1.Impact factor selection for non-fatal occupational injuries among manufacturing workers by LASSO regression
Yingheng XIAO ; Chunhua LU ; Juan QIAN ; Ying CHEN ; Yishuo GU ; Zeyun YANG ; Daozheng DING ; Liping LI ; Xiaojun ZHU
Journal of Environmental and Occupational Medicine 2025;42(2):133-139
Background As a pillar industry in China, the manufacturing sector has a high incidence of non-fatal occupational injuries. The factors influencing non-fatal occupational injuries in this industry are closely related at various levels, including individual, equipment, environment, and management, making the analysis of these influencing factors complex. Objective To identify influencing factors of non-fatal occupational injuries among manufacturing workers, providing a basis for targeted interventions and surveillance. Methods A total of
2.Correlation between adult mosquito density and meteorological factors in Pudong New Area of Shanghai, China
Ge GE ; Yongting YUAN ; Lei FENG ; Hanzhao LIU ; Chen LIN ; Ruohua GU ; Juan GE ; Jun LIU
Shanghai Journal of Preventive Medicine 2025;37(2):105-108
ObjectiveTo learn the density and seasonal variation of adult mosquitoes in Pudong New Area of Shanghai, and to explore the influence of meteorological factors on the density of adult mosquitoes. MethodsFrom April to November in 2017‒2021, adult mosquito density in Pudong New Area was monitored every ten days a time by using CO2 trapping light method. Meteorological data were collected during the same time, and Pearson correlation analysis and multiple linear regression model were used to investigate the correlation between adult mosquito density and meteorological factors. ResultsThe seasonal variation of adult mosquito density showed a single-peak pattern, with the peak of 7.09 mosquitoes·(set·time)-1 in July. The adult mosquito density was positively correlated with the monthly average temperature, monthly maximum temperature, monthly minimum temperature, and monthly average relative humidity (r=0.813, 0.793, 0.820, 0.617, all P<0.05), but negatively correlated with monthly average air pressure (r=-0.738, P<0.05). The regression equation of the adult mosquito density and monthly minimum temperature in Pudong New Area of Shanghai was Y=0.066 X3-0.884, with a corrected R2 of 0.673, indicating a good model fitting. ConclusionThe overall seasonal variation of adult mosquito density in Pudong New Area showed a single-peak pattern. The density of adult mosquitoes was correlated with the monthly average temperature, monthly maximum temperature, monthly minimum temperature, monthly average relative humidity, and monthly average air pressure, and linearly correlated with monthly minimum temperature.
3.Off-the-shelf human umbilical cord mesenchymal stromal cell product in acute-on-chronic liver failure: A multicenter phase I/II clinical trial.
Lina CUI ; Huaibin ZOU ; Shaoli YOU ; Changcun GUO ; Jundong GU ; Yulong SHANG ; Gui JIA ; Linhua ZHENG ; Juan DENG ; Xiufang WANG ; Ruiqing SUN ; Dawei DING ; Weijie WANG ; Xia ZHOU ; Guanya GUO ; Yansheng LIU ; Zhongchao HAN ; Zhibo HAN ; Yu CHEN ; Ying HAN
Chinese Medical Journal 2025;138(18):2347-2349
4.Crosstalk and the progression of hepatocellular carcinoma.
Lei-Rong GU ; Hui ZHANG ; Juan CHEN ; Sheng-Tao CHENG
Acta Physiologica Sinica 2025;77(2):267-276
Malignant proliferating liver cancer cells possess the ability to detect and respond to various body signals, thereby facilitating tumor growth, invasion, and metastasis. One crucial mechanism through which hepatocellular carcinoma (HCC) cells interpret these signals is crosstalk. Within liver cancer tissues, cancer cells engage in communication with hepatic stellate cells (HSCs), tumor-associated macrophages (TAMs), and immune cells. This interaction plays a pivotal role in regulating the proliferation, invasion, and metastasis of HCC cells. Crosstalk occurs in multiple ways, each characterized by distinct functions. Its molecular mechanisms primarily involve regulating immune cell functions through the expression of specific receptors, such as CD24 and CD47, modulating cell functions by secreting cytokines like transforming growth factor-β (TGF-β) and platelet-derived growth factor (PDGF), and mediating cell growth and proliferation by activating pathways such as Wnt/β-catenin and Hedgehog. A comprehensive understanding of the mechanisms and interactions within crosstalk is essential for unraveling the pathogenesis of HCC. It also opens up new avenues for the development of innovative therapeutic strategies. This article reviews the relationship between crosstalk and the progression of HCC, offering insights and inspiration for future research.
