Therapeutic Interventions for Advanced Glycation-End Products and its Receptor- Mediated Cardiovascular Disease
Bibliographic record
Abstract
Advanced glycation end products (AGEs) are heterogeneous group of molecules formed from nonenzymatic reaction of reducing sugars with amino group of proteins, lipids, and nucleic acid. Interaction of AGEs with its cell-bound receptor (RAGE) results in generation of oxygen radicals, nuclear factor kappa-β, proinflammatory cytokines and cell adhesion molecules, and is involved in the pathophysiology of cardiovascular diseases (CVD). Circulating soluble forms of RAGE (sRAGE) and endo-secretory RAGE (esRAGE) compete with RAGE for ligand binding and function as a decoy. This paper describes the endogenous and exogenous (high dietary AGEs, and cooking food under high dry heat, elevated pH, and longer period) sources of AGEs. AGERAGE- mediated CVD includes atherosclerosis, coronary artery disease, carotid artery disease, hypertension, peripheral vascular diseases, heart failure, cardiomyopathy, and microangiopathy. The therapeutic interventions with reduction in AGEs and RAGE, and elevation in sRAGE has been reported for the treatment of AGE-RAGEmediated CVD. Reduction in levels of AGEs can be achieved by reduction in consumption of food containing low amount of AGEs, cooking food at low temperature, moist heat, and shorter duration. AGE formation can be reduced with drugs, vitamins and stoppage of cigarette smoking. Statins, telmisartan, and curcumin have been used for suppression of RAGE. Statins, ACE-inhibitors, Rosiglitazone and vitamin D have been used to increase levels of sRAGE. Finally exogenous administration of sRAGE can be helpful in amelioration of CVD. In conclusion, AGE-RAGE-mediated CVD could be attenuated with reduction in consumption of AGEs, suppression of RAGE and elevation of sRAGE.
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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) | 0.001 | 0.001 |
| 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) | 0.006 | 0.001 |
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".