β-Sitosterol mitigates the development of high-fructose diet-induced nonalcoholic fatty liver disease in growing male Sprague–Dawley rats
Bibliographic record
Abstract
Fructose contributes to the development of nonalcoholic fatty liver disease (NAFLD). β-Sitosterol (Bst), a naturally occurring phytosterol, has antihyperlipidaemic and hepatoprotective properties. This study interrogated the potential protective effect of β-sitosterol against NAFLD in growing rats fed a high-fructose diet, modelling children fed obesogenic diets. Forty-four 21 day old male rat pups were randomly allocated to and administered the following treatments for 12 weeks: group I, standard rat chow (SRC) + plain drinking water (PW) + plain gelatine cube (PC); group II, SRC + 20% w/v fructose solution (FS) as drinking fluid + PC; group III, SRC + FS + 100 mg/kg fenofibrate in a gelatine cube; group IV, SRC + FS + 20 mg/kg β-sitosterol gelatine cube (Bst); group V, SRC + PW + Bst. Terminally, the livers were dissected out, weighed, total liver lipid content determined, and histological analyses done. Harvested plasma was used to determine the surrogate biomarkers of liver function. The high-fructose diet caused increased (p < 0.05) hepatic lipid (total) accretion (>10% liver mass), micro- and macrovesicular hepatic steatosis, and hepatic inflammation. β-Sitosterol and fenofibrate prevented the high-fructose diet-induced macrovesicular steatosis and prevented the progression of NAFLD to steatohepatitis. β-Sitosterol can prospectively be used to mitigate diet-induced NAFLD.
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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) | 0.002 | 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".