Effect of DHA supplementation on digestible starch utilization by rainbow trout
Bibliographic record
Abstract
Rainbow trout has a limited ability to utilize digestible carbohydrates efficiently. Trout feeds generally contain high levels of DHA, a fatty acid known to inhibit a number of glycolytic and lipogenic enzymes in animals. A study was conducted to determine whether carbohydrate utilization by rainbow trout might be affected by dietary DHA level. Two low-carbohydrate (<4 % digestible carbohydrate) basal diets were formulated to contain 1 (adequate) or 4 (excess) g/100 g DHA diet respectively. The two basal diets were diluted with increasing levels of digestible starch (0 %, 10 %, 20 % and 30 %, respectively) to produce eight diets. These diets were fed to fish for 12 weeks at 15 degrees C according to a pair-fed protocol that consisted of feeding the same amount of basal diet but different amounts of starch. Live weight, N and lipid gains, hepatic glycogen and plasma glucose values significantly increased, whereas feed efficiency (gain:feed) significantly decreased, with increasing starch intake (P<0.05). The retention efficiency of N (N gain/digestible N intake) improved with starch supplementation but was not affected by DHA level (P>0.05). Starch increased the activity of glucokinase, pyruvate kinase, glucose 6-phosphate dehydrogenase and fatty acid synthase (P<0.05) but did not affect hexokinase and malic enzyme activity. DHA had no effect on growth but increased plasma glucose and reduced carcass lipid and liver glycogen contents (P<0.05). Glycolytic and lipogenic enzymes were not affected by DHA level, except for pyruvate kinase, which was reduced by increasing DHA level. These results suggest only a marginal effect of dietary DHA on the ability of fish to utilize carbohydrate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".