Mechanisms underlying the beneficial effects of Kaiyu Granule for depression

The proprietary Chinese medicine preparation Kaiyu Granule is made of bupleurum, nutgrass ga- lingale rhizome, szechwan Iovage rhizome, turmeric root tuber, white peony alba, cape jasmine fruit fried semen ziziphi jujubae, and prepared liquorice root. It is a common recipe for the clinical treatment of depression in China. In this study, after 21 days of unpredictable stress exposure, Wistar rats exhibited similar behavioral changes to patients with depression. Moreover, G-protein-coupled inwardly rectifying K＋ channel 1 mRNA and protein expression were significantly reduced in rat hippocampal CA1 and CA3 regions. However, G-protein-coupled inwardly rectifying K＋ channel 1 mRNA, protein expression, and rat behavior were clearly better after administration of 12, 8, or 4 g/kg of Kaiyu Granule when depression model rats underwent stress. 12 g/kg of Kaiyu Granule had the most obvious effects on the increased expression of G-protein-coupled inwardly rectifying K＋ channel 1 mRNA and protein in rat hippocampal CA1 and CA3 regions. These results suggested that Kaiyu Granule improved depression by affecting G-protein-coupled inwardly recti- fying K＋ channel 1 expression in the rat hippocampus.

A Hybrid Channel Stock Model for Stock Price Forecasting with Multifaceted Feature Fusion

Stock market is volatile and predicting stock prices is a challenging task.Stock prices are influenced by multiple factors,and prediction using only numerical or image features is ineffective.To solve this problem,we propose a Hybrid Channel Stock model that incorporates multiple features of basic stock data,K-line charts and technical indicator factors for predicting the closing price of a stock on day n+1.The model combines multiple aspects of data and uses a multi-channel structure including improved CNN-TW,bidirectional LSTM and Transformer network.First,we construct the multi-channel branches of the multi-faceted feature fusion input network model;second,in this paper,we will use the concatenate method to stitch the output of each branch as the input of the rest of the network;the last layer in the network is the fully connected layer,which combines the linear activation function regression to output the predicted prices.Finally,we conducted extensive experiments on the Dow 30,SSH 50 and CSI100 indices.The experimental results show that the Hybrid Channel Stock method has the best performance with the smallest MSE,RMSE,MAE and MAPE compared with existing models.in addition,the experiments on different trading days validate the stability and effectiveness of the model,providing an important reference for investors to make stock investment decisions.