A toolbox of machine learning software to support microbiome analysis.

The human microbiome has become an area of intense research due to its potential impact on human health. However, the analysis and interpretation of this data have proven to be challenging due to its complexity and high dimensionality. Machine learning (ML) algorithms can process vast amounts of data to uncover informative patterns and relationships within the data, even with limited prior knowledge. Therefore, there has been a rapid growth in the development of software specifically designed for the analysis and interpretation of microbiome data using ML techniques. These software incorporate a wide range of ML algorithms for clustering, classification, regression, or feature selection, to identify microbial patterns and relationships within the data and generate predictive models. This rapid development with a constant need for new developments and integration of new features require efforts into compile, catalog and classify these tools to create infrastructures and services with easy, transparent, and trustable standards. Here we review the state-of-the-art for ML tools applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on ML based software and framework resources currently available for the analysis of microbiome data in humans. The aim is to support microbiologists and biomedical scientists to go deeper into specialized resources that integrate ML techniques and facilitate future benchmarking to create standards for the analysis of microbiome data. The software resources are organized based on the type of analysis they were developed for and the ML techniques they implement. A description of each software with examples of usage is provided including comments about pitfalls and lacks in the usage of software based on ML methods in relation to microbiome data that need to be considered by developers and users. This review represents an extensive compilation to date, offering valuable insights and guidance for researchers interested in leveraging ML approaches for microbiome analysis.

Overview of data preprocessing for machine learning applications in human microbiome research.

Although metagenomic sequencing is now the preferred technique to study microbiome-host interactions, analyzing and interpreting microbiome sequencing data presents challenges primarily attributed to the statistical specificities of the data (e.g., sparse, over-dispersed, compositional, inter-variable dependency). This mini review explores preprocessing and transformation methods applied in recent human microbiome studies to address microbiome data analysis challenges. Our results indicate a limited adoption of transformation methods targeting the statistical characteristics of microbiome sequencing data. Instead, there is a prevalent usage of relative and normalization-based transformations that do not specifically account for the specific attributes of microbiome data. The information on preprocessing and transformations applied to the data before analysis was incomplete or missing in many publications, leading to reproducibility concerns, comparability issues, and questionable results. We hope this mini review will provide researchers and newcomers to the field of human microbiome research with an up-to-date point of reference for various data transformation tools and assist them in choosing the most suitable transformation method based on their research questions, objectives, and data characteristics.

Advancing microbiome research with machine learning: key findings from the ML4Microbiome COST action.

The rapid development of machine learning (ML) techniques has opened up the data-dense field of microbiome research for novel therapeutic, diagnostic, and prognostic applications targeting a wide range of disorders, which could substantially improve healthcare practices in the era of precision medicine. However, several challenges must be addressed to exploit the benefits of ML in this field fully. In particular, there is a need to establish "gold standard" protocols for conducting ML analysis experiments and improve interactions between microbiome researchers and ML experts. The Machine Learning Techniques in Human Microbiome Studies (ML4Microbiome) COST Action CA18131 is a European network established in 2019 to promote collaboration between discovery-oriented microbiome researchers and data-driven ML experts to optimize and standardize ML approaches for microbiome analysis. This perspective paper presents the key achievements of ML4Microbiome, which include identifying predictive and discriminatory 'omics' features, improving repeatability and comparability, developing automation procedures, and defining priority areas for the novel development of ML methods targeting the microbiome. The insights gained from ML4Microbiome will help to maximize the potential of ML in microbiome research and pave the way for new and improved healthcare practices.

Citizen science helps to raise awareness about gut microbiome health in people at risk of developing non-communicable diseases.

