AGE-RAGE Axis in the Pathophysiology of Chronic Lower Limb Ischemia and a Novel Strategy for Its Treatment
Bibliographic record
Abstract
This review focuses on the role of advanced glycation end products (AGEs) and its cell receptor (RAGE) and soluble receptor (sRAGE) in the pathogenesis of chronic lower limb ischemia (CLLI) and its treatment. CLLI is associated with atherosclerosis in lower limb arteries. AGE-RAGE axis which comprises of AGE, RAGE, and sRAGE has been implicated in atherosclerosis and restenosis. It may be involved in atherosclerosis of lower limb resulting in CLLI. Serum and tissue levels of AGE, and expression of RAGE are elevated, and the serum levels of sRAGE are decreased in CLLI. It is known that AGE, and AGE-RAGE interaction increase the generation of various atherogenic factors including reactive oxygen species, nuclear factor-kappa B, cell adhesion molecules, cytokines, monocyte chemoattractant protein-1, granulocyte macrophage-colony stimulating factor, and growth factors. sRAGE acts as antiatherogenic factor because it reduces the generation of AGE-RAGE-induced atherogenic factors. Treatment of CLLI should be targeted at lowering AGE levels through reduction of dietary intake of AGE, prevention of AGE formation and degradation of AGE, suppression of RAGE expression, blockade of AGE-RAGE binding, elevation of sRAGE by upregulating sRAGE expression, and exogenous administration of sRAGE, and use of antioxidants. In conclusion, AGE-RAGE stress defined as a shift in the balance between stressors (AGE, RAGE) and antistressor (sRAGE) in favor of stressors, initiates the development of atherosclerosis resulting in CLLI. Treatment modalities would include reduction of AGE levels and RAGE expression, RAGE blocker, elevation of sRAGE, and antioxidants for prevention, regression, and slowing of progression of CLLI.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".