Objective To observe blood pressure variability (BPV) in elderly patients with primary hypertension and carotid artery intima-media thickness (IMT),and analyze the correlation between BPV and atherosclerosis.Methods One hundred and nine patients with primary hypertension were divided into non-carotid atherosclerosis group (IMT ＜ 1.0 mm,49 cases) and carotid atherosclerosis group (IMT ≥ 1.0mm,60 cases).24 h ambulatory blood pressure monitoring (ABPM) was used to measure the 24-hour mean systolic blood pressure (24 h SBP),24-hour mean diastolic pressure (24 h DBP) ; SBP of daytime (dSBP)and nighttime (nSBP) ; DBP of daytime (dDBP) and nighttime (nDBP).And their standard deviation and coetficient of variation (CV) was calculated.Results dSBP,24 h SBPCV,24 h DBPCV,dSBPCV,nSBPCV in carotid atherosclerosis group was higher than that in non-carotid atherosclerosis group [(132.3 ± 12.1)mm Hg(1 mm Hg =0.133 kPa) vs.(122.7 ± 10.8) mm Hg,0.118±0.011 vs.0.107 ± 0.023,0.142 ± 0.058vs.0.116 ±0.028,0.129 ±0.039 vs.0.105 ±0.017,0.119 ±0.060 vs.0.109 ±0.037],and there was significant difference (P ＜ 0.01 or ＜ 0.05).There was no significant difference in 24 h SBP,24 h DBP,dDBP,nSBP,nDBP,dSBPCV,nSBPCV between two groups (P ＞0.05).Conclusions BPV of carotid atherosclerosis is higher than that of non-carotid atherosclerosis.BPV and carotid artery IMT has a certain relevance.

OBJECTIVES: Glioma is the most common intracranial primary tumor in central nervous system. Glioma grading possesses important guiding significance for the selection of clinical treatment and follow-up plan, and the assessment of prognosis. This study aims to explore the feasibility of logistic regression model based on radiomics to predict glioma grading. METHODS: Retrospective analysis was performed on 146 glioma patients with confirmed pathological diagnosis from January, 2012 to December, 2018. A total of 41 radiomics features were extracted from contrast-enhanced T RESULTS: A total of 5 imaging features selected by LASSO were used to establish a logistic regression model for predicting glioma grading. The model showed good discrimination with AUC value of 0.919. Hosmer-Lemeshow test showed no significant difference between the calibration curve and the ideal curve ( CONCLUSIONS The logistic regression model using radiomics exhibits a relatively high accuracy for predicting glioma grading, which may serve as a complementary tool for preoperative prediction of giloma grading.
Brain Neoplasms/diagnostic imaging* ; Glioma/diagnostic imaging* ; Humans ; Logistic Models ; Magnetic Resonance Imaging ; ROC Curve ; Retrospective Studies

To explore the feasibility and efficacy of artificial neural network for differentiating high-grade glioma and low-grade glioma using image information.
 Methods: A total of 130 glioma patients with confirmed pathological diagnosis were selected retrospectively from 2012 to 2017. Forty one imaging features were extracted from each subjects based on 2-dimension magnetic resonance T1 weighted imaging with contrast-enhancement. An artificial neural network model was created and optimized according to the performance of feature selection. The training dataset was randomly selected half of the whole dataset, and the other half dataset was used to verify the performance of the neural network for glioma grading. The training-verification process was repeated for 100 times and the performance was averaged.
 Results: A total of 5 imaging features were selected as the ultimate input features for the neural network. The mean accuracy of the neural network for glioma grading was 90.32%, with a mean sensitivity at 87.86% and a mean specificity at 92.49%. The area under the curve of receiver operating characteristic curve was 0.9486.
 Conclusion: As a technique of artificial intelligence, neural network can reach a relatively high accuracy for the grading of glioma and provide a non-invasive and promising computer-aided diagnostic process for the pre-operative grading of glioma.
Brain Neoplasms ; diagnostic imaging ; pathology ; Glioma ; diagnostic imaging ; pathology ; Humans ; Magnetic Resonance Imaging ; Neoplasm Grading ; Neural Networks, Computer ; ROC Curve ; Retrospective Studies ; Sensitivity and Specificity