Size distribution and viral RNA load of influenza virus-laden airborne particles emitted from pigs over the course of an H1N1 infection
Bibliographic record
Abstract
Airborne influenza infections cause significant disease in animals and people. However, there is limited information on the dynamics of viral emissions and size distribution of airborne virus-laden particles generated by infected animals. In this study, we used pigs as a model for the airborne transmission of influenza A virus (IAV) and we quantified nasal shedding, viral RNA load of airborne particles emitted from pigs experimentally infected with a swine-origin H1N1 IAV, and characterized the size distribution of the virus-laden particles generated from infected pigs over the course of infection. We found that the peak of nasal shedding and airborne IAV-laden particles across multiple size ranges took place at 2 days post inoculation (DPI), with higher viral RNA load found in larger particles. The amount of airborne IAV emitted by infected pigs over the course of infection in particles > 8 μm was significantly higher than that in particles between 0.22 and 1.7 μm. These findings help understand the risk of airborne transmission of IAV in pigs and provide information to help control airborne infections more effectively.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".