Antimicrobial Activity and Phytochemical Screening of<i>Buchenavia tetraphylla</i>(Aubl.) R. A. Howard (Combretaceae: Combretoideae)
Bibliographic record
Abstract
This study evaluated the antimicrobial and hemolytic activities and phytochemical constituents of hydroalcoholic extract and its fractions from Buchenavia tetraphylla leaves. Cyclohexane (BTCF), ethyl acetate (BTEF), and n-butanol-soluble (BTSBF) and non-soluble (BTNBF) fractions were obtained from a liquid-liquid partition of hydroalcoholic extract (BTHE) from B. tetraphylla leaves. The hemolytic activity of active fractions was checked. The BTHE inhibited the growth of Micrococcus luteus (MIC: 0.10 mg/mL), Pseudomonas aeruginosa (MIC: 0.20 mg/mL), Mycobacterium smegmatis (MIC: 0.39 mg/mL), Proteus vulgaris, and Staphylococcus aureus (MIC: 0.78 mg/mL for both). The more active fractions were BTCF and BTBSF. BTCF showed better potential to inhibit M. luteus (0.10 mg/mL), P. aeruginosa (0.20 mg/mL), S. enteritidis (0.39 mg/mL), and S. aureus (1.56 mg/mL). BTBSF showed the best results for M. luteus (0.10 mg/mL), M. smegmatis, B. subtilis (0.39 mg/mL for both), and P. vulgaris (0.10 mg/mL). The HC50 were greater than observed MIC: 20.30, 4.70 and 2.53 mg/mL, respectively, to BTBF, BTHE and BTCF, which. The phytochemical analysis detected the presence of flavanoids, triterpene, carbohydrate, and tannin. Our work showed for the first time the broad-spread antimicrobial activity of B. tetraphylla, which has nonhemolytic action, creating a new perspective on the interesting association of traditional and scientific knowledge.
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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".