Antibiofilm peptides enhance the corrosion resistance of titanium in the presence of Streptococcus mutans
Bibliographic record
Abstract
Titanium alloys have gained popularity in implant dentistry for the restoration of missing teeth and related hard tissues because of their biocompatibility and enhanced strength. However, titanium corrosion and infection caused by microbial biofilms remains a significant clinical challenge leading to implant failure. This study aimed to evaluate the effectiveness of antibiofilm peptides 1018 and DJK-5 on the corrosion resistance of titanium in the presence of Streptococcus mutans . Commercially pure titanium disks were prepared and used to form biofilms. The disks were randomly assigned to different treatment groups (exposed to S. mutans supplied with sucrose) including a positive control with untreated biofilms, peptides 1018 or DJK-5 at concentrations of 5 μg/mL or 10 μg/mL, and a negative control with no S. mutans . Dynamic biofilm growth and pH variation of all disks were measured after one or two treatment periods of 48 h. After incubation, the dead bacterial proportion, surface morphology, and electrochemical behaviors of the disks were determined. The results showed that peptides 1018 and DJK-5 exhibited significantly higher dead bacterial proportions than the positive control group in a concentration dependent manner ( p < 0.01), as well as far less defects in microstructure. DJK-5 at 10 μg/mL killed 84.82% of biofilms and inhibited biofilm growth, preventing acidification due to S. mutans and maintaining a neutral pH. Potential polarization and electrochemical impedance spectroscopy data revealed that both peptides significantly reduced the corrosion and passive currents on titanium compared to titanium surfaces with untreated biofilms, and increased the resistance of the passive film ( p < 0.05), with 10 μg/mL of DJK-5 achieving the greatest effect. These findings demonstrated that antibiofilm peptides are effective in promoting corrosion resistance of titanium against S. mutans , suggesting a promising strategy to enhance the stability of dental implants by endowing them with antibiofilm and anticorrosion properties.
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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 | 0.000 | 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) | 0.000 | 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".