expressions of LC3-II and Beclin-1 in the BT+ group were obviously lower than those in the BT- group (LC3-II/β-actin: 0.38±0.04 vs. 0.70±0.09, Beclin-1/β-actin: 0.62±0.05 vs. 0.86±0.05, both P < 0.01), and there were autophagosomes in the intestinal mucosa. These results indicated that intestinal mucosal autophagy was activated after hvKp continuous gavage. Compared with CO group, the mRNA expressions of ZO-1 and Claudin-2 in the BT- and BT+ groups were significantly decreased. Compared with the BT- group, the mRNA expressions of ZO-1 and Claudin-2 in the BT+ group was further reduced [ZO-1 mRNA (2-ΔΔCT): 0.78±0.06 vs. 0.88±0.06, Claudin-2 mRNA (2-ΔΔCT): 0.40±0.04 vs. 0.70±0.06, both P < 0.01]. The immunofluorescence results showed that the fluorescence intensity of LC3-II, Beclin-1, ZO-1, and Claudin-2 in the BT+ group was significantly lower than that in the BT- group.
CONCLUSION
HvKp can activate intestinal mucosal autophagy and reduce the damage to intestinal mucosal barrier function by down-regulating oxidative stress level, reduce the occurrence of bacterial translocation.
Animals
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Oxidative Stress
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Mice, Inbred C57BL
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Autophagy
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Intestinal Mucosa/microbiology*
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Bacterial Translocation
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Mice
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Klebsiella Infections/microbiology*
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Superoxide Dismutase/metabolism*
;
Beclin-1
3.RA synovial fluid induces the polarization of neutrophils towards N1 type through the MEK/ERK pathway
Chuanhao XU ; Yanmeng LI ; Chi ZHANG ; Fengmei TAN ; Hong DENG ; Kaibo WANG ; Mei HAN
Immunological Journal 2025;41(3):165-172
Objective To investigate the effects of synovial fluid from patients with rheumatoid arthritis(RA)on the polarization of normal neutrophils and the MEK/ERK signaling pathway,thereby providing experimental evidence to elucidate the pathological mechanisms of RA and offering novel insights into its treatment.Methods Synovial fluid samples were collected from 20 RA patients and 20 osteoarthritis(OA)patients.ELISA was used to measure the levels of interferon-gamma(IFN-γ),tumor necrosis factor-alpha(TNF-α),transforming growth factor-beta(TGF-β)and interleukin-8(IL-8)in the synovial fluid.Neutrophils were isolated from the peripheral blood of healthy donors,and their purity was confirmed by flow cytometry.Neutrophils were then treated with synovial fluid and divided into four groups:Control(untreated),RA(treated with 10%RA synovial fluid),OA(treated with 10%OA synovial fluid),and anti-IFN-β(treated with 20 μg anti-IFN-β antibody as an N2-type control).Western blot was used to assess the changes in the expression of IFN-γ,TNF-α,TGF-β,MEK,p-MEK,ERK and p-ERK.Additionally,the MEK inhibitor PD0325901 was used to block the MEK/ERK pathway,and subsequent changes in IFN-γ and TGF-β expression in neutrophils were evaluated.Results The levels of IFN-γ,TNF-α,TGF-β and IL-8 in the synovial fluid of RA patients were significantly higher than those in OA patients.After intervention with RA synovial fluid,the relative expression of IFN-γ and TNF-α in neutrophils were significantly increased compared to those in the untreated control group and the OA group.While,TGF-β expression in the RA group was lower than that in both the control and anti-IFN-β groups.The relative expression of p-MEK and p-ERK in the RA group were significantly higher than those in the control,OA and Anti-IFN-β groups.Upon addition of the MEK inhibitor,the relative expression of p-MEK and p-ERK were reduced.Furthermore,in both the RA and OA groups,the relative expression levels of IFN-γ decreased,while the expression of TGF-β increased.Conclusion Synovial fluid from RA patients contains higher of IFN-γ,TNF-αand TGF-β,which activate neutrophils through phosphorylation of the MEK/ERK signaling pathway,promoting their polarization toward the pro-inflammatory N1 phenotype.
4.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.
5.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.
6.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.
7.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.
