Supplementing dams with both arachidonic and docosahexaenoic acid has beneficial effects on growth and immune development
Bibliographic record
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 n-3 LCPUFAs, arachidonic (20:4n-6, 0.6%wt ARA) and docosahexaenoic acid (22:6n-3, 0.32%wt DHA), or 1:1 and 2:1 ratios (ARA: DHA) 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 day 3 pp, Sprague-Dawley dams were fed a commercial chow. On day 3 pp litters were culled and pups (4 males and 2 females) randomly cross-fostered to dams who were randomized to one of the 5 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 ARA: DHA doubled 22:6n-3 in milk lipids (P<0.05). Feeding 1:1 or 2:1 ARA: DHA resulted in greater pup weight at weaning (P<0.05). Compared to the control pups, ARA + DHA fed pups had a lower proportion of splenic CD45RA+ lymphocytes. In summary, postpartum supplementation with a combination of ARA + DHA, compared to ARA or DHA alone, resulted in a higher content of ARA and DHA in dam's milk and tissues and had positive effects on growth, accompanied by evidence of progression toward a mature immune phenotype, and suggests a need for ARA when DHA is supplemented in the early diet. Additional investigations are needed of ARA immunomodulation to better understand and estimate nutritional requirements for LCPUFAs during early development.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".