Analgesic effects of neurotensin agonists in a rat bone cancer pain model
Bibliographic record
Abstract
Bone metastases are a source of intractable pain, resistant to conventional opioid and non-opioid analgesics. The neurotensin system represents a potential pathway toward bone cancer pain (BCP) relieve via the inhibition of its receptors NTS1 and NTS2. Capitalizing on our recent results using neurotensin analogs in inflammatory and neuropathic pain models, we here show, for the first time, a potential role for neurotensin receptors agonists in the treatment of BCP. The novel non-selective agonist JMV-2009 (300 μg/kg) reversed mechanical allodynia in our rodent BCP model at both early and late stages of the disease. The NTS2-selective agonist JMV-431 (90 μg/kg), in addition to anti-allodynia, also had an effect on weight bearing deficits. In parallel, we tested proven analgesics from several classes to put the effect of neurotensin analogs in perspective and found that morphine (3 mg/kg), tramadol (15 mg/kg) and amitriptyline (10 mg/kg) had mild effects on BCP while the cannabinoid nabilone (1 mg/kg) significantly reversed both allodynia and weight bearing deficits. Taken together, our results affirm the potential of the modulation of the neurotensin system for the development of new analgesics for the treatment of bone cancer pain.
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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 | 0.001 | 0.002 |
| 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".