Early healing events in a porcine model of contaminated wounds: effects of nanocrystalline silver on matrix metalloproteinases, cell apoptosis, and healing
Bibliographic record
Abstract
A porcine model of wound healing was employed to examine the impact of nanocrystalline silver-coated dressings on specific wound healing events. Full-thickness wounds were created on the backs of pigs, contaminated with an experimental inoculum containing Pseudomonas aeruginosa, Fusobacterium sp., and coagulase-negative staphylococci, and covered with dressing products either containing silver or not. Nanocrystalline silver-coated dressings promoted rapid wound healing, particularly during the first several days post-injury. Healing was characterized by rapid development of well vascularized granulation tissue that supported tissue grafting 4 days post-injury, unlike control dressed wounds. The proteolytic environment of wounds treated with nanocrystalline silver was characterized by reduced levels of matrix metalloproteinases. Matrix metalloproteinases have been shown to be present in chronic ulcers at abnormally high levels, as compared with acute wounds, and may contribute to the nonhealing nature of these wounds. Cellular apoptosis occurred at a higher frequency in the nanocrystalline silver-treated wounds than in wounds dressed with other products. The results suggest that nanocrystalline silver may play a role in altering or compressing the inflammatory events in wounds and facilitating the early phases of wound healing. These benefits are associated with reduced local matrix metalloproteinase levels and enhanced cellular apoptosis.
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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) | 0.000 | 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 | 0.001 | 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".