β-elemene relieves neuropathic pain in mice through the regulation on C-X-C motif chemokine receptor 3 and GABAA receptor
Bibliographic record
Abstract
β-elemene (Bel) is a sesquiterpene compound that has shown potential in the antinociceptive treatment. This study focused on the function of Bel in neuropathic pain relief in mice. A murine model with spared nerve injury (SNI) was established and treated with Bel. The paw withdrawal thresholds in response to mechanical and thermal stimulations were examined using von Frey filaments. The L4-L6 spinal dorsal horn tissue samples were collected for histological examination. Bel treatment reduced the sensitivities of model mice to mechanical and thermal stimulations, and it inhibited activation of microglia and the secretion of inflammatory cytokines including tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), and IL-6 in tissues. Bel treatment reduced the expression of nociceptor excitatory N-methyl-D-aspartate receptor (NMDAR), whereas it enhanced the expression of nociceptor inhibitory gamma-aminobutyric acid A (GABAA) receptor to relieve the nociception of mice. The C-X-C motif chemokine receptor 3 (CXCR3) is a downstream molecule mediated by Bel. Either overexpression of CXCR3 or downregulation of GABAA receptor in the tissues aggravated the neuropathic pain in SNI mice which was initially relieved by Bel. In conclusion, this study suggested that Bel might serve as a drug for nociception management by inhibiting CXCR3 and upregulating GABAA receptor. This study may offer novel insights into the field of neuropathic pain relief.
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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 | 0.001 |
| 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".