Search Results (3,467)

Primary dystrophin deficiency, caused by X-chromosome mutations within the DMD gene, encompasses a continuous clinical spectrum of neurological, muscular, and cardiac disorders known as dystrophinopathies that exhibit profound phenotypic variability driven by specific mutation profiles and epigenetic factors. This comprehensive review analyzes the clinical and molecular characteristics of seven primary classifications: Duchenne muscular dystrophy (DMD), a severe childhood myopathy caused by a complete absence of the protein that leads to loss of ambulation and fatal cardiorespiratory failure in youth; Becker muscular dystrophy (BMD), a milder variant with partial protein deficiency that preserves walking capabilities into adulthood and prolongs life expectancy; pseudometabolic dystrophinopathic syndrome, featuring exercise intolerance, cramps, and recurrent rhabdomyolysis that mimics metabolic diseases; asymptomatic dystrophinopathy, representing the mild end of the spectrum identified incidentally through chronically elevated creatine kinase levels; brain dystrophin-related syndrome, where the disruption of distal isoforms like Dp140 and Dp71 results in neurodevelopmental and neuropsychiatric comorbidities such as ADHD, autism, and intellectual disability; X-linked dilated cardiomyopathy (XLDCM), a cardiac-selective condition causing severe heart failure and arrhythmias while sparing skeletal muscle function; and female dystrophin-related syndrome, distinguishing between familial carriers—who can manifest symptoms due to skewed X-chromosome inactivation—and rare sporadic females who develop an exceptional, severe, Duchenne-like phenotype due to cytogenetic accidents such as Turner syndrome or chromosomal translocations. Ultimately, advancements in molecular testing (NGS and WGS) have significantly optimized diagnostic precision, proving essential for implementing early cardioprotective care, accurate genetic counseling, and the development of future tissue-specific targeted gene therapies. The present study also discusses the psychosocial impact that the disease has on patients. Full article

Effective conversation skills are vital for navigating social interactions successfully. However, many individuals with autism spectrum disorder (ASD) face challenges in developing and maintaining these skills. We aimed to create a brief assessment to evaluate small talk among individuals with ASD. Three individuals diagnosed with ASD participated in the study. Assessment sessions were conducted in person, with questions asked at varying intervals to evoke participant responses. The assessment was effective in identifying potential conversation skills requiring improvement. The findings can inform the design of individualized and naturalistic interventions to enhance conversation skills in adolescents and adults with ASD, fostering improved social communication and engagement. Full article

Background: School-aged children belong to a distinct developmental stage in which bullying and weight status can have significant short- and long-term consequences, particularly for those with autism spectrum disorder (ASD). Methods: Data were analyzed from the 2023 National Survey of Children’s Health (N = 17,689), including perpetration and victimization among school-aged children. Results: They were more likely to be White non-Hispanic compared to multiracial (OR 2.24; 95% CI 2.17–2.31), less likely to be female (OR 0.40; 95% CI 0.38–0.41), younger [6–9 years] (OR 0.42; 95% CI 0.41–0.43), from families within the 100–199% (OR 0.40; 95% CI 0.39–0.42) and 200–299% (OR 0.26; 95% CI 0.26–0.27) poverty level. Adjusted models indicated that children with ASD had higher odds of being underweight (OR 1.75; 95% CI 1.70–1.81) or overweight (OR 3.55; 95% CI 3.37–3.73) than of being obese. They were also more likely to be victimized 1–2 times/week (OR 1.30; 95% CI 1.24–1.37), but less likely to bully others 1–2 times/month (OR 0.31; 95%CI 0.29–0.33). Conclusions: These findings suggest that school-aged children with ASD may face elevated risks of victimization and unhealthy weight status. Full article

