Search Results (304)

Inflammasomes are cytosolic multiprotein complexes that detect pathogens, cellular stress, and damage-associated molecular signals, thereby orchestrating innate immune responses. Increasing evidence suggests that dysregulated inflammasome activation contributes to persistent neuroinflammation and to a wide range of neuropsychiatric disorders, including mood disorders, schizophrenia, Alzheimer’s disease, and autism spectrum disorders. Together, these findings emphasize the critical role of neuroimmune interactions in the pathophysiology of mental disorders. Recent molecular studies have substantially advanced our understanding of the crosstalk among neurons, microglia, astrocytes, and peripheral immune cells, uncovering complex regulatory networks mediated by cytokines, neurotrophins, and neurotransmitters. By examining key inflammatory mediators and cell type-specific mechanisms, this review consolidates current knowledge and proposes conceptual frameworks to guide future investigations and facilitate the development of targeted therapeutic strategies for neuropsychiatric disorders. Full article

Background/Objectives: Autism spectrum disorder (ASD) is a clinically and genetically heterogeneous neurodevelopmental condition, and the diagnostic yield of whole-exome sequencing (WES) varies across settings. This single-center study aimed to determine the molecular diagnostic yield of WES in pediatric ASD and to explore genotype–phenotype overlap using a structured, phenotype-driven reanalysis strategy. Methods: We enrolled 60 children with syndromic and non-syndromic ASD, who underwent detailed clinical and dysmorphology assessment. WES for single-nucleotide and copy-number variant (CNV) detection was performed in an accredited laboratory, followed by clinician-driven reinterpretation, integrating expanded phenotypic data and ACMG/AMP-based variant classification. Genes were considered if they harbored rare, potentially pathogenic variants and were previously reported or curated in established ASD-associated gene resources. Results: The initial external laboratory report identified 5 of 60 patients (8.3%) with a pathogenic (P) or likely pathogenic (LP) variant (positive result), 30 of 60 (50.0%) with a variant of unknown significance (VUS) (inconclusive result), and 25 of 60 (41.7%) with a negative result. Clinician-based variant reinterpretation identified pathogenic or likely pathogenic variants in 9 of 60 patients (15.0%), representing an 80% relative increase in diagnostic yield, as well as 43 VUSs distributed across 34 patients, while 17 patients had no reportable variants (negative result). Overall, reanalysis revealed 11 additional variants of interest (pathogenic, likely pathogenic, or VUS) that had not been reported in the initial assessment. In total, 52 sequence and copy-number variants in 46 genes were detected, most of which were VUSs (83%). Conclusions: In this pediatric ASD cohort, WES with phenotype-driven reinterpretation and CNV assessment yielded a clinically positive result in 15% of patients and uncovered additional candidate variants, highlighting both the value and the current interpretative challenge of comprehensive genomic testing in ASD. Full article

The etiology of autism spectrum disorder (ASD) implicates genetic predispositions and environmental chemicals, such as polybrominated diphenyl ethers (PBDEs). We aimed to identify whether mitochondrial quality control (MQC) was involved in ASD-relevant behavioral changes induced by decabromodiphenyl ether (deca-BDE, BDE-209) and the alleviation by melatonin. Pregnant rats exposed to BDE-209 (50 mg/kg i.g.) were administrated melatonin through drinking water (0.2 mg/mL) during gestation and lactation. Behavioral assessments integrated open-field test, three-chamber social test, and Morris water maze; mitochondrial detections took transmission electron microscopy, immunofluorescence, and homeostasis together; hippocampal molecular network was identified through transcriptomics profiles, combining dendritic morphology analysis after Golgi-Cox staining. Melatonin supplementation attenuated BDE-209-reduced social and cognitive ability, accompanied by improvements in hippocampal synaptic plasticity (dendritic spines, PSD95, SNAP25). Mitochondrial dysfunctions, shown as decreases in complex IV activity, ATP content, and mtDNA copies, plus redox imbalance (ROS/SOD2) and resultant mitochondrial membrane potential disruption and apoptosis, together with fusion/fission dynamic (MFN2/DRP1), biogenesis (SIRT1-PGC1α-TFAM), and mitophagy (SIRT3-FOXO3-PINK1) suppression, were reversed by melatonin partially through SIRT1 (Sirtuin-1)-dependent pathways, as these protections were abolished by inhibitor EX527. This study highlighted the SIRT1–SIRT3 axis in MQC and behavioral effects, providing novel intervention for PBDEs’ neurodevelopmental impairment. Full article

