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.
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The monitoring of antimicrobial resistance (AMR) in bacterial pathogens of animals is not currently coordinated at European level. To fill this gap, experts of the European Union Joint Action on Antimicrobial Resistance and Healthcare Associated Infections (EU-JAMRAI) recommended building the European Antimicrobial Resistance Surveillance network in Veterinary medicine (EARS-Vet). In this study, we (i) identified national monitoring systems for AMR in bacterial pathogens of animals (both companion and food-producing) among 27 countries affiliated to EU-JAMRAI, (ii) described their structures and operations, and (iii) analyzed their respective strengths, weaknesses, opportunities and threats (SWOT). Twelve countries reported having at least one national monitoring system in place, representing an opportunity to launch EARS-Vet, but highlighting important gaps in AMR data generation in Europe. In total, 15 national monitoring systems from 11 countries were described and analyzed. They displayed diverse structures and operations, but most of them shared common weaknesses (e.g., data management and representativeness) and common threats (e.g., economic vulnerability and data access), which could be addressed collectively under EARS-Vet. This work generated useful information to countries planning to build or improve their system, by learning from others' experience. It also enabled to advance on a pragmatic harmonization strategy: EARS-Vet shall follow the European Committee on Antimicrobial Susceptibility Testing (EUCAST) standards, collect quantitative data and interpret AMR data using epidemiological cut-off values.
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- časopisecké články MeSH
