材料科学
脚手架
软骨
肿胀 的
生物医学工程
极限抗拉强度
关节软骨修复
复合材料
关节软骨
骨关节炎
解剖
医学
替代医学
病理
作者
Minyue Luo,Mingxia Chen,Jiafan Bai,Taijun Chen,Siyuan He,Wenzhen Peng,Jianxin Wang,Zhi Wei,Jie Weng
标识
DOI:10.1016/j.colsurfb.2022.112821
摘要
Due to the avascular nature of cartilage, it is difficult to heal and regenerate spontaneously after injury. At present, tissue engineering has become a promising strategy for repairing damaged cartilage, but the use of seed cells and growth factors is limited. In addition, the importance of mechanical compatibility of scaffold materials is often ignored. In this study, osteochondral scaffold was designed as a bilayer structure with a dense γ-Polyglutamic acid/carboxymethyl chitosan/bacterial cellulose (PGA/CMCS/BC) hydrogel cartilage layer and a porous nano HA-containing PGA/CMCS/BC hydrogel osteogenic layer. In addition, bioactive ions were introduced into the hydrogel scaffold to adjust the mechanical and swelling properties of the material to match the mechanical properties of natural articular cartilage. At the same time, based on the structural characteristics of bone and cartilage, magnesium and copper ions were introduced into the double-layer hydrogel scaffold, respectively, to prepare the cartilage layer and the bone layer, which endowed the material with excellent antibacterial properties and achieved the purpose of the integrated repair of bone and cartilage. The results showed that, after adding magnesium ions, the tensile breaking strength of material was increased from 0.66 MPa to 1.37 MPa,the corresponding compression modulus of the material (strain 0-12%) increased from 0.15 MPa to 0.58 MPa whilst the maximum mass swelling rate decreased from 155% to 75%. The results of in vivo experiments show that the group with bioactive ions had a much better effect on the repair of osteochondral defects, compared with group without bioactive ions, demonstrating such double ion regulation strategy is a very practical strategy for the treatment of osteochondral defects.
科研通智能强力驱动
Strongly Powered by AbleSci AI