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: To analyze the clinical characteristics of venous thromboembolism (VTE) in patients with multiple myeloma (MM) and to identify the risk factors of VTE in MM patients. METHODS: 179 newly diagnosed MM (NDMM) patients admitted to The Second Hospital of Shanxi Medical University from January 2014 to December 2020 who were followed up for more than 6 months were collected, and they were divided into VTE group and control group according to whether combined with VTE. The clinical and laboratory data were compared between the two groups. Mann-whitney U test was used for inter-group comparison of measurement data, Chi-square test or Fisher's exact test was used for inter-group comparison of count data, and multivariate logistic regression analysis was performed to explore the risk factors of VTE in MM patients. RESULTS: Compared with control group, the serum albumin (ALB) level in VTE group was significantly lower (P =0.033), the fibrinogen (FIB) level was significantly higher (P =0.016), and the proportion of patients with D-dimer≥2 000 ng/ml was significantly higher than that in the control group (26.3% vs 4.4%, P =0.002). There was a significant difference in M-component type between the two groups (P =0.028), and the proportion of IgG type in VTE group was higher. There were no statistically significant differences between two groups in age, sex, body mass index (BMI), the proportions of patients with hypertension, diabetes, coronary heart disease and cerebral infarction, white blood cell (WBC) count, platelet (PLT) count, liver and kidney function, plasma cells ratio in bone marrow, serum globulin (GLO), lactate dehydrogenase (LDH), β₂-microglobulin (β₂-MG) level, C-reactive protein (CRP) level, erythrocyte sedimentation rate (ESR), prothrombin time (PT), activated partial thromboplastin time (APTT), disease stage, thrombosis prevention and the use of immunomodulators (P >0.05). Multivariate logistic regression analysis showed that FIB level (OR=1.578, 95%CI:1.035-2.407, P =0.034), D-dimer≥2 000 ng/ml (OR=5.467, 95%CI:1.265-23.621, P =0.023) and IgG type (OR=4.780, 95%CI: 1.221-18.712, P =0.025) were independent risk factors for VTE in MM patients. CONCLUSION MM patients are prone to VTE, and FIB level, D-dimer≥2 000 ng/ ml and IgG type are independent risk factors for VTE in MM patients.
Humans ; Venous Thromboembolism ; Multiple Myeloma/complications* ; Risk Factors ; Anticoagulants ; Immunoglobulin G ; Retrospective Studies

Objective: To analyze and compare therapy responses, outcomes, and incidence of severe hematologic adverse events of flumatinib and imatinib in patients newly diagnosed with chronic phase chronic myeloid leukemia (CML) . Methods: Data of patients with chronic phase CML diagnosed between January 2006 and November 2022 from 76 centers, aged ≥18 years, and received initial flumatinib or imatinib therapy within 6 months after diagnosis in China were retrospectively interrogated. Propensity score matching (PSM) analysis was performed to reduce the bias of the initial TKI selection, and the therapy responses and outcomes of patients receiving initial flumatinib or imatinib therapy were compared. Results: A total of 4 833 adult patients with CML receiving initial imatinib (n=4 380) or flumatinib (n=453) therapy were included in the study. In the imatinib cohort, the median follow-up time was 54 [interquartile range (IQR), 31-85] months, and the 7-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.2%, 88.4%, 78.3%, and 63.0%, respectively. The 7-year FFS, PFS, and OS rates were 71.8%, 93.0%, and 96.9%, respectively. With the median follow-up of 18 (IQR, 13-25) months in the flumatinib cohort, the 2-year cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) were 95.4%, 86.5%, 58.4%, and 46.6%, respectively. The 2-year FFS, PFS, and OS rates were 80.1%, 95.0%, and 99.5%, respectively. The PSM analysis indicated that patients receiving initial flumatinib therapy had significantly higher cumulative incidences of CCyR, MMR, MR(4), and MR(4.5) and higher probabilities of FFS than those receiving the initial imatinib therapy (all P<0.001), whereas the PFS (P=0.230) and OS (P=0.268) were comparable between the two cohorts. The incidence of severe hematologic adverse events (grade≥Ⅲ) was comparable in the two cohorts. Conclusion: Patients receiving initial flumatinib therapy had higher cumulative incidences of therapy responses and higher probability of FFS than those receiving initial imatinib therapy, whereas the incidence of severe hematologic adverse events was comparable between the two cohorts.
Adult ; Humans ; Adolescent ; Imatinib Mesylate/adverse effects* ; Incidence ; Antineoplastic Agents/adverse effects* ; Retrospective Studies ; Pyrimidines/adverse effects* ; Leukemia, Myelogenous, Chronic, BCR-ABL Positive/drug therapy* ; Treatment Outcome ; Benzamides/adverse effects* ; Leukemia, Myeloid, Chronic-Phase/drug therapy* ; Aminopyridines/therapeutic use* ; Protein Kinase Inhibitors/therapeutic use*

Objective To explore the role pathway and pattern of the myeloma cancer gene (MYC) and its mRNA interaction with the microRNAs(miRNAs) and circular RNA(circRNAs) at hour 0, hour 6 and hour 72 in the rat liver regeneration. Methods The rat 2/3 hepatectomy (PH) model was prepared as described by Higgins, the hepatocytes were isolated according to the method of Smedsrod et al. The expression changes of mRNA, miRNA and circRNA [together named as competing endogenous RNA (ceRNA)] were detected by the large-scale quantitative detection technology, the interaction network of ceRNA was constructed by Cytoscape 3.2 software, and their correlation in expression and role were analyzed by ceRNA comprehensive analysis. Results It was found that at hour 0 and hour 6 after PH, the ratio value of MYC mRNA showed 0.15±0.03 and 2.36±0.20, miR-134-5p indicated 3.22±0.61 and 0.08±0.02, circRNA_12112 displayed 0.68±0.21 and 13.35±3.53. At the same time, the cell cycle initiation-related genes ras association domain family member 1 (RASSF1), cyclin dependent kinase 2 (CDK2), superoxide dismutase 2 (SOD2), which were promoted in expression by MYC, were down-regulated at hour 0 after PH, but the cell cycle initiation-related genes nestin (NES), RAD21 cohesin complex component (RAD21), CUE domain containing 2 (CUEDC2), which are inhibieted in expression by MYC, had no meaningful express changes at hour 0 after PH. On the other hand, the cell cycle initiation-related gene SOD2, which was promoted in expression by MYC, was up-regulated at hour 6 after PH, but the cell cycle initiation-related genes NES, RAD21, CUEDC2, which are inhibieted in expression by MYC, were down-regulated at hour 6 after PH. In contrary, at hour 72 after PH, the ratio value of MYC mRNA showed 2.36±0.20, miR-880-3p indicated 0.54±0.01, circRNA_09599 displayd 0.54±0.16. At the same time, the cell cycle termination-related gene hepatocyte growth factor (HGF), which is promoted in expression by MYC, was up-regulated 72 hours after PH, the cell cycle termination-related genes MET proto-oncogene receptor tyrosine kinase (MET) and cyclin dependent kinase inhibitor 1A (CDKN1A), which are inhibieted in expression by MYC, were down-regulated 72 hours after PH. Conclusion The correlation in expression and role of the miRNAs, which are inhibited by circRNAs, MYC, its mRNA is inhibited by miRNAs, and the cell cycle initiation-related and cell cycle termination-related genes, which are regulated by MYC, are helpful for the hepatocyte to be in cell cycle initiation state at hour 6 after PH and to be in cell cycle termination state at hour 72 after PH.