RESUMEN
Luminescence thermometry is a non-contact method that can measure surface temperatures and the temperature of the area where the fluorescent probe is located, allowing temperature distribution visualizations with a camera. Ratiometric fluorescence thermometry, which uses the intensity ratio of fluorescence peaks at two wavelengths with different fluorescence intensity dependencies, is an excellent method for visualizing temperature distributions independent of the fluorophore spatial concentration, excitation light intensity and absolute fluorescence intensity. Herein, Nd3+/Yb3+/Er3+-doped Y2O3 nanomaterials with a diameter of 200 nm were prepared as phosphors for temperature distribution measurement of fluids at different temperatures. The advantages of this designed fluorescent material include non-aggregation in water and the fact that its near-infrared (NIR) fluorescence excitation (808 nm) is not absorbed by water, thereby minimizing sample heating upon irradiation. Under optical excitation at 808 nm, the ratio of the fluorescence intensities of Yb3+ (IYb; 975 nm) and Er3+ (IEr; 1550 nm), which exhibited different temperature responses, indicated the temperature distribution inside the fluid device. Thus, this technique using Nd3+/Yb3+/Er3+-doped Y2O3 is expected to be applied for temperature distribution mapping analysis inside fluidic devices as a ratiometric NIR fluorescence thermometer, which is unaffected by laser-induced heating.
RESUMEN
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. Aim: We investigated the ability of near-infrared hyperspectral imaging (NIR-HSI) to identify GC areas, including exposed and unexposed using surgical specimens, and explored the identifiable characteristics of the GC. Approach: Our study examined 10 patients with diagnosed GC who underwent surgery between 2020 and 2021. Specimen images were captured using NIR-HSI. For the specimens, the exposed area was defined as an area wherein the cancer was exposed on the surface, the unexposed area as an area wherein the cancer was present although the surface was covered by normal tissue, and the normal area as an area wherein the cancer was absent. We estimated the GC (including the exposed and unexposed areas) and normal areas using a support vector machine, which is a machine-learning method for classification. The prediction accuracy of the GC region in every area and normal region was evaluated. Additionally, the tumor thicknesses of the GC were pathologically measured, and their differences in identifiable and unidentifiable areas were compared using NIR-HSI. Results: The average prediction accuracy of the GC regions combined with both areas was 77.2%; with exposed and unexposed areas was 79.7% and 68.5%, respectively; and with normal regions was 79.7%. Additionally, the areas identified as cancerous had a tumor thickness of >2 mm. Conclusions: NIR-HSI identified the GC regions with high rates. As a feature, the exposed and unexposed areas with tumor thicknesses of >2 mm were identified using NIR-HSI.
