Ferulic acid exerts its antidiabetic effect by modulating insulin-signalling molecules in the liver of high-fat diet and fructose-induced type-2 diabetic adult male rat
Bibliographic record
Abstract
Ferulic acid (FA) is a phenolic phytochemical known for its antidiabetic property The present study is designed to evaluate the mechanism behind its antidiabetic property in high-fat and fructose-induced type 2 diabetic adult male rats. Animals were divided into 5 groups: (i) control, (ii) diabetic control, (iii) diabetic animals treated with FA (50 mg/(kg body weight · day)(-1), orally) for 30 days, (iv) diabetic animals treated with metformin (50 mg/(kg body weight · day)(-1), orally) for 30 days, and (v) control rats treated with FA. FA treatment to diabetic animals restored blood glucose, serum insulin, glucose tolerance, and insulin tolerance to normal range. Hepatic glycogen concentration, activity of glycogen synthase, and glucokinase were significantly decreased, whereas activity of glycogen phosphorylase and enzymes of gluconeogenesis (phosphoenolpyruvate carboxykinase (PEPCK) and glucose-6-phosphatase (G6Pase)) were increased in diabetic animals and FA restored these to normal levels similar to that of metformin. FA improved the insulin signalling molecules and reduced the negative regulators of insulin signalling. The messenger RNA of gluconeogenic enzyme genes (PEPCK and G6Pase) and the interaction between forkhead transcription factor-O1 and promoters of gluconeogenic enzyme genes (PEPCK and G6Pase) was reduced significantly by ferulic acid. It is concluded from the present study that FA treatment to type 2 diabetic rats improves insulin sensitivity and hepatic glycogenesis but inhibits gluconeogenesis and negative regulators of insulin signalling to maintain normal glucose homeostasis.
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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".