Visualization of hydrocarbon chain length and degree of saturation of fatty acids in mouse livers by combining near-infrared hyperspectral imaging and machine learning.

Fatty acids play various physiological roles owing to their diverse structural characteristics, such as hydrocarbon chain length (HCL) and degree of saturation (DS). Although the distribution of fatty acids in biological tissues is associated with lipid metabolism, in situ imaging tools are still lacking for HCL and DS. Here, we introduce a framework of near-infrared (1000-1400 nm) hyperspectral label-free imaging with machine learning analysis of the fatty acid HCL and DS distribution in the liver at each pixel, in addition to the previously reported total lipid content.

Evaluating the identification of the extent of gastric cancer by over-1000 nm near-infrared hyperspectral imaging using surgical specimens.

Significance: Determining the extent of gastric cancer (GC) is necessary for evaluating the gastrectomy margin for GC. Additionally, determining the extent of the GC that is not exposed to the mucosal surface remains difficult. However, near-infrared (NIR) can penetrate mucosal tissues highly efficiently.