Development of a quick and simple detection methodology for foot-and-mouth disease virus serotypes O, A and Asia 1 using a generic RapidAssay Device
Bibliographic record
Abstract
BACKGROUND: Outbreaks of Foot-and-mouth disease (FMD) have resulted in tremendous economic losses. Thus, the development of a rapid and easily performed test for FMD detection is important for controlling a FMD outbreak and containing its spread. The purpose of this project is to develop a lateral flow immunochromatographic (LFI) strip test for rapid detection of FMD virus serotypes O, A and Asia 1. METHODS: Specific monoclonal antibodies (mAbs) against each serotype were produced and used as the capture mAbs. A serotype independent mAb was selected and used as the detection mAb with the aim of subsequently developing a multi-serotype strip test. A new generation of the generic RapidAssay Device (gRAD) was used for the test. RESULT: Each strip test can specifically detect the FMDV O, A or Asia 1 viruses, but not other vesicular disease viruses. The LFI strip tests for serotypes A and Asia 1 were able to identify all tested serotype A (n= 39) and Asia 1 field isolates (n=17). Whereas the test for serotype O detected 45 out of 46 field isolates. The sensitivity of this strip test was comparable with the double antibody sandwich ELISA for viral antigen detection. All vesicular fluid and epithelium samples collected from experimentally infected animals with serotype O, A and Asia 1 were identified as positive by the LFI strip test. Swab samples (n=11) collected over the lesion area from experimentally inoculated animals (serotype A) were examined. All of them demonstrated positive results using the LFI serotype A strip test and double antibody sandwich (DAS) ELISA. CONCLUSIONS: The ability of strip tests to produce rapid results and high specificity makes it a valuable tool for early detection of FMDV O, A and Asia 1 in the field.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".