Treatment of Oral Biofilms by a D-Enantiomeric Peptide
Bibliographic record
Abstract
Almost all dental diseases are caused by biofilms that consist of multispecies communities. DJK-5, which is a short D-enantiomeric, protease-resistant peptide with broad-spectrum anti-biofilm activity, was tested for its effect on oral multispecies biofilms. Peptide DJK-5 at 10 μg/mL effectively prevented the growth of these microbes in culture media in a time-dependent manner. In addition to the prevention of growth, peptide DJK-5 completely killed both Streptococcus mutans and Enterococcus faecalis suspended from biofilms after 30 minutes of incubation in liquid culture media. DJK-5 also led to the effective killing of microbes in plaque biofilm. The proportion of bacterial cells killed by 10 μg/mL of DJK-5 was similar after 1 and 3 days, both exceeding 85%. DJK-5 was able to significantly prevent biofilm formation over 3 days (P = 0.000). After 72 hours of exposure, DJK-5 significantly reduced and almost completely prevented plaque biofilm production by more than 90% of biovolume compared to untreated controls (P = 0.000). The proportion of dead biofilm bacteria at the 10 μg/mL DJK-5 concentration was similar for 1- and 3-day-old biofilms, whereby >86% of the bacteria were killed. DJK-5 was also able to kill >79% and >85% of bacteria, respectively, after one-time and three brief treatments of 3-day-old biofilms. The combination of DJK-5 and chlorhexidine showed the best bacterial killing among all treatments, with ~83% and >88% of bacterial cells killed after 1 and 3 minutes, respectively. No significant difference was found in the percentage of biofilm killing amongst three donor plaque samples after DJK-5 treatment. In particular, DJK-5 showed strong performance in inhibiting biofilm development and eradicating pre-formed oral biofilms compared to L-enantiomeric peptide 1018. DJK-5 was very effective against oral biofilms when used alone or combined with chlorhexidine, and may be a promising agent for use in oral anti-biofilm strategies in the future.
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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".