Leukocyte recruitment induced by exogenous methylglyoxal: the role of endothelial adhesion molecules (173.27)
Bibliographic record
Abstract
Abstract Methylglyoxal (MG) is a reactive dicarbonyl metabolite formed during glucose, protein and fatty acid metabolism. In condition of hyperglycemia, excessive MG produced endogenously can lead to diabetic macro- and micro-vascular complications where inflammation plays an important part. However, the role of MG in the induction of inflammation and the underlying mechanisms are poorly understood. In this study, we applied MG to local, healthy tissue and used intravital microscopy to investigate the role of endothelial adhesion molecules in MG-induced leukocyte recruitment in mice. Acute administration of MG to the tissue dose-dependently induced leukocyte recruitment with ~90% recruited cells being neutrophils. The MG treatment upregulated the expression of endothelial adhesion molecules P-selectin, E-selectin, ICAM-1, but not VCAM-1. The role of these upregulated endothelial adhesion molecules in MG-induced leukocyte recruitment was confirmed by applying specific functional blocking antibodies to acute MG-treated live animals. Our data demonstrate that exogenous MG induces leukocyte recruitment through the upregulation of the expression of P-selectin, E-selectin, ICAM-1 but not VCAM-1. Our results provided mechanistic insights into the role of MG in the induction of inflammation response in conditions related to diabetic complications and revealed the role of endothelial adhesion molecules in this process.
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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".