Carbachol interactions with nonsteroidal anti-inflammatory drugs
Bibliographic record
Abstract
The inhibition of cyclooxygenase enzymes by nonsteroidal anti-inflammatory drugs (NSAIDs) does not completely explain the antinociceptive efficacy of these agents. It is known that cholinergic agonists are antinociceptive, and this study evaluates the interactions between carbachol and some NSAIDs. Antinociceptive activity was evaluated in mice by the acetic acid writhing test. Dose-response curves were constructed for NSAIDs and carbachol, administered either intraperitoneally (i.p.) or intrathecally (i.t.). The interactions of carbachol with NSAIDs were evaluated by isobolographic analysis after the simultaneous administration of fixed proportions of carbachol with each NSAID. All of the drugs were more potent after spinal than after systemic administration. The combinations of NSAIDs and carbachol administered i.p. were supra-additive; however, the i.t. combinations were only additive. Isobolographic analysis of the coadministration of NSAIDs and carbachol and the fact that atropine antagonized the synergistic effect suggest that carbachol may strongly modulate the antinociceptive activity of NSAIDs; thus, central cholinergic modulation would be an additional mechanism for the antinociceptive action of NSAIDs, unrelated to prostaglandin biosynthesis inhibition.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".