journal article·2022-03

Biomimetic, highly elastic conductive and hemostatic gelatin/rGO-based nanocomposite cryogel to improve 3D myogenic differentiation and guide in vivo skeletal muscle regeneration

Xin Zhao, Zhiyi Zhang, Jinlong Luo, Zhengying Wu, Zhuofan Yang, Shaowen Zhou, Youpeng Tu, Ying Huang, Yong Han, Baolin Guo

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Abstract

The conductive and hemostatic cryogel capable of mimicking skeletal muscle`s conductivity and extracellular matrix component presents huge potential for in situ skeletal muscle tissue engineering. Herein, elastic and conductive nanocomposite cryogels were synthesized using amide bond crosslinked gelatin and poly(tannic acid) reduced graphene oxide (rGO@PTA) via cryopolymerization. The cryogels possessed excellent elasticity, blood-trigged shape memory and 3D macroporous structure. The incorporation of rGO@PTA enhanced the mechanical property and conductivity of the cryogel, and endowed the cryogels with anti-oxidation, and without disturbing the elasticity and macroporous structure. Besides, the rGO@PTA improved the fast degradation of gelatin cryogel to prolong its service life. The cell proliferation and myogenic differentiation of C2C12 myoblasts during 3D culture were significantly improved by the conductive cryogel scaffold. Especially, the cryogel with 4 mg/mL rGO@PTA presented the best cell proliferation and myogenic differentiation, and significantly improved the repair efficiency of volumetric muscle loss. Furthermore, it had better hemostatic efficiency than those of commercial gelatin sponge and gelatin cryogel. This study provides a new strategy to develop highly elastic, hemostatic and conductive scaffolds, which integrates hemostasis and tissue repair to promote skeletal muscle or other electrical signal responsive soft tissue repair via in situ tissue engineering approach.
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Research context

OpenAlex topics: Graphene and Nanomaterials Applications · Electrospun Nanofibers in Biomedical Applications · Bone Tissue Engineering Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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