AGE-RAGE stress play a role in aortic aneurysm: A comprehensive review and novel potential therapeutic target
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Aortic aneurysms are mostly asymptomatic but have high rates of mortality when there is rupture or dissection. Matrix metalloproteinases is involved in the evolution of aortic aneurysms. Advanced glycation end products and its cell receptor RAGE (receptor for AGE) and sRAGE (soluble receptor of AGE) have been suggested to be involved in the pathogenesis of numerous diseases. This review addresses the role of AGE, RAGE and AGE-RAGE stress (AGE/sRAGE) in the pathogenesis of abdominal aortic aneurysm and thoracic aortic aneurysm in humans. AGERAGE interaction not only increases the generation of reactive oxygen species and inflammatory cytokines, but also activates NF-kB. There are increases in the levels of AGE in aortic tissue, skin and serum in patients with thoracic aortic aneurysm and abdominal aortic aneurysm. Levels of RAGE in tissue are elevated in abdominal aortic aneurysm. AGE-RAGE stress is elevated in patients with thoracic aortic aneurysm. The serum levels of cytokines and Matrix metalloproteinases are elevated in patients with thoracic aortic aneurysm and abdominal aortic aneurysm. The levels of AGE and AGE-RAGE stress correlate positively with cytokines and Matrix metalloproteinases, but the serum levels of sRAGE correlate negatively with cytokines and Matrix metalloproteinases. Cytokines levels are positively correlated with the levels of Matrix metalloproteinases in patients with thoracic aortic aneurysm. In conclusion, elevated levels of AGE, RAGE and AGE-RAGE stress, and reduced levels of sRAGE increase the levels of cytokines that in turn increase the production of Matrix metalloproteinases resulting in formation of aortic aneurysms. The data suggest that AGE-RAGE stress is involved in the pathogenesis of aortic aneurysms. Treatment options have also been discussed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it