Electrochemical Treatment of Contaminated Titanium Surfaces in Vitro: An Approach for Implant Surface Decontamination
Bibliographic record
Abstract
Bacterial contamination on titanium implants can cause inflammation and eventually implant failure. Currently used methods for decontamination of implants have demonstrated limited success. Metal surfaces can be disinfected electrochemically. However, the effect of electrochemical treatments on biofilm-contaminated titanium is largely unknown. We hypothesized that electrochemical treatments are able to safely remove organic contamination and bacteria from titanium implants without altering their surfaces. This study was designed to assess the electrochemical properties of bacteria-contaminated surfaces in order to develop new treatments to clean titanium. Surface morphology, composition, bacterial load, and electrochemical properties of polished titanium discs were analyzed before and after biofilm contamination and subsequent decontamination with various electrochemical methods. The effect of the combination of the electrochemical with titanium brush cleaning was also evaluated. Results were then analyzed and compared to baseline readings (prior to contamination) using repeated measures ANOVA. Biofilm contamination increased the levels of carbon, nitrogen, and live bacteria on titanium surfaces while reducing their open circuit potential and corrosion resistance. Optimized electrochemical treatments with alternating current (-2.3 mA, + 22.5 μA) and voltages (1.8 V), were bactericidal and able to completely decontaminate saliva-contaminated titanium surfaces within 5 min while preserving surface integrity and histological quality of mammalian tissues. Furthermore, with the aid of mechanical brushing, the optimized electrochemical treatment was able to achieve complete decontamination of biofilm-contaminated Ti surfaces. The electrochemical treatment seems to be promising and well worth investigating for the clinical management of peri-implant infections.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".