Indirect ELISA based on Hendra and Nipah virus proteins for the detection of henipavirus specific antibodies in pigs
Bibliographic record
Abstract
Hendra virus (HeV) and Nipah virus (NiV) belong to the genus Henipavirus in the family Paramyxoviridae. Henipavirus infections were first reported in the 1990's causing severe and often fatal outbreaks in domestic animals and humans in Southeast Asia and Australia. NiV infections were observed in humans in Bangladesh, India and in the first outbreak in Malaysia, where pigs were also infected. HeV infections occurred in horses in the North-Eastern regions of Australia, with singular transmission events to humans. Bats of the genus Pteropus have been identified as the reservoir hosts for henipaviruses. Molecular and serological indications for the presence of henipa-like viruses in African fruit bats, pigs and humans have been published recently. In our study, truncated forms of HeV and NiV attachment (G) proteins as well as the full-length NiV nucleocapsid (N) protein were expressed using different expression systems. Based on these recombinant proteins, Enzyme-linked Immunosorbent Assays (ELISA) were developed for the detection of HeV or NiV specific antibodies in porcine serum samples. We used the NiV N ELISA for initial serum screening considering the general reactivity against henipaviruses. The G protein based ELISAs enabled the differentiation between HeV and NiV infections, since as expected, the sera displayed higher reactivity with the respective homologous antigens. In the future, these assays will present valuable tools for serosurveillance of swine and possibly other livestock or wildlife species in affected areas. Such studies will help assessing the potential risk for human and animal health worldwide by elucidating the distribution of henipaviruses.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".