Supplementing dams with both arachidonic acid and docosahexaenoic acid has beneficial effects on growth and immune development beyond single fatty acid supplementation
Bibliographic record
Abstract
Abstract Omega-3 long-chain polyunsaturated fatty acids (LCPUFAS) modulate immune cells in vitro and in vivo. This study investigated the effects of enriching the maternal diet with the n-6 and -3 LCPUFAs, arachidonic (20:4n-6, 0.6%wt ARA) and docosahexaenoic acid (22:6n-3, 0.32%wt DHA), or a 2:1 ratio (2ARA:1DHA) on total lipids in milk, total lipids, and immunophenotypes in plasma, lymph nodes, and spleen from isolated immune cells from 28d old pups. From day 15 of gestation to post-partum day (ppd)3, Sprague-Dawley dams were fed a commercial chow. On ppd3 litters were culled and pups (4 males and 2 females) randomly cross-fostered to dams who were randomized to one of the 3 experimental diets resulting in 20 male and 10 female pups/diet group. Dams fed ARA or ARA:DHA had 28–36% more 20:4n-6 in milk and feeding DHA or 2:1 ARA:DHA doubled 22:6n-3 in milk lipids (P<0.05). Feeding 2ARA:1DHA or ARA alone resulted in greater pup weight at weaning (P<0.05). Feeding ARA (-DHA) resulted in lower DHA in plasma and spleen while feeding DHA (-ARA) resulted in higher DHA in plasma (P<0.05). Compared to the 2ARA:1DHA pups, there was general trend for ARA fed pups to have higher and DHA fed pups to have lower total, T helper, T cytotoxic, and B lymphocytes in blood, and spleen. In summary, perinatal supplementation with 2ARA:1DHA, compared to ARA or DHA alone, resulted in a higher content of ARA and DHA in breast milk and tissues and had positive effects on growth. DHA without ARA increased 22:6n-3 in milk, plasma, spleen, and reduced 20:4n-6 in milk. Manipulation of dietary LCPUFAs was reflected by lipid content in tissues, and accompanied by evidence of effects on immunophenotype. These changes suggest functional effects but this remains to be confirmed by future investigations.
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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 | 0.001 |
| 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".