Citizens lack knowledge about the impact of gut microbiota on health and how lifestyle and dietary choices can influence it, leading to Non-Communicable Diseases (NCDs) and affecting overall well-being. Participatory action research (PAR) is a promising approach to enhance communication and encourage individuals to adopt healthier behaviors and improve their health. In this study, we explored the feasibility of integrating the photovoice method with citizen science approaches to assess the impact of social and environmental factors on gut microbiota health. In this context, citizen science approaches entailed the involvement of participants in the collection of samples for subsequent analysis, specifically gut microbiome assessment via 16S rRNA gene sequencing. We recruited 70 volunteers and organized six photovoice groups based on age and educational background. Participants selected 64 photographs that represented the influence of daily habits on gut microbiota health and created four photovoice themes. Analysis of the gut microbiome using 16S rRNA gene sequencing identified 474 taxa, and in-depth microbial analysis revealed three clusters of people based on gut microbiome diversity and body mass index (BMI). Our findings indicate that participants enhanced their knowledge of gut microbiome health through PAR activities, and we found a correlation between lower microbial diversity, higher BMI, and better achievement of learning outcomes. Using PAR as a methodology is an effective way to increase citizens' awareness and engagement in self-care, maintain healthy gut microbiota, and prevent NCD development. These interventions are particularly beneficial for individuals at higher risk of developing NCDs.

AI4FoodDB: a database for personalized e-Health nutrition and lifestyle through wearable devices and artificial intelligence.

The increasing prevalence of diet-related diseases calls for an improvement in nutritional advice. Personalized nutrition aims to solve this problem by adapting dietary and lifestyle guidelines to the unique circumstances of each individual. With the latest advances in technology and data science, researchers can now automatically collect and analyze large amounts of data from a variety of sources, including wearable and smart devices. By combining these diverse data, more comprehensive insights of the human body and its diseases can be achieved. However, there are still major challenges to overcome, including the need for more robust data and standardization of methodologies for better subject monitoring and assessment. Here, we present the AI4Food database (AI4FoodDB), which gathers data from a nutritional weight loss intervention monitoring 100 overweight and obese participants during 1 month. Data acquisition involved manual traditional approaches, novel digital methods and the collection of biological samples, obtaining: (i) biological samples at the beginning and the end of the intervention, (ii) anthropometric measurements every 2 weeks, (iii) lifestyle and nutritional questionnaires at two different time points and (iv) continuous digital measurements for 2 weeks. To the best of our knowledge, AI4FoodDB is the first public database that centralizes food images, wearable sensors, validated questionnaires and biological samples from the same intervention. AI4FoodDB thus has immense potential for fostering the advancement of automatic and novel artificial intelligence techniques in the field of personalized care. Moreover, the collected information will yield valuable insights into the relationships between different variables and health outcomes, allowing researchers to generate and test new hypotheses, identify novel biomarkers and digital endpoints, and explore how different lifestyle, biological and digital factors impact health. The aim of this article is to describe the datasets included in AI4FoodDB and to outline the potential that they hold for precision health research. Database URL https://github.com/AI4Food/AI4FoodDB.

A comprehensive map of microbial biomarkers along the gastrointestinal tract for celiac disease patients.

Dysbiosis of the microbiome has been related to Celiac disease (CeD) progress, an autoimmune disease characterized by gluten intolerance developed in genetically susceptible individuals under certain environmental factors. The microbiome contributes to CeD pathophysiology, modulating the immune response by the action of short-chain fatty acids (SCFA), affecting gut barrier integrity allowing the entrance of gluten-derived proteins, and degrading immunogenic peptides of gluten through endoprolyl peptidase enzymes. Despite the evidence suggesting the implication of gut microbiome over CeD pathogenesis, there is no consensus about the specific microbial changes observed in this pathology. Here, we compiled the largest dataset of 16S prokaryotic ribosomal RNA gene high-throughput sequencing for consensus profiling. We present for the first time an integrative analysis of metataxonomic data from patients with CeD, including samples from different body sites (saliva, pharynx, duodenum, and stool). We found the presence of coordinated changes through the gastrointestinal tract (GIT) characterized by an increase in Actinobacteria species in the upper GIT (pharynx and duodenum) and an increase in Proteobacteria in the lower GIT (duodenum and stool), as well as site-specific changes evidencing a dysbiosis in patients with CeD' microbiota. Moreover, we described the effect of adherence to a gluten-free diet (GFD) evidenced by an increase in beneficial bacteria and a decrease in some Betaproteobacteriales but not fully restoring CeD-related dysbiosis. Finally, we built a Random Forest model to classify patients based on the lower GIT composition achieving good performance.

Gut Microbiota Induced by Pterostilbene and Resveratrol in High-Fat-High-Fructose Fed Rats: Putative Role in Steatohepatitis Onset.

