Biologically Active Implants Prevent Mortality in a Mouse Sepsis Model
Bibliographic record
Abstract
Implant-associated infections remain a significant complication in medicine. often leading to chronic infection, tissue damage, or implant failure. To address this, this work develops a modular, triple-action titanium implant that integrates bacterial repellency, bactericidal activity, and enhanced tissue integration. The implant comprises medical-grade titanium with a co-deposited layer of bacteriophages and collagen stably embedded within a repellent lubricant layer. The collagen layer promotes cell deposition and spreading in vitro. When tested against Pseudomonas aeruginosa, the coating reduces bacterial load by 3.2 logs on the surface and 1.9 logs in the medium, outperforming conventional liquid-infused surfaces. A modified version targeting Staphylococcus aureus achieves 4.1-log and 5.2-log reductions, respectively, after a 6-h incubation. When challenging the coating in a sepsis survival model of Pseudomonas aeruginosa infection, mice with the phage-activated implants exhibit a 100% survival rate and fully recover from the infection. In comparison, those with pathogen-repellent and untreated titanium implants show survival rates of only 30% and 10%, respectively. Furthermore, phage, but no bacteria, are detected in the bloodstream of mice implanted with phage-activated titanium, suggesting that locally implanted phage-biomaterials can distribute systemically to control blood infections. Therefore, the engineered phage-activated, triple-action biomaterials may prevent implant-associated infections locally and systemically.
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.
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".