Development of a Competitive Enzyme-Linked Immunosorbent Assay for Detection of Antibodies against the 3B Protein of Foot-and-Mouth Disease Virus
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) is one of the most highly contagious and economically devastating diseases, and it severely constrains the international trade of animals. Vaccination against FMD is a key element in the control of FMD. However, vaccination of susceptible animals raises critical issues, such as the differentiation of infected animals from vaccinated animals. The current study developed a reliable and rapid test to detect antibodies against the conserved, nonstructural proteins (NSPs) of the FMD virus (FMDV) to distinguish infected animals from vaccinated animals. A monoclonal antibody (MAb) against the FMDV NSP 3B was produced. A competitive enzyme-linked immunosorbent assay (cELISA) for FMDV/NSP antibody detection was developed using a recombinant 3ABC protein as the antigen and the 3B-specific MAb. Sera collected from naive, FMDV experimentally infected, vaccinated carrier, and noncarrier animals were tested using the 3B cELISA. The diagnostic specificity was 99.4% for naive animals (cattle, pigs, and sheep) and 99.7% for vaccinated noncarrier animals. The diagnostic sensitivity was 100% for experimentally inoculated animals and 64% for vaccinated carrier animals. The performance of this 3B cELISA was compared to that of four commercial ELISA kits using a panel of serum samples established by the World Reference Laboratory for FMD at The Pirbright Institute, Pirbright, United Kingdom. The diagnostic sensitivity of the 3B cELISA for the panel of FMDV/NSP-positive bovine serum samples was 94%, which was comparable to or better than that of the commercially available NSP antibody detection kits. This 3B cELISA is a simple, reliable test to detect antibodies against FMDV nonstructural proteins.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".