An update on molecular cat allergens: Fel d 1 and what else? Chapter 1: Fel d 1, the major cat allergen
Bibliographic record
Abstract
BACKGROUND: Cats are the major source of indoor inhalant allergens after house dust mites. The global incidence of cat allergies is rising sharply, posing a major public health problem. Ten cat allergens have been identified. The major allergen responsible for symptoms is Fel d 1, a secretoglobin and not a lipocalin, making the cat a special case among mammals. MAIN BODY: Given its clinical predominance, it is essential to have a good knowledge of this allergenic fraction, including its basic structure, to understand the new exciting diagnostic and therapeutic applications currently in development. The recent arrival of the component-resolved diagnosis, which uses molecular allergens, represents a unique opportunity to improve our understanding of the disease. Recombinant Fel d 1 is now available for in vitro diagnosis by the anti-Fel d 1 specific IgE assay. The first part of the review will seek to describe the recent advances related to Fel d 1 in terms of positive diagnosis and assessment of disease severity. In daily practice, anti-Fel d 1 IgE tend to replace those directed against the overall extract but is this attitude justified? We will look at the most recent arguments to try to answer this question. In parallel, a second revolution is taking place thanks to molecular engineering, which has allowed the development of various forms of recombinant Fel d 1 and which seeks to modify the immunomodulatory properties of the molecule and thus the clinical history of the disease via various modalities of anti-Fel d 1-specific immunotherapy. We will endeavor to give a clear and practical overview of all these trends.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".