Feeding a Docosahexaenoic Acid Rich Diet During the Suckling and Weaning Period Modulates Immune Function in Offspring
Bibliographic record
Abstract
The objective was to determine the effect of feeding a high docosahexaenoic acid (DHA) diet during the suckling and/or the weaning period on immune system development and function in offspring. Dams were randomized to one of the two nutritionally adequate diets: control diet (N=12, 0% DHA) or DHA diet (N=8, 2.2% DHA). Diets were fed to dams throughout the suckling period and then pups were randomly assigned to one of the two diets for the weaning period. At 6 weeks, immune cell phenotype and cytokine production by mitogen‐stimulated splenocytes were measured. Regardless of the weaning diet, pups fed the DHA diet during suckling had higher interleukin (IL)‐10 production by splenocytes stimulated with Concanavalin A (Con A, P=0.04) and lipopolysaccharide (LPS, P=0.06) vs. the control diet. Feeding a DHA diet at weaning resulted in a lower production of tumor necrosis factor (TNF)‐α in ConA‐stimulated splenocytes (P<0.01) and of IL‐1β and TNF‐α in LPS‐stimulated splenocytes (P<0.01) vs. the control diet. Feeding DHA during both periods was associated with higher proportion of total CD27 + cells (P<0.03). Our findings suggest that feeding a DHA diet during the suckling period had a programming effect on the ability of the offspring's cells to produce the regulatory cytokine IL‐10 which may contribute to the reported lower risk for atopic diseases associated with DHA intake during lactation. However, the major modulating effects occurred while DHA was fed during the weaning period with a higher proportion of mature B cells and a lower production of inflammatory cytokines which suggests beneficial effects of supplementing DHA in the weaning diet. Supported from the Natural Sciences and Engineering Research Council of Canada.
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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) | 0.001 | 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".