The effect of cannabis-derived terpenes on alveolar macrophage function
Bibliographic record
Abstract
Cannabis sativa (marijuana) is used by millions of people around the world. C. sativa produces hundreds of secondary metabolites including cannabinoids, flavones and terpenes. Terpenes are a broad class of organic compounds that give cannabis and other plants its aroma. Previous studies have demonstrated that terpenes may exert anti-inflammatory properties on immune cells. However, it is not known whether terpenes derived from cannabis alone or in combination with the cannabinoid ∆9-THC impacts the function of alveolar macrophages, a specialized pulmonary innate immune cell that is important in host defense against pathogens. Therefore, we investigated the immunomodulatory properties of two commercially-available cannabis terpene mixtures on the function of MH-S cells, a murine alveolar macrophage cell line. MH-S cells were exposed to terpene mixtures at sublethal doses and to the bacterial product lipopolysaccharide (LPS). We measured inflammatory cytokine levels using qRT-PCR and multiplex ELISA, as well as phagocytosis of opsonized IgG-coated beads or mCherry-expressing Escherichia coli via flow cytometry. Neither terpene mixture affected inflammatory cytokine production by MH-S cells in response to LPS. Terpenes increased MH-S cell uptake of opsonized beads but had no effect on phagocytosis of E. coli. Addition of ∆9-THC to terpenes did not potentiate cytotoxicity nor phagocytosis. These results suggest that terpenes from cannabis have minimal impact on the function of alveolar macrophages.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".