Evaluation of an interferon-gamma release assay for the detection of Mycobacterium bovis using QuantiFERON-TB gold tubes in bison (Bison bison athabascae)
Bibliographic record
Abstract
Mycobacterium bovis (M. bovis), which causes bovine tuberculosis, is endemic in Canada's Wood Buffalo National Park and threatens the conservation of free-ranging bison. It also poses spillover risks to humans and livestock. Accurate diagnostic tools are critical for screening this threatened and culturally significant animal. Interferon gamma (IFNγ) release assays (IGRA) are commonly used for tuberculosis diagnosis in humans and other wildlife species. Hence, this study aimed to develop an IGRA-based approach for detecting M. bovis infection in bison. Animals from a tuberculosis-free captive bison farm (n = 8) were experimentally infected with M. bovis. The whole blood was collected prior to and following M. bovis challenge and incubated overnight in QuantiFERON®-TB Gold (QFT) blood collection tubes and pokeweed mitogen tubes. Plasma was harvested after centrifugation. Concentrations of IFNγ in plasma were quantified using the Mabtech bovine IFNγ ELISA Flex kit. IGRA responses were calculated as the difference in IFNγ concentrations between TB antigen and the nil tubes. A diagnostic cut-off value of 59 pg/ml (Se = 81 %, 95 % CI 57-93 %; Sp =100 %, 95 % CI 89-100 %; AUC = 0.92) was determined using known infection status to distinguish infected bison. Additionally, cut-off values for IFNγ concentrations for plasma from QFT-nil and pokeweed mitogen tubes were calculated to increase confidence in IGRA validity interpretation. The combination of the QFT stimulation platform and Mabtech bovine IFNγ ELISA shows promise as a diagnostic test to distinguish between M. bovis-infected and uninfected bison. These findings support the use of this tool for surveillance in free-ranging and captive Canadian bison populations and warrant further field validation.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 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".