Antibacterial Properties of Garcinia mangostana Linn. Ethanolic and Methanolic Extracts Against Selected Gram-Positive and Gram-Negative Bacteria: A Meta-Analysis
Bibliographic record
Abstract
Garcinia mangostana Linn., has been studied for its antibacterial properties to augment commercial antibiotics and in the hope of easing reliance on these chemical medications in the future, however, the comparison of the fruit’s bactericidal capabilities relative to different bacterial species requires further analyses. This systematic review and meta-analysis compared the antibacterial activity of ethanolic and methanolic mangosteen extracts against three species that commonly cause Healthcare-Associated Infections (HAIs)—Staphylococcus aureus, Escherichia coli, and Pseudomonas aeruginosa. The results revealed no significant difference [mean difference: 1.42 (CI: -3.53 to 6.37, I2 = 99%, Z = 0.56 (P = 0.57))] between the effectiveness of the extracts against S. aureus and E. coli. But it was contrary when P. aeruginosa was compared with S. aureus [mean difference: 5.00 (CI: 4.48 to 5.52, I2= 0%, Z = 18.97 (P < 0.00001))] and E. coli [mean difference: 3.96 (CI: 2.01 to 5.92, I2 = 94%, Z = 3.97 (P < 0.0001))]. Literature search and screening were done following the PRISMA guidelines. Quality assessments utilized the JBI Critical Appraisal Tool and a remodified Newcastle-Ottawa Scale. A total of 13 studies were included in the review, only 7 of which were eligible for meta-analyses. In conclusion, G. mangostana extracts are indeed effective against multiple microbes, however, relative to the selected bacterial species, inhibition varied. Moreover, this study sheds light on further practical or in vivo applications of mangosteen as a treatment for bacterial infections.
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.045 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".