自愈水凝胶
细胞外基质
纤维
胶原纤维
明胶
组织工程
Ⅰ型胶原
材料科学
动态力学分析
生物物理学
生物医学工程
化学
高分子化学
聚合物
生物化学
复合材料
生物
内分泌学
医学
作者
Malachy Maher,John White,Veronica Glattauer,Zhilian Yue,Timothy C. Hughes,John A. M. Ramshaw,Gordon G. Wallace
出处
期刊:Polymers
[MDPI AG]
日期:2022-04-27
卷期号:14 (9): 1775-1775
被引量:8
标识
DOI:10.3390/polym14091775
摘要
As the most abundant protein in the extracellular matrix, collagen has become widely studied in the fields of tissue engineering and regenerative medicine. Of the various collagen types, collagen type I is the most commonly utilised in laboratory studies. In tissues, collagen type I forms into fibrils that provide an extended fibrillar network. In tissue engineering and regenerative medicine, little emphasis has been placed on the nature of the network that is formed. Various factors could affect the network structure, including the method used to extract collagen from native tissue, since this may remove the telopeptides, and the nature and extent of any chemical modifications and crosslinking moieties. The structure of any fibril network affects cellular proliferation and differentiation, as well as the overall modulus of hydrogels. In this study, the network-forming properties of two distinct forms of collagen (telo- and atelo-collagen) and their methacrylated derivatives were compared. The presence of the telopeptides facilitated fibril formation in the unmodified samples, but this benefit was substantially reduced by subsequent methacrylation, leading to a loss in the native self-assembly potential. Furthermore, the impact of the methacrylation of the collagen, which enables rapid crosslinking and makes it suitable for use in 3D printing, was investigated. The crosslinking of the methacrylated samples (both telo- and atelo-) was seen to improve the fibril-like network compared to the non-crosslinked samples. This contrasted with the samples of methacrylated gelatin, which showed little, if any, fibrillar or ordered network structure, regardless of whether they were crosslinked.
科研通智能强力驱动
Strongly Powered by AbleSci AI