Diet and temperature affect liver lipids and membrane properties in steelhead trout ( <i>Oncorhynchus mykiss</i> )
Bibliographic record
Abstract
Fish regulate cellular membrane fluidity in response to temperature by restructuring membrane lipid composition. This study evaluated the effect of diet and temperature on liver membranes in steelhead trout, an important aquaculture species. Oncorhynchus mykiss were fed three commercial diets with different levels of PUFA (lower (L-n3); medium (M-n3); higher (H-n3) omega 3) from marine, terrestrial, and vegetable oils; the effect of temperature changes on liver membrane was measured. Fish fed the H-n3 diet had the most linear response in Raman spectroscopy, indicating that they can adapt to changes in temperature with the least effect on liver membrane, due to the higher polyunsaturate:saturate ratio in the diet, counteracting the influence of low temperature. L-n3-fed fish presented increased membrane fluidity at all temperatures, highlighting the influence of terrestrial fatty acids on membrane properties. These results underscore changes in sterol:phospholipid ratios as a key response for membrane adaptability to environmental changes, and the necessity to include environmental variables when testing new diets. Substitution of fish oil with vegetable oils may compromise sterol:phospholipid ratios, affecting membrane adaptability. This study shows changes at cellular level in liver tissue for fish fed different diets and subjected to different water temperatures.
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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".