Development and Evaluation of a Real-Time Reverse Transcription-PCR Assay for Quantification of Gamma Interferon mRNA To Diagnose Tuberculosis in Multiple Animal Species
Bibliographic record
Abstract
Tuberculosis of free-ranging and captive wildlife, including species implicated in the maintenance and transmission of Mycobacterium bovis, is a difficult disease to diagnose and control. Historically, diagnosis of tuberculosis has relied largely upon assays of cell-mediated immunity (CMI), such as tuberculin skin testing. This approach, however, is problematic or impractical for use with many wildlife species. Increasingly, in vitro diagnostic tests, including gamma interferon (IFN-gamma)-based assays, are replacing or complementing skin testing of cattle and humans. Analogous assays are unavailable for most wildlife because of a lack of species-specific immunological reagents. This report describes the development and validation of a whole-blood assay to quantify antigen-specific IFN-gamma mRNA expression by quantitative real-time reverse transcription-PCR. Oligonucleotide primers and probes were designed and tested for reactivity towards several susceptible species of interest with respect to tuberculosis infection. The assay was subsequently optimized to quantify the IFN-gamma mRNA expression in elk and red deer (Cervus elaphus) and was evaluated for its ability to detect mycobacterial antigen-specific responses of experimentally tuberculosis-infected animals. The assay was a simple, rapid, and sensitive measure of antigen-specific CMI. The IFN-gamma mRNA responses correlated well with IFN-gamma protein production and showed performance in determining an animal's infection status superior to that of either lymphocyte proliferation or IFN-gamma protein enzyme-linked immunosorbent assay methods. An additional advantage is the ease with which the assay can be modified to reliably quantify IFN-gamma expression by using consensus sequences of closely related species or of other species for which IFN-gamma sequence information is available.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".