Production of Monoclonal Antibodies against Foot‐and‐Mouth Whole Virus Particles (Serotype O and A) and their Potential Use in Quantification of Intact Virus for Vaccine Manufacture
Bibliographic record
Abstract
Foot‐and mouth disease (FMD) is the most feared infection of domestic livestock. It causes production losses, particularly to the dairy and pig industries. Vaccination is a practical option to prevent the devastating consequences of a FMD outbreak. The essential immunogenic component of FMD vaccines is the whole viral particles (140S), since any degradation of the particle greatly reduces the potency of the vaccine. The concentration of 140S particles is usually measured using the sucrose gradient, which requires purified samples and is labour intensive. In contrast, ELISA offers greatly decreased assay times as well as increased simplicity and sensitivity. However, serological methods for the quantification of 140S are difficult since the whole virus and its subunit (12S) share many of the same epitopes. As a result, polyclonal and majority of monoclonal antibodies (mAb) cross react with 140S and 12S. In order to develop an ELISA tests for quantification FMD whole virus particle, two mAbs against FMD virus (FMDV) that bind to serotype O and A whole virus particle were produced. The antibody binding epitopes were characterized. Using the two mAbs, the double antibody sandwich ELISAs for the quantification of whole FMDV particles were developed for serotype O and A. The two mAbs described here cross‐react with all of the tested FMDV vaccine strains. The development of these two mAbs and their potential use in the sandwich ELISA make them an optimal tool for both quantification and qualification in monitoring the FMD whole virus particles during vaccine manufacture. This work was supported by the funds of the laboratories directorate of the Canadian Food Inspection Agency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".