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Record W4205464851 · doi:10.3390/biom12010141

Smart Bioinks for the Printing of Human Tissue Models

2022· review· en· W4205464851 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueBiomolecules · 2022
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of British ColumbiaUniversity of VictoriaUniversity of Ottawa
FundersNatural Sciences and Engineering Research Council of CanadaBiotalent CanadaCanada Research ChairsCanadian Institutes of Health ResearchAlzheimer's AssociationInnovate BCMichael Smith Health Research BC
KeywordsRegenerative medicine3D bioprintingComputer scienceTissue engineeringProcess (computing)Field (mathematics)NanotechnologyEngineeringArtificial intelligenceBiochemical engineeringBiomedical engineeringMaterials scienceBiologyStem cell

Abstract

fetched live from OpenAlex

3D bioprinting has tremendous potential to revolutionize the field of regenerative medicine by automating the process of tissue engineering. A significant number of new and advanced bioprinting technologies have been developed in recent years, enabling the generation of increasingly accurate models of human tissues both in the healthy and diseased state. Accordingly, this technology has generated a demand for smart bioinks that can enable the rapid and efficient generation of human bioprinted tissues that accurately recapitulate the properties of the same tissue found in vivo. Here, we define smart bioinks as those that provide controlled release of factors in response to stimuli or combine multiple materials to yield novel properties for the bioprinting of human tissues. This perspective piece reviews the existing literature and examines the potential for the incorporation of micro and nanotechnologies into bioinks to enhance their properties. It also discusses avenues for future work in this cutting-edge 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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.995
Threshold uncertainty score0.808

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.151
GPT teacher head0.393
Teacher spread0.242 · 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