Airway Surface Liquid Has Innate Antiviral Activity That is Reduced in Cystic Fibrosis
Bibliographic record
Abstract
Abstract Although chronic bacterial infections and inflammation are associated with progressive lung disease in patients with cystic fibrosis (CF), much less is known regarding the contributions of respiratory viral infections to this process. Clinical studies suggest that antiviral host defenses may be compromised in individuals with CF, and CF airway epithelia exhibit impaired antiviral responses in vitro. Here, we used the CF pig model to test the hypothesis that the antiviral activity of respiratory secretions is reduced in CF. We developed an in vitro assay to measure the innate antiviral activity present in airway surface liquid (ASL) from CF and non-CF pigs. We found that tracheal and nasal ASL from newborn non-CF pigs exhibited dose-dependent inhibitory activity against several enveloped and encapsidated viruses, including Sendai virus, respiratory syncytial virus, influenza A, and adenovirus. Importantly, we found that the anti–Sendai virus activity of nasal ASL from newborn CF pigs was significantly diminished relative to non-CF littermate controls. This diminution of extracellular antiviral defenses appears to be driven, at least in part, by the differences in pH between CF and non-CF ASL. These data highlight the novel antiviral properties of native airway secretions and suggest the possibility that defects in extracellular antiviral defenses contribute to CF pathogenesis.
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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 | 0.000 | 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".