Artificial intelligence in food science and nutrition: a narrative review.

In the late 2010s, artificial intelligence (AI) technologies became complementary to the research areas of food science and nutrition. This review aims to summarize these technological advances by systematically describing the following: the use of AI in other fields (eg, engineering, pharmacy, and medicine); the history of AI in relation to food science and nutrition; the AI technologies currently used in the agricultural and food industries; and some of the important applications of AI in areas such as immunity-boosting foods, dietary assessment, gut microbiome profile analysis, and toxicity prediction of food ingredients. These applications are likely to be in great demand in the near future. This review can provide a starting point for brainstorming and for generating new AI applications in food science and nutrition that have yet to be imagined.

Selective Labeling of Proteins on Living Cell Membranes Using Fluorescent Nanodiamond Probes.

The impeccable photostability of fluorescent nanodiamonds (FNDs) is an ideal property for use in fluorescence imaging of proteins in living cells. However, such an application requires highly specific labeling of the target proteins with FNDs. Furthermore, the surface of unmodified FNDs tends to adsorb biomolecules nonspecifically, which hinders the reliable targeting of proteins with FNDs. Here, we combined hyperbranched polyglycerol modification of FNDs with the ß-lactamase-tag system to develop a strategy for selective imaging of the protein of interest in cells. The combination of these techniques enabled site-specific labeling of Interleukin-18 receptor alpha chain, a membrane receptor, with FNDs, which eventually enabled tracking of the diffusion trajectory of FND-labeled proteins on the membrane surface.