Download full-text PDF |
Source |
|---|---|
| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6011828 | PMC |
| http://dx.doi.org/10.1002/ajmg.a.38641 | DOI Listing |
Publication Analysis
Top Keywords
Similar Publications
Effect of Probiotics on the Symptomatology of Autism Spectrum Disorder and/or Attention Deficit/Hyperactivity Disorder in Children and Adolescents: Pilot Study.
Res Child Adolesc Psychopathol
January 2025
Nutrition and Mental Health Research Group (NUTRISAM), Universitat Rovira I Virgili (URV), Carretera de Valls, S/N, 43007, Tarragona, Spain.
The aim of this study is to investigate the impact of using probiotics with strains related to dopamine and gamma-aminobutyric acid production on clinical features of autism spectrum disorder (ASD) and/or attention deficit/hyperactivity disorder (ADHD). This randomized, controlled trial involved 38 children with ADHD and 42 children with ASD, aged 5-16 years, who received probiotics (Lactiplantibacillus plantarum and Levilactobacillus brevis 109/cfu/daily) or placebo for 12 weeks. Parent-reported symptoms were assessed using Conners' 3rd-Ed and the Social Responsiveness Scale Test, 2nd-Ed (SRS-2), and children completed the Conners Continuous Performance Test, 3rd-Ed (CPT 3) or Conners Kiddie CPT, 2nd-Ed (K-CPT 2).
View Article and Find Full Text PDFResearch Tools for Eating Behavior in People with Autism Spectrum Disorder (ASD).
J Autism Dev Disord
January 2025
Federal Technological University of Paraná, Postgraduate Program in Science and Technology Teaching (PPGECT), Ponta Grossa, Paraná, Brasil.
Objective: To identify the tools used to assess eating behaviors in individuals with Autism Spectrum Disorder (ASD) and summarize their distribution, citation rates, journal publication, JCR scores, and psychometric properties.
Methods: A literature review was conducted to identify studies on eating behavior in individuals with ASD. The search included various descriptors and combinations of keywords in databases such as Medline/PubMed, Science Direct, Scopus, SciELO, and Web of Science.
Early childhood stress and amygdala structure in children and adolescents with neurodevelopmental disorders.
Brain Struct Funct
January 2025
Applied Psychology, Faculty of Education, University of Western Ontario, 1137 Western Rd, London, ON, N6G 1G7, Canada.
Children and adolescents with neurodevelopmental disorders such as autism spectrum disorder (ASD) and attention deficit hyperactivity disorder (ADHD) may be more susceptible to early life stress compared to their neurotypical peers. This increased susceptibility may be linked to regionally-specific changes in the striatum and amygdala, brain regions sensitive to stress and critical for shaping maladaptive behavioural responses. This study examined early life stress and its impact on striatal and amygdala development in 62 children and adolescents (35 males, mean age = 10.
View Article and Find Full Text PDFUsing Machine Learning to Diagnose Autism Based on Eye Tracking Technology.
Diagnostics (Basel)
December 2024
Computer Science Department, Yarmouk University, Irbid 21163, Jordan.
One of the key challenges in autism is early diagnosis. Early diagnosis leads to early interventions that improve the condition and not worsen autism in the future. Currently, autism diagnoses are based on monitoring by a doctor or specialist after the child reaches a certain age exceeding three years after the parents observe the child's abnormal behavior.
View Article and Find Full Text PDFDeveloping a simplified measure to predict the risk of autism spectrum disorders: Abbreviating the M-CHAT-R using a machine learning approach in China.
Psychiatry Res
January 2025
Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou 510630, China. Electronic address:
Background: Early screening for autism spectrum disorder (ASD) is crucial, yet current assessment tools in Chinese primary child care are limited in efficacy.
Objective: This study aims to employ machine learning algorithms to identify key indicators from the 20-item Modified Checklist for Autism in Toddlers, revised (M-CHAT-R) combining with ASD-related sociodemographic and environmental factors, to distinguish ASD from typically developing children.
Methods: Data from our prior validation study of the Chinese M-CHAT-R (August 2016-March 2017, n = 6,049 toddlers) were reviewed.
Want AI Summaries of new PubMed Abstracts delivered to your In-box?
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!