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Record W4323565419 · doi:10.3389/fdmed.2023.1144537

Role of junctional epithelium in maintaining dento-gingival adhesion and periodontal health

2023· review· en· W4323565419 on OpenAlexafffund
Letícia Helena Theodoro, Valdir Gouveia Garcia, Edílson Ervolino, James W. Holcroft, Christopher A. McCulloch, Bernhard Ganss

Bibliographic record

VenueFrontiers in Dental Medicine · 2023
Typereview
Languageen
FieldBiochemistry, Genetics and Molecular Biology
Topicdental development and anomalies
Canadian institutionsUniversity of Toronto
FundersDivision of Graduate EducationCanadian Institutes of Health ResearchCoordenação de Aperfeiçoamento de Pessoal de Nível Superior
KeywordsJunctional epitheliumPeriodontitisAdhesionBasal laminaEnamel paintOral cavityDentistryDental anatomyGingival and periodontal pocketDental laminaEpitheliumCell biologyMedicineBiologyChemistryAnatomyPathologyUltrastructure

Abstract

fetched live from OpenAlex

The dento-gingival junction comprises multiple epithelia including the junctional epithelium (JE), which is the most coronally-located structural element of the dento-gingival junction that demarcates external from internal periodontal environments. After tooth eruption into the oral cavity, a specialized basal lamina is formed that provides a firm attachment of the JE to the enamel. This attachment prevents microbial species and oral debris from entering subjacent periodontal tissues. Here we discuss the expression of certain JE adhesion molecules and enamel proteins that maintain the health of the dento-gingival junction but that are perturbed in the pathogenesis of periodontitis. We also consider how evolutionary processes have influenced the development of the JE as a specialized adhesion that is well-suited for protection of the dento-gingival junction. A detailed understanding of the biology of the JE will deepen current models of dento-gingival adhesion, potentially clarify inter-patient variability of susceptibility to periodontitis and help to identify new roles of enamel proteins in periodontal 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 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)0.000
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 score0.887
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.018
GPT teacher head0.302
Teacher spread0.284 · 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 teacher head, 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

Citations15
Published2023
Admission routes2
Has abstractyes

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