Bibliographic record
Abstract
Coronary artery atherosclerosis and atherosclerotic plaque rupture cause coronary artery disease (CAD). Advanced glycation end products (AGE) and its cell receptor RAGE, and soluble receptor (sRAGE) and endogenous secretory RAGE (esRAGE) may be involved in the development of atherosclerosis. AGE and its interaction with RAGE are atherogenic, while sRAGE and esRAGE have antiatherogenic effects. AGE-RAGE stress is a ratio of AGE/sRAGE. A high AGE-RAGE stress results in development and progression of CAD and vice-versa. AGE levels in serum and skin, AGE/sRAGE in patients with CAD, and expression of RAGE in animal model of atherosclerosis were higher, while serum levels of esRAGE were lower in patients with CAD compared with controls. Serum levels of sRAGE in CAD patients were contradictory, increased or decreased. This contradictory data may be due to type of patients used, because the sRAGE levels are elevated in diabetics and end-stage renal disease. AGE/sRAGE ratio is elevated in patients with reduced or elevated levels of serum sRAGE. It is to stress that AGE, RAGE, sRAGE, or esRAGE individually cannot serve as universal biomarker. AGE and sRAGE should be measured simultaneously to assess the AGE-RAGE stress. The treatment of CAD should be targeted at reduction in AGE levels, prevention of AGE formation, degradation of AGE in vivo, suppression of RAGE expression, blockade of RAGE, elevation of sRAGE, and use of antioxidants. In conclusion, AGE-RAGE stress would initiate the development and progression of atherosclerosis. Treatment modalities would prevent, regress, and slow the progression of CAD.
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 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) | 0.002 | 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".