Potential emergence of terbinafine resistance by squalene epoxidase gene mutations: An 18‐month cohort study of onychomycosis patients in the United States
Bibliographic record
Abstract
Abstract Background There is a concerning rise in antifungal‐resistant dermatophytosis globally, with resistance to terbinafine conferred by point mutations in the squalene epoxidase (SQLE) gene. Objectives Report changes in the prevalence and profile of SQLE mutations in onychomycosis patients in the United States. Methods A longitudinal cohort study of toenail samples was collected from suspected onychomycosis patients over an 18‐month period from 2022 to 2023. Samples were submitted from across the United States and subjected to multiplex real‐time polymerase chain reactions for dermatophyte detection, with further screening of SQLE mutations at four known hotspots (393Leu, 397Phe, 415Phe and 440His). Results A total of 62,056 samples were submitted (mean age: 57.5 years; female: 60.4%). Dermatophytes were detected in 38.5% of samples, primarily Trichophyton rubrum complex (83.6%) and T. mentagrophytes complex (10.7%). A survey of SQLE mutations was carried out in 22,610 dermatophyte samples; there was a significant increase in the prevalence of SQLE mutations between the first quarter of 2022 and the second quarter of 2023 (29.0 to 61.9 per 1000 persons). The Phe397Leu substitution was the predominant mutation; Phe415Ser and His440Tyr have also emerged which were previously reported as minor mutations in skin samples. The temporal change in mutation rates can be primarily attributed to the Phe415Ser substitution. Samples from elderly patients (>70 years) are more likely to be infected with the T. mentagrophytes complex including strains harbouring the Phe415Ser substitution. Conclusion The prevalence of SQLE mutations among onychomycosis patients with Trichophyton infections may be underestimated. Older individuals may have a higher risk.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".