[Analysis of clinical phenotype and variant of SLC2A1 gene in a Chinese pedigree affected with glucose transporter 1 deficiency syndrome].

OBJECTIVE: To analyze the clinical phenotype and variant of SLC2A1 gene in a Chinese pedigree affected with glucose transporter type 1 deficiency syndrome (GLUT1-DS). METHODS: Clinical data of a child who was treated due to delayed motor and language development and his family members were collected. DNA was extracted from peripheral blood samples and subjected to high-throughput medical exome sequencing. Candidate variant was verified by Sanger sequencing of his parents and sister. The genotype-phenotype correlation was explored. RESULTS: The child, his mother and sister had common manifestations such as delayed mental and motor development, poor exercise tolerance, easy fatigue and paroxysmal dystonia, but the difference was that the child and his mother had microcephaly and seizures, while his sister did not. A heterozygous missense SLC2A1 c.191T>C (p.L64P) variant was identified in all affected members, which was unreported previously. CONCLUSION: The missense SLC2A1 c.191T>C (p.L64P) variant probably underlay the disease in the proband and his mother and sister. Variability of the clinical phenotypes has reflected the genetic and phenotypic diversity of GLUT1-DS. Detection of the novel variant has enriched the spectrum of GLUT1-DS mutations.

[Research progress of epileptic seizure predictions based on electroencephalogram signals].

As a common disease in nervous system, epilepsy is possessed of characteristics of high incidence, suddenness and recurrent seizures. Timely prediction with corresponding rescues and treatments can be regarded as effective countermeasure to epilepsy emergencies, while most accidental injuries can thus be avoided. Currently, how to use electroencephalogram (EEG) signals to predict seizure is becoming a highlight topic in epilepsy researches. In spite of significant progress that made, more efforts are still to be made before clinical applications. This paper reviews past epilepsy studies, including research records and critical technologies. Contributions of machine learning (ML) and deep learning (DL) on seizure predictions have been emphasized. Since feature selection and model generalization limit prediction ratings of conventional ML measures, DL based seizure predictions predominate future epilepsy studies. Consequently, more exploration may be vitally important for promoting clinical applications of epileptic seizure prediction.