EFFECT OF INDIGENOUS VARIETY OF ALOE VERA ON BACTERIOCIN PRODUCTION BY PROBIOTICS CULTURE
Bibliographic record
Abstract
Background: Aloe vera has long been used as a medicinal plant in the preparation of various gels, lotions and ointments besides its use in various food products.Methodology: The present study was carried out at the Plants Genetics Research Institute, National Agricultural Research Centre, Islamabad. For the purpose of this study, an indigenous variety of Aloe vera was taken along with probiotics culture of Lactobacillus acidophilus, Lactococcus lactis and Lactobacillus helveticus. Aloe vera gel matrix was separated from outer cortex of leaves and media were prepared for the growth of respective probiotics cultures. Aloe vera was used in each probiotic culture against two pathogenic strains, i.e E. coli and S. aureus. In one plate, tryptone was replaced by 1% Aloe vera extract, while 0.5% and 1% Aloe vera extracts were added to other two plates. All the three probiotic cultures were separately inoculated in test tubes, which were incubated at 370C for 24 hours.Results: After incubation of 24 hours, plates were observed on the next day for the measurement of inhibition zones. Strong to medium zones of inhibition were formed against E. coli and S. aureus for all three probiotic cultures when 1% Aloe vera extract was used.Conclusions: This study revealed that the indigenous variety of Aloe vera exhibited significant anti microbial 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) | 0.000 | 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 | 0.001 |
| 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".