Detection of Antibodies Against Capripoxviruses Using an Inactivated Sheeppox Virus ELISA
Bibliographic record
Abstract
An indirect ELISA was developed to detect antibodies specific for capripoxviruses in goat, sheep and cattle sera. Heat-inactivated Nigerian sheeppox virus was used as the ELISA antigen. Sera obtained from sheep and goats that were experimentally infected with different capripoxvirus isolates were used to develop and evaluate the sensitivity of the ELISA. Virus neutralization indexes were determined for the experimental sera in OA3.Ts cells. The specificity of the ELISA was determined using 231 sera from capripoxvirus naïve sheep and goats from Canada. In addition, the ELISA was tested for cross-reactivity to anti-orf virus antibodies using orf-reactive sera and no cross-reactivity was observed. Using experimentally generated sera obtained from animals infected with virulent sheeppox or goatpox virus isolates, the diagnostic sensitivity of the ELISA was 96% with a diagnostic specificity of 95%, where the diagnostic sensitivity of the virus neutralization assay was 96% with a diagnostic specificity of 100%. Further evaluation of this ELISA, using 276 cattle serum samples that were positive by virus neutralization assays, revealed a diagnostic sensitivity of 88% with a specificity of 97%. These results indicated that the inactivated capripoxvirus ELISA can detect capripoxvirus-specific antibodies in sheep, goats and cattle that have been infected with virulent capripoxvirus isolates. Non-virulent capripoxvirus isolates, in contrast, did not elicit positive (>or=1.5 Log10 neutralization index) antibody responses.
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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) | 0.002 | 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 | 0.001 | 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".