8.X-ray irradiation promotes apoptosis in rat astrocytes by upregulating AQP4 and inhibiting autophagy
Yaozu XIONG ; Changhua YU ; Na CHEN ; Fengmei CUI ; Yu TU ; Xiaoting XU
Chinese Journal of Radiation Oncology 2025;34(4):369-378
Objective:To explore the changes of aquaporin-4 (AQP4) expression in rat astrocytes (RA) and rat model of radiation-induced brain injury (RIBI) after X-ray radiation, as well as its impact on cell functions.Methods:Logarithmic-phase RA were divided into four groups: Sham group (no treatment), AQP4 siRNA group (transfected with AQP4 lentivirus), IR group (single 20 Gy X-ray irradiation), and IR+AQP4 siRNA group (transfected with AQP4 lentivirus followed by single 20 Gy X-ray irradiation). The cell proliferation viability after radiation was detected using the CCK-8 assay. The relative expression of AQP4 mRNA in each group was measured by quantitative reverse transcription polymerase chain reaction, and the optimal AQP4 siRNA lentiviral sequence was selected for further studies. Western blot was used to detect the relative expression levels of AQP4, phosphorylated histone H2A family member X (γH2AX), autophagy-related proteins, glial fibrillary acidic protein (GFAP), proteins in the phosphatidylinositol-3-kinase (PI3K) / protein kinase B (AKT) / mammalian target of rapamycin (mTOR) pathway, and apoptosis-related proteins in cells from each group. Immunofluorescence (co-expression) staining was used to detect the expression of AQP4, γH2AX, GFAP, LC3, phosphatase and tensin homolog-induced kinase 1 (PINK1) in cells from each group, as well as the expression of AQP4 and LC3 in brain tissues from the rat RIBI model. Flow cytometry was used to detect the apoptosis rate of cells in each group. The differences between two groups were compared using the t-test, while the differences among multiple groups were evaluated using analysis of variance (ANOVA) and Tukey's multiple comparison test. Results:The proliferation viability of RA decreased to its lowest level at 24 h after irradiation. At the 24 h post-irradiation time point, the expression levels of γH2AX and AQP4 were significantly increased in the IR group compared with the Sham group. The fluorescence intensity of AQP4 in the AQP4 siRNA group was lower than that in the Sham group. The cells in the IR group showed significant enlargement and irregular morphology, with a significant increase in AQP4 fluorescence intensity, while the IR+AQP4 siRNA group showed mild cell enlargement and lower AQP4 fluorescence intensity compared with the IR group. Compared with the Sham group, the expression of microtubule-associated protein1 light chain 3 (LC3) and PINK1 proteins were decreased in the IR group, while the IR+AQP4 siRNA group showed higher expression levels compared with the IR group. Compared with the Sham group, the IR group showed increased expression of AQP4, GFAP, and sequestosome 1 (P62) proteins, decreased expression of Beclin-1 (BECN1) and LC3 proteins, and a reduced LC3-II/LC3-I ratio, while the IR+AQP4 siRNA group exhibited a restored LC3-II/LC3-I ratio. The apoptosis rate in the AQP4 siRNA group was similar to that in the Sham group, while the IR group showed a significantly increased apoptosis rate, and the IR+AQP4 siRNA group had a lower apoptosis rate than the IR group. The expression of caspase-3 (Caspase3) decreased in the IR group, while the expression of PI3K, AKT, mTOR, extracellular signal-regulated kinase (ERK)1/2, and cleaved Caspase3 protein increased; these changes were partially reversed in the IR+AQP4 siRNA group. In the rat RIBI model, the expression area and intensity of AQP4 were higher in the IR group compared with the Sham group, while LC3 expression showed the opposite pattern.Conclusions:The possible molecular mechanism of RA apoptosis caused by X-ray irradiation is that irradiation promotes the expression of AQP4, causes cell swelling, inhibits autophagy, and prevents cells from clearing damaged organelles in a timely manner, thereby promoting cell apoptosis.
9.Coinfection with coxsackievirus A6 and B1 in a Syrian hamster animal model
Jinghan HOU ; Suqin DUAN ; Hongjie XU ; Wenting SUN ; Mingxue LI ; Yanyan LI ; Weihua JIN ; Lixiong CHEN ; Quan LIU ; Yuan ZHAO ; Fengmei YANG ; Zhanlong HE
Chinese Journal of Comparative Medicine 2025;35(1):30-40
Objective To establish an animal model of hand,foot,and mouth disease(HFMD)in Syrian hamsters coinfected with coxsackievirus A6(CVA6)and coxsackievirus B1(CVB1).Methods 42 Syrian hamsters were divided into a CVA6 infection group,CVB1 infection group,CVA6 and CVB1 coinfection group and control group.A HFMD model was established by nasal instillation of virus solution and phosphate-buffered saline.Clinical and physiological indicators and detoxification status were monitored and recorded for 15 d,and animals were selected on day 7(D7)after infection for histopathology and viral antigen and nucleic acid testing.Results Hamsters in the single-infection and coinfection groups showed clinical symptoms similar to human HFMD.White blood cell,neutrophil,and lymphocyte result were characteristic of viral infection.Both viral nucleic acids were detected in throat swabs,feces,blood,and tissues and both viruses were isolated from fecal samples.Pathological damage and positive co-localization of CVA6 and CVB1 viral antigen proteins and nucleic acids were found in brain and other tissues.Conclusions Nasal instillation of a CVA6 and CVB1 mixture can successfully coinfect Syrian hamsters,replicate herpes infection similar to human HFMD,and cause pathological viral myocarditis and encephalitis damage.The result showed that the coinfection group was more seriously affected than the single-infection group,with worse clinical symptoms,increased viral replication,and obvious tissue pathological damage.This study provides a reference for further basic and clinical research into human enterovirus coinfection.
