Development and Validation of Serotype-Specific Blocking ELISA for the Detection of Anti-FMDV O/A/Asia1/SAT2 Antibodies
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) is one of the most infectious viral transboundary diseases of livestock, which causes devastating global economic losses. Different enzyme-linked immunosorbent assays (ELISAs) are used for sero-surveillance of the foot-and-mouth disease virus (FMDV). However, more sensitive, accurate, and convenient ELISAs are still required to detect antibodies against FMDV serotypes. The primary goal of this study was to establish serotype-specific monoclonal antibody (mAb)-based blocking ELISAs (mAb-bELISAs) that would provide better performance characteristics or be equivalent in performance characteristics compared with a conventional polyclonal antibody (pAb)-based competitive ELISA (pAb-cELISA). Four mAb-bELISAs were developed using FMDV serotype-specific mAbs for the detection of anti-FMDV/O/A/Asia1/SAT2 antibodies. Using a 50% cut-off, all four mAb-bELISAs exhibited species-independent 99.74%, 98.01%, 96.59%, and 98.55% diagnostic specificity (DSp) and 98.93%, 98.25%, 100%, and 87.50% diagnostic sensitivity (DSe) for FMDV serotypes O, A, Asia1, and SAT2, respectively. In addition, a 100% DSe of serotypes O- and SAT2-specific mAb-bELISAs was observed for porcine sera when the cut-off was 30%. All mAb-bELISAs developed in this study displayed high repeatability/reproducibility without cross-reactivity. Finally, the diagnostic performance of mAb-bELISAs was found to be better than or equivalent to compared with pAb-cELISAs, suggesting that mAb-bELISAs can be used to replace existing pAb-ELISAs for the detection of antibodies against these four FMDV serotypes.
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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".