Efficacy of gaseous nitric oxide in the treatment of skin and soft tissue infections
Bibliographic record
Abstract
Bacterial burden significantly interferes with the healing process in chronic ulcers. Nitric oxide (NO) plays a key role in regulating skin's response to infection and wound healing. In previous studies, we demonstrated that exogenous NO gas (gNO) at 200 parts per million (ppm) exhibits potent antimicrobial effects against a representative range of pathogens. The aim of the present study is to explore the antimicrobial properties of gNO in vivo and to determine skin cells' sensitivity to the cytotoxic effects of gNO. To test gNO's antimicrobial effects, full-thickness wounds were infected with Staphylococcus aureus on the dorsal skin surface of New Zealand White rabbit and treated with 200 ppm gNO for 8 hours/day for 3 consecutive days. Significant reduction in wound bacterial content was observed in the presence of gNO. In a separate experiment, primary cultures of human fibroblasts, keratinocytes, and endothelial cells were established to test gNO's cytotoxicity in the skin. Methyl thiazolyl tetrazolium proliferation assays demonstrated that human skin cells, unlike bacterial cells, exhibited significant resistance toward gNO cytotoxicity. In vitro migration studies on keratinocytes and endothelial cells revealed that gNO treatment does not seem to interfere with reepithelialization and angiogenesis during the process of wound healing. Following 24 hours of gNO treatment, fibroblasts expressed significantly higher levels of procollagen and, to a lesser degree, a decrease in matrix metalloproteinase -1 mRNA. In conclusion, the present study provides evidence for the potential application of high doses of gNO as an antimicrobial agent for the treatment of infection in chronic nonhealing ulcers or burn patients, without compromising the viability, and function of skin cells.
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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) | 0.001 | 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".