Dental plaque-inspired versatile nanosystem for caries prevention and tooth restoration
Bibliographic record
Abstract
Dental caries is one of the most prevalent human diseases resulting from tooth demineralization caused by acid production of bacteria plaque. It remains challenges for current practice to specifically identify, intervene and interrupt the development of caries while restoring defects. In this study, inspired by natural dental plaque, a stimuli-responsive multidrug delivery system (PMs@NaF-SAP) has been developed to prevent tooth decay and promote enamel restoration. Classic spherical core-shell structures of micelles dual-loaded with antibacterial and restorative agents are self-assembled into bacteria-responsive multidrug delivery system based on the pH-cleavable boronate ester bond, followed by conjugation with salivary-acquired peptide (SAP) to endow the nanoparticle with strong adhesion to tooth enamel. The constructed PMs@NaF-SAP specifically adheres to tooth, identifies cariogenic conditions and intelligently releases drugs at acidic pH, thereby providing antibacterial adhesion and cariogenic biofilm resistance, and restoring the microarchitecture and mechanical properties of demineralized teeth. Topical treatment with PMs@NaF-SAP effectively diminishes the onset and severity of caries without impacting oral microbiota diversity or surrounding mucosal tissues. These findings demonstrate this novel nanotherapy has potential as a promising biomedical application for caries prevention and tooth defect restoration while resisting biofilm-associated diseases in a controlled manner activated by pathological bacteria.
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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 | 0.000 | 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) | 0.001 | 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 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".