Humans
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Carcinoma, Hepatocellular/metabolism*
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Liver Neoplasms/metabolism*
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Hepatic Stellate Cells/physiology*
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Disease Progression
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Signal Transduction/physiology*
;
Transforming Growth Factor beta/metabolism*
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Cell Proliferation
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Hedgehog Proteins/metabolism*
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Tumor-Associated Macrophages
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Platelet-Derived Growth Factor/metabolism*
;
Cell Communication/physiology*
5.Expression and Prognostic Significance of MYCN in Adult Patients with Newly Diagnosed Acute Myeloid Leukemia.
Yue LIU ; Yang CAO ; Hui-Juan CHEN ; Jia-Yu LIU ; Ying-Jie MIAO ; Wei-Ying GU
Journal of Experimental Hematology 2025;33(3):733-737
OBJECTIVE:
This study aimed to determine the expression levels and prognostic significance of MYCN in bone marrow of adult patients with newly diagnosed acute myeloid leukemia (AML).
METHODS:
A total of 62 newly diagnosed patients with non-M3 AML were enrolled as the study group, and 20 healthy donors as the control group. Real-time quantitative reverse transcription-polymerase chain reaction (PCR) was performed to detect the expression level of MYCN, and the relationship between MYCN expression and prognosis of AML patients was analyzed.
RESULTS:
MYCN was up-regulated in newly diagnosed AML patients compared with normal controls (P < 0.001). Receiver operating characteristic (ROC) curve analysis revealed that MYCN could serve as a diagnostic biomarker for AML. Kaplan-Meier survival analysis showed that the patients with high MYCN expression had a shorter overall survival (OS) time than the patients with low MYCN expression (P =0.016). The expression level of MYCN was lower during the complete ressimion (CR) phase of AML compared to the initial diagnosis, but it returned to the initial diagnostic level or even higher during relapse phase. Multivariate Cox regression analysis showed that high expression of MYCN was an independent risk factor for OS of AML patients (P =0.021).
CONCLUSION
MYCN is highly expressed and associated with poor prognosis in de novo AML, which might be serve as a novel diagnostic and prognostic biomarker for adult AML.
Humans
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Leukemia, Myeloid, Acute/metabolism*
;
Prognosis
;
N-Myc Proto-Oncogene Protein
;
Adult
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Female
;
Male
;
Middle Aged
6.Predictive Value of MIC Typing for IDH1/2 Mutations in Patients with Acute Myeloid Leukemia.
Hui-Juan CHEN ; Yang-Ling SHEN ; Yan-Ting GUO ; Yi-Fang ZHOU ; Ying-Jie MIAO ; Wei-Min DONG ; Wei-Ying GU
Journal of Experimental Hematology 2025;33(4):939-944
OBJECTIVE:
To investigate the predictive value of morphology, immunology, and cytogenetics for isocitrate dehydrogenase 1 and 2 (IDH1/2) gene mutation in newly diagnosed acute myeloid leukemia (AML) patients.
METHODS:
The clinical data of 186 newly diagnosed AML patients (except M3 subtype) in the First People's Hospital of Changzhou were retrospectively analyzed, and the variables associated with IDH1/2 mutation in patients were screened using LASSO regression to construct a multivariate logistic regression analysis model. The Bootstrap method was used for internal validation of the model and nomograms were used to visualize the model, and receiver operating characteristic (ROC) curve was used to evaluate the predictive performance of the model.
RESULTS:
A total of 60 AML patients had IDH1/2 mutation at initial diagnosis. LASSO regression screened 9 predictive variables associated with IDH1/2 mutation, including CD7, CD56, CD11b, CD15, CD64, HLA-DR, platelet count≥50×109/L, isolated +8 and normal karyotype. The nomogram and ROC curve were plotted based on the above 9 variables. The area under the ROC curve (AUC) of the training set and the validation set were 0.871 and 0.806, respectively. Internal validation showed that the nomogram had good predictive ability.
CONCLUSION
The prediction model based on MIC typing constructed in this study has a good predictive ability for the presence of IDH1/2 mutations in newly diagnosed AML patients and has important clinical application value when the gene mutation detection results are unavailable.
