Neuraminidase as an enzymatic marker for detecting airborne <i>Influenza virus</i> and other viruses
Bibliographic record
Abstract
Little information is available regarding the effectiveness of air samplers to collect viruses and regarding the effects of sampling processes on viral integrity. The neuraminidase enzyme is present on the surface of viruses that are of agricultural and medical importance. It has been demonstrated that viruses carrying this enzyme can be detected using commercial substrates without having to process the sample by methods such as RNA extraction. This project aims at evaluating the effects of 3 aerosol-sampling devices on the neuraminidase enzyme activity of airborne viruses. The purified neuraminidase enzymes from Clostridium perfringens, a strain of Influenza A (H1N1) virus, the FluMist influenza vaccine, and the Newcastle disease virus were used as models. The neuraminidase models were aerosolized in aerosol chambers and sampled with 3 different air samplers (SKC BioSampler, 3-piece cassettes with polycarbonate filters, and Coriolis μ) to assess the effect on neuraminidase enzyme activity. Our results demonstrated that Influenza virus and Newcastle disease virus neuraminidase enzymes are resistant to aerosolization and sampling with all air samplers tested. Moreover, we demonstrated that the enzymatic neuraminidase assay is as sensitive as RT-qPCR for detecting low concentrations of Influenza virus and Newcastle disease virus. Therefore, given the sensitivity of the assay and its compatibility with air sampling methods, viruses carrying the neuraminidase enzyme can be rapidly detected from air samples using neuraminidase activity assay without having to preprocess the 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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".