Transcriptomic changes in oxidative stress, immunity, and cancer pathways caused by cannabis vapor on alveolar epithelial cells
Bibliographic record
Abstract
Abstract As legalization of cannabis increases worldwide, vaping cannabis is gaining popularity due to the belief that it is less harmful than smoking cannabis. However, the safety of cannabis vaping remains untested. To address this, we developed a physiologically relevant method for in vitro assessment of cannabis vapor on alveolar epithelial cell cultures. We compared the transcriptional response in three in vitro models of cannabis vapor exposure using A549 epithelial cells in submerged culture, pseudo-air liquid interface (ALI) culture, and ALI culture coupled with the expoCube™ advanced exposure system. Baseline gene expression in ALI-maintained A549 cells showed higher expression of type 2 alveolar epithelial (AEC2) genes related to surfactant production, ion movement, and barrier integrity. Acute exposure to cannabis vapor significantly affected gene expression in AEC2 cells belonging to pathways related to cancer, oxidative stress, and the immune response without being associated with a DNA damage response. This study identifies potential risks of cannabis vaping and underscores the need for further exploration into its respiratory health implications. Graphical Abstract • Vaporizing cannabis is increasingly popular but remains largely untested. • We used three in vitro models to assess the effects of cannabis vapor on alveolar epithelial cells. • Cannabis vapor exposure alters pathways linked to cancer and metabolism, without causing DNA damage.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".