The role of endothelial cell adhesion molecules <scp>P</scp>‐selectin, <scp>E</scp>‐selectin and intercellular adhesion molecule‐1 in leucocyte recruitment induced by exogenous methylglyoxal
Bibliographic record
Abstract
Methylglyoxal (MG) is a reactive dicarbonyl metabolite formed during glucose, protein and fatty acid metabolism. In hyperglycaemic conditions, increased MG level has been linked to the development of diabetes and its vascular complications at the macrovascular and microvascular levels where inflammation plays a role. To study the mechanism of MG-induced inflammation in vivo, we applied MG locally to healthy mice and used intravital microscopy to investigate the role of endothelial cell adhesion molecules in MG-induced leucocyte recruitment in cremasteric microvasculature. Administration of MG (25 and 50 mg/kg) to the tissue dose-dependently induced leucocyte recruitment at 4.0-5.5 hr, with 84-92% recruited cells being neutrophils. Such MG treatment up-regulated the expression of endothelial cell adhesion molecules P-selectin, E-selectin, intercellular adhesion molecule-1, but not vascular cell adhesion molecule-1. Activation of the nuclear factor-κB signalling pathway contributed to MG-induced up-regulation of these adhesion molecules and leucocyte recruitment. The role of the up-regulated endothelial cell adhesion molecules in MG-induced leucocyte recruitment was determined by applying specific functional blocking antibodies to MG-treated animals and observing changes in leucocyte recruitment parameters. Our data demonstrate that the up-regulation of P-selectin, E-selectin and intercellular adhesion molecule-1 contributes to the increased leucocyte rolling flux, reduced leucocyte rolling velocity, and increased leucocyte adhesion, respectively. Our results reveal the role of endothelial cell adhesion molecules in MG-induced leucocyte recruitment in microvasculature, an inflammatory condition related to diabetic vascular complications.
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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) | 0.000 | 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 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".