Chemotherapeutic decontamination of dental implants colonized by mature multispecies oral biofilm
Bibliographic record
Abstract
AIM: No studies have tested disinfectants on mature multispecies oral biofilms on titanium substrata. The aim of this study was to investigate the efficacy of commonly used antimicrobial agents in decontamination of multispecies mature oral biofilm on sandblasted, large-grit, acid-etched (SLA) titanium implants. METHODS: SLA titanium disks were inoculated with dental plaque and cultured anaerobically for 21 days. The disks were rinsed with 0.9% NaCl, exposed for 2 min. to tetracycline paste, 1% Chlorhexidine gel (CHX), 35% phosphoric acid gel (Etch) or a novel chemical formula (0.3% cetrimide, 0.1% CHX and 0.5% EDTA) and then rinsed again with 0.9% NaCl. Bacteria were quantified from scanning electron micrographs of the implant surfaces. Living bacteria were quantified with confocal laser scanning microscopy (CLSM). RESULTS AND CONCLUSIONS: Rinsing the surfaces with 0.9% NaCl removed the majority of the biofilm. However, bacteria persisted in all specimens and none of the disinfectants was superior to the double saline rinse group. CLSM analysis showed that CHX and Etch groups had a statistically significant reduction of viable bacteria, although small. Overall the results show that many disinfection agents used in the clinic are ineffective in biofilm removal and leave live bacteria on the surface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".