Hydrogen peroxide-based products alter inflammatory and tissue damage-related proteins in the gingival crevicular fluid of healthy volunteers: a randomized trial
Bibliographic record
Abstract
Abstract Hydrogen peroxide (H2O2)-based products are effective in tooth whitening; however, their safety is controversial as they may harm patient tissues/cells. These effects are suggested to be concentration-dependent; nonetheless, to date, there are no reports on H2O2-mediated oxidative damage in the gingival tissue, and neither whether this can be detected in gingival crevicular fluid (GCF) samples. We hypothesize that H2O2 whitening products may cause collateral oxidative tissue damage following in office application. Therefore, H2O2 and nitric oxide (NO) levels were investigated in GCF samples obtained from patients undergoing dental bleaching with H2O2 at different concentrations, in a randomized, double-blind, split-mouth clinical trial. A proteomic analysis of these samples was also performed. H2O2-based whitening products promoted inflammation which was detected in GCF samples and lasted for longer following 35% H2O2 bleaching. This included time-dependent changes in NO levels and in the abundance of proteins associated with NO synthesis, oxidative stress, neutrophil regulation, nucleic acid damage, cell survival and/or tissue regeneration. Overall, H2O2-based products used in office promote inflammation irrespective of their concentration. As the inflammation caused by 35% H2O2 is longer, patients may benefit better from using lower concentrations of this bleaching product, as they may result in less tissue damage.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".