In Vitro Assessment of Salivary Pellicle Disruption and Biofilm Removal on Titanium: Exploring the Role of Surface Hydrophobicity in Chemical Disinfection
Bibliographic record
Abstract
OBJECTIVES: Peri-implantitis is mostly caused by a pathological biofilm that forms through complex processes, initiated by the formation of the salivary pellicle on implant surfaces. Understanding the nature of these pellicles and biofilm and how to remove them is important for preventing peri-implant infections and improving the success of dental implants. This study explores the characteristics of the salivary pellicle on titanium surfaces and assesses the effectiveness of different decontamination agents in eliminating the salivary pellicle and related microbial contaminations. MATERIALS AND METHODS: Titanium surfaces were contaminated with salivary pellicles and pathological biofilms. The nature of the salivary pellicle was characterized using X-ray photoelectron spectroscopy (XPS), surface proteomics, contact angle measurements, and fluorescence microscopy. We tested six commonly used decontamination chemicals (chlorhexidine, essential oil-based mouthwash, citric acid, phosphoric acid, saline, and phosphate buffer saline) as well as newly proposed treatments such as surfactants and solvents (acetone, acetic acid, and Tween 20) for their capability to eliminate salivary pellicles and pathogenic biofilms from titanium surfaces. RESULTS: The hydrophobic nature of the salivary pellicle on titanium surfaces limits the efficacy of commonly used hydrophilic solutions in removing pellicles and bacteria. Organic solvents and surfactants, particularly acetic acid and Tween 20, demonstrated superior effectiveness in removing the pellicle and biofilm. Acetic acid was notably effective in restoring surface composition, reducing microbial levels, and removing multispecies biofilms. CONCLUSIONS: The use of surfactants and solvents could be a promising alternative for the treatment of biofilms on titanium surfaces. However, further studies are needed to explore their clinical applicability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".