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Record W3178323990 · doi:10.3390/molecules26144129

ILs and MMPs Levels in Inflamed Human Dental Pulp: A Systematic Review

2021· review· en· W3178323990 on OpenAlexaboutno aff
Konstantina Kritikou, Maria Greabu, Marina Imre, Daniela Miricescu, Alexandra Totan, Marian Burcea, Iulia-Ioana Stănescu-Spînu, Tudor Spinu

Bibliographic record

VenueMolecules · 2021
Typereview
Languageen
FieldDentistry
TopicEndodontics and Root Canal Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMatrix metalloproteinasePulp (tooth)PulpitisMedicineCochrane LibraryInflammationDentistryInternal medicineMeta-analysis

Abstract

fetched live from OpenAlex

A wide range of mediators are released from the pulp tissue because of bacterial invasion which causes inflammation. Interleukins (ILs) and matrix metalloproteinases (MMPs) have a leading role in initiating and spreading of inflammation because of their synergic action. Biomarkers such as ILs and MMPs can be identified via several methods, establishing the inflammatory response of the dental pulp. The aim of this systematic review is to evaluate the levels of ILs and/or MMPs in human dental pulp. PubMed, OVID, Cochrane, Scopus, Web of Science and Wiley online library databases were searched for original clinical studies. After applying inclusion and exclusion criteria, a quality assessment of studies was performed based on a modified Newcastle-Ottawa scale. In the review were included articles that evaluated the presence of ILs and/or MMPs in pulp tissue using enzyme-linked immunosorbent assay (ELISA) or western blot or multiplex assay. Six articles were included in the present synthesis. Although various diagnostic methods were used, statistically significant higher levels of ILs and/or MMPs were mostly found in the experimental groups compared to healthy pulp samples. The biomarkers studied can be a promising tool to evaluate pulp tissue health or even in pulpitis treatment.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.060
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.000
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 head0.066
GPT teacher head0.377
Teacher spread0.311 · 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 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

Citations28
Published2021
Admission routes1
Has abstractyes

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