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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 OpenAlexaff
Nátaly Domingues Almeida, Camila Alves Carneiro, Andréa Carvalho De Marco, Vinícius Carvalho Porto, Rodrigo França

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.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.006
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.005
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.002

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

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 designSystematic review
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

Citations22
Published2024
Admission routes1
Has abstractyes

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