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Record W4394847340 · doi:10.1080/14656566.2024.2343079

An update on antifungal resistance in dermatophytosis

2024· article· en· W4394847340 on OpenAlexaff
Aditya K. Gupta, Avantika Mann, Shruthi Polla Ravi, Tong Wang

Bibliographic record

VenueExpert Opinion on Pharmacotherapy · 2024
Typearticle
Languageen
FieldMedicine
TopicNail Diseases and Treatments
Canadian institutionsMediprobe Research (Canada)University of Toronto
Fundersnot available
KeywordsMedicineAntifungalItraconazoleDermatologyVoriconazoleTerbinafineIntensive care medicinePharmacology

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.501
Threshold uncertainty score0.927

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.027
GPT teacher head0.404
Teacher spread0.378 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2024
Admission routes1
Has abstractyes

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