Humans
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Isocitrate Dehydrogenase/genetics*
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Leukemia, Myeloid, Acute/genetics*
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Mutation
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Retrospective Studies
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Nomograms
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Female
;
Male
;
ROC Curve
;
Middle Aged
7.Investigation of the immune profile of multiple myeloma patients achieving long-term survival after autologous stem cell transplantation
Jingli GU ; Chuhang ZHONG ; Meilan CHEN ; Lifen KUANG ; Xiaozhe LI ; Beihui HUANG ; Junru LIU ; Juan LI
Chinese Journal of Internal Medicine 2024;63(4):365-370
Objective:To identify the characteristics of the bone marrow immune microenvironment associated with long-term survival in multiple myeloma (MM) patients.Methods:In the follow-up cohort of patients with newly diagnosed MM and who received “novel agent induction therapy and subsequent autologous stem cell transplantation and immunomodulator maintenance therapy” in the First Affiliated Hospital of Sun Yat-sen University, a cross-sectional study was carried out between August 2019 and May 2020. Using NanoString technology, the RNA expression of 770 bone marrow immune-related markers was compared between 16 patients who had progression-free survival ≥5 years and 5 patients with progressive disease. Among the 16 patients who achieved long-term survival, 9 achieved persistent minimal residual disease (MRD) negative while the other 7 had persistent positive MRD. The functional scores of each kind of immune cells were calculated based on the expression level of characteristic genes, so as to indirectly obtained the proportion of each immune cell subset. The Mann-Whitney U test and the Kruskal Wallis test were used for statistical analysis. Results:The proportion of neutrophils was significantly higher in long-surviving MM patients than in patients with progressive disease [functional scores, 13.61 (13.33, 14.25) vs. 12.93 (12.58, 13.38); Z=2.31, P=0.021]. Among long-surviving patients, those who were MRD-positive had a significantly greater number of mast cells compared with those who were MRD-negative [functional scores, 7.09 (6.49, 8.57) vs. 6.03 (5.18, 6.69); H=2.18, P=0.029]. Compared with patients with progressive disease, four genes (CTSG, IFIT2, S100B, and CHIT1) were significantly downregulated and six (C4B, TNFRSF17, CD70, IRF4, C2, and GAGE1) were upregulated in long-surviving patients. Among long-surviving patients, only gene CMA1 was significantly upgraded, 10 genes (ISG15, OAS3, MX1, IFIT2, DDX58, SIGLEC1, CXCL10, IL1RN, SERPING and TNFSF10) were significantly downregulated in the MRD-positive group compared with that in the MRD-negative group, the first 5 of which are related to the interferon response pathway. Conclusions:The increased neutrophil and mast cell numbers may be related to long-term survival in MM. Interferon signaling activation may be a key bone marrow immune profiling feature for MRD-negative, long-surviving patients with MM.
8.Changing distribution and resistance profiles of common pathogens isolated from urine in the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Yanming LI ; Mingxiang ZOU ; Wen'en LIU ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Fengbo ZHANG ; Ping JI ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; Yan JIN ; Chunhong SHAO ; Xuesong XU ; Chao YAN ; Shanmei WANG ; Yafei CHU ; Lixia ZHANG ; Juan MA ; Shuping ZHOU ; Yan ZHOU ; Lei ZHU ; Jinhua MENG ; Fang DONG ; Zhiyong LÜ ; Fangfang HU ; Han SHEN ; Wanqing ZHOU ; Wei JIA ; Gang LI ; Jinsong WU ; Yuemei LU ; Jihong LI ; Jinju DUAN ; Jianbang KANG ; Xiaobo MA ; Yanping ZHENG ; Ruyi GUO ; Yan ZHU ; Yunsheng CHEN ; Qing MENG ; Shifu WANG ; Xuefei HU ; Jilu SHEN ; Ruizhong WANG ; Hua FANG ; Bixia YU ; Yong ZHAO ; Ping GONG ; Kaizhen WENG ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):287-299
Objective To investigate the distribution and antimicrobial resistance profiles of the common pathogens isolated from urine from 2015 to 2021 in the CHINET Antimicrobial Resistance Surveillance Program.Methods The bacterial strains were isolated from urine and identified routinely in 51 hospitals across China in the CHINET Antimicrobial Resistance Surveillance Program from 2015 to 2021.Antimicrobial susceptibility was determined by Kirby-Bauer method,automatic microbiological analysis system and E-test according to the unified protocol.Results A total of 261 893 nonduplicate strains were isolated from urine specimen from 2015 to 2021,of which gram-positive bacteria accounted for 23.8%(62 219/261 893),and gram-negative bacteria 76.2%(199 674/261 893).The most common species were E.coli(46.7%),E.faecium(10.4%),K.pneumoniae(9.8%),E.faecalis(8.7%),P.mirabilis(3.5%),P.aeruginosa(3.4%),SS.agalactiae(2.6%),and E.cloacae(2.1%).The strains were more frequently isolated from inpatients versus outpatients and emergency patients,from females versus males,and from adults versus children.The prevalence of ESBLs-producing strains in E.coli,K.pneumoniae and P.mirabilis was 53.2%,52.8%and 37.0%,respectively.The prevalence of carbapenem-resistant strains in E.coli,K.pneumoniae,P.aeruginosa and A.baumannii was 1.7%,18.5%,16.4%,and 40.3%,respectively.Lower than 10%of the E.faecalis isolates were resistant to ampicillin,nitrofurantoin,linezolid,vancomycin,teicoplanin and fosfomycin.More than 90%of the E.faecium isolates were ressitant to ampicillin,levofloxacin and erythromycin.The percentage of strains resistant to vancomycin,linezolid or teicoplanin was<2%.The E.coli,K.pneumoniae,P.aeruginosa and A.baumannii strains isolated from ICU inpatients showed significantly higher resistance rates than the corresponding strains isolated from outpatients and non-ICU inpatients.Conclusions E.coli,Enterococcus and K.pneumoniae are the most common pathogens in urinary tract infection.The bacterial species and antimicrobial resistance of urinary isolates vary with different populations.More attention should be paid to antimicrobial resistance surveillance and reduce the irrational use of antimicrobial agents.
