Comprehensive Proteomics Approach in Characterizing and Quantifying Allergenic Proteins from Northern Shrimp: Toward Better Occupational Asthma Prevention
Bibliographic record
Abstract
Occupational asthma is a major chronic health dilemma among workers involved in the seafood industry. Several proteins notoriously known to cause asthma have been reported in different seafood. This work involves the application of an allergenomics strategy to study the most potent allergens of northern shrimp. The proteins were extracted from shrimp tissue and profiled by gel electrophoresis. Allergenic proteins were identified based on their reactivity to patient sera and were structurally identified using tandem mass spectrometry. Northern shrimp tropomyosin, arginine kinase, and sarcoplasmic calcium-binding protein were found to be the most significant allergens. Multiple proteolytic enzymes enabled 100% coverage of the sequence of shrimp tropomyosin by tandem mass specrometry. Only partial sequence coverage was obtained, however, for the shrimp allergen arginine kinase. Signature peptides, for both tropomyosin and arginine kinase, were assigned and synthesized for use in developing the multiple reaction monitoring tandem mass spectrometric method. Subsequently, air samples were collected from a shrimp processing plant and two aerosolized proteins quantified using tandem mass specrometry. Allergens were detected in all areas of the plant, reaching levels as high as 375 and 480 ng/m(3) for tropomyosine and arginine kinase, respectively. Tropomyosine is much more abundant than arginine kinase in shrimp tissues, so the high levels of arginine kinase suggest it is more easily aerosolized. The present study shows that mass spectrometric analysis is a sensitive and accurate tool in identifying and quantifying aerosolized 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 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".