Cannabinoid‐mediated antinociception is enhanced in rat osteoarthritic knees
Bibliographic record
Abstract
OBJECTIVE: To determine whether local administration of the cannabinoid 1 (CB(1)) receptor agonist arachidonyl-2-chloroethylamide (ACEA) can modulate joint nociception in control rat knee joints and in experimental osteoarthritis (OA). METHODS: OA was induced in male Wistar rats by intraarticular injection of 3 mg of sodium mono-iodoacetate, with a recovery period of 14 days. Electrophysiologic recordings were made of knee joint primary afferent nerve fibers in response to normal rotation and noxious hyperrotation of the joint both before and after close intraarterial injection of different doses of ACEA. RESULTS: Local application of the CB(1) agonist significantly reduced the firing rate of afferent nerve fibers by up to 50% in control knee joints (n=19) and up to 62% in OA knee joints (n=29; P<0.01). Coadministration of the CB(1) receptor antagonist AM251 or the transient receptor potential vanilloid 1 (TRPV-1) ion channel antagonist SB366791 significantly reduced the desensitizing effect of ACEA. The CB(1) receptor antagonist AM251 by itself had no effect in the control joint but significantly increased the firing rate of afferent nerve fibers in the OA joint. CONCLUSION: These findings indicate that activation of peripheral CB(1) receptors reduces the mechanosensitivity of afferent nerve fibers in control and OA knee joints. Blockade of either the CB(1) receptor or the TRPV-1 channel significantly reduced the efficacy of ACEA, which suggests that both receptors are involved in cannabinoid-mediated antinociception. The increased nerve activity observed following CB(1) receptor antagonism suggests a tonic release of endocannabinoids during OA. As such, peripheral CB(1) receptors may be important targets in controlling OA pain.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 | 0.000 |
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 teacher head, 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".