No abstract

Silymarin, a standardized extract of the milk thistle (Silybum marianum), has a long tradition as a herbal remedy, and was introduced as a hepatoprotective agent a few years ago. However, the therapeutic effects of silymarin remain undefined. Carbon tetrachloride (CCl4) is a xenobiotic used extensively to induce oxidative stress and is one of the most widely used hepatic toxins for experimental induction of liver fibrosis in the laboratory. In this study, we investigated the restoration of the CCl4-induced hepatic fibrosis by high dose of silymarin in rats. After treatment with oil (as normal group; n = 6) or CCl4 [as model (n = 7) and therapeutic (n = 7) groups] by intragastric delivery for 8 weeks for the induction of liver fibrosis, the rats in the normal and model group were administered orally normal saline four times a week for 3 weeks whilst the therapeutic group received silymarin (200 mg/kg). The histopathological changes were observed with Masson staining. The results showed that the restoration of the CCl4-induced damage of liver fibrosis in the therapeutic group was significantly increased as compared to that in the model group. Moreover, silymarin significantly decreased the elevation of aspartate aminotransferase (AST), alanine aminotransferase, and alkaline phosphatase in serum, and also reversed the altered expressions of alpha-smooth muscle actin in liver tissue. Therefore, these findings indicated that silymarin may have the potential to increase the resolution of the CCl4-induced liver fibrosis in rats.

Numerous studies have revealed various working memory (WM)-related brain activities that originate from various cortical regions and oscillate at different frequencies. However, multi-frequency band analysis of the brain network in WM in the cortical space remains largely unexplored. In this study, we employed a graph theoretical framework to characterize the topological properties of the brain functional network in the theta and alpha frequency bands during WM tasks. Twenty-eight subjects performed visual n-back tasks at two difficulty levels, i.e., 0-back (control task) and 2-back (WM task). After preprocessing, Electroencephalogram (EEG) signals were projected into the source space and 80 cortical brain regions were selected for further analysis. Subsequently, the theta- and alpha-band networks were constructed by calculating the Pearson correlation coefficients between the power series (obtained by concatenating the power values of all epochs in each session) of all pairs of brain regions. Graph theoretical approaches were then employed to estimate the topological properties of the brain networks at different WM tasks. We found higher functional integration in the theta band and lower functional segregation in the alpha band in the WM task compared with the control task. Moreover, compared to the 0-back task, altered regional centrality was revealed in the 2-back task in various brain regions that mainly resided in the frontal, temporal and occipital lobes, with distinct presentations in the theta and alpha bands. In addition, significant negative correlations were found between the reaction time with the average path length of the theta-band network and the local clustering of the alpha-band network, which demonstrates the potential for using the brain network metrics as biomarkers for predicting the task performance during WM tasks.

No abstract

We study the error performance of multiple-symbol differential detection of uncoded PSK signals transmitted over correlated flat Rayleigh fading channels. It is found that the optimal detector uses a decoding metric which is a quadratic form of Gaussian variates. By using the characteristic function/residue theorem approach, we are able to derive an exact expression for the pairwise error event probability for the optimal detector. Subsequently it is found that multiple-symbol differential detection is a very effective strategy for eliminating the irreducible error floor commonly associated with conventional differential detection. It is also found that the error performance of these detectors are not very sensitive to the mismatch between the decoding metric and the channel fading st at istics.