Development and laboratory evaluation of a competitive ELISA for serodiagnosis of Nipah and Hendra virus infection using recombinant Nipah glycoproteins and a monoclonal antibody
Bibliographic record
Abstract
Introduction: , are classified as Risk Group 4 (RG4) pathogens that cause respiratory disease in pigs and acute/febrile encephalitis in humans with high mortality. Methods: A competitive enzyme-linked immunosorbent assay (cELISA) using a monoclonal antibody (mAb) and recombinant NiV glycoprotein (G) was developed and laboratory evaluated using sera from experimental pigs, mini pigs and nonhuman primates. The test depends on competition between specific antibodies in positive sera and a virus-specific mAb for binding to NiV-G. Results: Based on 1,199 negative and 71 NiV positive serum test results, the cutoff value was determined as 35% inhibition. The diagnostic sensitivity and specificity of the NiV cELISA was 98.58 and 99.92%, respectively. When testing sera from animals experimentally infected with NiV Malaysia, the cELISA detected antibodies from 14 days post-infection (dpi) and remained positive until the end of the experiment (28 dpi). Comparisons using the Kappa coefficient showed strong agreement (100%) between the cELISA and a plaque reduction neutralization test (PRNT). Discussion: Because our cELISA is simpler, faster, and gives comparable or better results than PRNT, it would be an adequate screening test for suspect NiV and HeV cases, and it would also be useful for epidemiological surveillance of Henipavirus infections in different animal species without changing reagents.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".