Show simple item record Nettles, DL Haider, MA Chilkoti, A Setton, LA
dc.coverage.spatial United States 2011-04-15T16:46:35Z 2010-01
dc.identifier.citation Tissue Eng Part A, 2010, 16 (1), pp. 11 - 20
dc.description.abstract The successful design of biomaterial scaffolds for articular cartilage tissue engineering requires an understanding of the impact of combinations of material formulation parameters on diverse and competing functional outcomes of biomaterial performance. This study sought to explore the use of a type of unsupervised artificial network, a self-organizing map, to identify relationships between scaffold formulation parameters (crosslink density, molecular weight, and concentration) and 11 such outcomes (including mechanical properties, matrix accumulation, metabolite usage and production, and histological appearance) for scaffolds formed from crosslinked elastin-like polypeptide (ELP) hydrogels. The artificial neural network recognized patterns in functional outcomes and provided a set of relationships between ELP formulation parameters and measured outcomes. Mapping resulted in the best mean separation amongst neurons for mechanical properties and pointed to crosslink density as the strongest predictor of most outcomes, followed by ELP concentration. The map also grouped formulations together that simultaneously resulted in the highest values for matrix production, greatest changes in metabolite consumption or production, and highest histological scores, indicating that the network was able to recognize patterns amongst diverse measurement outcomes. These results demonstrated the utility of artificial neural network tools for recognizing relationships in systems with competing parameters, toward the goal of optimizing and accelerating the design of biomaterial scaffolds for articular cartilage tissue engineering.
dc.format.extent 11 - 20
dc.language eng
dc.language.iso en_US en_US
dc.relation.ispartof Tissue Eng Part A
dc.relation.isversionof 10.1089/ten.tea.2009.0134
dc.subject Animals
dc.subject Cartilage
dc.subject Chondrogenesis
dc.subject Elastin
dc.subject Humans
dc.subject Hydrogels
dc.subject Neural Networks (Computer)
dc.subject Tissue Engineering
dc.title Neural network analysis identifies scaffold properties necessary for in vitro chondrogenesis in elastin-like polypeptide biopolymer scaffolds.
dc.type Journal Article
dc.description.version Version of Record en_US 2010-1-0 en_US
duke.description.endpage 20 en_US
duke.description.issue 1 en_US
duke.description.startpage 11 en_US
duke.description.volume 16 en_US
dc.relation.journal Tissue Engineering Part a en_US
pubs.issue 1
pubs.organisational-group /Duke
pubs.organisational-group /Duke/Institutes and Provost's Academic Units
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers
pubs.organisational-group /Duke/Institutes and Provost's Academic Units/University Institutes and Centers/Duke Institute for Brain Sciences
pubs.organisational-group /Duke/Pratt School of Engineering
pubs.organisational-group /Duke/Pratt School of Engineering/Biomedical Engineering
pubs.organisational-group /Duke/School of Medicine
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers
pubs.organisational-group /Duke/School of Medicine/Institutes and Centers/Duke Cancer Institute
pubs.organisational-group /Duke/Trinity College of Arts & Sciences
pubs.organisational-group /Duke/Trinity College of Arts & Sciences/Chemistry
pubs.publication-status Published
pubs.volume 16
dc.identifier.eissn 1937-335X

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