Fusarium spp. have frequently been isolated from patients with onychomycosis. In Colombia, several studies have shown that Fusarium is the most common non-dermatophyte mould causing onychomycosis and its spread has increased in the past years. In this study, samples were collected in 2003 and 2004 from 137 patients who were diagnosed with onychomycosis caused by Fusarium spp. Three species of Fusarium were identified: Fusarium solani (64.9%), Fusarium oxysporum (32.8%) and Fusarium verticillioides (2.3%). The diseases were more common in women (73%) than in men (27%) and occurred mainly among adults between 31 and 40 years old. The percentage of patients who had received previous treatments was 63.5%. In the last years, new and improved antifungal agents like echinocandins or new triazoles like voriconazole have been developed. For this reason, susceptibility testing using voriconazole was performed, by broth microdilution and disk diffusion. The results showed that F. solani had the highest minimum inhibitory concentration. Using the disk diffusion test, many of the isolates showed variable susceptibility. Genetic diversity of F. oxysporum isolates was determined by random amplified polymorphic DNA. Twenty isolates belonging to different haplotypes were selected for PCR amplification of a region of the gene encoding α-l-arabinofuranosidase B, a specific test to determine if the isolates were F. oxysporum f. sp. dianthi. On the basis of these PCR results, we found that five out of the 20 F. oxysporum isolates corresponded to f. sp. dianthi.

Fusariosis have been increasing in Colombia in recent years, but its epidemiology is poorly known. We have morphologically and molecularly characterized 89 isolates of Fusarium obtained between 2010 and 2012 in the cities of Bogotá and Medellín. Using a multi-locus sequence analysis of rDNA internal transcribed spacer, a fragment of the translation elongation factor 1-alpha (Tef-1α) and of the RNA-dependent polymerase subunit II (Rpb2) genes, we identified the phylogenetic species and circulating haplotypes. Since most of the isolates studied were from onychomycoses (nearly 90 %), we carried out an epidemiological study to determine the risk factors associated with such infections. Five phylogenetic species of the Fusarium solani species complex (FSSC), i.e., F. falciforme, F. keratoplasticum, F. lichenicola, F. petroliphilum, and FSSC 6 as well as two of the Fusarium oxysporum species complex (FOSC), i.e., FOSC 3 and FOSC 4, were identified. The most prevalent species were FOSC 3 (38.2%) followed by F. keratoplasticum (33.7%). In addition, our isolates were distributed into 23 haplotypes (14 into FOSC and nine into FSSC). Two of the FSSC phylogenetic species and two haplotypes of FSSC were not described before. Our results demonstrate that recipients of pedicure treatments have a lower probability of acquiring onychomycosis than those not receiving such treatments. The antifungal susceptibility of all the isolates to five clinically available agents showed that amphotericin B was the most active drug, while the azoles exhibited lower in vitro activity.

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Introduction. Yeasts of the genus Malassezia form a normal component of skin flora, but are also associated with several dermatological disorders. Since 1996, the description of new species in this genus have led to new questions about their epidemiology and pathogenicity. Objective. Herein, the frequency of Malassezia species in individuals with pityriasis versicolor, atopic dermatitis, seborrhoeic dermatitis, seborrhoeic dermatitis was compared in HIV patients and healthy individuals. Three body sites were selected for examination -head, thorax, and upper and lower extremities. Material and methods. The 154 Malassezia species were isolated from 112 individuals and grouped as follows: 39 with seborrhoeic dermatitis (20 were HIV-positive patients), 18 with pityriasis versicolor, 18 with atopic dermatitis and 37 without dermatological leisions. HIV patient samples were examined microscopically, and specimens from both patients and healthy subjects were cultured on modified Dixon agar medium. Subsequently, isolates were identified by macroscopic, microscopic and physiological characteristics. Results. The most commonly isolated species were Malassezia globosa (37.5%), M. sympodialis (31.3%) and M. furfur (31.3%). Malazzerzia globosa predominated in patients with pityriasis versicolor (67%) and in HIV-positive patients with seborrhoeic dermatitis (85%). In non-HIV patients with atopic dermatitis or seborrhoeic dermatitis, M. furfur and M. restricta were isolated in 72% and 26% of the cases, respectively. Conclusion. Several conclusions were evident. First, Malassezia species was present in subjects with and without dermatological pathologies. Second, the species frequency in the sampled population differed from frequencies reported from other geographic areas. Third, Malassezia globosa was involved at high frequency in patients with dermatological pathologies, suggesting a higher pathogenicity of this species. Additional studies on each species are recommended to clarify their pathogenic roles in association with HIV-positive and normal subjects.

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Poster session 3, September 23, 2022, 12:30 PM - 1:30 PM Objective: To classify five fungi types using two different deep learning approaches and three different convolutional neural network models, VGG16, Inception V3, and ResNet50. Method: A mycological laboratory in Colombia donated the images used for the development of this research work. They were manually labeled into five classes and curated with subject matter expert assistance. The images were later cropped and modified with automated coding routines to produce the final dataset. Results We present experimental results classifying five types of fungi using two different deep learning approaches and three different convolutional neural network models, VGG16, Inception V3, and ResNet50. The first approach benchmarks the classification performance for the models trained from scratch, while the second approach benchmarks the classification performance using pre-trained models based on the ImageNet dataset. Using k-fold cross-validation testing on the 5-class dataset, the best performing model trained from scratch was Inception V3, reporting 73.2% accuracy. Likewise, the best performing model using transfer learning was VGG16, with 85.04% accuracy. Conclusion The statistics provided by the two approaches create an initial benchmark to encourage future research work to improve classification performance. Furthermore, the dataset built is published on Kaggle and GitHub to encourage future research.