Disparate effects on renal and oxidative parameters following RAGE deletion, AGE accumulation inhibition, or dietary AGE control in experimental diabetic nephropathy
Bibliographic record
Abstract
Advanced glycation end products (AGEs) and the receptor for AGEs (RAGE) generate ROS, and therefore this study evaluated the effects of RAGE deletion, decreasing AGE accumulation, or lowering dietary AGE content on oxidative parameters in diabetic nephropathy (DN). Control and diabetic male wild-type and RAGE-deficient (RAGE-/-) mice were fed high- or low-AGE diets, with two groups given the inhibitor of AGE accumulation, alagebrium chloride, and followed for 24 wk. Diabetic RAGE-/- mice were protected against albuminuria, hyperfiltration, glomerulosclerosis, decreased renal mitochondrial ATP production, and excess generation of both mitochondrial and cytosolic superoxide. Whereas glomerulosclerosis, tubulointerstitial expansion, and hyperfiltration were improved in diabetic mice treated with alagebrium, there was no effect on urinary albumin excretion. Both diabetic RAGE-/- and alagebrium-treated mice had an attenuation of renal RAGE expression and decreased renal and urinary AGE (carboxymethyllysine) levels. Low-AGE diets did not confer renoprotection, lower the AGE burden or renal RAGE expression, or improve cytosolic or mitochondrial superoxide generation. Renal uncoupling protein-2 gene expression and mitochondrial membrane potential were attenuated by all therapeutic interventions in diabetic mice. In the present study, diverse approaches to block the AGE-RAGE axis had disparate effects on DN, which has potential clinical implications for the way this axis should be targeted in humans.
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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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".