Development of a multiplex lateral flow strip test for foot-and-mouth disease virus detection using monoclonal antibodies
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) is one of the world's most highly contagious animal diseases with tremendous economic consequences. A rapid and specific test for FMD diagnosis at the site of a suspected outbreak is crucial for the implementation of control measures. This project developed a multiplex lateral flow immunochromatographic strip test (multiplex-LFI) for the rapid detection and serotyping of FMD viruses. The monoclonal antibodies (mAbs) against serotypes O, A, and Asia 1 were used as capture mAbs. The mAbs were conjugated with fluorescein, rhodamine or biotin for serotype O, A and Asia 1, respectively. The detection mAbs which consisted of a serotype-independent mAb in combination with one serotype A-specific mAb and one Asia 1-specific mAb, were each colloidal gold-conjugated. The strips used in this study contained one control line and three test lines, which corresponded to one of the three serotypes, O, A or Asia 1. The newly developed multiplex-LFI strip test specifically identified serotype O (n=46), A (n=45) and Asia 1 (n=17) in all tested field isolates. The sensitivity of this strip test was comparable to the double antibody sandwich ELISA for serotypes O and A, but lower than the ELISA for serotype Asia 1. The multiplex-LFI strip test identified all tissue suspensions from animals that were experimentally inoculated with serotypes O, A or Asia 1. FMD viruses were detected in 38% and 50% of the swab samples from the lesion areas of experimentally inoculated sheep for serotypes O and A, respectively. The capability of the multiplex-LFI strip tests to produce rapid results with high specificity for FMD viruses of multiple serotypes makes this test a valuable tool to detect FMD viruses at outbreak sites.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".