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

Zhang, Colin; Elvitigala, Kelum Chamara Manoj Lakmal; Mubarok, Wildan; Okano, Yasunori; Sakai, Shinji

doi:https://doi.org/10.1080/17452759.2024.2400330

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

Zhang, Colin; Elvitigala, Kelum Chamara Manoj Lakmal; Mubarok, Wildan et al.

Virtual and Physical Prototyping, 2024, 19(1), e2400330

Number of Access:570（2025-09-17 05:52 Counts）

Identifier to cite or link to this item: https://hdl.handle.net/11094/98349

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Title	Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting
Creator	CreatorName Zhang, Colin
	CreatorName Elvitigala, Kelum Chamara Manoj Lakmal
	CreatorName Mubarok, Wildan
	NRID 1000090204007 1000090204007 ORCID 0000-0003-3710-2492 0000-0003-3710-2492 researchmap permalink Yasunori_OKANO Yasunori_OKANO Scopus Author ID 7202082504 7202082504 CreatorName Okano, Yasunori
	NRID 1000020359938 1000020359938 ORCID 0000-0002-1041-4798 0000-0002-1041-4798 researchmap permalink sakaishinji sakaishinji Scopus Author ID 35450082900 35450082900 CreatorName Sakai, Shinji
Subject	3D bioprinting
	alginate-based bioink
	cell viability
	machine learning
	numerical analysis
	shear stress
Description	Zhang C., Elvitigala K.C.M.L., Mubarok W., et al. Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting. Virtual and Physical Prototyping 19, e2400330 (2024); https://doi.org/10.1080/17452759.2024.2400330.
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.
Publisher	Taylor and Francis Ltd.
Source Title	Virtual and Physical Prototyping
Volume (Issue)	19(1)
Page	e2400330
Date of Issued	2024-12-31
Language	English
Handle URL	https://hdl.handle.net/11094/98349
PISSN	17452759
eISSN	17452767
Relation.isIdenticalTo	DOI (Publisher Version) https://doi.org/10.1080/17452759.2024.2400330
Access Rights	open access
CopyRight	This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
oaire:version	Version of Record
Category	学術雑誌論文 Journal Article

Resource Type	journal article
Local Type	Journal Article
DCTERMS.bibliographicCitation	Virtual and Physical Prototyping. 2024, 19(1), p. e2400330
DC.title	Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting
DC.creator	Zhang, Colin
	Elvitigala, Kelum Chamara Manoj Lakmal
	Mubarok, Wildan
	Okano, Yasunori
	Sakai, Shinji
DC.publisher	Taylor and Francis Ltd.
DC.language' scheme='DCTERMS.RFC1766	English
DCTERMS.issued' scheme='DCTERMS.W3CDTF	2024-12-31
DC.identifier	https://doi.org/10.1080/17452759.2024.2400330
DC.subject	3D bioprinting
	alginate-based bioink
	cell viability
	machine learning
	numerical analysis
	shear stress
DC.description	Zhang C., Elvitigala K.C.M.L., Mubarok W., et al. Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting. Virtual and Physical Prototyping 19, e2400330 (2024); https://doi.org/10.1080/17452759.2024.2400330.
DCTERMS.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.
DC.rights	This article is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
DC.format	98349
citation_title	Machine learning-based prediction and optimisation framework for as-extruded cell viability in extrusion-based 3D bioprinting
citation_author	Zhang, Colin
	Elvitigala, Kelum Chamara Manoj Lakmal
	Mubarok, Wildan
	Okano, Yasunori
	Sakai, Shinji
citation_publisher	Taylor and Francis Ltd.
citation_language	English
citation_date	2024-12-31
citation_journal_title	Virtual and Physical Prototyping
citation_volume	19
citation_issue	1
citation_firstpage	e2400330
citation_doi	https://doi.org/10.1080/17452759.2024.2400330
citation_public_url	https://hdl.handle.net/11094/98349

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

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