Feeding a Mixture of Choline Forms during Lactation Improves Offspring Growth and Maternal Lymphocyte Response to Ex Vivo Immune Challenges
Bibliographic record
Abstract
Study objectives were to examine the impact of feeding a mixture of choline forms, or a diet high in glycerophosphocholine (GPC) on maternal immune function and offspring growth during lactation. Lactating Sprague-Dawley rat dams (n = 6/diet) were randomized to one of three diets, providing 1 g/kg total choline: Control (100% free choline (FC)), Mixed Choline (MC; 50% phosphatidylcholine (PC), 25% FC, 25% GPC), or High GPC (HGPC; 75% GPC, 12.5% PC, 12.5% FC). At 3 weeks, cell phenotypes and cytokine production with Concanavalin A (ConA)-or lipopolysaccharide (LPS)-stimulated splenocytes and mesenteric lymphocytes were measured. Feeding MC or HGPC diets improved pups’ growth compared to Control (+22% body weight, p < 0.05). In spleen, MC-and HGPC-fed dams had higher proportions of cytotoxic (CD8+) T cells expressing CD27, CD71 and CD127, total B cells (CD45RA+) and dendritic cells (OX6+OX62+), and produced less IL-6 and IFN-γ after ConA than Control-fed dams (p < 0.05). MC and HGPC LPS-stimulated splenocytes produced less IL-1β and IL-6 than Control. ConA-stimulated mesenteric lymphocytes from MC and HGPC dams produced more IL-2 and IFN-γ than Control (p < 0.05). In summary, feeding a mixture of choline forms during lactation improved offspring growth and resulted in a more efficient maternal immune response following mitogenic immune challenge.
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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) | 0.001 | 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".