Paradoxical levetiracetam (LEV) effects occurred in people with epilepsy, particularly in those with drug-resistant focal epilepsy. Furthermore, the occurrence of REDs in EEG was an independent factor associated with the paradoxical effects of LEV in people with epilepsy.
Pediatric Epilepsy
Infants exposed to invasive Group B Streptococcus (iGBS) meningitis during their first 3 months of life could have a greater risk of developing epilepsy in later childhood compared to infants who were not exposed.
SUDEP
Featuring the work of former CURE grantee Dr. Carl Faingold
The unifying hypothesis: seizure-induced adenosine release leads to respiratory depression. This can be reversed by serotonergic action on autoresuscitation and other restorative respiratory responses acting, in part, via the PAG. Therefore, we hypothesize that serotonergic or direct activation of this brainstem site may be a useful approach for SUDEP prevention.
We instead propose that improved feature selection alongside collaboration, data standardization, and model sharing is required to advance the field.
SUDEP
Featuring the work of former CURE Epilepsy grantee Dr. Edward Glasscock.
A study that could lead to the identification of biomarkers to help identify people at risk for sudden unexpected death in epilepsy (SUDEP).
Weaning was associated with increased seizures and behavioral disturbances in a subset. An approach targeting potassium channel dysfunction with ezogabine is warranted in patients with KCNQ2-related DEE.
This study provides insight into the perspectives of users and offers recommendations for implementing automated spike and seizure detection in EMUs.
Socioeconomic hardship (increased neighborhood disadvantage) exerts a significant adverse impact on the cognitive and academic status of youth with new and recent onset epilepsies, an impact that needs to be incorporated into etiological models of the neurobehavioral comorbidities of epilepsy.
Many state-of-the-art methods for seizure prediction, using the electroencephalogram, are based on machine learning models that are black boxes, weakening the trust of clinicians in them for high-risk decisions.
