OBJECTIVE: To screen out the key metabolites related to diabetic foot (DF) by integrating genome-wide association studies (GWAS) and metabolome genome-wide association studies (mGWAS). METHODS: The literature databases such as PubMed and China national knowledge infrastructure(CNKI), as well as genomics databases such as PAN UKBB, FinnGen, and IEU Open GWAS were systematically retrieved from database estobilishment to November 2024 on DF-related single nucleotide polymorphisms and genome-wide association studies. DF-single nucleotide polymorphism-metabolite network was constructed by mGWAS package and mGWAS-Explorer platform. The causal relationship between key factors was evaluated by two-sample Mendelian randomization. The genetic correlation between DF and 575 metabolites (source:IEU Open GWAS) was evaluated by linkage disequilibrium score regression. In vitro experiments were conducted to induce injury of human umbilical vein endothelial cells with 30 mM glucose and intervene with 20 μM γ-tocopherol. Changes in cell migration, scratch healing and tube formation function were detected. RESULTS: Twenty-senen literatures on single nucleotide polymorphism literatures and 3 studies on GWAS were included. Genetic analysis results showed DF-related single nucleotide polymorphisms were enriched in vascular endothelial dysfunction-related pathways (such as fluid shear stress and atherosclerosis). The results of metabolic network analysis screened out 19 associated metabolites, among which 12 such as γ -tocopherol and pyruvate had significant genetic correlations with DF. Mendelian randomization suggested matrix metalloproteinase-9(MMP-9) might be a potential driver of DF (β=0.658, P=0.063 8), and the occurrence of DF could reduce the level of high-density lipoprotein (β=-0.002, P=0.015 2). The results of in vitro experiments confirmed that γ -tocopherol could improve endothelial dysfunction induced by high glucose, specifically manifested as an increase in the number of cell migrations, improvement in the scratch healing rate, and recovery of tubule formation ability (P<0.05). CONCLUSION DF has a genetic basis centered on vascular endothelial dysfunction, and its occurrence can lead to further metabolic disorders. The key single nucleotide polymorphism loci integrated provided molecular markers for the risk stratification of foot ulcers in diabetic patients. In addition, γ -tocopherol has demonstrated clinical application potential as a therapeutic drug for DF by significantly improving the function of vascular endothelial cells in a high-glucose environment.
Humans ; Diabetic Foot/drug therapy* ; Polymorphism, Single Nucleotide ; Genome-Wide Association Study ; Genomics ; Metabolomics ; Metabolome

OBJECTIVE: Humans are exposed to complex mixtures of environmental chemicals and other factors that can affect their health. Analysis of these mixture exposures presents several key challenges for environmental epidemiology and risk assessment, including high dimensionality, correlated exposure, and subtle individual effects. METHODS: We proposed a novel statistical approach, the generalized functional linear model (GFLM), to analyze the health effects of exposure mixtures. GFLM treats the effect of mixture exposures as a smooth function by reordering exposures based on specific mechanisms and capturing internal correlations to provide a meaningful estimation and interpretation. The robustness and efficiency was evaluated under various scenarios through extensive simulation studies. RESULTS: We applied the GFLM to two datasets from the National Health and Nutrition Examination Survey (NHANES). In the first application, we examined the effects of 37 nutrients on BMI (2011-2016 cycles). The GFLM identified a significant mixture effect, with fiber and fat emerging as the nutrients with the greatest negative and positive effects on BMI, respectively. For the second application, we investigated the association between four pre- and perfluoroalkyl substances (PFAS) and gout risk (2007-2018 cycles). Unlike traditional methods, the GFLM indicated no significant association, demonstrating its robustness to multicollinearity. CONCLUSION GFLM framework is a powerful tool for mixture exposure analysis, offering improved handling of correlated exposures and interpretable results. It demonstrates robust performance across various scenarios and real-world applications, advancing our understanding of complex environmental exposures and their health impacts on environmental epidemiology and toxicology.
Humans ; Environmental Exposure/analysis* ; Linear Models ; Nutrition Surveys ; Environmental Pollutants ; Body Mass Index

Cervical spinal cord injury without fracture-dislocation (CSCIWFD) is referred to as a special type of cervical spinal cord injury characterized by traumatic spinal cord dysfunction and no significant bony structural abnormalities on imagines. Duo to the high risk of missed diagnosis during the initial consultation, CSCIWFD may lead to progressive neurological deterioration or even complete paralysis, severely impacting patients′ prognosis. Currently, there are no established consensuses over the diagnosis and treatment of CSCIWFD, such as the lack of evidence-based standards for indications of non-surgical treatment and risk of secondary neurological injury, as well as debates over the optimal timing for surgical intervention and indications for different surgical approaches. To address these issues, the Spine Trauma Group of the Orthopedic Branch of the Chinese Medical Doctor Association organized experts in the relevant fields to formulate Diagnosis and treatment guideline for acute cervical spinal cord injury without fracture- dislocation in adults ( version 2025) . Based on evidence-based medicine and the principles of scientific rigor and clinical applicability, the guidelines proposed 11 recommendations covering terminology, diagnosis, evaluation treatment, and rehabilitation, etc., aiming to standardize the management of CSCIWFD.