Resveratrol and its 2-methoxy derivative pterostilbene are two phenolic compounds that occur in foodstuffs and feature hepato-protective effects. This study is devoted to analysing and comparing the metabolic effects of pterostilbene and resveratrol on gut microbiota composition in rats displaying NAFLD induced by a diet rich in saturated fat and fructose. The associations among changes induced by both phenolic compounds in liver status and those induced in gut microbiota composition were also analysed. For this purpose, fifty Wistar rats were distributed in five experimental groups: a group of animals fed a standard diet (CC group) and four additional groups fed a high-fat high-fructose diet alone (HFHF group) or supplemented with 15 or 30 mg/kg bw/d of pterostilbene (PT15 and PT30 groups, respectively) or 30 mg/kg bw/d of resveratrol (RSV30 group). The dramatic changes induced by high-fat high-fructose feeding in the gut microbiota were poorly ameliorated by pterostilbene or resveratrol. These results suggest that the specific changes in microbiota composition induced by pterostilbene (increased abundances of Akkermansia and Erysipelatoclostridium, and lowered abundance of Clostridum sensu stricto 1) may not entirely explain the putative preventive effects on steatohepatitis.

Statistical and Machine Learning Techniques in Human Microbiome Studies: Contemporary Challenges and Solutions.

The human microbiome has emerged as a central research topic in human biology and biomedicine. Current microbiome studies generate high-throughput omics data across different body sites, populations, and life stages. Many of the challenges in microbiome research are similar to other high-throughput studies, the quantitative analyses need to address the heterogeneity of data, specific statistical properties, and the remarkable variation in microbiome composition across individuals and body sites. This has led to a broad spectrum of statistical and machine learning challenges that range from study design, data processing, and standardization to analysis, modeling, cross-study comparison, prediction, data science ecosystems, and reproducible reporting. Nevertheless, although many statistics and machine learning approaches and tools have been developed, new techniques are needed to deal with emerging applications and the vast heterogeneity of microbiome data. We review and discuss emerging applications of statistical and machine learning techniques in human microbiome studies and introduce the COST Action CA18131 "ML4Microbiome" that brings together microbiome researchers and machine learning experts to address current challenges such as standardization of analysis pipelines for reproducibility of data analysis results, benchmarking, improvement, or development of existing and new tools and ontologies.

Applications of Machine Learning in Human Microbiome Studies: A Review on Feature Selection, Biomarker Identification, Disease Prediction and Treatment.

The number of microbiome-related studies has notably increased the availability of data on human microbiome composition and function. These studies provide the essential material to deeply explore host-microbiome associations and their relation to the development and progression of various complex diseases. Improved data-analytical tools are needed to exploit all information from these biological datasets, taking into account the peculiarities of microbiome data, i.e., compositional, heterogeneous and sparse nature of these datasets. The possibility of predicting host-phenotypes based on taxonomy-informed feature selection to establish an association between microbiome and predict disease states is beneficial for personalized medicine. In this regard, machine learning (ML) provides new insights into the development of models that can be used to predict outputs, such as classification and prediction in microbiology, infer host phenotypes to predict diseases and use microbial communities to stratify patients by their characterization of state-specific microbial signatures. Here we review the state-of-the-art ML methods and respective software applied in human microbiome studies, performed as part of the COST Action ML4Microbiome activities. This scoping review focuses on the application of ML in microbiome studies related to association and clinical use for diagnostics, prognostics, and therapeutics. Although the data presented here is more related to the bacterial community, many algorithms could be applied in general, regardless of the feature type. This literature and software review covering this broad topic is aligned with the scoping review methodology. The manual identification of data sources has been complemented with: (1) automated publication search through digital libraries of the three major publishers using natural language processing (NLP) Toolkit, and (2) an automated identification of relevant software repositories on GitHub and ranking of the related research papers relying on learning to rank approach.

Isavuconazole is highly active in vitro against Candida species isolates but shows trailing effect.

