An update on molecular cat allergens: Fel d 1 and what else? Chapter 1: Fel d 1, the major cat allergen
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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 it