Effects of cannabidiol on the differentiation of human monocytes into macrophages
Bibliographic record
Abstract
Abstract Background Innate immune myeloid cells, such as macrophages, contribute to chronic inflammation when consistently activated. Recently, cannabidiol (CBD), an active non-psychoactive constituent of the Cannabis sativa plant (i.e., Marijuana), has sparked interest as a safe and effective anti-inflammatory and immunomodulatory agent. This study aimed to investigate the CBD’s effects on the differentiation of human macrophages and their cell surface receptors. Methods Peripheral blood was collected from healthy human donors. Monocytes, isolated from peripheral blood mononuclear cells, were differentiated with macrophage colony stimulating factor into monocyte-derived macrophages (MDM) with and without CBD (5 μM). Differentiated MDMs were then stained for different cell surface markers and analyzed by multicolor flow-cytometry. Proportions of anti-inflammatory (M2: CD206, CD71, CD163) and pro-inflammatory (M1: CD86, CD163) MDMs, and expression of myeloid lineage markers (CD14, CD16), chemokine receptor 5 (CCR5) and endocannabinoid type 2 receptor (CB2) were determined. Results CBD promoted a shift towards a greater proportion of M2-type vs. M1-type MDMs (p<0.05, Fisher’s exact test). Cells displayed a significantly reduced expression of CD14, CD163, CD86, CCR5 and CB2 (p<0.05, Student t-test). Conclusions CBD appears to promote differentiation of MDMs into anti-inflammatory phenotypes. Further studies are ongoing to determine the mechanistic pathways implicated in these processes, as these findings may have implications for the use of CBD in various disease conditions.
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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 | 0.000 |
| 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".