1.Changing antimicrobial resistance profiles of Burkholderia cepacia in hospitals across China:results from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Chunyue GE ; Yunjian HU ; Xiaoman AI ; 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(5):557-562
Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1%(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5%,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3%(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1%)were isolated from sputum and respiratory secretions,followed by urine(10.7%)and blood samples(8.1%).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1%.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4%in the patients<18 years old to 20.6%in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.
2.Predictive efficacy of multimodal MRI-based machine learning models for glioblastoma multiforme MGMT promoter methylation states
Hong-lin LI ; Shi-ting HU ; Zi-heng ZHOU ; Bing LI ; Zhi-ping QI ; Ruo-qi LI ; Kai LIU ; Chun-feng HU ; Hai-tao GE
Chinese Medical Equipment Journal 2025;46(6):7-13
Objective To explore the predictive efficacy of several multimodal MRI-based machine learning models for the promoter methylation states of O6-methylguanine-DNA methyltransferase(MGMT)of glioblastoma muliforme(GBM)patients in terms of the GBM heterogeneity and the complexity of the tumor microenvironment.Methods Firstly,the multimodal MRI images of 317 GBM patients from The University of Pennsylvania Glioblastoma(UPENN-GBM)dataset were pre-processed,with four sequences involved in including T1-weighted imaging(T1WI)sequence,T1-weighted contrast-enhanced imaging(T1CE)sequence,T2-weighted imaging(T2WI)sequence and fluid-attenuated inversion recovery(FLAIR)sequence,and the radiomics features were extracted for two regions of interest(ROIs)such as the tumor core region and the tumor edema region.Secondly,the data of the 317 GBM patients were randomly divided into a training set(254 cases)and a test set(63 cases),which underwent normalization with Z-scores and feature selection and dimensionality reduction with Lasso regression.Finally,three models were established respectively with particle swarm optimization-support vector machine(PSO-SVM),C-support vector classification(C-SVC)and adaptive boosting(adaptive boosting(Adaboost)algorithms,and the predictive efficacy of the three models for glioblastoma multiforme MGMT promoter methylation states were evaluated in terms of accuracy and AUC.Results The Adaboost model based on T2WI sequence and radiomics features of the tumor core region had the highest predictive efficacy with accuracy and AUC values of 67%and 0.74,respectively,higher than those of other combinations of sequences,models and regions of interest.Conclusion The multimodal MRI-based machine learning models can be used for the prediction of glioblastoma multiforme MGMT promoter methylation states,which provides powerful support for personalized treatment and prognostic assessment of GBM.[Chinese Medical Equipment Journal,2025,46(6):7-13]
3.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.
4.Effect of triglyceride-glucose index combined with C-reactive protein on new-onset nonalcoholic fatty liver disease
Yurui DU ; Fei TIAN ; Hong JI ; Yaochen WEI ; Yunpeng LI ; Xinyu GE ; Minghua LI ; Xiangming MA
Journal of Clinical Hepatology 2025;41(8):1541-1547
Objective To investigate whether there is a synergistic pathogenic effect between triglyceride glucose index(TyG)and C-reactive protein(CRP)on new-onset nonalcoholic fatty liver disease(NAFLD)by observing the influence of combinations of TyG and CRP at different levels,and to provide a basis for identifying the high-risk population of NAFLD.Methods A total of 31 935 employees in Kailuan Group who participated in physical examination in 2006-2007 were enrolled as the observation cohort,and they had no history of drinking,fatty liver disease,cardiovascular disease,or malignant tumor and did not take antidiabetic or lipid-lowering drugs.According to the median of TyG and CRP at baseline,the subjects were divided into TyG<8.42 and CRP<0.60 mg/L group,TyG<8.42 and CRP≥0.60 mg/L group,TyG≥8.42 and CRP<0.60 mg/L group,and TyG≥8.42 and CRP≥0.60 mg/L group.A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups,and an analysis of variance was used for comparison of continuous data with skewed distribution between groups after logarithmic transformation;the chi-square test was used for comparison of categorical data between groups.The Kaplan-Meier method was used to calculate the cumulative incidence rate of NAFLD in different combinations of CRP and TyG levels,and the multivariate Cox regression model was used to investigate the influence of different combinations of TyG and CRP on the incidence rate of NAFLD.Results After a mean follow-up time of 7.59 years,a total of 16 592 employees developed NAFLD.The cumulative incidence rate of NAFLD in the TyG<8.42 and CRP<0.60 mg/L group,TyG<8.42 and CRP≥0.60 mg/L group,TyG≥8.42 and CRP<0.60 mg/L group,and TyG≥8.42 and CRP≥0.60 mg/L group were 59.5%,67.1%,73.8%,and 80.8%,respectively(P<0.001).After adjustment for confounding factors,compared with the TyG<8.42 and CRP<0.60 mg/L group,the TyG≥8.42 and CRP≥0.60 mg/L group had the highest risk of developing NAFLD(hazard ratio[HR]=1.54,95%confidence interval[CI]:1.47-1.61),followed by the TyG≥8.42 and CRP<0.60 mg/L group(HR=1.43,95%CI:1.36-1.49)and the TyG<8.42 and CRP≥0.60 mg/L group(HR=1.17,95%CI:1.12-1.22).Conclusion With elevated TyG and CRP levels,the cumulative incidence of NAFLD increased,and rising levels of these markers significantly augmented the risk of NAFLD development.
