Alessia Visconti

TL;DR: A comprehensive blind assessment of over 30 network inference methods on Escherichia coli, Staphylococcus aureus, Saccharomyces cerevisiae and in silico microarray data defines the performance, data requirements and inherent biases of different inference approaches, and provides guidelines for algorithm application and development.

TL;DR: Analysis of data from a smartphone-based app designed for large-scale tracking of potential COVID-19 symptoms, used by over 2.5 million participants in the United Kingdom and United States, shows that loss of taste and smell sensations is predictive of potential SARS-CoV-2 infection.

TL;DR: The authors study the interplay between the microbiome and faecal and blood metabolome, and how the microbiome interacts in the dialogue between these metabolic compartments, identifying a key role for microbial functions and underscoring their relevance for microbiome therapeutic strategies.

TL;DR: The levels of 1,251 metabolites are measured in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals, and machine-learning algorithms reveal that diet and the microbiome are the determinants with the strongest predictive power for the levels of these metabolites.

TL;DR: Loss of taste and smell is a strong predictor of having been infected by the COVID-19 virus and is particularly relevant to healthcare and other key workers in constant contact with the public who have not yet been tested for the virus.