Vertebral refracture following percutaneous vertebral augmentation (PVA) is commonly seen in elderly patients with osteoporotic thoracolumbar compression fractures (OTLCF). It can lead to recurrent pain, loss of vertebral height, progression of kyphosis, and even neurological dysfunction, significantly impairing patients′ quality of life. Current diagnosis and treatment face multiple challenges, including high misdiagnosis rate, difficulty in choosing between surgical and non-surgical treatment options, lack of standardized surgical protocols, interference from intralesional bone cement during procedures, inadequate stability of internal fixation in osteoporotic bone, and suboptimal compliance of anti-osteoporotic therapy. Establishing a standardized diagnostic and therapeutic framework is urgently needed. To standardize the management process and improve outcomes for vertebral refractures after PVA in elderly OTLCF patients, Spinal Trauma Group of the Orthopedic Branch of Chinese Medical Doctor Association organized experts in the field to develop Guideline for the diagnosis and treatment of vertebral refracture after percutaneous vertebral augmentation in elderly patients with osteoporotic thoracolumbar compression fractures ( version 2025), based on current literature and clinical experience, and adhering to principles of scientific rigor and clinical applicability. A total of 11 recommendations were proposed, encompassing diagnosis, treatment, and rehabilitation of vertebral refracture after PVA in elderly patients with OTLCF, aiming to provide a foundation for a standardized management.

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 analyze the association of peripheral blood systemic immune-inflammation index(SII),neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR)with disease severity and progno-sis in elderly patients with chronic pulmonary heart disease(CPHD).Methods:A total of 180 elderly CPHD pa-tients admitted in Xuancheng Central Hospital between September 2021 and January 2023 were enrolled as case group.Healthy volunteers who simultaneously underwent physical examinations in our hospital were selected as con-trol group(n=50).According to the 28d prognosis,the case group was divided into death group(n=45)and sur-vival group(n=135).Levels of peripheral blood SII,NLR and PLR were compared among above-mentioned groups;Spearman correlation analysis was used to analyze the association of above indexes with cardiac function class and prognosis in these patients.Multivariate Logistic regression analysis was used to analyze risk factors for death in these patients.The predictive value of SII,NLR,and PLR for death in elderly CPHD patients was ana-lyzed using receiver operating characteristic(ROC)curve.Results:Compared with those in control group,those in the case group had significant higher levels of peripheral blood SII,NLR and PLR(P＜0.001 all).Compared with NYHA class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher levels of peripheral blood SII[(1759.87±179.43)vs.(1148.33±121.57)vs.(1392.44±146.36)],NLR[(8.65±0.89)vs.(7.14±0.75)vs.(7.76±0.81)],PLR[(152.45±16.79)vs.(125.29±13.46)vs.(138.77±13.58)];and levels of peripheral blood SII,NLR,PLR in class Ⅲ group were significantly higher than those of class Ⅱ group(P＜0.001 all).Com-pared with patients in survival group,those in death group had significant higher levels of peripheral blood SII[(1723.86±189.65)vs.(1296.81±142.33)],NLR[(8.24±0.89)vs.(7.63±0.78)],PLR[(148.75±15.26)vs.(134.41±14.58)](P＜0.001 all).Spearman correlation analysis indicated that the levels of peripheral blood SII,NLR and PLR were significant positively correlated with the severity and poor prognosis(r=0.336～0.432,P＜0.05 or＜0.01;r=0.319～0.504,P＜0.05 or＜0.01)in elderly CPHD patients.Multivariate Logistic regression analy-sis indicated that peripheral blood SII,NLR,PLR and smoking were independent risk factors for death(OR=1.024～9.514,P＜0.05 or＜0.01)in elderly CPHD patients.ROC curve indicated that area under curve(AUC)of combination of SII,NLR and PLR predicting death in elderly CPHD patients was 0.979(95％CI 0.946～0.995),significantly higher than those of each single detection[SII:0.847(95％CI 0.786～0.896),NLR:0.832(95％CI 0.769～0.883),PLR:0.881(95％CI 0.825～0.925),Z=3.988,4.386,4.217,P＜0.01 all].The nomogram calibration curve and decision curve showed good consistency and net benefit of the model.Conclusion:Peripheral blood SII,NLR and PLR are associat-ed with the severity and prognosis of elderly CPHD patients,and have certain predictive value for patient's prognosis.

Aim To investigate the antidepressant mechanism of hyperforin via the utilization of single-cell sequencing technology.Methods C57BL/6 mice were randomly divided into the control group,depres-sion model group,and hyperforin intervention group.The chronic unpredictable mild stress(CUMS)model was induced and drug interventions were administered for 28 d.Behavioral experiments were conducted to as-sess depressive symptoms,and hippocampal tissue was collected for single-cell RNA sequencing.Key cell populations and differentially expressed genes across groups were identified,followed by PPI network,GO,and KEGG enrichment analysis.Results Behavioral experiments indicated that CUMS successfully induced depressive symptoms in mice,while hyperforin im-proved depressive behavior.In the depression model group,the proportion of brain perivascular macrophages(PVM)increased,and this proportion decreased after hyperforin intervention,approaching the level seen in the control group.The top 20 common differentially ex-pressed genes in the PVM subpopulation were Saa3,Hbb-bs and Ccl24.PPI network analysis identified core targets,including Ccl2,Dhx9,C3,Msr1,Cxcl2 and Cx3cr1.KEGG enrichment analysis revealed pathways related to chemokines,phagosome formation,and inosi-tol phosphate metabolism.Conclusion The antide-pressant mechanism of hyperforin may be related to the regulation of Ccl24 and its related chemokine signaling pathway by PVM.

