Cannabis smoke suppresses antiviral immune responses to influenza A in mice
Bibliographic record
Abstract
Rationale Despite its increasingly widespread use, little is known about the impact of cannabis smoking on the response to viral infections like influenza A virus (IAV). Many assume that cannabis smoking will disrupt antiviral responses in a manner similar to cigarette smoking; however, since cannabinoids exhibit anti-inflammatory effects, cannabis smoke exposure may impact viral infection in distinct ways. Methods Male and female BALB/c mice were exposed daily to cannabis smoke and concurrently intranasally instilled with IAV. Viral burden, inflammatory mediator levels (multiplex ELISA), lung immune cells populations (flow cytometry) and gene expression patterns (RNA sequencing) were assessed in the lungs. Plasma IAV-specific antibodies were measured via ELISA. Results We found that cannabis smoke exposure increased pulmonary viral burden while decreasing total leukocytes, including macrophages, monocytes and dendritic cell populations in the lungs. Furthermore, infection-induced upregulation of certain inflammatory mediators (interferon-γ and C-C motif chemokine ligand 5) was blunted by cannabis smoke exposure, which in females was linked to the transcriptional downregulation of pathways involved in innate and adaptive immune responses. Finally, plasma levels of IAV-specific IgM and IgG1 were significantly decreased in cannabis smoke-exposed, infected mice compared to infected controls, only in female mice. Conclusions Overall, cannabis smoke exposure disrupted host-defence processes, leading to increased viral burden and dampened inflammatory signalling. These results suggest that cannabis smoking is detrimental to the maintenance of pulmonary homeostasis during viral infection and highlight the need for data regarding the impact on immune competency in humans.
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How this classification was reachedexpand
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 | 0.007 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".