Background/Objectives: The goal of the study was to identify the peculiarities of attention deficit hyperactivity disorder (ADHD) on the base of the behavioral characteristics and acoustic features of speech of children with ADHD and ADHD with comorbidity—ADHD and autism spectrum disorders (ASD) and ADHD and intellectual disabilities (ID)—within the framework of one test task. Behavioral characteristics were selected using DSM-V criteria; acoustic features of speech were considered by researchers as speech markers of ASD and ID detected for different languages. Methods: The study includes 92 children aged 5–13 years with ADHD, ADHD and ID, ADHD and ASD, and control groups of children diagnosed with ASD, ID and typically developing (TD) children. The children were tested using the test task “co-op play”. Video and audio recordings of children performing the test task were collected. We used a complex approach to study the peculiarities of children with ADHD through expert analysis of children’s behavior and play, acoustic spectrographic analysis of speech and questionnaires about early childhood development filled out by parents. Results: The characteristics of behavior, play, and acoustic features of speech of children with ADHD and ADHD and comorbidity were revealed. Children with ADHD had lower behavior scores in the play situation on the expert assessment than TD children, with the greatest differences for characteristics of play, “Playing for toy”, and of behavior “Displaced activity” and “Losing attention”. The speech of children with ADHD is characterized by low values of the third formant and the difference between the first two formants, compared to the corresponding speech features of children from other groups. The speech of children with ADHD+ASD is characterized by maximal pitch values (high voice), while that of children with ADHD+ID is characterized by low vowel articulation index values. Conclusions: Based on the analysis of behavior and speech of children with TD, ADHD, ADHD and comorbidity performing the “co-op play” test task, the set of characteristics specific to ADHD was identified. The obtained data expand our understanding of the specificity of children with ADHD and may contribute to the development of qualified support for families with children with ADHD. Full article

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by genetic heterogeneity. Post-transcriptional regulation—particularly alternative polyadenylation (APA)—plays a critical role in the pathogenesis of ASD. APA controls mRNA stability, translational efficiency, and subcellular localization through modulating the length of the 3′ untranslated region of mRNA. APA profiling can uncover functionally relevant post-transcriptional alterations often missed by conventional gene expression analyses. However, current ASD analyses still largely rely on differential gene expression or individual APA event detection, which ignores the collective explanatory power of ASD risk genes or co-dysregulated functional gene modules within specific cell types. In this study, we present an integrative computational framework that combines matrix factorization and machine learning to identify ASD-associated gene modules driven by APA and to predict cell-type-specific ASD-related cells. Applied to human brain single-nucleus RNA sequencing (snRNA-seq) data, our approach systematically uncovers APA regulatory patterns that are specific to cell type, brain region, and sex in ASD. The identified APA modules are significantly enriched in pathways related to synaptic function, neurodevelopment, and immune response, with the strongest signals observed in excitatory neurons of the prefrontal cortex. Using APA genes from these modules as features, we built a classification model that effectively distinguishes ASD cells from normal cells. Moreover, we found that integrating APA with gene expression—two complementary modalities—substantially improves prediction accuracy, underscoring APA as an independent and biologically informative regulatory layer. Our work delineates a high-resolution APA regulatory landscape in ASD, offering novel insights and potential therapeutic avenues beyond transcriptional abundance. Full article

