A global toxicogenomic analysis investigating the mechanistic differences between tobacco and marijuana smoke condensates in vitro
Bibliographic record
Abstract
Like tobacco smoking, habitual marijuana smoking causes numerous adverse pulmonary effects. However, the mechanisms of action involved, especially as compared to tobacco smoke, are still unclear. To uncover putative modes of action, this study employed a toxicogenomics approach to compare the toxicological pathways perturbed following exposure to marijuana and tobacco smoke condensate in vitro. Condensates of mainstream smoke from hand-rolled tobacco and marijuana cigarettes were similarly prepared using identical smoking conditions. Murine lung epithelial cells were exposed to low, medium and high concentrations of the smoke condensates for 6h. RNA was extracted immediately or after a 4h recovery period and hybridized to mouse whole genome microarrays. Tobacco smoke condensate (TSC) exposure was associated with changes in xenobiotic metabolism, oxidative stress, inflammation, and DNA damage response. These same pathways were also significantly affected following marijuana smoke condensate (MSC) exposure. Although the effects of the condensates were largely similar, dose-response analysis indicates that the MSC is substantially more potent than TSC. In addition, steroid biosynthesis, apoptosis, and inflammation pathways were more significantly affected following MSC exposure, whereas M phase cell cycle pathways were more significantly affected following TSC exposure. MSC exposure also appeared to elicit more severe oxidative stress than TSC exposure, which may account for the greater cytotoxicity of MSC. This study shows that in general MSC impacts many of the same molecular processes as TSC. However, subtle pathway differences can provide insight into the differential toxicities of the two complex mixtures.
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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 | 0.000 | 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 | 0.000 |
| 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".