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Nonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration.

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Authors
Edmunds, K J
Árnadóttir, Í
Gíslason, M K
Carraro, U
Gargiulo, P
Issue Date
2016

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Citation
Nonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration. 2016, 2016:8932950 Comput Math Methods Med
Abstract
Muscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration.
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Efst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files.
Additional Links
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223076/pdf/CMMM2016-8932950.pdf
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Archived with thanks to Computational and mathematical methods in medicine
ae974a485f413a2113503eed53cd6c53
10.1155/2016/8932950
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English Journal Articles (Peer Reviewed)

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