Development of a Point-of-Care Immunochromatographic Lateral Flow Strip Assay for the Detection of Nipah and Hendra Viruses
Bibliographic record
Abstract
Nipah virus (NiV) and Hendra virus (HeV), which both belong to the genus henipavirus, are zoonotic pathogens that cause severe systemic, neurological, and/or respiratory disease in humans and a variety of mammals. Therefore, monitoring viral prevalence in natural reservoirs and rapidly diagnosing cases of henipavirus infection are critical to limiting the spread of these viruses. Current laboratory methods for detecting NiV and HeV include virus isolation, reverse transcription quantitative real-time PCR (RT-qPCR), and antigen detection via an enzyme-linked immunosorbent assay (ELISA), all of which require highly trained personnel and specialized equipment. Here, we describe the development of a point-of-care customized immunochromatographic lateral flow (ILF) assay that uses recombinant human ephrin B2 as a capture ligand on the test line and a NiV-specific monoclonal antibody (mAb) on the conjugate pad to detect NiV and HeV. The ILF assay detects NiV and HeV with a diagnostic specificity of 94.4% and has no cross-reactivity with other viruses. This rapid test may be suitable for field testing and in countries with limited laboratory resources.
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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 | 0.002 |
| 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 | 0.000 |
| 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".