👤 Sevim Yener

We evaluated the accuracy of standard machine learning (ML) algorithms in predicting 1-year cognitive decline in Alzheimer's disease patients with mild cognitive impairment (ADMCI) using resting-state electroencephalographic (rsEEG) biomarkers enriched with APOE genotype, sex, age, and educational attainment data. The study analyzed datasets from 63 ADMCI patients obtained from an international archive. The ML algorithms included Simple Logistic Regression, Model Trees, Logistic Regression, K-nearest neighbor, and Support Vector Machine. Input features comprised lobar rsEEG source activities across delta (<4 Hz) to alpha (≈10-12 Hz) bands, cerebrospinal fluid (CSF Aβ1-42/p-tau), and structural magnetic resonance imaging (sMRI) biomarkers. Cognitive decline was assessed over a 1-year follow-up ("stable" vs. "decliner") based on Mini-Mental State Examination (MMSE) scores. The four independent ML algorithms accurately predicted changes in the MMSE score over a 1-year follow-up, with accuracies of 77-78% in ADMCI participants aged ≥ 70 years and 74-77% in those aged < 70 years. These findings suggest that rsEEG biomarkers in ADMCI patients may not only reveal underlying pathophysiological mechanisms affecting cortical arousal and vigilance but also hold predictive value for cognitive outcomes. Show less

Our study aimed to evaluate the genetic etiology of treatment-resistant nocturnal enuresis in children who have undergone at least 6 episodes of behavioral therapy, urotherapy, alarm therapy, and medical treatment. A total of 21 patients were included in the study. Inclusion criteria for the study comprised children aged 5-18 years diagnosed with treatment-resistant enuresis according to the International Children's Continence Society (ICCS) guidelines. The capture-based Sophia Hereditary Disease Panel by Sophia Genetics was used specifically for nocturnal enuresis, consisting of a panel of 19 genes (AGXT, AQP2, AVPR2, BNC2, CLCNKB, DLG3, ELN, FA2H, FAM20A, FOXP1, HPSE2, KCNJ10, MLXIPL, NPHP3, RNF168, SLC12A3, SLC25A13, SLC5A2, SMARCA2). Patients were analyzed for genetic variations in genes associated with nocturnal enuresis, including AGXT, AQP2, AVPR2, BNC2, CLCNKB, DLG3, ELN, FA2H, FAM20A, FOXP1, HPSE2, KCNJ10, MLXIPL, NPHP3, RNF168, SLC12A3, SLC25A13, SLC5A2, and SMARCA2. No pathogenic changes potentially explaining the etiology of the disease were detected in 20 patients. One patient exhibited a variant in the AQP2 gene at hg19:Chr12:50344908 exon 1, c.295G>A locus, classified as a Variant of Uncertain Significance (VUS) according to the American College of Medical Genetic and Genomics (ACMG) 2015 guidelines. The AQP2 gene is associated with autosomal dominant and autosomal recessive inherited nephrogenic diabetes insipidus (type 2) in the OMIM (Online Mendelian Inheritance in Man) database. Our study resembles studies indicating that nocturnal enuresis cases do not have a monogenic etiology but occur with multifactorial effects and have a weak correlation between genotype and phenotype. Show less