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Record W3204873255 · doi:10.1002/adma.202104730

Emerging Technologies in Multi‐Material Bioprinting

2021· review· en· W3204873255 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAdvanced Materials · 2021
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsMcGill University
FundersNational Institute of Biomedical Imaging and BioengineeringNational Institute on Deafness and Other Communication DisordersNational Science FoundationNational Institutes of HealthNational Cancer InstituteNational Heart, Lung, and Blood Institute
KeywordsBiofabrication3D bioprintingNanotechnologyMaterials scienceTissue engineeringSystems engineeringComputer scienceBiochemical engineeringBiomedical engineeringEngineering

Abstract

fetched live from OpenAlex

Bioprinting, within the emerging field of biofabrication, aims at the fabrication of functional biomimetic constructs. Different 3D bioprinting techniques have been adapted to bioprint cell-laden bioinks. However, single-material bioprinting techniques oftentimes fail to reproduce the complex compositions and diversity of native tissues. Multi-material bioprinting as an emerging approach enables the fabrication of heterogeneous multi-cellular constructs that replicate their host microenvironments better than single-material approaches. Here, bioprinting modalities are reviewed, their being adapted to multi-material bioprinting is discussed, and their advantages and challenges, encompassing both custom-designed and commercially available technologies are analyzed. A perspective of how multi-material bioprinting opens up new opportunities for tissue engineering, tissue model engineering, therapeutics development, and personalized medicine is offered.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.970
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
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 head0.051
GPT teacher head0.379
Teacher spread0.328 · 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