3D-printed composite scaffold with gradient structure and programmed biomolecule delivery to guide stem cell behavior for osteochondral regeneration
Source abstract
Abstract
The fabrication of osteochondral scaffolds with both structural and biochemical cues to regulate endogenous bone marrow-derived mesenchymal stem cells (BMSCs) behavior for cartilage and subchondral bone regeneration is still a challenge. To this end, a composite scaffold (BE-PSA) with gradient structure and programmed biomolecule delivery was prepared by fused deposition modeling (FDM) 3D printing and multi-material-based modification. The 3D-printed polycaprolactone (PCL) scaffold included upper pores of 200 μm for cartilage regeneration and lower pores of 400 μm for bone regeneration. For a sequential modulation of BMSCs behavior, fast-degrading sodium alginate (SA) hydrogel was used to deliver a burst release of E7 peptide to enhance BMSCs migration within 72 h, while a slowly-degrading silk fibroin (SF) porous matrix was used to provide a sustained release of B2A peptide to improve BMSCs dual-lineage differentiation lasting for >300 h, depending on the different degradation rates of SA hydrogel and SF matrix. The BE-PSA scaffold had good biocompatibility and could improve the migration and osteogenic/chondrogenic differentiation of BMSCs. Benefiting from the synergistic effects of spatial structures and programmed biomolecule delivery, the BE-PSA scaffold showed enhanced cartilage and subchondral bone regeneration in rabbit osteochondral defect model. This work not only provides a promising scaffold to guide BMSCs behavior for osteochondral regeneration but also offers a method for the fabrication of tissue engineering biomaterials based on the structural and biochemical modification.
Source: europepmc · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Bone Tissue Engineering Materials · Silk-based biomaterials and applications · Electrospun Nanofibers in Biomedical Applications
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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