Can we achieve biomimetic electrospun scaffolds with gelatin alone?

Roldan Ciudad, Elisa ORCID: https://orcid.org/0000-0002-7793-7542, Reeves, Neil D ORCID: https://orcid.org/0000-0001-9213-4580, Cooper, Glen and Andrews, Kirstie (2023) Can we achieve biomimetic electrospun scaffolds with gelatin alone? Frontiers in Bioengineering and Biotechnology, 11. p. 1160760. ISSN 2296-4185

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Abstract

Introduction: Gelatin is a natural polymer commonly used in biomedical applications in combination with other materials due to its high biocompatibility, biodegradability, and similarity to collagen, principal protein of the extracellular matrix (ECM). The aim of this study was to evaluate the suitability of gelatin as the sole material to manufacture tissue engineering scaffolds by electrospinning. Methods: Gelatin was electrospun in nine different concentrations onto a rotating collector and the resulting scaffold’s mechanical properties, morphology and topography were assessed using mechanical testing, scanning electron microscopy and white light interferometry, respectively. After characterizing the scaffolds, the effects of the concentration of the solvents and crosslinking agent were statistically evaluated with multivariate analysis of variance and linear regressions. Results: Fiber diameter and inter-fiber separation increased significantly when the concentration of the solvents, acetic acid (HAc) and dimethyl sulfoxide (DMSO), increased. The roughness of the scaffolds decreased as the concentration of dimethyl sulfoxide increased. The mechanical properties were significantly affected by the DMSO concentration. Immersed crosslinked scaffolds did not degrade until day 28. The manufactured gelatin-based electrospun scaffolds presented comparable mechanical properties to many human tissues such as trabecular bone, gingiva, nasal periosteum, oesophagus and liver tissue. Discussion: This study revealed for the first time that biomimetic electrospun scaffolds with gelatin alone can be produced for a significant number of human tissues by appropriately setting up the levels of factors and their interactions. These findings also extend statistical relationships to a form that would be an excellent starting point for future research that could optimize factors and interactions using both traditional statistics and machine learning techniques to further develop specific human tissue.