Engineered Salivary Peptides Reduce Enamel Demineralization Provoked by Cariogenic S. mutans Biofilm
Bibliographic record
Abstract
Engineering of the acquired enamel pellicle using salivary peptides has been shown to be a promising anticaries strategy. However, the mechanisms by which these peptides protect teeth against tooth decay are not fully understood. In this study, we evaluated the effect of the engineered salivary peptides DR9-DR9 and DR9-RR14 on enamel demineralization in two experimental conditions: (1) adsorbed onto the enamel surface forming the AEP, and (2) forming the AEP combined with their use to treat the biofilms 2×/day, using a validated cariogenic Streptococcus mutans in vitro biofilm model. Biofilms were grown for 144 h on enamel slabs and then collected to determine the bacterial viability (CFU/biofilm) and biofilm mass (mg protein/biofilm), and to extract cellular/extracellular proteins, which were characterized by mass spectrometry. The culture medium was changed 2×/day to fresh medium, and pH (indicator of biofilm acidogenicity) and calcium concentration (indicator of demineralization) was determined in used medium. DR9-RR14 peptide significantly reduced enamel demineralization (p < 0.0001) in both experimental conditions. However, this peptide did not have a significant effect on biofilm biomass (p > 0.05) nor did it modulate the expression of cellular and extracellular bacterial proteins involved in biofilm cariogenicity. These findings suggest that DR9-RR14 may control caries development mainly by a physicochemical mechanism.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".