CB1 and CB2 contribute to antinociceptive and anti-inflammatory effects of electroacupuncture on experimental arthritis of the rat temporomandibular joint
Bibliographic record
Abstract
Electroacupuncture (EA) and cannabinoids have been reported to have anti-inflammatory and antinociceptive effects in animal models of arthritis. Male Wistar rats were injected with saline or zymosan (2 mg) into the temporomandibular joint (TMJ). EA (10 Hz, 30 min) was performed 2 h after or 1 h before zymosan administration. AM251 or AM630 (3 mg/kg, i.p.)were administered before EA treatment. Mechanical hypernociception was accessed after zymosan administration. Rats were sacrificed 6 h after zymosan administration and the joint was removed for histopathological analysis. The gene expression of CB₁ and CB₂ receptors was assessed after sacrifice of the TMJ arthritic animals. EA inhibited zymosan-induced hypernociception (p < 0.05). AM251 reversed significantly the antinociceptive effect of EA, suggesting that the CB₁ receptor is involved in this effect. AM630 reversed the anti-inflammatory effect of EA. CB₁ and CB₂ receptor gene expression was upregulated 6 h after zymosan-induced arthritis in the EA-treated group. We observed downregulation of CB₂ receptor gene expression in the EA group at the 24th hour compared with the 6th hour. Higher CB₁ receptor gene expression was also found compared with the 6th hour. EA produced antinociceptive and anti-inflammatory effects, and these effects appeared to be mediated through CB₁ and CB₂ receptor activation.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 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".