Silver carp muscle hydrolysate ameliorated atherosclerosis and liver injury in apoE <sup>-/-</sup> mice: the modulator effects on enterohepatic cholesterol metabolism
Bibliographic record
Abstract
Atherosclerosis (AS) is a major cause of cardiovascular diseases (CVDs) and a strong link with hepatic steatosis. Silver carp muscle hydrolysate (SCH) possess various beneficial activities but its effect on AS and hepatic steatosis is yet unknown. This study aimed to investigate the effects of SCH on AS lesions and hepatic steatosis using apoE-/- mice. Results showed that SCH significantly reduced the vascular AS plaques and alleviated hepatic steatosis lesions in apoE-/- mice. Consistent with this, the lipid levels both in circulation and liver were lowered by SCH. The mechanism analysis showed SCH down-regulated the expression of genes involved in lipoproteins production while up-regulated the expression of genes related to reverse cholesterol transport (RCT) in liver. Meanwhile, SCH remarkably promoted transintestinal cholesterol excretion (TICE) process in intestine, partly contributing to the reduction of blood lipids. The peptide profile data indicated LYF, HWPW, FPK, and YPR are the main peptides in SCH that play a vital role in alleviating AS lesions and hepatic steatosis. Our findings provided new knowledge for the application of SCH in ameliorating CVDs and liver diseases.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".