Publication detail

Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion

HASHEMI, A. EZATI, M. ZUMBERG, I. VIČAR, T. CHMELÍKOVÁ, L. ČMIEL, V. PROVAZNÍK, V.

Original Title

Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion

Type

journal article in Web of Science

Language

English

Original Abstract

Bio-inks and biomaterial inks are crucial to the success of 3D bioprinting, as they form the foundation of almost every 3D bio-printed structure. Despite the use of various biomaterial inks with potential biomedical applications in 3D printing, developing printable biomaterial inks for extrusion-based 3D bioprinting remains a major challenge in additive manufacturing. To be effective, the inks must possess suitable mechanical properties, high biocompatibility, and the ability to print precisely. In this study, machine learning (ML) was employed to develop a chitosan-gelatin-agarose biomaterial ink. The ink's printability, rheological properties, hydrophilicity, degradability, and biological response were evaluated after an optimization process. The optimized ink exhibited adequate viscosity for reliable printing, and 3D structures were created to assess printability and shape integrity. Bone marrow mesenchymal stem/stromal cells (BMSCs) were cultured on the ink's surface, and cell adhesion, growth, and morphology were assessed. Results showed favorable cell morphology, and cell viability within the optimized ink. The ink consisting of 27 % agarose, 53 % chitosan, and 20 % gelatin (ACG), may be a suitable biomaterial for fabricating 3D complex tissue constructs.

Keywords

Bayesian optimization; Biomaterial ink development; Bone marrow mesenchymal stem/stromal cells; Extrusion 3D bioprinting; Machine learning-based optimization; Rheological characterization

Authors

HASHEMI, A.; EZATI, M.; ZUMBERG, I.; VIČAR, T.; CHMELÍKOVÁ, L.; ČMIEL, V.; PROVAZNÍK, V.

Released

8. 7. 2024

Publisher

Elsevier

Location

Amsterdam, Netherlands

ISBN

2352-4928

Periodical

Materials Today Communications

Year of study

40

Number

August 2024

State

United Kingdom of Great Britain and Northern Ireland

Pages from

1

Pages to

12

Pages count

12

URL

https://www.sciencedirect.com/science/article/pii/S2352492824017586

BibTex

@article{BUT189218,
  author="Amir {Hashemi} and Masoumeh {Ezati} and Inna {Zumberg} and Tomáš {Vičar} and Larisa {Chmelíková} and Vratislav {Čmiel} and Valentýna {Provazník}",
  title="Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion",
  journal="Materials Today Communications",
  year="2024",
  volume="40",
  number="August 2024",
  pages="1--12",
  doi="10.1016/j.mtcomm.2024.109777",
  issn="2352-4928",
  url="https://www.sciencedirect.com/science/article/pii/S2352492824017586"
}