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Biomineralization and Biomaterial Considerations in Dentin Remineralization

2016· article· en· W2959642863 on OpenAlexaff
Xu Zhang, Zuohui Xiao, Haorong Wang, Anil Kishen

Bibliographic record

VenueJournal of Operative Dentistry & Endodontics · 2016
Typearticle
Languageen
FieldDentistry
TopicDental Erosion and Treatment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsDemineralizationDentinBiomineralizationRemineralisationBiomaterialTooth RemineralizationDentistryChemistryMineralized tissuesMaterials scienceBiomedical engineeringNanotechnologyChemical engineeringEnamel paintEngineeringMedicine

Abstract

fetched live from OpenAlex

ABSTRACT Dentin is a composite hard tissue, comprising of inorganic and organic matrices, and regulated by many proteins during development. The demineralization of dentin results from the loss of inorganic matrix [mainly hydroxyapatite (HAP)], but the organic matrix (mainly type I collagen) will sustain for a period of time after demineralization. Over the past decade, there has been a growing interest on the remineralization of demineralized dentin, primarily in connection with minimally invasive caries management. More and more biomaterials and methods are currently being evaluated to achieve newer approaches for the remineralization of demineralized dentin. These strategies are mostly based on biomimetic approaches and aim to achieve the characteristics of natural hard tissue. This article will present a complete review on the basic compositions and properties of dentin, which formed the basis for the biomimetic remineralization of demineralized dentin. How to cite this article Zhang X, Xiao Z, Wang H, Kishen A. Biomineralization and Biomaterial Considerations in Dentin Remineralization. J Oper Dent Endod 2016;1(1):7-12.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.028
GPT teacher head0.320
Teacher spread0.292 · 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 designTheoretical or conceptual
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

Citations9
Published2016
Admission routes1
Has abstractyes

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Same venueJournal of Operative Dentistry & EndodonticsSame topicDental Erosion and TreatmentFrench-language works237,207