Growth performance, hepatic gene expression, and plasma biochemistry of rainbow trout fed full-fat meal, defatted meal, oil and chitin from black soldier flies
Bibliographic record
Abstract
Insects, such as black soldier flies (BSF), are sustainable alternatives to fishmeal and fish oil that have been shown maintain fish growth performance, especially at low dietary inclusions. However, BSF is commonly processed to remove chitin and oil that may have additional benefits to the immune response and disease resistance of fish. The aim of this study was to investigate the hepatic gene expression and plasma biochemistry of rainbow trout (Oncorhynchus mykiss) fed BSF and BSF components. A three-month feeding trial was performed where rainbow trout (90.0 ± 4.0g; mean ± SD) were fed either a control diet or diets that contained: 10% full-fat BSF, 10% defatted BSF, 4% BSF oil, and 1% BSF chitin. Liver was analyzed for gene expression via qPCR and plasma was analyzed for 21 biochemical markers. The 4% oil and 1% chitin diets had the highest effect on growth performance, i.e. final weight (p=0.043) and feed intake (p=0.017), compared to the control diet, while no effects of these diets were found on hepatic gene expression. The 10% full-fat BSF diet affected gene expression, specifically upregulating the pro-inflammatory cytokine IL-8 (p=0.033), which may be due to the higher content of saturated fatty acids (SFA) and subsequently immune cell activity in the fish or the combination of the SFA and antimicrobial peptides in BSF that stimulate the innate immune system. No other immune and oxidative stress genes, or 21 plasma biochemical markers were found to be significantly affected (p>0.05). This research provides support to include and not remove oil and chitin from BSF since these components can improve growth performance and immune response of rainbow trout.
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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".