Mechanisms Underlying Decreased Hepatic Triacylglycerol and Cholesterol by Dietary Bitter Melon Extract in the Rat
Bibliographic record
Abstract
In these studies, we focused on finding the mechanism(s) underlying the bitter melon (Momordica charantia L.) methanol fraction (MF)-dependent reduction in the concentration of hepatic triacylglycerol (TAG) and cholesterol in the rat. Rats were fed diets containing low (5 %) fat for 2 weeks (experiment 1), or low (5 %) and high (15 %) fat for a longer period of 8 weeks (experiment 2). MF was supplemented at 1 % level in both experiments. After feeding, rats were sacrificed, and their livers were prepared as slices and hepatocytes, followed by incubation with [1(2)-¹⁴C] acetate or [1-¹⁴C] oleic acid (18:1 n-6). Under these conditions, we found that rats fed diets containing MF, as compared to those without MF, showed: (1) no adverse effects on food intake and growth, (2) a decreased hepatic TAG and total cholesterol, irrespective of the difference in dietary fat level or feeding period, and (3) a decreased incorporation of [1(2)-(¹⁴C] acetate and [1-¹⁴C] oleic acid into TAG of liver slices and hepatocytes. MF-supplemented rats also showed no altered incorporation of labeled acetate into cholesterol and cholesterol ester, an increased fecal excretion of neutral steroids, but not of acidic steroids, and an enhanced mRNA abundance of carnitine palmitoylacyltransferase I, which is the rate-limiting enzyme for fatty acid oxidation. These results suggest that dietary MF decreases hepatic TAG synthesis while enhancing fatty acid oxidation, thereby reducing the concentration of hepatic TAG. The liver cholesterol-lowering effect of MF, however, is probably mediated through an increased fecal excretion of neutral steroids, without an effect on cholesterogenesis.
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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".