5.Changing antimicrobial resistance profiles of Burkholderia cepacia in hospitals across China:results from CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Chunyue GE ; Yunjian HU ; Xiaoman AI ; 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(5):557-562
Objective To examine the changing prevalence and antimicrobial resistance profiles of Burkholderia cepacia in 52 hospitals across China from 2015 to 2021.Methods A total of 9 261 strains of B.cepacia were collected from 52 hospitals between January 1,2015 and December 31,2021.Antimicrobial susceptibility of the strains was tested using Kirby-Bauer method or automated antimicrobial susceptibility testing systems according to a unified protocol.The results were interpreted according to the breakpoints released in the Clinical & Laboratory Standards Institute(CLSI)guidelines(2023 edition).Results A total of 9 261 strains of B.cepacia were isolated from all age groups,especially elderly patients.The proportion was 11.1%(1 032 strains)in children,significantly lower than the proportion in adults.About half(46.5%,4 310/9 261)of the strains were isolated from patients at least 60 years old and 42.3%(3 919/9 261)of the strains were isolated from young adults.Most isolates(71.1%)were isolated from sputum and respiratory secretions,followed by urine(10.7%)and blood samples(8.1%).B.cepacia isolates were highly susceptible to the five antimicrobial agents recommended in the CLSI M100 document(33rd edition,2023).B.cepacia isolates showed relatively higher resistance rates to meropenem and levofloxacin.However,the resistance rates to ceftazidime,trimethoprim-sulfamethoxazole,and minocycline remained below 8.1%.The percentage of B.cepacia strains resistant to levofloxacin was the highest compared to other antibiotics in any of the three age groups(from 12.4%in the patients<18 years old to 20.6%in the patients aged 60 years or older).Conclusions B.cepacia is one of the clinically important non-fermenting gram-negative bacteria.Accurate and timely reporting of antimicrobial susceptibility test results and ongoing antimicrobial resistance surveillance are helpful for rational prescription of antimicrobial agents and proper prevention and control of nosocomial infections.
6.Proteomic analysis of differentially expressed proteins in mice plasma following FLASH and conventional dose rate whole-lung irradiation
Hui LUO ; Liuxiang WANG ; Ron LEAVITT ; Jackie ROMERO ; Marie-Catherine VOZENIN ; Aymeric ALMEIDA ; Chengliang YANG ; Na LI ; Xuenan WANG ; Ronghu MAO ; Leijie MA ; Hongchang LEI ; Hong GE
Chinese Journal of Radiological Medicine and Protection 2025;45(10):941-948
Objective:To observe the tumor control and the degree of radiation-induced lung injury (RILI) between FLASH irradiation and conventional dose rate (CONV) irradiation, and compare the changes in plasma proteomic profiles of mice following whole-lung FLASH and CONV irradiation using proteomics method.Methods:A mouse model with metastatic lung cancer was established. After whole-lung irradiation, changes in normal lung capacity were monitored using CT scans. Then, a RILI model was constructed to examine pathological alterations in lung tissues following whole-lung CONV and FLASH irradiation. Plasma samples were collected from mice receiving whole-lung CONV irradiation ( n = 5) and whole-lung FLASH irradiation ( n = 5), followed by comparison with samples from the control group of healthy mice (also referred to as the healthy control group). These plasma samples were analyzed using isobaric tags for relative and absolute quantification (iTRAQ)-based proteomics, followed by the screening and identification of differentially expressed proteins using high-throughput bioinformatics. Moreover, protein-protein interaction (PPI) network analysis was conducted to identify hub genes using the STRING database and Cytoscape software. Results:Whole-lung FLASH and CONV irradiation produced consistent tumor control, with the former significantly reducing RILI compared to the latter. A total of 609 proteins were identified through proteomic analysis. Among them, 89 differentially expressed proteins were detected in the whole-lung FLASH group. Gene Ontology (GO) enrichment analysis indicated that up-regulated genes were primarily associated with stress and inflammatory responses, whereas down-regulated genes were related to ATP metabolism and angiogenesis regulation. Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis revealed that up-regulated genes were predominantly enriched in unfolded protein response pathways, while down-regulated genes were mainly involved in metabolic pathways and oxidative phosphorylation. Integrated PPI analysis and subsequent validation via reverse transcription-polymerase chain reaction (RT-PCR) revealed four key genes.Conclusions:Compared to the whole-lung CONV irradiation, whole-lung FLASH irradiation reduces the RILI of normal lung tissues while maintaining equivalent tumor control in metastatic lung cancer. Proteomic analysis of differentially expressed proteins in plasma after whole-lung FLASH and CONV irradiation provides valuable insights into the molecular mechanisms underlying the FLASH effect.
