Paracetamol is a centrally acting analgesic using mechanisms located in the periaqueductal grey
Bibliographic record
Abstract
Background and Purpose We previously demonstrated that paracetamol has to be metabolised in the brain by fatty acid amide hydrolase enzyme into AM404 (N‐(4‐hydroxyphenyl)‐5Z,8Z,11Z,14Z‐eicosatetraenamide) to activate CB1 receptors and TRPV1 channels, which mediate its analgesic effect. However, the brain mechanisms supporting paracetamol‐induced analgesia remain unknown. Experimental Approach The effects of paracetamol on brain function in Sprague‐Dawley rats were determined by functional MRI. Levels of neurotransmitters in the periaqueductal grey (PAG) were measured using in vivo 1H‐NMR and microdialysis. Analgesic effects of paracetamol were assessed by behavioural tests and challenged with different inhibitors, administered systemically or microinjected in the PAG. Key Results Paracetamol decreased the connectivity of major brain structures involved in pain processing (insula, somatosensory cortex, amygdala, hypothalamus, and the PAG). This effect was particularly prominent in the PAG, where paracetamol, after conversion to AM404, (a) modulated neuronal activity and functional connectivity, (b) promoted GABA and glutamate release, and (c) activated a TRPV1 channel‐mGlu5 receptor‐PLC‐DAGL‐CB1 receptor signalling cascade to exert its analgesic effects. Conclusions and Implications The elucidation of the mechanism of action of paracetamol as an analgesic paves the way for pharmacological innovations to improve the pharmacopoeia of analgesic agents.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".