Cigarette Smoke Suppresses Type I Interferon-Mediated Antiviral Immunity in Lung Fibroblast and Epithelial Cells
Bibliographic record
Abstract
The objective of this study was to investigate the impact of cigarette smoke on innate antiviral defense mechanisms; specifically, we examined the effects of cigarette smoke on the induction of type I interferon (IFN). We observed a dose-dependent decrease in the ability of human lung fibroblast and epithelial cells to elicit an antiviral response against a viral double-strand RNA (dsRNA) mimic, polyI:C, in the presence of cigarette smoke-conditioned medium (SCM). Mechanistically, SCM decreases the expression of IFN-stimulated gene 15 (ISG15) and IFN regulatory factor-7 (IRF-7) transcripts and suppresses the nuclear translocation of key transcription factors, nuclear factor-kappaB (NF-kappaB) and IRF-3, after polyI:C stimulation. Furthermore, we provide evidence that the intercellular defense strategy against viral infection is also impaired. We observed a decrease in the ability of fibroblasts to elicit an antiviral state in response to IFN-beta stimulation. This was associated with decreased nuclear translocation of phosphorylated Stat1 in response to IFN-beta treatment. The effects elicited by SCM are reversible and are almost entirely abrogated in the presence of an antioxidant, such as glutathione. Our findings suggest that cigarette smoke affects the immediate-early, inductive, and amplification phases of the type I IFN response.
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".