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Printability and Cell Viability in Extrusion-Based Bioprinting from Experimental, Computational, and Machine Learning
Ali Malekpour1, Xiongbiao Chen1,2
1Department of Mechanical Engineering, College of Engineering, University of Saskatchewan, 57 Campus Drive, Saskatoon, SK S7N5A9, Canada.
Journal of Functional Biomaterials
|April 25, 2022
Summary
This review explores extrusion bioprinting for tissue engineering, focusing on printability and cell viability. It identifies key parameters and methods, highlighting machine learning
Area of Science:
- Biomaterials Science
- Tissue Engineering
- Biotechnology
Background:
- Extrusion bioprinting precisely deposits biomaterials and cells (bioink) layer-by-layer for 3D tissue constructs.
- Printability (structural integrity) and cell viability (cell survival) are critical challenges in this process.
Purpose of the Study:
- To review literature identifying parameters affecting extrusion bioprinting printability and cell viability.
- To highlight methods for optimizing these critical parameters for improved outcomes.
Main Methods:
- Literature review focusing on experimental, computational, and machine learning (ML) approaches.
- Analysis of factors influencing bioink formulation, construct design, and bioprinting process parameters.
Main Results:
- Printability and cell viability are influenced by construct design, bioink properties, and printing process parameters.
- Various experimental, computational, and ML strategies exist for parameter determination and optimization.
Conclusions:
- Optimizing parameters is crucial for enhancing printability and cell viability in extrusion bioprinting.
- Machine learning shows significant potential as a powerful tool to advance bioprinting for tissue engineering applications.
