Centro Científico Tecnólogico - Rosario

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. 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. 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. 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.

Abstract We investigate projection constants within classes of multivariate polynomials over finite‐dimensional real Hilbert spaces. Specifically, we consider the projection constant for spaces of spherical harmonics and spaces of homogeneous polynomials as well as for spaces of polynomials of finite degree on the unit sphere. We establish a connection between these quantities and certain weighted ‐norms of specific Jacobi polynomials. As a consequence, we present exact formulas, computable expressions, and asymptotically accurate estimates for them.

Volatile organic compounds (VOCs) are key markers in applications ranging from food quality assessment to medical diagnostics that can be profiled, for example, by gas chromatography–mass spectrometry (GC-MS) or by direct injection mass spectrometry (e.g. proton transfer reaction mass spectrometry). The common practice in both cases is to construct a tabular dataset from the raw measurements by performing peak extraction across samples and use statistical or machine learning methods to analyze it. However, modeling VOC profiles is particularly challenging due to high dimensionality, noise, and small sample sizes. In this study, we evaluate the Tabular Prior-data Fitted Network (TabPFN), a foundation model recently introduced for tabular data, across diverse VOC datasets. Without requiring task-specific training, TabPFN achieves state-of-the-art performance in both classification and regression tasks, outperforming classical machine learning methods for most datasets. We further explore new strategies to enhance TabPFN’s performance, including ensembling and fine-tuning, finding that a plain ensemble seems to be the best option in this setting. Our results demonstrate that TabPFN is a highly effective modeling tool for VOC profiles obtained with different analytical approaches. It offers robust predictions even in the data-scarce, high-variability scenarios typical of real-world workflows.

Humanoid robots and mixed reality headsets benefit from the use of head-mounted sensors for tracking. While advancements in visual-inertial odometry (VIO) and simultaneous localization and mapping (SLAM) have produced new and high-quality state-of-the-art tracking systems, we show that these are still unable to gracefully handle many of the challenging settings presented in the head-mounted use cases. Common scenarios like high-intensity motions, dynamic occlusions, long tracking sessions, low-textured areas, adverse lighting conditions, saturation of sensors, to name a few, continue to be covered poorly by existing datasets in the literature. In this way, systems may inadvertently overlook these essential real-world issues. To address this, we present the Monado SLAM dataset, a set of real sequences taken from multiple virtual reality headsets. We release the dataset under a permissive CC BY 4.0 license, to drive advancements in VIO/SLAM research and development.

Background: Chronic Chagas disease can affect multiple organs, most notably the heart and gastrointestinal tract, and in some cases, the nervous system. However, the underlying pathophysiological mechanisms of this parasitic infection remain incompletely understood. Summary: Evidence from studies in both mice with acute Trypanosoma cruzi (T. cruzi) infection and in patients with Chagas disease has revealed a range of immune-neuroendocrine alterations and metabolic disruptions. In this review, we highlight key findings in human Chagas disease related to these abnormalities and discuss their potential contributions to disease pathogenesis. Key Messages: In the context of chronic Chagas disease, the neuroendocrine-immune axis operates as a dynamic interface, integrating systemic immune-endocrine processes with localized responses in the central nervous system (CNS), with each component influencing disease advancement and organ-specific pathology through distinct yet interconnected mechanisms.