Allergenicity of Soybean: New Developments in Identification of Allergenic Proteins, Cross-Reactivities and Hypoallergenization Technologies
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it