Characterization of post-transfusion anti-FEA 1 alloantibodies in transfusion-naive FEA 1-negative cats
Bibliographic record
Abstract
OBJECTIVES: The aim of this study was to characterize anti-feline erythrocyte antigen (FEA) 1 alloantibodies following sensitization of FEA 1-negative cats, including their rate of appearance, agglutination titer over time and immunoglobulin class. A secondary aim was to obtain polyclonal anti-FEA 1 alloantibodies to increase the availability of FEA 1 blood typing. We also describe a case study documenting an acute hemolytic transfusion reaction in a transfusion-naive FEA 1-negative feline patient that received FEA 1-positive blood. METHODS: In this prospective clinical study, 35 cats with blood group type A underwent extensive blood typing for FEA 1-5. Two cats were identified as FEA 1-negative; these cats were transfused uneventfully with 50 ml of FEA 1-positive, but otherwise compatible, packed red blood cells. Post-transfusion blood samples were collected routinely as long as anti-FEA 1 alloantibodies were detected. Appearance of anti-FEA 1 alloantibodies was detected using a gel column crossmatch method. RESULTS: Anti-FEA 1 alloantibodies were detected as early as 5 days post-transfusion and remained detectable for over 400 days in one cat. Agglutination titers in both cats were relatively weak (1:1 to 1:8). The main immunoglobulin class was IgM. CONCLUSIONS AND RELEVANCE: Transfusion of FEA 1-negative, transfusion-naive cats with FEA 1-positive blood results in production of post-transfusion anti-FEA 1 alloantibodies as early as 5 days post-transfusion. Our results confirm the potential immunogenicity of FEA 1 and support crossmatching prior to a blood transfusion, even in transfusion-naive cats. Further studies are needed to better document the clinical importance of these post-transfusion antibodies, as well as to facilitate routine blood typing for the FEA 1 antigen in cats.
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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".