1.The influence of diabetes mellitus and high-sensitivity C-reactive protein on the risk of diges-tive system malignancy: a prospective cohort study
Kuan LIU ; Jiaxing LI ; Chao MA ; Wanchao WANG ; Yuan TIAN ; Zhigang DONG ; Wenqiang WEI ; Shuohua CHEN ; Shouling WU ; Siqing LIU
Chinese Journal of Digestive Surgery 2025;24(1):93-102
Objective:To investigate the influence of diabetes mellitus (DM) and high-sen-sitivity C-reactive protein (Hs-CRP) on the risk of digestive system malignancy.Methods:The pro-spective cohort study was conducted. The clinical data of 93 928 participants who participated health examination in 9 hospitals at Tangshan, including Kailuan General Hospital Affiliated to North China University of Science and Technology et al, in 2006 were selected. According to the presence or absence of DM and the level of Hs-CRP, all participants were divided into 4 groups, including the DM(-)CRP(-) group defined as absence of DM and Hs-CRP ≤3 mg/L, the DM(-)CRP(+) group defined as absence of DM and Hs-CRP>3 mg/L, the DM(+)CRP(-) group defined as presence of DM and Hs-CRP ≤3 mg/L, and the DM(+)CRP(+) group defined as presence of DM and Hs-CRP >3 mg/L. The data of participants were collected by a fixed team of physicians. The first physical examination in 2006 was taken as the starting point for follow-up. The end event of follow-up was defined as the occurrence of digestive system malignancy or death, and the follow-up was up to December 31, 2021. Observation indicators: (1) comparison of clinical data among the 4 groups of participants; (2) the incidence and cumulative incidence rate of digestive system malignancy in participants; (3) influence of DM and Hs-CRP level on the risk of digestive system malignancy; (4) the combined influence of DM and Hs-CRP level on the risk of digestive system malignancy; (5) sensitivity analysis. Comparison of measurement data with normal distribution among multiple groups was conducted using the one-way analysis of variance. For pairwise comparison, least significant difference test was used for homogeneity of variance, and Dunnett′s T3 test was used for heterogeneity of variance. Comparison of measurement data with skewed distribution among multiple groups was conducted using the Kruskal-Wallis rank sum test, and Dunn-Bonferroni test was used for pairwise comparison. Comparison of count data among multiple groups was conducted using the chi-square test, and Bonferroni test was used among multiple comparisons. The Kaplan-Meier method was used to plot cumulative incidence curve, and Log-rank test was used for cumulative incidence rate analysis. The Cox proportional risk model was used for multivariate analysis. All models were adjusted for relevant confounders. Results:(1) Comparison of clinical data among the 4 groups of participants. Of the 93 928 participants, there were 70 743 cases in the DM(-)CRP(-) group, 14 644 cases in the DM(-)CRP(+) group, 6 425 cases in the DM(+)CRP(-) group, and 2 116 cases in the DM(+)CRP(+) group. There were significant differences in gender, age, fasting blood glucose, Hs-CRP, triglyceride, alanine aminotransferase, body mass index, marrital status, smoking, drinking, high school degree or above, physical exercise, high salt diet, high fat diet, positive hepatitis B virus surface antigen, fatty liver, liver cirrhosis, gallstone, taking hypoglycemic drugs, taking lipid-lowering drugs among the 4 groups of participants ( P<0.05). (2) The incidence and cumulative incidence rate of digestive system malignancy in participants. At the end-up of follow-up, 2 008 cases developed digestive system malignancy in the 93 928 participants, including 717 cases of colorectal cancer, 456 cases of liver cancer, 396 cases of gastric cancer, 195 cases of esophageal cancer, 144 cases of pancreatic cancer, 65 cases of gallbladder cancer or extrahepatic cholangiocarcinoma, 35 cases of small bowel cancer. The cumulative incidence rates of digestive system malignancy were 2.19%, 2.42%, 2.86%, 3.59% in participants of the DM(-)CRP(-) group, DM(-)CRP(+) group, DM(+)CRP(-) group, DM(+)CRP(+) group, respectively, showing a significant difference among the 4 groups ( χ2=31.72, P<0.05). (3) Influence of DM and Hs-CRP level on the risk of digestive system malignancy. After adjusting for the confounding factors of the participants, results of multivariate analysis showed that DM and Hs-CRP >3 mg/L were independent influencing factors for the incidence of digestive system malignancy ( hazard ratio=1.32, 1.19, 95% confidence interval as 1.13-1.56, 1.06-1.33, P<0.05). Futher analysis showed that there was a significant difference in interaction between DM