Alveolar epithelial cells of lung fibrosis patients are susceptible to severe virus-induced injury
Bibliographic record
Abstract
Patients with pulmonary fibrosis (PF) often experience exacerbations of their disease, characterised by a rapid, severe deterioration in lung function that is associated with high mortality. Whilst the pathobiology of such exacerbations is poorly understood, virus infection is a trigger. The present study investigated virus-induced injury responses of alveolar and bronchial epithelial cells (AECs and BECs, respectively) from patients with PF and age-matched controls (Ctrls). Air-liquid interface (ALI) cultures of AECs, comprising type I and II pneumocytes or BECs were inoculated with influenza A virus (H1N1) at 0.1 multiplicity of infection (MOI). Levels of interleukin-6 (IL-6), IL-36γ and IL-1β were elevated in cultures of AECs from PF patients (PF-AECs, n = 8-11), being markedly higher than Ctrl-AECs (n = 5-6), 48 h post inoculation (pi) (P<0.05); despite no difference in H1N1 RNA copy numbers 24 h pi. Furthermore, the virus-induced inflammatory responses of PF-AECs were greater than BECs (from either PF patients or controls), even though viral loads in the BECs were overall 2- to 3-fold higher than AECs. Baseline levels of the senescence and DNA damage markers, nuclear p21, p16 and H2AXγ were also significantly higher in PF-AECs than Ctrl-AECs and further elevated post-infection. Senescence induction using etoposide augmented virus-induced injuries in AECs (but not viral load), whereas selected senotherapeutics (rapamycin and mitoTEMPO) were protective. The present study provides evidence that senescence increases the susceptibility of AECs from PF patients to severe virus-induced injury and suggests targeting senescence may provide an alternative option to prevent or treat the exacerbations that worsen the underlying disease.
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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) | 0.002 | 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".