A global toxicogenomic analysis investigating the mechanistic differences between tobacco and marijuana smoke condensates in vitro
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it