Smart Materials for Nerve Regeneration and Neural Tissue Engineering

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2017-01-01
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Mallapragada, Surya
Uz, Metin
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Mallapragada, Surya
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Chemical and Biological Engineering
Abstract

Stimuli-responsive smart-biomaterial-based approaches have been identified as a promising tool for nerve regeneration and neural tissue engineering. Understanding the stimuli-responsive behavior of the smart materials, along with the fundamentals of cellular interactions, is the key to future strategies for neural tissue engineering. Advances in the development and application of smart biomaterials and 3-D scaffold fabrication techniques as well as cellular reprogramming and transdifferentiation technologies make it possible to combine stem cells, cellular engineering, drug/gene delivery systems, nanotechnology and biomaterial-based therapies to develop experimental and clinical strategies for neural tissue engineering. The application of smart biomaterials in these technologies is likely to contribute synergistically to the improvement of therapeutic strategies for clinical translation. This review chapter focuses on the use of strategies combining stimuli-responsive smart biomaterials with other technologies in neural tissue engineering. A specific emphasis on temperature, pH, enzyme, photo-triggered, self-assembling and electrical stimuli-sensitive mono or multi-responsive smart biomaterials in neural tissue engineering is presented. A summary of the clinical potential and applications of smart materials in neural tissue engineering is also presented at the end to illustrate how smart materials can be effective in combination with these technologies to enhance neural regeneration.

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This book chapter is published as Uz, M., and Mallapragada, S.K., “Smart Materials for Nerve Regeneration and Neural Tissue Engineering," Chapter 14:382-408 in Smart Materials for Tissue Engineering: Applications. RSC Smart Materials Series. (2017) London, UK : Royal Society of Chemistry. http://dx.doi.org/10.1039/9781788010542. Posted with permission.

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Sun Jan 01 00:00:00 UTC 2017
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