Development of 2 types of competitive enzyme-linked immunosorbent assay for detecting antibodies to the rinderpest virus using a monoclonal antibody for a specific region of the hemagglutinin protein
Bibliographic record
Abstract
A competitive enzyme-linked immunosorbent assay (C-ELISA) has been developed and standardized for the detection of antibodies to the rinderpest virus (RPV) in sera from cattle, sheep, and goats. The test is specific for rinderpest because it does not detect antibodies to peste-des-petits-ruminants virus (PPRV). The test depends on the ability of the monoclonal antibody (MAb) directed against the hemagglutinin (H) protein of RPV to compete with the binding of RPV antibodies in the positive serum to the H protein of this virus. This MAb recognized a region from amino acids 575 to 583 on the H protein of RPV that is unique to the RPV H protein and is not present on the hemagglutinin-neuraminidase protein of PPRV. Another C-ELISA (peptide C-ELISA) was set up using this specific region as an antigen. A threshold value of 64.4% inhibition was established for the RPV C-ELISA, with 90 known RPV-negative and 30 RPV-positive serum samples. Using common serum samples, a cutoff value of 43.0% inhibition for the peptide C-ELISA was established. Based on statistical analysis, the overall sensitivity and specificity of the RPV C-ELISA, relative to those of a commercial kit, were found to be 90.00% and 103.33%, respectively. However, the sensitivity and specificity of the peptide C-ELISA were found to be 180.00% and 73.33%, respectively. Although a common MAb in 2 new C-ELISA systems was used, variation in their percent inhibition, due to the use of different antigens, was observed. Taking into consideration the difference in percent inhibition of the 2 described assays and the commercial kit (50%), it was found that the RPV C-ELISA and the peptide C-ELISA are more specific and sensitive tools than the commercial kit for assessing herd immune status and for epidemiologic surveillance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".