The development of a differentiating of infected from vaccinated animals (diva) elisa to detect antibody against senecavirus a
Bibliographic record
Abstract
Senecavirus A (SVA) is the causative agent of porcine idiopathic vesicular disease (PIVD), a condition clinically indistinguishable from other vesicular diseases in pigs, such as foot and mouth disease (FMD). This similarity complicates differential diagnosis, impacting the global swine industry, including Thailand. Rapid and accurate diagnostic methods are essential for effective disease control. Therefore, developing a highly sensitive and specific diagnostic tool for detecting SVA infection is crucial for distinguishing it from other vesicular diseases and controlling its spread. In this study, we conducted a retrospective analysis of SVA emergence in diagnostic samples from 2010 to 2021 in Thailand, with the goal of developing serological diagnostic tools for SVA. These included enzyme-linked immunosorbent assays (ELISA), starting with the SVA VP1 ELISA followed by the differentiating infected from vaccinated animals (DIVA) ELISA to detect SVA antibodies in pig serum. We utilized baculovirus and Escherichia coli (E. coli) expression systems to produce the SVA VP1 structural protein (SP) and the SVA 3AB non-structural protein (NSP), which served as coating antigens in the ELISAs to evaluate the performance of different protein expression systems. Additionally, we developed a prototype colloidal gold nanoparticle-based Lateral Flow Immunochromatographic (LFI) strip test using SVA NSP to detect SVA antibodies. Our results revealed that the first detection of SVA in Thailand occurred in 2016, with Thai SVA isolates closely related to the Canadian strain (11-55910-3), sharing 85% identity in the SVA VP1 gene and, showing a more distant relationship to other strains. All three assays were optimized, validated, and demonstrated high sensitivity and specificity: SVA VP1 ELISA (baculovirus: 100% sensitivity, 96.67% specificity; E. coli: 96.67% sensitivity and specificity) and SVA DIVA ELISA (baculovirus: 96.67% sensitivity, 96.67% specificity; E. coli: 100% sensitivity, 93.33% specificity). The SVA LFI strip test showed 97.97% sensitivity, and 90% specificity compared to VNA, and 98.62% sensitivity and 86.79% specificity compared to ELISA. All assays showed no cross-reactivity with other viruses and demonstrated strong agreement. These findings highlight the emergence of SVA in Thailand and emphasize the development of reliable diagnostic tools for detecting SVA antibodies, which could be valuable for sero-surveillance, disease control, and managing SVA vaccination in future pig herds.
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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 | 0.000 |
| 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) | 0.000 | 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".