Real-time quantification of matrix metalloproteinase and integrin αvβ3 expression during biomaterial-associated infection in a murine model
Bibliographic record
Abstract
Biomaterial implants and devices increase the risk of microbial infections due to the biofilm mode of growth of infecting bacteria on implant materials, in which bacteria are protected against antibiotic treatment and the local immune system. Matrix-metalloproteinases (MMPs) and cell surface integrin receptors facilitate transmigration of inflammatory cells toward infected or inflamed tissue. This study investigates the relationship between MMP- and integrin-expression and the clearance of infecting Staphylococcus aureus around implanted biomaterials in a murine model.MMP- and integrin αvβ3-expression were monitored in mice, with and without subcutaneously implanted biomaterial samples, in the absence and presence of bioluminescent S. aureus Xen36. Staphylococcal persistence was imaged longitudinally over time using bioluminescence imaging. The activatable MMPSense®680 and integrin-targeted IntegriSense®750 probes were injected on different days after implantation and their signal intensity and localisation monitored using fluorescence imaging. After sacrifice 7 or 16 days post-implantation, staphylococci from biomaterial samples and surrounding tissues were cultured on agar-plates and presence of host inflammatory cells was histologically evaluated.MMP- and integrin-expression were equally enhanced in presence of staphylococci or biomaterials up to 7 days post-implantation, but their localisation along the biomaterial samples differed. Bacterial clearance from tissue was higher in the absence of biomaterials. It is of clinical relevance that MMP- and integrin-expression were enhanced in presence of both staphylococci and biomaterials, although the immune system in the presence of biomaterials remained hampered in eradicating bacteria during the first 7 days post-implantation.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".