Determination of susceptibility/resistance to antifungal drugs of<i>Trichophyton mentagrophytes</i>isolates by a macrodilution method
Bibliographic record
Abstract
Onychomycosis is a common adult human mycosis, and dermatophytes of the Trichophyton genera are the most frequently isolated microorganism. Globally, from 3% to 10% of the human population is attacked by ony cho mycosis, and many cases involve toenails. The aim of this work was to determine the minimal inhibitory concentrations (MICs) of antifungal drugs (fluconazole, ketoconazole, itraconazole, terbinafine, and griseofulvin) often used for the treatment of ungueal dermatophytosis caused by Trichophyton mentagrophytes. The MICs were determined by the broth medium macrodilution method. The results showed that activities of terbinafine and itraconazole were significantly higher (MIC <0.007-0.015 microg.mL -1 and MIC = 0.062-1.000 microg.mL -1, respectively). All isolates had reduced susceptibility to fluconazole (MIC = 16 to >64 microg.mL -1). The MICs of ketoconazole and griseofulvin varied among strains, ranging from 0.125 to 2.000 microg.mL -1 for ketoconazole and from 0.25 to 2.00 microg.mL -1 for griseofulvin. These MICs were higher than those of other studies cited, possibly because of differences in culture medium used in the other studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".