ObjectiveTo characterize the dynamic expression of Robo3 in the rat model of temporal lobe epilepsy(TLE), and assess the potential contribution of Robo3 to epileptogenesis. MethodsMale Sprague-Dawley (SD) rats were randomly divided into the control group (n=6) and the experimental groups (n=30, 6 per group). The experimental groups were injected intraperitoneally (i.p.) with an aqueous solution of lithium-pilocarpine, and sacrificed at different time points (1, 7, 14, 30 and 60 days) following the seizure. The control group was i.p. with 0.9% sodium chloride instead of pilocarpine. Quantitative real-time PCR were used to detected the mRNA expression of Robo3 and Western bolt were used to detected the protein expression of Robo3. ResultsQuantitative real-time PCR showed that the expression of Robo3 were significantly lower in the rat temporal lobe tissues of the latent and the chronic period group as compared with the controls(P < 0.05), but no significant differences were identified between the acute period group and the controls(P > 0.05). Western blot showed that the protein expression of Robo3 were significantly lower in the rat temporal lobe tissues of the latent and the chronic period group as compared with the controls(P < 0.05), no significant differences were identified between the acute period group and the controls(P > 0.05). ConclusionsRobo3 may be involved in the pathogenesis of temporal lobe epilepsy.
Electroencephalogram (EEG) is the primary tool in investigation of the brain science. It is necessary to carry out a deepgoing study into the characteristics and information hidden in EEGs to meet the needs of the clinical research. In this paper, we present a wavelet-nonlinear dynamic methodology for analysis of nonlinear characteristic of EEGs and delta, theta, alpha, and beta sub-bands. We therefore studied the effectiveness of correlation dimension (CD), largest Lyapunov exponen, and approximate entropy (ApEn) in differentiation between the interictal EEG and ictal EEG based on statistical significance of the differences. The results showed that the nonlinear dynamic characteristic of EEG and EEG subbands could be used as effective identification statistics in detecting seizures.