Decontamination of rough implant surfaces colonized by multispecies oral biofilm by application of leukocyte‐ and platelet‐rich fibrin
Bibliographic record
Abstract
BACKGROUND: Decontamination of biofilm-infected rough implant surfaces is challenging. Platelet rich blood products have been shown to have anti-microbial properties against periodontal pathogens. Our aim was to investigate the effect of a potential biological implant surface disinfectant, leukocyte- and platelet-rich fibrin (L-PRF), on a mature oral multispecies biofilm on a rough titanium surface. METHODS: Sandblasted, large grit, acid-etched (SLA) titanium disks were inoculated with subgingival dental plaque and cultured anaerobically for 21 days. The L-PRF membranes were collected from 12 donors in three trials (four donors in each trial). The disks were rinsed with 0.9% NaCl and exposed to the cell-rich portion of the L-PRF membranes for 48 hours followed by scanning electron microscope (SEM) analysis immediately or after rinsing with 0.9% NaCl prior to fixation. The presence of platelet factor-4 in the rinse samples was analyzed by Western blotting. Remaining bacteria were quantified from SEM images of the implant surfaces and their numbers statistically compared. RESULTS: The L-PRF-treated samples without rinsing displayed numerous cells with multiple pseudopodia in immediate contact with bacteria that appeared perforated and increased in size. The cells were identified as platelets based on morphological criteria and by positive reaction for platelet factor-4 by Western blotting. After post-treatment rinsing, the L-PRF-treated disks displayed a significant reduction in bacterial counts (in average 92% reduction). CONCLUSION: Application of L-PRF significantly reduced bacterial counts on contaminated SLA titanium surface, most likely through anti-microbial action by platelets.
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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".