Search Results (157)

Autism spectrum disorders (ASD) are a heterogeneous group of neurodevelopmental disorders characterized by a number of dysfunctions in communication, social interactions and repetitive rigid patterns of behavior, interests, and activities. Despite much research, the causes of ASD remain elusive. In addition to genetic and epigenetic etiology, scientists have indicated inflammation, deregulation of cytokines, anti-brain autoantibodies, gut microbiota, and deregulated immunity as mechanisms possibly involved in the development of ASD phenotype. The aim of the study was to analyze the levels of IgA, IgE, and IgM immunoglobulins in the blood serum in patients with ASD to find out whether certain blood parameters are deregulated in that group of patients. The results suggest altered production of the immune cells in ASD patients which may be considered in the assessment of immune functions. Also, PCT% and LYMPH elevated values in patients with ASD might be of clinical relevance, possibly of predictive value for clinical preliminary diagnosis and therapy. Full article

(1) Background: Quantitative EEG (QEEG) offers potential for identifying objective neurophysiological biomarkers in psychiatric disorders and guiding neurofeedback interventions. This study examined whether three nonlinear QEEG metrics—Lempel–Ziv Complexity, Tsallis Entropy, and Renyi Entropy—can distinguish children with autism spectrum disorder (ASD) from typically developing (TD) peers, and assessed their relevance for neurofeedback targeting. (2) Methods: EEG recordings from 19 scalp channels were analyzed in children with ASD and TD. The three nonlinear metrics were computed for each channel. Group differences were evaluated statistically, while machine learning classifiers assessed discriminative performance. Dimensionality reduction with t-distributed Stochastic Neighbor Embedding (t-SNE) was applied to visualize clustering. (3) Results: All metrics showed significant group differences across multiple channels. Machine learning classifiers achieved >90% accuracy, demonstrating robust discriminative power. t-SNE revealed distinct ASD and TD clustering, with nonlinear separability in specific channels. Visual processing–related channels were prominent contributors to both classifier predictions and t-SNE cluster boundaries. (4) Conclusions: Nonlinear QEEG metrics, particularly from visual processing regions, differentiate ASD from TD with high accuracy and may serve as objective biomarkers for neurofeedback. Combining complexity and entropy measures with machine learning and visualization techniques offers a relevant framework for ASD diagnosis and personalized intervention planning. Full article

Background: Autism Spectrum Disorder (ASD) encompasses a range of neurodevelopmental conditions characterized by impairments in social communication, restricted and repetitive behaviors, and sensory sensitivities. Despite increased awareness, timely diagnosis in Türkiye remains limited due to the lack of culturally appropriate, psychometrically robust assessment tools. Objective: This study aimed to develop, validate, and standardize the Autism Behavior Assessment Scale (ABAS) as a reliable and culturally adapted tool for assessing ASD-related behaviors in individuals aged 3–24 years in Türkiye. Methods: Employing a three-phase, nine-step scale development framework, data were gathered from 1275 informants (parents and professionals) across 14 provinces. The ABAS comprises 36 items rated on a three-point Likert scale, spanning four subscales: Restricted Repetitive Behaviors & Sensory Sensitivity (RRBSS), Social Interaction (SI), Social Communication (SC), and Non-Developmental Speech (NDS). Psychometric analyses included exploratory and confirmatory factor analysis, reliability testing, and validation against established instruments. Results: The four-factor structure was confirmed via EFA and CFA with excellent model fit. The ABAS demonstrated strong internal consistency (α = 0.91–0.96), test–retest reliability (r = 0.83), and criterion validity (r = 0.93 with GARS-2-TV; r = 0.84 with U-ODKL). Discriminant validity analyses showed that the ABAS accurately differentiated individuals with ASD from individuals with intellectual disabilities (ID) and individuals with hearing impairments (AUC = 0.99). Conclusions: The ABAS is a psychometrically sound, developmentally sensitive, and culturally grounded instrument for identifying and monitoring ASD-related behaviors in Türkiye. It holds promise for improving early detection and guiding educational and clinical interventions. Full article

