Low Levels of Serum Soluble Receptors for Advanced Glycation End Products, Biomarkers for Disease State: Myth or Reality
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Advanced glycation end products (AGEs) interact with the receptor for AGEs (RAGE) on the membrane and induce deleterious effects via activation of nuclear factor kappa-B, and increased oxidative stress and inflammatory mediators. AGEs also combine with circulating soluble receptors (endogenous secretory RAGE [esRAGE] and soluble receptor for RAGE [sRAGE]) and sequester RAGE ligands and act as a cytoprotective agent. esRAGE is secreted from the cells and is a spliced variant of RAGE. The sRAGE on the other hand is proteolytically cleaved from cell surface receptor via matrix metalloproteinase (MMPs). sRAGE is elevated in type 1 and type 2 diabetes and in patients with decreased renal function. Serum levels of sRAGE are reduced in diseases including coronary artery disease, atherosclerosis, essential hypertension, chronic obstructive lung disease, heart failure, and hypercholesterolemia. Serum levels of AGEs are elevated in patients with coronary artery disease and atherosclerosis. However, the increases in serum AGEs are very high in patients with diabetes and renal disease. There is a positive correlation between serum levels of AGEs and RAGE and sRAGE. The elevated levels of sRAGE in patients with diabetes and impaired renal function may be due to increased levels of MMPs. AGEs increase in the expression and production of MMPs, which would increase the cleavage of sRAGE from cell surface. In conclusion, low level of serum sRAGE is a good biomarker for disease other than diabetes and renal disease. A unified formula that takes into consideration of AGEs, sRAGE, and esRAGE such as AGE/sRAGE or AGEs/esRAGE would be better biomarker than sRAGE or esRAGE for all AGE-RAGE-associated diseases including diabetes and renal disease.
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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 | 0.001 | 0.003 |
| 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 | 0.001 | 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 it