Dioscorea nipponica rhizoma (DNR) is commonly used for the cure of hyperthyroidism resulting from Graves’ disease (GD) or thyroid nodules. However, its therapeutic mechanism remains unclear. This study aimed to utilize network pharmacology integrated molecular docking and experimental verification to reveal the potential pharmacological mechanism of DNR against GD. First, the active componds of DNR were collected from the HERB database and a literature search was conducted. Then, according to multisource database, the predicted genes of DNR and GD were collected to generate networks. The analysis of protein–protein interaction and GO enrichment and KEGG pathway were employed to discover main mechanisms associated with therapeutic targets. Moreover, molecular docking simulation was applied in order to verify the interactions between the drug and target. Finally, our experiments validated the ameliorated effects of diosgenin, the main component of DNR, in terms of phosphorylation deactivation in IGF-1R, which in turn inhibited the phosphorylation and activation of PI3K-AKT and Rap1-MEK signaling pathways, promoting cell apoptosis and GD remission. Our present study provided a foundation for further investigation of the in-depth mechanisms of diosgenin in GD and will provide new scientific evidence for clinical application.

The parameters selection of ESN (Echo State Network) is excessively dependent on human experience, it is difficult to produce the corresponding optimal parameters for specific problem, resulting in severely restricted in practice. In view of this, a chaotic time series prediction model is proposed in this paper, and the model is based on differential evolution algorithm and the echo state network. With this model, training the input sample sequence to find the network's parameters which is suitable for the data characteristics at first, then use the ideal parameters to predict chaotic time series. In the prediction of the typical chaotic time series generated by Lorenz system, this method can establish a suitable echo state network based on the data characteristics effectively, and gets satisfactory results.