BACKGROUND: Blood glucose and serum albumin have been associated with cardiovascular disease prognosis, but the impact of admission-blood-glucose-to-albumin ratio (AAR) on adverse outcomes in critical ill coronary artery disease (CAD) patients was not investigated. METHODS: Patients diagnosed with CAD were non-consecutively selected from the MIMIC-IV database and categorized into quartiles based on their AAR. The primary outcome was 1-year mortality, and secondary endpoints were in-hospital mortality, acute kidney injury (AKI), and renal replacement therapy (RRT). A restricted cubic splines model and Cox proportional hazard models assessed the association between AAR and adverse outcomes in CAD patients. Kaplan-Meier survival analysis determined differences in endpoints across subgroups. RESULTS: A total of 8360 patients were included. There were 726 patients (8.7%) died in the hospital and 1944 patients (23%) died at 1 year. The incidence of AKI and RRT was 63% and 4.3%, respectively. High AAR was markedly associated with in-hospital mortality (HR = 1.587, P = 0.003), 1-year mortality (HR = 1.502, P < 0.001), AKI incidence (HR = 1.579, P < 0.001), and RRT (HR = 1.640, P < 0.016) in CAD patients in the completely adjusted Cox proportional hazard model. Kaplan-Meier survival analysis noted substantial differences in all endpoints based on AAR quartiles. Stratified analysis and interaction test demonstrated stable correlations between AAR and outcomes. CONCLUSIONS The results highlight that AAR may be a potential indicator for assessing in-hospital mortality, 1-year mortality, and adverse renal prognosis in critical CAD patients.

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 compare the characteristics and temperature changes of single and double fiber Nd∶YAG laser in fresh isolated pig liver,and to provide reference for clinical ablation treatment.Methods:Single-needle single-point and double-needle double-point ablations were perf ormed on fresh isolated pig livers using a 5 W power laser,and the morphology,range,and surrounding temperature changes of the ablation lesions caused by the two in vitro liver tissues were observed.Results:The ablation lesions were divided into carbonized area,necrotic area and deformed area from inside to outside.The carbonized area in the center of the ablation lesion in the double-fiber group was larger and the cell necrosis was more thorough.The aspect ratio(LD/TD)of the laser ablation lesion in the single-fiber group was larger than that in the double fiber group(P＜0.001).The transverse diameter(TD)and volume(V)of the ablation lesion in the double-fiber group were larger than those in the single-fiber group(P＜0.001).There was no significant difference in the longitudinal diameter(LD)of the ablation lesion between the double-fiber group and the single-fiber group(P＞0.05).There was no significant difference in the temperature of 20 s,40 s and 60 s at 5 mm and 10 mm beside the ablation center between the two groups(all P＞0.05).Conclusion:Under the condition of 5 W,the temperature changes around the single and double fiber ablation are similar.The single fiber is suitable for small tumor ablation,and the double fiber ablation range is larger,which can be used for one-time full coverage ablation of larger cancer nodules.

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 compare the characteristics and temperature changes of single and double fiber Nd∶YAG laser in fresh isolated pig liver,and to provide reference for clinical ablation treatment.Methods:Single-needle single-point and double-needle double-point ablations were perf ormed on fresh isolated pig livers using a 5 W power laser,and the morphology,range,and surrounding temperature changes of the ablation lesions caused by the two in vitro liver tissues were observed.Results:The ablation lesions were divided into carbonized area,necrotic area and deformed area from inside to outside.The carbonized area in the center of the ablation lesion in the double-fiber group was larger and the cell necrosis was more thorough.The aspect ratio(LD/TD)of the laser ablation lesion in the single-fiber group was larger than that in the double fiber group(P＜0.001).The transverse diameter(TD)and volume(V)of the ablation lesion in the double-fiber group were larger than those in the single-fiber group(P＜0.001).There was no significant difference in the longitudinal diameter(LD)of the ablation lesion between the double-fiber group and the single-fiber group(P＞0.05).There was no significant difference in the temperature of 20 s,40 s and 60 s at 5 mm and 10 mm beside the ablation center between the two groups(all P＞0.05).Conclusion:Under the condition of 5 W,the temperature changes around the single and double fiber ablation are similar.The single fiber is suitable for small tumor ablation,and the double fiber ablation range is larger,which can be used for one-time full coverage ablation of larger cancer nodules.

