Metabolomics investigation of dietary effects on flesh quality in grass carp (<i>Ctenopharyngodon idellus</i>)
Bibliographic record
Abstract
Background: The ultrahigh density intensive farming model of grass carp (Ctenopharyngodon idellus) may elicit growth inhibition, decrease flesh quality, and increase disease susceptibility of fish. The degradation in quality and excessive fat accumulation in cultured C. idellus have long been attributed to possible alterations in the lipid metabolism of fish muscle tissues as a result of overnutrition from artificial diets. To investigate the effects of different diets on fish muscle quality, a large-scale metabolomics study was performed on 250 tails of C. idellus. Findings: The experimental fish were divided into four groups based on sex and diet-female artificial feed (FAF), female grass feed, male artificial feed (MAF), and male grass feed (MGF). After a 113-day rearing period, the artificial feed (AF) group showed a significantly higher total mass of muscle fat (P < 0.01), with the FAF group being the highest. Metabolomics profiling based on liquid chromatography-mass spectrometry revealed distinctive patterns of clustering according to the four groups. Overall, artificial feeding was associated with higher concentrations of docosapentaenoic acid, dihomo-gamma-linolenic acid, and arachidonic acid, whereas grass feeding was associated with elevated n-3 unsaturated fatty acids (UFAs) such as eicosapentaenoic acid, alpha-linolenic acid, and gamma-linolenic acid. Artificial feeding also resulted in significant increased docosahexaenoic acid in MAF muscle than in MGF fish, whereas there was no significance in the comparison of female samples. Metabolic pathway analyses using both targeted and untargeted approaches consistently revealed that arachidonic acid metabolism and steroid hormone biosynthesis pathways were significantly different between AF and grass fed groups. Conclusions: Our results suggest that grass is a better source of dietary fatty acid and protein when compared to artificial feed. Grass feeding could effectively lower triglycerides in serum, reduce fat accumulation, and alter lipid compositions in fish muscle by increasing the concentrations of n-3 UFAs, leading to better nutrition and health.
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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".