AGE–RAGE Stress, Stressors, and Antistressors in Health and Disease
Bibliographic record
Abstract
Adverse effects of advanced glycation end-products (AGEs) on the tissues are through nonreceptor- and receptor-mediated mechanisms. In the receptor-mediated mechanism, interaction of AGEs with its cell-bound receptor of AGE (RAGE) increases generation of oxygen radicals, activates nuclear factor-kappa B, and increases expression and release of pro-inflammatory cytokines resulting in the cellular damage. The deleterious effects of AGE and AGE-RAGE interaction are coined as "AGE-RAGE stress." The body is equipped with defense mechanisms to counteract the adverse effects of AGE and RAGE through endogenous enzymatic (glyoxalase 1, glyoxalase 2) and AGE receptor-mediated (AGER1, AGER2) degradation of AGE, and through elevation of soluble receptor of AGE (sRAGE). Exogenous defense mechanisms include reduction in consumption of AGE, prevention of AGE formation, and downregulation of RAGE expression. We have coined AGE and RAGE as "stressors" and the defense mechanisms as "anti-stressors." AGE-RAGE stress is defined as a shift in the balance between stressors and antistressors in the favor of stressors. Measurements of stressors or antistressors alone would not assess AGE-RAGE stress. For true assessment of AGE-RAGE stress, the equation should include all the stressors and antistressors. The equation for AGE-RAGE stress, therefore, would be the ratio of AGE + RAGE/sRAGE + glyoxalase1 + glyoxalase 2 + AGER1 +AGER2. This is, however, not practical in patients. AGE-RAGE stress may be assessed simply by the ratio of AGE/sRAGE. A high ratio of AGE/sRAGE indicates a relative shift in stressors from antistressors, suggesting the presence of AGE-RAGE stress, resulting in tissue damage, initiation, and progression of the diseases and their complications.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".