The effect of Hydrogen Peroxide treatments on dental enamel porosity and protein structure and its long-term implications on tooth hardness and optical properties
Bibliographic record
Abstract
OBJECTIVE: The aim of this study is to investigate how hydrogen peroxide (HP) treatments affect enamel porosity and protein structure, as well as their impact on tooth shade and hardness over time. METHODS: Fifty healthy teeth were collected from adult patients. Teeth were randomly divided into 2 groups, the first was incubated with 30 % HP while the second, the control group, was incubated with distilled water (DW). Tooth shade and enamel microhardness were evaluated using a digital spectrophotometer and a Vickers tester, respectively, at different time intervals after treatment. The specific surface area of the enamel was measured using a surface area analyzer and the Brunauer-Emmett-Teller (BET) equation. Protein structure was analyzed using circular dichroism (CD) spectroscopy and dynamic light scattering (DLS). RESULTS: Shade analysis revealed that, in the short term, HP treatment significantly increased lightness and Hue and decreased chroma compared to DW (p < 0.05); however, 1 week after treatment some of the initial gains in tooth lightness were partially lost. Hardness analysis revealed significant decreases in microhardness in the bleached group compared to the control group (p < 0.05). BET analysis revealed that HP treatment increased enamel surface area and reduced its average pore size. CD and DLS analyses showed that proteins from HP-treated teeth mostly adopted a non-random conformation and had smaller average protein sizes compared to the control. CONCLUSION: HP treatment lightened tooth shade and significantly reduced enamel microhardness over time. This could be related to changes in surface area and porosity caused by denaturalization of the enamel proteins. CLINICAL SIGNIFICANCE: Our findings showed that HP induced denaturation of enamel proteins, resulting in increased enamel porosity and reduced microhardness. These changes in enamel properties could help explain clinical complications observed with these treatments such as increased sensitivity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".