Soybean Allergens Affecting North American Patients Identified by 2D Gels and Mass Spectrometry
Why this work is in the frame
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Bibliographic record
Abstract
In this study, we compared the immunoglobulin E immunological reactions of 23 soy-allergic, nine soy-sensitive, and four non-allergic human sera to soybean proteins separated by 1D and 2D gel electrophoresis and screened by Western blot. This method led to novel allergen identification in soybean proteins by tandem mass spectrometry analysis of reactive proteins. Soybean is one of the eight most significant foods which provoke allergic reactions among North Americans. Previous studies have identified several putative allergens present in soybean seeds; however, many of these reports did not employ mass spectrometry to conclusively identify the allergenic proteins. Reviews have suggested that soybean seeds may contain between 10 and 20 different proteins responsible for provoking allergic reactions among sensitive patients. We report, in this study of North American patients, a total of 19 potentially allergenic proteins including ten identified by mass spectrometry and five novel allergens. We have also made extensive use of soybean lines lacking various subunits of the major seed storage proteins, glycinin and β-conglycinin. By using these knockout lines in Western blots with patient serum, it was conclusively demonstrated that some patients react predominantly to only a few proteins, while most react to four to six proteins. The findings herein describe the main allergic proteins present in soybean seeds, the relative significance of these allergens among North Americans, and some genetic lines of soybean lacking individual allergens.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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