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]

This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.

Objective:The genotoxicity risk of graphene artificial nerve sheath conduit was systematically evaluated to provide scientific evidence for their clinical safety and to establish methodological references for the genotoxicity assessment of nanomaterial medical devices.Methods:The potential effects of graphene artificial nerve sheath conduit on genetic and chromosomal endpoints were analyzed by integrating bacterial reverse mutation assays,in vitro chromosome aberration assays,mouse lymphoma cell TK gene mutation tests,and mammalian erythrocyte Pig-a gene mutation assays.Results:In the bacterial reverse mutation assay,all plates showed good background growth.There was no significant difference in the average number of revertant colonies between the test group and the negative control group,with a ratio around 1.0.In the in vitro chromosome aberration assay,the chromosomal aberration rate in the test group was less than 5％,showing no significant increase compared to the negative control group.In the mouse lymphoma cell TK gene mutation assay,the mutation frequency in the test group was less than twice that of the negative control group,with no significant difference.In the mammalian erythrocyte Pig-a gene mutation assay,the mutation frequencies of erythrocytes and reticulocytes in the test group were both less than 3× 10-6,showing no significant difference compared to the negative control group.Conclusions:Graphene artificial nerve sheath conduit exhibited no detectable genotoxicity under the tested conditions,the research results can provide reference and guidance for the genotoxicity evaluation of nanomaterial medical devices.

Relevant literature on mobile medical devices combined with the ecological momentary assessment(EMA)method applied to lung cancer patient caring was collected from some databases of CNKI,Wanfang,VIP,China Biomedical Literature Database,PubMed,Embase,Cochrane Library,CINAHL and Web of Science.The method of scoping review was used to sort out the general characteristics of the included literature,types and application of mobile medical devices,assessment content elements and outcome indicators.The feasibility and validity of mobile medical devices combined with the EMA method for the symptom assessment of lung cancer patients were described,whose advantages in monitoring during lung cancer caring and application prospects were elaborated.The problems of mobile medical devices during practical application were pointed out and some countermeasures were put forward accordingly.References were provided for personalized remote caring of lung cancer patients and development of intelligent multi-modal mobile devices.[Chinese Medical Equipment Journal,2025,46(10):71-77]

Relevant literature on mobile medical devices combined with the ecological momentary assessment(EMA)method applied to lung cancer patient caring was collected from some databases of CNKI,Wanfang,VIP,China Biomedical Literature Database,PubMed,Embase,Cochrane Library,CINAHL and Web of Science.The method of scoping review was used to sort out the general characteristics of the included literature,types and application of mobile medical devices,assessment content elements and outcome indicators.The feasibility and validity of mobile medical devices combined with the EMA method for the symptom assessment of lung cancer patients were described,whose advantages in monitoring during lung cancer caring and application prospects were elaborated.The problems of mobile medical devices during practical application were pointed out and some countermeasures were put forward accordingly.References were provided for personalized remote caring of lung cancer patients and development of intelligent multi-modal mobile devices.[Chinese Medical Equipment Journal,2025,46(10):71-77]

