Cocktail Approach with Polyserotonin Nanoparticles and Peptides for Treatment of <i>Streptococcus mutans</i>
Bibliographic record
Abstract
Dental plaque, formed by a Streptococcus mutans biofilm, is a major contributor to cavity formation. While antimicrobial strategies exist, the growing risk of antibiotic resistance necessitates alternative therapeutic solutions. Polyserotonin nanoparticles (PSeNPs), recently recognized for their photothermal property and promising biomedical applications, open up a new avenue for antimicrobial use. Here, we introduced a UV-initiated synthetic route for PSeNPs with improved yield. Using these PSeNPs, a cocktail treatment to reduce the viability of this cavity-causing bacteria was developed. This cocktail comprises an S. mutans -targeting antimicrobial peptide (GH12), an intraspecies competence-stimulating peptide that triggers altruistic cell death in S. mutans, and laser-activated heating of PSeNPs. The “peptide + PSeNP + laser” combination effectively inhibits S. mutans growth in both planktonic and biofilm states. Moreover, the cocktail approach remains effective in reducing the viability of S. mutans in a more virulent dual-species biofilm with Candida albicans . Overall, our results reinforce the utility of a multipronged therapeutic strategy to reduce cariogenic bacteria in the complex model oral biofilm.
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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 | 0.001 |
| 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".