The AhR is a Critical Regulator of the Pulmonary Response to Cannabis Smoke
Bibliographic record
Abstract
Abstract Cannabis use is prevalent worldwide, with smoking being the most common method of consumption. When smoking cannabis, users are exposed to both harmful combustion products as well as cannabinoids such as tetrahydrocannabinol (THC) and cannabidiol (CBD). THC and CBD have purported anti-inflammatory effects through activation of cannabinoid receptors; however, the minimal expression of these receptors in lung tissue suggests that respiratory effects of cannabis may be mediated through alternative pathways. One potential mediator of these effects is the aryl hydrocarbon receptor (AhR), a transcription factor involved in xenobiotic metabolism. Notably, the AhR is activated by both combustion products and cannabinoids. This receptor is also known to dampen lung inflammation induced by tobacco smoke or air pollution. Therefore, we hypothesized that AhR activation would reduce susceptibility to the harmful effects of inhaled cannabis smoke. To investigate this hypothesis, Ahr +/- and Ahr -/- mice were exposed to air or cannabis smoke using a controlled puff regimen over a three-day period. In the first study to characterize the effects of cannabis smoke on lung tissue and the pulmonary secretome, including extracellular vesicles and secreted proteins, we found that acute exposure induced neutrophilia, vascular leakage, and activation of tissue remodeling pathways, all of which were regulated by AhR. These findings highlight not only the detrimental effects of cannabis smoke on lung health but also the pivotal role of the AhR as a key regulator of the pulmonary response to cannabis smoke exposure.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".