Objective:To compare the short-term effect and adverse reaction of venetoclax(VEN)combined with azacitidine(AZA)versus"7+3"regimen in newly diagnosed elder patients with acute myeloid leukemia(AML).Methods:From January 2021 to January 2022,the clinical data of seventy-nine newly diagnosed elder patients with AML at the Second Hospital of Shanxi Medical University and the Shanxi Bethune Hospital were retrospectively analyzed,including VEN+AZA group(41 cases)and"7+3"group(38 cases).The propensity score matching(PSM)method was used to balance confounding factors,then response,overall survival(OS),progression-free survival(PFS)and adverse reactions between the two groups were compared.Results:The ORR of VEN+AZA group and"7+3"group was 68％and 84％,respectively,and the CRc was 64％and 72％,respectively,the differents were not statistically significant(P＞0.05).In the VEN+AZA group,there were 5 non-remission(NR)patients,4 with chromosome 7 abnormality(7q-/-7),and 1 with ETV6 gene mutation.Median followed-up time between the two groups was 8 months and 12 months,respectively,and the 6-months OS was 84％vs 92％(P=0.389),while 6-months PFS was 84％vs 92％(P=0.258).The main hematological adverse reactions in two groups were stage Ⅲ-Ⅳmyelosuppression,and the incidence rate was not statistically different(P＞0.05).The median time of neutrophil recovery in two groups was 27(11-70)d,25(14-61)d(P=0.161),and platelet recovery was 27(11-75)d,25(16-50)d(P=0.270),respectively.The infection rate of VEN+AZA group was lower than that of"7+3"group(56％vs 88％,P=0.012).The rate of lung infections of two groups was 36％and 64％,respectively,the difference was statistically significant(P=0.048).Conclusion:The short-term effect of VEN+AZA group and"7+3"regimens in eldrly AML patients are similar,but the VEN+AZA regimen had a lower incidence of infection.The presence of chromosome 7 abnormality(7q-/-7)may be a poor prognostic factor for elderly AML patients treated with VEN+AZA.

Pogostemon cablin is a famous southern medicine.As the important raw material for modern medicine and industry,Pogostemon cablin becomes required with a large marketing demand.However,due to the serious continuous cropping obstacles in the growth process of Pogostemon cablin,the aggravation of diseases of Pogostemon cablin and the degradation of its quality arose.This paper outlined the ecological factors such as climate factors,soil factors and topographic factors suitable for the growth of Pogostemon cablin,analyzed the continuous cropping obstacles and diseases arising in the cultivation,reviewed the current ecological planting mode of Pogostemon cablin such as crop rotation,intercropping,relay-cropping and under-forest planting,and also made a comprehensive evaluation of the economic benefits,ecological benefits and social benefits of the ecological planting mode of Pogostemon cablin,aiming to provide a theoretical basis and a reference for the promotion of the ecological planting mode of Pogostemon cablin.

