Antifungal susceptibility and genotypical pattern of<i>Microsporum canis</i>strains
Bibliographic record
Abstract
Dermatophytes are a group of fungi that are capable of invading keratinized tissues of humans and other animals. Antifungal susceptibility analysis and genetic studies by random amplification of polymorphic DNA (RAPD), have been used to detect polymorphism as well as determining the possible resistance of dermatophytes to antifungals. The aim of this study was to evaluate the possible correlation between the antifungal susceptibility and genotypical pattern of Microsporum canis strains isolated in dogs and cats with dermatophytosis in Northeast Brazil. The antifungal susceptibility study was conducted using the broth microdilution test with griseofulvine, ketoconazole, itraconazole, and fluconazole. The genotypical analysis was performed using the RAPD method. The antifungal susceptibility analysis showed that all the strains of M. canis analyzed (n = 22) were sensitive to griseofulvine (0.25 microg/mL < or minimum inhibitory concentration (MIC) < or = 1 microg/mL), ketoconazole (0.25 microg/mL < or = MIC < or = 2 microg/mL), itraconazole (0.25 microg/mL < or = MIC < or = 1 microg/mL), and fluconazole (1 microg/mL < or = MIC < or = 16 microg/mL). The RAPD results showed that all analyzed strains are genetically similar. Thus, based on antifungal susceptibility analysis and RAPD data, a possible correlation can be shown between the antifungal susceptibility and the genotypical pattern of the strains of M. canis from Northeast Brazil.
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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) | 0.000 | 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".