Lipid-Lowering Effects of Lotus Leaf Alcoholic Extract on Serum, Hepatopancreas, and Muscle of Juvenile Grass Carp via Gene Expression
Bibliographic record
Abstract
Compared with wild grass carp (Ctenopharyngodon idellus), intensively cultured fish displayed disordered lipid metabolism, showing excess lipid deposition in the hepatopancreas and muscle. Lotus leaf prevents fat accumulation in humans and may have similar effects on fish. This study explored the regulatory mechanisms by which the dietary addition of an alcoholic extract of lotus leaf (AELL) reduced lipid deposition in the hepatopancreas and muscle of juvenile grass carp. The fish (average initial weight: 34.00 ± 0.40 g) were fed four experimental diets containing different AELL levels (0, 0.07, 0.14, and 0.21%) for 8 weeks. Serum components, lipid droplet size, triacylglycerol (TAG) content, enzymatic activities, and mRNA levels of genes related to lipid metabolism in the hepatopancreas and muscle were analyzed. The results show that dietary AELL supplementation significantly reduced the TAG content and lipid droplet area in the histological sections as well as the fatty acid synthase (FAS) activity in both the hepatopancreas and muscle but enhanced the activities of lipoprotein lipase (LPL) and carnitine palmitoyltransferase I (CPT1) in both tissues. In addition, dietary AELL supplementation decreased the mRNA expression of genes involved in fatty acid uptake (cd36, fatp1/fatp4/fatp6, fabp10/fabp11, acsl1/acsl4) and de novo lipid synthesis (pgd, g6pd, and fasn) as well as the transcription factors pparg and srebf1 in the hepatopancreas and muscle but increased the mRNA levels of genes relating to lipid catabolism (cpt1a, lipe, pnpla2, lpl), lipid transportation (apob), and the transcription factor ppara in both tissues. In conclusion, dietary AELL supplementation reduced lipid accumulation in the hepatopancreas and muscle by affecting the gene expression of proteins with known effects on lipid metabolism in juvenile grass carp.
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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) | 0.000 | 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 | 0.000 |
| 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".