Development of an ante-mortem diagnostic test for enzootic nasal tumor virus and detection of neutralizing antibodies in host serum
Bibliographic record
Abstract
Enzootic nasal adenocarcinoma (ENA) is a contagious neoplasm of the nasal mucosa of sheep and goats and is associated with enzootic nasal tumour virus (ENTV). As ENA is a common disease in North America and there are no vaccines against ENTV-1, diagnostic tests that can identify infected animals and assist with eradication and disease surveillance efforts are greatly needed. In this study, we endeavoured to develop a novel, non-invasive diagnostic tool that could be used not only to validate clinical signs of ENA but also to detect ENTV-1 infection prior to the onset of disease signs (i.e. pre-clinical diagnosis). Cytology, serology and reverse transcription (RT)-PCR-based diagnostic methods were investigated. Although the cytology-based assay was able to detect ENTV-1 infection in some animals, it had poor sensitivity and specificity and thus was not developed further as an ante-mortem diagnostic method. Three different assays, including ELISA, Western blotting and virus neutralization, were developed to detect the presence of ENTV-1-specific antibodies in sheep serum. Whilst a surprisingly large number of sheep mounted an antibody-mediated immune response against ENTV-1, and in some cases neutralizing, correlation with disease status was poor. In contrast, RT-PCR on RNA extracted from nasal swabs reliably detected exogenous ENTV-1 sequences, did not amplify endogenous ovine betaretroviral sequences, demonstrated high concordance with immunohistochemical staining for ENTV-1 envelope protein, and had perfect sensitivity and specificity. This report describes a practical and highly specific RT-PCR technique for the detection of clinical and pre-clinical ENA that may prove beneficial in future control or eradication programmes.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".