Saikosaponin a (SSa), a main constituent of the Chinese herb Bupleurum chinense DC., has been demonstrated to have antiepileptic activity. Recent studies have shown that SSa could inhibit NMDA receptor current and persistent sodium current. However, the effects of SSa on potassium (K+) currents remain unclear. In this study, we tested the effect of SSa on 4AP-induced epileptiform discharges and K+ currents in CA1 neurons of rat hippocampal slices. We found that SSa significantly inhibited epileptiform discharges frequency and duration in hippocampal CA1 neurons in the 4AP seizure model in a dose-dependent manner with an IC 50 of 0.7 μM. SSa effectively increased the amplitude of I Total and I A, significantly negative-shifted the activation curve, and positive-shifted steady-state curve of I A. However, SSa induced no significant changes in the amplitude and activation curve of I K. In addition, SSa significantly increased the amplitude of 4AP-sensitive K+ current, while there was no significant change in the amplitude of TEA-sensitive K+ current. Together, our data indicate that SSa inhibits epileptiform discharges induced by 4AP in a dose-dependent manner and that SSa exerts selectively enhancing effects on I A. These increases in I A may contribute to the anticonvulsant mechanisms of SSa.

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Document similarity computation is an exciting research topic in information retrieval (IR) and it is a key issue for automatic document categorization, clustering analysis, fuzzy query and question answering. Topic model is an emerging field in natural language processing (NLP), IR and machine learning (ML). In this paper, we apply a latent Dirichlet allocation (LDA) topic model-based method to compute similarity between documents. By mapping a document with term space representation into a topic space, a distribution over topics derived for computing document similarity. An empirical study using real data set demonstrates the efficiency of our method.