Development and Evaluation of Recombinant NS3 Protein-based ELISA for the Detection of Bovine Viral Diarrhea Virus (BVDV) Antibodies
Bibliographic record
Abstract
Background: Bovine Viral Diarrhea (BVD) is an important infectious disease that has a significant economic impact on dairy farms. Early detection of antibodies against BVD virus within herds can help reduce economic losses resulting from impaired animal health and reproductive performance. However, commercial ELISA tests tend to be expensive and unaffordable for smallholder farmers in Thailand. This study aims to develop an in- house indirect ELISA for the detection of antibodies to BVD. Methods: Gene synthesis and codon optimization were performed prior to protein expression. The prepared recombinant NS3 protein was confirmed through western blot analysis. Assay validation involved a comparison with an indirect commercial ELISA using a set of field serum samples (n=497) as references to evaluate its diagnostic accuracy. Measurements for test agreement and assay repeatability were calculated to assess performance. Result: The area under the receiver operating characteristic (ROC) curve was 0.86, indicating good discrimination between BVDV-positive and negative animals. The kappa coefficient, with an identified optimal cut-off value, was 0.577, indicated moderate agreement between the two tests. Additionally, the assay exhibited acceptable repeatability, with coefficients of variations (CVs) of 8.8% and 15.1% for intra-assay and inter-assay variations, respectively. This developed ELISA demonstrates both discriminatory ability and repeatability, making it a valuable alternative tool for the serodiagnosis of BVDV infection, especially in low-resource settings.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| 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".