Identification of feline erythrocyte antigen 6 and lack of alloimmunization to feline erythrocyte antigen 4 in cats
Bibliographic record
Abstract
BACKGROUND: New feline erythrocyte antigens (FEAs) have been described based on the presence of naturally occurring alloantibodies (NOAb), but their immunogenicity and clinical relevance are poorly understood. HYPOTHESIS/OBJECTIVES: Describe the immunogenicity of FEA 4 after sensitizing FEA 4-negative cats lacking NOAb and characterize anti-FEA 4 alloantibodies produced, including their rate of appearance, agglutination titer, and immunoglobulin class. ANIMALS: Nineteen healthy type A cats were blood typed for FEAs 1 to 5 to identify suitable donor-recipient pairs for FEA 4 sensitization. METHODS: Four FEA 4-negative cats were transfused with FEA 4-positive red blood cells. Using a gel column technique, posttransfusion samples were screened daily for a week, weekly for a month, and monthly thereafter for anti-FEA 4 alloantibodies. RESULTS: Alloantibodies were not detected in the first 3 recipients despite repeated transfusions (1 and 3 additional transfusions for 2 and 1 recipients, respectively). In the 4th recipient, alloantibodies against its donor red blood cells were detected 21 days postsensitization. However, they were not directed against FEA 4, but rather against a novel FEA not yet described. The alloantibodies, named anti-FEA 6, remained detectable for >4 months after sensitization and were determined to be mostly immunoglobulin M based on sulfhydryl treatment. CONCLUSIONS AND CLINICAL IMPORTANCE: Feline erythrocyte antigen 4 does not appear to be immunogenic because repeated sensitization of 4 cats failed to produce detectable anti-FEA 4 alloantibodies. A new immunogenic antigen, named FEA 6, has been discovered, but additional studies are needed to document its clinical importance.
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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 | 0.000 | 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".