Background: The early detection of autism spectrum disorder (ASD) is imperative for enhancing long-term developmental outcomes. Nevertheless, conventional screening methods depend on time-consuming, expert-driven behavioral assessments and are characterized by limited scalability. Automated video-based analysis provides a noninvasive and objective approach for the extraction of behavioral biomarkers from naturalistic recordings. Methods: A modular multimodal framework was developed that integrates motion-based video analysis and facial feature extraction for the purpose of ASD versus typically developing (TD) classification. The system is capable of processing RGB videos, skeleton/stickman representations, and motion trajectory streams. A comprehensive set of kinematic features was extracted, encompassing joint trajectories, velocity and acceleration profiles, posture variability, movement smoothness, and bilateral asymmetry. The repetitive stereotypical behaviors exhibited by the subjects were characterized using frequency-domain analysis via FFT within the 0.3–7.0 Hz band. Facial expression features derived from normalized face crops and landmark-based morphological descriptors were integrated as complementary modalities. The feature-level fusion process was executed subsequent to z-score normalization, and the classification procedure was conducted using a Random Forest model with stratified 5-fold cross validation. The implementation of GPU acceleration was instrumental in facilitating near real-time inference. Results: The motion-based ComplexVideos pipeline demonstrated a cross-validated accuracy of 94.2 ± 2.1% with an area under the ROC curve (AUC) of 0.93. Skeleton-based KinectStickman inputs demonstrated moderate performance, with an accuracy range of 60–80%. In contrast, facial-only models exhibited an accuracy of approximately 60%. The integration of multiple modalities through feature fusion has been demonstrated to enhance the robustness of classification algorithms and mitigate the occurrence of false negative outcomes, thereby surpassing the performance of single-modality models. The mean inference time remained below one second per video frame under standard operating conditions. Conclusions: The experimental results demonstrate that the integration of multimodal cues, including motion and facial features, facilitates the development of effective and efficient video-based screening methods for autism spectrum disorder (ASD). The proposed framework is designed to offer a scalable, extensible, and computationally efficient solution that can support early screening in clinical and remote assessment settings. Full article

Autism Spectrum Disorder (ASD) is estimated to affect about 1% of children globally. While there is currently no cure, early detection and targeted interventions can significantly enhance the well-being and daily functioning of children with ASD. This paper presents an intelligent, content-based recommender system designed to suggest personalized activities aligned with each child’s preferences and developmental needs. The proposed system integrates social stories, educational videos, and interactive exercises supported by machine learning techniques to foster communication, social interaction, emotional regulation, and cognitive development—while reducing the need for constant parental supervision. Unlike traditional content-based systems, our approach incorporates the child’s emotional state (mood) to provide more diverse and context-aware recommendations, avoiding the filter bubble effect and enhancing personalization and engagement. A key contribution of this work lies in its focus on personalized and interactive learning experiences, made possible through the combination of multiple assistive technologies. Additionally, the study addresses the problem of data scarcity by providing a publicly available dataset to facilitate further research in ASD-focused intelligent systems. Preliminary feedback from therapists and parents indicates that the system holds strong potential to substantially improve the educational, communicative, and emotional skills of children with ASD. These promising results motivate future large-scale empirical evaluations to validate its effectiveness and establish it as a valuable tool for ASD intervention and inclusive education. Full article

Caregivers of children with Autism Spectrum Disorder (ASD) frequently experience chronic psychological stress, thereby necessitating accessible support. Although artificial intelligence (AI)-based assisted technologies have the potential to reduce caregiver workload, most existing solutions lack robust privacy control and clinical interoperability, which significantly limits their adoption in regulated healthcare environments. To address these challenges, this paper proposes a Privacy-by-Design (PbD) multi-agent architecture that enables consent-aware, auditable, and privacy-preserving AI-assisted support for caregivers of children with ASD. The effectiveness of the proposed architecture was evaluated using two datasets: one focusing on clinically grounded autism-related knowledge and another reflecting naturalistic caregiver observation language. System performance was assessed using a Retrieval-Augmented Generation Assessment (RAGAs)-based framework with a Large Language Model (LLM)-as-a-Judge approach implemented via a locally deployed Llama 3 8B model. The system achieved answer relevancy scores of 0.767 for the clinical dataset and 0.750 for the observational dataset, with corresponding Recall@K values of 0.400 and 0.742, respectively. Context precision ranged from 0.599 to 0.631, and no harmful content was detected. Overall, the proposed architecture demonstrates secure caregiver–specialist collaboration through consent-aware routing, anonymised data storage, and controlled data reconstruction, providing a regulation-aligned design option for privacy-preserving AI integration in assisted care platforms. Full article

