Atorvastatin-mediated protection of the retina in a model of diabetes with hyperlipidemia
Bibliographic record
Abstract
Insulin resistance, a key feature of obesity, metabolic syndrome, and type 2 diabetes mellitus (T2DM), results in a variety of metabolic and vascular abnormalities. Metabolic disturbances associated with diabetes could contribute to disrupting the structural and (or) functional integrity of the retina. The effects of atorvastatin on retinal cells in hyperlipidemic T2DM rats have not yet been investigated. We used Goto-Kakizaki (GK) rats fed with an atherogenic diet (AD) for 4 months to investigate whether atorvastatin (administered for 1 month) would slow-down or reverse the progression of lesions in the diabetic retina. Fluorogenic substrates were used to measure the proteasome activities in retinal cells. The production of reactive oxygen species was determined by immunofluorescence in frozen retina sections, using dihydroethydium. Nitrotyrosine levels were assessed using immunohistochemistry. Protein levels of ubiquitin conjugates, free ubiquitin, and ubiquitin activating enzyme E1 were determined with Western blotting. Atorvastatin significantly reduced the levels of oxidative stress that were induced by the AD and restored the proteasome activities in the diabetic GK rats. Atorvastatin therapy significantly improved local oxidative stress levels in GK rats fed with AD. Atorvastatin can, at least in part, restore the ubiquitin proteasome system, and may represent a pharmacological approach to prevent some of the complications associated with diabetic retinopathy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".