Antimicrobial and antibiofilm effects of crude and microencapsulated guava leaf extracts against Enterococcus faecalis and Staphylococcus epidermidis
Bibliographic record
Abstract
Background Staphylococcus epidermidis and Enterococcus faecalis are nosocomial microorganisms that have gained attention in recent times due to the increasing reports of antimicrobial-resistant strains, which are leading to infections that are progressively harder to eradicate. One of the most important resistance mechanisms employed by these two bacteria is biofilm formation, which provide them with physical and chemical protection from antimicrobial agents. Methods This study assessed the antimicrobial activity of crude and microencapsulated extracts of Psidium guajava L., an agro-industrial waste product widely available in guava-producing countries, using the microdilution technique. Additionally, anti-adhesion activity was analyzed in microplates and by confocal laser scanning microscopy (CLSM). Results Guava leaf extract reduced the growth of all three bacterial strains evaluated. For Staphylococcus epidermidis (ATCC 12228), the minimum inhibitory concentrations (MICs) were 25 mg/ml for the crude extract and 0.625 mg/ml for the microencapsulated form. In contrast, for Enterococcus faecalis (ATCC 29212 and a vaginal clinical isolate), MIC values were greater than 50 mg/ml and 5 mg/ml, respectively. Furthermore, both extracts exhibited anti-biofilm activity by reducing bacterial adhesion. Conclusion microencapsulation allowed a reduction in the extract concentration and guava leaf extract shows potential as an antimicrobial agent for future application.
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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 | 0.000 |
| 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".