Autoenhanced Raman Spectroscopy via Plasmonic Trapping for Molecular Sensing.

As a label-free and sensitive biosensor, surface-enhanced Raman spectroscopy (SERS) is a rapidly emerging technique. However, because SERS spectra are obtained in the area of light excitation and the enhancement effect can be varied depending on the position of a substrate, it is important to match the enhanced area with an illuminated spot. Here, in order to overcome such difficulty, we demonstrated a new technique combining SERS with plasmonic trapping. By plasmonic trapping, we can collect gold nanoparticles (GNPs) in the middle of initially fabricated nanobowtie structures where a laser is excited. As a result of trapping GNPs, hot-spots are formed at that area. Because SERS is measured in the area irradiated by a laser, hot-spot can be simultaneously coincided with a detection site for SERS. By using this, we detected Rhodamine 6G to 100 pM. To further verify and improve the reproducibility of our technique, we also calculated the electric field distribution, trapping force and trapping potential.

Single test-based diagnosis of multiple cancer types using Exosome-SERS-AI for early stage cancers.

Early cancer detection has significant clinical value, but there remains no single method that can comprehensively identify multiple types of early-stage cancer. Here, we report the diagnostic accuracy of simultaneous detection of 6 types of early-stage cancers (lung, breast, colon, liver, pancreas, and stomach) by analyzing surface-enhanced Raman spectroscopy profiles of exosomes using artificial intelligence in a retrospective study design. It includes classification models that recognize signal patterns of plasma exosomes to identify both their presence and tissues of origin. Using 520 test samples, our system identified cancer presence with an area under the curve value of 0.970. Moreover, the system classified the tumor organ type of 278 early-stage cancer patients with a mean area under the curve of 0.945. The final integrated decision model showed a sensitivity of 90.2% at a specificity of 94.4% while predicting the tumor organ of 72% of positive patients. Since our method utilizes a non-specific analysis of Raman signatures, its diagnostic scope could potentially be expanded to include other diseases.