Antimicrobial, Antioxidant Activity of Ethyl Acetate Extract of Streptomyces sp. PERM2, its Potential Modes of Action and Bioactive Compounds
Bibliographic record
Abstract
Background: Microorganisms belonging to Streptomyces sp. are Gram-positive bacteria known for their unsurpassed capacity for the production of secondary metabolites with diverse biological activities. The aim of this study was to evaluate the antimicrobial and antioxidant properties of ethyl acetate Streptomyces sp. PERM2 extract, its potential modes of action and bioactive secondary metabolites. Results: The ethyl acetate PERM2 extract showed antimicrobial activity more pronounced on both Gram-positive and Gram-negative bacteria and fungi with a Minimum Inhibitory Concentration value (MIC) of 0.5 mg/mL and Minimum Bactericidal Concentration (MBC) of 2 - 4 mg/mL against bacterial pathogens. MIC value against pathogenic fungi was 2 mg/mL and Minimum Fungicidal Concentration (MFC) of 0.01 - 0.05 mg/mL against pathogenic fungi. PERM2 crude extract showed the ability to inhibit bacteria cell wall synthesis at 0.5 and 1 MIC. The extract was found to possess dose-dependent 2,2-Diphenyl-picrylhadrazyl (DPPH) free radical scavenging and Ferric reducing activity. The gas chromatography-mass spectrometry (GC-MS) analysis revealed the presence of three major compounds identified as 9,12-octadecadienoic acid (Z, Z) (29.75%), tridecyl trifluoroacetate (24.82%) and 1-(+)-ascorbic acid 2, 6-dihexadecanoate (22.34%). The liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis revealed the presence of 22 non-volatile metabolites in PERM2 extract and only the compound 3, 30-O-dimethylellagic acid was identified. Conclusion: The results of this study indicate that ethyl acetate Streptomyces sp. PERM2 extract possesses antibacterial, antifungal, and antioxidant activities; inhibits bacteria cell wall and protein synthesis; and contains significant bioactive secondary metabolites which could be used as an alternative to multi-resistance antibiotics.
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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".