Epilepsy remains a significant global health challenge, with a substantial portion of patients experiencing inadequate seizure control despite existing therapeutic options. This review aimed at exploring recent advances in critical areas that, in the authors’ opinion, hold promise for improving epilepsy treatment outcomes. Our paper briefly discusses issues related to gene therapy, including recently initiated clinical trials using this technique. Additionally, we explore the utility of machine learning techniques in diagnostics and predicting responses to antiseizure treatment. Next, we present novel drug delivery routes, such as intranasal and transdermal systems, which provide alternative methods to enhance drug efficacy and minimise side effects. Finally, we briefly characterise new antiseizure medication (ASMs) introduced within the European Union in the last five years, as well as ASMs currently in phase III clinical trials. In summary, this review highlights the potential impact of innovative treatments and technologies on epilepsy management. Keywords: gene therapy machine learning new delivery routes for antiseizure medications new antiseizure medications

Objectives: In this study, we explore the molecular basis of the literature-reported inverse association between brain neoplasms and neurodegenerative disorders, including Parkinson’s disease (PD). As miRNAs are post-transcriptional regulators, we selected them as candidates underlying opposite processes of neurodegeneration and glioma development. Methods: We used bioinformatic analyses for disease-gene extraction, miRNA target prediction, enrichment analyses, and miRNA ranking. We identified 953 shared genes between PD and glioblastoma (GBM) in DisGeNET, then prioritized miRNAs predicted to regulate the largest number of shared targets. Next, we collected peripheral blood from patients with PD (n = 12), patients with gliomas (the most advanced—grade IV, n = 10 and grade III n = 3) and controls undergoing spinal surgery for disk pathology (n = 10). Blood samples were obtained pre-operatively in controls and glioma patients. Tumor and peritumoral tissues were obtained from glioma patients, whereas tissue sampling is not feasible in PD. Brain tissues and plasma were analyzed using RT-qPCR (miRNA) and ELISA (p53). Results: We observed increased levels of miR-16-5p (p < 0.05) and p53 protein (p < 0.05) in tumor tissues compared with peritumoral tissue. Additionally, miR-16-5p and miR-32-5p plasma levels were elevated in glioma patients compared with both PD patients (p < 0.01 and p < 0.001, respectively) and controls (p < 0.01 and p < 0.001, respectively). Plasma levels in PD did not differ from controls. Conclusions: Although these analyses highlight miR-16-5p and miR-32-5p as candidate biomarkers associated with glioma related pathways, the results did not provide evidence for the expected opposite regulation between PD and glioma. Future studies with a larger cohort of patients using high-throughput methods are needed to validate these findings and to elucidate the mechanisms driving neurodegeneration or excessive proliferation.