OBJECTIVE: To investigate the effects of histone deacetylase (HDAC) levels on the proliferation and apoptosis of Burkitt lymphoma cells, and the changes in related signaling molecules in the PI3K/AKT/mTOR signaling pathway, so as to explore the pathogenesis of Burkitt lymphoma. METHODS: HDAC levels in Burkitt lymphoma were detected by RT-PCR and Western blot. CA46 and RAJI cells were treated with the HDAC selective inhibitor VPA. CCK8 assay was used to detect the proliferation ability of cells. Western Blot was used to measure the expression of apoptosis-related proteins, PI3K/AKT/mTOR signaling pathway proteins and their phosphorylation levels. RESULTS: The expression levels of classⅠ HDAC in Burkitt lymphoma were higher than those in normal cells, and the HDAC1 inhibitor VPA could inhibit the proliferation of CA46 and RAJI cells. VPA decreased HDAC expression in CA46 and RAJI cells, inhibited the phosphorylation of PI3K/AKT/mTOR pathway molecules AKT and p70S6K, increased the expression of apoptotic proteins Cleaved Caspase-3, Cleaved Caspase-8, Cleaved Caspase-9 and Bax, and decreased the expression of anti-apoptotic proteins Bcl-2 and PARP. CONCLUSION Inhibition of HDAC activity can Attenuate the proliferation of Burkitt lymphoma cells and induce apoptosis by inhibiting the PI3K/AKT/mTOR signaling pathway activity.
Humans ; Burkitt Lymphoma/pathology* ; Apoptosis ; Cell Proliferation ; Signal Transduction ; Proto-Oncogene Proteins c-akt/metabolism* ; Phosphatidylinositol 3-Kinases/metabolism* ; Cell Line, Tumor ; Histone Deacetylases/metabolism* ; TOR Serine-Threonine Kinases/metabolism* ; Histone Deacetylase Inhibitors/pharmacology* ; Phosphorylation

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.

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]

Objective To investigate the changing antibiotic resistance profiles of E.coli isolated from patients in the 52 hospitals participating in the CHINET program from 2015 to 2021.Methods Antimicrobial susceptibility was tested for clinical isolates of E.coli according to the unified protocol of CHINET program.WHONET 5.6 and SPSS 20.0 software were used for data analysis.Results Atotal of 289 760 nonduplicate clinical strains ofE.coli were isolated from 2015 to 2021,mainly from urine samples(44.7±3.2)％.The proportion of E.coli strains isolated from urine samples was higher in females than in males(59.0％vs 29.5％).The proportion of E.coli strains isolated from respiratory tract and cerebrospinal fluid samples was significantly higher in children than in adults(16.7％vs 7.8％,0.8％vs 0.1％,both P＜0.05).The isolates from internal medicine department accounted for the largest proportion(28.9±2.8)％with an increasing trend over years.Overall,the prevalence of ESBLs-producing E.coli and carbapenem resistant E.coli(CREco)was 55.9％and 1.8％,respectively during the 7-year period.The prevalence of ESBLs-producing E.coli was the highest in tertiary hospitals each year from 2015 to 2021 compared to secondary hospitals.The prevalence of CREco was higher in children's hospitals compared to secondary and tertiary hospitals each year from 2015 to 2021.The prevalence of ESBLs-producing E.coli in tertiary hospitals and children's hospitals and the prevalence of CREco in children's hospitals showed a decreasing trend over the 7-year period.The prevalence of CREco in secondary and tertiary hospitals increased slowly.Antibiotic resistance rates changed slowly from 2015 to 2021.Carbapenem drugs(imipenem,meropenem)were the most active drugs amongβ-lactams against E.coli(resistance rate≤2.1％).The resistance rates of E.coli to β-lactam/β-lactam inhibitor combinations(piperacillin-tazobactam,cefoperazone-sulbactam),aminoglycosides(amikacin),nitrofurantoin and fosfomycin(for urinary isolates only)were all less than 10％.The resistance rate of E.coli strains to antibiotics varied with the level of hospitals and the departments where the strains were isolated,especially for cefazolin and ciprofloxacin,to which the resistance rate of E.coli strains from children in non-ICU departments was significantly lower than that of the strains isolated from other departments(P＜0.05).The E.coli isolates from ICU showed higher resistance rate to most antimicrobial agents tested(excluding tigecycline)than the strains isolated from other departments.The E.coli strains isolated from tertiary hospitals showed higher resistance rates to the antimicrobial agents tested(excluding tigecycline,polymyxin B,cefepime and carbapenems)than the strains from secondary hospitals and children's hospitals.Conclusions E.coli is an important pathogen causing clinical infection.More than half of the clinical isolates produced ESBL.The prevalence of CREco is increasing in secondary and tertiary hospitals over the 7-year period even though the overall prevalence is still low.This is an issue of concern.

