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Abstract
Cancerous tumors are among the most fatal diseases worldwide, claiming nearly 10 million lives in 2020. Due to their complex and dynamic nature, modeling tumors accurately is a challenging task. Current models suffer from inadequate translation between in vitro and in vivo results, primarily due to the isotropic nature of tumors and their microenvironment's relationship. To address these limitations, hydrogel-based 3D bioprinting is emerging as a promising approach to mimic cancer development and behavior. It provides precise control over individual elements' size and distribution within the cancer microenvironment and enables the use of patient-derived tumor cells, rather than commercial lines. Consequently, hydrogel bioprinting is expected to become a state-of-the-art technique for cancer research. This manuscript presents an overview of cancer statistics, current modeling methods, and their limitations. Additionally, we highlight the significance of bioprinting, its applications in cancer modeling, and the importance of hydrogel selection. We further explore the current state of creating models for the five deadliest cancers using 3D bioprinting. Finally, we discuss current trends and future perspectives on the clinical use of cancer modeling using hydrogel bioprinting.
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Authors (6)
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Michael R. Hamblin
- Laser Research Centre, Faculty of Health Science, University of Johannesburg, Doornfontein 2028, South Africa
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Masoud Mozafari
- Research Unit of Health Sciences and Technology, Faculty of Medicine, University of Oulu, Oulu, Finland
Cite as
Full text
- Publication version
- Accepted or Published Version
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.tranon.2023.101750
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Keywords
Details
- Category:
- Articles
- Type:
- artykuły w czasopismach
- Published in:
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Translational Oncology
no. 37,
ISSN: 1944-7124 - Language:
- English
- Publication year:
- 2023
- Bibliographic description:
- Gnatowski P., Piłat E., Kucińska-Lipka J., Saeb M., Hamblin M. R., Mozafari M.: Recent advances in 3D bioprinted tumor models for personalized medicine// Translational Oncology -,iss. 37 (2023), s.101750-
- DOI:
- Digital Object Identifier (open in new tab) 10.1016/j.tranon.2023.101750
- Sources of funding:
-
- COST_FREE
- Verified by:
- Gdańsk University of Technology
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