Detection and Identification of Allergens from Canadian Mustard Varieties of Sinapis alba and Brassica juncea
Bibliographic record
Abstract
Currently, information on the allergens profiles of different mustard varieties is rather scarce. Therefore, the objective of this study was to assess protein profiles and immunoglobulin E (IgE)-binding patterns of selected Canadian mustard varieties. Optimization of a non-denaturing protein extraction from the seeds of selected mustard varieties was first undertaken, and the various extracts were quantitatively and qualitatively analyzed by means of protein recovery determination and protein profiling. The IgE-binding patterns of selected mustard seeds extracts were assessed by immunoblotting using sera from mustard sensitized and allergic individuals. In addition to the known mustard allergens—Sin a 2 (11S globulins), Sin a 1, and Bra j 1 (2S albumins)—the presence of other new IgE-binding protein bands was revealed from both Sinapis alba and Brassica juncea varieties. Mass spectrometry (MS) analysis of the in-gel digested IgE-reactive bands identified the unknown ones as being oleosin, β-glucosidase, enolase, and glutathione-S transferase proteins. A bioinformatic comparison of the amino acid sequence of the new IgE-binding mustard proteins with those of know allergens revealed a number of strong homologies that are highly relevant for potential allergic cross-reactivity. Moreover, it was found that Sin a 1, Bra j 1, and cruciferin polypeptides exhibited a stronger IgE reactivity under non-reducing conditions in comparison to reducing conditions, demonstrating the recognition of conformational epitopes. These results further support the utilization of non-denaturing extraction and analysis conditions, as denaturing conditions may lead to failure in the detection of important immunoreactive epitopes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".