2010
DOI: 10.1089/ten.tea.2009.0134
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Neural Network Analysis Identifies Scaffold Properties Necessary for In Vitro Chondrogenesis in Elastin-like Polypeptide Biopolymer Scaffolds

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 … Show more

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Cited by 39 publications
(22 citation statements)
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“…The sulfated glycosaminoglycan (sGAG) and DNA contents for papain digests of cell-hydrogel samples cultured for 7 days (n=5–6 per hydrogel formulation/cell type) were determined [41, 66]. In brief, papain digests of each cell seeded HA-PEG hydrogel (10 units/mg papain: Sigma Aldrich #P4762, St. Louis, MO; 1X PBS, 5mM cysteine HCL, 5mM EDTA, pH=6.0; mixed for 2h at 37°C then filtered; digested for less than 24 hours at 60°C) were analyzed by a dimethylmethylene blue (DMMB) assay for sGAG content using commercially available chondroitin-4-sulfate as a standard (Sigma).…”
Section: Methodsmentioning
confidence: 99%
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“…The sulfated glycosaminoglycan (sGAG) and DNA contents for papain digests of cell-hydrogel samples cultured for 7 days (n=5–6 per hydrogel formulation/cell type) were determined [41, 66]. In brief, papain digests of each cell seeded HA-PEG hydrogel (10 units/mg papain: Sigma Aldrich #P4762, St. Louis, MO; 1X PBS, 5mM cysteine HCL, 5mM EDTA, pH=6.0; mixed for 2h at 37°C then filtered; digested for less than 24 hours at 60°C) were analyzed by a dimethylmethylene blue (DMMB) assay for sGAG content using commercially available chondroitin-4-sulfate as a standard (Sigma).…”
Section: Methodsmentioning
confidence: 99%
“…Statistical approaches such as principal components analysis or cluster analyses have been used to group biomaterial features based on multiple “outcomes” for material selection, particularly when large datasets are generated. Artificial neural network (ANN) analysis is another, probability-based approach to identify relationships between material formulations parameters and biological outcomes [4143]. ANN analyses to generate a “self-organizing map” will identify connectivity amongst formulations based on similarities in a diverse array of measured outcomes [44–46].…”
Section: Introductionmentioning
confidence: 99%
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“…[65][66][67][68][69][70] In a study by Nettles et al, 16 lysine-containing ELP formulations were evaluated for their mechanical properties and their ability to promote cartilage matrix synthesis by encapsulated primary chondrocytes. 69 These ELP formulations di®ered in the frequencies of lysine along the ELP backbone (termed the K-period), their solution concentrations and molecular weights. An arti¯cial neural network analysis was used to evaluate relationships between the properties of ELP-associated biological and mechanical outcomes.…”
Section: Cartilage Tissue Engineeringmentioning
confidence: 99%
“…Elastin tubes fi lled with agarose gel containing basic fi broblast growth factor for sustained release of the growth factor showed signifi cantly more cell infi ltration at 28 days compared to those without growth factor (Kurane et al, 2007). Elastin scaffolds formed from cross-linked elastin-like polypeptide hydrogels were investigated to identify relationships between scaffold formulation parameters (crosslink density, molecular weight, and concentration) and properties including mechanical, matrix accumulation, metabolite use and production, and histological appearance (Nettles et al, 2010). Crosslink density was the strongest predictor of most outcomes related to neuron functions, followed by elastin-like polymer concentration (Nettles et al, 2010).…”
Section: Elastin-derived Scaffoldsmentioning
confidence: 99%