Glioma is the most common and aggressive primary brain tumor in adults with high morbidity and mortality. Rapid proliferation and diffuse migration are the main obstacles to successful glioma treatment. Xanthatin, a sesquiterpene lactone purified from Xanthium strumarium L., possesses a significant antitumor role in several malignant tumors. In this study, we report that xanthatin suppressed glioma cells proliferation and induced apoptosis in a time‐ and concentration‐dependent manner, and was accompanied by autophagy inhibition displaying a significantly reduced LC3 punctate fluorescence and LC3II/I ratio, decreased level of Beclin 1, while increased accumulation of p62. Notably, treating glioma cells with xanthatin resulted in obvious activation of the PI3K‐Akt–mTOR signaling pathway, as indicated by increased mTOR and Akt phosphorylation, decreased ULK1 phosphorylation, which is important in modulating autophagy. Furthermore, xanthatin‐mediated pro‐apoptosis in glioma cells was significantly reversed by autophagy inducers (rapamycin or Torin1), or PI3K‐mTOR inhibitor NVP‐BEZ235. Taken together, these findings indicate that anti‐proliferation and pro‐apoptosis effects of xanthatin in glioma are most likely by inhibiting autophagy via activation of PI3K‐Akt–mTOR pathway, suggesting a potential therapeutic strategy against glioma.

Electroencephalogram (EEG) signals are the gold standard tool for detecting epileptic seizures. Long-term EEG signal monitoring is a promising method to realize real-time and automatic epilepsy detection with the assistance of computer-aided techniques and the Internet of Medical Things (IoMT) devices. Machine learning (ML) algorithms combined with advanced feature extraction methods have been widely explored to precisely recognize EEG signals, while among which, little attention has been paid to high computing costs and severe information losses. The lack of model interpretability also impedes the wider application and deeper understanding of ML methods in epilepsy detection. In this research, a novel feature extraction method based on an autoencoder (AE) is proposed in the time domain. The architecture and mechanism are elaborated. In this method, specified features are defined and calculated on the basis of signal reconstruction quantification of the AE. The EEG recognition is performed to validate the effectiveness of the proposed detection method, and the prediction accuracy reached 97%. To further investigate the superiority of the proposed AE-based feature extraction method, a widely used feature extraction method, PCA, is allocated for comparison. In order to understand the underlying working mechanism, permutation importance and SHapley Additive exPlanations (SHAP) are conducted for model interpretability, and the results further confirm the reasonability and effectiveness of the extracted features by AE reconstruction. With high computing efficiency in the time domain and an extensively satisfactory accuracy, the proposed epilepsy detection method exhibits great superiority and potential in almost real-time and automatic epilepsy monitoring.