Background. Parents of a child with neurological problems such as seizures and epilepsy experience significant mental distress. Little is known about the mental state of parents in such a stressful situation. This study aims to determine the prevalence of self-reported depression, anxiety, sleep disorders, and quality of life in parents of children with epilepsy and first unprovoked seizure. Methods. This cross-sectional study was conducted among the parents of children diagnosed with first unprovoked seizure and epilepsy admitted to the Pediatric Neurology Department, Outpatient Unit of İnönü University Medical Faculty Hospital. Participants filled out a questionnaire investigating demographic variables, Beck Anxiety Inventory (BAI), Beck Depression Inventory (BDI), Pittsburgh Sleep Quality Index (PSQI), and 36-Item Short-Form Health Survey (SF-36). Results. 113 parents participated in the study. Depression was found in 7%, anxiety in 14%, and sleep quality disorder in 33.3% of parents of children diagnosed with epilepsy on the basis of moderate or higher severity, while depression was found in 8.9%, anxiety in 14.3%, and sleep disorder in 21.4% of parents of children diagnosed with first unprovoked seizure. There was no statistically significant difference between the groups. Mothers were at higher risk for loss of physical function and social functionality. There was a positive correlation between BAI, BDI, and PSQI scores. Quality of life sub-dimension measured by SF-36 was associated with different levels of depression, anxiety, and sleep quality. Conclusion. Addressing parental psychiatric problems by professionals involved in the treatment of children with a history of seizures may have the potential to provide further support for the family and the care of patients.

EEG signals are the letters of the brain and reflect neural activity. Abnormal EEG patterns indicate brain disorders such as epilepsy. Recently, machine learning has enabled automated EEG interpretation with high accuracy. This study introduces an explainable EEG classification model based on feature engineering. A novel feature extractor, Differential Quadruple Pattern (DiffQuadPat), is proposed. DiffQuadPat computes relations between four channel values using difference-based transformations and combinational transition tables. Feature selection is performed by Cumulative Weight Neighborhood Component Analysis (CWNCA), and classification is achieved with t-algorithm-based k-Nearest Neighbors (tkNN). For interpretability, Directed Lobish (DLOB) is used to produce symbolic explanations. The proposed DiffQuadPat-centric XFE framework was validated on two EEG datasets: Amyotrophic Lateral Sclerosis (ALS) and neonatal epilepsy detection. The model achieved over 98% accuracy under 10-fold cross-validation. Furthermore, cortical and hemispheric connectome diagrams were generated, enabling transparent visualization of brain-level interactions.

Background Pediatric acute liver failure (PALF) is still life -threatening and requires urgent care. The presence of encephalopathy is a clinical diagnosis, but it is more difficult to diagnose in children than in adults, and an electroencephalogram (EEG) can be invaluable. The role of EEG in managing the treatment of patients with PALF, other than the identification of encephalopathy, is unknown. This study aimed to investigate patients' EEGs, which may guide in choosing the most appropriate treatment in encephalopathy children. A further aim was to investigate a new score method, based on the laboratory results, which might indicate the presence of encephalopathy in cases with PALF. Methods Medical data of 33 PALF patients followed in our clinic were reviewed retrospectively. This study included 33 patients, whose EEG recording was taken on the first day of supportive treatment due to liver failure in the pediatric intensive care unit (PICU). The EEG findings were categorized into three classes: normal, epileptic and non -epileptic paroxysmal, and background encephalopathic patterns including widespread slowing and voltage suppression. Result This retrospective study included 13 male and 20 female patients with a mean age at presentation of 4.82 +/- 4.81 months whose EEG was performed on the first day of supportive therapy for liver failure in the PICU. The EEG findings were categorized into three groups: normal, epileptic and non -epileptic paroxysms, and encephalopathic patterns including diffuse background slowing and voltage suppression. Comparing EEG findings and treatments, we found that the normal EEG group responded well to liver -supporting therapy and the rate of plasmapheresis treatment was significantly higher in the diffuse slowing group. Patients with diffuse slowing of the EEG were 9.6 times more likely to receive plasmapheresis. We found that above a cut-off of >= 7.5 for the TAI (total bilirubin, albumin, and international normalized ratio (INR)) score used in our study, the risk of developing encephalopathy increased 14.4 -fold. Conclusions In PALF, EEG findings can provide findings that will help clinicians in determining treatment selection and prognosis, as well as detecting epileptic focus and encephalopathy. The TAI score can be used to assess the risk of encephalopathy in cases of PALF, when it is challenging to identify encephalopathy or when an EEG is not possible.

Background Although the underlying genetic causes of intellectual disability (ID) continue to be rapidly identified, the biological pathways and processes that could be targets for a potential molecular therapy are not yet known. This study aimed to identify ID-related shared pathways and processes utilizing enrichment analyses.Methods In this multicenter study, causative genes of patients with ID were used as input for Disease Ontology (DO), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes enrichment analysis.Results Genetic test results of 720 patients from 27 centers were obtained. Patients with chromosomal deletion/duplication, non-ID genes, novel genes, and results with changes in more than one gene were excluded. A total of 558 patients with 341 different causative genes were included in the study. Pathway-based enrichment analysis of the ID-related genes via ClusterProfiler revealed 18 shared pathways, with lysine degradation and nicotine addiction being the most common. The most common of the 25 overrepresented DO terms was ID. The most frequently overrepresented GO biological process, cellular component, and molecular function terms were regulation of membrane potential, ion channel complex, and voltage-gated ion channel activity/voltage-gated channel activity, respectively.Conclusion Lysine degradation, nicotine addiction, and thyroid hormone signaling pathways are well-suited to be research areas for the discovery of new targeted therapies in ID patients.