7.Association of Chinese visceral adiposity index and high-sensitivity C-reactive protein with the risk of digestive malignancies
Shuqing CUI ; Chao MA ; Jiaxing LI ; Yunpeng LI ; Ze WANG ; Fei TIAN ; Hong JI ; Xinyu GE ; Shouling WU ; Xiangming MA
Journal of Clinical Hepatology 2025;41(7):1380-1387
Objective To investigate the association of Chinese visceral adiposity index(CVAI)and high-sensitivity C-reactive protein(hs-CRP)with the risk of digestive malignancies in the Kailuan study population,and to provide a basis for the prevention and control of digestive malignancies in the population.Methods A prospective cohort study was conducted,and a total of 94 377 Kailuan workers who participated in the 2006 health examination,had no history of cancer,and had complete data on CVAI,CRP,and related covariates were selected as the observation cohort.According to the levels of CVAI and CRP,the subjects were divided into low CVAI+CRP≤3 mg/L group[CVAI(-)CRP(-)group],low CVAI+CRP>3 mg/L group[CVAI(-)CRP(+)group],high CVAI+CRP≤3 mg/L group[CVAI(+)CRP(-)group],and high CVAI+CRP>3 mg/L group[CVAI(+)CRP(+)group].An analysis of variance was used for comparison of normally distributed continuous data between groups,and the non-parametric Kruskal-Wallis H test was used for comparison of continuous data with skewed distribution between groups;the chi-square test was used for comparison of categorical data between groups.The Cox proportional-hazards regression model was used to assess the impact of CVAI and CRP alone or in combination on the risk of digestive malignancies.Results There were significant differences between the four groups in age,male/female ratio,total cholesterol,triglycerides,high-density lipoprotein cholesterol,systolic blood pressure,diastolic blood pressure,fasting blood glucose,high-sensitivity C-reactive protein,waist circumference,body mass index,marital status,alcohol consumption,smoking,reported income,and physical exercise(all P<0.05).During a mean follow-up time of 14.08±2.76 years,2 043 new-onset cases of digestive malignancies were identified by the end of follow-up on December 31,2021.The Cox proportional-hazards regression model showed that after adjustment for CRP and other factors,compared with the low CVAI group,the high CVAI group had a hazard ratio(HR)of 1.34(95%confidence interval[CI]:1.23-1.47)for the risk of digestive malignancies.After adjustment for CVAI and other factors,compared with the CRP≤3 mg/L group,the CRP>3 mg/L group had an HR of 1.14(95%CI:1.02-1.28)for the risk of digestive malignancies.Compared with the CVAI(-)CRP(-)group(n=40 978),the CVAI(-)CRP(+)group(n=6 210),the CVAI(+)CRP(-)group(n=36 502),and the CVAI(+)CRP(+)group(n=10 687)had an HR of 1.05(95%CI:1.01-1.09,P<0.05),1.32(95%CI:1.20-1.45,P<0.05),and 1.48(95%CI:1.28-1.70,P<0.05),respectively,for the risk of digestive malignancies.As for digestive malignancies at specific locations,the CVAI(+)CRP(+)group had an increased risk of liver cancer,gastric cancer,pancreatic cancer,colorectal cancer,and small intestinal cancer with an HR of 1.35(95%CI:1.05-1.81,P<0.05),1.48(95%CI:1.09-2.00,P<0.05),1.60(95%CI:1.07-2.41,P<0.05),1.76(1.40-2.21,P<0.05),and 3.85(95%CI:1.43-10.33,P<0.05),respectively.Conclusion A high level of CVAI,a high level of CRP,and high levels of CVAI and CRP in combination can all increase the risk of digestive malignancies,among which the high levels of CVAI and CRP in combination may lead to a higher risk.
