Allergenicity of Soybean: New Developments in Identification of Allergenic Proteins, Cross-Reactivities and Hypoallergenization Technologies
Bibliographic record
Abstract
Soybean is considered one of the "big eight" foods that are believed to be responsible for 90% of all allergenic reactions. Soy allergy is of particular importance, because soybeans are widely used in processed foods and, therefore, represent a particularly insidious source of hidden allergens. Although significant advances have been made in the identification and characterization of soybean allergens, scientists are not completely certain about which proteins in soy cause allergic reactions. At least 16 allergens have been identified. Most of them, as with other plant food allergens, have a metabolic, storage, or protective function. These allergens belong to protein families which have conserved structural features in relation with their biological activity, which explains the wide immunochemical cross-recognition observed among members of the legume family. Detailed analysis of the structure-allergenicity relationships has been hampered by the complexity and heterogeneity of soybean proteins. A variety of technological approaches have been attempted to decrease soybean allergenicity. This paper provides a comprehensive review of the current body of knowledge on the identification and characterization of soybean allergens, as well as an update on current hypoallergenization techniques.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".