ObjectiveThe abnormal autophagy fluxis involved in the pathophysiological process of drug-resistance temporal lobe epilepsy (TLE).Hippocampal sclerosis (HS) is the main pathological type of drug-resistance TLE.Different subtypes of HS have various prognosis, etiology and pathophysiology.However, whether theabnormal block ofautophagy flux involved in this process has not been reported.This study proposed a preliminary comparison of autophagy fluxin typical and atypical HS to investigate the potential pathogenesis and drug-resistance mechanism of atypical HS. MethodsSurgical excision of hippocampal and temporal lobe epilepsy foci were performed in 17 patients with drug-resistance TLE.Patients were grouped according to the HS classification issued by International League Against Epilepsy in 2013.The distribution and expression of LC3B, beclin-1 and P62 were detected by immunohistochemistry and Western blot in each group. ResultsLC3B, beclin-1 and P62 are mainly expressed in neuronal cytoplasm, which is consistent with previous reports.Taking β-actin as internal reference, we found that LC3B and Beclin-1, the downstream products of autophagy flux, have increased significantly (P < 0.01) in the atypical HS group compared to typical HS group.However, the autophagy flux substrate P62 has no difference between the groups.This result suggested that compared with the typical HS group, atypical HS group had autophagy substrate accumulation and autophagy flux abnormal block.Besides, we found that glyceraldehycle-3-phosphate dehydrogenase(GAPDH) was significantly different between the two groups (P=0.003). ConclusionThere is abnormal phenomenon of autophagy flux in atypical HS, and GAPDH elevation may be involved in its mechanism, which might provide new targets and ideas for future treatment of atypical HS.
Objective This study aimed to determine whether quantitative volumetric measures from MRI and metabolic indices from FDG-PET can independently or jointly predict surgical outcome in patients with drug-resistant temporal lobe epilepsy (TLE), particularly when accounting for the well-established prognostic role of hippocampal sclerosis (HS) co-pathology. Methods We retrospectively reviewed patients who underwent temporal lobe resection for drug-resistant epilepsy, comprising 80 with MRI data, 77 with FDG-PET data, and a subset of 42 with both modalities available (fusion cohort). Quantitative asymmetry indices for temporal lobe subregions were derived using the AAL atlas. To assess whether imaging features contributed prognostic information beyond HS status and clinical covariates, we employed hierarchical logistic regression. Data-driven predictive performance was further evaluated through machine learning models using nested leave-one-out cross-validation with permutation testing. Results Across all cohorts, HS co-pathology consistently emerged as the strongest predictor of favorable outcome (MRI cohort: OR=18.4, P<0.001; PET cohort: OR=42.0, P<0.001). When examined individually, neither MRI-derived nor PET-derived quantitative features added significantly to the predictive model beyond HS (MRI: P=0.085; PET: P=0.386). By contrast, combining both modalities in the fusion cohort yielded a significant incremental contribution over HS and clinical variables (likelihood ratio test, P=0.009), with the AUC rising from 0.778 to 0.963. Of particular interest, amygdala volumetric asymmetry on MRI was identified as an independent predictor not previously reported (OR=83.7, P=0.041). Machine learning approaches yielded only modest discrimination (fusion cohort AUC=0.692, P=0.075) and did not outperform the hypothesis-driven statistical framework. Conclusion Integrating MRI volumetric and FDG-PET metabolic data offers meaningful prognostic value that extends beyond what HS status alone can provide. Amygdala asymmetry on MRI represents a novel independent predictor warranting further validation. Our findings favor comprehensive multimodal presurgical workup over reliance on a single imaging modality and suggest that, for surgical outcome prediction in TLE, hypothesis-driven analytical approaches may hold advantages over purely data-driven machine learning strategies.