Candidiasis: Changes and Challenges in Its Epidemiology, Pathogenesis, Diagnosis, Treatment and Prevention, 2nd Edition

A Special Issue of Journal of Fungi (ISSN 2309-608X) belonging to the section "Fungal Pathogenesis and Disease Control".

Deadline for manuscript submissions: 30 November 2026 | Viewed by 339

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Department of Immunology, Microbiology and Parasitology, Faculty of Medicine and Nursing, University of the Basque Country, UPV/EHU, 48080 Bilbao, Spain
Interests: medical mycology; mycoses; candida; candidiasis; mycobiome; biofilms; diagnosis; antifungal agents
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Special Issue Information

Dear Colleagues,

Candidiasis presents a serious threat to public health and is associated with prolonged hospitalizations and considerable morbidity and mortality rates. The epidemiology of candidiasis has changed significantly in recent decades. Candida albicans remains the most frequent etiology, but its relative prevalence has decreased. Candidozyma auris (Candida auris) is a new emerging antifungal resistant species, with a great capacity for persistence and nosocomial transmission, causing candidiasis outbreaks in hospitals worldwide. For these reasons, the World Health Organization has included both species in their list of priority fungal pathogens. Candidiasis caused by C. albicans and C. auris have similarities, but there are also important differences in their epidemiology, pathogenesis, diagnostics and therapeutics. The aim of this Special Issue is to provide the latest knowledge on candidiasis induced by both species to improve the diagnostic and therapeutic management of these patients through comprehensive reviews, original studies, and novel perspectives.

Prof. Dr. Guillermo Quindos
Guest Editor

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Keywords

  • medical mycology
  • mycoses
  • candida
  • candidiasis
  • mycobiome
  • biofilms
  • diagnosis
  • antifungal agents

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Published Papers (1 paper)

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Research

28 pages, 4007 KB  
Article
Machine Learning Reveals Distinct Phenotypic Virulence Clusters in Candida Isolates Recovered from the Nasopharynx of COVID-19 Convalescents
by Yekaterina Koloskova, Bakhyt Ramazanova, Kamilya Mustafina, Tolkyn Begadilova, Rustam Yussupov, Zamzagul Khandilla, Darya Bunyayeva, Azat Kalmyrzayev, Mohamed Ahmed Mabrouk and Akmaral Bissekenova
J. Fungi 2026, 12(10), 711; https://doi.org/10.3390/jof12100711 (registering DOI) - 22 Sep 2026
Abstract
Background: Candida spp. are opportunistic fungi whose pathogenic potential arises from interactions among multiple phenotypic traits that may not be captured by conventional single-factor analyses. This study aimed to identify multidimensional phenotypic virulence profiles among clinical Candida isolates using unsupervised machine learning. [...] Read more.
Background: Candida spp. are opportunistic fungi whose pathogenic potential arises from interactions among multiple phenotypic traits that may not be captured by conventional single-factor analyses. This study aimed to identify multidimensional phenotypic virulence profiles among clinical Candida isolates using unsupervised machine learning. Methods: A total of 107 nasopharyngeal isolates recovered from COVID-19 convalescent patients in Almaty, Kazakhstan, during 2021–2022 were evaluated for biofilm formation, phospholipase, proteinase, hemolytic activities and susceptibility to fluconazole, voriconazole, itraconazole, and amphotericin B. Phenotypic data were analyzed using principal component analysis, Uniform Manifold Approximation and Projection, hierarchical clustering, permutational multivariate analysis of variance (PERMANOVA), Random Forest, and SHAP analysis. Results: Candida albicans predominated among the isolates (69.2%), and non-albicans Candida species accounted for 30.8%. Interspecies differences were observed in biofilm formation (p = 0.048) and itraconazole minimum inhibitory concentrations (p < 0.001). Three phenotypic clusters independent of species were identified (PERMANOVA: pseudo-F = 6.72, p = 0.0002). Across 20 repeated 5-fold cross-validation runs, the Random Forest model achieved an aggregated accuracy of 0.785, a mean balanced accuracy of 0.518 (95% CI: 0.461–0.577), and a macro-F1 score of 0.504 (95% CI: 0.444–0.559). SHAP identified itraconazole susceptibility, biofilm formation, and phospholipase activity as the strongest predictors. Conclusions: These findings demonstrate that integrating microbiological phenotyping with machine learning can reveal multidimensional virulence profiles beyond conventional species-based classification. Full article
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