Biofilm Production in Intensive Care Units: Challenges and Implications.

The prevalence of infections amongst intensive care unit (ICU) patients is inevitably high, and the ICU is considered the epicenter for the spread of multidrug-resistant bacteria. Multiple studies have focused on the microbial diversity largely inhabiting ICUs that continues to flourish despite treatment with various antibiotics, investigating the factors that influence the spread of these pathogens, with the aim of implementing sufficient monitoring and infection control methods. Despite joint efforts from healthcare providers and policymakers, ICUs remain a hub for healthcare-associated infections.

Disease prediction with multi-omics and biomarkers empowers case-control genetic discoveries in the UK Biobank.

The emergence of biobank-level datasets offers new opportunities to discover novel biomarkers and develop predictive algorithms for human disease. Here, we present an ensemble machine-learning framework (machine learning with phenotype associations, MILTON) utilizing a range of biomarkers to predict 3,213 diseases in the UK Biobank. Leveraging the UK Biobank's longitudinal health record data, MILTON predicts incident disease cases undiagnosed at time of recruitment, largely outperforming available polygenic risk scores.