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A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury

Overview of attention for article published in PLOS ONE, July 2008
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Title
A Patient-Specific in silico Model of Inflammation and Healing Tested in Acute Vocal Fold Injury
Published in
PLOS ONE, July 2008
DOI 10.1371/journal.pone.0002789
Pubmed ID
Authors

Nicole Y. K. Li, Katherine Verdolini, Gilles Clermont, Qi Mi, Elaine N. Rubinstein, Patricia A. Hebda, Yoram Vodovotz

Abstract

The development of personalized medicine is a primary objective of the medical community and increasingly also of funding and registration agencies. Modeling is generally perceived as a key enabling tool to target this goal. Agent-Based Models (ABMs) have previously been used to simulate inflammation at various scales up to the whole-organism level. We extended this approach to the case of a novel, patient-specific ABM that we generated for vocal fold inflammation, with the ultimate goal of identifying individually optimized treatments. ABM simulations reproduced trajectories of inflammatory mediators in laryngeal secretions of individuals subjected to experimental phonotrauma up to 4 hrs post-injury, and predicted the levels of inflammatory mediators 24 hrs post-injury. Subject-specific simulations also predicted different outcomes from behavioral treatment regimens to which subjects had not been exposed. We propose that this translational application of computational modeling could be used to design patient-specific therapies for the larynx, and will serve as a paradigm for future extension to other clinical domains.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 3 4%
Korea, Republic of 1 1%
Portugal 1 1%
Canada 1 1%
United Kingdom 1 1%
Unknown 71 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 18 23%
Researcher 13 17%
Student > Bachelor 9 12%
Professor > Associate Professor 7 9%
Student > Master 7 9%
Other 15 19%
Unknown 9 12%
Readers by discipline Count As %
Agricultural and Biological Sciences 18 23%
Medicine and Dentistry 13 17%
Computer Science 7 9%
Engineering 6 8%
Nursing and Health Professions 5 6%
Other 14 18%
Unknown 15 19%