Evaluation of antimicrobial and wound healing activities of Jatropha podagrica extracts
Bibliographic record
Abstract
Background: Jatropha podagrica has been traditionally used to treat skin infections and promote wound healing. However, there is currently no scientific research validating its antimicrobial or wound-healing properties. Objectives: This study aimed to evaluate the antimicrobial and wound-healing activities of various extracts from all parts of J. podagrica, using different solvents. In addition, the chemical composition of the most effective extract was analyzed by Gas Chromatography-Mass Spectrometry (GC-MS). Materials and Methods: The leaves, stem, and root of J. podagrica were extracted using hexane, 95% ethanol, ethanol-soluble hexane residue, and aqueous decoction. The antimicrobial effects of the extracts were assessed against five common pathogens found in infected wounds using a microtiter plate-based method. Wound healing activity was evaluated by measuring cell proliferation through the MTT assay and wound closure through the scratch assay in HaCaT cell. The chemical composition of the most effective extract was analyzed by GC-MS. Results: The hexane root macerated with hexane (RMH) demonstrated the strongest antimicrobial activity compared to the ethanolic root macerated with ethanol (RME), ethanolsoluble residue of root macerated with ethanol (RRE), and aqueous extract root decoction in water (RRE). RMH exhibited the highest antimicrobial effect against Bacillus subtilis, with the values of minimal inhibitory concentration (MIC) and minimal bactericidal concentration as 312.50 μg/mL, while ampicillin had an MIC of 0.78 μg/mL. However, none of the extracts inhibited the growth of Pseudomonas aeruginosa or Candida albicans. In the wound healing assays, the aqueous extracts showed the most significant effects on cell proliferation and wound closure. The major compounds identified in the root, such as linoleic acid, palmitic acid, and 5-hydroxymethylfurfural, are known to possess antibacterial properties. Conclusion: The hexane extract exhibited the most potent antimicrobial effect, while the aqueous extract showed the best wound healing promotion. These findings support the traditional use of J. podagrica root extract as a potential alternative treatment. Further studies in animal models and clinical trials are necessary to confirm its efficacy.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".