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Record W3135426067 · doi:10.1016/j.actbio.2021.03.016

Biofabrication and bioprinting using cellular aggregates, microtissues and organoids for the engineering of musculoskeletal tissues

2021· review· en· W3135426067 on OpenAlexaff
Ross Burdis, Daniel J. Kelly

Bibliographic record

VenueActa Biomaterialia · 2021
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsTrinity College
FundersEuropean Research CouncilEuropean Regional Development FundScience Foundation Ireland
KeywordsBiofabricationOrganoidTissue engineering3D bioprintingBiomedical engineeringMaterials scienceNanotechnologyCell biologyEngineeringBiology

Abstract

fetched live from OpenAlex

The modest clinical impact of musculoskeletal tissue engineering (TE) can be attributed, at least in part, to a failure to recapitulate the structure, composition and functional properties of the target tissue. This has motivated increased interest in developmentally inspired TE strategies, which seek to recapitulate key events that occur during embryonic and post-natal development, as a means of generating truly biomimetic grafts to replace or regenerate damaged tissues and organs. Such TE strategies can be substantially enabled by emerging biofabrication and bioprinting strategies, and in particular the use of cellular aggregates, microtissues and organoids as 'building blocks' for the development of larger tissues and/or organ precursors. Here, the application of such biological building blocks for the engineering of musculoskeletal tissues, from vascularised bone to zonally organised articular cartilage, will be reviewed. The importance of first scaling-down to later scale-up will be discussed, as this is viewed as a key component of engineering functional grafts using cellular aggregates or microtissues. In the context of engineering anatomically accurate tissues of scale suitable for tissue engineering and regenerative medicine applications, novel bioprinting modalities and their application in controlling the process by which cellular aggregates or microtissues fuse and self-organise will be reviewed. Throughout the paper, we will highlight some of the key challenges facing this emerging field. STATEMENT OF SIGNIFICANCE: The field of bioprinting has grown substantially in recent years, but despite the hype and excitement it has generated, there are relatively few examples of bioprinting strategies producing implants with superior regenerative potential to that achievable with more traditional tissue engineering approaches. This paper provides an up-to-date review of emerging biofabrication and bioprinting strategies which use cellular aggregates and microtissues as 'building blocks' for the development of larger musculoskeletal tissues and/or organ precursors - a field of research that can potentially enable functional regeneration of damaged and diseased tissues. The application of cellular aggregates and microtissues for the engineering of musculoskeletal tissues, from vascularised bone to zonally organised articular cartilage, will be reviewed. In the context of engineering anatomically accurate tissues of scale, novel bioprinting modalities and their application in controlling the process by which cellular aggregates or microtissues self-organise is addressed, as well as key challenges facing this emerging field.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.037
GPT teacher head0.329
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations95
Published2021
Admission routes1
Has abstractyes

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