OBJECTIVE: Isavuconazole is a triazole previously shown to have potent in vitro activity against Aspergillus spp., Mucorales, and Candida spp. Unlike for other azoles, it is unclear if isavuconazole may induce a trailing effect. We studied isavuconazole MICs for a large collection of Candida isolates from blood samples and determined the extent of the trailing effect when using the EUCAST Edef 7.3.1 method. METHODS: 761 molecularly identified Candida isolates from blood samples of 742 patients admitted to the hospital (January 2007 to September 2017) were evaluated and further tested for in vitro susceptibility to isavuconazole following the EUCAST E.Def 7.3.1 test method. RESULTS: C. albicans showed the highest susceptibility, followed by C. parapsilosis and C. tropicalis (geometric mean MIC 0.003 vs 0.005/0.006, respectively; P < 0.001). In contrast, C. glabrata, and C. krusei had significantly higher MIC values (geometric mean MIC 0.094 vs 0.093, respectively). Isavuconazole MIC distributions were not truncated at the lowest concentration tested, except for C. albicans. Overall, the mean percentage of trailing was 12.9% but differences among species were observed: C. glabrata, C. albicans, and C. tropicalis exhibited higher trailing in comparison to C. parapsilosis and non-Candida yeasts (P < 0.001). The percentage of non-wild-type C. albicans (considering the heavy trailer isolates as wild-type), C. parapsilosis and C. glabrata isolates were 0.56% (2/355), 1.5% (3/200), and 4.65% (4/86), respectively. CONCLUSIONS: Isavuconazole showed high in vitro activity against Candida spp., particularly against C. albicans. Trailing effect is commonly observed with isavuconazole, particularly with C. glabrata.

Candida isolates causing candidemia show different degrees of virulence in Galleria mellonella.

We aim to assess intra- and interspecies differences in the virulence of Candida spp. strains causing candidemia using the invertebrate Galleria mellonella model. We studied 739 Candida spp. isolates (C. albicans [n = 373], C. parapsilosis [n = 203], C. glabrata [n = 92], C. tropicalis [n = 53], and C. krusei [n = 18]) collected from patients with candidemia admitted to Gregorio Marañon Hospital (Madrid, Spain). Species-specific infecting inocula (yeast cells/larva) were adjusted (5 × 105 [C. albicans, and C. tropicalis], 2 × 106-5 × 106 [C. parapsilosis, C. glabrata, and C. krusei]) and used to infect 10 larvae per isolate; percentage of survival and median survival per isolate were calculated. According to the interquartile range of the median survival, isolates with a median survival under P25 were classified as of high-virulence and isolates with a median survival over P75 as of low virulence. The median survival of larvae infected with different species was variable: C. albicans (n = 2 days, IQR <1-3 days), C. tropicalis (n = 2 days, IQR 1.5-4 days), C. parapsilosis (n = 2 days, IQR 2-3.5 days), C. glabrata (n = 3 days, IQR 2-3 days), and C. krusei (n = 7 days, 6.5->8 days) (P < .001). Differences in virulence among species were validated by histological examination (day +1 post-infection) in the larvae infected by the isolates of each virulence category and species. Virulence-related gene expression in C. albicans isolates did not reach statistical significance. We report species-specific virulence patterns of Candida spp. and show that isolates within a given species have different degrees of virulence in the animal model.

Persistent Candidemia in adults: underlying causes and clinical significance in the antifungal stewardship era.

To investigate the causes and the clinical significance of persistent candidemia (PC) in adults diagnosed in a tertiary hospital with an active antifungal stewardship program. Retrospective cohort including all adults with candidemia from 2010 to 2018. PC was defined as any positive follow-up blood culture (BC) obtained ≥ 5 days from the first BCs yielding the same Candida species. PC was detected in 35/255 (13.7%) patients. There were no differences regarding antifungal adequacy in PC vs. non-PC (94.3% vs. 82.3%, p = 0.084) and primary source control (63.3% vs. 76.4%, p = 0.172) at the time of the follow-up BCs. The average time until source control (2 [0-37] vs. 2 days [0-44], p = 0.311) or adequate antifungal treatment (2 [0-26] vs. 2 days [- 2-10], p = 0.748) was similar. Patients with PC had more non-ocular complications (31.4% vs. 10.5%, p = 0.002). No impact on 30-day mortality was observed (31.4% vs. 22.3%, p = 0.238). The only independent factor associated with PC was to have a previously undetected site of infection [OR 4.28, 95%CI (1.77-10.34), p = 0.001]. Persistent candidemia was not associated with inadequate or delayed therapeutic management, nor higher 30-day mortality rates. Timely screening and control of unexpected infection sources are encouraged to shorten hospitalization and improve patient care.

