Febrile seizures (FS) are one of the most common neurological disorders in pediatrics, commonly seen in children from three months to five years of age. Most children with FS have a good prognosis, but some febrile convulsions progress to refractory epilepsy (RE). Epilepsy is a common chronic neurological disorder , and refractory epilepsy accounts for approximately one-third of epilepsies. The etiology of refractory epilepsy is currently complex and diverse, and its mechanisms are not fully understood. There are many pathophysiological changes that occur after febrile convulsions, such as inflammatory responses, changes in the blood-brain barrier, and oxidative stress, which can subsequently potentially lead to refractory epilepsy, and inflammation is always in tandem with all physiological changes as the main response. This article focuses on the pathogenesis of refractory epilepsy resulting from post-febrile convulsions.
It is a significant challenge to improve the blood-brain barrier (BBB) permeability of central nervous system (CNS) drugs in their development. Compared with traditional pharmacokinetic property tests, machine learning techniques have been proven to effectively and cost-effectively predict the BBB permeability of CNS drugs. In this study, we introduce a high-performance BBB permeability prediction model named balanced-stacking-learning based BBB permeability predictor(BSL-B3PP). Firstly, we screen out the feature set that has a strong influence on BBB permeability from the perspective of medicinal chemistry background and machine learning respectively, and summarize the BBB positive(BBB+) quantification intervals. Then, a combination of resampling algorithms and stacking learning(SL) algorithm is used for predicting the BBB permeability of CNS drugs. The BSL-B3PP model is constructed based on a large-scale BBB database (B3DB). Experimental validation shows an area under curve (AUC) of 97.8% and a Matthews correlation coefficient (MCC) of 85.5%. This model demonstrates promising BBB permeability prediction capability, particularly for drugs that cannot penetrate the BBB, which helps reduce CNS drug development costs and accelerate the CNS drug development process.