Development and Validation of Competitive ELISA for Detection of H5 Hemagglutinin Antibodies
Bibliographic record
Abstract
Influenza A viruses (IAVs) belonging to the goose/Guangdong (Gs/GD)-lineage H5Nx remain a major concern for the global poultry industry, wildlife, and humans. The hemagglutinin (HA) protein is the dominant antigenic epitope carrier within IAV, which in turn triggers substantial immunogenic responses in the infected host. The current study describes the development and validation of a highly sensitive competitive H5 ELISA (cELISA) based on a novel monoclonal antibody developed in mice immunized with inactivated virus H5N1 (A/Turkey/ON/6213/1966). The cELISA is capable of detecting the H5 antibody response to a wider range of H5-subtype viruses belonging to both North American and Eurasia lineages, including the Gs/GD H5Nx from clade 2.3.4.4b that is currently causing the highly pathogenic avian influenza outbreaks in Eurasia, Africa, and Latin and North America. The developed H5 cELISA provides a specific, sensitive, and species-independent serological assay for the rapid detection of H5 antibodies. The assay is more robust and more sensitive than the hemagglutination inhibition assay, which is the “Gold standard”. The assay can be used in serological diagnosis, serosurveillance, and vaccine monitoring of serum samples collected from different species of animals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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".