Dietary <i>Rosa rubiginosa</i> petal supplementation improves growth performance, skin pigmentation, immunity, antioxidant capacity, and intestinal microbiota in <i>Carassius auratus</i>
Bibliographic record
Abstract
Natural additives are gaining attention in ornamental fish aquaculture due to their potential to enhance health and colouration. Rose petals (RP) contain diverse bioactive compounds, yet their functional effects in goldfish (Carassius auratus) are not well understood. This study aimed to evaluate the effects of dietary RP powder on growth, pigmentation, antioxidant defense, immune gene expression, and gut microbiota in goldfish. A total of 300 fish (6.75–6.90 g/fish) were fed diets containing 0, 5, 10, 20, or 40 g/kg RP for eight weeks. Fish fed RP diets, particularly at 40 g/kg, showed significantly greater final weight and weight gain than the control (p < 0.05). Skin redness increased in RP-20 and RP-40 groups, while yellowness was highest in RP-40 (p < 0.05). Serum antioxidant indices improved with increasing RP levels, evidenced by elevated ABTS and SOD activities and reduced MDA levels (p < 0.05). Intestinal expression of antioxidant (hsp70, cyp1a), growth (igf, tgf), and immune (lyz, tnfα) genes was upregulated in RP-20–RP-40 (p < 0.05). RP supplementation significantly shifted gut microbiota composition (β-diversity, p = 0.017), with higher Staphylococcus and lower Alloprevotella abundances, but did not alter α-diversity. Overall, RP inclusion enhanced growth, colouration, antioxidant status, and immune responses in goldfish, demonstrating its potential as a sustainable functional additive for ornamental fish diets.
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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".