Supplementation with n‐3 docosahexaenoic acid (DHA) during pregnancy increases DHA in human milk in women.
Bibliographic record
Abstract
Human milk provides the fatty acids needed for growth and development of the breast‐fed infant, as well as essential n‐3 fatty acids needed for central nervous system development. Previous studies have suggested about 50% of milk fatty acids are derived from maternal tissues, and an association between maternal docosahexaenoic acid (DHA, 22:6n‐3) supplementation and infant neural development has been reported. We determined the effect of maternal DHA supplementation in gestation on human milk fatty acids. Pregnant women, n=50, were given 400mg/day DHA or a vegetable oil placebo from 16 to 36 wk gestation. Maternal RBC phosphatidylethanolamine (RBC PE) fatty acids were determined at 16 and 36 wk gestation, and milk fatty acids were determined at 1 and 2 mth postpartum. DHA supplementation resulted in a significant increase in maternal RBC PE DHA, and lower 22:5n‐6, 22:4n‐6 and 22:5n‐3, but no change in arachidonic acid (ARA, 20:4n‐6). Human milk levels of DHA were significantly higher in women who received DHA compared to the placebo during gestation; the increase in DHA was associated with lower milk oleic acid (18:1), with no significant change in other n‐6 or n‐3 fatty acids. Our results suggest maternal n‐3 fatty acid nutrition during gestation is important to human development, both through effects in gestation and through supporting later higher DHA in human milk. Supported by Canadian Institute for Health Research
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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".