https://repositorio.fleni.org.ar/xmlui/handle/123456789/13 2026-06-30T16:00:05Z 2026-06-30T16:00:05Z Armeno, Marisa Laura Massaro, Mario Boccoli, Julia Caballero, Eugenia Chacon, Santiago Diz, Mariana Espeche, Alberto Fasulo, Lorena Galarza, Nadia Gonzalez, Lara Kobayashi, Virginia Loos, Mariana Semprino, Marcos Veneruzzo, Gabriel Verini, Antonella Caraballo, Roberto https://repositorio.fleni.org.ar/xmlui/handle/123456789/1513 2026-06-29T18:37:39Z 2026-02-02T00:00:00Z

Glucose transporter type 1 deficiency syndrome : Phenotypes, molecular findings, and ketogenic therapy implementation in Argentina Armeno, Marisa Laura; Massaro, Mario; Boccoli, Julia; Caballero, Eugenia; Chacon, Santiago; Diz, Mariana; Espeche, Alberto; Fasulo, Lorena; Galarza, Nadia; Gonzalez, Lara; Kobayashi, Virginia; Loos, Mariana; Semprino, Marcos; Veneruzzo, Gabriel; Verini, Antonella; Caraballo, Roberto Objective Glucose transporter type 1 deficiency syndrome (Glut1DS) is a rare metabolic encephalopathy caused by pathogenic SLC2A1 variants. Ketogenic dietary therapy (KDT) is the mainstay of treatment. In Latin America, Glut1DS remains underdiagnosed due to limited awareness and restricted access to genetic testing. This study describes the clinical and genetic features, management, and response to KDT in an Argentine cohort. Methods A retrospective multicenter study was conducted including patients with a clinical and/or genetic diagnosis of Glut1DS. Clinical data, seizure types, neurodevelopmental features, treatment response, and KDT characteristics were collected from medical records using a standardized form. Genetic confirmation was obtained by SLC2A1 sequencing. Descriptive and comparative analyses were performed. Results Thirty-nine patients with Glut1DS (64% males) were included. Mean age at evaluation was 13.7 years. Median ages at symptom onset and diagnosis were 6 and 55 months, respectively, with a median diagnostic delay of 49 months. Cognitive impairment was present in two-thirds of patients, and movement disorders in 79%. Epilepsy occurred in 74%. Of 39 patients, all but one received KDT, with MCT oil in 64%. Thirty patients remained on KDT, achieving seizure freedom in 86% and >50% reduction in four others. Improvements were reported in motor coordination (38%), cognition and attention (10%), energy (10%), and behavior (8%). No major adverse effects were reported. Significance This first national report underscores the clinical diversity of Glut1DS in Argentina and a positive trend toward earlier KDT initiation. Strengthening early diagnosis, systematic follow-up, and equitable access to therapy remains essential.

2026-02-02T00:00:00Z Adell, Matias F. Balda, Javier Casas, Facundo D’Giano, Carlos Quintero-Rincón, Antonio https://repositorio.fleni.org.ar/xmlui/handle/123456789/1458 2025-12-02T14:08:57Z 2025-06-01T00:00:00Z

Pipeline to detect spike-and-wave EEG patterns based on polynomial regression modeling and Taylor series feature selection Adell, Matias F.; Balda, Javier; Casas, Facundo; D’Giano, Carlos; Quintero-Rincón, Antonio Epilepsy is a common neurological disorder diagnosed and monitored through EEG recordings. Accurate spike-and-wave (SW) pattern classification is crucial for distinguishing this epileptic seizure disorder from normal brain wave activity (NW). However, mathematically modeling SW remains challenging, affecting classification accuracy. This study proposes a pipeline in two stages combining polynomial regression techniques, and data processing, in a machine-learning classification scheme. At the first stage of decision-making, the idea is to create a generalized waveform mother that represents all the waveforms of the EEG patterns, such as SW and NW. This waveform is derived from a polynomial regression model that is assessed by the truncation error of the Taylor series. In the second stage, a feature selection algorithm based on a vector that includes the coefficients from Taylor and the statistical properties of the SW and NW waveforms was designed for the machine learning classifier. This algorithm uses the confidence interval to extract the Taylor series points that do not represent the generalized mother equation. This yields a dimensional reduction of this vector, which can be used in a classification and detection scheme. Three polynomial regression models, such as Fourier, Gaussian, and sums-of-sines were evaluated using the pipeline methodology. The best model was the Fourier regression, which achieved an accuracy of 96.2% using the SVM classifier with a Gaussian kernel to detect spike-and-wave patterns.

