Induced pluripotent stem cell-derived macrophages as a platform for modelling human disease.

Macrophages are innate immune cells that are present in essentially all tissues, where they have vital roles in tissue development, homeostasis and pathogenesis. The importance of macrophages in tissue function is reflected by their association with various human diseases, and studying macrophage functions in both homeostasis and pathological tissue settings is a promising avenue for new targeted therapies that will improve human health. The ability to generate macrophages from induced pluripotent stem (iPS) cells has revolutionized macrophage biology, with the generation of iPS cell-derived macrophages (iMacs) providing unlimited access to genotype-specific cells that can be used to model various human diseases involving macrophage dysregulation.

Molecular Recognition in Mechanochemistry: Insights from Solid-State NMR Spectroscopy.

Molecular-recognition events are highly relevant in biology and chemistry. In the present study, we investigated such processes in the solid state under mechanochemical conditions using the formation of racemic phases upon reacting enantiopure entities as example. As test systems, α-(trifluoromethyl)lactic acid (TFLA) and the amino acids serine and alanine were used.

Machine learning approach as an early warning system to prevent foodborne Salmonella outbreaks in northwestern Italy.

Salmonellosis, one of the most common foodborne infections in Europe, is monitored by food safety surveillance programmes, resulting in the generation of extensive databases. By leveraging tree-based machine learning (ML) algorithms, we exploited data from food safety audits to predict spatiotemporal patterns of salmonellosis in northwestern Italy. Data on human cases confirmed in 2015-2018 (n = 1969) and food surveillance data collected in 2014-2018 were used to develop ML algorithms.