Ms. Dan Liu Profile

Dan Liu

at Fujian Normal Univ

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Publications (2)

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Proceedings Article | 9 October 2021 Paper

Label-free detection of plasma using surface-enhanced Raman spectroscopy for bladder cancer screening

Xin Bai, Dan Liu, Yamin Lin, Xiang Wu, QingJiang Xu, Tao Li, Wei Gong, Yun Yu, Juqiang Lin

Proceedings Volume 11900, 119001K (2021) https://doi.org/10.1117/12.2600406

KEYWORDS: Bladder cancer, Plasma, Raman spectroscopy, Surface enhanced Raman spectroscopy, Cancer, Receivers, Silver, Tumors, Proteins, Oncology

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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.

Proceedings Article | 9 October 2021 Paper

Surface-enhanced Raman spectroscopy of plasma for the noninvasive differentiation of renal-calculi

Dan Liu, Jiamin Gao, Yamin Lin, Wei Gong, Xiang Wu, QingJiang Xu, Tao Li, Yun Yu, Juqiang Lin

Proceedings Volume 11900, 119001I (2021) https://doi.org/10.1117/12.2600199

KEYWORDS: Renal calculi, Surface enhanced Raman spectroscopy, Plasma, Raman spectroscopy, Principal component analysis, Silver, Proteins, Blood, Statistical analysis, Diagnostics

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Renal calculi (kidney stones) that occur in human urethra is considered as one of the most painful urological disorders. Recurrence rates are close to 50%, it affects 5-15% of the population worldwide and the costs to individuals and society are high. Accurate analysis of such calculi plays a vital role in the evaluation of urolithiasis patients and in taking appropriate preventive measures to inhibit the formation or growth of kidney stones. In this paper, surface-enhanced Raman spectroscopy (SERS) was used to detect and analyze the plasma from patients with kidney stones and normal volunteer. In this preliminary experiment, principal component analysis (PCA) was used as a spectral dimensionality reduction method, and linear discriminant analysis (LDA) was used to classify the SERS spectra of plasma samples from patients with kidney stones (n=10) and healthy volunteers (n=10). The high quality SERS spectra were obtained in the range of 400-1800cm-1. The discriminant sensitivity and specificity were 80% and 100%, respectively. The difference spectrum analysis combined with the assignment of SERS bands indicated that there were subtle but significant changes between the normal and the kidney stone plasma, indicating that the contents of nucleic acids, proteins, lipids and other bio-molecules in different cell lines had special changes. The (ROC) curve of receiver performance further confirms the effectiveness of the diagnosis algorithm based on PCA-LDA. This exploratory work shows that the combination of SERS technology and PCA-LDA algorithm has great potential in the development of a label-free, non-invasive and accurate detection and screening of kidney stone.

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