Low erucic acid canola oil does not induce heart triglyceride accumulation in neonatal pigs fed formula
Bibliographic record
Abstract
Canola oil is not approved for use in infant formula largely because of concerns over possible accumulation of triglyceride in heart as a result of the small amounts of erucic acid (22:1n-9) in the oil. Therefore, the concentration and composition of heart triglyceride were determined in piglets fed from birth for 10 (n = 4-6) or 18 (n = 6) d with formula containing about 50% energy fat as 100% canola oil (0.5% 22:1n-9) or 100% soybean oil, or 26% canola oil or soy oil (blend) with palm, high-oleic sunflower and coconut oil, providing amounts of 16:0 and 18:1 closer to milk, or a mix of soy, high-oleic sunflower and flaxseed oils with C16 and C18 fatty acids similar to canola oil but without 22:1. Biochemical analysis found no differences in heart triglyceride concentrations among the groups at 10 or 18 d. Assessment of heart triglycerides using Oil Red O staining in select treatments confirmed no differences between 10-d-old piglets fed formula with 100% canola oil (n = 4), 100% soy oil (n = 4), or the soy oil blend (n = 2). Levels of 22:1n-9 in heart triglyceride and phospholipid, however, were higher (P<0.01) in piglets fed 100% canola oil or the canola oil blend, with higher levels found in triglycerides compared with phospholipids. The modest accumulation of 22:1n-9 associated with feeding canola oil was not associated with biochemical evidence of heart triglyceride accumulation at 10 and 18 d.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".