Formulation and Evaluation of Antimicrobial Activities of Herbal Cream Containing Ethanolic Extracts of Azadirachta indica Leaves and Aloe Vera Gel
Bibliographic record
Abstract
The antimicrobial activity of ethanolic extract of dried leaves of Azadirachta indica (Neem), fresh gel of Aloe vera, combination of the two extracts and the creams formulated with these extracts were evaluated.The preliminary in vitro antimicrobial activity of the extracts at various concentrations and those of their creams were determined against some microorganisms using the agar cup plate method. The growth inhibition zones of the extracts on the microorganisms were noted. The minimum inhibitory concentration (MIC) was also determined by agar dilution method. The physical properties of the creams formulated with these extracts were evaluated using standard procedures.Gram positive bacteria were more susceptible to Neem extract of which Staphylococcus aureus was the most susceptible with the lowest MIC value (2.5mg/ml). The fungal strain Candida albicans had the lowest MIC value (2.0mg/ml) for the Aloe vera gel extract. The MIC values (mg/ml) of Neem leaves against Bacillus subtilis, Escherichia coli, Staphylococcus aureus, Pseudomonas aeroginosa, Candida albicans and Aspergillus niger were 5.00, 5.00, 2.50, 10.00, 2.50, 5.00 respectively, while MIC of Aloe extract were 8.00, 8.00, 4.00, 8.00, 2.00, 4.00 respectively. Among the formulated creams, the formulation containing equal concentrations of the extracts (1:1) showed the highest antimicrobial activity, however the commercial brand Funbact A® had better antimicrobial activity. Most of the creams showed comparable physical properties.The study showed that the creams containing equal concentrations of the two ethanolic extracts have high potentials as topical antimicrobial agents especially against skin infections due to the tested Gram positive bacteria and Candida albicans.
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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) | 0.001 | 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 | 0.001 |
| 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".