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Management of First Non-febrile Convulsive Seizure in Pediatric Emergency Department: Retrospective Study 2015-2021

A retrospective analysis of 167 children presenting to a pediatric emergency department with first non-febrile convulsive seizures, categorizing cases as epileptic seizures (59.3%), non-epileptic paroxysmal events (38.3%), or acute symptomatic seizures (2.4%). The study evaluated clinical examination findings and semiological features to develop a practical management algorithm for distinguishing seizure types in pediatric emergency settings.

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Listening across frequencies: Capturing seizures of all patients to advance antiseizure medication development

The paper critiques the traditional requirement of ≥4 seizures per month for epilepsy trial enrollment, noting this threshold excludes many patients as improved treatments have reduced baseline seizure burden. The authors propose alternative time-to-event trial designs that enable broader patient inclusion while maintaining rigor, alongside integration of cognitive outcome measures alongside seizure control assessments.

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Modelling precise responses to anti-seizure medication using brain organoids carrying SCN2A-GoF and SCN2A-LoF mutations

This study uses brain organoid models to investigate how gain-of-function and loss-of-function SCN2A mutations affect response to anti-seizure medications, potentially enabling personalized treatment approaches for epilepsy patients. The research applies modern neurodevelopmental modeling techniques to understand drug responsiveness at the cellular level.

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Ganglioglioma surgery and anti-seizure medication outcomes: A nationwide Swedish registry cohort study

This registry-linked study of 144 Swedish patients with surgically treated ganglioglioma examined anti-seizure medication (ASM) discontinuation rates following surgery. Five years post-surgery, 36% of adults and 59% of children had discontinued ASMs, with no significant difference between those treated in specialized epilepsy surgery programs versus other surgical settings.

StudiuEpilepsieÎncredere bună19.06.2026

TFFBN-HDLF: Hybrid Deep Learning Framework for Epileptic Seizure Detection Using Time-Frequency Functional Brain Networks

This study presents a hybrid deep learning framework combining time-frequency functional brain networks with CNN and Transformer modules to improve epilepsy seizure detection in elderly patients from EEG signals. The method uses Pearson correlation and phase lag index to construct brain networks that capture synchronous neural interactions, converting complex EEG data into clinically useful features for automated seizure detection.