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Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology

Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and acc...

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Autores principales: Anton, Aurore, Plinet, Mathilde, Peyret, Thomas, Cazaudarré, Thomas, Pesant, Stéphanie, Rouquet, Yannick, Tricoteaux, Marie-Andrée, Bernier, Matthieu, Bayette, Jérémy, Fournier, Remi, Marguerettaz, Mélanie, Rolland, Pierre, Bayol, Thibaud, Abbaoui, Nadia, Berry, Antoine, Iriart, Xavier, Cassaing, Sophie, Chauvin, Pamela, Bernard, Elodie, Fabre, Richard, François, Jean-Marie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670032/
https://www.ncbi.nlm.nih.gov/pubmed/37998565
http://dx.doi.org/10.3390/diagnostics13223430
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author Anton, Aurore
Plinet, Mathilde
Peyret, Thomas
Cazaudarré, Thomas
Pesant, Stéphanie
Rouquet, Yannick
Tricoteaux, Marie-Andrée
Bernier, Matthieu
Bayette, Jérémy
Fournier, Remi
Marguerettaz, Mélanie
Rolland, Pierre
Bayol, Thibaud
Abbaoui, Nadia
Berry, Antoine
Iriart, Xavier
Cassaing, Sophie
Chauvin, Pamela
Bernard, Elodie
Fabre, Richard
François, Jean-Marie
author_facet Anton, Aurore
Plinet, Mathilde
Peyret, Thomas
Cazaudarré, Thomas
Pesant, Stéphanie
Rouquet, Yannick
Tricoteaux, Marie-Andrée
Bernier, Matthieu
Bayette, Jérémy
Fournier, Remi
Marguerettaz, Mélanie
Rolland, Pierre
Bayol, Thibaud
Abbaoui, Nadia
Berry, Antoine
Iriart, Xavier
Cassaing, Sophie
Chauvin, Pamela
Bernard, Elodie
Fabre, Richard
François, Jean-Marie
author_sort Anton, Aurore
collection PubMed
description Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and accurate species-specific analytical methods for diagnosis and therapeutic management. Based on these requirements, we investigated the feasibility of DendrisCHIP(®) technology as an innovative molecular diagnostic method for the identification of a subset of 13 pathogens potentially responsible for dermatophytosis infections in clinical samples. This technology is based on DNA microarray, which potentially enables the detection and discrimination of several germs in a single sample. A major originality of DendrisCHIP(®) technology is the use of a decision algorithm for probability presence or absence of pathogens based on machine learning methods. In this study, the diagnosis of dermatophyte infection was carried out on more than 284 isolates by conventional microbial culture and DendrisCHIP(®)DP, which correspond to the DendrisCHIP(®) carrying oligoprobes of the targeted pathogens implicated in dermatophytosis. While convergence ranging from 75 to 86% depending on the sampling procedure was obtained with both methods, the DendrisCHIP(®)DP proved to identify more isolates with pathogens that escaped the culture method. These results were confirmed at 86% by a third method, which was either a specific RT-PCR or genome sequencing. In addition, diagnostic results with DendrisCHIP(®)DP can be obtained within a day. This faster and more accurate identification of fungal pathogens with DendrisCHIP(®)DP enables the clinician to quickly and successfully implement appropriate antifungal treatment to prevent the spread and elimination of dermatophyte infection. Taken together, these results demonstrate that this technology is a very promising method for routine diagnosis of dermatophytosis.
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spelling pubmed-106700322023-11-11 Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology Anton, Aurore Plinet, Mathilde Peyret, Thomas Cazaudarré, Thomas Pesant, Stéphanie Rouquet, Yannick Tricoteaux, Marie-Andrée Bernier, Matthieu Bayette, Jérémy Fournier, Remi Marguerettaz, Mélanie Rolland, Pierre Bayol, Thibaud Abbaoui, Nadia Berry, Antoine Iriart, Xavier Cassaing, Sophie Chauvin, Pamela Bernard, Elodie Fabre, Richard François, Jean-Marie Diagnostics (Basel) Article Dermatophytosis is a superficial fungal infection with an ever-increasing number of patients. Culture-based mycology remains the most commonly used diagnosis, but it takes around four weeks to identify the causative agent. Therefore, routine clinical laboratories need rapid, high throughput, and accurate species-specific analytical methods for diagnosis and therapeutic management. Based on these requirements, we investigated the feasibility of DendrisCHIP(®) technology as an innovative molecular diagnostic method for the identification of a subset of 13 pathogens potentially responsible for dermatophytosis infections in clinical samples. This technology is based on DNA microarray, which potentially enables the detection and discrimination of several germs in a single sample. A major originality of DendrisCHIP(®) technology is the use of a decision algorithm for probability presence or absence of pathogens based on machine learning methods. In this study, the diagnosis of dermatophyte infection was carried out on more than 284 isolates by conventional microbial culture and DendrisCHIP(®)DP, which correspond to the DendrisCHIP(®) carrying oligoprobes of the targeted pathogens implicated in dermatophytosis. While convergence ranging from 75 to 86% depending on the sampling procedure was obtained with both methods, the DendrisCHIP(®)DP proved to identify more isolates with pathogens that escaped the culture method. These results were confirmed at 86% by a third method, which was either a specific RT-PCR or genome sequencing. In addition, diagnostic results with DendrisCHIP(®)DP can be obtained within a day. This faster and more accurate identification of fungal pathogens with DendrisCHIP(®)DP enables the clinician to quickly and successfully implement appropriate antifungal treatment to prevent the spread and elimination of dermatophyte infection. Taken together, these results demonstrate that this technology is a very promising method for routine diagnosis of dermatophytosis. MDPI 2023-11-11 /pmc/articles/PMC10670032/ /pubmed/37998565 http://dx.doi.org/10.3390/diagnostics13223430 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Anton, Aurore
Plinet, Mathilde
Peyret, Thomas
Cazaudarré, Thomas
Pesant, Stéphanie
Rouquet, Yannick
Tricoteaux, Marie-Andrée
Bernier, Matthieu
Bayette, Jérémy
Fournier, Remi
Marguerettaz, Mélanie
Rolland, Pierre
Bayol, Thibaud
Abbaoui, Nadia
Berry, Antoine
Iriart, Xavier
Cassaing, Sophie
Chauvin, Pamela
Bernard, Elodie
Fabre, Richard
François, Jean-Marie
Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology
title Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology
title_full Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology
title_fullStr Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology
title_full_unstemmed Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology
title_short Rapid and Accurate Diagnosis of Dermatophyte Infections Using the DendrisCHIP(®) Technology
title_sort rapid and accurate diagnosis of dermatophyte infections using the dendrischip(®) technology
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10670032/
https://www.ncbi.nlm.nih.gov/pubmed/37998565
http://dx.doi.org/10.3390/diagnostics13223430
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