This study evaluated the effects of a strictly auditory–vocal instructional package, combining cued vocal rehearsal, forward chaining, and multiple stimulus control arrangements, on the acquisition of forward intraverbal story recall (RC, divergent relations) and the emergent performance on untaught reverse character identification (CI, convergent relations) probes across nine children with autism spectrum disorder (ASD). Utilizing a concurrent multiple-baseline design across character chains replication, the 1:1 intervention integrated the auditory–vocal components without initial permanent visual aids. All nine participants met the mastery criterion for the directly taught forward story-recall chains (mean = 4.6, 4.9, and 4.3 sessions across target characters). Following instruction, unreinforced probe performance on untaught CI relations increased above pre-instruction levels for all participants, with seven achieving mastery on these convergent tasks. Maintenance probes conducted at 2 and 4 weeks post-instruction demonstrated sustained responding for the majority of participants, though marked individual variability—including notable performance decays in story recall for two participants—was observed. Because a formal component analysis was not conducted, the specific contribution of vocal rehearsal cannot be isolated from the overall package, and theoretical mechanisms such as joint control serve only as a cautious, tentative conceptual account. While limited by CI’s functional problem-solving evidence being tempered by its measurement format within the staggered concurrent arrangement, the absence of component isolation, and direct classroom generalization probes, these preliminary findings suggest that package utilizing strictly auditory–vocal arrangements demonstrates initial feasibility for establishing complex intraverbal forward chains under controlled 1:1 clinical conditions. This foundation allows future research on adapting this package, considering temporal location parameter adjustments, to support multi-component stimulus control for vocal independence within inclusive classroom settings like Learning in Regular Classrooms (LRC). Full article

Neuroligin-2 (NLGN2) is a cell adhesion molecule implicated in neurodevelopmental disorders (NDDs), including autism spectrum disorder (ASD) and intellectual disability (ID). While NLGN2 is well known as a postsynaptic organizer of predominantly inhibitory synapses, accumulating evidence suggests that NLGN family proteins are also involved in neuronal morphogenesis during early developmental stages. However, a gap remains in our understanding of how loss of function in NLGN2 potentially leads to abnormal neuronal morphogenesis. Here, we investigated the molecular basis of excessive neuronal process formation induced by depletion of NLGN2 using the N1E-115 cell line, an established model of neuronal differentiation. Clustered regularly interspaced short palindromic repeat (CRISPR)/Cas13-mediated knockdown of NLGN2 promoted neuronal process elongation and neuronal differentiation marker expression. Mechanistically, NLGN2 knockdown resulted in activation of coiled-coil and hook domain-containing protein 88A (CCDC88A, also known as Girdin or GIV), a non-receptor guanine nucleotide exchange factor for heterotrimeric G proteins. Transfection of the regulator of G protein signaling (RGS) domain of RGS3, a negative regulator of G proteins, or the G protein-binding domain of engulfment and cell motility 1 (ELMO1) effectively decreased the excessive process elongation phenotype. Similar effects were observed in primary cortical neurons. Furthermore, these interventions normalized elevated Rac1 activity induced by NLGN2 knockdown. Collectively, our findings identify the CCDC88A-G protein-ELMO signaling pathway as a key mediator of excessive neuronal morphogenesis following NLGN2 knockdown. These results provide valuable insight into the mechanisms by which NLGN2 dysfunction may contribute to abnormal neuronal morphogenesis and suggest potential recovery strategies. Full article

Background: Brain MRI in patients with autism spectrum disorder (ASD) is generally considered when additional neurologic, developmental, or syndromic concerns warrant structural evaluation. In the study practice, ASD alone was not used as the reason to obtain MRI. We examined documented MRI use and broad result coding while explicitly separating selection for imaging from abnormal-result status. Methods: This retrospective specialty-practice cohort included 1884 patients with ASD. The primary outcome was the proportion of documented MRI examinations coded abnormal. Secondary analyses examined associations of age, sex, seizure history, EEG status, and genetic test status with abnormal versus normal MRI coding and with documented MRI utilization. MRI categories and the pediatric restriction were exploratory and sensitivity analyses. Results: An MRI result was recorded for 704 patients (37.4%; 95% CI 35.2–39.6); 322 were coded abnormal (45.7%; 95% CI 42.0–49.5). The abnormal-result models had low in-sample discrimination (AUC 0.528 and 0.553), and adjusted confidence intervals for all measured variables included 1.00; these results do not demonstrate equivalence between clinical groups. Seizure history was associated with documented imaging in the full cohort (aOR 2.47, 95% CI 1.94–3.14), and abnormal EEG was associated in the exploratory complete-case utilization model (aOR 2.83, 95% CI 1.93–4.14). Conclusions: Broad MRI abnormalities were frequent among patients selected for imaging because of additional clinical concerns, but a broad abnormal code is not equivalent to clinically actionable yield. The routinely captured variables available in this dataset were insufficient to distinguish abnormal from normal MRI coding. These findings favor individualized MRI decisions based on the clinical question prompting imaging and motivate future studies with richer neurologic and developmental phenotyping. Full article

