Antibiofilm peptides against biofilms on titanium and hydroxyapatite surfaces
Bibliographic record
Abstract
Biofilms are the main challenges in the treatment of common oral diseases such as caries, gingival and endodontic infection and periimplantitis. Oral plaque is the origin of microbes colonizing in the form of biofilms on hydroxyapatite (tooth) and titanium (dental implant) surfaces. In this study, hydroxyapatite (HA) and titanium (Ti) disks were introduced, and their surface morphology was both qualitatively and quantitatively analyzed by a scanning electron microscope (SEM) and atomic force microscope (AFM). The average roughness of Ti disks (77.6 ± 18.3 nm) was less than that of HA (146.1 ± 38.5 nm) (p < 0.05). Oral multispecies biofilms which were cultured on Ti and HA disks for 6 h and three weeks were visualized by SEM. We investigated the ability of two new antibiofilm peptides, DJK-5 and 1018, to induce killing of bacteria in oral multispecies biofilms on Ti and HA disks. A 6-h treatment by DJK-5 and 1018 (2 or 10 μg/mL) significantly reduced biomass of the multispecies biofilms on both Ti and HA disks. DJK-5 was able to kill more bacteria (40.4–75.9%) than 1018 (30.4–67.0%) on both surfaces (p < 0.05). DJK-5 also led to a more effective killing of microbes after a 3-min treatment of 3-day-old and 3-week-old biofilms on Ti and HA surfaces, compared to peptide 1018 and chlorhexidine (p < 0.05). No significant difference was found in the amount of biofilm killing between Ti and HA surfaces. Both peptide DJK-5 and 1018 may potentially be used as effective antibiofilm agents in clinical dentistry.
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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".