Enhancing growth, immunity, and gene expression in Nile Tilapia (Oreochromis niloticus) through dietary supplementation with avocado (Persea americana) seed powder
Bibliographic record
Abstract
This study evaluated the effects of dietary supplementation with different doses (0, 10, 20, 40, and 80 g kg −1 ) of powdered avocado seed (AS) on the growth performance, immunological response, and gene expression of Nile tilapia ( Oreochromis niloticus ) reared in a biofloc system over 8 weeks. A total of 300 Nile tilapia fingerlings (average weight 14.67 ± 0.07 g) were randomly assigned to five treatment groups, each with three replicates, and 20 fish per tank. The results demonstrated significant improvements ( p < 0.05 ) in growth and immune response in AS-supplemented fish, particularly in those fed the 10 g kg −1 AS diet (AS10), which showed the most notable increases. In contrast, fish fed higher AS doses (AS20, AS40, and AS80) exhibited no statistically significant differences compared to the control group ( p > 0.05 ). Additionally, the AS10 group exhibited a significant upregulation ( p < 0.05 ) in the mRNA expression of key immune-related genes ( IL-1 , IL-8 , and LBP ) and antioxidant-related genes ( GST-α, GPX , and GSR ) in both liver and intestinal tissues, indicating enhanced immune and antioxidant responses. The highest expression levels were found in the AS10 group. These findings suggest that the inclusion of 10 g kg −1 powdered avocado seed in the diet substantially enhances growth, immune function, and gene expression in Nile tilapia reared in a biofloc system. The results highlight avocado seed as a promising feed additive for improving the sustainability of Nile tilapia aquaculture. • A 10 g kg −1 diet avocado seed diet (AS10) improved growth and feed utilization in fish. • AS10 diet enhanced skin mucus and serum immunity in fish. • AS10 diet upregulated IL-1, IL-8, LBP, GSTα, GPX, and GSR gene expression.
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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) | 0.000 | 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".