Objective To explore the utility of CT for the evaluation of abdominal aortic calcification(AAC)in chronic kidney disease(CKD).Methods Laboratory examination indexes and abdominal plain CT imaging of 132 CKD stage 3-5 patients were analyzed retrospectively.The ACC score was evaluated according to quantitative method,the risk factors related to AAC were analyzed.Results In correlation analyses,AAC score was positively correlated with creatinine(Cr)level,CO2 combining power(P＜0.05),and negatively corrected with albumin(ALB).Logistic regression analysis showed that Cr level were risk factors for AAC,and ALB were protective factors.Conclusion Based on abdominal plain CT images,Cr level,CO2 combining power and ALB level are related to AAC.Quantitative method based on CT has a high application value in evaluating AAC.

Objective:The study aimed to evaluate the application value of a machine learning method based on general linear model (GLM) in the localization of individual motor function in patients with glioma after blood oxygen level dependent functional magnetic resonance imaging (BOLD-fMRI).Methods:A retrospective study was conducted, and strict clinical screening was performed in the Neurosurgery Department of the First Affiliated Hospital of Xi'an Jiaotong University from November 2017 to November 2021. A total of 38 pathologically confirmed patients with glioma located in the motor area were selected and included in the validation set of the machine learning model (25 males, 13 females; aged 24-69), and 50 healthy volunteers were recruited and included in the training set (26 males, 14 females; aged 22-68). Extracting the resting-state fMRI (rs-fMRI) features from 98 subjects in the Human Connectome Project (HCP) using the independent component analysis (ICA). A machine learning model based on GLM was trained using the correlation between the rs-fMRI and task-based fMRI (tb-fMRI) features of healthy subjects. (1) GLM-predicted activation and actual activation were compared by Pearson correlation coefficient (CC) analysis; (2) the dice coefficient (DC) was used as a quantitative indicator of the prediction efficiency of the model and used in comparing the prediction efficiency of GLM and ICA methods.Results:(1) GLM-prediction activation in glioma patients was highly similar to task-state function activation (CC>0.30 in 89.47% [34/38] of patients). (2) GLM was better than ICA in predicting task-state motor function activation. The DC was 0.34 (0.27, 0.42), and 0.26 (0.16, 0.30), respectively, the difference was statistically significant ( Z=-3.88; P<0.001). In the tumor-containing hemisphere, GLM was better than ICA in predicting task-state activation, with DCs of 0.36 (0.17, 0.48) and 0.34 (0.04, 0.45), respectively ( Z=-2.43, P=0.015). The prediction effects of the two methods in the nontumor hemisphere was significantly higher than that in the tumor hemisphere ( Z=-4.33, -3.59; all P values<0.001). Conclusion:GLM-based machine learning can predict tb-fMRI motor activation in patients with glioma after rs-fMRI and before surgery and is more efficient than ICA.

"大健康"是根据时代发展、社会需求与疾病谱的改变提出的一种全局理念,医学人才的培养正是推进"大健康"时代的关键生产力.如何推进医学教育改革、提高医学人才质量,已经成为我国现阶段高等医学教育改革的核心.笔者通过在美国及德国医学院访问学习的经历,将中、美、德医学教育体系进行比较分析,进而总结美、德医学教育模式中值得借鉴之处,以期能够促进我国医学卓越人才的培养.