Does the composition of polystyrene trays affect Candida spp. biofilm formation?

The biofilm formation ability of Candida species seems to have a role in the prognosis of patients with candidemia. Biofilm formation is usually tested using 96 well flat bottom polystyrene microtiter plates, although the type of plastic used is not commonly reported. This study compares biofilm formation by Candida spp. on six types of plates from three brands (three non-tissue-treated and three tissue-treated). Thirty isolates of each of the following species were selected: C. albicans, C. parapsilosis, C. glabrata, C. tropicalis, as well as 15 isolates of C. krusei (n = 135 isolates) from patients with candidemia. Biofilm production was evaluated by measuring biomass production and metabolic activity. Our results show higher biomass production and metabolic activity of biofilms formed on non-tissue-treated plates in comparison to those formed on tissue-treated plates (P < .001). We only found significant differences in metabolic activity of biofilms formed on non-tissue-treated plates (P < .003). All comparisons including biofilm formation and metabolic activity among plates of the same brand yielded higher biofilm formation on non-treated plates compared to treated plates (P < .001). Significant difference in biomass production by C. parapsilosis was only seen when comparing between the various tissue-treated plastics (P < .03). In contrast, comparisons of different non-tissue-treated tray brands yielded significant metabolic activity differences for all species except for C. parapsilosis (P < .05). Biofilm formation and metabolic activity is significantly affected by the plastic composition of non-tissue-treated trays leading to increased biofilm formation.

Resistance to Echinocandins in Candida Can Be Detected by Performing the Etest Directly on Blood Culture Samples.

We examined the rapid evaluation of susceptibility to echinocandins in Candida spp. using the Etest performed directly on positive blood cultures and anidulafungin-containing agar plates. We prospectively collected 80 positive blood cultures (Bactec-FX system, Becton-Dickinson, Cockeysville, MD, USA) with echinocandin-susceptible Candida spp. (n = 60) and echinocandin-intermediate Candida parapsilosis (n = 20) from patients with candidemia. Additionally, blood culture bottles of nonfungemic/bacteremic patients were spiked with 35 echinocandin-resistant Candida species isolates. A total of 2 to 4 drops of medium from each bottle were stroked directly onto both RPMI 1640 agar plates with micafungin and anidulafungin Etest strips (ETDIR) and Sabouraud agar plates containing 2 mg/liter of anidulafungin. The isolates were tested according to the EUCAST method and Etest standard (ETSD). Essential and categorical agreement between the methods was calculated. The essential agreement and categorical agreement between the EUCAST method and ETDIR and ETSD were both >97.4%. The essential agreement between ETDIR and the EUCAST method for both echinocandins was >97%. The categorical agreement between the FKS sequence and ETDIR was 97.4%. The ETDIR MICs of anidulafungin and micafungin (≥0.19 mg/liter and ≥0.064 mg/liter, respectively) effectively separated all susceptible FKS wild-type isolates from the resistant FKS mutant isolates. The categorical agreement (62.6%) between the EUCAST method and growth on anidulafungin-containing plates was poor, with the best agreement observed for Candida glabrata (94.2%). When performed directly on positive blood cultures from patients with candidemia, the Etest with micafungin and anidulafungin is a reliable procedure for the rapid testing of susceptibility to echinocandins in Candida species isolates.

Mutant Prevention Concentration and Mutant Selection Window of Micafungin and Anidulafungin in Clinical Candida glabrata Isolates.

