Breastfeeding and the developmental origins of mucosal immunity: how human milk shapes the innate and adaptive mucosal immune systems
Bibliographic record
Abstract
PURPOSE OF REVIEW: Breastfeeding provides passive immunity while the neonatal immune system matures, and may also protect against chronic immune-mediated conditions long after weaning. This review summarizes current knowledge and new discoveries about human milk and mucosal immunity. RECENT FINDINGS: New data suggest that certain microbes in maternal milk may seed and shape the infant gut microbiota, which play a key role in regulating gut barrier integrity and training the developing immune system. Human milk oligosaccharides, best known for their prebiotic functions, have now been shown to directly modulate gene expression in mast and goblet cells in the gastrointestinal tract. Epidemiologic data show a reduced risk of peanut sensitization among infants breastfed by peanut-consuming mothers, suggesting a role for milk-borne food antigens in tolerance development. Cross-fostering experiments in mice suggest the soluble Toll-like receptor 2, found in human milk, may be critical in this process. Finally, interest in human milk antibodies surged during the pandemic with the identification of neutralizing severe acute respiratory syndrome coronavirus 2 antibodies in maternal milk following both natural infection and vaccination. SUMMARY: Human milk provides critical immune protection and stimulation to breastfed infants. Understanding the underlying mechanisms could identify new therapeutic targets and strategies for disease prevention across the lifespan.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".