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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".