A concerning trend has emerged in the diagnosis and treatment of autism spectrum disorder (ASD) that has a negative impact on care. Quite often, a clinician's diagnosis of ASD using DSM-5 criteria is no longer sufficient for individuals with ASD to access services. Insurance companies, school districts, and developmental disability agencies commonly require an Autism Diagnostic Observation Schedule (ADOS) to be eligible for services.
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http://dx.doi.org/10.1016/j.jaac.2019.07.933 | DOI Listing |
Front Psychiatry
January 2025
Laboratoire Lorrain de Psychologie et Neurosciences de la Dynamique des Comportements, Université de Lorraine, Nancy, Lorraine, France.
Background: This study examined the profiles of adaptive behavior development in adults with autism spectrum disorder (ASD) and severe intellectual disability (ID), and the relationships between the levels of the different domains and subdomains of adaptive development and the intensity of autistic symptomatology.
Participants: This study involved 71 adults (44 men and 27 women with average ages of 39 years 7 months and 36 years 2 months, respectively) living in medico-social institutions and having a level of adaptive development corresponding to age below 3 years 4 months and a level of cognitive development corresponding to ages between 12 and 24 months.
Methods: ASD was diagnosed using Pervasive Development Disorder-Mental Retardation Scale (PDD-MRS) and Childhood Autistic Rating Scale (CARS), ID and its severity were determined based on the Diagnostic Statistical Manual-5 (DSM-5) criteria, and the very low cognitive developmental level was assessed using the Socio-emotional Cognitive Evaluation Battery (Adrien, Pearson-ECPA, 2007), adapted for adults (SCEB-A).
Pharmgenomics Pers Med
January 2025
Department of Clinical Medicine, North Sichuan Medical College, Nanchong, Sichuan, 637000, People's Republic of China.
Background: Autism spectrum disorder (ASD) is a complex neurodevelopmental condition marked by diverse symptoms affecting social interaction, communication, and behavior. This research aims to explore bacterial lipopolysaccharide (LPS)- and immune-related (BLI) molecular subgroups in ASD to enhance understanding of the disorder.
Methods: We analyzed 89 control samples and 157 ASD samples from the GEO database, identifying BLI signatures using least absolute shrinkage and selection operator regression (LASSO) and logistic regression machine learning algorithms.
Proc Natl Acad Sci U S A
January 2025
Shenzhen Key Laboratory of Biomolecular Assembling and Regulation, Department of Neuroscience, School of Life Sciences, Southern University of Science and Technology, Shenzhen 518055, China.
Ankyrin Repeat Domain-containing Protein 11 () is a causative gene for KBG syndrome, a significant risk factor for Cornelia de Lange syndrome (CdLS), and a highly confident autism spectrum disorder gene. Mutations of lead to developmental abnormalities in multiple organs/tissues including the brain, craniofacial and skeletal bones, and tooth structures with unknown mechanism(s). Here, we find that ANKRD11, via a short peptide fragment in its N-terminal region, binds to the cohesin complex with a high affinity, implicating why mutation can cause CdLS.
View Article and Find Full Text PDFPediatr Rep
January 2025
ASL Salerno, 84124, Salerno, Italy.
Motor skills in early and middle childhood are essential for physical play, social interactions, and academic development. Children with autism spectrum disorder (ASD) often exhibit atypical sensory responses, which can impact self-care and other developmental areas. This study explores the impact of sensory and motor rehabilitation using a Motor Sensory Room to stimulate motor development in children with ASD.
View Article and Find Full Text PDFJAMIA Open
February 2025
Institute for Informatics, Data Science and Biostatistics, Washington University, Saint Louis, MO 63110, United States.
Objective: Dimensionality reduction techniques aim to enhance the performance of machine learning (ML) models by reducing noise and mitigating overfitting. We sought to compare the effect of different dimensionality reduction methods for comorbidity features extracted from electronic health records (EHRs) on the performance of ML models for predicting the development of various sub-phenotypes in children with Neurofibromatosis type 1 (NF1).
Materials And Methods: EHR-derived data from pediatric subjects with a confirmed clinical diagnosis of NF1 were used to create 10 unique comorbidities code-derived feature sets by incorporating dimensionality reduction techniques using raw International Classification of Diseases codes, Clinical Classifications Software Refined, and Phecode mapping schemes.
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