Nociceptive responses in melatonin MT<sub>2</sub> receptor knockout mice compared to MT<sub>1</sub> and double MT<sub>1</sub>/MT<sub>2</sub> receptor knockout mice
Bibliographic record
Abstract
Abstract Melatonin, a neurohormone that binds to two G protein‐coupled receptors MT1 and MT2, is involved in pain regulation, but the distinct role of each receptor has yet to be defined. We characterized the nociceptive responses of mice with genetic inactivation of melatonin MT1 (MT1−/−), or MT2 (MT2−/−), or both MT1/MT2 (MT1−/−/MT2−/−) receptors in the hot plate test (HPT), and the formalin test (FT). In HPT and FT, MT1−/− display no differences compared to their wild‐type littermates (CTL), whereas both MT2−/− and MT1−/−/MT2−/− mice showed a reduced thermal sensitivity and a decreased tonic nocifensive behavior during phase 2 of the FT in the light phase. The MT2 partial agonist UCM924 induced an antinociceptive effect in MT1−/− but not in MT2−/− and MT1−/−/MT2−/− mice. Also, the competitive opioid antagonist naloxone had no effects in CTL, whereas it induced a decrease of nociceptive thresholds in MT2−/− mice. Our results show that the genetic inactivation of melatonin MT2, but not MT1 receptors, produces a distinct effect on nociceptive threshold, suggesting that the melatonin MT2 receptor subtype is selectively involved in the regulation of pain responses.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".