ABSTRACT Short tandem repeat (STR) expansion is a major genetic mechanism underlying numerous neurogenetic disorders. However, traditional PCR amplification and short‐read next‐generation sequencing‐based methods often fail to detect large‐scale, complex expansions and to capture methylation information. Thus, this study aimed to modify an amplification‐free nanopore Cas9‐targeted sequencing (nCATS) platform to achieve uniform coverage across 56 currently defined STR loci using a single test with genomic DNA from patient‐derived blood cells and to develop a dedicated analysis algorithm, STRiker, capable of identifying internal motif contexts and de novo repeat structures. Ultimately, this study identified pathogenic repeat expansions in 12 of 37 patients (32.4%) with cerebellar ataxia who remained genetically undiagnosed despite extensive prior genetic testing, in FGF14 (n = 4), ATXN8OS, NOP56, RFC1 (n = 2 each), and PRNP and NOTCH2NLC (n = 1 each). Additionally, family‐based cascade screening revealed six relatives with repeat expansions in five families. These results demonstrate a broader diversity of pathogenic repeat structures, particularly in FGF14, and illustrate that CpG methylation can mitigate the pathogenic effects of repeat expansions. This nCATS–STRiker workflow offers a powerful strategy for improving the diagnosis of STR‐related neurogenetic diseases, such as cerebellar ataxia and other diseases.
Seungbok Lee, Chan-Ju Jung, Minjeong Kim et al.· Advancement of science· 0 citations
Inroduction This study aimed to develop and evaluate a machine learning framework for classifying seizure onset zone lateralization and localization in patients with focal cortical dysplasia using ictal scalp electroencephalography (EEG). Methods We retrospectively analyzed ictal scalp EEG recordings from 69 patients with focal cortical dysplasia, including 63 surgical and 6 non-surgical patients. EEG signals were preprocessed using common average referencing, segmented into overlapping 1-s windows, and filtered into five frequency bands. Morphological and connectivity features were extracted, and principal component analysis was applied for dimensionality reduction. Automated machine learning was used to select optimal classifiers for lateralization and localization. Model performance was assessed using three-fold cross-validation in 51 surgical patients, internal validation in 12 surgical patients, and extra validation in 6 non-surgical patients. Results Before principal component analysis, connectivity features generally outperformed morphological features. Covariance-based connectivity achieved the highest area under the receiver operating characteristic curve for lateralization (0.781), whereas the full connectivity feature set achieved the highest area under the receiver operating characteristic curve for localization (0.786). After principal component analysis, morphology-based energy features showed improved performance, achieving area under the receiver operating characteristic curve values of 0.811 for lateralization and 0.829 for localization in the early post-onset window. Discussion These findings suggest that ictal scalp EEG combined with machine learning enables accurate and interpretable seizure onset zone classification in patients with focal cortical dysplasia. The proposed framework may serve as a noninvasive decision-support tool to improve presurgical evaluation and guide invasive EEG planning in focal cortical dysplasia-related epilepsy.
You-Min Shin, Sungeun Hwang, Seung-Bo Lee et al.· Frontiers in Neuroscience· 0 citations
This time-resolved analysis suggests that early seizure control is primarily influenced by surgical and tumor burden-related factors, whereas long-term seizure outcomes and recurrence appear to be predominantly determined by the clinical presentation at diagnosis rather than treatment-related variables.
Kyung-Il Park, Chul-Kee Park, Soon-Tae Lee et al.· Epilepsia· 0 citations
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