Aldose Reductase and Cardiovascular Diseases, Creating Human-Like Diabetic Complications in an Experimental Model
Bibliographic record
Abstract
Hyperglycemia and reduced insulin actions affect many biological processes. One theory is that aberrant metabolism of glucose via several pathways including the polyol pathway causes cellular toxicity. Aldose reductase (AR) is a multifunctional enzyme that reduces aldehydes. Under diabetic conditions AR converts glucose into sorbitol, which is then converted to fructose. This article reviews the biology and pathobiology of AR actions. AR expression varies considerably among species. In humans and rats, the higher level of AR expression is associated with toxicity. Flux via AR is increased by ischemia and its inhibition during ischemia reperfusion reduces injury. However, similar pharmacological effects are not observed in mice unless they express a human AR transgene. This is because mice have much lower levels of AR expression, probably insufficient to generate toxic byproducts. Human AR expression in LDL receptor knockout mice exacerbates vascular disease, but only under diabetic conditions. In contrast, a recent report suggests that genetic ablation of AR increased atherosclerosis and increased hydroxynonenal in arteries. It was hypothesized that AR knockout prevented reduction of toxic aldehydes. Like many in vivo effects found in genetically manipulated animals, interpretation requires the reproduction of human-like physiology. For AR, this will require tissue specific expression of AR in sites and at levels that approximate those 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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".