Decontamination of multispecies oral biofilm from rough implant surface by airflow with glycine
Bibliographic record
Abstract
OBJECTIVES: Decontamination of biofilm-colonized rough implant surfaces remains challenging. We investigated the effect of airflow with glycine powder (AFG) on decontamination of mature oral multispecies biofilm from a sandblasted and acid etched (SLA) titanium surface. MATERIALS AND METHODS: Subgingival dental plaque was cultured on SLA disks anaerobically for 21 days. AFG with various settings and distances was applied directly on the disks with or without previous rinse of 0.9% NaCl. The specimens were then analyzed through scanning electron microscope and remaining bacteria on the implant surface were quantified and statistically compared. RESULTS: Mature oral biofilm with cocci and rods as major morphotypes, as well as spiral- and filamentous-shaped organisms, was formed on the untreated disks. Saline rinsing removed the thick biofilm layer but left numerous of coccoid bacteria in rough surface pits. AFG effectively removed most of the bacteria from the pits. Both 25% and 50% power settings were equally effective at 3-mm distance. With 50% power, AFG successfully removed bacteria at both 3- and 6-mm distance. When AFG was applied on native biofilm without prior rinsing with saline, it effectively removed the biofilm including bacteria in the pits. CONCLUSION: Application of AFG appears effective in removing bacteria from rough implant surfaces.
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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) | 0.001 | 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".