Emerging Strategies to Prevent Bacterial Infections on Titanium‐Based Implants
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Titanium and titanium alloys remain the gold standard for dental and orthopedic implants. These materials are heavily used because of their bioinert nature, robust mechanical properties, and seamless 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 develop novel strategies to protect the surface of medical implants from bacteria. In this review, details on emerging strategies to prevent infection in titanium implants are reported. 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. Additionally, commercially available technologies and those under preclinical trials are examined while discussing current and future trends.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
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 it