AGE–RAGE Stress in the Pathophysiology of Atrial Fibrillation and Its Treatment
Bibliographic record
Abstract
Atrial fibrillation (AF) is the most common of cardiac arrhythmias. Mechanisms such as atrial structural remodeling and electrical remodeling have been implicated in the pathogenesis of AF. The data to date suggest that advanced glycation end products (AGEs) and its cell receptor RAGE (receptor for AGE) and soluble receptor (sRAGE) are involved in the pathogenesis of AF. This review focuses on the role of AGE-RAGE axis in the pathogenesis of AF. Interaction of AGE with RAGE generates reactive oxygen species, cytokines, and vascular cell adhesion molecules. sRAGE is a cytoprotective agent. The data show that serum levels of AGE and sRAGE, and expression of RAGE, are elevated in AF patients. Elevated levels of sRAGE did not protect the development of AF. This might be due to greater elevation of AGE than sRAGE. Measurement of AGE-RAGE stress (AGE/sRAGE) would be appropriate as compared with measurement of AGE or RAGE or sRAGE alone in AF patients. AGE and its interaction with RAGE can induce AF through alteration in cellular protein and extracellular matrix. AGE and its interaction with RAGE induce atrial structural and electrical remodeling. The treatment strategy should be directed toward reduction in AGE levels, suppression of RAGE expression, blocking of binding of AGE to RAGE, and elevation of sRAGE and antioxidants. In conclusion, AGE-RAGE axis is involved in the development of AF through atrial structural and electrical remodeling. The treatment modalities for AF should include lowering of AGE, suppression of RAGE, elevation of sRAGE, and use of antioxidants.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".