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  1. Article ; Online: Epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome in a European child with mutations

    Antigone Papavasiliou / Katerina Foska / John Ioannou / Mato Nagel

    SAGE Open Medical Case Reports, Vol

    A case report

    2017  Volume 5

    Abstract: Background: Epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome is a multi-organ disorder that links to autosomal recessive mutations in the KCNJ10 gene, which encodes for the Kir4.1 potassium channel. It is mostly described in consanguineous, ...

    Abstract Background: Epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome is a multi-organ disorder that links to autosomal recessive mutations in the KCNJ10 gene, which encodes for the Kir4.1 potassium channel. It is mostly described in consanguineous, non-European families. Case Report: A European male of non-consanguineous birth, with early-onset, static ataxic motor disorder, intellectual disability and epilepsy, imitating cerebral palsy, presented with additional findings of renal tubulopathy, sensorineural deafness and normal neuroimaging leading to the diagnosis of epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome. The patient was heterozygous for two KCNJ10 mutations: a missense mutation (p.R65C) that is already published and a not yet published duplication (p.F119GfsX25) that creates a premature truncation of the protein. Both mutations are likely damaging. Parental testing has not been performed, and therefore, we do not know for certain whether the mutations are on different alleles. This young man presents some clinical and laboratory features that differ from previously reported patients with epilepsy, ataxia, sensorineural deafness, tubulopathy syndrome. Conclusion: The necessity of accurate diagnosis through genetic testing in patients with static motor disorders resembling cerebral palsy phenotypes, atypical clinical features and noncontributory neuroimaging is emphasized.
    Keywords Medicine (General) ; R5-920
    Subject code 616
    Language English
    Publishing date 2017-07-01T00:00:00Z
    Publisher SAGE Publishing
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  2. Article ; Online: Towards stratified treatment of JIA

    Stephanie J.W. Shoop-Worrall / Saskia Lawson-Tovey / Lucy R. Wedderburn / Kimme L. Hyrich / Nophar Geifman / Aline Kimonyo / Alyssia McNeece / Andrew Dick / Andrew Morris / Annie Yarwood / Athimalaipet Ramanan / Bethany R. Jebson / Chris Wallace / Daniela Dastros-Pitei / Damian Tarasek / Elizabeth Ralph / Emil Carlsson / Emily Robinson / Emma Sumner /
    Fatema Merali / Fatjon Dekaj / Helen Neale / Hussein Al-Mossawi / Jacqui Roberts / Jenna F. Gritzfeld / Joanna Fairlie / John Bowes / John Ioannou / Melissa Kartawinata / Melissa Tordoff / Michael Barnes / Michael W. Beresford / Michael Stadler / Paul Martin / Rami Kallala / Sandra Ng / Samantha Smith / Sarah Clarke / Soumya Raychaudhuri / Stephen Eyre / Sumanta Mukherjee / Teresa Duerr / Thierry Sornasse / Vasiliki Alexiou / Victoria J. Burton / Wei-Yu Lin / Wendy Thomson / Zoe Wanstall

    EBioMedicine, Vol 100, Iss , Pp 104946- (2024)

    machine learning identifies subtypes in response to methotrexate from four UK cohortsResearch in context

    2024  

    Abstract: Summary: Background: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To ... ...

    Abstract Summary: Background: Methotrexate (MTX) is the gold-standard first-line disease-modifying anti-rheumatic drug for juvenile idiopathic arthritis (JIA), despite only being either effective or tolerated in half of children and young people (CYP). To facilitate stratified treatment of early JIA, novel methods in machine learning were used to i) identify clusters with distinct disease patterns following MTX initiation; ii) predict cluster membership; and iii) compare clusters to existing treatment response measures. Methods: Discovery and verification cohorts included CYP who first initiated MTX before January 2018 in one of four UK multicentre prospective cohorts of JIA within the CLUSTER consortium. JADAS components (active joint count, physician (PGA) and parental (PGE) global assessments, ESR) were recorded at MTX start and over the following year.Clusters of MTX ‘response’ were uncovered using multivariate group-based trajectory modelling separately in discovery and verification cohorts. Clusters were compared descriptively to ACR Pedi 30/90 scores, and multivariate logistic regression models predicted cluster-group assignment. Findings: The discovery cohorts included 657 CYP and verification cohorts 1241 CYP. Six clusters were identified: Fast improvers (11%), Slow Improvers (16%), Improve-Relapse (7%), Persistent Disease (44%), Persistent PGA (8%) and Persistent PGE (13%), the latter two characterised by improvement in all features except one. Factors associated with clusters included ethnicity, ILAR category, age, PGE, and ESR scores at MTX start, with predictive model area under the curve values of 0.65–0.71. Singular ACR Pedi 30/90 scores at 6 and 12 months could not capture speeds of improvement, relapsing courses or diverging disease patterns. Interpretation: Six distinct patterns following initiation of MTX have been identified using methods in artificial intelligence. These clusters demonstrate the limitations in traditional yes/no treatment response assessment (e.g., ACRPedi30) and can form the basis ...
    Keywords Juvenile idiopathic arthritis ; Machine learning ; Treatment outcome ; Epidemiology ; Methotrexate ; Medicine ; R ; Medicine (General) ; R5-920
    Subject code 310
    Language English
    Publishing date 2024-02-01T00:00:00Z
    Publisher Elsevier
    Document type Article ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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