Development and Evaluation of Models for the Study of Enamel Remineralization
Bibliographic record
Abstract
Hydroxyapatite (HAP) products have been approved as an anti-caries agent in Canada in 2015 and is currently being explored as fluoride-free alternative for the remineralization of WSL. In the first phase of our study, we performed a scoping review comparing the use of fluoride and HAP on caries prevention in vitro and in vivo. The results of our study suggest that HAP toothpaste performs equally as well as fluoride toothpaste at preventing caries in school age children. In the second phase, we used a lactic acid buffer to create artificial white spot lesions (WSL) in human extracted molars. Using this protocol, we were successfully able to induce enamel WSL and characterize them using both micro computed tomography (microCT) and optical coherence tomography (OCT). The WSL using this protocol were then compared to naturally occurring WSL. In the last phase of our study we investigated the use of amelotin (AMTN) coated HAP nanoparticles on the remineralization of artificially induced WSL. Our preliminary data suggest that 2 hours of incubation with AMTN-HAP nanoparticles significantly increased the mean mineral density and decreased the artificial WSL depth compared to controls. Collectively our early results suggest that AMTN-HAP nanoparticles are effective at remineralizing artificial WSL under the in vitro model system used. The research presented may be used to help with the development of novel therapeutic strategies for caries prevention.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".