دورية أكاديمية

Analysis of Friedreich's ataxia patient clinical data reveals importance of accurate GAA repeat determination in disease prognosis and gender differences in cardiac measures

التفاصيل البيبلوغرافية
العنوان: Analysis of Friedreich's ataxia patient clinical data reveals importance of accurate GAA repeat determination in disease prognosis and gender differences in cardiac measures
المؤلفون: Mohammadmersad Ghorbani, Françoise Pousset, Allan Tucker, Stephen Swift, Paola Giunti, Michael Parkinson, David Gilbert, XiaoHui Liu, Annette Payne
المصدر: Informatics in Medicine Unlocked, Vol 17, Iss , Pp - (2019)
بيانات النشر: Elsevier, 2019.
سنة النشر: 2019
المجموعة: LCC:Computer applications to medicine. Medical informatics
مصطلحات موضوعية: Computer applications to medicine. Medical informatics, R858-859.7
الوصف: Friedreich's ataxia (FRDA) is a rare autosomal recessive inherited neurodegenerative disease which is the result of a triplet repeat expansion in the intronic region of the frataxin FXN gene resulting in depleted frataxin protein expression. Disease onset is usually in childhood and causes progressive damage to the nervous system resulting in progressive disability. This work uses computer aided classification techniques to identify which measures of the disease progression, including accurate determination of the shortest allele repeat length, are the most informative when trying to predict likely disease progression and prognosis. Further we investigate the possibility of a gender difference in the progression of the disease. Our results highlight the importance of accurate determination GAA repeat length in any clinical predictions showing that the number of repeats is the best prognostic tool in FRDA and is strongly linked to the age at onset disease. Further that there are possible gender dependent differences in cardiac measurements recorded from patients of similar age of onset and GAA repeat length. Keywords: Friedrich's ataxia, Nucleotide repeat, Computerised classification, Neurology, Clinical prognosis, Intelligent data analysis
نوع الوثيقة: article
وصف الملف: electronic resource
اللغة: English
تدمد: 2352-9148
العلاقة: http://www.sciencedirect.com/science/article/pii/S2352914819301741Test; https://doaj.org/toc/2352-9148Test
DOI: 10.1016/j.imu.2019.100266
الوصول الحر: https://doaj.org/article/2d307d3054ab4bc8a9bdc31a4e6c8624Test
رقم الانضمام: edsdoj.2d307d3054ab4bc8a9bdc31a4e6c8624
قاعدة البيانات: Directory of Open Access Journals
الوصف
تدمد:23529148
DOI:10.1016/j.imu.2019.100266