Antimicrobial activities of different solvent extracts from stem and seeds of Peganum Harmala L
Bibliographic record
Abstract
Wild medicinal herbs have been used as folk and traditional medicines all across the world since well before recorded history. This present study was designed to test the antimicrobial activities of five different solvent extracted samples ( n-hexane , n-butanol , ethyl acetate , methanol , and water ) of Peganum harmala using stems and seeds. Two different strains of Gram-negative bacteria ( Escherichia coli and Klebsiella pneumonia) , two Gram-positive bacteria (Bacillus subtilus and Staphylococcus aureus) , and one fungal strain ( Candida albicans ) were used. The antimicrobial activities were measured using a disc diffusion assay. Two concentrations of the extracts (1 and 2mgDisc -1 ) were used. Ethyl acetate fraction was found more affective among the tested solvents and showed maximum activity (zone of inhibition) against S . aureus ( 65.53 and 81.10% ) , E . coli ( 46.22 and 61.29% ) while n-butanol and water fractions gave maximum activity against S . aureus (78.86 and 70.00%) and K . pneumonia (57.00 and 61.39%) respectively. Water fraction showed maximum activity against C . albicans (60.00 and 81.88%). In the case of the stem, Ethyl acetate again showed more activity against B . subtilus (38.57 and 42.10%) and S. aureus (36.66 and 46.66%) while n-butanol showed maximum activity against K . pneumonia (24.55 and 32.44%) and E . coli (27.93 and 37.61%). Methanol was found more effective against C . albicans (25.71 and 43.80%). Seed extracted samples were found more effective compared to the stem. Ethyl acetate , butanol , and aqueous extracted samples showed good activity against the tested microbes, so these fractions are recommended for study their mechanism of actions and isolation of bioactive metabolites responsible for antimicrobial activities. The P . harmala should be evaluated for their bioactive compounds to be used in future studies. Our objective is to provide the framework for future study on the roles of P . harmala as traditional medicines.
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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 | 0.000 |
| 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".