Evaluation of the selective antibacterial activity of <i>Eucalyptus globulus</i> and <i>Pimenta pseudocaryophyllus</i> essential oils individually and in combination on <i>Enterococcus faecalis</i> and <i>Lactobacillus rhamnosus</i>
Bibliographic record
Abstract
Essential oils (EOs), as substitutes for antibiotics in animal diets, should have selective antibacterial activity between pathogenic and beneficial bacteria from the animal gut. Thus, this study evaluated the selective antibacterial activity of Eucalyptus globulus (EG) and Pimenta pseudocaryophyllus (PP) EOs on Enterococcus faecalis as a surrogate model of pathogenic bacterium and on Lactobacillus rhamnosus as a beneficial bacterium model. The EOs antibacterial activity was evaluated by determination of minimal inhibitory concentrations (MICs), minimal bactericidal concentration (MBCs), and fractional inhibitory concentration (FIC) indices. The time-kill and sequential exposure assays were also performed, but using only the EG oil, which was the best selective EO, since it had a MIC lower on E. faecalis (7.4 mg/mL) than on L. rhamnosus (14.8 mg/mL). FIC index values showed that the combination of the two EOs had an indifferent effect (1.25 and 2.03) on E. faecalis and an additive effect (1.00) on L. rhamnosus. The time-kill assay showed that EG oil was able to kill E. faecalis within 15 min of treatment (∼5 log reduction) and caused a reduction ∼3 log of L. rhamnosus viability. The sequential exposure assay showed that EG oil (at MIC/2) produced higher reduction on E. faecalis viability (∼3 log) than on L. rhamnosus (∼2 log) as well. Therefore, L. rhamnosus presented higher tolerance to the antibacterial activity of EG oil than E. faecalis did.
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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) | 0.000 | 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".