Clustering may uncover latent behavioral structure in Autism Spectrum Disorder (ASD) screening data without using outcome labels, but the resulting partitions depend strongly on data geometry and the adopted similarity measure. Quantum-hybrid clustering offers alternative distance and similarity estimators, yet whether these subroutines improve on classical methods under controlled conditions remains unclear. We conduct a benchmark of k-means, DBSCAN, agglomerative clustering, and spectral clustering against their quantum-hybrid counterparts. All methods are evaluated in a common 13-dimensional representation, with hyperparameters selected exclusively through internal validation indices. The evaluation covers four synthetic geometries and a 13-dimensional PCA representation of an ASD screening dataset, each containing 300 samples and evaluated over 10 seed-defined stochastic runs. Clustering quality is measured using the Silhouette Index (SI), Davies–Bouldin Index (DBI), Calinski–Harabasz Index (CHI), Adjusted Mutual Information (AMI), and Adjusted Rand Index (ARI). Under this validation protocol, Q-means exactly recovers the Gaussian clusters and improves label agreement on anisotropic data, but it does not outperform classical k-means on the ASD screening data. Q-spectral significantly reduces DBI on Two Moons, Concentric Rings, and ASD screening data, although these reductions do not consistently translate into higher AMI or ARI. Q-DBSCAN and Q-agglomerative exhibit greater sensitivity to distance distortions and finite-shot noise. Overall, the results reveal geometry-dependent trade-offs rather than uniform quantum-hybrid superiority. We relate these findings to theoretical complexity, measurement noise, state-preparation costs, and eigensolver bottlenecks in near-term implementations. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by impaired social interactions, communication deficits, and excessive repetitive behaviors. While ASD has a strong genetic basis, growing evidence suggests that epileptic seizures may serve as environmental risk factors for ASD development. The high comorbidity between epilepsy and ASD (20–30%) suggests potential shared neurobiological mechanisms, yet the causal relationship remains poorly understood. To investigate the causal role of seizures in the development of ASD-like pathology, we exposed adolescent mice (3 weeks old) to electroconvulsive seizures (ECS) for 10 consecutive days. This repeated ECS exposure led to the emergence of autistic-like behaviors including significantly decreased sociability, increased repetitive self-grooming, enhanced marble burying behavior, and anxiety-like behaviors, without affecting general locomotor activity. Additionally, repeated exposure to ECS induced significant changes in glutamatergic neurotransmission in the mice’s prefrontal cortex and hippocampus, brain regions critically involved in social cognition and behavioral regulation. Interestingly, these changes occurred without alterations in other excitatory/inhibitory neuronal markers, suggesting a specific impact on glutamate receptor expression rather than a general disruption of excitatory/inhibitory balance. These findings suggest that repeated seizures may contribute to ASD-like symptoms by specifically affecting key glutamatergic neurotransmitter systems, providing insights into the neurobiological mechanisms underlying the comorbidity between epilepsy and autism. In addition, repeated ECS differentially regulated histone deacetylase (HDAC) transcripts in a region-specific manner and produced seizure-intensity-dependent transcriptomic signatures. Full article

Variation in gaze behavior due to age is currently a considerable challenge in building reliable eye-tracking systems for Autism Spectrum Disorder (ASD) diagnosis. However, existing strategies often focus on static gaze representation or dataset-based information, which can lead to limited generalization of findings depending on developmental groups and heterogeneous recording conditions. In this paper, we present a temporal transformer-based system for ASD classification using eye-tracking sequences. This allows you to model gaze behavior as a structured temporal process in the context of contextual attention, as well as employing entropy-based modeling for various distributions of variability over time and temporal consistency constraints to capture sequential gaze dynamics related to ASD behavioral patterns. The framework was evaluated using public eye-tracking corpus containing temporally ordered gaze recordings from ASD and TD participants across age groups. Five sequential experiments on baseline classification, class-balancing analysis, cross-age evaluation, ablation analysis, and cross-dataset transfer learning were performed to conduct experiment-based evaluations. Model performed 0.91 in in-domain Area Under the Receiver Operating Characteristic Curve (AUC) and 0.81 in F1-score on the primary eye-tracking dataset. In the cross-dataset assessment stage, the framework presented a relatively stable performance, with an AUC of 0.85 and an average F1-score of 0.74, irrespective of differences in participant distributions and recording conditions. Ablation analysis also revealed that entropy regularization and temporal consistency mechanisms played a significant role in model stability and classification performance. The ablation analysis provides additional insight into the contribution of the proposed framework components beyond the overall classification performance. Removing the entropy-based regularization reduced the model’s ability to represent variability in gaze allocation, whereas removing the temporal-consistency regularization resulted in less stable sequence representations during learning. These observations indicate that the proposed components complement the transformer-based sequence encoder by improving representation stability and preserving diagnostically relevant temporal information. Rather than acting as independent classifiers, the regularization mechanisms serve as supporting constraints that enhance the quality and robustness of the learned temporal representations. The results indicate that temporally structured gaze modeling is more robust, interpretable, and general in comparison to static gaze representations. In summary, the presented framework can represent a scalable and developmentally appropriate approach to gaze-based ASD classification and support the implementation of trusted neurodevelopmental screening systems. Full article