This study employed the "disease-medicine-dose" pattern to mine the medication rules of traditional Chinese medicine(TCM) prescriptions containing Astragali Radix in ancient Chinese medical books, aiming to provide a scientific basis for the clinical application of Astragali Radix and the development of new medicines. The TCM prescriptions containing Astragali Radix were retrieved from databases such as Chinese Medical Dictionary and imported into Excel 2020 to construct the prescription library. Statical analysis were performed for the prescriptions regarding the indications, syndromes, medicine use frequency, herb effects, nature and taste, meridian tropism, dosage forms, and dose. SPSS statistics 26.0 and IBM SPSS Modeler 18.0 were used for association rules analysis and cluster analysis. A total of 2 297 prescriptions containing Astragali Radix were collected, involving 233 indications, among which sore and ulcer, consumptive disease, sweating disorder, and apoplexy had high frequency(>25), and their syndromes were mainly Qi and blood deficiency, Qi and blood deficiency, Yin and Yang deficiency, and Qi deficiency and collateral obstruction, respectively. In the prescriptions, 98 medicines were used with the frequency >25 and they mainly included Qi-tonifying medicines and blood-tonifying medicines. Glycyrrhizae Radix et Rhizoma, Angelicae Sinensis Radix, Ginseng Radix et Rhizoma, Atractylodis Macrocephalae Rhizoma, and Citri Reticulatae Pericarpium were frequently used. The medicines with high frequency mainly have warm or cold nature, and sweet, pungent, or bitter taste, with tropism to spleen, lung, heart, liver, and kidney meridians. In the treatment of sore and ulcer, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to promote granulation and heal up sores. In the treatment of consumptive disease, Astragali Radix was mainly used with the dose of 37.30 g and combined with Ginseng Radix et Rhizoma to tonify deficiency and replenish Qi. In the treatment of sweating disorder, Astragali Radix was mainly used with the dose of 3.73 g and combined with Glycyrrhizae Radix et Rhizoma to consolidate exterior and stop sweating. In the treatment of apoplexy, Astragali Radix was mainly used with the dose of 7.46 g and combined with Glycyrrhizae Radix et Rhizoma to dispell wind and stop convulsions. Astragali Radix can be used in the treatment of multiple system diseases, with the effects of tonifying Qi and ascending Yang, consolidating exterior and stopping sweating, and expressing toxin and promoting granulation. According to the manifestations of different diseases, when combined with other medicines, Astragali Radix was endowed with the effects of promoting granulation and healing up sores, tonifying deficiency and Qi, consolidating exterior and stopping sweating, and dispelling wind and replenishing Qi. The findings provide a theoretical reference and a scientific basis for the clinical application of Astragali Radix and the development of new medicines.
Drugs, Chinese Herbal/history* ; Humans ; Medicine, Chinese Traditional/history* ; History, Ancient ; Astragalus Plant/chemistry* ; China ; Astragalus propinquus

By employing the analytic hierarchy process(AHP), the CRITIC method(a weight determination method based on indicator correlations), and the AHP-CRITIC hybrid weighting method, the weight coefficients of evaluation indicators were determined, followed by a comprehensive score comparison. The grey correlation analysis was then performed to analyze the results calculated using the hybrid weighting method. Subsequently, a backpropagation-artificial neural network(BP-ANN) model was constructed to predict the extraction process parameters and optimize the extraction process for Shenxiong Huanglian Jiedu Granules(SHJG). In the extraction process, an L_9(3~4) orthogonal experiment was designed to optimize three factors at three levels, including extraction frequency, water addition amount, and extraction time. The evaluation indicators included geniposide, berberine, ginsenoside Rg_1 + Re, ginsenoside Rb_1, ferulic acid, and extract yield. Finally, the optimal extraction results obtained by the orthogonal experiment, grey correlation analysis, and BP-ANN method were compared, and validation experiments were conducted. The results showed that the optimal extraction process involved two rounds of aqueous extraction, each lasting one hour; the first extraction used ten times the amount of added water, while the second extraction used eight times the amount. In the validation experiments, the average content of each indicator component was higher than the average content obtained in the orthogonal experiment, with a higher comprehensive score. The optimized extraction process parameters were reliable and stable, making them suitable for subsequent preparation process research.
Drugs, Chinese Herbal/analysis* ; Neural Networks, Computer

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

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]

This study was aimed at exploring novel auxiliary diagnostic biomarkers for brucellosis and their potential miR-NA-mRNA regulatory networks.High-throughput sequencing was used to compare miRNA expression differences in serum ex-osomes between patients with brucellosis and healthy controls.Subsequently,RT-qPCR was used to validate the expression of significantly upregulated exosomal miRNAs.The diagnostic value of these miRNAs was assessed with ROC curves,and bioin-formatics analyses were performed to investigate the potential roles of the miRNAs in brucellosis infection.The ROC curve a-nalysis indicated that the area under the curve for exosomal hsa-miR-11400(P＜0.05),hsa-miR-199a-5p(P＜0.05),and hsa-miR-148a-5p(P＜0.05)was 0.79,0.81,and 0.74,respectively.A total of 465 differentially expressed miRNAs and their tar-get genes were predicted,including 25 immune-related target genes,most of which were closely associated with cancer-related proteoglycans,NF-kappa B signaling pathways,and IL-17 signaling pathways.The constructed differentially expressed gene network indicated that the immune genes PLXNA2,IL17RA,PRKCA,CD22,ACVR1B,and CBL might be regulated by hsa-miR-199a-5p and hsa-miR-148a-5p.These findings suggest that exosomal miRNAs might serve as auxiliary diagnostic indicators for brucellosis.Our exosomal miRNA-mRNA regulatory network provides new insights into the pathogenesis and treatment of brucellosis.