Systematic Understanding of Mechanism of Yi-Qi-Huo-Xue Decoction Against Intracerebral Hemorrhagic Stroke Using a Network Pharmacology Approach.

BACKGROUND Intracerebral hemorrhage (ICH), a fatal type of stroke, profoundly affects public health. Yi-Qi-Huo-Xue decoction (YQHXD), a traditional Chinese medicine (TCM) prescription, is verified to be an efficient method to treat ICH stroke among the Chinese population. Nevertheless, the pharmacological mechanisms of YQHXD have been unclear. MATERIAL AND METHODS We used a strategy based on network pharmacology to explore the possible multi-component, multi-target, and multi-pathway pattern of YQHXD in treating ICH. First, candidate targets for YQHXD were identified using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Then, these candidate YQHXD targets were used in combination with the known targets for the treatment of ICH stroke to construct the core network (cPPI) using data on protein-protein interaction (PPI). We calculated 5 topological parameters for identification of the main hubs. Pathway enrichment and GO biological process enrichment analyses were performed after the incorporation of the main hubs into ClueGO. RESULTS In total, 55 candidate YQHXD targets for ICH were recognized to be the major hubs in accordance with their topological importance. As suggested by enrichment analysis, the YQHXD targets for ICH were roughly classified into several biological processes (related to redox equilibrium, cell-cell communication, adhesion and collagen biosynthesis, cytokine generation, lymphocyte differentiation and activation, neurocyte apoptosis and development, neuroendocrine system, and vascular development) and related pathways (VEGF, mTOR, NF-kappaB, RAS/MAPK, JAK/STAT and cytokine-cytokine receptors interaction), indicating those mechanisms underlying the therapeutic effect of YQHXD. CONCLUSIONS The present results may serve as a pharmacological framework for TCM studies in the future, helping to promote the use of YQHXD in clinical treatment of ICH.

Performance of dynamic features in classifying scalp epileptic interictal and normal EEG.

Over 50 million people worldwide suffer from epilepsy. Recently, researchers have proposed computer-aided epilepsy diagnostic systems based on classifying scalp epileptic interictal and normal EEG. Features used in the classification can be divided into two groups: classical spectral features and dynamic features. Classical spectral features are similar to major frequency component identification that physicians use in conventional EEG reading. Because dynamic features are new compared to classical spectral features, we are interested in knowing whether they are suitable for this classification problem. To study this, we build such a system and compare the results between using classical spectral features and dynamic features. Furthermore, we study which dynamic features are more suitable, i.e., more discriminative, by ranking them using F-score. According to the result, we discuss redesigning certain dynamic features for better classification. This research is a preliminary study of using dynamic features of scalp interictal EEG for epilepsy diagnosis.

Automated epilepsy diagnosis using interictal scalp EEG.

Over 50 million people worldwide suffer from epilepsy. Traditional diagnosis of epilepsy relies on tedious visual screening by highly trained clinicians from lengthy EEG recording that contains the presence of seizure (ictal) activities. Nowadays, there are many automatic systems that can recognize seizure-related EEG signals to help the diagnosis. However, it is very costly and inconvenient to obtain long-term EEG data with seizure activities, especially in areas short of medical resources. We demonstrate in this paper that we can use the interictal scalp EEG data, which is much easier to collect than the ictal data, to automatically diagnose whether a person is epileptic. In our automated EEG recognition system, we extract three classes of features from the EEG data and build Probabilistic Neural Networks (PNNs) fed with these features. We optimize the feature extraction parameters and combine these PNNs through a voting mechanism. As a result, our system achieves an impressive 94.07% accuracy.