Optimization and validation of ELISAs for interferon-gamma determination in bison
Bibliographic record
Abstract
Bovine tuberculosis, caused by Mycobacterium bovis , is endemic in the Wood Buffalo National Park, Canada, home to free-ranging and threatened wood bison. This disease poses a threat to the conservation of this culturally and ecologically important animal species, as well as potentially impacting the health of humans and other animal species via zoonosis and spillover, respectively. The ability to detect infection early will minimize and prevent the potential risk of M. bovis transmission. Interferon-gamma (IFNγ) assays are a reliable detection method for M. bovis in cattle and other wildlife species and may have diagnostic value in bison as well. We aimed to optimize and partially validate 2 commercial IFNγ ELISAs to detect endogenous bison IFNγ in mitogen-stimulated whole blood. Parameters evaluated included antibody identification, sample matrix effect, dilution linearity, assay reproducibility, and limit of quantification. The optimized assays demonstrated linear responses to recombinant bovine and endogenous bison IFNγ (range: 1–125 pg/mL; R 2 = 0.99), with good recovery and fair reproducibility, and a low limit of quantification of 1 pg/mL. Mabtech bovine Flex and Pro kits have the same antibodies but in 2 different assay formats; an in-house assay platform (Flex kit) and precoated plates (Pro kit) are considered suitable for measuring bison IFNγ, offering flexibility depending on available resources.
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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 | 0.007 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".