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Record W2795484731 · doi:10.1002/jbio.201700368

Whole‐field macro‐ and micro‐deformation characteristic of unbound water‐loss in dentin hard tissue

2018· article· en· W2795484731 on OpenAlexafffund
Zhenning Chen, Bobby Nadeau, Kevin Yu, Xinxing Shao, Xiaoyuan He, M. Cynthia Goh, Anil Kishen

Bibliographic record

VenueJournal of Biophotonics · 2018
Typearticle
Languageen
FieldDentistry
TopicDental materials and restorations
Canadian institutionsUniversity of Toronto
FundersUniversity of TorontoNational Natural Science Foundation of China
KeywordsDentinDentinal TubuleMaterials scienceComposite materialDeformation (meteorology)Human toothBiomedical engineeringEnamel paint

Abstract

fetched live from OpenAlex

High-resolution deformation measurements in a functionally graded hard tissue such as human dentin are essential to understand the unbound water-loss mediated changes and their role in its mechanical integrity. Yet a whole-field, 3-dimensional (3D) measurement and characterization of fully hydrated dentin in both macro- and micro-scales remain to be a challenge. This study was conducted in 2 stages. In stage-1, a stereo-digital image correlation approach was utilized to determine the water-loss and load-induced 3D deformations of teeth in a sagittal section over consecutively acquired frames, from a fully hydrated state to nonhydrated conditions for a period up to 2 hours. The macroscale analysis revealed concentrated residual deformations at the dentin-enamel-junction and the apical regions of root in the direction perpendicular to the dentinal tubules. Significant difference in the localized deformation characteristics was observed between the inner and outer aspects of the root dentin. During quasi-static loadings, further increase in the residual deformation was observed in the dentin. In stage-2, dentin microstructural variations induced by dynamic water-loss were assessed with environmental scanning electron microscopy and atomic force microscopy (AFM), showing that the dynamic water-loss induced distention of dentinal tubules with concave tubular edges, and concurrent contraction of intertubular dentin with convex profile. The findings from the current macro- and micro-scale analysis provided insight on the free-water-loss induced regional deformations and ultrastructural changes in human dentin.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.093
Threshold uncertainty score0.273

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.010
GPT teacher head0.266
Teacher spread0.256 · 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

Citations9
Published2018
Admission routes2
Has abstractyes

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