Polyunsaturated fatty acids and T‐cell function: Implications for the neonate
Bibliographic record
Abstract
Infant survival depends on the ability to respond effectively and appropriately to environmental challenges. Infants are born with a degree of immunological immaturity that renders them susceptible to infection and abnormal dietary responses (allergies). T-lymphocyte function is poorly developed at birth. The reduced ability of infants to respond to mitogens may be the result of the low number of CD45RO+ (memory/antigen-primed) T cells in the infant or the limited ability to produce cytokines [particularly interferon-y, interleukin (IL)-4, and IL-10. There have been many important changes in optimizing breast milk substitutes for infants; however, few have been directed at replacing factors in breast milk that convey immune benefits. Recent research has been directed at the neurological, retinal, and membrane benefits of adding 20:4n-6 (arachidonic acid; AA) and 22:6n-3 (docosahexaenoic acid; DHA) to infant formula. In adults and animals, feeding DHA affects T-cell function. However, the effect of these lipids on the development and function of the infant's immune system is not known. We recently reported the effect of adding DHA + AA to a standard infant formula on several functional indices of immune development. Compared with standard formula, feeding a formula containing DHA + AA increased the proportion of antigen mature (CD45RO+) CD4+ cells, improved IL-10 production, and reduced IL-2 production to levels not different from those of human milk-fed infants. This review will briefly describe T-cell development and the potential immune effect of feeding long-chain polyunsaturated fatty acids to the neonate.
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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".