Low levels of soluble receptor for advanced glycation end products in non-ST elevation myocardial infarction patients
Bibliographic record
Abstract
BACKGROUND: Interaction of the receptors for advanced glycation end products (RAGEs) with advanced glycation end products (AGEs) results in expression of inflammatory mediators (tumor necrosis factor-alpha [TNF-α] and soluble vascular cell adhesion molecule-1 [sVCAM-1]), activation of nuclear factor-kappa B and induction of oxidative stress - all of which have been implicated in atherosclerosis. Soluble RAGE (sRAGE) acts as a decoy for the RAGE ligand and is protective against atherosclerosis. OBJECTIVES: To determine whether levels of serum sRAGE are lower, and whether levels of serum AGEs, TNF-α and sVCAM-1 are higher in non-ST elevation myocardial infarction (NSTEMI) patients than in healthy control subjects; and whether sRAGE or the ratio of AGEs to sRAGE (AGEs/sRAGE) is a predictor/biomarker of NSTEMI. METHODS: Serum levels of sRAGE, AGEs, TNF-α and sVCAM-1 were measured in 46 men with NSTEMI and 28 age- and sex-matched control subjects. Angiography was performed in the NSTEMI patients. RESULTS: sRAGE levels were lower, and levels of AGEs, TNF-α, sVCAM-1 and AGEs/sRAGE were higher in NSTEMI patients than in control subjects. sRAGE levels were negatively correlated with the number of diseased coronary vessels, serum AGEs, AGEs/sRAGE, TNF-α and sVCAM-1. The sensitivity of the AGEs/sRAGE test is greater than that of the sRAGE test, while the specificity and predictive values of the sRAGE test are greater than those of the AGEs/sRAGE test for identifying NSTEMI patients. CONCLUSIONS: Serum levels of sRAGE were low in NSTEMI patients, and were negatively correlated with extent of lesion, inflammatory mediators, AGEs and AGEs/sRAGE. Both sRAGE and AGEs/sRAGE may serve as biomarkers/predictors for identifying NSTEMI patients.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.002 | 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".