and Hs-CRP >3 mg/L ( P<0.05). (4) The combined influence of DM and Hs-CRP level on the risk of digestive system malign-ancy. After adjusting for confounding factors, results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(-)CRP(+) group, DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratio=1.14, 1.23, 1.79, 95% confidence interval as 1.01-1.29, 1.02-1.48, 1.38-2.31, P<0.05). In the site-specific analysis of digestive system malignancy, using the DM(-)CRP(-) group as the control group, the risk of incidence of liver cancer increased in the DM(-)CRP(+) group ( hazard ratio=1.37, 95% confidence interval as 1.07-1.75, P<0.05), the risk of incidence of liver cancer and pancrea-tic cancer increased in the DM(+)CRP(-) group ( hazard ratio=1.60, 1.74, 95% confidence interval as 1.16-2.21, 1.00-3.02, P<0.05), the risk of incidence of small bowel cancer, pancreatic cancer and colorectal cancer increased in the DM(+)CRP(+) group ( hazard ratio=5.05, 2.31, 2.23, 95% confidence interval as 1.57-16.21, 1.00-5.31, 1.54-3.24, P<0.05). (5) Sensitivity analysis. After adjusting for confounding factors of excluding 3 types of participants (103 cases of digestive system malignancy within 1 year of follow-up, 2 370 cases of taking glucose-lowering drugs, and 915 cases of taking lipid-lowering drugs), results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratioexcluding cases of digestive system malignancy within 1 year of follow-up=1.26, 1.66, 95% confidence interval as 1.04-1.52, 1.26-2.18, P<0.05; hazard ratioexcluding cases taking glucose-lowering drugs=1.23, 1.75, 95% confidence interval as 1.02-1.49, 1.31-2.33, P<0.05; hazard ratioexcluding cases taking lipid-lowering drugs=1.24, 1.80, 95% confidence interval as 1.03-1.49, 1.39-2.34, P<0.05). Conclusions:DM and Hs-CRP >3 mg/L are independent influencing factors for the incidence of digestive system malignancy. There is an interation and synergistic effect between DM and Hs-CRP to promote the incidence of digestive system malignancy.
2.Screening and validation of glucose metabolism genes in osteoarthritis
Kexin LIU ; Chao MA ; Kai LIU ; Maochen HAO ; Xingru WANG ; Lingting MENG ; Mei DONG ; Jianzhong WANG
Chinese Journal of Tissue Engineering Research 2025;29(20):4181-4189
BACKGROUND:Glucose metabolism plays a crucial role in maintaining the normal physiological function of the body.Glucose metabolism disorder can lead to a range of health problems.At present,the molecular mechanism of glucose metabolism and potential gene targets in osteoarthritis need to be further studied.OBJECTIVE:To analyze the genes related to glucose metabolism in osteoarthritis by bioinformatics methods,and to verify them by cell experiments in vitro,so as to provide new ideas for prevention and treatment of osteoarthritis from the perspective of glucose metabolism.METHODS:Differentially expressed genes and glucose metabolism related genes were screened out from GEO database and GeneCards database.The genes related to both osteoarthritis and glucose metabolism were obtained.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were used to screen the functions and pathways of these genes.To further investigate the interactions between these genes,a protein-protein interaction network was constructed and computational methods using Cytoscape software were utilized to identify key genes(Hub genes)for osteoarthritis glucose metabolism.In addition,CIBERSORT algorithm was used to analyze immune cell infiltration in GSE98918 data set.Finally,the expression of Hub gene was verified by cell experiment in vitro.RESULTS AND CONCLUSION:A total of 134 osteoarthritis glucose metabolism-related genes were obtained.GO enrichment analysis showed that GO was mainly involved in the reaction of toxic substances,the positive regulation of inflammatory reaction,the reaction of lipopolysaccharide and so on.KEGG enrichment analysis showed that it was closely related to PI3K-Akt signaling pathway,interleukin-17 signaling pathway,and AGE-RAGE signaling pathway in diabetic complications.Macrophages,monocytes,resting natural killer cells,regulatory T cells,and CD8+T cells were the main infiltrating cells obtained by immune infiltration analysis.In vitro cell experiments showed that the expression of Hub genes SERPINF1,TAC1,GLUL,APOE,and TMEM176A in the experimental group was significantly different from that in the control group.The mRNA expression of HLA-DRA was not statistically significant.The results show that SERPINF1,TAC1,Glul,APOE,and TMEM176A may be the key genes of glucose metabolism in osteoarthritis,and may be potential new targets for the prevention and treatment of osteoarthritis.
