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Cited by 683

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

3-D bioprinting technologies in tissue engineering and regenerative medicine: Current and future trends

Open access
Bishop, Elliot S., Mostafa, Sami, Pakvasa, Mikhail, Luu, Hue H., Lee, Michael J., Wolf, Jennifer Moriatis, Ameer, Guillermo A., He, Tong-Chuan, Reid, Russell R.·2017·Genes & Diseases

Abstract

Advances in three-dimensional (3D) printing have increased feasibility towards the synthesis of living tissues. Known as 3D bioprinting, this technology involves the precise layering of cells, biologic scaffolds, and growth factors with the goal of creating bioidentical tissue for a variety of uses. Early successes have demonstrated distinct advantages over conventional tissue engineering strategies. Not surprisingly, there are current challenges to address before 3D bioprinting becomes clinically relevant. Here we provide an overview of 3D bioprinting technology and discuss key advances, clinical applications, and current limitations. While 3D bioprinting is a relatively novel tissue engineering strategy, it holds great potential to play a key role in personalized medicine.

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Crossref · 10.1016/j.gendis.2017.10.002

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Metadata

Authors
Bishop, E. S., Mostafa, S., Pakvasa, M., Luu, H. H., Lee, M. J., Wolf, J. M., Ameer, G. A., He, T.-C., & Reid, R. R.
Venue
Genes & Diseases
Publisher
Elsevier BV
Published
2017-12
Volume
4
Issue
4
Pages
185-195
ISSN
2352-3042 (ISSN-L)
OpenAlex
W2768673761
Cited by
683
References
6
FWCI
20.83
Type
journal article
Language
en
Open access
gold

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CoChat. (2026). Citation check: 3-D bioprinting technologies in tissue engineering and regenerative medicine: Current and future trends. https://app.cochat.ai/papers/10.1016/j.gendis.2017.10.002
The paper
Bishop, E. S., Mostafa, S., Pakvasa, M., Luu, H. H., Lee, M. J., Wolf, J. M., Ameer, G. A., He, T.-C., & Reid, R. R. (2017). 3-D bioprinting technologies in tissue engineering and regenerative medicine: Current and future trends. Genes & Diseases. https://doi.org/10.1016/j.gendis.2017.10.002

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.