Secretion of Peanut Protein With IgE Crosslinking Capacity in Human Milk
Bibliographic record
Abstract
ABSTRACT Background Human milk provides an infant one of the first routes of exposure to dietary antigens. A better understanding of the levels and functional capacity of maternal dietary proteins in human milk (HM) and their impact on infant food allergy (FA) is needed. Objective To measure the quantity and biologic activity of maternal dietary proteins in HM and determine factors associated with their presence. Methods HM samples from mothers of infants with or without FA were collected before and serially after consumption of a meal with prespecified amounts of peanut, egg, and cow's milk protein. Ara h 2 from peanut, egg ovalbumin, and bovine β‐lactoglobulin in HM was detected using ELISA. IgE‐crosslinking capacity of Ara h 2 was assessed by the basophil activation test (BAT). Maternal intestinal permeability was measured with the lactulose/mannitol test. Results Thirty‐nine mothers were included and 69% had measurable Ara h 2 in HM. Secretion of food allergens was variable between different mothers and across allergens, but did not correlate with intestinal permeability. Atopic mothers' milk had a higher peak Ara h 2 concentration (median 246 pg/mL, range 2.0–1634) than nonatopic mothers (median 0 pg/mL, range 0–135, p = 0.017). In samples with the most BAT reactivity, there was a significant correlation between BAT reactivity and Ara h 2 level ( R = 0.72, p = 2 × 10 −8 ). Multiple monoclonal antibodies directed at different epitopes detected Ara h 2 at similar levels. Conclusion Ara h 2 is secreted in HM as intact protein or significant parts of it that are capable of IgE‐crosslinking, found in higher levels in atopic mothers. HM levels of peanut lack correlation with other dietary proteins, which suggests antigen specificity of the secretion of dietary protein into HM.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 0.000 |
| 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".