9.Changing resistance profiles of Enterococcus in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Na CHEN ; Ping JI ; Yang YANG ; Fupin HU ; Demei ZHU ; Yingchun XU ; Xiaojiang ZHANG ; Yi XIE ; Mei KANG ; Chuanqing WANG ; Pan FU ; Yuanhong XU ; Ying HUANG ; Ziyong SUN ; Zhongju CHEN ; Yuxing NI ; Jingyong SUN ; Yunzhuo CHU ; Sufei TIAN ; Zhidong HU ; Jin LI ; Yunsong YU ; Jie LIN ; Bin SHAN ; Yan DU ; Sufang GUO ; Lianhua WEI ; Fengmei ZOU ; Hong ZHANG ; Chun WANG ; Yunjian HU ; Xiaoman AI ; Chao ZHUO ; Danhong SU ; Dawen GUO ; Jinying ZHAO ; Hua YU ; Xiangning HUANG ; 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 WEN ; Yirong ZHANG ; Jiangshan LIU ; Longfeng LIAO ; Hongqin GU ; Lin JIANG ; Wen HE ; Shunhong XUE ; Jiao FENG ; Chunlei YUE
Chinese Journal of Infection and Chemotherapy 2024;24(3):300-308
Objective To understand the distribution and changing resistance profiles of clinical isolates of Enterococcus in hospitals across China from 2015 to 2021.Methods Antimicrobial susceptibility testing was conducted for the clinical isolates of Enterococcus according to the unified protocol of CHINET program by automated systems,Kirby-Bauer method,or E-test strip.The results were interpreted according to the Clinical & Laboratory Standards Institute(CLSI)breakpoints in 2021.WHONET 5.6 software was used for statistical analysis.Results A total of 124 565 strains of Enterococcus were isolated during the 7-year period,mainly including Enterococcus faecalis(50.7%)and Enterococcus faecalis(41.5%).The strains were mainly isolated from urinary tract specimens(46.9%±2.6%),and primarily from the patients in the department of internal medicine,surgery and ICU.E.faecium and E.faecalis strains showed low level resistance rate to vancomycin,teicoplanin and linezolid(≤3.6%).The prevalence of vancomycin-resistant E.faecalis and E.faecium was 0.1%and 1.3%,respectively.The prevalence of linezolid-resistant E.faecalis increased from 0.7%in 2015 to 3.4%in 2021,while the prevalence of linezolid-resistant E.faecium was 0.3%.Conclusions The clinical isolates of Enterococcus were still highly susceptible to vancomycin,teicoplanin,and linezolid,evidenced by a low resistance rate.However,the prevalence of linezolid-resistant E.faecalis was increasing during the 7-year period.It is necessary to strengthen antimicrobial resistance surveillance to effectively identify the emergence of antibiotic-resistant bacteria and curb the spread of resistant pathogens.
