Reduction of human monocytic cell neurotoxicity and cytokine secretion by ligands of the cannabinoid‐type CB2 receptor
Bibliographic record
Abstract
1 Two cannabinoid receptors, CB1 and CB2, have been identified. The CB1 receptor is preferentially expressed in brain, and the CB2 receptor in cells of leukocyte lineage. We identified the mRNA for the CB1 receptor in human neuroblastoma SH-SY5Y cells, and the mRNA and protein for the CB2 receptor in human microglia and THP-1 cells. 2 Delta(9)-and Delta(8)-tetrahydrocannabinol (THC) were toxic when added directly to SH-SY5Y neuroblastoma cells. The toxicity of Delta(9)- THC was inhibited by the CB1 receptor antagonist SR141716A but not by the CB2 receptor antagonist SR144528. The endogenous ligand anandamide was also toxic, and this toxicity was enhanced by inhibitors of its enzymatic hydrolysis. 3 The selective CB2 receptor ligands JWH-015 and indomethacin morpholinylamide (BML-190), when added to THP-1 cells before stimulation with lipopolysaccharide (LPS) and IFN-gamma, reduced the toxicity of their culture supernatants to SH-SY5Y cells. JWH-015 was more effective against neurotoxicity of human microglia than THP-1 cells. The antineurotoxic activity of JWH-015 was blocked by the selective CB2 receptor antagonist SR144528, but not by the CB1 receptor antagonist SR141716A. This activity of JWH-015 was synergistic with that of the 5-lipoxygenase (5-LOX) inhibitor REV 5901. 4 Cannabinoids inhibited secretion of IL-1beta and tumor necrosis factor-alpha (TNF-alpha) by stimulated THP-1 cells, but these effects could not be directly correlated with their antineurotoxic activity. 5 Specific CB2 receptor ligands could be useful anti-inflammatory agents, while avoiding the neurotoxic and psychoactive effects of CB1 receptor ligands such as Delta(9)-THC.
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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 | 0.000 |
| 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 | 0.001 |
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".