Background/Objectives: Ultrasound imaging is widely employed to assess kidney health and diagnose renal diseases. Accurate segmentation of renal structures in ultrasound images plays a critical role in the diagnosis and treatment of related kidney diseases. However, challenges such as speckle noise and low contrast still hinder precise segmentation. Methods: In this work, we propose an encoder–decoder architecture, named MAT-UNet, which incorporates two distinct attention mechanisms to enhance segmentation accuracy. Specifically, the multi-convolution pixel-wise attention module utilizes the pixel-wise attention to enable the network to focus more effectively on important features at each stage. Furthermore, the triple-branch multi-head self-attention mechanism leverages the different convolution layers to obtain diverse receptive fields, capture global contextual information, compensate for the local receptive field limitations of convolution operations, and boost the segmentation performance. We evaluate the segmentation performance of the proposed MAT-UNet using the Open Kidney US Data Set (OKUD). Results: For renal capsule segmentation, MAT-UNet achieves a Dice Similarity Coefficient (DSC) of 93.83%, a 95% Hausdorff Distance (HD95) of 32.02 mm, an Average Surface Distance (ASD) of 9.80 mm, and an Intersection over Union (IOU) of 88.74%. Additionally, MAT-UNet achieves a DSC of 84.34%, HD95 of 35.79 mm, ASD of 11.17 mm, and IOU of 74.26% for central echo complex segmentation; a DSC of 66.34%, HD95 of 82.54 mm, ASD of 19.52 mm, and IOU of 51.78% for renal medulla segmentation; and a DSC of 58.93%, HD95 of 107.02 mm, ASD of 21.69 mm, and IOU of 43.61% for renal cortex segmentation. Conclusions: The experimental results demonstrate that our proposed MAT-UNet achieves superior performance in multiple renal structure segmentation in ultrasound images. Full article

Background: Autism spectrum disorder (ASD) represents a neurobiological disorder with a high prevalence in the children’s population. The aim of the present review was to assess the current evidence on the use of salivary biomarkers for the early diagnosis of ASD. Materials and methods: A search was conducted on the electronic databases PUBMED/Medline, Google Scholar and Scopus for the retrieval of articles concerning the study topic. Results: A total of 22 studies have been included in the present review considering 21 articles identified from databases and 1 article included using a manual search. A wide range of biomarkers have been proposed for early detection of ASD diseases including nonspecific inflammation markers like interleukin-1β (IL-1β), interleukin-6 (IL-6), interleukin-8 (IL-8), tumor necrosis factor α (TNFα), oxidative stress markers like superoxide dismutase and glutathione peroxidase, hormones such as cortisol and oxytocin, various microRNAs including miR-21, miR-132 and miR-137, and exosomes. The techniques used for biomarke detection may vary according to molecule type and concentration. Conclusions: salivary biomarkers could represent a potential useful tool for the primary detection of several systemic diseases including ASD, taking advantage of non-invasiveness and cost-effective capability compared to other biofluid-based diagnostic techniques. Full article

Background/Objectives: There is a growing need for autism spectrum disorder (ASD) assessment tools that are diagnostically aligned, clinically usable, and accessible across diverse service contexts. The Diagnostic Autism Spectrum Interview—Version 2 (DASI-2) is a freely available, semi-structured clinical interview mapped directly to DSM-5 and ICD-11 criteria. This pilot study aimed to adapt DASI-2 into Hungarian and explore the (1) acceptability of DASI-2 administration, (2) agreement with prior clinical ASD diagnoses, and (3) relationship between DASI-2 observational ratings and ADOS-2 classifications. Methods: Following a multistep translation procedure, DASI-2 was administered to seven children previously assessed for ASD in a multidisciplinary Hungarian clinical setting. The assessment included a parent interview, direct assessment with the child or young person, and completion of the DASI observational record (OR1–OR4). DASI diagnostic outcomes were compared with prior clinical decisions, and OR scores were analyzed in relation to ADOS-2 classifications. Results: All participants completed the DASI-2 interview in full. Agreement with prior clinical diagnosis was found in six of seven cases (κ = 0.70, indicating substantial agreement). When exploring the one non-aligned case, the divergence in diagnostic outcome was due to broader contextual information considered by the initial clinical team which influenced clinical opinion. The five participants diagnosed with ASD showed substantially higher DASI observational scores (mean = 15.26) than the two who were not diagnosed (mean = 1.57), mirroring ADOS-2 severity classifications. Conclusions: These findings support the acceptability and preliminary validity of DASI-2. Its inclusive structured observational record may provide a practical complement to resource-intensive tools such as the ADOS-2; however, further validation in larger and more diverse samples is needed. Full article

