The <scp>AhR</scp> 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 and cannabinoids. The aryl hydrocarbon receptor (AhR), a transcription factor activated by both cannabinoids and combustion products, is known to regulate pulmonary responses to environmental insults. 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, we show that cannabis smoke activates AhR in lung tissue, leading to distinct immunological and proteomic responses across lung tissue, extracellular vesicles (EVs), and bronchoalveolar lavage fluid (BALF). AhR deficiency exacerbated neutrophilic inflammation, epithelial barrier disruption, and caused systemic cytokine elevation. Proteomic profiling revealed that AhR drives the activation of detoxification and metabolic pathways in lung tissue while suppressing cytoskeletal and adhesion proteins in response to cannabis smoke. In contrast, AhR loss shifted the proteomic response in EVs and BALF, altering coagulation, protease regulation, and metabolic stability. These findings demonstrate that AhR coordinates compartment‐specific responses to cannabis smoke and plays a central role in preserving lung homeostasis and restraining inflammatory injury following cannabis exposure. 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 | 0.000 |
| Research integrity | 0.000 | 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".