We report the mutant prevention concentration (MPC) and mutant selection window (MSW) for micafungin and anidulafungin administered to treat Candida glabrata We also determine the mutation frequency. We studied 20 echinocandin-susceptible, fluconazole-intermediate, and FKS wild-type C. glabrata isolates. Adjusted inocula were stroked directly onto Sabouraud agar plates containing different concentrations of micafungin or anidulafungin and visually inspected daily for up to 5 days of incubation. Individual colonies growing on the plates containing echinocandins at 1 mg/liter were selected for antifungal susceptibility testing. The FKS genes of the resulting individual phenotypically resistant colonies were sequenced, and the MPC, MSW, and mutation frequency were determined. Biofilm was quantified, and the growth kinetics and virulence (Galleria mellonella model) of the resulting individual FKS mutant colonies were studied. For micafungin and anidulafungin, we found similar results for the MPC (0.06 to 2 mg/liter and 0.25 to 2 mg/liter, respectively), MSW (0.015 to 2 mg/liter for both echinocandins), and mutation frequency (3.7 × 10-8 and 2.8 × 10-8, respectively). A total of 12 isolates were able to grow at 1 mg/liter on echinocandin-containing plates, yielding a total of 32 phenotypically resistant colonies; however, FKS2 mutations (ΔF658, S663P, W715L, and E655A) were observed only in 21 colonies. We did not find differences in biofilm formation, the kinetic parameters studied, or the median survival of larvae infected by wild-type isolates and the resulting individual FKS2 mutant colonies. Echinocandin concentrations lower than 2 mg/liter can lead to selection of resistance mutations in C. glabrata isolates in vitro.

Low and constant micafungin concentrations may be sufficient to lead to resistance mutations in FKS2 gene of Candida glabrata.

We studied the ability of five echinocandin-susceptible C. glabrata isolates to acquire in vitro resistance to anidulafungin and micafungin. All isolates became phenotypically resistant after 2-4 days of exposure to low and constant micafungin concentrations (P < .05). Mutations in the HS1 region of the FKS2 gene were found in all isolates. The acquisition of resistance was not related to the previous use of antifungal treatment in the patients or the presence of mutations at MSH2 gene. We found differences (P < .0001) in the median survival of Galleria mellonella larvae infected with FKS2 mutant isolates (5 days) and wild-type isolates (3 days).

The Etest Performed Directly on Blood Culture Bottles Is a Reliable Tool for Detection of Fluconazole-Resistant Candida albicans Isolates.

We assessed the ability of the Etest performed directly on positive blood cultures (ETDIR) to detect fluconazole susceptibility in 6 fluconazole-resistant and 12 fluconazole-susceptible Candida albicans isolates, according to CLSI M27-A3 and EUCAST EDef 7.2 procedures. Categorical agreement between ETDIR and broth microdilution was 100% when the trays were incubated at 25°C and trailing effect was ruled out. ETDIR is a reliable procedure when screening for the presence of fluconazole resistance in C. albicans.

Candida guilliermondii Complex Is Characterized by High Antifungal Resistance but Low Mortality in 22 Cases of Candidemia.

The objectives of our study were to describe the characteristics of patients with Candida guilliermondii candidemia and to perform an in-depth microbiological characterization of isolates and compare them with those of patients with C. albicans candidemia. We described the risk factors and outcomes of 22 patients with candidemia caused by the C. guilliermondii complex. Incident isolates were identified using molecular techniques, and susceptibility to fluconazole, anidulafungin, and micafungin was studied. Biofilm formation was measured using the crystal violet assay (biomass production) and the XTT reduction assay (metabolic activity), and virulence was studied using the Galleria mellonella model. Biofilm formation was compared with that observed for C. albicans The main conditions predisposing to infection were malignancy (68%), immunosuppressive therapy (59%), and neutropenia (18%). Clinical presentation of candidemia was less severe in patients infected by the C. guilliermondii complex than in patients infected by C. albicans, and 30-day mortality was lower in C. guilliermondii patients (13.6% versus 33.9%, respectively; P = 0.049). Isolates were identified as C. guilliermondiisensu stricto (n = 17) and Candida fermentati (n = 5). The isolates produced biofilms with low metabolic activity and moderate biomass. The G. mellonella model showed that C. guilliermondii was less virulent than C. albicans (mean of 6 days versus 1 day of survival, respectively; P < 0.001). Patients with candidemia caused by the C. guilliermondii complex had severe and debilitating underlying conditions. Overall, the isolates showed diminished susceptibility to fluconazole and echinocandins, although poor biofilm formation and the low virulence were associated with a favorable outcome.