2025-06-01T00:00:00Z Weil, Alexander G. Dimentberg, Evan Lewis, Evan C. Ibrahim, George M. Kola, Olivia Tseng, Chi-Hong Chen, Jia-Shu Lin, Kao-Min Cai, Li-Xin Liu, Qing-Zhu Lin, Jiu-Luan Zhou, Wen-Jing Mathern, Gary W. Smyth, Matthew D. O'Neill, Brent R. Dudley, Roy Ragheb, John Pociecha, Juan Chamorro, Noelia Muro, Valeria L. https://repositorio.fleni.org.ar/xmlui/handle/123456789/978 2024-02-08T12:56:38Z 2023-06-22T00:00:00Z

Development of an online calculator for the prediction of seizure freedom following pediatric hemispherectomy using the Hemispherectomy Outcome Prediction Scale (HOPS) Weil, Alexander G.; Dimentberg, Evan; Lewis, Evan C.; Ibrahim, George M.; Kola, Olivia; Tseng, Chi-Hong; Chen, Jia-Shu; Lin, Kao-Min; Cai, Li-Xin; Liu, Qing-Zhu; Lin, Jiu-Luan; Zhou, Wen-Jing; Mathern, Gary W.; Smyth, Matthew D.; O'Neill, Brent R.; Dudley, Roy; Ragheb, John; Pociecha, Juan; Chamorro, Noelia; Muro, Valeria L. Objectives: Although hemispheric surgeries are among the most effective procedures for drug-resistant epilepsy (DRE) in the pediatric population, there is a large variability in seizure outcomes at the group level. A recently developed HOPS score provides individualized estimation of likelihood of seizure freedom to complement clinical judgement. The objective of this study was to develop a freely accessible online calculator that accurately predicts the probability of seizure freedom for any patient at 1-, 2-, and 5-years post-hemispherectomy. Methods: Retrospective data of all pediatric patients with DRE and seizure outcome data from the original Hemispherectomy Outcome Prediction Scale (HOPS) study were included. The primary outcome of interest was time-to-seizure recurrence. A multivariate Cox proportional-hazards regression model was developed to predict the likelihood of post-hemispheric surgery seizure freedom at three time points (1-, 2- and 5- years) based on a combination of variables identified by clinical judgment and inferential statistics predictive of the primary outcome. The final model from this study was encoded in a publicly accessible online calculator on the International Network for Epilepsy Surgery and Treatment (iNEST) website (https://hops-calculator.com/). Results: The selected variables for inclusion in the final model included the five original HOPS variables (age at seizure onset, etiologic substrate, seizure semiology, prior non-hemispheric resective surgery, and contralateral fluorodeoxyglucose-positron emission tomography [FDG-PET] hypometabolism) and three additional variables (age at surgery, history of infantile spasms, and magnetic resonance imaging [MRI] lesion). Predictors of shorter time-to-seizure recurrence included younger age at seizure onset, prior resective surgery, generalized seizure semiology, FDG-PET hypometabolism contralateral to the side of surgery, contralateral MRI lesion, non-lesional MRI, non-stroke etiologies, and a history of infantile spasms. The area under the curve (AUC) of the final model was 73.0%. Significance: Online calculators are useful, cost-free tools that can assist physicians in risk estimation and inform joint decision-making processes with patients and families, potentially leading to greater satisfaction. Although the HOPS data was validated in the original analysis, the authors encourage external validation of this new calculator.

2023-06-22T00:00:00Z Quintero Rincón, Antonio D'Giano, Carlos Batatia, Hadj https://repositorio.fleni.org.ar/xmlui/handle/123456789/626 2023-02-09T00:48:30Z 2019-08-28T00:00:00Z

A quadratic linear-parabolic model-based EEG classification to detect epileptic seizures Quintero Rincón, Antonio; D'Giano, Carlos; Batatia, Hadj The two-point central difference is a common algorithm in biological signal processing and is particularly useful in analyzing physiological signals. In this paper, we develop a model-based classification method to detect epileptic seizures that relies on this algorithm to filter electroencephalogram (EEG) signals. The underlying idea was to design an EEG filter that enhances the waveform of epileptic signals. The filtered signal was fitted to a quadratic linear-parabolic model using the curve fitting technique. The model fitting was assessed using four statistical parameters, which were used as classification features with a random forest algorithm to discriminate seizure and non-seizure events. The proposed method was applied to 66 epochs from the Children Hospital Boston database. Results showed that the method achieved fast and accurate detection of epileptic seizures, with a 92% sensitivity, 96% specificity, and 94.1% accuracy.

2019-08-28T00:00:00Z