Aim To investigate the antidepressant mechanism of hyperforin via the utilization of single-cell sequencing technology.Methods C57BL/6 mice were randomly divided into the control group,depres-sion model group,and hyperforin intervention group.The chronic unpredictable mild stress(CUMS)model was induced and drug interventions were administered for 28 d.Behavioral experiments were conducted to as-sess depressive symptoms,and hippocampal tissue was collected for single-cell RNA sequencing.Key cell populations and differentially expressed genes across groups were identified,followed by PPI network,GO,and KEGG enrichment analysis.Results Behavioral experiments indicated that CUMS successfully induced depressive symptoms in mice,while hyperforin im-proved depressive behavior.In the depression model group,the proportion of brain perivascular macrophages(PVM)increased,and this proportion decreased after hyperforin intervention,approaching the level seen in the control group.The top 20 common differentially ex-pressed genes in the PVM subpopulation were Saa3,Hbb-bs and Ccl24.PPI network analysis identified core targets,including Ccl2,Dhx9,C3,Msr1,Cxcl2 and Cx3cr1.KEGG enrichment analysis revealed pathways related to chemokines,phagosome formation,and inosi-tol phosphate metabolism.Conclusion The antide-pressant mechanism of hyperforin may be related to the regulation of Ccl24 and its related chemokine signaling pathway by PVM.

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 analyze the association of peripheral blood systemic immune-inflammation index(SII),neutrophil-to-lymphocyte ratio(NLR),platelet-to-lymphocyte ratio(PLR)with disease severity and progno-sis in elderly patients with chronic pulmonary heart disease(CPHD).Methods:A total of 180 elderly CPHD pa-tients admitted in Xuancheng Central Hospital between September 2021 and January 2023 were enrolled as case group.Healthy volunteers who simultaneously underwent physical examinations in our hospital were selected as con-trol group(n=50).According to the 28d prognosis,the case group was divided into death group(n=45)and sur-vival group(n=135).Levels of peripheral blood SII,NLR and PLR were compared among above-mentioned groups;Spearman correlation analysis was used to analyze the association of above indexes with cardiac function class and prognosis in these patients.Multivariate Logistic regression analysis was used to analyze risk factors for death in these patients.The predictive value of SII,NLR,and PLR for death in elderly CPHD patients was ana-lyzed using receiver operating characteristic(ROC)curve.Results:Compared with those in control group,those in the case group had significant higher levels of peripheral blood SII,NLR and PLR(P＜0.001 all).Compared with NYHA class Ⅱ group and class Ⅲ group,those in class Ⅳ group had significant higher levels of peripheral blood SII[(1759.87±179.43)vs.(1148.33±121.57)vs.(1392.44±146.36)],NLR[(8.65±0.89)vs.(7.14±0.75)vs.(7.76±0.81)],PLR[(152.45±16.79)vs.(125.29±13.46)vs.(138.77±13.58)];and levels of peripheral blood SII,NLR,PLR in class Ⅲ group were significantly higher than those of class Ⅱ group(P＜0.001 all).Com-pared with patients in survival group,those in death group had significant higher levels of peripheral blood SII[(1723.86±189.65)vs.(1296.81±142.33)],NLR[(8.24±0.89)vs.(7.63±0.78)],PLR[(148.75±15.26)vs.(134.41±14.58)](P＜0.001 all).Spearman correlation analysis indicated that the levels of peripheral blood SII,NLR and PLR were significant positively correlated with the severity and poor prognosis(r=0.336～0.432,P＜0.05 or＜0.01;r=0.319～0.504,P＜0.05 or＜0.01)in elderly CPHD patients.Multivariate Logistic regression analy-sis indicated that peripheral blood SII,NLR,PLR and smoking were independent risk factors for death(OR=1.024～9.514,P＜0.05 or＜0.01)in elderly CPHD patients.ROC curve indicated that area under curve(AUC)of combination of SII,NLR and PLR predicting death in elderly CPHD patients was 0.979(95％CI 0.946～0.995),significantly higher than those of each single detection[SII:0.847(95％CI 0.786～0.896),NLR:0.832(95％CI 0.769～0.883),PLR:0.881(95％CI 0.825～0.925),Z=3.988,4.386,4.217,P＜0.01 all].The nomogram calibration curve and decision curve showed good consistency and net benefit of the model.Conclusion:Peripheral blood SII,NLR and PLR are associat-ed with the severity and prognosis of elderly CPHD patients,and have certain predictive value for patient's prognosis.