Treatment of Oral Multispecies Biofilms by an Anti-Biofilm Peptide
Bibliographic record
Abstract
Human oral biofilms are multispecies microbial communities that exhibit high resistance to antimicrobial agents. Dental plaque gives rise to highly prevalent and costly biofilm-related oral infections, which lead to caries or other types of oral infections. We investigated the ability of the recently identified anti-biofilm peptide 1018 to induce killing of bacterial cells present within oral multispecies biofilms. At 10 μg/ml (6.5 μM), peptide 1018 was able to significantly (p<0.05) prevent biofilm formation over 3 days. The activity of the peptide on preformed biofilms was found to be concentration-dependent since more than 60% of the total plaque biofilm cell population was killed by 10 μg/ml of peptide 1018 in 3 days, while at 5 μg/ml 50% of cells were dead and at 1 μg/ml the peptide triggered cell death in around 30% of the total bacterial population, as revealed by confocal microscopy. The presence of saliva did not affect peptide activity, since no statistically significant difference was found in the ability of peptide 1018 to kill oral biofilms using either saliva coated and non-saliva coated hydroxyapatite surfaces. Scanning electron microscopy experiments indicated that peptide 1018 induced cell lysis in plaque biofilms. Furthermore, combined treatment using peptide 1018 and chlorhexidine (CHX) increased the anti-biofilm activity of each compound compared to when these were used alone, resulting in >50% of the biofilm being killed and >35% being dispersed in only 3 minutes. Peptide 1018 may potentially be used by itself or in combination with CHX as a non-toxic and effective anti-biofilm agent for plaque disinfection in clinical dentistry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".