Plant- and Animal-Derived Dietary Sources of Phosphatidylcholine Have Differential Effects on Immune Function in The Context of A High-Fat Diet in Male Wistar Rats
Bibliographic record
Abstract
BACKGROUND: Phosphatidylcholine (PC) derived from eggs has been shown to beneficially modulate T cell response and intestinal permeability under the context of a high-fat diet. OBJECTIVES: The objective of this study was to determine whether there is a differential effect of plant and animal-derived sources of PC on immune function. METHODS: Four-week-old male Wistar rats were randomly assigned to consume 1 of 4 diets (n = 10/group) for 12 wk, all containing 1.5 g of total choline/kg of diet but differing in choline forms: 1-Control Low-Fat [CLF, 20% fat, 100% free choline (FC)]; 2-Control High-Fat (CHF, 50% fat, 100% FC); 3-High-Fat Egg-derived PC (EPC, 50% fat, 100% Egg-PC); 4-High-Fat Soy-derived PC (SPC, 50% fat, 100% Soy-PC). Immune cell functions and phenotypes were measured in splenocytes by ex vivo cytokine production after mitogen stimulation and flow cytometry, respectively. RESULTS: The SPC diet increased splenocyte IL-2 production after PMA+I stimulation compared with the CHF diet. However, the SPC group had a lower proportion of splenocytes expressing the IL-2 receptor (CD25+, P < 0.05). After PMA+I stimulation, feeding EPC normalized splenocyte production of IL-10 relative to the CLF diet, whereas SPC did not (P < 0.05). In mesenteric lymph node lymphocytes, the SPC diet group produced more IL-2 and TNF-α after PMA+I stimulation than the CHF diet, whereas the EPC diet group did not. CONCLUSIONS: Our results suggest that both egg- and soy-derived PC may attenuate high-fat diet-induced T cell dysfunction. However, egg-PC enhances, to a greater extent, IL-10, a cytokine involved in promoting the resolution phase of inflammation, whereas soy-PC appears to elicit a greater effect on gut-associated immune responses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".