ObjectTo investigate the pathogenesis of drug-resistant epilepsy by examining the expression of mRNA and protein of Cell Division Cycle 42 GTP-binding protein (Cdc42), Neural Wiskott-Aldrich Syndrome Protein (N-WASP) and Actin-related protein 2/3(Arp2/3) in peripheral blood of patients with drug-resistant epilepsy (DRE).MethodsSeventy two essential epilepsy patients who were attended at outpatients and inpatients in the Department of Neurology of the Affiliated Hospital of Youjiang Medical University for Nationalities were selected from October 2016 to October 2018. According to the 2010 International League Against Epilepsy’s definition of Drug-Resistant Epilepsy, the patients were divided into 2 groups: 32 patients with DRE were defined as DRE group, 40 patients with anti-epilepsy drugs (AEDs) well controlled were defined as the well controlled group. Thirty two healthy persons were selected as control group. The expression of mRNA and protein of Cdc42, N-WASP and Arp2/3 in peripheral blood were measured by quantitative real-time PCR (RT-qPCR) and Western blot(WB). Experimental data were analyzed by ANOVA or rank-sum test.ResultsCompared with well-controlled group and healthy persons group, Cdc42, N-WASP, Arp2/3 in DRE group were significantly increased, the differences were statistically significant (P<0.05). Compared with the control group, Cdc42, N-WASP, Arp2/3 in well-controlled group were significantly increased, with statistically significant differences (P<0.05).ConclusionThe expression of Cdc42, N-WASP, Arp2/3 in peripheral blood of patients with DRE significantly increased, being closely related to the occurrence and development of DRE, and used as indicators in peripheral blood predicting the occurrence of DRE.
The segmentation of organs at risk is an important part of radiotherapy. The current method of manual segmentation depends on the knowledge and experience of physicians, which is very time-consuming and difficult to ensure the accuracy, consistency and repeatability. Therefore, a deep convolutional neural network (DCNN) is proposed for the automatic and accurate segmentation of head and neck organs at risk. The data of 496 patients with nasopharyngeal carcinoma were reviewed. Among them, 376 cases were randomly selected for training set, 60 cases for validation set and 60 cases for test set. Using the three-dimensional (3D) U-NET DCNN, combined with two loss functions of Dice Loss and Generalized Dice Loss, the automatic segmentation neural network model for the head and neck organs at risk was trained. The evaluation parameters are Dice similarity coefficient and Jaccard distance. The average Dice Similarity coefficient of the 19 organs at risk was 0.91, and the Jaccard distance was 0.15. The results demonstrate that 3D U-NET DCNN combined with Dice Loss function can be better applied to automatic segmentation of head and neck organs at risk.