In vitro susceptibility of the seven Malassezia species to ketoconazole, voriconazole, itraconazole and terbinafine
Bibliographic record
Abstract
Fifty-five strains, either authentic or ex-type, of seven Malassezia species were investigated for in vitro susceptibility to various concentrations (0.03-64.0 microg/mL) of three azole drugs, ketoconazole, voriconazole and itraconazole, as well as the allylamine terbinafine, using the agar dilution method. All strains of the seven Malassezia species were susceptible to the three azole drugs at low concentrations. M. furfur, M. sympodialis, M. slooffiae, M. pachydermatis, M. globosa, M. obtusa and M. restricta were most sensitive to ketoconazole and itraconazole, with minimum inhibitory concentrations (MICs) ranging from < or = 0.03 to 0.125 microg/mL. The recently introduced antifungal, voriconazole, was also very effective, with MIC80 values < or = 0.03 microg/mL for 80% of strains. MICs of terbinafine against the seven Malassezia species ranged from </= 0.03 to 64.0 microg/mL. There were variations in susceptibility of the seven Malassezia species to ketoconazole, voriconazole, itraconazole and terbinafine. Strains of M. furfur, M. globosa and M. obtusa were more tolerant to terbinafine than the remaining Malassezia species; M. sympodialis was highly susceptible. M. furfur strains tested with terbinafine ranged from highly susceptible to relatively resistant. Correct identification of Malassezia species could facilitate selection of appropriate antifungal therapy.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".