An update on antifungal resistance in dermatophytosis
Bibliographic record
Abstract
Introduction The reports of resistance to antifungal agents used for treating onychomycosis and other superficial fungal infections are increasing. This rise in antifungal resistance poses a public health challenge that requires attention.Areas covered This review explores the prevalence of dermatophytes and the current relationship between dermatophyte species, their minimum inhibitory concentrations (MICs) for terbinafine (an allylamine) and itraconazole (an azole), and various mutations prevalent in these species. The most frequently isolated dermatophyte associated with resistance in patients with onychomycosis and dermatophytosis was T. mentagrophytes. However, T. indotineae emerged as the most prevalent isolate with mutations in the SQLE gene, exhibiting the highest MIC of 8 µg/ml for terbinafine and MICs of 8 µg/ml and ≥ 32 µg/ml for itraconazole.Overall, the most prevalent SQLE mutations were Phe397Leu, Leu393Phe, Ala448Thr, Phe397Leu/Ala448Thr, and Lys276Asn/Leu415Phe (relatively recent).Expert opinion Managing dermatophyte infections requires a personalized approach. A detailed history should be obtained including details of travel, home and occupational exposure, and clinical examination of the skin, nails and other body systems. Relevant testing includes mycological examination (traditional and molecular). Additional testing, where available, includes MIC evaluation and detection of SQLE mutations. In case of suspected terbinafine resistance, itraconazole or voriconazole (less commonly) should be considered.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.004 |
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".