The Raman enhancement effect of Ag-based nanomaterial hydroxyapatite was studied using Rhodamine 6G and adenine. The composite is a nano-rod hydroxyapatite wrapped with Ag particles, which acts as a substrate for surface-enhanced Raman scattering of the material to be tested. The results show that the substrate has a significant enhancement effect for the Raman spectra of Rhodamine 6G and adenine. Compared with the traditional silver colloids substrate, this is an interesting substrate. Nano-level hydroxyapatite is expected to carry drugs into the living body, and then on the basis of silver coating, in vivo SERS detection becomes possible.
Prostate cancer (PCa) is an epithelial malignant tumor occurring in the prostate gland and is the most common malignancy of the male genitourinary system. Prostate cancer is often asymptomatic in its early stage, and the best treatment time is usually missed when it is found. Therefore, early diagnosis and treatment is the key to reduce the mortality of prostate cancer patients. In this work, we developed a method to identify patients with prostate cancer and healthy volunteers. We collected serum samples from patients with prostate cancer and healthy volunteers, detected the SERS spectra of these serum samples, and analyzed the SERS spectrum of the obtained serum samples with a preliminary spectral peak assignment. Then principal component analysis (PCA) combined with linear discriminant analysis (LDA) was used to diagnose the SERS spectra of serum from patients with prostate cancer and healthy volunteers. Difference spectrum analysis showed that there are obvious differences in several characteristic peaks between the serum of patients with prostate cancer and the serum of healthy volunteers, which may be related to the special changes of nucleic acids, proteins, lipids and other biological molecules in the process of carcinogenesis. Using principal component analysis (PCA) combined with linear discriminant analysis (LDA) multivariate statistical method, the diagnostic sensitivity and specificity were 85% and 95%, respectively. The receiver operating characteristic (ROC) curve further proves the effectiveness of diagnosis algorithm based on PCA-LDA technology, and the area under the curve (AUC) is 0.998. These results show that the combination of SERS technology and PCA-LDA algorithm has great potential in screening prostate cancer patients.
At present, bladder cancer has become a common malignant tumor around the world, and the number of deaths from bladder cancer is also increasing year by year. Therefore, it is necessary to develop a powerful technology for further analysis. In this paper, we used a new method of surface enhanced Raman spectroscopy (SERS) to detect the plasma of 10 normal volunteers and 10 patients with bladder cancer, and successfully recorded their spectra. At the same time, the plasma of normal persons and patients was analyzed by difference spectrum analysis, principal component analysis(PCA), linear discriminant analysis(LDA) algorithm, and receiver operating characteristic (ROC) curves. The difference spectrum analysis shows that there are slight but significant differences in the spectra between normal plasma and bladder cancer plasma, which may indicate that some changes have taken place in the contents of protein, nucleic acid and lipid in patients with bladder cancer. PCA was used to investigate the correlation of multiple variables, so as to reduce the dimension, and combined with the LDA algorithm to distinguish normal samples and patient samples, the sensitivity and specificity are 80% and 100%, respectively. Finally, the area under the receiver operating characteristic(ROC) curve is 0.97, which further proves the validity of the diagnostic algorithm based on the PCA-LDA diagnostic algorithm. The exploratory work showed that the combination of SERS technology and PCA-LDA algorithm could distinguish the plasma of normal people and bladder cancer patients. And further showed that SERS could be used as a simple and effective method for the detection of clinical cancer.
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