Title |
An algorithm to predict phenotypic severity in mucopolysaccharidosis type I in the first month of life
|
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Published in |
Orphanet Journal of Rare Diseases, July 2013
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DOI | 10.1186/1750-1172-8-99 |
Pubmed ID | |
Authors |
Sandra DK Kingma, Eveline J Langereis, Clasine M de Klerk, Lida Zoetekouw, Tom Wagemans, Lodewijk IJlst, Ronald JA Wanders, Frits A Wijburg, Naomi van Vlies |
Abstract |
Mucopolysaccharidosis type I (MPS I) is a progressive multisystem lysosomal storage disease caused by deficiency of the enzyme alpha-L-iduronidase (IDUA). Patients present with a continuous spectrum of disease severity, and the most severely affected patients (Hurler phenotype; MPS I-H) develop progressive cognitive impairment. The treatment of choice for MPS I-H patients is haematopoietic stem cell transplantation, while patients with the more attenuated phenotypes benefit from enzyme replacement therapy.The potential of newborn screening (NBS) for MPS I is currently studied in many countries. NBS for MPS I, however, necessitates early assessment of the phenotype, in order to decide on the appropriate treatment. In this study, we developed an algorithm to predict phenotypic severity in newborn MPS I patients. |
X Demographics
Geographical breakdown
Country | Count | As % |
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Spain | 1 | 50% |
Unknown | 1 | 50% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 2 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Netherlands | 1 | 2% |
Unknown | 63 | 98% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 19% |
Researcher | 12 | 19% |
Student > Master | 9 | 14% |
Student > Doctoral Student | 5 | 8% |
Student > Bachelor | 4 | 6% |
Other | 8 | 13% |
Unknown | 14 | 22% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 23 | 36% |
Biochemistry, Genetics and Molecular Biology | 11 | 17% |
Agricultural and Biological Sciences | 4 | 6% |
Psychology | 2 | 3% |
Nursing and Health Professions | 1 | 2% |
Other | 6 | 9% |
Unknown | 17 | 27% |