The Potential of Different Extraction Methods of Soursop (<i>Annona Muricata</i> Linn) Leaves as Antimicrobial Agents for Aquatic Animals
Bibliographic record
Abstract
A cup plate diffusion method was employed to determine the antibacterial and antifungal activities of aqueous, ethanolic and methanolic extracts of soursop ( Annona muricata ) leaves against four clinical strains of bacteria isolates from Clarias gariepinus and Oreochromis niloticus . They were Bacillus subtilis , Staphylococcus aureus , Streptococcus iniae , Aeromonas hydrophila and Aspergillus niger . Phytochemical screening and Minimum Inhibitory Concentration (MIC) of soursop leaves were established through standard methods. Data obtained were subjected to ANOVA at P = 0.05. The diameter of zone of inhibition varies depending on pathogens and method of extraction. An average diameter of zone of inhibition ranges from without inhibition zone in control to 20 ± 0.01 mm, without inhibition zone in control to 21 ± 0.02 mm and without inhibition zone in control 25 ± 0.01 mm for aqueous, ethanolic and methanolic extracts respectively. The extracts displayed higher activities to the Gram positive organisms. Chloramphenicol and distilled water were used as control. Phytochemical screening indicated the presence of tannins, glucosinolates, phenols, amino acids and polysterols and absence of saponins. The result of MIC of soursop leaves extracts on the pathogens investigated was 500 µg/ ml, 500 µg/ ml and 1000 µg/ml for aqueous, ethanolic and methanolic extracts respectively. The results indicated that different methods of extraction of soursop leaves had antimicrobial activity on the pathogens ( B. subtilis , S. aureus , S. iniae , A. hydrophila and A. niger ) and suggested that soursop extracts can be used in fish farming to inhibit bacterial growth and improved fish health.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".