Evidence for 5‐HT<sub>1A</sub> receptor‐mediated antiallodynic and antihyperalgesic effects of apigenin in mice suffering from mononeuropathy
Bibliographic record
Abstract
Background and Purpose Neuropathic pain places a devastating health burden, with very few effective therapies. We investigated the potential antiallodynic and antihyperalgesic effects of apigenin, a natural flavonoid with momoamine oxidase (MAO) inhibitory activity, against neuropathic pain and investigated the mechanism(s). Experimental Approach The neuropathic pain model was produced by chronic constriction injury of sciatic nerves in male C57BL/6J mice, with pain‐related behaviours being assayed by von Frey test and Hargreaves test. In this model the role of 5‐HT and 5‐HT1A receptor‐related mechanisms were investigated in vivo/in vitro. Key Results Apigenin repeated treatment (p.o., once per day for 2 weeks), in a dose‐related manner (3, 10 and 30 mg·kg−1), ameliorated the allodynia and hyperalgesia in chronic nerve constriction injury in mice. These effects seem dependent on neuronal 5‐hydroxytryptamine, because (i) the antihyperalgesia and antiallodynia were attenuated by depletion of 5‐HT with p‐chlorophenylalanine and potentiated by 5‐hydroxytryptophan and (ii), apigenin‐treated chronic constriction injury mice caused an increased level of spinal 5‐HT, associated with diminished MAO activity. In vivo administration, spinally or systematically, of the 5‐HT1A antagonist WAY‐100635 inhibited the apigenin‐induced antiallodynia and antihyperalgesia. In vitro, apigenin acted as a positive allosteric modulator to increase the efficacy (stimulation of [35S]GTPγS binding) of the 5‐HT1A agonist 8‐OH‐DPAT. Apigenin attenuated neuronal changes caused by chronic constriction of the sciatic nerve in mice, without causing a hypertensive crisis. Conclusion and Implications Apigenin antiallodynic and antihyperalgesic actions against neuropathic pain crucially involve spinal 5‐HT1A receptors and indicate it could be used to treat neuropathic 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) | 0.000 | 0.000 |
| 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 | 0.000 | 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".