Global Dermatophyte Infections Linked to Human and Animal Health: A Scoping Review
Bibliographic record
Abstract
Dermatophytes are commonly encountered pathogens in clinical practice causing superficial infections of the skin, hair, and nails. These pathogens are often found on animals such as livestock (e.g., cattle, rabbits) and pets (e.g., cats, hedgehogs) that can lead to spillover infections in human populations. Here, we reviewed published reports (2009–2024) of dermatophyte infections in animals and in humans with a history of animal contact. A literature search was completed in October 2024 using PubMed, Embase (Ovid), and Web of Science (Core Collection), which identified 250 articles. Generally, dermatophytes tend to infect younger animals with long hair and exhibit a species-specific host range. Microsporum canis was the most commonly reported species—linked to cats—that can cause tinea capitis, especially concerning the development of kerion in children. Trichophyton verrucosum is strongly associated with cattle. The Trichophyton mentagrophytes complex shows a diverse range of animal hosts, with rabbits being most frequently reported; however, T. mentagrophytes var. erinacei is almost exclusively isolated from hedgehogs, and T. mentagrophytes var. benhamiae is more commonly found on rodents (e.g., guinea pigs). Lastly, the geophilic Nannizia gypsea has been isolated from both dogs and cats. Managing dermatophyte zoonoses is an ongoing challenge, as healthcare providers may empirically treat with corticosteroids or antibacterial agents due to its atypical inflammatory appearance. Evidence of in vitro resistance against griseofulvin and fluconazole has been documented in multiple zoonotic dermatophyte species. Resistance development against terbinafine and itraconazole is also a possibility, although the number of reports is scarce. Under the principles of the One Health approach, research on human fungal diseases should take animal and environmental factors into account. A renewed call for increased testing efforts is warranted.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".