In vitro Antibacterial Activity of Zingiber officinale and Orthosiphon stamineus on Enterococcus faecalis
Bibliographic record
Abstract
This study evaluates the antibacterial effects of Zingiber officinale essential oil and Orthosiphon stamineus water extract against Enterococcus faecalis. The herbs were prepared in various concentrations to determine their minimum inhibitory concentrations (MIC) and growth inhibitory effect. Anti-adhesion activities of the herbs were determined by co-incubation with E. faecalis cultures for 6 and 24 h. Biofilm disruption activities were determined by adding the studied herbs into preformed E. faecalis biofilm. The effects on the morphology of E. faecalis grown as biofilm were studied using scanning electron microscopy (SEM). The MICs of ginger oil and O. stamineus extract were 0.31 and 25 mg/mL, respectively. Between the tested herbs, ginger exhibited greater inhibitory effects on the growth of E. faecalis grown in suspension mode. Both herbs generally showed anti-adhesion activities in inverse concentration-dependent manner. No significant biofilm disruption activities by both herbs were observed. SEM analyses showed E. faecalis cell surface changes in the treated biofilm. The studied herbs may have compromised the integrity of the bacterial cell membrane. These findings suggest that the studied herbs may have better antibacterial activities against E. faecalis in suspension mode compared to biofilm mode, with ginger oil showed greater antibacterial activity compared to O. stamineus extract.
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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".