Systematic Review: United States Workforce for Autism-Related Child Healthcare Services

A diversity of United States health professional disciplines provide services for children with autism spectrum disorder (ASD). We conducted a systematic review examining the availability, distribution and competencies of the U.S. workforce for autism-related child healthcare services, and assess studies ’ strength of evidence.
Source: Journal of the American Academy of Child and Adolescent Psychiatry - Category: Psychiatry Authors: Tags: Review Source Type: research

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Publication date: December 2020Source: Research in Developmental Disabilities, Volume 107Author(s): Roshini Chandroo, Iva Strnadová, Therese M. Cumming
Source: Research in Developmental Disabilities - Category: Disability Source Type: research
Publication date: November 2020Source: Research in Autism Spectrum Disorders, Volume 79Author(s): Ju Hee Park, Young-Shin Kim, Yun-Joo Koh, Bennett L. Leventhal
Source: Research in Autism Spectrum Disorders - Category: Psychiatry Source Type: research
Authors: Bellomo TR, Prasad S, Munzer T, Laventhal N Abstract In the unprecedented disruption and social isolation of the COVID-19 pandemic, families around the world are faced with questions of how their children can thrive in these conditions. On top of the ubiquitous challenges for all children, this public health crisis imparts unique difficulties for children with special health needs. We identify children with Autism Spectrum Disorder (ASD) as being particularly vulnerable to negative impacts of the COVID-19 pandemic. In this paper, we examine why children with ASD are uniquely vulnerable, recommend strategie...
Source: Journal of Pediatric Rehabilitation Medicine - Category: Rehabilitation Tags: J Pediatr Rehabil Med Source Type: research
2-CHANNEL CONVOLUTIONAL 3D DEEP NEURAL NETWORK (2CC3D) FOR FMRI ANALYSIS: ASD CLASSIFICATION AND FEATURE LEARNING. Proc IEEE Int Symp Biomed Imaging. 2018 Apr;2018:1252-1255 Authors: Li X, Dvornek NC, Papademetris X, Zhuang J, Staib LH, Ventola P, Duncan JS Abstract In this paper, we propose a new whole brain fMRI-analysis scheme to identify autism spectrum disorder (ASD) and explore biological markers in ASD classification. To utilize both spatial and temporal information in fMRI, our method investigates the potential benefits of using a sliding window over time to measure temporal statistics (mean an...
Source: Proceedings - International Symposium on Biomedical Imaging - Category: Radiology Tags: Proc IEEE Int Symp Biomed Imaging Source Type: research
Authors: Li X, Dvornek NC, Zhuang J, Ventola P, Duncan JS Abstract Autism spectrum disorder (ASD) is a complex neurodevelopmental disorder. Finding the biomarkers associated with ASD is extremely helpful to understand the underlying roots of the disorder and can lead to earlier diagnosis and more targeted treatment. Although Deep Neural Networks (DNNs) have been applied in functional magnetic resonance imaging (fMRI) to identify ASD, understanding the data driven computational decision making procedure has not been previously explored. Therefore, in this work, we address the problem of interpreting reliable biomark...
Source: MICCAI International Conference on Medical Image Computing and Computer-Assisted Intervention - Category: Radiology Tags: Med Image Comput Comput Assist Interv Source Type: research
Authors: Li X, Dvornek NC, Zhou Y, Zhuang J, Ventola P, Duncan JS Abstract Discovering imaging biomarkers for autism spectrum disorder (ASD) is critical to help explain ASD and predict or monitor treatment outcomes. Toward this end, deep learning classifiers have recently been used for identifying ASD from functional magnetic resonance imaging (fMRI) with higher accuracy than traditional learning strategies. However, a key challenge with deep learning models is understanding just what image features the network is using, which can in turn be used to define the biomarkers. Current methods extract biomarkers, i.e., i...
Source: Inf Process Med Imaging - Category: Radiology Tags: Inf Process Med Imaging Source Type: research
Contributors : Zhou Dan ; Xuhao Mao ; Qisha Liu ; Zhi LiuSeries Type : OtherOrganism : feces metagenomeHere we report 16S rRNA data in gut microbiota of autism spectrum disorders compared with healthy volunteers. A total of 1322 operational taxonomic units (OTUs) were identified in the sequence data. The Bacteroidetes and Firmicutes were both dominated phylum in ausitic subjects and healthy controls. Phylum level analysis showed a clear alteration of the bacterial gut community in ASD characterized by a higher Firmicutes (P
Source: GEO: Gene Expression Omnibus - Category: Genetics & Stem Cells Tags: Other feces metagenome Source Type: research
Contributors : Xingyin Liu ; Xuhua Mao ; Qisha Liu ; Zhou DanSeries Type : OtherOrganism : feces metagenomeHere we report metagenomic sequencing data in gut microbiota of autism spectrum disorders (ASD) compared with healthy volunteers (30 for ASD children and 30 for healthy controls, respectively). The genes changed in autistic subjects involved 1,312,364 analytes that compare to 1,335,835 analytes in healthy controls. The number of taxa in autistic subjects were significantly increased as compared to the healthy controls based on the phylum and genus level (P = 0.001). However, the number of species were significantly d...
Source: GEO: Gene Expression Omnibus - Category: Genetics & Stem Cells Tags: Other feces metagenome Source Type: research
Series Type : OtherOrganism : feces metagenomeThis SuperSeries is composed of the SubSeries listed below.
Source: GEO: Gene Expression Omnibus - Category: Genetics & Stem Cells Tags: Other feces metagenome Source Type: research
Publication date: Available online 25 September 2020Source: Life SciencesAuthor(s): Nayeon Goo, Ho Jung Bae, Geontae Park, Jaehoon Kim, Yongwoo Jeong, Mudan Cai, Kyungnam Cho, Seo Yun Jung, Dong-Hyun Kim, Jong Hoon Ryu
Source: Life Sciences - Category: Biology Source Type: research
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