Autism-specific screening and developmental surveillance in primary care aid identification of autism. In this study, we assessed primary care clinicians’ (PCCs’) reported confidence in screening scores from the Modified Checklist for Autism in Toddlers, Revised (M-CHAT-R) and in their own diagnostic impressions. Four PCCs provided data for 50 children aged 16–36 months for whom they had any developmental concern. PCCs’ diagnostic impressions were “Definitely Autism” for 15 children (30%), “Unsure—Needs Further Evaluation” for 25 children (50%) and “Definitely Not Autism” for 10 children (20%). They reported High Confidence on the screener score in 33 cases (66%). Of the 17 cases for whom PCCs reported having Low Confidence on the M-CHAT-R, 14 children (82.3%) had a Low Likelihood score, with no significant association between M-CHAT-R likelihood and PCC’s confidence in the screening score. PCCs’ diagnostic impressions were concordant with the M-CHAT-R autism likelihood in 42% of cases, with a significantly higher mean in confidence rating when compared to the non-concordant cases. Language development and social engagement were the most frequently endorsed concerns by PCCs, with significant associations between these concerns and M-CHAT-R likelihood. Our results suggest that, when developmental concerns exist, PCCs may place greater confidence in the M-CHAT-R when scores indicate a higher likelihood of autism, and that confidence in their own diagnostic impressions may be associated with concordance with the screener score. Full article

It is well established that early diagnosis and subsequent intervention can result in significant benefits in infants with neurodevelopmental disorders such as cerebral palsy (CP). This scoping review aimed to assess the current state of the literature regarding the use of innovative and emerging technologies for early CP screening, diagnosis and phenotyping in pre-ambulatory children. Searches were performed across PubMed, Embase and Cochrane databases; articles were screened by four independent reviewers at the title/abstract and full-text levels. Forty-eight studies met the inclusion criteria. The most frequently used modalities included wearable sensors (e.g., accelerometers, inertial measurement units) and video-based motion analysis. These movement-tracking systems were used to screen for a variety of pediatric-onset neurodevelopmental disorders and have been useful in quantifying spontaneous infant movements, detecting the absence or abnormality of fidgety movement, or identifying atypical motor patterns. Although CP was our primary focus, several studies applied a similar pipeline to autism spectrum disorder (ASD) and spinal muscular atrophy (SMA), underscoring broader relevance for early neurodevelopmental screening, diagnosing and phenotyping. Overall, technology-assisted motor assessment demonstrated promising feasibility and diagnostic potential; however, most studies are limited by small sample sizes, short follow-up durations, and heterogeneous validation methods. Given the benefits of early intervention and the emerging capabilities of wearable and video-based analytics, larger multi-site and longitudinal datasets are needed to support early diagnosis, risk stratification, and functional phenotyping in CP. Full article

Background: Children with autism spectrum disorders (ASD) are generally less involved in physical activity and sport. Therefore, the present pilot study aimed at determining the effect of a sport-based training program on motor coordination development and functioning in children with ASD. Methods: Twenty children with ASD (age: 8.7 ± 1.6 years, 5 females) were included in a sport-based training program for 6 months. Participants were free to select their own sport discipline. Before and after the program, reaction time was evaluated using a simple (by identifying the targeted stimulus) and a complex (by discriminating the targeted stimulus among confounding signals) reactive test, while fine and gross motor coordination was assessed by transferring pennies, jumping in place (same sides synchronized), tapping feet and fingers (same side synchronized), and the Flamingo test. Results: The analysis showed a significant reduction (p = 0.016, d = 0.16) in complex reactive test (pre: 15.8 ± 14.8 s; post: 13.6 ± 11.1 s) and a significant improvement in transferring pennies test (pre: 6.3 ± 3.4 pt.; post: 7.8 ± 3.8 pt.; p = 0.034, d = 0.42). Furthermore, two of the low-functioning children, who did not perform any motor test before the program, were able to complete both reactive tests and transferring pennies test. No significant differences emerged for the remaining tests. Conclusions: A sport-based extra-curricular program improved reaction time and fine motor coordination in children with ASD. The complex reactive and transferring pennies tests were particularly effective in detecting changes, even in low-functioning children. These findings support the promotion of diverse physical activities to aid physical and cognitive development. Full article

