Analgesic effects of adenylyl cyclase inhibitor NB001 on bone cancer pain in a mouse model
Bibliographic record
Abstract
BACKGROUND: Cancer pain, especially the one caused by metastasis in bones, is a severe type of pain. Pain becomes chronic unless its causes and consequences are resolved. With improvements in cancer detection and survival among patients, pain has been considered as a great challenge because traditional therapies are partially effective in terms of providing relief. Cancer pain mechanisms are more poorly understood than neuropathic and inflammatory pain states. Chronic inflammatory pain and neuropathic pain are influenced by NB001, an adenylyl cyclase 1 (AC1)-specific inhibitor with analgesic effects. In this study, the analgesic effects of NB001 on cancer pain were evaluated. RESULTS: Pain was induced by injecting osteolytic murine sarcoma cell NCTC 2472 into the intramedullary cavity of the femur of mice. The mice injected with sarcoma cells for four weeks exhibited significant spontaneous pain behavior and mechanical allodynia. The continuous systemic application of NB001 (30 mg/kg, intraperitoneally, twice daily for three days) markedly decreased the number of spontaneous lifting but increased the mechanical paw withdrawal threshold. NB001 decreased the concentrations of cAMP and the levels of GluN2A, GluN2B, p-GluA1 (831), and p-GluA1 (845) in the anterior cingulate cortex, and inhibited the frequency of presynaptic neurotransmitter release in the anterior cingulate cortex of the mouse models. CONCLUSIONS: NB001 may serve as a novel analgesic to treat bone cancer pain. Its analgesic effect is at least partially due to the inhibition of AC1 in anterior cingulate cortex.
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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 | 0.000 |
| 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) | 0.002 | 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 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".