The addition of docosahexaenoic and arachidonic acid to the diet of artificially reared pups improves the response of splenocytes to lipopolysaccharide
Bibliographic record
Abstract
Immune cells from infants are reported to have a lower cytokine (IFNγ) response to bacterial antigens such as lipopolysaccharide (LPS), which may contribute to their higher susceptibility to infections. Recently, it was reported that the addition of the long chain polyunsaturated fats (PUFA), docosahexaenoic (DHA) and arachidonic (ARA) acid to infant formula reduced the risk of infections. Using the artificially reared rodent model, we studied the effect of feeding isocaloric nutritionally adequate rat milk substitute with or without long chain PUFA (0.24% DHA + 0.36% AA) for one week (12–21 d of age). Both groups of rats grew similar to suckled pups and there was no difference in body or spleen weight. Except for a higher (P<0.05) proportion of dendritic cells (OX62+) in PUFA‐fed rats, supplementation with PUFA had minimal effects on the major lymphocyte phenotypes in spleen. After ex vivo LPS stimulation (48h), isolated splenocytes from the PUFA‐fed rats produced more IL‐1β (1.4X) and IFNγ (1.9X) than those from the control group (P<0.05). These results suggest that adding DHA and AA to the diet of formula‐fed rats significantly improved the immune response to LPS. Funded by NSERC.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".