3.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.
4.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.
5.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.
6.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.
7.Screening and validation of glucose metabolism genes in osteoarthritis
Kexin LIU ; Chao MA ; Kai LIU ; Maochen HAO ; Xingru WANG ; Lingting MENG ; Mei DONG ; Jianzhong WANG
Chinese Journal of Tissue Engineering Research 2025;29(20):4181-4189
BACKGROUND:Glucose metabolism plays a crucial role in maintaining the normal physiological function of the body.Glucose metabolism disorder can lead to a range of health problems.At present,the molecular mechanism of glucose metabolism and potential gene targets in osteoarthritis need to be further studied.OBJECTIVE:To analyze the genes related to glucose metabolism in osteoarthritis by bioinformatics methods,and to verify them by cell experiments in vitro,so as to provide new ideas for prevention and treatment of osteoarthritis from the perspective of glucose metabolism.METHODS:Differentially expressed genes and glucose metabolism related genes were screened out from GEO database and GeneCards database.The genes related to both osteoarthritis and glucose metabolism were obtained.Gene Ontology(GO)and Kyoto Encyclopedia of Genes and Genomes(KEGG)enrichment analysis were used to screen the functions and pathways of these genes.To further investigate the interactions between these genes,a protein-protein interaction network was constructed and computational methods using Cytoscape software were utilized to identify key genes(Hub genes)for osteoarthritis glucose metabolism.In addition,CIBERSORT algorithm was used to analyze immune cell infiltration in GSE98918 data set.Finally,the expression of Hub gene was verified by cell experiment in vitro.RESULTS AND CONCLUSION:A total of 134 osteoarthritis glucose metabolism-related genes were obtained.GO enrichment analysis showed that GO was mainly involved in the reaction of toxic substances,the positive regulation of inflammatory reaction,the reaction of lipopolysaccharide and so on.KEGG enrichment analysis showed that it was closely related to PI3K-Akt signaling pathway,interleukin-17 signaling pathway,and AGE-RAGE signaling pathway in diabetic complications.Macrophages,monocytes,resting natural killer cells,regulatory T cells,and CD8+T cells were the main infiltrating cells obtained by immune infiltration analysis.In vitro cell experiments showed that the expression of Hub genes SERPINF1,TAC1,GLUL,APOE,and TMEM176A in the experimental group was significantly different from that in the control group.The mRNA expression of HLA-DRA was not statistically significant.The results show that SERPINF1,TAC1,Glul,APOE,and TMEM176A may be the key genes of glucose metabolism in osteoarthritis,and may be potential new targets for the prevention and treatment of osteoarthritis.
