Identification of 5 novel feline erythrocyte antigens based on the presence of naturally occurring alloantibodies
Bibliographic record
Abstract
BACKGROUND: Since the discovery of the Mik antigen, several studies have described blood incompatibilities unrelated to the AB system in cats. OBJECTIVE: To estimate the prevalence of cats with non-AB incompatibilities associated with naturally occurring alloantibodies (NOAb), and to begin mapping the corresponding feline erythrocyte antigens (FEA). ANIMALS: Two hundred and fifty-eight type A cats. METHODS: Prospectively, cats were evaluated for the presence of NOAb by crossmatching in groups of 4-6 cats. When NOAb were detected in a cat, its plasma was used as reagent to assess for the presence of the corresponding FEA in all cats included thereafter, and agreement observed between results of this extensive blood typing was evaluated. RESULTS: The chance of detecting incompatibilities by randomly crossmatching 2 cats was 3.9%, which resulted in at least 7% of type A cats having NOAb. Blood typing and agreement analyses performed with 7 newly detected NOAb allowed the identification of 5 presumably distinct FEA. Feline erythrocyte antigens 1 and 5 were most frequent with prevalence of 84% and 96%, respectively. Only FEA 1-negative status was associated with a higher risk of presenting NOAb; with 16.7% of 42 FEA 1-negative cats having NOAb compared to 5.1% of 216 FEA 1-positive cats. CONCLUSIONS AND CLINICAL IMPORTANCE: This study represents a first step of FEA identification outside the AB system. Because of its prevalence and association with NOAb, FEA 1 might correspond to the Mik antigen.
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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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".