Pubmed du 22/12/20

mardi 22 décembre 2020

1. Al-Mazeedi SH, Al-Ayadhi LY. Plasma levels of Alpha and Gamma Synuclein in Autism Spectrum Disorder : A Severity Indicator. Medical principles and practice : international journal of the Kuwait University, Health Science Centre. 2020.

OBJECTIVES : To correlate plasma levels of α-synuclein and γ-synuclein in ASD children in order to elucidate their possible contribution to the pathogenesis of ASD and to study their association with the severity of the disorder. SUBJECTS AND METHODS : plasma level of Alpha and Gamma Synuclein in 38 male children diagnosed with autism spectrum disorder (ASD) compared with 40 healthy age-matched male children by using enzyme linked immunosorbent assay (ELISA). RESULTS : Our results showed that plasma levels of α-synuclein (18.02 ± 5.3 pg/mL) were significantly higher in ASD children compared to control children (14.39 ± 2 pg/mL) and plasma levels of γ-synuclein were decreased in ASD group (23.74 ± 7.7 pg/mL) compared to control group (32.40 ± 6.8 pg/mL) (p<0.0001). Our data also indicates that plasma levels of both α-synuclein and γ-synuclein are significantly associated with the severity of ASD. CONCLUSIONS : Our study showed that alteration in synaptic proteins Alpha and Gamma Synuclein might be associated with ASD pathogenesis and might be an indicator of the severity of the disorder.

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2. Andrews DS, Lee JK, Harvey DJ, Waizbard-Bartov E, Solomon M, Rogers SJ, Nordahl CW, Amaral DG. A Longitudinal Study of White Matter Development in Relation to Changes in Autism Severity Across Early Childhood. Biol Psychiatry. 2020.

BACKGROUND : Cross-sectional diffusion-weighted magnetic resonance imaging studies suggest that young autistic children have alterations in white matter structure that differ from older autistic individuals. However, it is unclear whether these differences result from atypical neurodevelopment or sampling differences between young and older cohorts. Furthermore, the relationship between altered white matter development and longitudinal changes in autism symptoms is unknown. METHODS : Using longitudinal diffusion-weighted magnetic resonance imaging acquired over 2 to 3 time points between the ages of approximately 2.5 to 7.0 years in 125 autistic children and 69 typically developing control participants, we directly tested the hypothesis that autistic individuals have atypical white matter development across childhood. Additionally, we sought to determine whether changes in white matter diffusion parameters were associated with longitudinal changes in autism severity. RESULTS : Autistic children were found to have slower development of fractional anisotropy in the cingulum bundle, superior longitudinal fasciculus, internal capsule, and splenium of the corpus callosum. Furthermore, in the sagittal stratum, autistic individuals who increased in autism severity over time had a slower developmental trajectory of fractional anisotropy compared with individuals whose autism decreased in severity. In the uncinate fasciculus, autistic individuals who decreased in autism symptom severity also had greater increases in fractional anisotropy with age. CONCLUSIONS : These longitudinal findings indicate that previously reported differences in diffusion-weighted magnetic resonance imaging measures between younger and older autism cohorts are attributable to an atypical developmental trajectory of white matter. Differences in white matter development between individuals whose autism severity increased, remained stable, or decreased suggest that these functional differences are associated with fiber development in the autistic brain.

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3. El Hayek L, Tuncay IO, Nijem N, Russell J, Ludwig S, Kaur K, Li X, Anderton P, Tang M, Gerard A, Heinze A, Zacher P, Alsaif HS, Rad A, Hassanpour K, Abbaszadegan MR, Washington C, DuPont BR, Louie RJ, Couse M, Faden M, Rogers RC, Abou Jamra R, Elias ER, Maroofian R, Houlden H, Lehman A, Beutler B, Chahrour MH. KDM5A mutations identified in autism spectrum disorder using forward genetics. eLife. 2020 ; 9.

Autism spectrum disorder (ASD) is a constellation of neurodevelopmental disorders with high phenotypic and genetic heterogeneity, complicating the discovery of causative genes. Through a forward genetics approach selecting for defective vocalization in mice, we identified Kdm5a as a candidate ASD gene. To validate our discovery, we generated a Kdm5a knockout mouse model (Kdm5a(-/-)) and confirmed that inactivating Kdm5a disrupts vocalization. In addition, Kdm5a(-/-) mice displayed repetitive behaviors, sociability deficits, cognitive dysfunction, and abnormal dendritic morphogenesis. Loss of KDM5A also resulted in dysregulation of the hippocampal transcriptome. To determine if KDM5A mutations cause ASD in humans, we screened whole exome sequencing and microarray data from a clinical cohort. We identified pathogenic KDM5A variants in nine patients with ASD and lack of speech. Our findings illustrate the power and efficacy of forward genetics in identifying ASD genes and highlight the importance of KDM5A in normal brain development and function.

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4. Grossi E, Valbusa G, Buscema M. Detection of an Autism EEG Signature From Only Two EEG Channels Through Features Extraction and Advanced Machine Learning Analysis. Clinical EEG and neuroscience. 2020 : 1550059420982424.

