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Reinforcement of Hydrogels with a 3D-Printed Polycaprolactone (PCL) Structure Enhances Cell Numbers and Cartilage ECM Production under Compression.

Hamed Alizadeh SardroudXiongbiao ChenB Frank Eames
Published in: Journal of functional biomaterials (2023)
Hydrogels show promise in cartilage tissue engineering (CTE) by supporting chondrocytes and maintaining their phenotype and extracellular matrix (ECM) production. Under prolonged mechanical forces, however, hydrogels can be structurally unstable, leading to cell and ECM loss. Furthermore, long periods of mechanical loading might alter the production of cartilage ECM molecules, including glycosaminoglycans (GAGs) and collagen type 2 (Col2), specifically with the negative effect of stimulating fibrocartilage, typified by collagen type 1 (Col1) secretion. Reinforcing hydrogels with 3D-printed Polycaprolactone (PCL) structures offer a solution to enhance the structural integrity and mechanical response of impregnated chondrocytes. This study aimed to assess the impact of compression duration and PCL reinforcement on the performance of chondrocytes impregnated with hydrogel. Results showed that shorter loading periods did not significantly affect cell numbers and ECM production in 3D-bioprinted hydrogels, but longer periods tended to reduce cell numbers and ECM compared to unloaded conditions. PCL reinforcement enhanced cell numbers under mechanical compression compared to unreinforced hydrogels. However, the reinforced constructs seemed to produce more fibrocartilage-like, Col1-positive ECM. These findings suggest that reinforced hydrogel constructs hold potential for in vivo cartilage regeneration and defect treatment by retaining higher cell numbers and ECM content. To further enhance hyaline cartilage ECM formation, future studies should focus on adjusting the mechanical properties of reinforced constructs and exploring mechanotransduction pathways.
Keyphrases
  • extracellular matrix
  • tissue engineering
  • single cell
  • cell therapy
  • drug delivery
  • hyaluronic acid
  • risk assessment
  • artificial intelligence
  • mass spectrometry
  • deep learning