8.Predictive value of different obesity indicators for colorectal cancer in different sex populations
Chao MA ; Jiaxing LI ; Kuan LIU ; Wanchao WANG ; Yuan TIAN ; Taixian JIANG ; Zhigang DONG ; Wenqiang WEI ; Shouling WU ; Siqing LIU
Chinese Journal of Gastrointestinal Surgery 2025;28(1):75-80
Objective:To investigate the predictive value of different obesity indicators for colorectal cancer (CRC) risk in different gender populations.Methods:This observational study was conducted within the Kailuan Study (Registration Number: ChiCTR-TNC-11001489). From July 2006 to October 2007, a total of 101,510 employed and retired individuals underwent health examinations, including gastrointestinal disease screening, hematological tests, and questionnaires, at Kailuan General Hospital and its 10 affiliated hospitals. After excluding those with incomplete data, 93,606 participants were included in this study and divided into male (74 852) and female (18 754) groups. CRC incidence was collected through physical examinations and questionnaires every two years. Each participant's follow-up period began at the time of the questionnaire and ended upon CRC diagnosis, death, or December 31, 2021. Body Mass Index (BMI), waist circumference, waist-to-hip ratio (WHR), and waist-to-height ratio (WHtR) were quartiled (Q1, Q2, Q3, Q4), with Q1 serving as the control group. After adjusting for traditional risk factors such as age, total cholesterol, triglycerides, diabetes, hypertension, smoking status, alcohol consumption, and physical exercise, Cox regression models were used to calculate the correlations between BMI, waist circumference, WHR, WHtR, and CRC incidence in both male and female populations.Results:The age of all patients was (51±12) years, BMI was (25.06±3.49) kg/m 2, waist circumference was (86.94±9.97) cm, hip circumference was (97.30±8.81) cm, WHR was 0.89±0.07, and WHtR was 0.52±0.06.Female participants had significantly lower BMI, waist circumference, WHR, and WHtR compared to males, with statistically significant differences (all P<0.05). The mean follow-up duration for all participants was 15.01 (14.10±2.66) years, during which 718 CRC cases were identified, including 626 males (0.83%) and 92 females (0.49%). Cox proportional hazards models for males showed that CRC risk increased with waist circumference from Q3 (HR=1.43, 95%CI: 1.13-1.79, P=0.003) to Q4 (HR=1.45,95%CI: 1.14-1.82, P=0.002). Similarly, CRC risk increased with WHR from Q3 (HR=1.22, 95%CI: 1.01-1.53, P=0.007) to Q4 (HR=1.43, 95%CI: 1.14-1.79, P=0.002) and with WHtR from Q3 (HR=1.37, 95%CI: 1.08-1.74, P=0.009) to Q4 (HR=1.68, 95%CI: 1.33-2.12, P<0.001). For females, CRC risk increased with waist circumference from Q2 (HR=2.37, 95%CI: 1.20-4.67, P=0.012) to Q3 (HR=2.42, 95%CI: 1.21-4.84, P=0.013) but decreased in Q4 ( HR=2.08, 95%CI: 1.02-4.25, P=0.043). CRC risk increased significantly with WHR from Q2 (HR=2.20, 95%CI: 1.11-4.39, P=0.024) to Q3 (HR=2.89, 95%CI: 1.48-5.67, P=0.002) in females but was not statistically significant in Q4 ( P=0.074). Among females, CRC risk also increased significantly with WHtR in Q2 (HR=2.30, 95% CI: 1.16-4.56, P=0.017) and Q4 (HR=2.64, 95%CI: 1.32-5.29, P=0.006). There were no statistically significant differences in CRC risk associated with BMI in either male or female populations (both P>0.05). Conclusion:Waist circumference, WHR, and WHtR were better predictors of CRC risk than BMI in both male and female populations.
