Journal
PROGRESS IN BIOMEDICAL ENGINEERING
Volume 4, Issue 4, Pages -Publisher
IOP Publishing Ltd
DOI: 10.1088/2516-1091/ac7833
Keywords
glioma; 3D bioprinting; brain cancer; cancer treatment; in vitro models
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Funding
- Tubitak 2232 International Fellowship for Outstanding Researchers Award [118C391]
- Alexander von Humboldt Research Fellowship
- Marie Sklodowska-Curie Individual Fellowship [101003361]
- Royal Academy Newton-Katip Celebi Transforming Systems Through Partnership award
- Science Academy's Young Scientist Awards Program (BAGEP)
- Outstanding Young Scientists Awards (GEB.IP)
- Bilim Kahramanlari Dernegi The Young Scientist Award
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This article reviews the 3D bioprinted models developed for recapitulating the brain environment and glioma tumors, and discusses the potential use of 4D printing and machine learning applications in glioma modeling.
Glioma is one of the most malignant types of cancer and most gliomas remain incurable. One of the hallmarks of glioma is its invasiveness. Furthermore, glioma cells tend to readily detach from the primary tumor and travel through the brain tissue, making complete tumor resection impossible in many cases. To expand the knowledge regarding the invasive behavior of glioma, evaluate drug resistance, and recapitulate the tumor microenvironment, various modeling strategies were proposed in the last decade, including three-dimensional (3D) biomimetic scaffold-free cultures, organ-on-chip microfluidics chips, and 3D bioprinting platforms, which allow for the investigation on patient-specific treatments. The emerging method of 3D bioprinting technology has introduced a time- and cost-efficient approach to create in vitro models that possess the structural and functional characteristics of human organs and tissues by spatially positioning cells and bioink. Here, we review emerging 3D bioprinted models developed for recapitulating the brain environment and glioma tumors, with the purpose of probing glioma cell invasion and gliomagenesis and discuss the potential use of 4D printing and machine learning applications in glioma modelling.
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