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]

Objective To explore the molecular mechanism of chronic osteomyelitis and to clarify the role of MAPK signal pathway in the pathogenesis of chronic osteomyelitis,by collecting and analyzing the transcriptional information of bone tissue in patients with chronic osteomyelitis.Methods Four cases of traumatic osteomyelitis in limbs from June 2019 to June 2020 were selected,and the samples of necrotic osteonecrosis from chronic osteomyelitis(necrotic group),and normal bone tissue(control group)were collected.Transcriptome information was collected by Illumina Hiseq Xten high throughput sequencing platform,and the gene expression in bone tissue was calculated by FPKM.The differentially expressed genes were screened by comparing the transcripts of the Necrotic group and control group.Genes were enriched by GO and KEGG.MAP3K7 and NFATC1 were selected as differential targets in the verification experiments,by using rat osteomyelitis animal model and im-munohistochemical analysis.Results A total of 5548 differentially expressed genes were obtained by high throughput sequenc-ing by comparing the necrotic group and control group,including 2701 up-regulated and 2847 down-regulated genes.The genes enriched in MAPK pathway and osteoclast differentiation pathway were screened,the common genes expressed in both MAPK and osteoclast differentiation pathway were(inhibitor of nuclear factor κ subunit Beta,IκBKβ),(mitogen-activated protein ki-nase 7,MAP3K7),(nuclear factor of activated t cells 1,NFATC1)and(nuclear factor Kappa B subunit 2,NFκB2).In rat os-teomyelitis model,MAP3K7 and NFATC1 were highly expressed in bone marrow and injured bone tissue.Conclusion Based on the transcriptome analysis,the MAPK signaling and osteoclast differentiation pathways were closely related to chronic os-teomyelitis,and the key genes IκBKβ,MAP3K7,NFATC1,NFκB2 might be new targets for clinical diagnosis and therapy of chronic osteomyelitis.

Objective To explore the effect of cardiac shock wave therapy(CSWT)on ST deviation of electrocardiogram and myocardial perfusion imaging in coronary artery disease(CAD)patients.Methods CAD patients who received CSWT in Cardiology Department of Beijing Hospital from December 2016 to August 2022 were enrolled.Three months of CSWT were conducted with a total of 9 times shock wave treatment.Clinical data,myocardial perfusion imaging data and stress electrocardiogram data were collected.Myocardial perfusion score,electrocardiographic data were compared before and after CSWT.Results A total of 55 patients were finally enrolled.There were 43 male and 12 female patients with an average age of(67.45±8.96)years old.ST deviation on 12 leads of electrocardiogram did not show significant difference before and after CSWT.Myocardial perfusion imaging showed global stress perfusion score(P=0.031)and reverse perfusion score(P=0.024).Global rest ischemia score reduced after CSWT(P=0.034).Target stress perfusion score(P=0.002),target reverse perfusion score(P=0.002),target reverse ischemic area(P=0.001)were improved after CSWT.Conclusions CSWT may not influence ST deviation of electrocardiogram,but may improve myocardial ischemia in CAD patients,