9.Administrative burden among primary healthcare professionals and its impact mechanism on job burnout:An exploratory sequential mixed-methods study
Shi-chao ZHAO ; Ming-ze XIN ; Zi-qian TANG ; Ya-fang DONG ; He-xi LI ; Hui-fen MA ; Tao WANG
Chinese Journal of Health Policy 2025;18(9):31-38
Objective:To examine the manifestations and causes of administrative burden among primary healthcare professionals,and to explore its impact on job burnout through the mediating role of role conflict,providing theoretical and empirical support for governance-level burden-reduction strategies.Methods:An exploratory sequential mixed-methods design was employed,focusing on primary healthcare professionals in Shandong Province.In the first phase,in-depth interviews were conducted with 175 participants;in the second phase,a questionnaire survey of 1,096 participants and follow-up interviews with 107 participants were carried out.Results:The proportions of respondents who reported"heavy"or"very heavy"burdens were 62.7%for inspection,54.8%for documentation,51.8%for reporting,and 24.4%for meetings.Structural equation modeling showed that administrative burden had a direct effect on job burnout(0.150)and an indirect effect through role conflict(0.093).Qualitative findings further indicated that administrative burden largely stemmed from public health traceability requirements and medical insurance policies,and operated through both resource-based and value-based conflicts.Conclusions:Primary healthcare professionals face considerable administrative burdens,which may heighten job burnout through role conflict.Governance reforms should optimize inspection and assessment,streamline data reporting,refine record-keeping,and promote collaborative governance to break the chain of institutional pressure leading to burnout.
10.The influence of diabetes mellitus and high-sensitivity C-reactive protein on the risk of diges-tive system malignancy: a prospective cohort study
Kuan LIU ; Jiaxing LI ; Chao MA ; Wanchao WANG ; Yuan TIAN ; Zhigang DONG ; Wenqiang WEI ; Shuohua CHEN ; Shouling WU ; Siqing LIU
Chinese Journal of Digestive Surgery 2025;24(1):93-102
Objective:To investigate the influence of diabetes mellitus (DM) and high-sen-sitivity C-reactive protein (Hs-CRP) on the risk of digestive system malignancy.Methods:The pro-spective cohort study was conducted. The clinical data of 93 928 participants who participated health examination in 9 hospitals at Tangshan, including Kailuan General Hospital Affiliated to North China University of Science and Technology et al, in 2006 were selected. According to the presence or absence of DM and the level of Hs-CRP, all participants were divided into 4 groups, including the DM(-)CRP(-) group defined as absence of DM and Hs-CRP ≤3 mg/L, the DM(-)CRP(+) group defined as absence of DM and Hs-CRP>3 mg/L, the DM(+)CRP(-) group defined as presence of DM and Hs-CRP ≤3 mg/L, and the DM(+)CRP(+) group defined as presence of DM and Hs-CRP >3 mg/L. The data of participants were collected by a fixed team of physicians. The first physical examination in 2006 was taken as the starting point for follow-up. The end event of follow-up was defined as the occurrence of digestive system malignancy or death, and the follow-up was up to December 31, 2021. Observation indicators: (1) comparison of clinical data among the 4 groups of participants; (2) the incidence and cumulative incidence rate of digestive system malignancy in participants; (3) influence of DM and Hs-CRP level on the risk of digestive system malignancy; (4) the combined influence of DM and Hs-CRP level on the risk of digestive system malignancy; (5) sensitivity analysis. Comparison of measurement data with normal distribution among multiple groups was conducted using the one-way analysis of variance. For pairwise comparison, least significant difference test was used for homogeneity of variance, and Dunnett′s T3 test was used for heterogeneity of variance. Comparison of measurement data with skewed distribution among multiple groups was conducted using the Kruskal-Wallis rank sum test, and Dunn-Bonferroni test was used for pairwise comparison. Comparison of count data among multiple groups was conducted using the chi-square test, and Bonferroni test was used among multiple comparisons. The Kaplan-Meier method was used to plot cumulative