Feeding a Diet Enriched in Docosahexaenoic Acid to Lactating Dams Improves the Tolerance Response to Egg Protein in Suckled Pups
Bibliographic record
Abstract
The objective of this study was to determine the effect of feeding a maternal diet supplemented with docosahexaenoic acid (DHA) during the suckling period on the development of the immune system and oral tolerance (OT) in offspring. Dams were randomized to consume one of two nutritionally adequate diets throughout the suckling period: control (N = 12, 0% DHA) or DHA (N = 8, 0.9% DHA) diet. At 11 days, pups from each dam were randomly assigned to a mucosal OT challenge: the placebo or the ovalbumin (OVA) treatment. At three weeks, plasma immunoglobulins and splenocyte cytokine production ex vivo were measured. OVA-tolerized pups had a lower Th2 (IL-13) response to OVA despite the presence of more activated T cells and memory cells (CD27+, all p < 0.05). Feeding a high DHA diet improved the ability of splenocytes to respond to mitogens toward a skewed Th1 response and led to a higher IL-10 and a lower TGF-β production after stimulation with OVA (all p < 0.05). Untolerized DHA-fed pups had lower plasma concentrations of OVA-specific immunoglobulin E (p for interaction < 0.05). Overall, feeding a high DHA maternal diet improves the tolerance response in untolerized suckled pups in a direction that is thought to be beneficial for the establishment of OT.
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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".