Development and Validation of an Indirect and Blocking ELISA for the Serological Diagnosis of African Swine Fever
Bibliographic record
Abstract
African swine fever (ASF) is an economically devastating viral disease of pigs caused by the ASF virus (ASFV). The rapid global spread of ASF has increased the demand for ASF diagnostics to be readily available and accessible. No commercial ASF enzyme-linked immunosorbent assay (ELISA) kits are manufactured and licensed in North America. Here, we report the development of two serological diagnostic assays, a blocking ELISA (bELISA) based on ASFV glycoprotein p54 and an indirect ELISA (iELISA) based on ASFV glycoproteins p54 and p72. The assays showed high sensitivity and specificity and detected anti-ASFV antibodies in serum samples from experimentally infected animals as early as 8 days post-infection. The two assays were produced commercially (AsurDx™ bELISA and iELISA) and subjected to extensive validation. Based on data from a set of characterized reference sera, the prototype commercial assays, while maintaining 100.00% specificity, showed 97.67% (AsurDx™ bELISA) and 83.72% (AsurDx™ iELISA) sensitivity. Both prototype assays detected anti-ASFV antibodies in serum samples collected from pigs experimentally infected with multiple ASFV strains and field samples collected from sick, recovering, and vaccinated animals. The two commercially available assays can be used in routine ASF diagnostics, serological surveys, and for evaluating serological responses to ASF vaccine candidates.
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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 | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 | 0.001 |
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".