Results 131 to 140 of about 106,718 (217)

Response to anti‐seizure medications in children carrying novel or previously reported HCN1 gene variants

open access: yesEpilepsia Open, EarlyView.
Abstract Objective Variants in the HCN1 gene cause a syndrome of childhood epilepsy and developmental disability with a broad phenotypic range. Many affected children manifest with early infantile epileptic encephalopathy (EIEE) and highly drug‐resistant epilepsy.
Marium N. Khan, Nicholas P. Poolos
wiley   +1 more source

Whole-Exome Sequencing in Undiagnosed Muscular Dystrophies: A High Diagnostic Yield and Novel Insights From Iranian Families. [PDF]

open access: yesHum Mutat
Soltani N   +13 more
europepmc   +1 more source

Long‐term developmental outcome in infantile epileptic spasms syndrome after high‐dose prednisolone and vigabatrin treatment

open access: yesEpilepsia Open, EarlyView.
Abstract Objective To evaluate long‐term developmental outcomes and identify independent predictors of favorable developmental outcomes at 3 years of age in children with infantile epileptic spasms syndrome (IESS) treated with a standardized stepwise vigabatrin and high‐dose prednisolone protocol.
Soyoung Jang   +5 more
wiley   +1 more source

From first seizure to specific antiseizure medication in Dravet syndrome: Quantifying delays in the DS'coverED study

open access: yesEpilepsia Open, EarlyView.
Abstract Objective Dravet syndrome (DS) is a rare early‐onset developmental and epileptic encephalopathy with persistent delays between seizure onset and diagnosis. The DS'coverED study aimed to characterize current diagnostic timelines by examining each step and its duration, identifying residual barriers, and actionable solutions to optimize the ...
Loucas Christodoulou   +9 more
wiley   +1 more source

Predicting Oral Cancer From Precursor Lesions: The Case for a Standardized Framework of Analysis to Improve Prediction Modeling

open access: yesHead &Neck, EarlyView.
ABSTRACT Oral Cancer often occurs from the transformation of precursor lesions, and this offers an opportunity for early detection. Current methods to assess risk of precursor lesion progression to oral cancer incompletely predict risk. A multimodal framework that leverages machine learning is needed to improve prediction.
Michael E. Troka, James C. Gates
wiley   +1 more source

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