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Record W2739268315 · doi:10.3390/ijms18071597

Application of Extrusion-Based Hydrogel Bioprinting for Cartilage Tissue Engineering

2017· review· en· W2739268315 on OpenAlexafffund
Fu You, B. Frank Eames, Daniel Chen

Bibliographic record

VenueInternational Journal of Molecular Sciences · 2017
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Saskatchewan
FundersChina Scholarship CouncilNatural Sciences and Engineering Research Council of CanadaSaskatchewan Health Research Foundation
Keywords3D bioprintingSelf-healing hydrogelsExtrusionMaterials scienceTissue engineeringCartilageBiofabricationBiocompatibilityNanotechnologyRegenerative medicine3D printingBiomedical engineeringComposite materialEngineeringChemistryCellAnatomyPolymer chemistry

Abstract

fetched live from OpenAlex

Extrusion-based bioprinting (EBB) is a rapidly developing technique that has made substantial progress in the fabrication of constructs for cartilage tissue engineering (CTE) over the past decade. With this technique, cell-laden hydrogels or bio-inks have been extruded onto printing stages, layer-by-layer, to form three-dimensional (3D) constructs with varying sizes, shapes, and resolutions. This paper reviews the cell sources and hydrogels that can be used for bio-ink formulations in CTE application. Additionally, this paper discusses the important properties of bio-inks to be applied in the EBB technique, including biocompatibility, printability, as well as mechanical properties. The printability of a bio-ink is associated with the formation of first layer, ink rheological properties, and crosslinking mechanisms. Further, this paper discusses two bioprinting approaches to build up cartilage constructs, i.e., self-supporting hydrogel bioprinting and hybrid bioprinting, along with their applications in fabricating chondral, osteochondral, and zonally organized cartilage regenerative constructs. Lastly, current limitations and future opportunities of EBB in printing cartilage regenerative constructs are reviewed.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.985
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.054
GPT teacher head0.395
Teacher spread0.342 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations178
Published2017
Admission routes2
Has abstractyes

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