Modulation of Streptococcus mutans Adherence to Hydroxyapatite by Engineered Salivary Peptides
Bibliographic record
Abstract
Since the modification of the proteinaceous components of the Acquired Enamel Pellicle (AEP) could influence the adhesion of Streptococcus mutans, the most cariogenic bacteria, to dental surfaces, we assessed if engineered salivary peptides would affect the adherence and modulate the bacterial proteome upon adherence. Single-component AEPs were formed onto hydroxyapatite (HAp) discs by incubating them with statherin, histatin-3, DR9, DR9-DR9, DR9-RR14, RR14, and parotid saliva. Then, the discs were inoculated with S. mutans UA159 and the bacteria were allowed to adhere for 2 h, 4 h, and 8 h (n = 12/treatment/time point). The number of bacteria adhered to the HAp discs was determined at each time point and analyzed by two-way ANOVA and Bonferroni tests. Cell-wall proteins were extracted from adhered, planktonic, and inoculum (baseline) bacteria and proteome profiles were obtained after a bottom-up proteomics approach. The number of adhered bacteria significantly increased over time, being the mean values obtained at 8 h, from highest to lowest, as follows: DR9-RR14 > statherin > RR14 = DR9-DR9 > DR9 = histatin3 > saliva (p < 0.05). Treatments modulated the bacterial proteome upon adherence. The findings suggested a potential use of our engineered peptide DR9-DR9 to control S. mutans biofilm development by reducing bacterial colonization.
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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) | 0.000 | 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 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".