Robust BICP0-gB indirect ELISA for the accurate diagnosis of bovine alphaherpesvirus 1 infections
Bibliographic record
Abstract
Bovine alphaherpesvirus 1 (BoHV-1) represents a significant threat to the cattle industry, emphasizing the need for reliable diagnostic tools that facilitate effective disease management. Current diagnostic methods, including virus neutralization tests (VNTs), are often complex and labor intensive, but existing antibody detection assays may lack sufficient accuracy. In this study, we developed a novel indirect enzyme-linked immunosorbent assay (iELISA) utilizing both BICP0 and gB proteins to enhance the detection of BoHV-1 infections. The inclusion of BICP0, a pivotal protein during the early stages of viral infection, markedly improved the assay's specificity and sensitivity. The BICP0-gB iELISA exhibited a high degree of concordance with the VNT, demonstrating superior sensitivity and specificity. Preliminary clinical evaluations revealed a real prevalence of 40.3 % (95 % CI: 33.1 %-48.0 %) in serum samples from yaks in Qinghai Province, which aligns with the documented prevalence of BoHV-1 in the region. These results underscore that BICP0-gB iELISA is a robust and reliable diagnostic tool for the differential diagnosis of BoHV-1, providing a cost-effective and efficient solution for high-throughput screening in the livestock industry.
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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 | 0.002 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".