Geraniol inhibits both planktonic cells and biofilms of the <i>Candida parapsilosis</i> species complex: Highlight for the improved efficacy of amphotericin B, caspofungin and fluconazole plus Geraniol
Bibliographic record
Abstract
The Candida parapsilosis species complex poses a recognized threat to the nosocomial environment. In the scenario of the global rise of resistant strains to antifungals, geraniol, a terpene isolated from different essential oils, has shown promising antimicrobial activity. We evaluated: (1) the effects of geraniol against the C. parapsilosis species complex, in planktonic and biofilm forms; (2) the strains' susceptibility to clinical antifungals and (3) the geraniol interaction with antifungals. Eighteen isolates were subjected to in vitro susceptibility testing by the broth microdilution protocol, using geraniol, amphotericin B, caspofungin, itraconazole and fluconazole to determine the minimum inhibitory concentration (MIC) and subsequently, we measured the fungicidal activity. Geraniol was tested against biofilms by the measurement of the metabolic activity and biomass. Pharmacological interactions were performed by the checkerboard method. Geraniol's MIC range was between 256 and 512 µg/ml. MIC range for clinical antifungals was ≤ 0.031-4 µg/ml. Geraniol also showed antibiofilm activity with average reductions of metabolic activity (38.33%) and biomass (30.69%), at MIC concentration. Furthermore, geraniol showed synergistic/additive effects with antifungals. Briefly, geraniol inhibits both planktonic cells and biofilms of the C. parapsilosis species complex and besides it improves the efficacy of amphotericin B, caspofungin and fluconazole.
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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".