Neuraminidase Activity as a Potential Enzymatic Marker for Rapid Detection of Airborne Viruses
Bibliographic record
Abstract
Viruses offer a limited range of targets for their detection. To date, PCR and RT-PCR have been widely used for detection of viruses. In the case of environmental air sampling, the ability to detect a broad range of viruses would constitute a significant advantage for preventing outbreaks of airborne-transmitted viral infections. Given that neuraminidase is found on some respiratory virus species of medical or agricultural importance, this enzyme could theoretically be used to detect several different airborne viruses in a single assay. The aim of the present study was to evaluate the potential of neuraminidase activity as a marker for rapid detection of airborne viruses. We first validated the use of a low-pathogenic strain of Newcastle disease virus (NDV) as a model airborne virus. Our findings revealed that neuraminidase activity-based assays are almost as sensitive as RT-PCR assays currently used for detection of NDV. We also validated the utilization of a neuraminidase substrate specific to viral neuraminidase. Experiments conducted in a controlled chamber demonstrated that the neuraminidase activity is preserved after aerosolization, air sampling using impingement and handling. Finally, we tested our method with swine barn air samples. Our results demonstrate that neuraminidase activity-based assays are suitable for detection of viruses in air samples.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".