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      Differentiating Females with Rett Syndrome and Those with Multi-Comorbid Autism Spectrum Disorder Using Physiological Biomarkers: A Novel Approach

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          Abstract

          This study explored the use of wearable sensor technology to investigate autonomic function in children with autism spectrum disorder (ASD) and Rett syndrome (RTT). We aimed to identify autonomic biomarkers that can correctly differentiate females with ASD and Rett Syndrome using an innovative methodology that applies machine learning approaches. Our findings suggest that we can predict (95%) the status of ASD/Rett. We conclude that physiological biomarkers may be able to assist in the differentiation between patients with RTT and ASD and could allow the development of timely therapeutic strategies.

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          The WEKA data mining software

          L Mark Hall, Eibe Frank, Geoffrey Holmes … (2009)
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            Rett syndrome: revised diagnostic criteria and nomenclature.

            Jeffrey Neul, Walter Kaufmann, Daniel G. Glaze … (2010)
            Rett syndrome (RTT) is a severe neurodevelopmental disease that affects approximately 1 in 10,000 live female births and is often caused by mutations in Methyl-CpG-binding protein 2 (MECP2). Despite distinct clinical features, the accumulation of clinical and molecular information in recent years has generated considerable confusion regarding the diagnosis of RTT. The purpose of this work was to revise and clarify 2002 consensus criteria for the diagnosis of RTT in anticipation of treatment trials. RettSearch members, representing the majority of the international clinical RTT specialists, participated in an iterative process to come to a consensus on a revised and simplified clinical diagnostic criteria for RTT. The clinical criteria required for the diagnosis of classic and atypical RTT were clarified and simplified. Guidelines for the diagnosis and molecular evaluation of specific variant forms of RTT were developed. These revised criteria provide clarity regarding the key features required for the diagnosis of RTT and reinforce the concept that RTT is a clinical diagnosis based on distinct clinical criteria, independent of molecular findings. We recommend that these criteria and guidelines be utilized in any proposed clinical research.
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              Next-Generation Machine Learning for Biological Networks

              James Costello, Katherine Collins, Rani K. Powers … (2018)
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                Author and article information

                Journal
                Journal ID (nlm-ta): J Clin Med
                Journal ID (iso-abbrev): J Clin Med
                Journal ID (publisher-id): jcm
                Title: Journal of Clinical Medicine
                Publisher: MDPI
                ISSN (Electronic): 2077-0383
                Publication date (Electronic): 02 September 2020
                Publication date Collection: September 2020
                Volume: 9
                Issue: 9
                Electronic Location Identifier: 2842
                Affiliations
                [1 ]Department of Child and Adolescent Psychiatry, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK; niakovid@ 123456gmail.com (N.I.); jatinder.singh@ 123456kcl.ac.uk (J.S.); federico.fiori@ 123456kcl.ac.uk (F.F.)
                [2 ]Child and Adolescent Neuropsychiatry Unit, Infermi Hospital, 47923 Rimini, Italy; evamaria.lanzarini@ 123456gmail.com
                [3 ]Centre for Personalised Medicine in Rett Syndrome (CPMRS) & Centre for Interventional Paediatric Psychopharmacology and Rare Diseases (CIPPRD), South London and Maudsley NHS Foundation Trust, London SE5 8AZ, UK
                [4 ]HealthTracker Limited, 76–78 High Street Medical Dental, High Street, Gillingham, Kent ME7 1AY, UK
                Author notes
                Article
                Publisher ID: jcm-09-02842
                DOI: 10.3390/jcm9092842
                PMC ID: 7563706
                PubMed ID: 32887357
                SO-VID: 2b59b17c-677b-423a-a5b7-0387dcc94651
                Copyright © © 2020 by the authors.
                License:

                Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

                History
                Date received : 07 August 2020
                Date accepted : 30 August 2020
                Categories
                Subject: Article

                Keywords: rett syndrome,autism spectrum disorder,children,machine learning,physiological biomarkers
                Data availability:
                Keywords: rett syndrome, autism spectrum disorder, children, machine learning, physiological biomarkers

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