Feeding a Mixture of Choline Forms to Lactating Dams Improves the Development of the Immune System in Sprague-Dawley Rat Offspring
Bibliographic record
Abstract
Dietary choline is essential during lactation, but few studies have examined the implications of feeding a mixture of choline forms on immune function. This study investigates the impact of feeding lactating dams different mixtures of choline forms, similar to those in human diets, on the development and later immune function of suckled offspring. Sprague-Dawley lactating dams (n = 6/diet) were randomized to consume one of three diets, containing 1 g/kg choline: Control (100% free choline (FC)), Mixed Choline (MC: 50% phosphatidylcholine (PC), 25% FC, 25% glycerophosphocholine (GPC)), or High GPC (HGPC: 75% GPC, 12.5% PC, 12.5% FC). At weaning, female pups (n = 2/dam) were fed the Control diet until 10 weeks. At 3 weeks, MC and HGPC pups were heavier and their splenocytes had a higher proportion of helper T cells expressing CD25 and CD28 and produced less interferon gamma (IFN-γ) and tumor-necrosis factor-α (TNF-α) after Concanavalin A stimulation vs. Control pups (p < 0.05). At 10 weeks, MC and HGPC offspring had a lower proportion of macrophages and dendritic cells and produced less interleukin (IL)-1β but more IL-10 after lipopolysaccharide stimulation vs. Control pups (p < 0.05). In summary, feeding mixed choline diets during lactation improved T cell phenotype/function at the end of suckling and programmed a less inflammatory response later in life.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 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".