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Record W4318253628 · doi:10.4322/bds.2023.e3666

Insights on the role of cytokines in carious lesions

2023· article· en· W4318253628 on OpenAlexaff
L.L. Gonçalves, Eui Kim, Janaína Freitas Bortolatto, Marilia Rabello Buzalaf, L Alreshaid, Anuradha Prakki

Bibliographic record

VenueBrazilian Dental Science · 2023
Typearticle
Languageen
FieldDentistry
TopicOral microbiology and periodontitis research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsPathogenesisPulp (tooth)InterleukinTumor necrosis factor alphaPathologicalMedicineImmune systemImmunologyProinflammatory cytokineDiseaseBiomarkerCytokineDentistryInflammationPathologyBiology

Abstract

fetched live from OpenAlex

Objectives: The dentin-pulp immune response to caries pathogenesis is still poorly understood due to the complexinterplay of the involving processes. The aim of this review was to explore the role of cytokines and its relevance inthe pathogenesis of dental caries. Results: Dental caries can result in a host inflammatory response in the dentalpulp, characterized by the accumulation of inflammatory cells leading to the release of inflammatory cytokinessuch as Interleukin-4 (IL-4), Interleukin (IL-6), Interleukin-8 (IL-8) and Tumor necrosis factor–a (TNF-a). IL-4seems to be correlated to the depth of carious lesions; IL-6 is strongly correlated to caries disease and is considereda potent biomarker; IL-8 can be a potent biomarker for both caries and other changes present in the pulp and,its release is correlated to TNF-a and IL-6; TNF-a plays an important role not only in caries progression, but alsoin other pathological processes. Conclusion: Specific mediators have a great potential to serve as biomarkersalluding to the presence and progress of caries disease, urging further investigations in the field. KEYWORDSBiomarkers; Cytokines; Dental caries; Dental pulp; Interleukins.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.631
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.020
GPT teacher head0.307
Teacher spread0.287 · 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.

Study designObservational
Domainnot available
GenreEmpirical

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

Citations1
Published2023
Admission routes1
Has abstractyes

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