Serological Hendra Virus Diagnostics Using an Indirect ELISA-Based DIVA Approach with Recombinant Hendra G and N Proteins
Bibliographic record
Abstract
Since the identification of Hendra virus (HeV) infections in horses in Australia in 1994, more than 80 outbreaks in horses have been reported, and four out of seven spillover infections in humans had a fatal outcome. With the availability of a subunit vaccine based on the HeV-Glycoprotein (HeV-G), there is a need to serologically Differentiate the Infected from the Vaccinated Animals (DIVA). We developed an indirect ELISA using HeV-G expressed in Leishmania tarentolae and HeV-Nucleoprotein (HeV-N) expressed in recombinant baculovirus-infected insect cells as antigens. During evaluation, we tested panels of sera from naïve, vaccinated and infected horses that either originated from a Hendra-virus free region, or had been pre-tested in validated diagnostic tests. Our data confirm the reliability of this approach, as HeV-N-specific antibodies were only detected in sera from infected horses, while HeV-G-specific antibodies were detected in infected and vaccinated horses with a high level of specificity and sensitivity. Given the excellent correlation of data obtained for German and Australian HeV-negative horses, we assume that this test can be applied for the testing of horse serum samples from a variety of geographical regions.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.000 |
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".