Fluconazole-containing agar Sabouraud dextrose plates are not useful when screening for susceptibility in Candida albicans / Las placas de agar dextrosado de Sabouraud con fluconazol no son útiles para la detección de resistencia en Candida albicans

Introduction. Fluconazole is an alternative for candidemic patients who are not critically ill. Fluconazole is mainly fungistatic and does not completely inhibit visual Candida albicans growth. We studied the usefulness of fluconazolecontaining Sabouraud dextrose agar plates for detecting susceptibility to fluconazole in C. albicans. Material and methods. Adjusted inocula of 19 isolates were transferred directly onto fluconazole-containing Sabouraud dextrose plates (concentrations ranging from 0.125 mg/L to 128 mg/L). The fluconazole MIC in fresh isolates and after growth on the fluconazole-containing plate at 128 mg/L was recorded following the EUCAST EDef 7.2 guidelines. Then isolates were classified according to their degree of trailing production, based on microdilution procedure. Results. All isolates were able to grow on all fluconazolecontaining plates, even those isolates susceptible to fluconazole. In fact, we selected isolates with different degrees of trailing based on microdilution procedures. 50% of isolates classified as heavy trailers, 35.71% as moderate trailers, and 14.28% as slight trailers. Conclusions. The use of fluconazole-containing Sabouraud dextrose agar plates was not a reliable method to detect fluconazole susceptibility in C. albicans isolates; growth of the isolates was a trailing effect rather than true resistance (AU)

Introducción. Fluconazol es el antifúngico de elección en pacientes con candidemia que no están en estado crítico. Fluconazol es un fármaco fungistático y no inhibe por completo el crecimiento de Candida albicans. En este estudio se evalúa la posibilidad de emplear placas de agar dextrosado de Sabouraud con diferentes concentraciones de fluconazol como método para evaluar la sensibilidad a este fármaco de aislados de C. albicans. Material y métodos. El inóculo ajustado procedente de 19 aislados de C. albicans se transfirió directamente a placas que contenían concentraciones de fluconazol comprendidas entre 0,125 mg/L y 128 mg/L. La CMI de fluconazol se calculó en los aislados originales y en los aislados crecidos en la placa de fluconazol de 128 mg/L, según el protocolo de EUCAST EDef 7.2. Los aislados se clasificaron según su grado de producción de efecto de arrastre, siguiendo el procedimiento de microdilución. Resultados. Todos los aislados fueron capaces de crecer en todas las placas con diferentes concentraciones de fluconazol, incluso las cepas sensibles. Los aislados mostraron diferentes grados de efecto de arrastre. El 50% de los aislados se clasificaron como altamente productores, el 35,71% como moderadamente productores y el 14,28% como poco productores de efecto de arrastre. Conclusión. El uso de placas de agar dextrosado de Sabouraud con fluconazol no demostró ser un método útil para el estudio de la sensibilidad de C. albicans a este fármaco ya que el crecimiento de los aislados fue interpretado como efecto de arrastre y no como una verdadera resistencia (AU)

The novel oral glucan synthase inhibitor SCY-078 shows in vitro activity against sessile and planktonic Candida spp.

Objectives: We studied the antifungal activity of SCY-078 (an orally bioavailable 1,3-ß -d- glucan synthesis inhibitor), micafungin and fluconazole against the planktonic and sessile forms of 178 Candida and non- Candida isolates causing fungaemia in patients recently admitted to a large European hospital. Methods: The in vitro activity of SCY-078, micafungin and fluconazole against the planktonic form of the isolates was assessed using EUCAST EDef 7.3 and CLSI M27-A3. Antibiofilm activity was assessed using the XTT reduction assay. Results: SCY-078 and micafungin showed potent in vitro activity against Candida and non- Candida isolates. The in vitro activity of both drugs was similar, but SYC-078 displayed significantly lower MIC values than micafungin against Candida parapsilosis and non- Candida isolates, whereas micafungin displayed significantly lower MIC values for the remaining species ( P <0.001). In contrast, SCY-078 and micafungin showed essentially the same activity against the biofilms with the exception of Candida glabrata , in which the micafungin sessile MIC values were significantly lower ( P <0.001). These observations were confirmed by assessing biofilm structure by scanning electron microscopy after antifungal treatment. Conclusions: Our study showed that the high in vitro activity of SCY-078 against invasive Candida isolates in both sessile and planktonic forms is comparable to that of micafungin.