Evidence map›Paper›PMID 34967148›Full record

ArticleAdvanced healthcare materials2022

Template-Enabled Biofabrication of Thick 3D Tissues with Patterned Perfusable Macrochannels.

Elham Davoodi, Hossein Montazerian, Masoud Zhianmanesh, Reza Abbasgholizadeh, Reihaneh Haghniaz, Avijit Baidya, Homeyra Pourmohammadali, Nasim Annabi, Paul S Weiss, Ehsan Toyserkani and 1 more

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Advanced healthcare materials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
1.8field-weighted citation impact, top 16% of its field
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

13 citing papers in PubMed, 26 citations in OpenAlex.

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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

11 authors at 5 institutions in 3 countries.

Elham DavoodiMulti-Scale Additive Manufacturing Laboratory, Mechanical and Mechatronics Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0001-8578-9431
Hossein MontazerianDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.ORCID 0000-0001-6972-2667
Masoud ZhianmaneshSchool of Biomedical Engineering, University of Sydney, Sydney, New South Wales, 2006, Australia.ORCID 0000-0001-9645-1276
Reza AbbasgholizadehTerasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA.ORCID 0000-0003-0904-6489
Reihaneh HaghniazTerasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA.ORCID 0000-0003-0033-891X
Avijit BaidyaDepartment of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.ORCID 0000-0001-5215-2856
Homeyra PourmohammadaliDepartment of System Design Engineering, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0001-8545-4981
Nasim AnnabiDepartment of Chemical and Biomolecular Engineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.ORCID 0000-0003-1879-1202
Paul S WeissDepartment of Bioengineering, University of California, Los Angeles, Los Angeles, CA, 90095, USA.ORCID 0000-0001-5527-6248
Ehsan ToyserkaniMulti-Scale Additive Manufacturing Laboratory, Mechanical and Mechatronics Engineering Department, University of Waterloo, 200 University Avenue West, Waterloo, ON N2L 3G1, Canada.ORCID 0000-0002-6716-9500
Ali KhademhosseiniTerasaki Institute for Biomedical Innovation, Los Angeles, CA, 90024, USA.ORCID 0000-0002-2692-1524
California NanoSystems Institute · USTerasaki Foundation · USUniversity of California, Los Angeles · USUniversity of Waterloo · CAThe University of Sydney · AU

Funding

3D printed muscle-bone organ implant for treating large injuriesR01AR073135 · NIAMS · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI DOKMECI, MEHMET REMZI, JANG, HAE LIN · 2018 to 2022
$2.2M
NIAMS NIH HHS R01 AR073135
6 · The paper itself

Abstract

Interconnected pathways in 3D bioartificial organs are essential to retaining cell activity in thick functional 3D tissues. 3D bioprinting methods have been widely explored in biofabrication of functionally patterned tissues; however, these methods are costly and confined to thin tissue layers due to poor control of low-viscosity bioinks. Here, cell-laden hydrogels that could be precisely patterned via water-soluble gelatin templates are constructed by economical extrusion 3D printed plastic templates. Tortuous co-continuous plastic networks, designed based on triply periodic minimal surfaces (TPMS), serve as a sacrificial pattern to shape the secondary sacrificial gelatin templates. These templates are eventually used to form cell-encapsulated gelatin methacryloyl (GelMA) hydrogel scaffolds patterned with the complex interconnected pathways. The proposed fabrication process is compatible with photo-crosslinkable hydrogels wherein prepolymer casting enables incorporation of high cell populations with high viability. The cell-laden hydrogel constructs are characterized by robust mechanical behavior. In vivo studies demonstrate a superior cell ingrowth into the highly permeable constructs compared to the bulk hydrogels. Perfusable complex interconnected networks within cell-encapsulated hydrogels may assist in engineering thick and functional tissue constructs through the permeable internal channels for efficient cellular activities in vivo.

Indexed as

BioprintingGelatinHydrogelsMethacrylatesPlasticsPrinting, Three-DimensionalTissue EngineeringTissue ScaffoldsGelatingelatin methacryloylHydrogelsMethacrylatesPlastics3D bioprintingadditive manufacturingbiofabricationcell-laden hydrogelsgelatin methacryloyl

Identifiers

PMID34967148
PMCPMC8986588
OpenAlexW4200467027

What OpenQuestion holds

Textmetadata
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.