Advanced Glycation End Products and its Soluble Receptors in the Pathogenesis of Thoracic Aortic Aneurysm
Bibliographic record
Abstract
BACKGROUND: Matrix metalloproteinases (MMPs) have been implicated in the pathogenesis of thoracic aortic aneurysms (TAAs). Cytokines [Interleukin (IL)-Iβ, IL-2, IL-6, and TNF-α)] increase the expression of MMP-2 and -3. Advanced glycation end products (AGEs) interact with cell receptors to increase the release of cytokines. Circulating soluble receptors for AGEs (sRAGE) and endogenous secretory RAGE (esRAGE) compete with membrane bound RAGE for binding with AGEs and reduce the production of cytokines. It is hypothesized that low levels of serum sRAGE and esRAGE and high levels of AGEs, AGEs/sRAGE, and AGEs/esRAGE would increase the levels of cytokines that would increase the levels MMPs, thus contributing to the formation of TAAs. METHODS: The study population was composed of 17 control subjects and 20 patients with TAA. Blood samples were collected for measurement of serum sRAGE, esRAGE, AGEs, cytokines, and MMPs. AGEs, sRAGE, and esRAGE were measured using ELISA kits, whereas the remaining parameters were measured using the Luminex Multi-Analyte system. RESULTS: The levels of sRAGE were lower, while the levels of AGEs, AGEs/sRAGE, AGEs/esRAGE, cytokines and MMPs were higher in patients with TAA compared to controls. The levels of sRAGE were inversely correlated with cytokines and MMPs, while AGEs, AGEs/sRAGE and AGEs/esRAGE were positively correlated with cytokines and MMPs. Cytokines were positively correlated with MMPs. CONCLUSIONS: The data suggest that the AGE-RAGE axis may be involved in the pathogenesis of TAA and that low levels of sRAGE and high levels of AGEs, AGEs/sRAGE, and AGEs/esRAGE are risk factors for TAA.
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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 | 0.000 |
| 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 | 0.000 |
| 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".