Autism spectrum disorder (ASD) is a complex and heterogeneous neurodevelopmental disorder with a significant impact on public health. ASD diagnosis is based on clinical observation and typically occurs around three years of age. The identification of reliable ASD markers could facilitate early diagnosis and help pinpoint therapeutic targets for effective interventions. Long non-coding RNAs (lncRNAs), particularly those derived from blood, have been recently proposed as potential biomarkers in many pathological conditions, including neurological diseases. This manuscript summarizes original studies examining human dysregulated blood-derived lncRNAs as potential ASD biomarkers. LncRNAs are described by grouping them according to the selection strategy used by the authors: (i) lncRNAs involved in biological processes impaired in ASD or in pathological conditions sharing the disrupted signaling pathways of ASD; and (ii) lncRNAs identified through high-throughput analysis. The study highlights key priorities for future research: assessing the ability of lncRNAs to distinguish ASD from other neurological disorders, extending analyses to larger and younger cohorts to validate candidate biomarkers in early life, and integrating multiple data sources to establish validated biomarker networks for clinical application. This review indicates that research on blood-derived lncRNAs in ASD is still in its early stages. Full article

Deficits in social interaction and communication characterize Autism Spectrum Disorder (ASD). Although widely recognized by its symptoms, diagnosing ASD remains challenging due to its wide range of clinical presentations. Methods: In this study, we propose a method to assist in the early diagnosis of autism, which is currently primarily based on clinical assessments. Our approach aims to develop an early differential diagnosis based on electroencephalogram (EEG) signals, seeking to identify patterns associated with ASD. In this study, we used EEG data from 56 participants obtained from the Sheffield dataset, including 28 individuals diagnosed with Autism Spectrum Conditions (ASC) and 28 neurotypical controls, applying numerical techniques to handle missing data. Subsequently, after a detailed analysis of the signals, we applied three different starting approaches: one with the original database and the other two with selection of the most significant attributes using the PSO and evolutionary search methods. In each of these approaches, we applied a series of machine learning models, where relatively high performances for classification were observed. Results: We achieved accuracies of 99.13% ± 0.44 for the dataset with original signals, 99.23% ± 0.38 for the dataset after applying PSO, and 93.91% ± 1.10 for the dataset after the evolutionary search methodology. These results were obtained using classical classifiers, with SVM being the most effective among the first two approaches, while Random Forest with 500 trees proved more efficient in the third approach. Conclusions: Even with all the limitations of the base, the results of the experiments demonstrated promising findings in identifying patterns associated with Autism Spectrum Disorder through the analysis of EEG signals. Finally, we emphasize that this work is the starting point for a larger project with the objective of supporting and democratizing the diagnosis of ASD both in children early and later in adults. Full article

This study examined the distribution characteristics of the standard scores on the Japanese version of the Cognitive Assessment System (CAS)’s Planning, Attention, Simultaneous Processing, and Successive Processing (PASS) scale by clustering the scores using the k-means method, focusing on clinical groups. In Study 1, 140 clinical cases evaluated using the CAS at University A’s educational counseling service were analyzed. The k-means clustering method was applied based on the full-scale standard scores, PASS scale scores, score discrepancies, and subtest scaled scores. Study 2 applied the same clustering method to a clinical group of 91 cases with ADHD, ASD, or comorbid ADHD–ASD, excluding those with intellectual developmental disorders or other disorders. In Study 1, a group with lower full-scale standard scores indicating general intellectual development was identified. Study 2 identified a cluster of cases with ADHD, ASD, or comorbid ADHD–ASD that showed distinct discrepancies among the four standard scores. In addition, there were no significant differences in the diagnoses across clusters. The Japanese version of the CAS provides valid cognitive profile insights in clinical settings, which can aid in planning support interventions beyond clinical diagnosis. Full article

