A Simplified mAb-Based Antigen Detection Assay for Rapid Serotyping of Foot-and-Mouth Disease Virus
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) is a devastating infectious viral disease of cloven-hoofed animals. Differentiating FMD from other vesicular diseases is difficult based on only clinical symptoms, requiring an appropriate laboratory diagnostic test. The double-antibody sandwich (DAS)-ELISA is a reliable diagnostic technique for antigen detection and serotyping of FMDV. However, classical DAS-ELISAs use polyclonal antibodies (pAbs), which are inconsistent in yields and limited in large-scale applications compared to hybridoma cell-secreted laboratory-made monoclonal antibodies (mAbs). Therefore, this study aimed to develop simplified and sensitive FMD serotype-specific DAS-ELISAs using HRP-conjugated mAbs and a TMB substrate. Six FMDV serotype-specific mAb-DAS-ELISAs were developed. All assays were optimized using BEI-inactivated FMD antigens. Real-time reverse-transcriptase PCR (RRT-PCR) was also used to verify the detection efficiency of all assays. Known negative and positive 10% tissue suspensions of different animal origins were examined to calculate the diagnostic specificity (DSp) and sensitivity (DSe). Serotype-specific mAb-DAS-ELISAs demonstrated 100%, 97%, 97%, 99%, 99%, and 94% DSp and 100%, 95%, 90%, 95%, 100%, and 100% DSe for serotypes O, A, Asia-1, SAT-1, SAT-2, and SAT-3, respectively. The detection efficiency of mAb-DAS-ELISAs was better than that of classical DAS-ELISAs. Also, all assays demonstrated minimal cross-reactivity and optimal reproducibility. Therefore, the mAb-DAS-ELISAs developed in this study could be useful for detecting and serotyping FMDV and ultimately replacing the classical DAS-ELISA.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".