Toward a Multiplexed Serotyping Immunoassay for Foot-and-Mouth Disease Virus
Bibliographic record
Abstract
Initial results demonstrating the feasibility of a multiplexed liquid array immunoassay for foot-and-mouth disease viral antigen detection and simultaneous serotype differentiation are presented. Serotype-specific antibodies from rabbit and guinea pig hyperimmunesera were isolated and prepared for use in a multiplexed, bead-based assay. The performance of all of the available antibodies as both capture and detector reagents was evaluated in the multiplexed system to establish a combination exhibiting the highest homotypic responses and lowest heterotypic reactions. The multiplexed assay was evaluated against inactivated cell culture supernatant samples of the same subtype as the virus used to raise the capture and detector antibodies. Distinct serotype differentiation was observed, except in the case of serotype SAT1. Subsequently, cell culture supernatant samples from a larger pool of viral subtypes were analyzed. Distinct serotype differentiation was obtained when analyzing cell culture supernatant samples from viral serotypes C, Asia, and SAT3, irrespective of the subtype. However, limitations of the current antibody pairs were realized in some inconclusive results obtained when analyzing samples from a broader range of O, A, and SAT2 subtypes. The results obtained in this initial study will be used to further optimize the assay using polyvalent or monoclonal antibodies and move toward the analysis of clinical samples.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".