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Medical Optics and Biotechnology|2 Article(s)
High Sensitivity Detection of Gene-like Tumor Markers Based on SERS Characteristics of Hollow Sea-urchin Gold Nanoparticles and Noble Metal/semiconductor Substrate
Le PENG, Lu ZHOU, Yan-ping QING, Li-ying TONG, Zhao-heng LIANG, Hao FU, and Jun ZHOU
Trace detection of genetic tumor marker microRNA (miRNA) has important application value for early diagnosis of cancer. According to the Surface-enhanced Raman Scattering characteristics of hollow sea urchin gold nanoparticles and Ag/ZnO nanostructures, and based on the principle of complementary base pairing, a "sandwich" structure of probe-nucleic acid-substrate is constructed and a highly sensitive quantitative detection scheme for genetic tumor marker miRNA is proposed. First, the captured DNA is linked to the hollow sea urchin gold nanoparticles modified with 4-mercaptobenzoic acid (4-MBA) as a probe. At the same time, the target DNA is modified on the Ag/ZnO nanostructure, and the SERS signal is detected to obtain the corresponding dose-response curve after complementary hybridization with miRNA-106a. The experimental results show that the detection limit of miRNA-106a reached 1.84 fmol·L-1 within the detection range of 1 fmol·L-1~1 nmol·L-1. Meanwhile, the reliability of the miRNA detection scheme based on the hollow sea urchin gold nanoparticles and Ag/ZnO nano-structure SERS characteristics was verified by the Real-time quantitative Polynucleotide Chain Reaction (RT-qPCR) method. Trace detection of genetic tumor marker microRNA (miRNA) has important application value for early diagnosis of cancer. According to the Surface-enhanced Raman Scattering characteristics of hollow sea urchin gold nanoparticles and Ag/ZnO nanostructures, and based on the principle of complementary base pairing, a "sandwich" structure of probe-nucleic acid-substrate is constructed and a highly sensitive quantitative detection scheme for genetic tumor marker miRNA is proposed. First, the captured DNA is linked to the hollow sea urchin gold nanoparticles modified with 4-mercaptobenzoic acid (4-MBA) as a probe. At the same time, the target DNA is modified on the Ag/ZnO nanostructure, and the SERS signal is detected to obtain the corresponding dose-response curve after complementary hybridization with miRNA-106a. The experimental results show that the detection limit of miRNA-106a reached 1.84 fmol·L-1 within the detection range of 1 fmol·L-1~1 nmol·L-1. Meanwhile, the reliability of the miRNA detection scheme based on the hollow sea urchin gold nanoparticles and Ag/ZnO nano-structure SERS characteristics was verified by the Real-time quantitative Polynucleotide Chain Reaction (RT-qPCR) method.
Acta Photonica Sinica
- Publication Date: Aug. 25, 2020
- Vol. 49, Issue 8, 0817002 (2020)
Measuring Optical Parameters γ of Biological Tissues by Artificial Neural Network Method
Qiu-sheng ZHU, and Ying LIU
An artificial neural network method is proposed for estimating reduced scattering coefficient μs' and phase function parameter γ of biological tissues from spatially resolved reflectance profiles in the sub-diffusive regime. Monte Carlo simulation method is used to obtain data samples of diffuse reflection from biological tissues. These data samples are used to train back-propagation neural network get the information of γ predicted from the sub-diffused scattered light. Since there is a large error occurs when predicting μs' and γ simultaneously, the segmenting data train of two back-propagation networks is performed to identify the μs' and γ in turn. It is found that 3.64lth (lth representing the average transport free path) is an insensitive points of γ. The network trained with data samples near this point is used for predicting μs', while the network trained with data samples in the 2lth is used for predicting γ. Monte Carlo simulation result show that within the range 1.3 ≤ γ ≤ 1.9, the relative root mean square error between the predicted result and the true value is within 1%. Compared with the existing measurement methods, the proposed method is simpler and has improved accuracy. An artificial neural network method is proposed for estimating reduced scattering coefficient μs' and phase function parameter γ of biological tissues from spatially resolved reflectance profiles in the sub-diffusive regime. Monte Carlo simulation method is used to obtain data samples of diffuse reflection from biological tissues. These data samples are used to train back-propagation neural network get the information of γ predicted from the sub-diffused scattered light. Since there is a large error occurs when predicting μs' and γ simultaneously, the segmenting data train of two back-propagation networks is performed to identify the μs' and γ in turn. It is found that 3.64lth (lth representing the average transport free path) is an insensitive points of γ. The network trained with data samples near this point is used for predicting μs', while the network trained with data samples in the 2lth is used for predicting γ. Monte Carlo simulation result show that within the range 1.3 ≤ γ ≤ 1.9, the relative root mean square error between the predicted result and the true value is within 1%. Compared with the existing measurement methods, the proposed method is simpler and has improved accuracy.
Acta Photonica Sinica
- Publication Date: Aug. 25, 2020
- Vol. 49, Issue 8, 0817001 (2020)
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