In Vitro Investigation of the Effect of Oral Bacteria in the Surface Oxidation of Dental Implants
Bibliographic record
Abstract
BACKGROUND: Bacteria are major contributors to the rising number of dental implant failures. Inflammation secondary to bacterial colonization and bacterial biofilm is a major etiological factor associated with early and late implant failure (peri-implantitis). Even though there is a strong association between bacteria and bacterial biofilm and failure of dental implants, their effect on the surface of implants is yet not clear. PURPOSE: To develop and establish an in vitro testing methodology to investigate the effect of early planktonic bacterial colonization on the surface of dental implants for a period of 60 days. MATERIALS AND METHODS: Commercial dental implants were immersed in bacterial (Streptococcus mutans in brain-heart infusion broth) and control (broth only) media. Immersion testing was performed for a period of 60 days. During testing, optical density and pH of immersion media were monitored. The implant surface was surveyed with different microscopy techniques post-immersion. Metal ion release in solution was detected with an electrochemical impedance spectroscopy sensor platform called metal ion electrochemical biosensor (MIEB). RESULTS: Bacteria grew in the implant-containing medium and provided a sustained acidic environment. Implants immersed in bacterial culture displayed various corrosion features, including surface discoloration, deformation of rough and smooth interfaces, pitting attack, and severe surface rusting. The surface features were confirmed by microscopic techniques, and metal particle generation was detected by the MIEB. CONCLUSION: Implant surface oxidation occurred in bacteria-containing medium even at early stages of immersion (2 days). The incremental corrosion resulted in dissolution of metal ions and debris into the testing solution. Dissolution of metal ions and particles in the oral environment can trigger or contribute to the development of peri-implantitis at later stages.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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".