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This study examines temporal patterns and prognostic factors in seizures associated with glioblastoma, a primary brain malignancy. The research aims to characterize how seizure presentation timing relates to patient outcomes and disease progression.
This pharmacovigilance study analyzed FDA adverse event reports to investigate whether ALK inhibitors (crizotinib, ceritinib, alectinib, brigatinib, lorlatinib) are associated with seizures using Bayesian statistical methods. Results showed varying signal detection scores across different ALK inhibitors, with ceritinib, brigatinib, alectinib, and lorlatinib demonstrating stronger associations with seizure disorders compared to crizotinib.
This retrospective study analyzed EEG recordings to determine optimal recording duration for detecting epileptiform abnormalities in patients with first seizure versus established epilepsy. Results showed detection rates of 37.6% and 57.4% respectively, with factors like anti-seizure medication use and seizure frequency significantly influencing detection, suggesting individualized EEG protocols based on clinical presentation.
This systematic review and meta-analysis examined alterations in gut microbiota composition in epilepsy patients compared to healthy controls across multiple databases. While alpha diversity did not differ significantly, specific bacterial taxa showed significant changes including increased Verrucomicrobia and Akkermansia but decreased Lactobacillus in epilepsy patients.
This study presents a machine learning model designed to anticipate epileptic seizures by analyzing EEG scalogram images through a multi-serial cascaded network with optimized feature selection. The approach uses the Archimedes Optimization algorithm for feature selection followed by Bi-directional LSTM processing to provide timely seizure detection.