Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F24%3APU151830" target="_blank" >RIV/00216305:26220/24:PU151830 - isvavai.cz</a>
Result on the web
<a href="https://www.sciencedirect.com/science/article/pii/S2352492824017586" target="_blank" >https://www.sciencedirect.com/science/article/pii/S2352492824017586</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1016/j.mtcomm.2024.109777" target="_blank" >10.1016/j.mtcomm.2024.109777</a>
Alternative languages
Result language
angličtina
Original language name
Characterization and optimization of a biomaterial ink aided by machine learning-assisted parameter suggestion
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
30404 - Biomaterials (as related to medical implants, devices, sensors)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Materials Today Communications
ISSN
2352-4928
e-ISSN
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Volume of the periodical
40
Issue of the periodical within the volume
August 2024
Country of publishing house
GB - UNITED KINGDOM
Number of pages
12
Pages from-to
1-12
UT code for WoS article
001326033100001
EID of the result in the Scopus database
2-s2.0-85197803419