Trigonelline attenuates hepatic complications and molecular alterations in high-fat high-fructose diet-induced insulin resistance in rats
Bibliographic record
Abstract
The present study aimed to evaluate the effect of trigonelline (TRG) on the hepatic complications associated with high-fat high-fructose (HFHF) diet-induced insulin resistance (IR) in rats. IR was induced by giving a saturated fat diet and 10% fructose in drinking water to rats for 8 weeks. Insulin-resistant rats were orally treated with TRG (50 and 100 mg/kg), sitagliptin (SIT; 5 mg/kg), or a combination of TRG (50 mg/kg) and SIT (5 mg/kg) for 14 days. Liver homogenates were used for assessment of hepatic lipids, oxidative stress biomarkers, and inflammatory cytokines. Histopathological and DNA cytometry examinations were carried out for hepatic and pancreatic tissues. Hepatic tissues were examined using Fourier-transform infrared spectroscopy for assessment of any molecular changes. Results of the present study revealed that oral treatment of insulin-resistant rats with TRG or TRG in combination with SIT significantly decreased homeostatic model assessment of IR, hepatic lipids, oxidative stress biomarkers, and the inflammatory cytokines. TRG or TRG in combination with SIT ameliorated the histopathological, DNA cytometry, and molecular alterations induced by a HFHF diet. Finally, it can be concluded that TRG has beneficial effects on the hepatic complications associated with IR due to its hypoglycemic effect and antioxidant potential.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".