The antinociceptive and anti-inflammatory effects of<i>Salvia officinalis</i>leaf aqueous and butanol extracts
Bibliographic record
Abstract
CONTEXT: The leaf of sage Salvia officinalis L. (Lamiaceae) is reputed in the folk medicine of Arabia, and Jordan in particular, to relieve pain associated with gastrointestinal disturbance. OBJECTIVES: Evaluation of the antinociceptive and anti-inflammatory activities of aqueous and butanol extracts of S. officinalis leaf. MATERIALS AND METHODS: The analgesic effects of the aqueous extract (10, 31.6, 100, 316, 1000 mg/kg) and butanol extract (10, 31.6, 100, 316 mg/kg) were studied using the hot-plate test for mice and the formalin-induced paw licking in rats. The effects were compared to those of morphine and the influence of naloxone on these effects was also evaluated. The same concentrations of both extracts were used to evaluate their anti-inflammatory effects using the cotton pellet granuloma and carrageenan-induced paw edema in rats. RESULTS: The aqueous extract (10, 31.6, 100, 316, 1000 mg/kg) and butanol extract (10, 31.6, 100, 316 mg/kg) caused analgesic effect in the hot-plate latency assay as well as in early and late phases of formalin-induced paw licking in rats. These effects were reduced by the opioid receptor antagonist, naloxone (5 mg/kg). The same range of doses of both extracts caused dose-dependent inhibition of carrageenan-induced paw edema in rats as well as inhibition of cotton pellet granuloma. DISCUSSION AND CONCLUSION: These observations suggest that the sage leaf aqueous and butanol extracts have analgesic and anti-inflammatory effects, confirming the traditional use of this plant for pain alleviation.
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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 | 0.000 | 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 | 0.000 |
| 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".