Objectives The purpose of this study is to verify the phenytoin-resistant mesial temporal lobe epilepsy (MTLE) induced by Li-pilocarpine and screened by antiepilepsy drug (AEDs). Methods The rats with MTLE were induced by Li-pilocarpine, which were screened by effect of phenytoin treatment monitored by vedio-EEG. The living microdialysis technology was used for verification of drug concentration in brain of drug-resistant and drug-responsive rat model, and the P-glycoprotein expression was detected by immunohistochemical method. Results Sixteen rats with chronic MTLE were successfully induced in total 30 rats, among which, 6 drug-resistant rats with MTLE were screened. The brain/plasma ratio of area under the curve in drug-resistant rats was significantly lower than that of drug-responsive rats (0.15±0.03 vs. 0.28±0.05, P<0.05). In addition, the P-glycoprotein expression in brain of drug-responsive rats was obviously higher than that of drug-responsive rats (P<0.05). Conclusions The low concentration of phenytoin in drug-resistant rat model with MTLE was verified that might be related to the over-expressed P-glycoprotein in brain.
ObjectiveIn order to evaluate that whether the P-glycoprotein-inhibitor verapamil (VPM) could effect the distribution of antiepileptic drug phenytoin (PHT) in a rat model of mesial temporal lobe epilepsy (MTLE).MethodsThe rat models of MTLE were induced by li-pilocarpine and were randomly divided into two groups (PHT group and VPM+PHT treatment group) to compare the PHT distribution in brain, liver and kidney. Brain dialysate samples were collected by microdialysis technology. And the analysis of samples for PHT concentration was performed by high performance liquid chromatography (HPLC). The comparisons were carried out by t test (or Wilcoxon test).ResultsIn VPM+PHT treatment group, 4 out of 9 rats were dead within 30 minutes after drug administration. The significantly decreased area under the curve (AUC) ratio of brain/plasma in VPM+PHT group was 0.11±0.06 when compared with PHT group 0.21±0.02 (t=3.237, P=0.025), while there were no significant differences in ratios of liver/plasma ［PHT (1.12±0.37) vs. VPM+PHT (0.99±0.27), Z=−0.490, P=0.624］ and kidney/plasma ［PHT (0.74±0.16) vs. VPM+PHT (0.49±0.26), t=1.872, P=0.103］ between two groups.ConclusionsThe P-glycoprotein-inhibitor VPM significantly decreased PHT level in brain of rat with MTLE.
Cognitive impairment is one of the three primary symptoms of schizophrenic patients and shows important value in early detection and warning for high-risk individuals. To study the specifics of electroencephalogram (EEG) in patients with schizophrenia under the cognitive load, we collected EEG signals from 17 schizophrenic patients and 19 healthy controls, extracted signals of each band based on wavelet transform, calculated the characteristics of nonlinear dynamic and functional brain networks, and automatically classified the two groups of people by using a machine learning algorithm. Experimental results indicated that the correlation dimension and sample entropy showed significant differences in α, β, θ, and γ rhythm of the Fp1 and Fp2 electrodes between groups under the cognitive load. These results implied that the functional disruptions in the frontal lobe might be the important factors of cognitive impairments in schizophrenic patients. Further results of the automatic classification analysis indicated that the combination of nonlinear dynamics and functional brain network properties as the input characteristics of the classifier showed the best performance, with the accuracy of 76.77%, sensitivity of 72.09%, and specificity of 80.36%. The results of this study demonstrated that the combination of nonlinear dynamics and function brain network properties may be potential biomarkers for early screening and auxiliary diagnosis of schizophrenia.
A great number of studies have demonstrated the structural and functional abnormalities in chronic schizophrenia (SZ) patients. However, few studies analyzed the differences between first-episode, drug-naive SZ (FESZ) patients and normal controls (NCs). In this study, we recruited 44 FESZ patients and 56 NCs, and acquired their multi-modal magnetic resonance imaging (MRI) data, including structural and resting-state functional MRI data. We calculated gray matter volume (GMV), regional homogeneity (ReHo), amplitude of low frequency fluctuation (ALFF), and degree centrality (DC) of 90 brain regions, basing on an automated anatomical labeling (AAL) atlas. We then applied these features into support vector machine (SVM) combined with recursive feature elimination (RFE) to discriminate FESZ patients from NCs. Our results showed that the classifier using the combination of ReHo and ALFF as input features achieved the best performance (an accuracy of 96.97%). Moreover, the most discriminative features for classification were predominantly located in the frontal lobe. Our findings may provide potential information for understanding the neuropathological mechanism of SZ and facilitate the development of biomarkers for computer-aided diagnosis of SZ patients.