The One Health approach emphasizes the importance of antimicrobial resistance (AMR) as a major concern both in public health and in food animal production systems. As a general classification, E. coli can be distinguished based on the ability to cause infection of the gastrointestinal system (IPEC) or outside of it (ExPEC). Among the different pathogens, E. coli are becoming of great importance, and it has been suggested that ExPEC may harbor resistance genes that may be transferred to pathogenic or opportunistic bacteria. ExPEC strains are versatile bacteria that can cause urinary tract, bloodstream, prostate, and other infections at non-intestinal sites. In this context of rapidly increasing multidrug-resistance worldwide and a diminishingly effective antimicrobial arsenal to tackle resistant strains. ExPEC infections are now a serious public health threat worldwide. However, the clinical and economic impact of these infections and their optimal management are challenging, and consequently, there is an increasing awareness of the importance of ExPECs amongst healthcare professionals and the general public alike. This review aims to describe pathotype characteristics of ExPEC to increase our knowledge of these bacteria and, consequently, to increase our chances to control them and reduce the risk for AMR, following a One Health approach.

The integration of artificial intelligence technologies has propelled the progress of clinical and genomic medicine in recent years. The significant increase in computing power has facilitated the ability of artificial intelligence models to analyze and extract features from extensive medical data and images, thereby contributing to the advancement of intelligent diagnostic tools. Artificial intelligence (AI) models have been utilized in the field of personalized medicine to integrate clinical data and genomic information of patients. This integration allows for the identification of customized treatment recommendations, ultimately leading to enhanced patient outcomes. Notwithstanding the notable advancements, the application of artificial intelligence (AI) in the field of medicine is impeded by various obstacles such as the limited availability of clinical and genomic data, the diversity of datasets, ethical implications, and the inconclusive interpretation of AI models' results. In this review, a comprehensive evaluation of multiple machine learning algorithms utilized in the fields of clinical and genomic medicine is conducted. Furthermore, we present an overview of the implementation of artificial intelligence (AI) in the fields of clinical medicine, drug discovery, and genomic medicine. Finally, a number of constraints pertaining to the implementation of artificial intelligence within the healthcare industry are examined.

Recent advances in the field of meta-omics sciences and related bioinformatics tools have allowed a comprehensive investigation of human-associated microbiota and its contribution to achieving and maintaining the homeostatic balance. Bioactive compounds from the microbial community harboring the human gut are involved in a finely tuned network of interconnections with the host, orchestrating a wide variety of physiological processes. These includes the bi-directional crosstalk between the central nervous system, the enteric nervous system, and the gastrointestinal tract (i.e., gut–brain axis). The increasing accumulation of evidence suggest a pivotal role of the composition and activity of the gut microbiota in neurodegeneration. In the present review we aim to provide an overview of the state-of-the-art of meta-omics sciences including metagenomics for the study of microbial genomes and taxa strains, metatranscriptomics for gene expression, metaproteomics and metabolomics to identify and/or quantify microbial proteins and metabolites, respectively. The potential and limitations of each discipline were highlighted, as well as the advantages of an integrated approach (multi-omics) to predict microbial functions and molecular mechanisms related to human diseases. Particular emphasis is given to the latest results obtained with these approaches in an attempt to elucidate the link between the gut microbiota and the most common neurodegenerative diseases, such as multiple sclerosis (MS), Alzheimer’s disease (AD), Parkinson’s disease (PD), and amyotrophic lateral sclerosis (ALS).