Background: The cerebellum integrates sensory information to maintain balance and posture, allowing movement guidance. Perinatal damage to this brain region correlates with an increased incidence of Autism Spectrum Disorder (ASD), a major neurodevelopmental condition where poor motor performance in eye–hand coordination, balance and gait is observed. The cerebellum of ASD individuals is affected by reduced GABAergic signaling that leads to an excitatory/inhibitory imbalance. Methods: Motor coordination behavior were tested and Western blot essays performed to study the developmental expression of GABAergic signaling proteins, namely GABA_A receptor (α1, α2 and β1) subunits and glutamate decarboxylase 67 (GAD67), in CD1 mice prenatally exposed to valproic acid (VPA), a preclinical model of ASD. Results: The VPA group exhibited motor coordination deficits on postnatal day 30 (P30) compared to the control. The expression profiles for the control group revealed that GABA_A-α1 increased linearly, while GABA_A-β1 displayed the opposite pattern and GABA_A-α2 presented one peak of expression (P8). GAD67 decreased from embryonic day 16 (E16) to P8 but increased linearly after the first week of postnatal development (P8-P30). The developmental expression profile for all these proteins was disrupted by prenatal exposure to VPA. Conclusions: Motor coordination deficits correlate with a downregulated expression of GABA_A (α1, α2 and β1) subunits and GAD67 through cerebellar development in individuals that were prenatally exposed to VPA. Full article

Deep learning and social–emotional learning (SEL) are increasingly recognized as essential for preparing children to thrive in an increasingly complex and interconnected world. However, research examining the relationship between SEL and deep learning among children with autism spectrum disorder (ASD) remains limited. This study investigated the association between SEL and deep learning during inclusive play among 13 children with ASD (IQ ≤ 50; 2 females and 11 males; Mage = 6.23 years, SD = 0.60). The results indicated that (1) children’s social–emotional competencies (SECs) were significantly higher at post-test than at pre-test over the period of participation in inclusive play; (2) children with ASD demonstrated changes in SEL within a dynamic process involving teacher scaffolding, interactions with diverse peers, imitation of peer behaviors, and internalization of activity routines during inclusive play; and (3) Several SEC dimensions were positively associated with deep learning, and regression analyses indicated a significant predictive relationship between SECs and deep learning. These findings highlight the potential of inclusive play for supporting both SEL and deep learning among children with ASD and provide empirical support for considering the development of SECs as a potential predictor of deep learning. However, the findings should be interpreted cautiously given the small sample size (n = 13) and the absence of a control group. Full article

Background/Objectives: Children now spend a substantial part of daily life in digital environments, and their digital, media, and information literacies shape their online safety, social-emotional development, and mental health, making these competencies a concern for child public health and preventive paediatric care. This study applies a Synthetic Knowledge Synthesis (SKS) to map how digital, media, and information literacies (hereafter digital literacies) among children have evolved, comparing neurotypical and neurodivergent children. SKS is a semi-automated approach that combines descriptive bibliometrics, keyword co-occurrence mapping, and qualitative content analysis to map an entire research field or topic. Methods: We treat digital literacies as relevant to children’s health, well-being, and safe online participation, and we analyse Scopus-indexed literature from 1996 to 2025 using descriptive bibliometrics, keyword co-occurrence mapping, and qualitative content analysis. Results: The mapping shows strong growth in general digital-literacy scholarship alongside a small but persistent body of work on a “double digital divide” affecting children with autism spectrum disorder (ASD), ADHD, and dyslexia. The two bodies of work differ in emphasis: the general literature foregrounds social integration and critical agency, while research on neurodivergent children foregrounds inclusive pedagogy, implicit learning, and multimodal expression. It also shifts from general digital-safety awareness toward protective mediations for vulnerabilities such as social-emotional decoding differences and impulsivity, which bear on children’s online safety and mental health. Because bibliometric mapping reveals where research is concentrated rather than what works in practice, the synthesis points to evidence gaps rather than proven methods. Conclusions: On this basis, we argue for an integrated public-health and socio-educational framework that complements universal literacy standards with adaptive, assistive safety nets, so that all children can take part in digital life safely and on equal terms. Full article

