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Record W4281481783 · doi:10.1038/s41598-022-13081-9

Effect of an enamel matrix derivative (Emdogain) on the microhardness and chemical composition of human root dentin: an in vitro study

2022· article· en· W4281481783 on OpenAlexaff
Karime Tavares Lima da Silva, Renata Grazziotin‐Soares, Rafael Resende de Miranda, Veridiana Resende Novais, Edilausson Moreno Carvalho, Gisele Rodrigues da Silva, José Bauer, Ceci Nunes Carvalho

Bibliographic record

VenueScientific Reports · 2022
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsUniversity of Saskatchewan
FundersFundação de Amparo à Pesquisa e ao Desenvolvimento Científico e Tecnológico do MaranhãoConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsDentinEnamel matrix derivativeDentistryKnoop hardness testFourier transform infrared spectroscopyEnamel paintRoot canalScanning electron microscopeMaterials scienceIndentation hardnessNuclear chemistryChemistryComposite materialMicrostructureMedicineChemical engineering

Abstract

fetched live from OpenAlex

The advantage of using an Enamel matrix derivative EMD Emdogain as an intracanal medication could be a manner to strength the tooth structure, improving the physical and chemical properties of dentin. We tested, in vitro, the effect of Emdogain on the surface microhardness and chemical composition of root dentin. Ten human teeth were used to produce dentin specimens originated from the canal walls (n = 30) that remained in contact to Emdogain gel for 90 days. Baseline and 90-days after Emdogain treatment measurements were performed using Fourier Transform Infrared Spectroscopy (ATR/FTIR), Scanning Electron Microscopy/Energy Dispersive Spectroscopy (SEM/EDS) and Knoop indenters. The use of EMD (Emdogain) for 90 days in contact with human root canal dentin specimens did not alter the microhardness and morphology of dentin. The elemental structure of dentin was altered because there was a reduction in carbonate content.

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)0.002
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.403

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.013
GPT teacher head0.313
Teacher spread0.300 · 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 designBench or experimental
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

Citations6
Published2022
Admission routes1
Has abstractyes

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