Production and characterization of a murine anti-dal monoclonal antibody for blood typing in dogs
Bibliographic record
Abstract
Considering the strong immunogenicity of the Dal antigen, and that > 98% of dogs, including blood donors, are Dal-positive, finding compatible blood for a previously transfused Dal-negative patient may be challenging. This is exacerbated by limited access to typing reagents, which currently rely on polyclonal antibodies (PAb) produced following sensitization of dogs. Therefore, the objective of this study was to produce and characterize an anti-Dal murine monoclonal antibody (MAb). Conventional hybridoma technology was used to produce MAb directed against canine red blood cells (cRBC). Briefly, female BALB/c mice were immunized via repeated intraperitoneal injections of washed Dal-positive cRBC (DEA 1,3,7 negative; DEA 4,5 positive) until serologic titers were sufficient (>1:1000). Following fusion with myeloma cells, 573 hybridoma cell culture supernatants were obtained and screened for MAb of interest using a gel column agglutination technique and known Dal-negative and Dal-positive cRBC. Fifteen supernatants led to cRBC agglutination, but only one had the desired pattern (i.e. anti-Dal). To assess its specificity and sensitivity, Dal blood typing of 62 canine EDTA-blood samples was performed using the anti-Dal MAb and two canine PAb: 45 Dal-positive and 17 Dal-negative were identified with 100% agreement between reagents (kappa =1). The anti-Dal MAb produced was further determined to be an IgG1. Conventional hybridoma technology, aided by a gel column technique, has enabled the production of a murine MAb specific against the canine Dal antigen. This will ensure long-term perennity of Dal blood typing, facilitate clinical management and research, as well as avoid resorting to repeat dog sensitization.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".