Monoaminergic mediation of hyperalgesic and analgesic descending control of nociception in mice
Bibliographic record
Abstract
ABSTRACT: Descending control of nociception (DCN; also known as conditioned pain modulation [CPM], the behavioral correlate of diffuse noxious inhibitory controls) is the phenomenon whereby pain inhibits pain in another part of the body and is the subject of increasing study because it may represent a biomarker of chronic pain. We recently discovered that pain modulation on the application of a DCN paradigm involving low-intensity test stimuli occurs in the direction of hyperalgesia in healthy mice and rats, whereas the use of high-intensity stimuli produces analgesia. To elucidate the physiological mechanisms underlying hyperalgesic DCN, we administered agonists and antagonists of norepinephrine (NE) and serotonin (5-HT) receptors, key neurochemical players in the production of analgesic DCN. We find that 3 different monoamine reuptake inhibitors-the NE-selective reboxetine, the 5-HT-selective fluoxetine, and the dual NE/5-HT agonist duloxetine-all abolish hyperalgesic DCN when administered into the spinal cord (but not systemically), with no effect on heat or mechanical pain sensitivity. The reversal by reboxetine of hyperalgesic DCN is mediated by α 2 -adrenergic receptors (ie, blocked by atipamezole), and the fluoxetine reversal is mediated by 5-HT 7 receptors (ie, blocked by SB269970). By contrast, analgesic DCN was found to be reversed by atipamezole and SB269970 themselves, with no effect of reboxetine or fluoxetine. Thus, hyperalgesic DCN seems to be the neurochemical opposite to analgesic DCN. These data further validate and help elucidate a preclinical paradigm that mimics dysfunctional CPM and thus may form the basis of translational experiments that aim to reveal preventative pharmacological strategies for individuals predisposed to persistent 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| 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".