Degree of hydrolysis is a poor predictor of the sensitizing capacity of whey- and casein-based hydrolysates in a Brown Norway rat model of cow’s milk allergy
Bibliographic record
Abstract
The use of infant formulas (IFs) based on hydrolyzed cow's milk proteins to prevent cow's milk allergy (CMA) is highly debated. The risk of sensitization to milk proteins induced by IFs may be affected by the degree of hydrolysis (DH) as well as other physicochemical properties of the cow's milk-based protein hydrolysates within the IFs. The immunogenicity (specific IgG1 induction) and sensitizing capacity (specific IgE induction) of 30 whey- or casein-based hydrolysates with different physicochemical characteristics were compared using an intraperitoneal model of CMA in Brown Norway rats. In general, the whey-based hydrolysates demonstrated higher immunogenicity than casein-based hydrolysates, inducing higher levels of hydrolysate-specific and intact-specific IgG1. The immunogenicity of the hydrolysates was influenced by DH, peptide size distribution profile, peptide aggregation, nano-sized particle formation, and surface hydrophobicity. Yet, only the surface hydrophobicity was found to affect the sensitizing capacity of hydrolysates, as high hydrophobicity was associated with higher levels of specific IgE. The whey- and casein-based hydrolysates exhibited distinct immunological properties with highly diverse molecular composition and physicochemical properties which are not accounted for by measuring DH, which was a poor predictor of sensitizing capacity. Thus, future studies should consider and account for physicochemical characteristics when assessing the sensitizing capacity of cow's milk-based protein hydrolysates.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".