8.Association of Chinese visceral adiposity index and high-sensitivity C-reactive protein with the risk of digestive malignancies
Shuqing CUI ; Chao MA ; Jiaxing LI ; Yunpeng LI ; Ze WANG ; Fei TIAN ; Hong JI ; Xinyu GE ; Shouling WU ; Xiangming MA
Journal of Clinical Hepatology 2025;41(7):1380-1387
Objective To investigate the association of Chinese visceral adiposity index(CVAI)and high-sensitivity C-reactive protein(hs-CRP)with the risk of digestive malignancies in the Kailuan study population,and to provide a basis for the prevention and control of digestive malignancies in the population.Methods A prospective cohort study was conducted,and a total of 94 377 Kailuan workers who participated in the 2006 health examination,had no history of cancer,and had complete data on CVAI,CRP,and related covariates were selected as the observation cohort.According to the levels of CVAI and CRP,the subjects were divided into low CVAI+CRP≤3 mg/L group[CVAI(-)CRP(-)group],low CVAI+CRP>3 mg/L group[CVAI(-)CRP(+)group],high CVAI+CRP≤3 mg/L group[CVAI(+)CRP(-)group],and high CVAI+CRP>3 mg/L group[CVAI(+)CRP(+)group].An analysis of variance was used for comparison of normally distributed continuous data between groups,and the non-parametric Kruskal-Wallis H test was used for comparison of continuous data with skewed distribution between groups;the chi-square test was used for comparison of categorical data between groups.The Cox proportional-hazards regression model was used to assess the impact of CVAI and CRP alone or in combination on the risk of digestive malignancies.Results There were significant differences between the four groups in age,male/female ratio,total cholesterol,triglycerides,high-density lipoprotein cholesterol,systolic blood pressure,diastolic blood pressure,fasting blood glucose,high-sensitivity C-reactive protein,waist circumference,body mass index,marital status,alcohol consumption,smoking,reported income,and physical exercise(all P<0.05).During a mean follow-up time of 14.08±2.76 years,2 043 new-onset cases of digestive malignancies were identified by the end of follow-up on December 31,2021.The Cox proportional-hazards regression model showed that after adjustment for CRP and other factors,compared with the low CVAI group,the high CVAI group had a hazard ratio(HR)of 1.34(95%confidence interval[CI]:1.23-1.47)for the risk of digestive malignancies.After adjustment for CVAI and other factors,compared with the CRP≤3 mg/L group,the CRP>3 mg/L group had an HR of 1.14(95%CI:1.02-1.28)for the risk of digestive malignancies.Compared with the CVAI(-)CRP(-)group(n=40 978),the CVAI(-)CRP(+)group(n=6 210),the CVAI(+)CRP(-)group(n=36 502),and the CVAI(+)CRP(+)group(n=10 687)had an HR of 1.05(95%CI:1.01-1.09,P<0.05),1.32(95%CI:1.20-1.45,P<0.05),and 1.48(95%CI:1.28-1.70,P<0.05),respectively,for the risk of digestive malignancies.As for digestive malignancies at specific locations,the CVAI(+)CRP(+)group had an increased risk of liver cancer,gastric cancer,pancreatic cancer,colorectal cancer,and small intestinal cancer with an HR of 1.35(95%CI:1.05-1.81,P<0.05),1.48(95%CI:1.09-2.00,P<0.05),1.60(95%CI:1.07-2.41,P<0.05),1.76(1.40-2.21,P<0.05),and 3.85(95%CI:1.43-10.33,P<0.05),respectively.Conclusion A high level of CVAI,a high level of CRP,and high levels of CVAI and CRP in combination can all increase the risk of digestive malignancies,among which the high levels of CVAI and CRP in combination may lead to a higher risk.
