γ-Terpinene complexed with β-cyclodextrin attenuates spinal neuroactivity in animals with cancer pain by Ca2+ channel block
Bibliographic record
Abstract
OBJECTIVES: Considering that γ-terpinene (γ-TPN) is a monoterpene found in Cannabis oil, with high lipophilicity and limited pharmacokinetics, our objective was to evaluate whether its complexation in β-cyclodextrin (γ-TPN/β-CD) could improve its physicochemical properties and action on cancer pain, as well as verify the mechanisms of action involved. METHODS: The γ-TPN/β-CD was prepared and submitted to physicochemical characterization. Animals with sarcoma 180 were treated (vehicle, γ-TPN 50 mg/kg, γ-TPN/β-CD 5 mg/kg or morphine) and assessed for hyperalgesia, TNF-α and IL-1β levels, iNOS and c-Fos activity. The effects of γ-TPN on calcium channels were studied by patch-clamp and molecular docking. RESULTS: β-CD improved the physicochemical properties and prolonged the anti-hyperalgesic effect of γ-TPN. This compound also reduced the levels of IL-1β, TNF-α and iNOS in the tumour, and c-Fos protein in the spinal cord. In addition, it reduced Ca2+ current, presenting favourable chemical interactions with different voltage-dependent calcium channels. CONCLUSION: These results indicate that the complexation of γ-TPN into β-CD increases its stability and time effect, reducing spinal neuroactivity and inflammation by blocking calcium channels.
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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) | 0.001 | 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".