10.Changing resistance profiles of Enterobacter isolates in hospitals across China:results from the CHINET Antimicrobial Resistance Surveillance Program,2015-2021
Shaozhen YAN ; Ziyong SUN ; Zhongju CHEN ; Yang YANG ; Fupin HU ; Demei ZHU ; Yi XIE ; Mei KANG ; Fengbo ZHANG ; Ping JI ; Zhidong HU ; Jin LI ; Sufang GUO ; Han SHEN ; Wanqing ZHOU ; Yingchun XU ; Xiaojiang ZHANG ; Xuesong XU ; Chao YAN ; Chuanqing WANG ; Pan FU ; Wei JIA ; Gang LI ; Yuanhong XU ; Ying HUANG ; Dawen GUO ; Jinying ZHAO ; Wen'en LIU ; Yanming LI ; Hua YU ; Xiangning HUANG ; Bin SHAN ; Yan DU ; Shanmei WANG ; Yafei CHU ; Yuxing NI ; Jingyong SUN ; Yunsong YU ; Jie LIN ; Chao ZHUO ; Danhong SU ; Lianhua WEI ; Fengmei ZOU ; Yan JIN ; Chunhong SHAO ; Jihong LI ; Lixia ZHANG ; Juan MA ; Yunzhuo CHU ; Sufei TIAN ; Jinju DUAN ; Jianbang KANG ; Ruizhong WANG ; Hua FANG ; Fangfang HU ; Yunjian HU ; Xiaoman AI ; Fang DONG ; Zhiyong LÜ ; Hong ZHANG ; Chun WANG ; Yong ZHAO ; Ping GONG ; Lei ZHU ; Jinhua MENG ; Xiaobo MA ; Yanping ZHENG ; Jinsong WU ; Yuemei LU ; Ruyi GUO ; Yan ZHU ; Kaizhen WEN ; Yirong ZHANG ; Chunlei YUE ; Jiangshan LIU ; Wenhui HUANG ; Shunhong XUE ; Xuefei HU ; Hongqin GU ; Jiao FENG ; Shuping ZHOU ; Yan ZHOU ; Yunsheng CHEN ; Qing MENG ; Bixia YU ; Jilu SHEN ; Rui DOU ; Shifu WANG ; Wen HE ; Longfeng LIAO ; Lin JIANG
Chinese Journal of Infection and Chemotherapy 2024;24(3):309-317
Objective To examine the changing antimicrobial resistance profile of Enterobacter spp.isolates in 53 hospitals across China from 2015 t0 2021.Methods The clinical isolates of Enterobacter spp.were collected from 53 hospitals across China during 2015-2021 and tested for antimicrobial susceptibility using Kirby-Bauer method or automated testing systems according to the CHINET unified protocol.The results were interpreted according to the breakpoints issued by the Clinical & Laboratory Standards Institute(CLSI)in 2021(M100 31st edition)and analyzed with WHONET 5.6 software.Results A total of 37 966 Enterobacter strains were isolated from 2015 to 2021.The proportion of Enterobacter isolates among all clinical isolates showed a fluctuating trend over the 7-year period,overall 2.5%in all clinical isolates amd 5.7%in Enterobacterale strains.The most frequently isolated Enterobacter species was Enterobacter cloacae,accounting for 93.7%(35 571/37 966).The strains were mainly isolated from respiratory specimens(44.4±4.6)%,followed by secretions/pus(16.4±2.3)%and urine(16.0±0.9)%.The strains from respiratory samples decreased slightly,while those from sterile body fluids increased over the 7-year period.The Enterobacter strains were mainly isolated from inpatients(92.9%),and only(7.1±0.8)%of the strains were isolated from outpatients and emergency patients.The patients in surgical wards contributed the highest number of isolates(24.4±2.9)%compared to the inpatients in any other departement.Overall,≤ 7.9%of the E.cloacae strains were resistant to amikacin,tigecycline,polymyxin B,imipenem or meropenem,while ≤5.6%of the Enterobacter asburiae strains were resistant to these antimicrobial agents.E.asburiae showed higher resistance rate to polymyxin B than E.cloacae(19.7%vs 3.9%).Overall,≤8.1%of the Enterobacter gergoviae strains were resistant to tigecycline,amikacin,meropenem,or imipenem,while 10.5%of these strains were resistant to polycolistin B.The overall prevalence of carbapenem-resistant Enterobacter was 10.0%over the 7-year period,but showing an upward trend.The resistance profiles of Enterobacter isolates varied with the department from which they were isolated and whether the patient is an adult or a child.The prevalence of carbapenem-resistant E.cloacae was the highest in the E.cloacae isolates from ICU patients.Conclusions The results of the CHINET Antimicrobial Resistance Surveillance Program indicate that the proportion of Enterobacter strains in all clinical isolates fluctuates slightly over the 7-year period from 2015 to 2021.The Enterobacter strains showed increasing resistance to multiple antimicrobial drugs,especially carbapenems over the 7-year period.

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