Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting

This repository contains scripts used for the paper "Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting."

Abstract

Extrusion-based 3D bioprinting has revolutionised tissue engineering, enabling complex biostructure manufacturing. However, extrusion imposes substantial shear stress on cells, compromising cell viability. Predicting and optimising cell viability remains challenging due to rheological modelling complexity and cell-type dependency. To address these challenges, this study developed a quantitative framework integrating numerical simulation and machine learning. Support vector regression and simulation were utilised to evaluate alginate ink viscosity and shear stress profiles, while multi-layer perceptron regressors were trained on experimental datasets for diverse cell types to predict as-extruded cell viability based on wall shear stress magnitude and exposure time. Results showed vascular endothelial cells were most susceptible to shear stress, with viability dropping to 80% at 2.05 kPa for 400 ms, while mesenchymal stem, cervical cancer, and embryonic fibroblast cells showed such decrease at 2.65, 2.85, and 3.72 kPa, respectively. This versatile framework enables rapid bioink optimisation across various cell types.

Keywords: 3D bioprinting; cell viability; shear stress; numerical analysis; machine learning; alginate-based bioink

Citation

Colin Zhang, Kelum C. M. L. Elvitigala, Wildan Mubarok, Yasunori Okano, and Shinji Sakai. (2024). Machine learning-based prediction and optimization framework for as-extruded cell viability in extrusion-based 3D bioprinting. Virtual and Physical Prototyping, 19(1), e2400330. https://doi.org/10.1080/17452759.2024.2400330.

@article{zhang_machine_2024,
  author = {Colin Zhang and Kelum Chamara Manoj Lakmal Elvitigala and Wildan Mubarok and Yasunori Okano and Shinji Sakai},
  title = {Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting},
  journal = {Virtual and Physical Prototyping},
  volume = {19},
  number = {1},
  pages = {e2400330},
  year = {2024},
  publisher = {Taylor \& Francis},
  doi = {10.1080/17452759.2024.2400330},
  url = {https://doi.org/10.1080/17452759.2024.2400330}
}

Disclaimer

Please note that some scripts in this repository might require the corresponding data files to run successfully. The data files are available upon reasonable request from the corresponding author.