Generation of monoclonal antibodies against foot‐and‐mouth disease virus <scp>SAT</scp> 2 and the development of a lateral flow strip test for virus detection
Bibliographic record
Abstract
Foot-and-mouth disease (FMD) remains a major economic concern for the livestock productivity in many developing countries and a continued threat to countries that are disease free because of its potential devastating impact on agricultural, food chain and tourism sectors. FMD virus (FMDV) is recognized as having seven serotypes: O, A, C, Asia 1, South African Territories (SAT) 1, 2, 3 and multiple subtypes within each serotype. FMD outbreaks due to SAT 2 have been reported in many African countries. The development of a rapid and easily performed test for FMD detection is critical for controlling FMD outbreaks and containing its spread. The present project developed a lateral flow immunochromatographic (LFI) strip test for the rapid detection of FMDV SAT 2. A panel of monoclonal antibodies (mAbs) against FMDV serotype SAT 2 was produced and characterized. One mAb (#10) was selected as the capture mAb because it reacted to all 23 SAT 2 isolates archived at the National Center for Foreign Animal Disease. The LFI strip test was developed using biotin-conjugated mAb #10, and the colloid gold-conjugated FMDV serotype-independent mAb as the detection mAb. A generic Rapid Assay Device (gRAD) with one test line and a control line was used for the test. The LFI strip test detected all 23 tested SAT 2 isolates and recent outbreak strains. The results indicated that the diagnostic specificity and sensitivity of the LFI strip test were greater than the double antibody sandwich (DAS) DAS ELISA. The ability of the LFI strip test to produce rapid diagnostic results will be useful for early on-site diagnosis during FMD outbreaks.
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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 | 0.000 | 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) | 0.001 | 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 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".