Effects of Artemisia argyi leaf water extract (AWE) on growth performance, muscle quality, intestinal microbial, and metabolomics of common carp (Cyprinus carpio)
Bibliographic record
Abstract
This study aimed to investigate the impact of Artemisia argyi leaf water extract (AWE) on the growth and metabolism of common carp (Cyprinus carpio). Over a period of 56 days, varying concentrations of AWE—0 % (A0), 0.1 % (A1), 0.2 % (A2), and 0.4 % (A3)—were incorporated into the feed for carp. The effects of AWE supplementation were assessed through various parameters including growth performance, flesh quality, biochemical indices, tissue structure, intestinal microbiota composition, and metabolomics analysis. Notably, treatment with A1 significantly enhanced the growth performance of carp while increasing body fat content and reducing protein levels (p < 0.05). Furthermore, A1 led to a significant increase in intestinal villi length (p < 0.05) and a reduction in liver fat deposition levels. Additionally, A1 concentration elevated protease activity within the intestine, with Trypsin exhibiting markedly higher activity compared to other groups (p < 0.05). The antioxidant capacity was also significantly improved across experimental groups (p < 0.05). Analysis of intestinal microbiota revealed that Cetobacterium and Aeromonas may play crucial roles as genus-level microorganisms in modulating metabolic processes associated with AWE in carp intestines. Metabolomic analyses indicated that pathways related to valine, leucine, and isoleucine biosynthesis were among those most profoundly affected by AWE supplementation. This experiment demonstrates that AWE exerts a growth-promoting effect on common carp at an optimal concentration near 0.1 %. These findings enhance our understanding of the potential applications of AWE in aquaculture practices and provide a theoretical basis for its utilization.
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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 | 0.001 |
| 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".