Synergic interaction between pomegranate extract and antibiotics against <i>Staphylococcus aureus</i>
Bibliographic record
Abstract
We evaluated the interaction between Punica granatum (pomegranate) methanolic extract (PGME) and antibiotics against 30 clinical isolates of methicillin-resistant Staphylococcus aureus (MRSA) and methicillin-sensitive Staphylococcus aureus (MSSA). Susceptibility testing of the isolates to PGME and antibiotics was performed by the broth dilution method. Synergic activity was detected between PGME and the 5 antibiotics tested, chloramphenicol, gentamicin, ampicillin, tetracycline, and oxacillin, ranging from 38% to 73%. For some isolates, PGME did not interfere with the action of any of the antibiotics tested. The bactericidal activity of PGME (0.1 x MIC) in combination with ampicillin (0.5 x MIC) was assessed using chosen isolates by time-kill assays, and they confirmed the synergic activity. Using this combination, cell viability was reduced by 99.9% and 72.5% in MSSA and MRSA populations, respectively. PGME increased the post-antibiotic effect (PAE) of ampicillin from 3 to 7 h. In addition, PGME demonstrated the potential to either inhibit the efflux pump NorA or to enhance the influx of the drug. The detection of in vitro variant colonies of S. aureus resistant to PGME was low and they did not survive. In conclusion, PGME dramatically enhanced the activity of all antibiotics tested, and thus, offers an alternative for the extension of the useful lifetime of these antibiotics.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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) | 0.002 | 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".