BACKGROUND AND OBJECTIVE : In 2 previous studies, we have shown the ability of special machine learning systems applied to standard EEG data in distinguishing children with autism spectrum disorder (ASD) from non-ASD children with an overall accuracy rate of 100% and 98.4%, respectively. Since the equipment routinely available in neonatology units employ few derivations, we were curious to check if just 2 derivations were enough to allow good performance in the same cases of the above-mentioned studies. METHODS : A continuous segment of artifact-free EEG data lasting 1 minute in ASCCI format from C3 and C4 EEG channels present in 2 previous studies, was used for features extraction and subsequent analyses with advanced machine learning systems. A features extraction software package (Python tsfresh) applied to time-series raw data derived 1588 quantitative features. A special hybrid system called TWIST (Training with Input Selection and Testing), coupling an evolutionary algorithm named Gen-D and a backpropagation neural network, was used to subdivide the data set into training and testing sets as well as to select features yielding the maximum amount of information after a first variable selection performed with linear correlation index threshold. RESULTS : After this intelligent preprocessing, 12 features were extracted from C3-C4 time-series of study 1 and 36 C3-C4 time-series of study 2 representing the EEG signature. Acting on these features the overall accuracy predictive capability of the best artificial neural network acting as a classifier in deciphering autistic cases from typicals (study 1) and other neuropsychiatric disorders (study 2) resulted in 100 % for study 1 and 94.95 % for study 2. CONCLUSIONS : The results of this study suggest that also a minor part of EEG contains precious information useful to detect autism if treated with advanced computational algorithms. This could allow in the future to use standard EEG from newborns to check if the ASD signature is already present at birth.

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5. Joshi G, Wilens T, Firmin ES, Hoskova B, Biederman J. Pharmacotherapy of attention deficit/hyperactivity disorder in individuals with autism spectrum disorder : A systematic review of the literature. Journal of psychopharmacology (Oxford, England). 2020 : 269881120972336.

AIM : To assess the empirical evidence for the treatment of attention deficit/hyperactivity disorder (ADHD) in populations with autism spectrum disorder (ASD). METHODS : A systemic PubMed, PsychINFO, Embase, and Medline database search of peer-reviewed literature was conducted. Included in the review were controlled trials published in English with sample sizes ⩾10 participants examining the safety and efficacy of anti-ADHD medication in ASD populations. Data was extracted on relevant variables of study design, demographics, associated psychopathology, medication dose, efficacy, and tolerability. RESULTS : Nine controlled trials met the inclusion and exclusion criteria : five with methylphenidate, three with atomoxetine, and one with guanfacine. Sample sizes ranged from 10 to 128 with 430 children participating across all the trials. In all the trials, treatment response was significantly superior to placebo. However, almost all trials assessed only hyperactivity, and most included only participants with intellectual disability with high levels of irritability. None of the trials distinguished agitation from hyperactivity. The response on hyperactivity for methylphenidate and atomoxetine was less than that observed in the neurotypical population ; however, the response for guanfacine surpassed results observed in neurotypical populations. Treatment-emergent mood lability (i.e. mood dysregulation and mood-related adverse events) was frequently associated with methylphenidate and guanfacine treatments. Worse treatment outcomes were associated with individuals with lower intellectual capability compared with those with higher IQs. CONCLUSIONS : here is a scarcity of controlled trials examining ADHD treatments in ASD populations, particularly in intellectually capable individuals with ASD and in adults. Response to ADHD medications in ASD were adversely moderated by the presence of intellectual disability and mood lability.

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6. Pierson LM, Thompson JL, Ganz JB, Wattanawongwan S, Haas AN, Yllades V. Coaching Parents of Children With Developmental Disabilities to Implement a Modified Dialogic Reading Intervention Using Low Technology via Telepractice. American journal of speech-language pathology. 2020 : 1-18.

Purpose Storybook reading provides a naturalistic context to promote bonding and increase oral communication between the reader and child. This study investigated the impact of modified dialogic reading procedures, which included a prompting component on the language skills of children with autism spectrum disorder and Down syndrome in the children’s homes. Method A multiple-probe-across-participants design was used to investigate the efficacy of the intervention for this population. Parent training and coaching were provided via telepractice. Maintenance and generalization sessions were also conducted. Results A functional relation was observed between parent implementation and telepractice coaching. Conclusion While the child responses to comprehension questions did not change, changes in the parent implementation of modified dialogic reading procedures in response to coaching via telepractice were noted in this study. Supplemental Material https://doi.org/10.23641/asha.13382831.

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7. Sapiets SJ, Totsika V, Hastings RP. Factors influencing access to early intervention for families of children with developmental disabilities : A narrative review. J Appl Res Intellect Disabil. 2020.

BACKGROUND : Early intervention (EI) can improve a range of outcomes for families of children with developmental disabilities. However, research indicates the level of access does not always match the level of need. To address disparities, it is essential to identify factors influencing access. METHOD : We propose a framework where access to EI is conceptualised as a process that includes three main phases. A narrative review examined potential barriers, facilitators and modifiers of access for each phase. RESULTS : The process of access to EI includes the following : 1) recognition of need, 2) identification or diagnosis and 3) EI provision or receipt. Several factors affecting access to EI for each phase were identified, related to the family, services, the intersection between family and services, and the context. CONCLUSION : A broad range of factors appear to influence the process of access to EI for this population. Our framework can be used in future research investigating access. Broad implications for policy, practice and future research to improve access to EI are discussed.

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8. Spivack E. Medical comorbidities associated with autism spectrum disorder and their impact on dental care. General dentistry. 2020 ; 69(1) : 62-8.

Autism spectrum disorder is a neurodevelopmental disorder affecting communication, behavior, and socialization in a large number of children and adults. In addition to the classically considered features of autism, individuals with this disorder also often present with multiple significant medical comorbidities that affect dental treatment. This article discusses the most common of these neurologic, psychiatric, and gastrointestinal issues and explores their relevance to dental care. It is incumbent on dentists and members of the dental team to be familiar with the features and comorbidities of autism spectrum disorder and effectively use this knowledge to provide care for patients with autism.

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