World Allergy Organization (WAO) Diagnosis and Rationale for Action against Cow's Milk Allergy (DRACMA) Guidelines update - III - Cow's milk allergens and mechanisms triggering immune activation
Bibliographic record
Abstract
Background: The immunopathogenesis of cow's milk protein allergy (CMPA) is based on different mechanisms related to immune recognition of protein epitopes, which are affected by industrial processing. Purpose: The purpose of this WAO DRACMA paper is to: (i) give a comprehensive overview of milk protein allergens, (ii) to review their immunogenicity and allergenicity in the context of industrial processing, and (iii) to review the milk-related immune mechanisms triggering IgE-mediated immediate type hypersensitivity reactions, mixed reactions and non-IgE mediated hypersensitivities. Results: The main cow's milk allergens - α-lactalbumin, β-lactoglobulin, serum albumin, caseins, bovine serum albumins, and others - may determine allergic reactions through a range of mechanisms. All marketed milk and milk products have undergone industrial processing that involves heating, filtration, and defatting. Milk processing results in structural changes of immunomodulatory proteins, leads to a loss of lipophilic compounds in the matrix, and hence to a higher allergenicity of industrially processed milk products. Thereby, the tolerogenic capacity of raw farm milk, associated with the whey proteins α-lactalbumin and β-lactoglobulin and their lipophilic ligands, is lost. Conclusion: The spectrum of immunopathogenic mechanisms underlying cow's milk allergy (CMA) is wide. Unprocessed, fresh cow's milk, like human breast milk, contains various tolerogenic factors that are impaired by industrial processing. Further studies focusing on the immunological consequences of milk processing are warranted to understand on a molecular basis to what extent processing procedures make single milk compounds into allergens.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.006 |
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".