DENTA: A Dual Enzymatic Nanoagent for Self‐Activating Tooth Whitening and Biofilm Disruption
Bibliographic record
Abstract
Abstract Traditional tooth whitening agents, particularly those based on hydrogen peroxide, are effective in achieving significant dental whitening but are prone to side effects such as tooth hypersensitivity and structural damage because of the highly concentrated production of reactive oxygen species (ROS). Although various nanoparticles, including metal oxides, ceramics, and piezoelectric materials, have been explored as alternative whitening agents, they often exhibit limitations such as insufficient ROS generation, poor biocompatibility, and an increased risk of bacterial infections. In this study, an innovative nanoapatite‐based whitening material is presented, termed DENTA (Dual Enzymatic Nanoagent for Tooth‐whitening and Antibiofilm Activity), which harnesses dual enzyme catalysis by integrating glucose oxidase and catalase. Designed to penetrate the dentinal tubules, DENTA leverages naturally occurring salivary glucose to continuously produce ROS, enabling effective and prolonged whitening while mitigating the side effects of conventional agents through controlled and reduced ROS concentrations. Moreover, DENTA exhibits distinctive antibacterial activity driven by glucose‐responsive ROS generation within biologically relevant concentration ranges, while maintaining excellent biocompatibility with oral cells under glucose‐supplemented conditions. This groundbreaking enzymatic nano‐whitening approach, validated under simulated tooth‐brushing conditions, positions DENTA as a promising candidate for safe, effective, and multifunctional tooth whitening in practical at‐home applications.
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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".