Silver oxysalts promote cutaneous wound healing independent of infection
Bibliographic record
Abstract
Abstract Chronic wounds often exist in a heightened state of inflammation whereby excessive inflammatory cells release high levels of proteases and reactive oxygen species (ROS). While low levels of ROS play a fundamental role in the regulation of normal wound healing, their levels need to be tightly regulated to prevent a hostile wound environment resulting from excessive levels of ROS. Infection amplifies the inflammatory response, augmenting levels of ROS which creates additional tissue damage that supports microbial growth. Antimicrobial dressings are used to combat infection; however, the effects of these dressing on the wound environment and healing independent of infection are rarely assessed. Cytotoxic or adverse effects on healing may exacerbate the hostile wound environment and prolong healing. Here we assessed the effect on healing independent of infection of silver oxysalts which produce higher oxidative states of silver (Ag2+/Ag3+). Silver oxysalts had no adverse effect on fibroblast scratch wound closure whilst significantly promoting closure of keratinocyte scratch wounds (34% increase compared with control). Furthermore, dressings containing silver oxysalts accelerated healing of full‐thickness incisional wounds in wild‐type mice, reducing wound area, promoting reepithelialization, and dampening inflammation. We explored the mechanisms by which silver oxysalts promote healing and found that unlike other silver dressings tested, silver oxysalt dressings catalyze the breakdown of hydrogen peroxide to water and oxygen. In addition, we found that silver oxysalts directly released oxygen when exposed to water. Collectively, these data provide the first indication that silver oxysalts promote healing independent of infection and may regulate oxidative stress within a wound through catalysis of hydrogen peroxide.
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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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".