Standardized Cannabis Smoke Extract Induces Inflammation in Human Lung Fibroblasts
Bibliographic record
Abstract
Cannabis (marijuana) is the most commonly used illicit product in the world and is the second most smoked plant after tobacco. There has been a rapid increase in the number of countries legalizing cannabis for both recreational and medicinal purposes. Smoking cannabis in the form of a joint is the most common mode of cannabis consumption. Combustion of cannabis smoke generates many of the same chemicals as tobacco smoke. Although the impact of tobacco smoke on respiratory health is well-known, the consequence of cannabis smoke on the respiratory system and, in particular, the inflammatory response is unclear. Besides the combustion products present in cannabis smoke, cannabis also contains cannabinoids including Δ9-tetrahydrocannabinol (Δ9-THC) and cannabidiol (CBD). These compounds are hydrophobic and not present in aqueous solutions. In order to understand the impact of cannabis smoke on pathological mechanisms associated with adverse respiratory outcomes, the development of in vitro surrogates of cannabis smoke exposure is needed. Therefore, we developed a standardized protocol for the generation of cannabis smoke extract (CaSE) to investigate its effect on cellular mechanisms in vitro. First, we determined the concentration of Δ9-THC, one of the major cannabinoids, by ELISA and found that addition of methanol to the cell culture media during generation of the aqueous smoke extract significantly increased the amount of Δ9-THC. We also observed by LC-MS/MS that CaSE preparation with methanol contains CBD. Using a functional assay in cells for CB1 receptors, the major target of cannabinoids, we found that this CaSE contains Δ9-THC which activates CB1 receptors. Finally, this standardized preparation of CaSE induces an inflammatory response in human lung fibroblasts. This study provides an optimized protocol for aqueous CaSE preparation containing biologically active cannabinoids that can be used for in vitro experimentation of cannabis smoke and its potential impact on various indices of pulmonary health.
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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 | 0.001 |
| 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) | 0.003 | 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".