Antimicrobial Activity of Chromolaena odorata Extracts against Bacterial Human Skin Infections
Bibliographic record
Abstract
Leaf, stem and root of Chromolaena odorata were extracted by maceration extraction method using water, ethanol, methanol and hexane as solvents. The average percentage yield of leaf extracts in water (12.16 ± 0.13%), ethanol (8.42 ± 0.115%), methanol (10.45 ± 0.012%) and hexane (2.37 ± 0.215%) were significantly higher compared with stem and root extracts using the same solvents. All extracts were tested for antimicrobial activity against ten bacterial strains associated with human skin infections. Leaf extracts with ethanol, methanol and hexane solvents gave the best inhibitory activity against six gram-positive bacterial strains (Bacillus cereus TISTR 687, Enterococcus faecalis TISTR 379, Staphylococcus epidermidis TISTR 518, Staphylococcus aureus TISTR 1466, Streptococcus pyogenes ATCC 19615 and Propionibacterium acnes DMST 14916) and one gram-negative bacterial strain (Proteus vulgaris ATCC 13315). The hexane stem extract showed greater inhibitory activity against Pseudomonas aeruginosa ATCC 27853(15.3±0.5 mm), B. cereus TISTR 687(14.6±0.8 mm) and Klebsiella pneumoniae TISTR 1843 (14.0±1.0 mm), while hexane root extract showed high inhibitory activity against Enterococcus faecalis TISTR 379 (14.5±0.9 mm) and Kleb. pneumoniae TISTR 1843 (14.7±0.6 mm). The lowest minimum inhibitory concentration (MIC) of the ethanolic leaf extract was 0.81 mg/mL against Staph. aureus TISTR 1466, followed by methanolic and hexane leaf extracts with equal MIC of 1.62 mg/mL against both Staph. aureus TISTR 1466 and Strep. pyogenes ATCC 19615. The antimicrobial activity of the methanolic leaf extract of C. odorata was correlated with the amount of both total phenolic and flavonoid compounds. The results obtained suggest that the ethanolic, methanolic and hexane leaf extracts could be developed to treat bacterial skin infections. The hexane was sutiable solvent for extraction of the stem and root parts of C. odorata.
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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".