Does nanocrystalline silver have a transferable effect?
Bibliographic record
Abstract
This study examined the mechanism of nanocrystalline silver antiinflammatory activity, and tested nanocrystalline silver for systemic antiinflammatory effects. Secondary ion mass spectroscopy of skin treated directly with nanocrystalline silver for 24 hours showed that at skin surfaces there were significant deposits at weights corresponding to Ag, AgO, AgCl, AgNO(3), Ag(2)O, and silver clusters Ag(2-6), but silver penetration was minimal. To test for translocation of the effect, a porcine contact dermatitis model in which wounds were induced on one side of the back and then treated with nanocrystalline silver on the opposite side of the back was used. Visual and histological data showed improvement relative to animals treated with saline only. Significantly increased induction of apoptosis in the inflammatory cells present in the dermis was observed with remote nanocrystalline silver treatments. In addition, immunohistochemical analysis showed decreased levels of proinflammatory cytokines tumor necrosis factor-alpha and interleukin-8, and increased levels of antiinflammatory cytokine interleukin-4, epidermal growth factor, keratinocyte growth factor, and keratinocyte growth factor-2. Thus, the antiinflammatory effects of nanocrystalline silver appear to be induced by interactions with cells in the top layers of the skin, which then release biological signals resulting in widespread antiinflammatory activity.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".