Objective To establish a dynamic prediction model of fatal massive hemorrhage in trauma based on the vital signs time series data and machine learning algorithms.Methods Retrospectively analyze the vital signs time series data of 7522 patients with trauma in the Medical Information Mart for Intensive Care-Ⅳ(MIMIC-Ⅳ)database from 2008 to 2019.According to the occurrence of posttraumatic fatal massive hemorrhage,the patients were divided into two groups:fatal massive hemorrhage group(n=283)and non-fatal massive hemorrhage group(n=7239).Six machine learning algorithms,including logistic regression(LR),support vector machine(SVM),random forests(RF),adaptive boosting(AdaBoost),gated recurrent unit(GRU),and GRU-D were used to develop a dynamic prediction models of fatal massive hemorrhage in trauma.The probability of fatal massive hemorrhage in the following 1,2,and 3 h was dynamically predicted.The performance of the models was evaluated by accuracy,sensitivity,specificity,positive predictive value,negative predictive value,Youden index,and area under receiver operating characteristic curve(AUC).The models were externally validated based on the trauma database of the Chinese PLA General Hospital.Results In the MIMIC-Ⅳ database,the set of dynamic prediction models based on the GRU-D algorithm was the best.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.946±0.029,0.940±0.032,and 0.943±0.034,respectively,and there was no significant difference(P=0.905).In the trauma dataset,GRU-D model achieved the best external validation effect.The AUC for predicting fatal major bleeding in the next 1,2,and 3 h were 0.779±0.013,0.780±0.008,and 0.778±0.009,respectively,and there was no significant difference(P=0.181).This set of models was deployed in a public web calculator and hospital emergency department information system,which is convenient for the public and medical staff to use and validate the model.Conclusion A set of dynamic prediction models has been successfully developed and validated,which is greatly significant for the early diagnosis and dynamic prediction of fatal massive hemorrhage in trauma.

Objective This study aimed to investigate the potential relationship between urinary metals copper (Cu), arsenic (As), strontium (Sr), barium (Ba), iron (Fe), lead (Pb) and manganese (Mn) and grip strength. Methods We used linear regression models, quantile g-computation and Bayesian kernel machine regression (BKMR) to assess the relationship between metals and grip strength.Results In the multimetal linear regression, Cu (β=-2.119), As (β=-1.318), Sr (β=-2.480), Ba (β=0.781), Fe (β= 1.130) and Mn (β=-0.404) were significantly correlated with grip strength (P < 0.05). The results of the quantile g-computation showed that the risk of occurrence of grip strength reduction was -1.007 (95％ confidence interval:-1.362, -0.652; P < 0.001) when each quartile of the mixture of the seven metals was increased. Bayesian kernel function regression model analysis showed that mixtures of the seven metals had a negative overall effect on grip strength, with Cu, As and Sr being negatively associated with grip strength levels. In the total population, potential interactions were observed between As and Mn and between Cu and Mn (Pinteractions of 0.003 and 0.018, respectively).Conclusion In summary, this study suggests that combined exposure to metal mixtures is negatively associated with grip strength. Cu, Sr and As were negatively correlated with grip strength levels, and there were potential interactions between As and Mn and between Cu and Mn.

The present study aims to construct an elderly vitality index evaluation system and develop a comprehensive vitality evaluation scale for the elderly to reasonably evaluate the vitality level of the elderly in China, so as to provide a reference for promoting the realization of "active aging" and "healthy aging". Literature research and in-depth interview were used to collect the senile vitality sensitive indexes. The indexes were screened and corrected by Delphi expert consultation method, item analysis method based on classical test theory, factor analysis method, and reliability and validity analysis method. The analytic hierarchy process was used to calculate the weight of each level of indexes. An elderly vitality evaluation system including 4 first-level indexes and 24 second-level indexes was constructed. The consistency test results of all levels of indicators showed that the consistency index (CI) and consistent ratio (CR) were both less than 0.1, which met the requirements and showed satisfactory consistency. The weights of exercise vitality, nutritional vitality, psychological vitality and social vitality were 0.263, 0.141, 0.455 and 0.141, respectively. In conclusion, the comprehensive vitality scale constructed for the Chinese elderly is reliable and scientific, and can be used to evaluate the vitality of the elderly.
Humans ; Aged ; Analytic Hierarchy Process ; Reproducibility of Results ; Delphi Technique ; Aging ; China ; Surveys and Questionnaires