Generation of mAbs to foot–and–mouth disease virus serotype A and application in a competitive ELISA for serodiagnosis
Bibliographic record
Abstract
BACKGROUND: Foot-and-mouth disease (FMD) is an economically devastating disease that severely limits international trade of animals. Of the seven FMD virus (FMDV) serotypes, serotype A is one of the most widespread cross the world. Currently antibodies to FMDV are detected in animals using the virus neutralization test (VNT) and the enzyme-linked immunosorbent assay (ELISA). The VNT is laborious, time-consuming and reliant on live virus and cell cultures, while ELISA has the advantage of using inactivated antigens and often provides more reproducible results. The aim of this study was to develop a reliable and rapid competitive ELISA (cELISA) for the detection of antibodies to FMDV serotype A (FMDV/A). RESULTS: A panel of FMDV/A specific monoclonal antibodies (mAbs) was generated and their ability to compete with a polyclonal serum from FMDV/A-infected cattle was examined. Two mAbs inhibited the binding of a polyclonal serum to FMDV/A viruses. The binding epitopes of each were determined as conformational and located on the VP2 viral capsid protein. The FMDV/A cELISA was developed using these two mAbs and FMDV/A inactivated virus as antigen. The diagnostic specificity and sensitivity were 99.7 and 99.3% (98.5-100%) respectively, based on a predetermined cut-off of 50% inhibition. When analysing sera from animals experimentally infected with FMDV/A, the cELISA detected antibodies from 5-days post infection (dpi) and remained positive for at least 21-28 days post infection. Comparison based on the Kappa coefficient showed strong agreement (90-94%) between cELISA and VNT. CONCLUSION: The cELISA results are comparable to the VNT for antibody detection making it a simple and reliable test to detect antibodies against FMDV/A.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".