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.This study was supported by COST Action CA18131 “Statistical and machine learning techniques in human microbiome studies”. Estonian Research Council grant PRG548 (JT). Spanish State Research Agency Juan de la Cierva Grant IJC2019-042188-I (LM-Z). EO was founded and OA was supported by Estonian Research Council grant PUT 1371 and EMBO Installation grant 3573. AG was supported by Statutory Research project of the Department of Computer Networks and Systems

Species distribution and antifungal susceptibility patterns of Candida isolates from a public tertiary teaching hospital in the Eastern Cape Province, South Africa

vital:49389Candida species are the leading cause of invasive fungal infections, and over the past decade there has been an increased isolation of drug resistant Candida species. This study aimed to identify the species distribution of Candida isolates and to determine their unique antifungal susceptibility and resistance patterns. During a cross-sectional study, 209 Candida isolates (recovered from 206 clinical samples) were collected and their species distribution was determined using ChromAgar Candida. The Vitek-2 system (Biomerieux, South Africa) was used to determine minimum inhibitory concentrations (MICs) to azoles (fluconazole, voriconazole), echinocandins (caspofungin, micafungin), polyenes (amphotericin B) and flucytosine. Four species of Candida were isolated, of which C. albicans was the most frequent, isolated in 45.4 percent (95/209) of the isolates, followed by C. glabrata: 31.1 percent (65/209). The MICs of the different antifungal drugs varied amongst the species of Candida. From the 130 isolates tested for MICs, 90.77 percent (112/130) were susceptible to all antifungal drugs and 6.9 percent (9/130) of the isolates were multi-drug resistant. C. dubliniensis (n=2) isolates were susceptible to all the above mentioned antifungal drugs. There was no significant difference in species distribution amongst clinical specimens and between patients’ genders (P40.05). An increase in MIC values for fluconazole and flucytosine towards the resistance range was observed. To our knowledge, this is the first report on surveillance of Candida species distribution and antifungal susceptibility at a public tertiary teaching hospital in Eastern Cape, South Africa. Key words: Candida species; Distribution; Antifungal susceptibility; Identification; South Afric

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

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Clusters of patients with candidaemia due to genotypes of Candida albicans and Candida parapsilosis: differences in frequency between hospitals

The presence of clusters (identical genotypes infecting different patients) suggests patient-to-patient transmission or a common source for strains. We report the results of a genotyping study based on microsatellite markers of Candida albicans (n = 179) and Candida parapsilosis (n = 76) causing candidaemia, to assess and compare the percentage of patients grouped in clusters during the study period (January 2010 to December 2012). The study was performed in two large tertiary hospitals in Madrid, Spain. We detected 145 C. albicans genotypes (21 in clusters) and 63 C. parapsilosis genotypes (seven in clusters). Clusters involved two to seven patients each. Most of the clusters in the two centres involved two patients for both species, but the number of patients included in each cluster differed between hospitals. Considering both species, the percentage of patients per cluster ranged from 19% to 38% (p <0.05). Up to 2.9% of genotypes were present in both hospitals. Clusters of C. albicans and C. parapsilosis genotypes causing candidaemia differed between hospitals, suggesting differences in strain transmission. Occasionally, the same genotypes were found in patients admitted to different hospitals located in the same city

SUSCEPTIBILITY TEST FOR FUNGI: CLINICAL AND LABORATORIAL CORRELATIONS IN MEDICAL MYCOLOGY

Nas últimas décadas, os testes de suscetibilidade a antifúngicos foram padronizados e, atualmente, servem tal como os testes de suscetibilidade a antibacterianos em laboratórios de microbiologia. Métodos de referência norte americanos e europeus foram desenvolvidos, assim como os equivalentes sistemas comerciais, estes últimos mais apropriados a laboratórios clínicos. A detecção de cepas resistentes por meio de tais sistemas permitiu o estudo e a compreensão das bases moleculares dos mecanismos de resistência de espécies fúngicas a fármacos antifúngicos. Além disso, foram realizados muitos estudos sobre a correlação de resultados obtidos in vitro com o desfecho clínico de pacientes permitindo a conclusão de que infecções por cepas resistentes têm pior evolução em relação às causadas por cepas sensíveis. Os estudos permitiram o estabelecimento de pontos de corte interpretativos (interpretative breakpoints development) para Candida spp. e Aspergillus spp., os agentes etiológicos mais frequentes de infecções fúngicas em todo o mundo. Em resumo, os testes de suscetibilidade representam uma ferramenta essencial para a orientação do tratamento de doenças fúngicas, para o conhecimento da epidemiologia local e global, bem como para a identificação de resistência a antifúngicos.During recent decades, antifungal susceptibility testing has become standardized and nowadays has the same role of the antibacterial susceptibility testing in microbiology laboratories. American and European standards have been developed, as well as equivalent commercial systems which are more appropriate for clinical laboratories. The detection of resistant strains by means of these systems has allowed the study and understanding of the molecular basis and the mechanisms of resistance of fungal species to antifungal agents. In addition, many studies on the correlation of in vitro results with the outcome of patients have been performed, reaching the conclusion that infections caused by resistant strains have worse outcome than those caused by susceptible fungal isolates. These studies have allowed the development of interpretative breakpoints for Candida spp. and Aspergillus spp., the most frequent agents of fungal infections in the world. In summary, antifungal susceptibility tests have become essential tools to guide the treatment of fungal diseases, to know the local and global disease epidemiology, and to identify resistance to antifungals