Screening the in vitro Antimicrobial and Antioxidant Activities of Methanolic Extract of Epiphyllum oxypetalum (DC.) Haw. Leaf
Bibliographic record
Abstract
Epiphyllum oxypetalum (E. oxypetalum) is traditionally used to cure liver infections, for wound healing, and to alleviate viral–related diseases. However, as the alcohol and aqueous solvents have low total phenolic and flavonoid content (TPC and TFC), antioxidant, and antibacterial capabilities, a different polarity would be more suitable. Thus, this study aimed to evaluate the methanolic extract of E. oxypetalum leaf. The chemical constituents were identified through TPC and TFC, as well as GC–MS analysis. The methanolic extract of E. oxypetalum leaf was evaluated for antimicrobial properties against five bacterial strains - Klebsiella pneumoniae, Staphylococcus aureus, Escherichia coli, Staphylococcus epidermidis, and a fungal strain of Candida albicans using the disc diffusion method. The antioxidant activity of the methanolic extract of E. oxypetalum leaf was also assessed using DPPH assay. The highest values of the TPC and TFC of the methanolic extract of E. oxypetalum leaf were 179.86 ± 0.17 mg GAE/g and 75.07 ± 0.17 mg QE/g, respectively. The GC–MS had 27 m/z peaks, indicating the presence of various bioactive compounds including phenolic compounds and fatty acids. In the antimicrobial study, the zone of inhibition (ZOI) was between 2.8–3.5 mm for antibacterial activity and there was also a significant antifungal activity of 1.8 mm against Candida albicans. The IC50 DPPH assay value of methanolic extract of E. oxypetalum leaf was 14 µg/ml, indicating high antioxidant properties. This study provides evidence that the methanolic extract of E. oxypetalum leaf possesses significant antioxidant and antimicrobial properties which could be attributed to its diverse chemical constituents. These findings suggest the potential of E. oxypetalum leaf as a natural source of medicine, antimicrobial, and antioxidant properties.
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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 | 0.000 |
| 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) | 0.001 | 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".