Influenza virus infection of well-differentiated human airway epithelial cells by infectious aerosols: insights into the earliest stages of infection
Bibliographic record
Abstract
Background: Influenza virus is a major human pathogen, yet surprisingly little data is available on the earliest stage of infection. We have developed a novel method to study natural transmission influenza infection by aerosol and to observe the effects of early infection on the ciliated airway epithelium using high-speed video microscopy. Methods: Primary human ciliated epithelial cultures were infected with influenza A (H1N1), delivered either by aerosol or by liquid immersion. Cells were stained for viral antigens and the level of inflammatory mediators, and the number of motile ciliated cells and ciliary beat frequency and pattern was measured. Results: Infection by aerosol and liquid inoculums of influenza virus was shown to be trophic for ciliated cells. Infection by both methods also led to a significant decrease in the number of cells with motile cilia over the first 24 hours; however, the ciliary beat frequency and beat pattern of the remaining cilia was maintained over 24 hours. Conclusions: Influenza virus aerosols readily infect human ciliated nasal epithelial cells resulting in early loss of motile ciliated cells. Delivery of the virus by aerosol elicited an anti-inflammatory Th2 response, which was distinct from cells exposed to virus by liquid immersion delivery. This suggests our aerosol model may provide a more clinically relevant model for studying the early effects of influenza infection.
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 | 0.001 |
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".