Inhibition of calcium-stimulated adenylyl cyclase subtype 1 (AC1) for the treatment of neuropathic and inflammatory pain in adult female mice
Bibliographic record
Abstract
Cortical long-term potentiation (LTP) serves as a cellular model for chronic pain. As an important subtype of adenylyl cyclases (ACs), adenylyl cyclase subtype 1 (AC1) is critical for the induction of cortical LTP in the anterior cingulate cortex (ACC). Genetic deletion of AC1 or pharmacological inhibition of AC1 blocked behavioral allodynia in animal models of neuropathic and inflammatory pain. Our previous experiments have identified a lead candidate AC1 inhibitor, NB001, which is highly selective for AC1 over other AC isoforms, and found that NB001 is effective in inhibiting behavioral allodynia in animal models of chronic neuropathic and inflammatory pain. However, previous experiments were carried out in adult male animals. Considering the potential gender difference as an important issue in researches of pain and analgesia, we investigated the effect of NB001 in female chronic pain animal models. We found that NB001, when administered orally, has an analgesic effect in female animal models of neuropathic and inflammatory pain without any observable side effect. Genetic deletion of AC1 also reduced allodynia responses in models of neuropathic pain and chronic inflammation pain in adult female mice. In brain slices of adult female mice, bath application of NB001(20 μM) blocked the induction of LTP in ACC. Our results indicate that calcium-stimulated AC1 is required for injury-related cortical LTP and behavioral allodynia in both sexes of adult animals, and NB001 can be used as a potential therapeutic drug for treating neuropathic and inflammatory pain in man and woman.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".