Fish Allergenicity Ladder and Parvalbumin Epitopes for Predicting Clinical Cross-reactivity and Reintroduction
Bibliographic record
Abstract
not-yet-known not-yet-known not-yet-known unknown Background: IgE-mediated fish allergy has long been considered an umbrella term due to the high cross-reactivity of parvalbumin, the major fish allergen. Yet, clinical tolerance to certain fish highlights allergenicity differences. In this study, we sought to construct a fish allergenicity ladder and identify fish parvalbumin epitopes to improve the diagnosis of fish allergy. Methods: Reported clinical history and the serum-specific IgE (sIgE) responses of 200 fish allergic patients were collected and analyzed, while the relative parvalbumin content in different fish were measured for the construction of fish allergenicity ladder. Double-blind placebo-controlled food challenge (DBPCFC) and open challenge against salmon, grass carp and grouper were performed in 58 selected patients for validation of the ladder. Epitope mapping was performed by peptide array against parvalbumins of salmon (both β-1 and β-2), cod, grouper, and grass carp with sera from fish allergic (n=11), partial fish tolerant (n=12), and complete fish tolerant (n=5) patients diagnosed based on oral food challenge outcome. Results: The distribution pattern of clinical, sIgE and molecular data and their strong positive correlation led to the construction of a 4-step fish allergenicity ladder comprising: step 1 of the least allergenic fishes (tuna, halibut, salmon), steps 2 (cod) and 3 (herring and grouper) of moderately allergenic fishes to step 4 of highly allergenic fishes (catfish, grass carp and tilapia). Epitope mapping revealed one epitope from grouper parvalbumin (AA64-78) for diagnosing general fish allergy and one epitopic region from salmon parvalbumin (AA19-33) as biomarker of specific fish tolerance. Only epitope-specific IgE differentiated these patients but not sIgE to fish extract or parvalbumin. Conclusion: The fish ladder and epitopes discovery can precisely differentiate fish-allergic and tolerant subjects and guide fish reintroduction by stepping up the ladder, which innovate fish allergy care in the next millennium.
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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 | 0.001 |
| 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) | 0.001 | 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".