Strategies to Prevent Bacterial Infections on Titanium-Based Orthopedic and Dental Implants
Bibliographic record
Abstract
Titanium and titanium alloys remain the gold standard for dental and orthopedic implants. These materials are heavily used because they are bioinert, have strong mechanical properties, and promote integration with bone. However, implant-associated infections (IAIs) remain one of the leading causes of implant failure. Eradicating an IAI can be difficult since bacteria can form biofilms on the medical implant, protecting the bacterial cells against systemic antibiotics and the host’s immune system. If the infection is not treated promptly and aggressively, device failure is inevitable, leading to costly multi-step revision surgeries. To circumvent this dire situation, scientists and engineers continue to fabricate novel strategies to protect the surface of medical implants from bacteria. In this review, we report on emerging strategies to prevent infection in titanium implants. These strategies include anti-adhesion properties provided by polymers, superhydrophobic, superhydrophilic, and liquid-infused surface coatings, as well as strategies and coatings employed to lyse the bacteria. We also explore commercially available technologies or under clinical trials and discuss future trends.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".