Hyperbaric oxygen treatment reduces neutrophil‐endothelial adhesion in chronic wound conditions through <scp>S</scp>‐nitrosation
Bibliographic record
Abstract
Hyperbaric oxygen (HBO) therapy is an effective treatment for diabetic chronic wounds. HBO reduces inflammation and accelerates wound healing, by mechanisms that remain unclear. Here we examined a mechanism by which HBO may reduce neutrophil recruitment, through changes in endothelial and neutrophil adhesion molecule expression and function. Human umbilical vein endothelial cells and neutrophils were exposed to selected chronic wound conditions, comprising hypoxia in the presence of lipopolysaccharide and tumor necrosis factor-alpha, and then treated with HBO. We observed neutrophil adhesion to endothelial cells following treatment with chronic wound conditions, which was reversed by HBO treatment. This was partly explained by reduced expression of endothelial intercellular adhesion molecule-1 and vascular cell adhesion molecule-1 by HBO. No changes in neutrophil adhesion molecule expression (CD18, CD11b, CD62L, CD31) were observed following HBO treatment. However, HBO decreased hydrogen peroxide generation by neutrophils, and induced nitrous oxide-related protein modifications. The transnitrosating agent S-nitroso-L-cysteine ethyl ester (600 μM) also reduced neutrophil adhesion to human umbilical vein endothelial cell monolayers, and the iNOS inhibitor 1400 W (10 μM) and HgCl2, which promotes the decomposition of S-nitrosothiols (1 mM), reversed the effect of HBO, suggesting that S-nitrosation may inhibit neutrophil-endothelial cell adhesion. This study indicates that HBO could reduce inflammation in wounds through reduced neutrophil recruitment, mediated by S-nitrosation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".