Generation and diagnostic application of monoclonal antibodies against <i>Seneca Valley virus</i>
Bibliographic record
Abstract
Seneca Valley virus (SVV), a member of the Picornaviridae family, was implicated in a suspicious vesicular disease discovered in pigs from Canada in 2007. Because any outbreak of vesicular disease in pigs is assumed to be foot-and-mouth disease (FMD) until confirmed otherwise, a test for diagnosing the presence of SVV would be a very useful tool. To develop the diagnostic tests for SVV infection, 5 monoclonal antibodies (mAbs) were produced from mice immunized with binary ethylenimine (BEI)-inactivated SVV. Using a dot blot assay, the reactivity of the mAbs was confirmed to be specific for SVV, not reacting with any of the other vesicular disease viruses tested. The mAbs demonstrated reactivity with SVV antigen in infected cells by an immunohistochemistry assay. An SVV-specific competitive enzyme-linked immunosorbent assay (cELISA) was developed using BEI-inactivated SVV antigen and a mAb for serodiagnosis. The cELISA results were compared to the indirect isotype (immunoglobulin [Ig]M and IgG) ELISA and the virus neutralization test. All SVV experimentally inoculated pigs exhibited a positive SVV-specific antibody response at 6 days postinoculation, and the sera remained positive until the end of the experiment on day 57 (>40% inhibition) using the cELISA. The cELISA reflected the profile of the indirect ELISA for both IgM and IgG. This panel of SVV-specific mAbs is valuable for the identification of SVV antigen and the serological detection of SVV-specific antibodies.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.001 | 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".