9.Effect of triglyceride-glucose index combined with C-reactive protein on new-onset nonalcoholic fatty liver disease
Yurui DU ; Fei TIAN ; Hong JI ; Yaochen WEI ; Yunpeng LI ; Xinyu GE ; Minghua LI ; Xiangming MA
Journal of Clinical Hepatology 2025;41(8):1541-1547
Objective To investigate whether there is a synergistic pathogenic effect between triglyceride glucose index(TyG)and C-reactive protein(CRP)on new-onset nonalcoholic fatty liver disease(NAFLD)by observing the influence of combinations of TyG and CRP at different levels,and to provide a basis for identifying the high-risk population of NAFLD.Methods A total of 31 935 employees in Kailuan Group who participated in physical examination in 2006-2007 were enrolled as the observation cohort,and they had no history of drinking,fatty liver disease,cardiovascular disease,or malignant tumor and did not take antidiabetic or lipid-lowering drugs.According to the median of TyG and CRP at baseline,the subjects were divided into TyG<8.42 and CRP<0.60 mg/L group,TyG<8.42 and CRP≥0.60 mg/L group,TyG≥8.42 and CRP<0.60 mg/L group,and TyG≥8.42 and CRP≥0.60 mg/L group.A one-way analysis of variance was used for comparison of normally distributed continuous data between multiple groups,and an analysis of variance was used for comparison of continuous data with skewed distribution between groups after logarithmic transformation;the chi-square test was used for comparison of categorical data between groups.The Kaplan-Meier method was used to calculate the cumulative incidence rate of NAFLD in different combinations of CRP and TyG levels,and the multivariate Cox regression model was used to investigate the influence of different combinations of TyG and CRP on the incidence rate of NAFLD.Results After a mean follow-up time of 7.59 years,a total of 16 592 employees developed NAFLD.The cumulative incidence rate of NAFLD in the TyG<8.42 and CRP<0.60 mg/L group,TyG<8.42 and CRP≥0.60 mg/L group,TyG≥8.42 and CRP<0.60 mg/L group,and TyG≥8.42 and CRP≥0.60 mg/L group were 59.5%,67.1%,73.8%,and 80.8%,respectively(P<0.001).After adjustment for confounding factors,compared with the TyG<8.42 and CRP<0.60 mg/L group,the TyG≥8.42 and CRP≥0.60 mg/L group had the highest risk of developing NAFLD(hazard ratio[HR]=1.54,95%confidence interval[CI]:1.47-1.61),followed by the TyG≥8.42 and CRP<0.60 mg/L group(HR=1.43,95%CI:1.36-1.49)and the TyG<8.42 and CRP≥0.60 mg/L group(HR=1.17,95%CI:1.12-1.22).Conclusion With elevated TyG and CRP levels,the cumulative incidence of NAFLD increased,and rising levels of these markers significantly augmented the risk of NAFLD development.
10.Prediction of gamma pass rate for thoracic intensity-modulated radiotherapy plan dose verification using a machine learning model based on planomics
Tiantian CUI ; Xiangyue LIU ; Nan MENG ; Yongqiang WANG ; Hong GE ; Zhaoyang LOU ; Bing LI
Chinese Journal of Radiation Oncology 2025;34(1):81-87
Objective:To construct a machine learning classification prediction model using planning-omics (planomics) features to predict the γ pass rate of intensity-modulated radiotherapy (IMRT) plan dose verification in fixed-field thoracic tumors, and evaluate the application of planomics in radiotherapy quality assurance.Methods:The fixed-field IMRT plans of 240 patients with chest tumors admitted to Department of Radiotherapy, Henan Cancer Hospital from August 2022 to March 2023 were retrospectively analyzed. All plans underwent dose verification using the electronic portal imaging system detector on the Varian accelerator to collect field dose data. The dose verification results were analyzed through Portal Dosimetry in the treatment planning system of Eclipse. The γ pass rate standard was set at 2%/2 mm with a 10% dose threshold. From the planning documents, 48 conventional planning features, 2476 planomics features, and the combination of the previous two feature sets were extracted. Subsequently, an auto-encoder classification model was constructed. To evaluate the classification efficacy of various feature sets, 20 random train-test divisions were conducted by calculating the area under the receiver operating characteristic curve (AUC) values along with the accuracy rates.Results:After the feature selection, 2 conventional features and 16 planomics features were finally selected. In the testing set, the AUC values for the model using combined features, planomics features, and conventional planned features were 0.802±0.030, 0.740±0.069, and 0.673±0.083, respectively. In contrast, in the training set, these AUC values were 0.844±0.074, 0.816±0.047, and 0.687±0.036, respectively. The accuracy rates were 0.752±0.083, 0.703±0.110, and 0.648±0.081 in the testing set, and 0.753±0.098, 0.751±0.075, and 0.624±0.054 in the training set for the combined, planomics, and conventional planning feature sets, respectively.Conclusions:For thoracic fixed-field adjusted radiotherapy planning, the machine learning method based on planomics features can be utilized to build a classification model for predicting the γ pass rate. Combining planomics features with conventional planned features can enhance the predictive performance of the classification models.

Result Analysis
Print
Save
E-mail