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

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30404 - Biomaterials (as related to medical implants, devices, sensors)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Materials Today Communications

ISSN

2352-4928

e-ISSN

—

Svazek periodika

40

Číslo periodika v rámci svazku

August 2024

Stát vydavatele periodika

GB - Spojené království Velké Británie a Severního Irska

Počet stran výsledku

12

Strana od-do

1-12

Kód UT WoS článku

001326033100001

EID výsledku v databázi Scopus

2-s2.0-85197803419