Background/Objectives: Regular participation in physical activity is essential for maintaining health and well-being and plays a key role in disease prevention and quality of life, both in the general population and among people with disabilities, who represent approximately 16% of the global population. Martial arts-based physical activity programs incorporating safe-falling techniques may contribute to improving physical fitness, reducing fall-related injuries, and supporting adherence to the World Health Organization physical activity recommendations. This systematic review primarily aimed to identify martial arts-based fall-training programs, particularly judo-based interventions, and examine their effects on health and well-being in individuals with intellectual disability. As a secondary objective, evidence from other populations and martial arts disciplines was considered to identify relevant fall-training protocols and transferability. Methods: The research question was formulated using the PEO framework. Eligibility criteria included: people with intellectual and developmental disabilities or special educational needs; exposure to exercise programs involving martial arts, combat sports, or judo; and outcomes related to safe falling, falls, balance, motor skills, and physical condition. A systematic literature search was conducted between December 2025 and April 2026 on the Web of Science Core Collection, Scopus, PubMed, and SPORTDiscus in accordance with PRISMA guidelines. Results: From a total of 658 records identified, 27 studies met the inclusion criteria, encompassing 1892 participants with various disabilities, including intellectual disability, autism spectrum disorder, visual impairment, and deafness. The interventions involved different martial arts modalities, such as judo, taekwondo, karate, tai chi, and capoeira. Overall, the available evidence suggests beneficial effects on physical fitness, motor abilities, balance, psychosocial outcomes, and safe-falling skills across different populations; however, substantial clinical and methodological heterogeneity was observed among the included studies. Conclusions: No studies investigating structured martial arts-based fall-training programs in individuals with intellectual disability were identified. Although evidence from other populations is promising, it remains heterogeneous. Further high-quality studies are needed to evaluate these interventions in individuals with intellectual disabilities. Full article

Self-determination is critical in promoting classroom engagement among students with autism spectrum disorder. The Self-Determined Learning Model of Instruction (SDLMI), an evidence-based practice for enhancing self-determination in students with disabilities, has gained increasing scholarly attention. However, empirical studies in Chinese contexts remain limited. This exploratory, descriptive implementation study used an A–B–M single-case approach with two individual cases to examine descriptive patterns in self-determination skills, perceived opportunities for self-determination, and classroom engagement before, during, and after the implementation of an SDLMI-based action-plan in Mandarin-speaking special education classrooms in Fujian Province, China. Preliminary findings indicated positive trends in students’ self-determination skills and classroom engagement after the implementation of the SDLMI, as well as increased perceived school-based opportunities for self-determination and mixed changes in perceived home-based opportunities. These findings are consistent with the existing international literature and provide preliminary case-level support for the cultural feasibility of adapting SDLMI practices within Chinese special education settings. Further research with larger samples and more rigorous experimental designs must fully assess its effectiveness and generalizability. Full article

Artificial intelligence has emerged as a promising paradigm for advancing the screening, diagnosis support, and monitoring of autism spectrum disorder (ASD) through scalable and data-driven clinical augmentation. Recent advances in machine learning, multimodal sensing, and digital phenotyping have enabled AI systems to analyze behavioral, neurophysiological, speech, and clinical data to identify early markers of ASD. Despite encouraging experimental results, major barriers to clinical translation remain, including limited generalizability, fragmented datasets, insufficient evaluation rigor, lack of semantic interoperability, and unresolved ethical and regulatory concerns. This systematic review provides a comprehensive technical review of AI for ASD, covering data modalities, feature engineering, learning paradigms, evaluation protocols, deployment architectures, and knowledge representation frameworks. Particular emphasis is placed on system-level and translational considerations, including cloud–edge infrastructures, explainable clinical decision-support systems, privacy-aware deployment, and ontology-driven reasoning. Beyond summarizing existing work, this paper critically analyzes challenges related to reproducibility, dataset bias, interpretability, and clinical integration and derives design requirements for next-generation trustworthy ASD AI systems. We argue that meaningful clinical impact will require the integration of multimodal learning, semantic knowledge representation, explainable reasoning, and human-in-the-loop decision processes to support safe, interpretable, and clinically deployable AI systems in pediatric healthcare environments. Full article