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Language: search.filters.language.EnglishSubject: search.filters.subject.Autism

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    Opinion Mining-Driven Classification Model for Early Autism Spectrum Disorders Identification Based on Standardized Assessments
    (MDPI AG, 2026)
    Grande-Ramírez, José Roberto
    ;
    Roldán-Reyes, Eduardo
    ;
    Cortés-Robles, Guillermo
    ;
    Delgado-Maciel, Jesús
    ;
    Morales-Saucedo, Marisol
    The efforts to achieve early detection of autism spectrum disorders (ASD) are becoming increasingly important due to the high prevalence that continues to persist globally. The World Health Organization (WHO) and other official institutions agree that in marginalized regions, it is urgently necessary to develop effective alternatives and methods to improve the quality of life of children and their families. This study presents an integrated model for the early detection of ASD, based on the analysis of parental observations and supported by validated diagnostic tools. The proposed approach consists of four sequential modules, aiming to improve early detection through techniques such as natural language processing (NLP) and machine learning (ML) metrics. Records from two Latin American countries were standardized, thereby consolidating a single database comprising 153 records of children aged 2 to 6 years. The Parent Interview Instrument (PII) was administered by specialists to caregivers and subsequently compared with standardized tests. Encouraging results were obtained from the support vector machine (SVM) classification algorithm, yielding an accuracy range of 89.88–91.34%, a maximum precision of 90.02%, a recall of 89.02%, and a maximum F-measure of 91.12%. The results of the case study allow us to identify disorders related to autism, such as the repetition of behaviors, difficulties in social interaction, and issues with verbal expression. This contribution aligns with the United Nations Sustainable Development Goal 3, which promotes health and well-being. ©The authors ©MDPI.
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    Modeling the functional impact of CPEB3 and CPEB4 dysregulation in autism: A theoretical–computational framework
    (Elsevier BV, 2026)
    González-Paz, Lenin
    ;
    Vivas, Alejandro
    ;
    Cardozo-Urdaneta, Arlene
    ;
    Lossada, Carla
    ;
    Mendez, Anibal
    Autism spectrum disorder (ASD) involves impaired synaptic plasticity tightly coupled to local mRNA translation. Cytoplasmic polyadenylation element-binding proteins 3 and 4 (CPEB3 and CPEB4) are post-transcriptional regulators of neuronal mRNA translation that may contribute to ASD-related molecular alterations. In this theoretical–computational study, we develop a weighted functional impact model that integrates transcriptomic expression with intrinsic molecular constraints of CPEB3 and CPEB4 to estimate regional and cell type–specific vulnerability in ASD. Coarse-grained molecular dynamics (MD) simulations were quantitatively analyzed to assess aggregation, diffusion, and cluster stability under cell type–specific cytoplasmic conditions, with statistical uncertainty explicitly evaluated. The anterior cingulate cortex and thalamus emerged as primary vulnerability sites. Despite higher CPEB4 expression—mainly in glial cells—our weighted functional impact model predicted greater theoretical susceptibility linked to CPEB3 dysfunction, particularly in inhibitory and excitatory neurons. MD simulations revealed that CPEB3 forms transient diffusion-permissive aggregates, whereas CPEB4 tends to assemble into more stable condensates. These complementary behaviors suggest differential but interdependent regulation of neuronal and glial functions. Importantly, the proposed framework provides experimentally testable predictions on how protein–protein interactions, microexon loss, and cytoplasmic crowding influence translational control in ASD. This integrative approach provides a quantitative and biologically grounded framework to investigate how post-transcriptional regulators contribute to ASD-relevant molecular vulnerability. ©The authors ©Sciencedirect ©Elsevier.