The Anti-Inflammatory Effects of Cannabis Sativa Extracts on LPS-Induced Cytokines Release in Immune and Non-Immune Cells in Vitro
Bibliographic record
Abstract
Inflammation is the response of the innate immune system to any type of injury. Although acute inflammation is critical for survival, dysregulation of innate immune response leads to chronic in-flammation. Many synthetic anti-inflammatory drugs have side effects, and thus, natural an-ti-inflammatory compounds are still needed. Cannabis sativa L. may provide a good source of in-flammation-reducing molecules. Here, we tested the anti-inflammatory properties of cannabis ex-tracts and pure cannabinoids in lipopolysaccharide (LPS)-induced inflammation in human THP-1 macrophages. We found that pre-treatment with cannabidiol (CBD), delta-9-tetrahydrocannabinol (THC), or extracts containing high levels of CBD or THC reduce the level of induction of various cytokines. The CBD was more efficient than THC, and the extracts were more efficient than pure cannabinoids. Finally, IL-6, IL-10 and monocyte chemoattractant protein-1 (MCP-1) were most sensitive to pre-treatments with CBD and THC, while IL-1β, IL-8 and TNF-α were less responsive. Thus, our work demonstrates the potential of use of cannabinoids or/and cannabis extracts for the reduction of inflammation and establishes IL-6 and MCP-1 as the sensitive markers for the analysis of the effect of cannabinoids on inflammation in 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) | 0.001 | 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) | 0.002 | 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".