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Mathematical model for alopecia areata

Overview of attention for article published in Journal of Theoretical Biology, June 2015
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Article details
Title
Mathematical model for alopecia areata
Published in
Journal of Theoretical Biology, June 2015
DOI 10.1016/j.jtbi.2015.05.033
Pubmed ID
Authors
Abstract

Alopecia areata (AA) is an autoimmune disease, and its clinical phenotype is characterized by the formation of distinct hairless patterns on the scalp or other parts of the body. In most cases hair falls out in round patches. A well-established hypothesis for the pathogenesis of AA states that collapse of hair follicle immune privilege is one of the essential elements in disease development. To investigate the dynamics of alopecia areata, we develop a mathematical model that incorporates immune system components and hair follicle immune privilege agents whose involvement in AA has been confirmed in studies and experiments. We perform parameter sensitivity analysis in order to determine which inputs have the greatest effect on outcome variables. Our findings suggest that, among all processes reflected in the model, immune privilege guardians and the pro-inflammatory cytokine interferon-γ govern disease dynamics. These results agree with the immune privilege collapse hypothesis for the development of AA.

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Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
Unknown 19 100%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 4 21%
Student > Master 3 16%
Student > Postgraduate 3 16%
Professor 2 11%
Student > Ph. D. Student 2 11%
Other 2 11%
Unknown 3 16%
Readers by discipline
Readers by discipline Count As %
Mathematics 4 21%
Medicine and Dentistry 3 16%
Biochemistry, Genetics and Molecular Biology 2 11%
Veterinary Science and Veterinary Medicine 1 5%
Environmental Science 1 5%
Other 5 26%
Unknown 3 16%