Advances in the Molecular Based Techniques for the Diagnosis and Characterization of Avian Influenza Virus Infections
Bibliographic record
Abstract
There have been remarkable advances in the molecular diagnosis and characterization of avian influenza virus infections in domestic poultry and free-living birds in the past two decades. Rapid pathotyping became possible with the recognition that the amino acid sequence of the connecting peptide of the haemagglutinin precursor, HA(0), is a major virulence determinant for H5 and H7 subtype viruses. This in turn resulted in nucleic acid sequencing as a relatively routine method for identifying highly pathogenic avian influenza virus isolates. Subsequent development of diagnostic methods based on reverse transcription-polymerase chain reaction (RT-PCR), real-time RT-PCR, nucleic acid sequence-based amplification and loop-mediated isothermal amplification has made the rapid detection of group A influenza and H5 and H7 subtype viruses possible. Further development of these assay platforms has enabled the specific detection of H5N1 Eurasian subtype viruses and the inference of their HA(0) cleavage sites. Identification of additional virulence determinants of influenza A viruses for birds and mammals will allow the emerging area of microarray technology to further extend our understanding of their ecology, epidemiology and pathogenesis.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
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".