Cytokines and Soluble Receptors in Breast Milk as Enhancers of Oral Tolerance Development
Bibliographic record
Abstract
The postpartum period is an important window during which environmental factors can shape the life-long health of the infant. This time period often coincides with substantial milk consumption either in the form of breast milk or from cow's milk sources, such as infant formulas. Although breast milk is the most beneficial source of nutrients for infants during the first 6 months after birth, its role in regulating food allergy development, through regulation of oral tolerance, is still controversial. Breast milk contains several factors that can impact mucosal immune function, including immune cells, antibodies, microbiota, oligosaccharides, cytokines, and soluble receptors. However, there is considerable variation in the assessed levels of cytokines and soluble receptors between studies and across the lactation period. Most of these cytokines and soluble receptors are absent, or only found in limited quantities, in commercial baby formulas. Differences in content of these pluripotent factors, which impact on both the mother and the neonate, could contribute to the controversy surrounding the role of breast milk regulating oral tolerance. This review highlights current knowledge about the importance of cytokines and soluble receptors in breast milk on the development of oral tolerance and tolerance-relateddisorders. Understanding the mechanisms by which such milk components might promote oral tolerance could aid in the development of improved strategies for allergy prevention.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".