Influence of time and phenotype on salivary Fel d1 in domestic shorthair cats
Bibliographic record
Abstract
Objectives Fel d1 is a major allergen that may affect humans sensitive to cat allergens, and it can be detected in the saliva and on the hair of cats. We studied the variability of salivary Fel d1 in typical house cats (ie, neutered domestic shorthair cats) and the factors that could be associated with that variability. Methods Saliva samples were collected from 64 cats, twice daily, every other day, for a year, at two locations (Missouri, USA, and Ontario, Canada). Salivary Fel d1 levels were measured using an immunoassay. Correlations and linear mixed-effects model analyses were run to assess which factors significantly affected the Fel d1 levels. Results Salivary Fel d1 levels varied significantly both within and among cats. Cat averages over the year ranged from 0.4–35 µg/ml, and a higher average correlated with a higher SD ( P <0.001). The first collection of the day tended to be higher than the afternoon collection ( P <0.001). Sex, coat color or body size did not relate to cats’ average Fel d1 production, but older cats tended to have lower salivary Fel d1 levels ( P <0.001). Fel d1 levels from four samples were reliable in identifying cats producing stable low levels of Fel d1. Conclusions and relevance We observed a wide and continuous range of salivary Fel d1 production in domestic shorthair cats. In particular, a subset of cats had stable low levels throughout the course of the year, and they can be identified by analyzing a few saliva samples rather than their physical appearance.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".