Antibacterial Potentials of the Ethanolic Extract of the Stem Bark of Combretum micranthum G. Don and Its Fractions
Bibliographic record
Abstract
Ethanolic extract of the stem bark of Combretum micranthum and its fractions were evaluated for their antibacterial potentials against two Gram positive organisms (Staphylococcus aureus and Bacillus subtilis) and two Gram negative organisms (Escherichia coli and Pseudomonas aeruginosa)-all of both medical and pharmaceutical importance. The anti-bacterial potential was assesed using both the agar and tube dilution methods; Phytochemical and TLC analysis were also done to identify the content of the stem bark of the plant. Phytochemical screening results indicated the presence of flavonoids, tannin, saponins, anthraquinones and cardiac glycosides. The extract and its fractions exhibited potent antimicrobial activities against the test organisms with n- hexane fraction showing the mildest activity. The most pronounced activity was observed with the aqueous fractions and interestingly, against the Gram negative organisms. Results of the Minimum Inhibitory concentration (MIC) of the ethanolic extract and fractions range from 0.234375mg/ml-15mg/ml. The Minimum Bactericidal concentration (MBC) range from 0.9375mg/ml-30mg/ml confirming that the extracts are bactericidal and that their activity is concentration dependent. Thin Layer Chromatography (TLC) results showed a number of likely bioactive constituents which may have been responsible for the observed activities. Results are discussed in the context of the relevance of plant constituents in the control of organisms of health and pharmaceutical importance.
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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 | 0.001 | 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".