T
Tianyue Yang
Researcher at Shenyang Ligong University
Publications - 23
Citations - 562
Tianyue Yang is an academic researcher from Shenyang Ligong University. The author has contributed to research in topics: Raman spectroscopy & Lung cancer. The author has an hindex of 10, co-authored 19 publications receiving 435 citations.
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Journal ArticleDOI
Spectral analysis of human saliva for detection of lung cancer using surface-enhanced Raman spectroscopy.
TL;DR: Saliva SERS of saliva showed the ability to predict lung cancer in the authors' experiment, with accuracy, sensitivity, and specificity being 80%, 78%, and 83%, respectively.
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Surface-enhanced Raman spectroscopy (SERS)-based immunochromatographic assay (ICA) for the simultaneous detection of two pyrethroid pesticides
TL;DR: In this paper, a surface-enhanced Raman scattering (SERS)-based immunochromatographic assay (ICA) method was proposed for the dual detection of two pyrethroid pesticides cypermethrin and esfenvalerate.
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Surface-Enhanced Raman Spectroscopic Analysis of Phorate and Fenthion Pesticide in Apple Skin Using Silver Nanoparticles:
TL;DR: The results showed that the characteristic wavenumbers of the two organophosphorus pesticides are more easily identified using SERS, and this method can be used as a quantitative analytical reference for the detection of phorate and fenthion.
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Surface Enhanced Raman Spectroscopy (SERS) for the Multiplex Detection of Braf, Kras, and Pik3ca Mutations in Plasma of Colorectal Cancer Patients.
TL;DR: The suggested PCR-SERS method is multiplexed, flexible in probe design, easy to incorporate into existing PCR conditions, and was sensitive enough to detect mutations in blood plasma.
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Raman spectroscopy combined with principal component analysis and k nearest neighbour analysis for non-invasive detection of colon cancer
TL;DR: In this article, the feasibility of using Raman spectroscopy for the diagnosis of colon cancer was investigated, and the multivariate statistical techniques of principal component analysis (PCA) and k nearest neighbor analysis (KNN) were utilized to develop diagnostic algorithms for classification.