Impaired activation and regulation of the extinction of inflammatory cells and molecules in injured neuronal tissues are key factors in the development of epilepsy. SerpinA3N is mainly associated with the acute phase response and inflammatory response. In our current study, transcriptomics analysis, proteomics analysis, and Western blotting showed that the expression level of Serpin clade A member 3N (SerpinA3N) is significantly increased in the hippocampus of mice with kainic acid (KA)-induced temporal lobe epilepsy, and this molecule is mainly expressed in astrocytes. Notably, in vivo studies using gain- and loss-of-function approaches revealed that SerpinA3N in astrocytes promoted the release of proinflammatory factors and aggravated seizures. Mechanistically, RNA sequencing and Western blotting showed that SerpinA3N promoted KA-induced neuroinflammation by activating the NF-κB signaling pathway. In addition, co-immunoprecipitation revealed that SerpinA3N interacts with ryanodine receptor type 2 (RYR2) and promotes RYR2 phosphorylation. Overall, our study reveals a novel SerpinA3N-mediated mechanism in seizure-induced neuroinflammation and provides a new target for developing neuroinflammation-based strategies to reduce seizure-induced brain injury.

The present study presents a novel approach for identifying epileptogenic tubers in patients with tuberous sclerosis complex (TSC) and automating tuber segmentation using a three-dimensional convolutional neural network (3D CNN). The study retrospectively included 31 TSC patients whose lesions were manually annotated from multiparametric neuroimaging data. Epileptogenic tubers were determined via presurgical evaluation and stereoelectroencephalography recording. Neuroimaging metrics were extracted and compared between epileptogenic and non-epileptogenic tubers. Additionally, five datasets with different preprocessing strategies were used to construct and train 3D CNNs for automated tuber segmentation. The normalized positron emission tomography (PET) metabolic value was significantly lower in epileptogenic tubers defined via presurgical evaluation (p = 0.001). The CNNs showed high performance for localizing tubers, with an accuracy between 0.992 and 0.994 across the five datasets. The automated segmentations were highly correlated with clinician-based features. The neuroimaging characteristics for epileptogenic tubers were demonstrated, increasing surgical confidence in clinical practice. The validated deep learning detection algorithm yielded a high performance in determining tubers with an excellent agreement with reference clinician-based segmentation. Collectively, when coupled with our investigation of minimal input requirements, the approach outlined in this study represents a clinically invaluable tool for the management of TSC.