Modulation of pulmonary immune function by inhaled cannabis products and consequences for lung disease
Bibliographic record
Abstract
The lungs, in addition to participating in gas exchange, represent the first line of defense against inhaled pathogens and respiratory toxicants. Cells lining the airways and alveoli include epithelial cells and alveolar macrophages, the latter being resident innate immune cells important in surfactant recycling, protection against bacterial invasion and modulation of lung immune homeostasis. Environmental exposure to toxicants found in cigarette smoke, air pollution and cannabis can alter the number and function of immune cells in the lungs. Cannabis (marijuana) is a plant-derived product that is typically inhaled in the form of smoke from a joint. However, alternative delivery methods such as vaping, which heats the plant without combustion, are becoming more common. Cannabis use has increased in recent years, coinciding with more countries legalizing cannabis for both recreational and medicinal purposes. Cannabis may have numerous health benefits owing to the presence of cannabinoids that dampen immune function and therefore tame inflammation that is associated with chronic diseases such as arthritis. The health effects that could come with cannabis use remain poorly understood, particularly inhaled cannabis products that may directly impact the pulmonary immune system. Herein, we first describe the bioactive phytochemicals present in cannabis, with an emphasis on cannabinoids and their ability to interact with the endocannabinoid system. We also review the current state-of-knowledge as to how inhaled cannabis/cannabinoids can shape immune response in the lungs and discuss the potential consequences of altered pulmonary immunity. Overall, more research is needed to understand how cannabis inhalation shapes the pulmonary immune response to balance physiological and beneficial responses with potential deleterious consequences on the lungs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".