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High-Throughput 2018, 7(1), 1;

Gradient Material Strategies for Hydrogel Optimization in Tissue Engineering Applications

The Vivian L. Smith Department of Neurosurgery, Center for Stem Cell & Regenerative Medicine, and Department of Nanomedicine and Biomedical Engineering, McGovern Medical School, University of Texas Health Science Center at Houston, Houston, TX 77030, USA
Received: 6 December 2017 / Revised: 30 December 2017 / Accepted: 2 January 2018 / Published: 4 January 2018
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Although a number of combinatorial/high-throughput approaches have been developed for biomaterial hydrogel optimization, a gradient sample approach is particularly well suited to identify hydrogel property thresholds that alter cellular behavior in response to interacting with the hydrogel due to reduced variation in material preparation and the ability to screen biological response over a range instead of discrete samples each containing only one condition. This review highlights recent work on cell–hydrogel interactions using a gradient material sample approach. Fabrication strategies for composition, material and mechanical property, and bioactive signaling gradient hydrogels that can be used to examine cell–hydrogel interactions will be discussed. The effects of gradients in hydrogel samples on cellular adhesion, migration, proliferation, and differentiation will then be examined, providing an assessment of the current state of the field and the potential of wider use of the gradient sample approach to accelerate our understanding of matrices on cellular behavior. View Full-Text
Keywords: gradient; combinatorial method; cell–material interface gradient; combinatorial method; cell–material interface

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Smith Callahan, L.A. Gradient Material Strategies for Hydrogel Optimization in Tissue Engineering Applications. High-Throughput 2018, 7, 1.

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