10.Changing distribution and antimicrobial resistance profiles of clinical isolates in children:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Qing MENG ; Lintao ZHOU ; Yunsheng CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Chuanqing WANG ; Aimin WANG ; Lei ZHU ; Jinhua MENG ; Hong ZHANG ; Chun WANG ; Fang DONG ; Zhiyong LÜ ; Shuping ZHOU ; Yan ZHOU ; Shifu WANG ; Fangfang HU ; Yingchun XU ; Xiaojiang ZHANG ; Zhaoxia ZHANG ; Ping JI ; Wei JIA ; Gang LI ; Kaizhen WEN ; Yirong ZHANG ; Yan JIN ; Chunhong SHAO ; Yong ZHAO ; Ping GONG ; Chao ZHUO ; Danhong SU ; Bin SHAN ; Yan DU ; Sufang GUO ; Jiao FENG ; Ziyong SUN ; Zhongju CHEN ; Wen'en LIU ; Yanming LI ; Xiaobo MA ; Yanping ZHENG ; Dawen GUO ; Jinying ZHAO ; Ruizhong WANG ; Hua FANG ; Lixia ZHANG ; Juan MA ; Jihong LI ; Zhidong HU ; Jin LI ; Yuxing NI ; Jingyong SUN ; Ruyi GUO ; Yan ZHU ; Yi XIE ; Mei KANG ; Yuanhong XU ; Ying HUANG ; Shanmei WANG ; Yafei CHU ; Hua YU ; Xiangning HUANG ; Lianhua WEI ; Fengmei ZOU ; Han SHEN ; Wanqing ZHOU ; Yunzhuo CHU ; Sufei TIAN ; Shunhong XUE ; Hongqin GU ; Xuesong XU ; Chao YAN ; Bixia YU ; Jinju DUAN ; Jianbang KANG ; Jiangshan LIU ; Xuefei HU ; Yunsong YU ; Jie LIN ; Yunjian HU ; Xiaoman AI ; Chunlei YUE ; Jinsong WU ; Yuemei LU
Chinese Journal of Infection and Chemotherapy 2025;25(1):48-58
Objective To understand the changing composition and antibiotic resistance of bacterial species in the clinical isolates from outpatient and emergency department(hereinafter referred to as outpatients)and inpatient children over time in various hospitals,and to provide laboratory evidence for rational antibiotic use.Methods The data on clinically isolated pathogenic bacteria and antimicrobial susceptibility of isolates from outpatients and inpatient children in the CHINET program from 2015 to 2021 were collected and analyzed.Results A total of 278 471 isolates were isolated from pediatric patients in the CHINET program from 2015 to 2021.About 17.1%of the strains were isolated from outpatients,primarily group A β-hemolytic Streptococcus,Escherichia coli,and Staphylococcus aureus.Most of the strains(82.9%)were isolated from inpatients,mainly SS.aureus,E.coli,and H.influenzae.The prevalence of methicillin-resistant S.aureus(MRSA)in outpatients(24.5%)was lower than that in inpatient children(31.5%).The MRSA isolates from outpatients showed lower resistance rates to the antibiotics tested than the strains isolated from inpatient children.The prevalence of vancomycin-resistant Enterococcus faecalis or E.faecium and penicillin-resistant S.pneumoniae was low in either outpatients or inpatient children.S.pneumoniae,β-hemolytic Streptococcus and S.viridans showed high resistance rates to erythromycin.The prevalence of erythromycin-resistant group A β-hemolytic Streptococcus was higher in outpatients than that in inpatient children.The prevalence of β-lactamase-producing H.influenzae showed an overall upward trend in children,but lower in outpatients(45.1%)than in inpatient children(59.4%).The prevalence of carbapenem-resistant Klebsiella pneumoniae(CRKpn),carbapenem-resistant Pseudomonas aeruginosa(CRPae)and carbapenem-resistant Acinetobacter baumannii(CRAba)was 14%,11.7%,47.8%in outpatients,but 24.2%,20.6%,and 52.8%in inpatient children,respectively.The prevalence of multidrug-resistant E.coli,K.pneumoniae,Proteus mirabilis,P.aeruginosa and A.baumannii strains was lower in outpatients than in inpatient children.The prevalence of fluoroquinolone-resistant E.coli,ESBLs-producing K.pneumoniae,ESBLs-producing P.mirabilis,carbapenem-resistant E.coli(CREco),CRKpn,and CRPae was lower in children in outpatients than in inpatient children,but the prevalence of CRAba in 2021 was higher than in inpatient children.Conclusions The distribution of clinical isolates from children is different between outpatients and inpatients.The prevalence of MRSA,ESBL,and CRO was higher in inpatient children than in outpatients.Antibiotics should be used rationally in clinical practice based on etiological diagnosis and antimicrobial susceptibility test results.Ongoing antimicrobial resistance surveillance and prevention and control of hospital infections are crucial to curbing bacterial resistance.

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