3D-Printed Alginate/Gelatin/ Platelet-Rich Plasma Composite Scaffold With Magnesium-Containing Bioactive Glass for Enhancing the Chondrogenic Differentiation of Mesenchymal Stem Cells in Cartilage Tissue Engineering Publisher Pubmed



Ashrafnia Menarbazari A ; Saberi F ; Kalantarzadeh R ; Fatahi Y ; Heydari A ; Simorgh S
Source: International Journal of Biological Macromolecules Published:2026

Abstract

Three-dimensional (3D) printed hydrogels have been recognized as a promising approach for cartilage tissue engineering (CTE) owing to their ability to accurately reproduce complex tissue architectures and to create a biomimetic microenvironment conducive to cell proliferation and differentiation. In this study, a novel 3D-printed composite scaffold composed of alginate, gelatin, and platelet-rich plasma (AG/PRP), incorporating magnesium-containing bioactive glass (Mg-BG), was developed (AG/PRP/BG). AG/PRP hydrogel formulations (10% w/v gelatin, 2% w/v alginate, and 0.2% w/v freeze-dried PRP) with varying Mg-BG concentrations (0.25, 0.5, and 1 wt%) were prepared to evaluate their effects on rheological behavior, printability, and biological performance. Rheological analyses revealed that all hydrogel formulations exhibited shear-thinning behavior, with the 0.5 wt% Mg-BG formulation (AG/PRP/BG2) demonstrating the highest print fidelity (printability index: 1.01 ± 0.02). Furthermore, bone marrow–derived mesenchymal stem cells (BM-MSCs) cultured on the 3D-printed scaffolds for 21 days showed enhanced chondrogenic differentiation in Mg-BG–containing group, as evidenced by increased SOX9 and collagen II gene expression, decreased collagen I gene expression, and elevated glycosaminoglycan (GAG) deposition. These findings demonstrate that the optimal incorporation of Mg-BG improves hydrogel viscosity, mechanical performance, and print fidelity, while promoting chondroinductive potential and maintaining high BM-MSCs viability, highlighting the suitability of AG/PRP/Mg-BG scaffolds as a robust candidate for advanced CTE applications. © 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.