Evidence map›Paper›PMID 35487196›Full record

ReviewBiofabrication2022

Engineered assistive materials for 3D bioprinting: support baths and sacrificial inks.

Lucia G Brunel, Sarah M Hull, Sarah C Heilshorn

Abstract readReview
In one paragraph

Review in Biofabrication, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 45 papers.

0numbers the graph read from it
0cells of the map it votes in
45citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

45 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
  4. Article
  5. Review
  6. Computational approaches in bioprinting processes.Nature reviews bioengineering · 2026
    Article
  7. Article
  8. Review
  9. Article
  10. Review
  11. Article
  12. Article
  13. Review
  14. Omnidirectional 3D Printing of Anisotropic Nanofibrous Peptide Hydrogels.bioRxiv : the preprint server for biology · 2025
    Article
  15. Review
  16. Article
  17. Review
  18. Article
  19. Review
  20. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lucia G BrunelDepartment of Chemical Engineering, Stanford University, Stanford, CA, United States of America.ORCID 0000-0003-0327-5635
Sarah M HullDepartment of Chemical Engineering, Stanford University, Stanford, CA, United States of America.ORCID 0000-0002-3741-654X
Sarah C HeilshornDepartment of Materials Science and Engineering, Stanford University, Stanford, CA, United States of America.ORCID 0000-0002-9801-6304

Funding

Injectable Hydrogels to Deliver Gene Therapy for Myocardial InfarctR01HL151997 · NHLBI · STANFORD UNIVERSITY · PI HEILSHORN, SARAH C · 2020 to 2023
$1.7M
Engineered matrix microarrays to enhance the regenerative potential of iPSC-derived endothelial cellsR01HL142718 · NHLBI · STANFORD UNIVERSITY · PI HEILSHORN, SARAH C, HUANG, NGAN F. · 2018 to 2021
$1.6M
Injectable Hydrogels to Protect Transplanted Cells from HypoxiaR01EB027666 · NIBIB · STANFORD UNIVERSITY · PI HEILSHORN, SARAH C, PLANT, GILES · 2019 to 2022
$1.4M
Engineered biomaterials to modulate cell-cell signaling for the robust expansion of stem cellsR01EB027171 · NIBIB · STANFORD UNIVERSITY · PI HEILSHORN, SARAH C · 2019 to 2022
$1.4M
Combinatorial matrix-mimetic recombinant proteins as engineered nerve guidance conduitsR21NS114549 · NINDS · STANFORD UNIVERSITY · PI GEORGE, PAUL, HASTIE, TREVOR J. · 2020 to 2020
$436k
Engineered Protein Hydrogels to Modulate Adipose-derived Stromal Cell Secretome and Exosomes for Injectable Myocardial Infarction TherapyR21HL138042 · NHLBI · STANFORD UNIVERSITY · PI HEILSHORN, SARAH C · 2017 to 2018
$435k
Characterization of corneal stromal stem cells encapsulated within bioorthogonally crosslinked collagen gels for delivery to the ocular surfaceF31EY030731 · NEI · STANFORD UNIVERSITY · PI HULL, SARAH · 2019 to 2022
$131k
NEI NIH HHS F31 EY030731NHLBI NIH HHS R01 HL142718NHLBI NIH HHS R01 HL151997NHLBI NIH HHS R21 HL138042NIBIB NIH HHS R01 EB027171NIBIB NIH HHS R01 EB027666NINDS NIH HHS R21 NS114549
6 · The paper itself

Abstract

Three-dimensional (3D) bioprinting is a promising technique for spatially patterning cells and materials into constructs that mimic native tissues and organs. However, a trade-off exists between printability and biological function, where weak materials are typically more suited for 3D cell culture but exhibit poor shape fidelity when printed in air. Recently, a new class of assistive materials has emerged to overcome this limitation and enable fabrication of more complex, biologically relevant geometries, even when using soft materials as bioinks. These materials include support baths, which bioinks are printed into, and sacrificial inks, which are printed themselves and then later removed. Support baths are commonly yield-stress materials that provide physical confinement during the printing process to improve resolution and shape fidelity. Sacrificial inks have primarily been used to create void spaces and pattern perfusable networks, but they can also be combined directly with the bioink to change its mechanical properties for improved printability or increased porosity. Here, we outline the advantages of using such assistive materials in 3D bioprinting, define their material property requirements, and offer case study examples of how these materials are used in practice. Finally, we discuss the remaining challenges and future opportunities in the development of assistive materials that will propel the bioprinting field forward toward creating full-scale, biomimetic tissues and organs.

Indexed as

BioprintingBathsHydrogelsInkPrinting, Three-DimensionalTissue EngineeringTissue ScaffoldsHydrogelsassistive materialbiofabricationbioprintinghydrogelsacrificial inksupport bathtissue engineering

Identifiers

PMID35487196
PMCPMC10788121

What OpenQuestion holds

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LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.