Estimation of ellagic acid and/or repaglinide effects on insulin signaling, oxidative stress, and inflammatory mediators of liver, pancreas, adipose tissue, and brain in insulin resistant/type 2 diabetic rats
Bibliographic record
Abstract
Even though ellagic acid has previously been valued in many models of cancer, so far its full mechanistic effect as a natural antiapoptotic agent in the prevention of type 2 diabetes complications has not been completely elucidated, which was the goal of this study. We fed albino rats a high-fat fructose diet (HFFD) for 2 months to induce insulin resistance/type 2 diabetes and then treated the rats with ellagic acid (10 mg/kg body weight, orally) and/or repaglinide (0.5 mg/kg body weight, orally) for 2 weeks. At the serum level, ellagic acid challenged the consequences of HFFD, significantly improving the glucose/insulin balance, liver enzymes, lipid profile, inflammatory cytokines, redox level, adipokines, ammonia, and manganese. At the tissue level (liver, pancreas, adipose tissue, and brain), ellagic acid significantly enhanced insulin signaling, autophosphorylation, adiponectin receptors, glucose transporters, inflammatory mediators, and apoptotic markers. Remarkably, combined treatment with both ellagic acid and repaglinide had a more pronounced effect than treatment with either alone. These outcomes give new insight into the promising molecular mechanisms by which ellagic acid modulates numerous factors induced in the progression of diabetes.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".