journal article·2020-09-01

A combinatorial approach for spinal cord injury repair using multifunctional collagen-based matrices: development, characterization and impact on cell adhesion and axonal growth

Zehra Betül Ahi, Rita Catarina Assunção-Silva, António Jose Salgado, Kadriye Tuzlakoglu

Biomedical Materials· Volume 15· Issue 5
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Abstract

Abstract Spinal cord injury is a devastating condition of the central nervous system, in which traditional treatments are largely ineffective due to the complex nature of the injured tissue. Therefore, biomaterial-based systems have been developed as possible alternative strategies to repair the damaged tissue. In the present study, we aimed to design bioactive agent loaded scaffolds composed of two layers with distinct physical properties to improve tissue regeneration. An electrospun layer with aligned nanofibers was made of collagen (Col) Type-I, poly(lactide-co-glycolide) (PLGA) and laminin to promote cell attachment of mesenchymal-like stem cells towards the direction of fibers, while a Col-based second layer was fabricated by plastic compression to act as a releasing system for NT-3 and chondroitinase ABC, so that axonal growth could be stimulated. Results showed that a source of mesenchymal stem cell (MSC)-like cells, adipose tissue-derived stem cells cultured on the fibrous layer of the matrices were able to adhere and proliferate, where the aligned fibers promoted the cell growth in an organized way. Furthermore, the bilayered matrices also promoted dorsal root ganglion neurite outgrowth. The bilayered matrice with Col/PLGA + laminin top layer appears to promote higher neurite growth. Collectively, the designed constructs show promising structural properties and biological performance for being employed as a scaffold for engineering the spinal cord tissue.
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Research context

OpenAlex topics: Nerve injury and regeneration · Electrospun Nanofibers in Biomedical Applications · Tissue Engineering and Regenerative Medicine

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

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