Acquired Enamel Pellicle Engineered Peptides: Effects on Hydroxyapatite Crystal Growth
Bibliographic record
Abstract
Abstract The aim of this study was to test the hypothesis that duplication/hybridization of functional domains of naturally occurring pellicle peptides amplified the inhibitory effect of hydroxyapatite crystal growth, which is related to enamel remineralization and dental calculus formation. Histatin 3, statherin, their functional domains (RR14 and DR9), and engineered peptides (DR9-DR9 and DR9-RR14) were tested at seven different concentrations to evaluate the effect on hydroxyapatite crystal growth inhibition. A microplate colorimetric assay was used to quantify hydroxyapatite crystal growth. The half-maximal inhibitory concentration (IC50) was determined for each group. ANOVA and Student-Newman-Keuls pairwise comparisons were used to compare the groups. DR9-DR9 increased the inhibitory effect of hydroxyapatite crystal growth compared to single DR9 (p < 0.05), indicating that functional domain multiplication represented a strong protein evolution pathway. Interestingly, the hybrid peptide DR9-RR14 had an intermediate inhibitory effect compared to DR9 and DR9-DR9. This study used an engineered peptide approach to investigate a potential evolution protein pathway related to duplication/hybridization of acquired enamel pellicle’s natural peptide constituents, contributing to the development of synthetic peptides for therapeutic use against dental caries and periodontal disease.
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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".