Modes of onset of epilepsy and differential diagnosis

Camfield, Peter; Camfield, Carol

doi:10.1016/b978-0-444-52891-9.00048-8

Cited by 5 publications

(3 citation statements)

References 35 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Our electrophysiological results with Ts4 corroborate with the previous study using mice challenged with this toxin where contractions of hind legs were observed in the animals [ 29 ] and, undoubtedly, epilepsy can cause body jerks and spasms [ 54 ].…”

Section: Discussionsupporting

confidence: 89%

Revealing the Function and the Structural Model of Ts4: Insights into the “Non-Toxic” Toxin from Tityus serrulatus Venom

Pucca

Cerni

Peigneur

et al. 2015

Toxins

View full text Add to dashboard Cite

The toxin, previously described as a “non-toxic” toxin, was isolated from the scorpion venom of Tityus serrulatus (Ts), responsible for the most severe and the highest number of accidents in Brazil. In this study, the subtype specificity and selectivity of Ts4 was investigated using six mammalian Nav channels (Nav1.2→Nav1.6 and Nav1.8) and two insect Nav channels (DmNav1 and BgNav). The electrophysiological assays showed that Ts4 specifically inhibited the fast inactivation of Nav1.6 channels, the most abundant sodium channel expressed in the adult central nervous system, and can no longer be classified as a “non-toxic peptide”. Based on the results, we could classify the Ts4 as a classical α-toxin. The Ts4 3D-structural model was built based on the solved X-ray Ts1 3D-structure, the major toxin from Ts venom with which it shares high sequence identity (65.57%). The Ts4 model revealed a flattened triangular shape constituted by three-stranded antiparallel β-sheet and one α-helix stabilized by four disulfide bonds. The absence of a Lys in the first amino acid residue of the N-terminal of Ts4 is probably the main responsible for its low toxicity. Other key amino acid residues important to the toxicity of α- and β-toxins are discussed here.

show abstract

Section: Discussionsupporting

confidence: 89%

Revealing the Function and the Structural Model of Ts4: Insights into the “Non-Toxic” Toxin from Tityus serrulatus Venom

Pucca

Cerni

Peigneur

et al. 2015

Toxins

View full text Add to dashboard Cite

show abstract

“…Epilepsies beginning in neonates or infants encompass a large group of disorders from self-limiting epilepsies with good outcome to devastating disorders known as epileptic encephalopathies, in which the infant or child typically has several types of seizures and abundant epileptiform activity on EEG, associated with developmental slowing or regression that might follow seizure onset. The spectrum of epilepsies, syndromes and comorbid neurodevelopmental disorders has expanded greatly in the latest decade and specific, aetiologybased diagnoses can be very challenging [7][8][9]. Seizure types, semiology and age at onset comprise important clinical information, yet often insufficient to frame a specific diagnosis and predict evolution and long-term outcome.…”

Section: Discussionmentioning

confidence: 99%

First reported case of an inheritedPACS2pathogenic variant with variable expression

Cesaroni¹,

Matricardi²,

Cappanera³

et al. 2022

Epileptic Disorders

View full text Add to dashboard Cite

Neonatal epilepsy, cerebellar dysgenesis and facial dysmorphisms may be associated with de novo PACS2 missense pathogenic variants (EIEE 66) (OMIM #618067). Here, we report a toddler boy with neonatal-onset seizures, developmental delay with hypotonia, facial dysmorphisms and prominence of the cisterna magna, mild inferior vermian and cerebellar hypoplasia. A nextgeneration epilepsy gene panel revealed a known pathogenic PACS2 missense variant, p.Glu209Lys, that was inherited from his mildly affected mother. We describe the first PACS2 pathogenic variant to be inherited, expanding the clinical spectrum, associated with a mild phenotype in the mother and a more severe phenotype in her son, in keeping with previously reported descriptions.

show abstract

“…Seizures manifest as sudden and temporary irregularities in the brain’s electrical activity, leading to disruptive symptoms. These symptoms range from momentary lapses in attention to sensory hallucinations or even full-body convulsions [ 3 ]. Epilepsy affects over 70 million people worldwide, of which around 12 million cases are in India [ 4 ].…”

Section: Introductionmentioning

confidence: 99%

The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review

Singh¹,

Velagala²,

Kumar³

et al. 2023

Cureus

View full text Add to dashboard Cite

Epilepsy is a neurological disorder characterized by recurrent seizures affecting millions worldwide. Medically intractable seizures in epilepsy patients are not only detrimental to the quality of life but also pose a significant threat to their safety. Outcomes of epilepsy therapy can be improved by early detection and intervention during the interictal window period. Electroencephalography is the primary diagnostic tool for epilepsy, but accurate interpretation of seizure activity is challenging and highly time-consuming. Machine learning (ML) and deep learning (DL) algorithms enable us to analyze complex EEG data, which can not only help us diagnose but also locate epileptogenic zones and predict medical and surgical treatment outcomes. DL models such as convolutional neural networks (CNNs), inspired by visual processing, can be used to classify EEG activity. By applying preprocessing techniques, signal quality can be enhanced by denoising and artifact removal. DL can also be incorporated into the analysis of magnetic resonance imaging (MRI) data, which can help in the localization of epileptogenic zones in the brain. Proper detection of these zones can help in good neurosurgical outcomes. Recent advancements in DL have facilitated the implementation of these systems in neural implants and wearable devices, allowing for real-time seizure detection. This has the potential to transform the management of drug-refractory epilepsy. This review explores the application of ML and DL techniques to Electroencephalograms (EEGs), MRI, and wearable devices for epileptic seizure detection. This review briefly explains the fundamentals of both artificial intelligence (AI) and DL, highlighting these systems' potential advantages and undeniable limitations.

show abstract

Modes of onset of epilepsy and differential diagnosis

Cited by 5 publications

References 35 publications

Revealing the Function and the Structural Model of Ts4: Insights into the “Non-Toxic” Toxin from Tityus serrulatus Venom

Revealing the Function and the Structural Model of Ts4: Insights into the “Non-Toxic” Toxin from Tityus serrulatus Venom

First reported case of an inheritedPACS2pathogenic variant with variable expression

The Application of Deep Learning to Electroencephalograms, Magnetic Resonance Imaging, and Implants for the Detection of Epileptic Seizures: A Narrative Review

Contact Info

Product

Resources

About