Background: Autism Spectrum Disorders (ASD) are neurodevelopmental disorders characterized by repetitive behaviors and social interaction deficits. While the severity of ASD is classified into levels (1–3) by the DSM-5, reliable circulating biomarkers to differentiate these levels are lacking. This retrospective pilot study examines plasma amino acid levels in children with ASD to identify the potential biomarkers of disease severity. Methods: Plasma samples from 30 children diagnosed with ASD (24 males, 6 females, aged 3–12 years) were analyzed. Participants were stratified into two groups based on the Autism Diagnostic Observation Schedule Calibrated Severity Score (ADOS CSS): Group 1, presenting with mild symptoms (Level 1, n = 11), and Group 2, characterized by moderate-to-severe symptoms (Levels 2–3, n = 19). This was further confirmed by the identification of electroencephalogram (EEG) anomalies (21.1%) and magnetic resonance imaging (MRI) abnormalities (5.3%), which were detected exclusively in Group 2 and absent in Group 1. Amino acid levels were measured by ion-exchange chromatography. Statistical analyses (Mann–Whitney U test and chi-square test) were used to compare AA levels between groups. Results: Statistically significant differences were observed in the levels of phosphoethanolamine, aspartic acid, and glutamic acid between the two groups. These amino acids (AA) were significantly higher in the moderate-to-severe symptoms group (Levels 2–3) compared to the mild symptoms group (Level 1) (p < 0.05). All AA values remained within age-appropriate reference ranges. Conclusions: Plasma levels of phosphoethanolamine, aspartic acid, and glutamic acid may serve as potential biomarkers for ASD severity in children. Results from this exploratory analysis suggest that AA profiling could differentiate ASD severity and identify specific metabolic pathways, such as excitatory neurotransmission and phospholipid turnover. Further studies with larger cohorts are necessary to validate these findings and explore the role of AAs in ASD pathophysiology. Full article

This study examined potential visual search advantages in individuals with autism spectrum disorder (ASD) and explored the roles of distractor grouping and perceptual load by comparing their performance with that of typically developing (TD) controls. Participants were required to search for large or small targets under two levels of perceptual load, with distractors being either large or small. The results showed the following: (1) Search speed in the ASD group was slower than that of the TD group. (2) The effect of distractor grouping was stronger in the Target–Nontarget (T-N) size-inconsistent condition than in the consistent condition. Both groups showed a T-N size-consistency effect—response speeds in the T-N size-inconsistent condition were faster, indicating that distractor grouping improves search efficiency. (3) Under high load, the TD group exhibited a stronger T-N size-consistency effect than the ASD group, whereas no significant difference was observed under low load. This suggests that distractor grouping in the ASD group is less effective than in TD participants under high load. (4) Under the T-N size-inconsistent condition, participants with ASD detected small targets faster under low load, whereas TD participants detected large targets faster under high load. This indicates that distractor grouping facilitates visual search in ASD under low load. Both groups focus more on targets under high load. In conclusion, although ASD shows no search advantage, improving distractor grouping can speed up target search. Nevertheless, under high load, distractor grouping in individuals with ASD is weaker than in TD individuals, consistent with the weak central coherence theory. Additionally, ASD displays size asymmetry that is influenced by load, with distractor grouping aiding target detection in low load and reducing distractor processing under high load. Full article

Background: Autism spectrum disorder (ASD) is associated with distinct visual attention patterns that provide insight into underlying social-cognitive mechanisms. Methods: This systematic review (PROSPERO: CRD42023429316), conducted per PRISMA guidelines, synthesizes evidence from 14 peer-reviewed studies using eye-tracking to compare oculomotor strategies in autistic children and typically developing (TD) controls. A comprehensive literature search was conducted in PubMed, Web of Science, and Science Direct up to March 2025. Study inclusion criteria focused on ASD versus TD group comparisons in individuals under 18 years, with key metrics, fixation duration and count, spatial distribution, saccadic parameters systematically extracted. Risk of bias was assessed using the QUADAS-2 tool, revealing high heterogeneity in both index tests and patient selection. Results: The results indicate that autistic children exhibit reduced fixation on socially salient stimuli, atypical saccadic behavior, and more variable spatial exploration compared to controls. Conclusions: These oculomotor differences suggest altered mechanisms of social attention and information processing in ASD. Findings suggest that eye-tracking can contribute valuable information about heterogeneous gaze profiles in ASD, providing preliminary insight that may inform future studies to develop more sensitive diagnostic tools. This review highlights visual attention patterns as promising indicators of neurocognitive functioning in ASD. Full article

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. Full article