Antifungal resistance in dermatophytes – review of the epidemiology, diagnostic challenges and treatment strategies for managing <i>Trichophyton indotineae</i> infections
Bibliographic record
Abstract
Introduction There is an increasing number of reports of Trichophyton indotineae infections. This species is usually poorly responsive to terbinafine.Areas covered A literature search was conducted in May 2024. T.indotineae infections detected outside the Indian subcontinent are generally associated with international travel. Reports of local spread are mounting.As a newly identified dermatophyte species closely related to the T. mentagrophytes complex with limited genetic and phenotypic differences, there is an unmet need to develop molecular diagnosis for T. indotineae. Terbinafine has become less effective as a first-line agent attributed to mutations in the squalene epoxidase gene (Leu393Phe, Phe397Leu). Alternative therapies include itraconazole for a longer time-period or a higher dose (200 mg/day or higher). Generally, fluconazole and griseofulvin are not effective. In some cases, especially when the area of involvement is relatively small, topical non-allylamine antifungals may be an option either as monotherapy or in combination with oral therapy. In instances when the patient relapses after apparent clinical cure then itraconazole may be considered. Good antifungal stewardship should be considered at all times.Expert Opinion When both terbinafine and itraconazole are ineffective, options include off-label triazoles (voriconazole and posaconazole). We present four patients responding to these newer triazoles.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".