posted on 2024-01-31, 18:12authored byThanh
Mien Nguyen, SinSung Jeong, Seok Kyung Kang, Seung-Wook Han, Thu M. T. Nguyen, Seungju Lee, Youn Joo Jung, You Hwan Kim, Sunwoo Park, Gyeong-Ha Bak, Young-Chai Ko, Eun-Jung Choi, Hyun Yul Kim, Jin-Woo Oh
The surface-enhanced
Raman scattering (SERS) technique has garnered
significant interest due to its ultrahigh sensitivity, making it suitable
for addressing the growing demand for disease diagnosis. In addition
to its sensitivity and uniformity, an ideal SERS platform should possess
characteristics such as simplicity in manufacturing and low analyte
consumption, enabling practical applications in complex diagnoses
including cancer. Furthermore, the integration of machine learning
algorithms with SERS can enhance the practical usability of sensing
devices by effectively classifying the subtle vibrational fingerprints
produced by molecules such as those found in human blood. In this
study, we demonstrate an approach for early detection of breast cancer
using a bottom-up strategy to construct a flexible and simple three-dimensional
(3D) plasmonic cluster SERS platform integrated with a deep learning
algorithm. With these advantages of the 3D plasmonic cluster, we demonstrate
that the 3D plasmonic cluster (3D-PC) exhibits a significantly enhanced
Raman intensity through detection limit down to 10–6 M (femtomole-(10–17 mol)) for p-nitrophenol (PNP) molecules. Afterward, the plasma of cancer subjects
and healthy subjects was used to fabricate the bioink to build 3D-PC
structures. The collected SERS successfully classified into two clusters
of cancer subjects and healthy subjects with high accuracy of up to
93%. These results highlight the potential of the 3D plasmonic cluster
SERS platform for early breast cancer detection and open promising
avenues for future research in this field.