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Record W4401482572 · doi:10.3390/biomimetics9080480

3D Bioprinting Techniques and Bioinks for Periodontal Tissues Regeneration—A Literature Review

2024· review· en· W4401482572 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

VenueBiomimetics · 2024
Typereview
Languageen
FieldEngineering
Topic3D Printing in Biomedical Research
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsRegeneration (biology)3D bioprintingBiomedical engineeringPeriodontal fiberRegenerative medicineDentistryTissue engineeringProcess (computing)Computer scienceMedicineStem cellCell biologyBiology

Abstract

fetched live from OpenAlex

The periodontal tissue is made up of supporting tissues and among its functions, it promotes viscoelastic properties, proprioceptive sensors, and dental anchorage. Its progressive destruction by disease leads to the loss of bone and periodontal ligaments. For this reason, biomaterials are constantly being developed to restore tissue function. Various techniques are being used to promote regenerative dentistry, including 3D bioprinting with bioink formulations. This paper aims to review the different types of bioink formulations and 3D bioprinting techniques used in periodontal tissue regeneration. Different techniques have been formulated, and the addition of different materials into bioinks has been conducted, with the intention of improving the process and creating a bioink that supports cell viability, proliferation, differentiation, and stability for periodontal tissue regeneration.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.843
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.039
GPT teacher head0.369
Teacher spread0.330 · 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