Effect of Probenecid on the Pain-Related Behaviour and Morphological Markers in Orofacial Formalin Test of the Rat
Bibliographic record
Abstract
Probenecid has been widely used in the treatment of gout, but evidence suggests that it may also have antinociceptive effects in different inflammatory and pain conditions. We examined the potential modulatory effects of probenecid on behavioural and morphological markers in the orofacial formalin test of the rat. One hour after pre-treatment with vehicle or probenecid (1 mmol/kg body weight) intraperitoneally, 50μl 1.5% formalin solution or physiological saline was injected subcutaneously into the right whisker pad of rats. The rubbing activity directed to the injected whisker pad was then measured for a period of 45 minutes. Four hours after formalin injection, the caudal part of spinal trigeminal nucleus was removed and subjected to c-Fos and neuronal nitric oxide synthase (nNOS) immunohistochemistry and to interleukin-1β and NAD(P)H: quinone oxidoreductase 1 (NQO1) Western blot. There was a significant decrease in formalin-induced biphasic behavioural response and c-Fos and nNOS immunoreactivity in the rats that were pre-treated with probenecid. However there were no alterations in expression of interleukin-1β or NQO1 after formalin administration. Our results suggest that probenecid has an anti-nociceptive effect in the trigeminal inflammatory pain model. This effect may be through influencing the release of prostaglandin E2 or desensitizing the transient receptor potential channel subtype A member 1 or the transient receptor potential channel subtype V member 2 or the effect may be through modulating kynurenic acid levels in the central nervous system. Thus, probenecid might be a potential candidate for the treatment of trigeminal activation related pain conditions.
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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 | 0.000 |
| 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 | 0.001 | 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".