iRAGE AS A NOVEL CARBOXYMETHYLATED PEPTIDE THAT PREVENTS AGE-INDUCED APOPTOSIS AND ENDOPLASMIC RETICULUM STRESS IN VASCULAR SMOOTH MUSCLE CELLS
Bibliographic record
Abstract
RAGE overexpression in smooth muscle cells of vulnerable atheromatous plaques suggests a central role in their apoptosis and in diabetes-related cardiovascular events. With the goal of preventing RAGE activation, we synthesized a peptide (iRAGE) whose sequence is derived from an 11-amino acid site surrounding lysine-378, a frequently glycated binding site of carboxymethyllysine-human serum albumin (CML-HSA) in vivo. Pretreatment with iRAGE was successful to prevent RAGE signaling via NF-κB, a major contributor to chronic inflammation. Moreover, as RAGE activation induced apoptosis of more than 75.6% of smooth muscle cells stimulated with CML-HSA, the peptide inhibited RAGE-induced apoptosis and normalized the expression of Bcl-2 homologs, key regulators of the mitochondrial pathway of apoptosis. Interestingly, RAGE blockade diminished the generation of endoplasmic reticular stress, as observed by reduced formation of stress granules and lesser expression of stress marker HuR. This result is of particular interest since HuR can regulate apoptosis by increasing the expression of caspase-9, an event that is prevented by treatment with iRAGE. As activation of RAGE involves the oligomerization of the receptor, our results support the model by which iRAGE inhibits RAGE signaling by hindering the binding of multimeric ligands and by stabilizing the receptors in a monomer state.
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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 | 0.001 |
| 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".