The receptor for advanced glycation endproducts (RAGE) and cardiovascular disease
Bibliographic record
Abstract
Recent and compelling investigation has expanded our view of the biological settings in which the products of nonenzymatic glycation and oxidation of proteins and lipids - the advanced glycation endproducts (AGEs) - form and accumulate. Beyond diabetes, natural ageing and renal failure, AGEs form in inflammation, oxidative stress and in ischaemia-reperfusion. The chief signal transduction receptor for AGEs - the receptor for AGEs (RAGE) - is a multiligand-binding member of the immunoglobulin superfamily. In addition to AGEs, RAGE binds certain members of the S100/calgranulin family, high-mobility group box 1 (HMGB1), and beta-amyloid peptide and beta-sheet fibrils. Recent studies demonstrate beneficial effects of RAGE antagonism and genetic deletion in rodent models of atherosclerosis and ischaemia-reperfusion injury in the heart and great vessels. Experimental evidence is accruing that RAGE ligand generation and release during ischaemia-reperfusion may signal through RAGE, thus suggesting that antagonism of this receptor might provide a novel form of therapeutic intervention in heart disease. However, it is plausible that innate, tissue-regenerative roles for these RAGE ligands may also impact the failing heart - perhaps through RAGE and/or distinct receptors. In this review, we focus on RAGE and the consequences of its activation in the cardiovasculature.
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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 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.006 |
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".