Enzyme-Linked Immunosorbent Assay Using Henipavirus-Receptor EphrinB2 and Monoclonal Antibodies for Detecting Nipah and Hendra Viruses
Bibliographic record
Abstract
The Nipah virus (NiV) and the Hendra virus (HeV) are highly pathogenic zoonotic diseases that can cause fatal infections in humans and animals. Early detection is critical for the control of NiV and HeV infections. We present the development of two antigen-detection ELISAs (AgELISAs) using the henipavirus-receptor EphrinB2 and monoclonal antibodies (mAbs) to detect NiV and HeV. The NiV AgELISA detected only NiV, whereas the NiV/HeV AgELISA detected both NiV and HeV. The diagnostic specificities of the NiV AgELISA and the NiV/HeV AgELISA were 100% and 97.8%, respectively. Both assays were specific for henipaviruses and showed no cross-reactivity with other viruses. The AgELISAs detected NiV antigen in experimental pig nasal wash samples taken at 4 days post-infection. With the combination of both AgELISAs, NiV can be differentiated from HeV. Complementing other henipavirus detection methods, these two newly developed AgELISAs can rapidly detect NiV and HeV in a large number of samples and are suitable for use in remote areas where other tests are not available.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".