journal article·2024-04-01Open access

Cation-crosslinked κ-carrageenan sub-microgel medium for high-quality embedded bioprinting

Hua Zhang, Yang Luo, Zeming Hu, Mengxi Chen, Shang Chen, Yudong Yao, Jie Yao, Xiaoqi Shao, Kerong Wu, Yabin Zhu, Jun Fu

Biofabrication· Volume 16· Issue 2

Abstract

Abstract Three-dimensional (3D) bioprinting embedded within a microgel bath has emerged as a promising strategy for creating intricate biomimetic scaffolds. However, it remains a great challenge to construct tissue-scale structures with high resolution by using embedded 3D bioprinting due to the large particle size and polydispersity of the microgel medium, as well as its limited cytocompatibility. To address these issues, novel uniform sub-microgels of cell-friendly cationic-crosslinked kappa-carrageenan (κ-Car) are developed through an easy-to-operate mechanical grinding strategy. These κ-Car sub-microgels maintain a uniform submicron size of around 642 nm and display a rapid jamming-unjamming transition within 5 s, along with excellent shear-thinning and self-healing properties, which are critical for the high resolution and fidelity in the construction of tissue architecture via embedded 3D bioprinting. Utilizing this new sub-microgel medium, various intricate 3D tissue and organ structures, including the heart, lungs, trachea, branched vasculature, kidney, auricle, nose, and liver, are successfully fabricated with delicate fine structures and high shape fidelity. Moreover, the bone marrow mesenchymal stem cells encapsulated within the printed constructs exhibit remarkable viability exceeding 92.1% and robust growth. This κ-Car sub-microgel medium offers an innovative avenue for achieving high-quality embedded bioprinting, facilitating the fabrication of functional biological constructs with biomimetic structural organizations.
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Research context

OpenAlex topics: 3D Printing in Biomedical Research · Innovative Microfluidic and Catalytic Techniques Innovation · Bone Tissue Engineering Materials

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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