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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".