Reduction of Advanced-Glycation End Products Levels and Inhibition of RAGE Signaling Decreases Rat Vascular Calcification Induced by Diabetes
Bibliographic record
Abstract
Advanced-glycation end products (AGEs) were recently implicated in vascular calcification, through a process mediated by RAGE (receptor for AGEs). Although a correlation between AGEs levels and vascular calcification was established, there is no evidence that reducing in vivo AGEs deposition or inhibiting AGEs-RAGE signaling pathways can decrease medial calcification. We evaluated the impact of inhibiting AGEs formation by pyridoxamine or elimination of AGEs by alagebrium on diabetic medial calcification. We also evaluated if the inhibition of AGEs-RAGE signaling pathways can prevent calcification. Rats were fed a high fat diet during 2 months before receiving a low dose of streptozotocin. Then, calcification was induced with warfarin. Pyridoxamine was administered at the beginning of warfarin treatment while alagebrium was administered 3 weeks after the beginning of warfarin treatment. Results demonstrate that AGEs inhibitors prevent the time-dependent accumulation of AGEs in femoral arteries of diabetic rats. This effect was accompanied by a reduced diabetes-accelerated calcification. Ex vivo experiments showed that N-methylpyridinium, an agonist of RAGE, induced calcification of diabetic femoral arteries, a process inhibited by antioxidants and different inhibitors of signaling pathways associated to RAGE activation. The physiological importance of oxidative stress was demonstrated by the reduction of femoral artery calcification in diabetic rats treated with apocynin, an inhibitor of reactive oxygen species production. We demonstrated that AGE inhibitors prevent or limit medial calcification. We also showed that diabetes-accelerated calcification is prevented by antioxidants. Thus, inhibiting the association of AGE-RAGE or the downstream signaling reduced medial calcification in diabetes.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".