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Article . 2022 . Peer-reviewed
License: CC BY
Data sources: Crossref
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Article . 2023
Data sources: DOAJ
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Digital light processing (DLP)‐based (bio)printing strategies for tissue modeling and regeneration

Authors: Hongbin Li; Jiliang Dai; Zixuan Wang; Heshan Zheng; Wanlu Li; Mian Wang; Feng Cheng;

Digital light processing (DLP)‐based (bio)printing strategies for tissue modeling and regeneration

Abstract

AbstractDigital light processing (DLP)‐based bioprinting technology has recently aroused considerable concerns as a strategy to deliver biomedical materials and/or specific cells to create sophisticated structures for various tissue modeling and regeneration. In this review, we display a concise introduction of DLP bioprinting, and a further discussion on the design and manufacture of DLP (bio)printer with varied bioinks and their biomedical applications toward drug screening, disease modeling, tissue repair, and regenerative medicine. Finally, the advantages, challenges, and perspectives of the DLP printing platforms are detailed. It is believed that DLP bioprinting will play a decisive role in the field of tissue model and regenerative medicine, mainly due to its time‐efficient, higher resolution, and amenability to automation for various tissue needs.

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Keywords

Chemistry, QH301-705.5, disease modeling, DLP bioprinting, drug screening, Biology (General), QD1-999, tissue modeling

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    selected citations
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    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    78
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Top 1%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Top 10%
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 1%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
78
Top 1%
Top 10%
Top 1%
gold