Objective: This study explores the current state of science regarding DSM-5 diagnostic criteria for Autism Spectrum Disorder (ASD) in young children. It examines the effectiveness of existing diagnostic methods and evaluates the potential of transdiagnostic approaches for early intervention. Method: A systematic literature review was conducted using MEDLINE, PsycINFO, and the Psychology and Behavioral Sciences Collection, focusing on peer-reviewed studies published between 2020 and 2023. The search followed PRISMA guidelines, selecting articles investigating ASD diagnosis in toddlers and preschoolers using DSM-5 criteria, behavioral assessments, and emerging diagnostic tools. Results: Findings indicate that DSM-5 provides a structured framework for ASD diagnosis, but it has limitations in early identification. It is necessary to integrate multiple assessment tools. Recent research highlights transdiagnostic models, which move beyond rigid diagnostic categories to capture the complexities of ASD presentation in young children. Conclusion: The literature supports a shift towards a transdiagnostic approach that combines behavioral, biological, and environmental assessments. This study underscores the need for interdisciplinary collaboration to refine ASD diagnostic frameworks to ensure more accurate and timely diagnoses that better serve affected children and their families. Full article

Background: Autism spectrum disorder (ASD) diagnosis traditionally relies on behavioral assessments, which can be subjective and often lead to delayed identification. Recent advances in metabolomics and machine learning offer promising alternatives for more objective and precise diagnostic approaches. Methods: First-morning urine samples were collected from 52 children (32 with ASD and 20 neurotypical controls), aged 5.04 ± 1.87 and 5.50 ± 1.74 years, respectively. Using liquid chromatography-mass spectrometry (LC-MS), 293 metabolites were identified and categorized into 189 endogenous and 104 exogenous metabolites. Various machine learning classifiers (random forest, logistic regression, random tree, and naïve Bayes) were applied to differentiate ASD and control groups through 10-fold cross-validation. Results: The random forest classifier achieved 85% accuracy and an area under the curve (AUC) of 0.9 using all 293 metabolites. Classification based solely on endogenous metabolites yielded 85% accuracy and an AUC of 0.86, whereas using exogenous metabolites alone resulted in lower performance (71% accuracy and an AUC of 0.72). Conclusion: This study demonstrates the potential of urine metabolomic profiling, particularly endogenous metabolites, as a complementary diagnostic tool for ASD. The high classification accuracy highlights the feasibility of developing assistive diagnostic methods based on metabolite profiles, although further research is needed to link these profiles to specific behavioral characteristics and ASD subtypes. Full article

Autism Spectrum Disorder (ASD) has been associated with disruptions in one-carbon metabolism and vitamin D pathways. Nutritional exposures—particularly vitamin D, vitamin B₁₂, and homocysteine—may influence neurodevelopmental outcomes. However, a comprehensive, lifespan-spanning synthesis of these modifiable nutritional biomarkers has not been conducted. This systematic review and stratified meta-analysis critically synthesized data on vitamin D, vitamin B₁₂, and homocysteine to elucidate their relationships with ASD risk and symptomatology. Our central question was: How do levels of vitamin D, vitamin B₁₂, and homocysteine—measured before and after birth—affect the risk, severity, and potential treatment outcomes for ASD? We conducted a PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) compliant systematic review and stratified meta-analysis (2015–2025) of 35 studies (11 randomized controlled trials, 24 observational), examining prenatal, neonatal, and postnatal biomarker levels. Eligibility criteria were defined using the PICOS (Population, Intervention, Comparator, Outcome, and Study Design) framework to ensure scientific rigor and clinical relevance, including studies involving human participants aged 0–18 years with a formal Autism Spectrum Disorder (ASD) diagnosis or prenatal exposures potentially linked to later ASD onset, while excluding animal studies, adult-only ASD populations, and studies lacking ASD cohorts or biomarker data. The search strategy, developed according to PRISMA, and Cochrane best practices, encompassed five major databases (PubMed/MEDLINE, Cochrane Library, Google Scholar, ClinicalTrials.gov, and ProQuest) alongside manual searches of key references, grey literature, and clinical trial registries to ensure comprehensive retrieval of both published and unpublished studies. Study quality was assessed using version 2 of the Cochrane risk-of-bias tool for RCTs (RoB2) and the Newcastle–Ottawa Scale (NOS) for observational studies; certainty of evidence was graded via GRADE (Grading of Recommendations Assessment, Development and Evaluation). Random-effects meta-analyses were stratified by biomarker and study design. Heterogeneity, small-study effects, and publication bias were evaluated using Cochran’s Q, I², Egger’s test, and trim-and-fill. Prenatal vitamin D deficiency was associated with approximately two-fold increased odds of Autism Spectrum Disorder (ASD) in offspring (pooled OR ≈ 2.0; p < 0.05), while excessively elevated maternal B₁₂ concentrations, often co-occurring with folate excess, were similarly linked to increased ASD risk. Meta-analytic comparisons revealed significantly lower circulating vitamin D (SMD ≈ −1.0; p < 0.001) and B₁₂ levels (SMD ≈ −0.7; p < 0.001), alongside elevated homocysteine (SMD ≈ 0.7; p < 0.001), in children with ASD versus neurotypical controls. Early-life vitamin D/B₁₂ insufficiency and elevated homocysteine are important, modifiable correlates of ASD risk and severity. Adequate maternal and child nutritional status could have risk-reducing and symptom-mitigating effects, although causality remains to be confirmed. This evidence supports tailored nutritional interventions as a component of ASD risk reduction and management strategies, within the bounds of overall developmental healthcare. The article processing charges (APC) were supported by “Dunărea de Jos” University of Galati, Romania. No external funding was received for the execution of the research. The review was not prospectively registered in PROSPERO or any other systematic review registry. Full article