Objective: The aim of this study was to investigate the efficacy of endoscopically visualized radiofrequency for treating sacroiliac joint pain. Methods: The study protocol was preregistered on INPLASY (INPLASY202450011). A systematic search was carried out across multiple databases, including PubMed, Embase, Cochrane CENTRAL, and Web of Science, from their inception until May 6, 2024. Peer-reviewed studies on human participants with low back pain diagnosed with sacroiliac joint pain and treated with endoscopically visualized radiofrequency ablation (RFA) were included. The study focused on evaluating changes in the visual analogue scale (VAS) and Oswestry Disability Index (ODI) from before the commencement of endoscopically visualized radiofrequency to postoperation. The quantitative syntheses employed a random-effects model, with effect sizes reported using the mean difference. Subgroup analyses were conducted based on 6-month and 12-month postoperative time points. Results: Four studies were ultimately included in this meta-analysis. Three of the studies were case series, while one was a retrospective cohort study. The mean difference of VAS scores between the preoperative and 6-month and 12-month postoperative assessments was -5.60 and -5.96, respectively. The mean difference of the ODI between preoperative and 6-month and 12-month postoperative assessments was -21.03 and -23.67, respectively. A subgroup analysis of both outcome measurement indices at the 2 follow-up time points did not reveal any statistically significant differences. Conclusion Endoscopically visualized RFA demonstrates potential as a treatment modality for sacroiliac joint pain; however, there is currently insufficient evidence to substantiate its long-term efficacy.

Objective: To identify the main metals involved in cognitive impairment in the Chinese oldest old, and explore the association between these metal exposures and cognitive impairment. Methods: A cross-sectional study was conducted on 1 568 participants aged 80 years and older from Healthy Aging and Biomarkers Cohort Study (2017 to 2018). Fasting venous blood was collected to measure the levels of nine metals (selenium, lead, cadmium, arsenic, antimony, chromium, manganese, mercury, and nickel). The cognitive function of these participants was evaluated by using the Chinese version of the Mini-Mental State Examination (CMMSE). The random forest (RF) was applied to independently identify the main metals that affected cognitive impairment. The multivariate logistic regression model and restricted cubic splines (RCS) model were used to further verify the association of the main metals with cognitive impairment. Results: The age of 1 568 study subjects was (91.8±7.6) years old, including 912 females (58.2%) and 465 individuals (29.7%) with cognitive function impairment. Based on the RF model (the out-of-bag error rate was 22.9%), the importance ranking of variables was conducted and the feature screening of five times ten-fold cross-validation was carried out. It was found that selenium was the metal that affected cognitive function impairment, and the other eight metals were not included in the model. After adjusting for covariates, the multivariate logistic regression model showed that with every increase of 10 μg/L of blood selenium levels, the risk of cognitive impairment decreased (OR=0.921, 95%CI: 0.889-0.954). Compared with the lowest quartile(Q₁) of blood selenium, the ORs (95%CI) of Q₃ and Q₄ blood selenium were 0.452 (0.304-0.669) and 0.419 (0.281-0.622) respectively. The RCS showed a linear dose-response relationship between blood selenium and cognitive impairment (P_nonlinear>0.05). Conclusion: Blood selenium is negatively associated with cognitive impairment in the Chinese oldest old.
Aged, 80 and over ; Female ; Humans ; Selenium ; Cohort Studies ; Cross-Sectional Studies ; Metals/analysis* ; Cognitive Dysfunction/epidemiology* ; China/epidemiology*

ObjectiveTo assess the prognostic value of ¹⁸F-FDG PET/CT parameters for predicting therapeutic response in diffuse large B-cell lymphoma （DLBCL）. MethodsWe retrospectively analyzed the clinical data and ¹⁸F-FDG PET/CT radiomics features of 81 DLBCL patients enrolled between June 2015 and October 2020. Multivariate logistic regression analysis was used to identify the predictive factors for therapeutic response of DLBCL， based on which a predictive model was developed accordingly. The performance of the model was evaluated by receiver operating characteristic （ROC） curves and calibration plots. ResultsDuring the two years after first chemotherapy， 23 patients （28.3%） developed relapse and 58 patients （71.7%） had progression-free survival （PFS）. The analysis for the predictive capability of the binary logistic regression model incorporating the PET/CT features revealed that the imaging features of ¹⁸F-FDG PET/CT after chemotherapy were independent prognostic factors for PFS. Among them， SUV_THR-mean2 was the most important factor for predicting therapeutic response in DLBCL patients after chemotherapy， with a cutoff value of 2.00 （AUC=0.81）. Conclusions¹⁸F-FDG PET/CT showed a valuable prognostic performance for PFS in DLBCL patients after chemotherapy， with the imaging feature after chemotherapy SUV_TLR-mean2 being the optimal independent predictor. Our predictive model of imaging features might have an important prognostic value in assessing the risk of disease progression， guiding the treatment and follow-up protocol， improving therapeutic efficiency and cutting down the medical cost.