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We explore the efficacy of multimodal behavioral cues for explainable prediction of personality and interview-specific traits. We utilize elementary head-motion units named kinemes, atomic facial movements termed action units and speech features to estimate these human-centered traits. Empirical results confirm that kinemes and action units enable discovery of multiple trait-specific behaviors while also enabling explainability in support of the predictions.

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Objective: To compare the prevalence of neurodevelopmental and mental health diagnoses in a national sample of youth with sex chromosome trisomies (SCTs) with matched controls.

Methods: Patients in PEDSnet and a diagnosis code mapping to 47,XXY/Klinefelter syndrome (n = 1171), 47,XYY/Double Y syndrome (n = 243), or 47,XXX/Trisomy X syndrome (n = 262) were matched with controls using propensity scores. Generalized estimating equations computed odds ratios (OR) with 95% confidence intervals (CI) for the prevalence of diagnoses within the neurodevelopmental and mental health composites, psychotropic medication prescriptions, and encounters with behavioral health and therapy providers.

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Graph Neural Networks-Based Prediction of Drug Gene Interactions of RTK-VEGF4 Receptor Family in Periodontal Regeneration.

J Clin Exp Dent

December 2024

DDS. Titular Professor. Universidad de Antioquia U de A, Medellín, Colombia. Biomedical Stomatology Research Group, Universidad de Antioquia U de A, Medellín, Colombia.

Background: The RTK-VEGF4 receptor family, which includes VEGFR-1, VEGFR-2, and VEGFR-3, plays a crucial role in tissue regeneration by promoting angiogenesis, the formation of new blood vessels, and recruiting stem cells and immune cells. Machine learning, particularly graph neural networks (GNNs), has shown high accuracy in predicting these interactions. This study aims to predict drug-gene interactions of the RTK-VEGF4 receptor family in periodontal regeneration using graph neural networks.

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Background: Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) encompasses a spectrum of histological conditions ranging from simple steatosis to fibrosing steatohepatitis, and is a risk factor for cardiovascular diseases (CVD). While oxidised apolipoproteins A and B have been linked to obesity and CVD, the association between other oxidised apolipoproteins and MASLD is yet to be established. To fill this gap, we characterised the circulating serum peptidome of patients with MASLD.

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Eutectogels are recently emerged as promising alternatives to hydrogels owing to their good environmental stability derived from deep eutectic solvents (DES). However, construction of competent eutectogels with both high conductivity and mechanical toughness is still difficult to achieve yet highly demanded. In this work, new LMNP-PEDOT-CMC-AA (LPCA) eutectogels are prepared using acrylic acid (AA) and carboxymethylcellulose sodium (CMC) as polymeric networks, liquid metal nanoparticle-poly(3,4-ethylenedioxythiophene) (LMNP-PEDOT) are added as multifunctional soft fillers.

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