Early diagnosis of autism spectrum disorder (ASD) is vital for effective intervention and improves social and behavioral development. The previous literature has shown that the Early Social Responsiveness (ESR) assessment is effective at detecting ASD risk in individuals as early as 13 months of age (“parent study”). However, an item analysis that examines individual item scores has not been conducted to further elucidate the strength of this assessment. In this study, we analyzed an existing dataset (collected in the parent study) containing individual item responses from the ESR assessment of 120 children (n = 61 males and n = 59 females; age range = 15–24 months). Through item analysis, we determined which ESR items or item sets are best at differentiating ASD risk from non-ASD risk. Ease of social engagement (i.e., questions assessing the administrator’s perceived level of effort in engaging the child) was the most effective risk indicator, with the hat and tickle activities being least effective at indicating ASD risk. These results could contribute to optimizing the scale and facilitating its clinical adoption. Full article

Background: Autism spectrum disorder (ASD) is a lifelong neurodevelopmental condition affecting 1.1% of adults. The increasing incidence of ASD has led to pressurised diagnostic services. Objective: We aimed to determine the number needed to harm (NNH) of criteria-informed triage assessment in an adult autism diagnostic service in the UK. Methods: The study was conducted at a specialist adult Autism Service in West Yorkshire, UK, from November 2021 to August 2022. All eligible referrals were accepted, with criteria requiring service users to be over 18 years old and without an intellectual disability. The evaluation consisted of 60 cases. Results: None of the evaluation cases resulted in a clinical diagnosis of ASD, yielding an infinite number needed to harm (NNH), demonstrating that every case benefited from the triage process without significant risk of harm. Conclusions: Triage enables services to gather comprehensive information about individual presentations and clinical needs, facilitating informed decision-making and better service utilisation. The evaluation demonstrates the safety and effectiveness of the triage process, with directions for further research discussed. Full article

Background and Objectives: In light of the growing need to incorporate primary care physicians (PCPs) in the complex care system for autistic patients, this study aims to assess the level of physicians’ knowledge of the autism spectrum in Poland. Materials and Methods: After a literature review, an online survey consisting of 20 items assessing the knowledge of autism etiology, diagnosis criteria, and patient support was developed. Of 250 invitations, 166 physicians filled out the form (a 66.4% response rate). For the statistical analysis, the normal distribution was excluded for all data based on the Shapiro–Wilk test. The U-Mann–Whitney test was performed for two variables to verify the comparison of variables. The threshold of statistical significance was at the level of p = 0.05. Results: Correct responses regarding autism etiology, diagnosis, and support were 37.95%, 42.69%, and 70.05%, respectively. Female physicians presented a higher level of knowledge regarding all categories. The level of general knowledge is statistically higher in pediatricians than in general practitioners, and the knowledge of physicians in training is higher in contrast to specialists. The knowledge of physicians from small towns, as well as physicians with more clinical experience, was low. Conclusions: This study revealed an insufficient level of knowledge relating to autism spectrum disorder among primary care physicians, which is similar to the findings of other studies conducted in different regions of the world. The lack of knowledge is especially evident in the theoretical preparation of physicians regarding ASD. Full article