Oral ethinylestradiol–levonorgestrel normalizes fructose-induced hepatic lipid accumulation and glycogen depletion in female rats
Bibliographic record
Abstract
The present study investigated the effects of oral ethinylestradiol–levonorgestrel (EEL) on hepatic lipid and glycogen contents during high fructose (HF) intake, and determined whether pyruvate dehydrogenase kinase-4 (PDK-4) and glucose-6-phosphate dehydrogenase (G6PD) activity were involved in HF and (or) EEL-induced hepatic dysmetabolism. Female Wistar rats weighing 140–160 g were divided into groups. The control, EEL, HF, and EEL+HF groups received water (vehicle, p.o.), 1.0 μg ethinylestradiol plus 5.0 μg levonorgestrel (p.o.), fructose (10% w/v), and EEL plus HF, respectively, on a daily basis for 8 weeks. Results revealed that treatment with EEL or HF led to insulin resistance, hyperinsulinemia, increased hepatic uric acid production and triglyceride content, reduced glycogen content, and reduced production of plasma or hepatic glutathione- and G6PD-dependent antioxidants. HF but not EEL also increased fasting glucose and hepatic PDK-4. Nonetheless, these alterations were attenuated by EEL in HF-treated rats. Our results demonstrate that hepatic lipid accumulation and glycogen depletion induced by HF is accompanied by increased PDK-4 and defective G6PD activity. The findings also suggest that EEL would attenuate hepatic lipid accumulation and glycogen depletion by suppression of PDK-4 and enhancement of a G6PD-dependent antioxidant barrier.
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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 | 0.000 |
| 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".