Soluble Receptors for Advanced Glycation End Products (sRAGE) as a Predictor of Restenosis Following Percutaneous Coronary Intervention
Bibliographic record
Abstract
BACKGROUND: Interaction of advanced glycation end products (AGEs) with their receptor (RAGE) increases expression of inflammatory mediators (tumor necrosis factor alpha [TNF-α] and soluble vascular cell adhesion molecule-1 [sVCAM-1]) and induces oxygen radicals that are implicated in atherosclerosis. Balloon-injury-induced atherosclerosis is associated with increased expression of AGEs and RAGE. The soluble receptor for AGE (sRAGE), which acts as a decoy for RAGE ligands (AGEs), prevents atherosclerosis in this model. HYPOTHESIS: We evaluated: 1) whether post-percutaneous coronary intervention (PCI) restenosis is associated with low pre-PCI serum sRAGE, high serum AGEs, TNF-α, and sVCAM-1, and high AGE/sRAGE ratio; 2) whether pre-PCI and post-PCI levels of these markers are similar in patients with or without restenosis; and 3) whether sRAGE and AGE/sRAGE ratio have predictive value for post-PCI restenosis. METHODS: Angiography was performed in 46 patients with non-ST-segment elevation myocardial infarction for assessment of restenosis. Serum sRAGE, AGEs, TNF-α, and sVCAM-1 were measured in these patients and 20 control subjects. RESULTS: : Nineteen of the 46 patients developed post-PCI restenosis, which was associated with lower sRAGE and higher TNF-α and sVCAM-1 levels, and higher AGE/sRAGE ratio compared with patients without restenosis. Pre-PCI and post-PCI levels of these biomarkers were similar in both groups, except in patients with restenosis, in whom the post-PCI level of sRAGE was lower and TNF-α was higher than the pre-PCI levels. The sensitivity and negative predictive value of sRAGE were 100%, and were higher than those of AGE/sRAGE ratio in identifying post-PCI restenosis. CONCLUSIONS: Both low serum sRAGE levels and high AGE/sRAGE ratio have predictive value for post-PCI restenosis.
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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 | 0.002 |
| 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 | 0.001 | 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".