Effects of high‐γ‐linolenic acid canola oil compared with borage oil on reproduction, growth, and brain and behavioral development in mice
Bibliographic record
Abstract
Previous research in rats and mice has suggested that gamma-linolenic acid (GLA) derived from borage oil (BO: 23% GLA) may be an appropriate source for increasing levels of long-chain n-6 FA in the developing brain. Recently, transgenic technology has made available a highly enriched GLA seed oil from the canola plant (HGCO: 36% GLA). The first objective of this study was to compare the effects of diets containing equal levels of GLA (23%) from either BO or HGCO on reproduction, pup development, and pup brain FA composition in mice. The second objective was to compare the effects of the HGCO diluted to 23% GLA (GLA-23) with those of undiluted HGCO containing 36% GLA (GLA-36). The diets were fed to the dams prior to conception and throughout pregnancy and lactation, as well as to the pups after weaning. The behavioral development of the pups was measured 12 d after birth, and anxiety in the adult male offspring was assessed using the plus maze. The findings show that despite equivalent levels of GLA, GLA-23 differed from BO in that it reduced pup body weight and was associated with a slight increase in neonatal pup attrition. However, there were no significant effects on pup behavioral development or on performance in the plus maze. An increase in dietary GLA resulted in an increase in brain 20:4n-6 and 22:4n-6, with a corresponding decrease in 22:6n-3. Again, despite their similar levels of GLA, these effects tended to be larger in GLA-23 than in BO. In comparison with GLA-23, GLA-36 had larger effects on growth and brain FA composition but no differences with respect to effects on reproduction and behavioral development. These findings suggest that the HGCO can be used as an alternative source of GLA.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".