incidence curve, and Log-rank test was used for cumulative incidence rate analysis. The Cox proportional risk model was used for multivariate analysis. All models were adjusted for relevant confounders. Results:(1) Comparison of clinical data among the 4 groups of participants. Of the 93 928 participants, there were 70 743 cases in the DM(-)CRP(-) group, 14 644 cases in the DM(-)CRP(+) group, 6 425 cases in the DM(+)CRP(-) group, and 2 116 cases in the DM(+)CRP(+) group. There were significant differences in gender, age, fasting blood glucose, Hs-CRP, triglyceride, alanine aminotransferase, body mass index, marrital status, smoking, drinking, high school degree or above, physical exercise, high salt diet, high fat diet, positive hepatitis B virus surface antigen, fatty liver, liver cirrhosis, gallstone, taking hypoglycemic drugs, taking lipid-lowering drugs among the 4 groups of participants ( P<0.05). (2) The incidence and cumulative incidence rate of digestive system malignancy in participants. At the end-up of follow-up, 2 008 cases developed digestive system malignancy in the 93 928 participants, including 717 cases of colorectal cancer, 456 cases of liver cancer, 396 cases of gastric cancer, 195 cases of esophageal cancer, 144 cases of pancreatic cancer, 65 cases of gallbladder cancer or extrahepatic cholangiocarcinoma, 35 cases of small bowel cancer. The cumulative incidence rates of digestive system malignancy were 2.19%, 2.42%, 2.86%, 3.59% in participants of the DM(-)CRP(-) group, DM(-)CRP(+) group, DM(+)CRP(-) group, DM(+)CRP(+) group, respectively, showing a significant difference among the 4 groups ( χ2=31.72, P<0.05). (3) Influence of DM and Hs-CRP level on the risk of digestive system malignancy. After adjusting for the confounding factors of the participants, results of multivariate analysis showed that DM and Hs-CRP >3 mg/L were independent influencing factors for the incidence of digestive system malignancy ( hazard ratio=1.32, 1.19, 95% confidence interval as 1.13-1.56, 1.06-1.33, P<0.05). Futher analysis showed that there was a significant difference in interaction between DM and Hs-CRP >3 mg/L ( P<0.05). (4) The combined influence of DM and Hs-CRP level on the risk of digestive system malign-ancy. After adjusting for confounding factors, results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(-)CRP(+) group, DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratio=1.14, 1.23, 1.79, 95% confidence interval as 1.01-1.29, 1.02-1.48, 1.38-2.31, P<0.05). In the site-specific analysis of digestive system malignancy, using the DM(-)CRP(-) group as the control group, the risk of incidence of liver cancer increased in the DM(-)CRP(+) group ( hazard ratio=1.37, 95% confidence interval as 1.07-1.75, P<0.05), the risk of incidence of liver cancer and pancrea-tic cancer increased in the DM(+)CRP(-) group ( hazard ratio=1.60, 1.74, 95% confidence interval as 1.16-2.21, 1.00-3.02, P<0.05), the risk of incidence of small bowel cancer, pancreatic cancer and colorectal cancer increased in the DM(+)CRP(+) group ( hazard ratio=5.05, 2.31, 2.23, 95% confidence interval as 1.57-16.21, 1.00-5.31, 1.54-3.24, P<0.05). (5) Sensitivity analysis. After adjusting for confounding factors of excluding 3 types of participants (103 cases of digestive system malignancy within 1 year of follow-up, 2 370 cases of taking glucose-lowering drugs, and 915 cases of taking lipid-lowering drugs), results of multivariate analysis showed that using the DM(-)CRP(-) group as the control group, the risk of incidence of digestive system malignancy increased in the DM(+)CRP(-) group, and DM(+)CRP(+) group, respectively ( hazard ratioexcluding cases of digestive system malignancy within 1 year of follow-up=1.26, 1.66, 95% confidence interval as 1.04-1.52, 1.26-2.18, P<0.05; hazard ratioexcluding cases taking glucose-lowering drugs=1.23, 1.75, 95% confidence interval as 1.02-1.49, 1.31-2.33, P<0.05; hazard ratioexcluding cases taking lipid-lowering drugs=1.24, 1.80, 95% confidence interval as 1.03-1.49, 1.39-2.34, P<0.05). Conclusions:DM and Hs-CRP >3 mg/L are independent influencing factors for the incidence of digestive system malignancy. There is an interation and synergistic effect between DM and Hs-CRP to promote the incidence of digestive system malignancy.

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