Pharmacological properties and related constituents of stem bark of Pterocarpus erinaceus Poir. (Fabaceae)
Bibliographic record
Abstract
OBJECTIVE: To screen methanol and dichloromethane extracts of stem bark of Pterocarpus erinaceus for anti-inflammatory, analgesic, in vitro antioxidant activities and phytochemical analysis. METHODS: Anti-inflammatory activity was determined by using carrageenan induced-edema of mice paw and croton oil-induced edema of mice ear; analgesic effect was evaluated using acetic acid-induced writhing. Phytochemical screening of extracts was performed by thin layer chromatography. The chromatographic fractionation led to the isolation of main active components as friedelin, lupeol and epicathechin. The structures were established by TLC and nuclear magnetic resonance studies. RESULTS: Both methanol and dichloromethane extracts, friedelin, lupeol and epicatechin showed a significant anti-inflammatory effect using croton oil induced-ear edema. Furthermore, the action of dichloromethane extract was more important. At the doses of 100 and 200 mg/kg, the methanol extract was able to reduce the carrageenan induced-hind paw edema, while at the doses of 100, 200 and 400 mg/kg, it showed an important analgesic effect against writhing induced by acetic acid injection of 38.8%, 68.0% and 74.3%, respectively. Antioxidative properties of methanol extract and its dichloromethane and ethyl acetate fractions were assessed by using the 1,1-diphenyl-2-picrylhydrazyl method. The methanol extract showed the stronger radical scavenging activity than dichloromethane and ethyl acetate fractions, with an antiradical power of 5, 3.5 and 2 respectively. The main components isolated from these extracts as friedelin, lupeol and epicathechin were responsible of these activities. CONCLUSIONS: The results suggest that the stem bark extracts of Pterocarpus erinaceus possessed important anti-inflammatory, analgesic activities and strong antioxidant properties, therefore, they could be used as potential natural ingredients in the pharmaceutical industry.
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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 | 0.000 |
| 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".