Effect of providing a formula supplemented with long-chain polyunsaturated fatty acids on immunity in full-term neonates
Bibliographic record
Abstract
To determine the effect of feeding formula containing long-chain PUFA (LCP) on immune function, healthy term infants were randomised at age 2 weeks to either a standard term formula (Formula; n 14) or the same formula supplemented with the LCP 20 : 4n-6 and 22 : 6n-3 (Formula+LCP; n 16). Peripheral blood was collected at 2 and 6 weeks to measure immune cell response (the rate of [3H]thymidine uptake and cytokine production after stimulation with phytohaemagglutinin (PHA)). Compared with cells from infants receiving only human milk (HM), the rate of [3H]thymidine uptake in response to PHA, but not IL-2 production, was lower for Formula+LCP infants (P < 0.05). Compared with HM-fed infants, Formula-fed infants (but not Formula+LCP infants) produced more TNF-alpha (unstimulated) and had a fewer CD3+CD44+ cells before stimulation and fewer CD11c+ cells post-stimulation (P < 0.05). However, compared with Formula-fed infants, the Formula+LCP infants had an immune cell distribution (higher percentage CD3+CD44+ and CD4+CD28+ cells) and cytokine profile (lower production of TNF-alpha post-stimulation) that did not differ from HM infants. Additionally, it was found that feeding infants formula during the first 10 d of life influenced immune function. These infants had a higher percentage of CD3+, CD4+CD28+, and lower percentage of CD14+ cells and produced more TNF-alpha and interferon-gamma after PHA stimulation than HM-fed infants (P < 0.05). These results demonstrate that early diet influences both the presence of specific cell types and function of infant blood immune cells. Since many diseases have a strong immunological component, these immune changes may be of physiological importance to the developing infant.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".