著者

Hui Liu Yixin Shi Jia Zhan Yingchun Liu Jing Zhou Biao Su Yue Chen Ling Wang Lin Chen

出版者

International Research and Cooperation Association for Bio & Socio-Sciences Advancement

雑誌

Drug Discoveries & Therapeutics (ISSN:18817831)

巻号頁・発行日

vol.17, no.1, pp.26-36, 2023-02-28 (Released:2023-03-11)

参考文献数

50

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Cervical lymph node metastasis (CLNM) of papillary thyroid carcinoma (PTC) is directly associated with clinical management and prognosis. In this study, we aimed to evaluate the value of conventional ultrasound (US) combined with ENST00000438158 in predicting CLNM of PTC. Fourty-nine PTC patients underwent US examination and US-guided fine needle aspiration (FNA). ENST00000438158 expression in FNA cytological specimens and PTC cell lines was detected using real-time reverse transcription polymerase chain reaction (qRT-PCR). The role of ENST00000438158 expression in the proliferation, migration, invasion, apoptosis, and cell cycle of PTC cells was investigated by Cell Counting Kit-8 (CCK8) and clone formation experiments, transwell assay, and flow cytometry, respectively. Calcification, capsule contact, and low ENST00000438158 expression were independently associated with PTC with CLNM (all p < 0.05). The combination of multiple US features was more valuable than a single US feature in predicting CLNM in PTC. Adding ENST0000438158 to US greatly improved the value of differentiation of PTC with or without CLNM. In conclusion, ENST00000438158 is a potential molecular marker for predicting CLNM in PTC. ENST00000438158 combined with US features is highly valuable for predicting CLNM in PTC.

著者

Xiwen Yang Ping Jiang Yahui Luo Yixin Shi

出版者

Japan Oil Chemists' Society

雑誌

Journal of Oleo Science (ISSN:13458957)

巻号頁・発行日

vol.72, no.1, pp.69-77, 2023 (Released:2023-01-07)

参考文献数

22

被引用文献数

1

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As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R2) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (RC2) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (RP2) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.

著者

Xiwen Yang Ping Jiang Yahui Luo Yixin Shi

出版者

Japan Oil Chemists' Society

雑誌

Journal of Oleo Science (ISSN:13458957)

巻号頁・発行日

pp.ess22139, (Released:2022-12-12)

被引用文献数

1

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As a unique traditional vegetable oil in China, camellia seed oil has very high edible value. Camellia seed kernel is mainly composed of fatty acids, which not only determines the oil yield of camellia seed, but also exert an important impact on the storage performance of camellia seed. In order to quickly and accurately determine the fatty acid content of camellia seed, this paper took camellia seed as the research object, used hyperspectral technology to determine the fatty acid content of camellia seed, and establishes a spectral model. 8 pretreatment methods, such as Savitzky-Golay smoothing, normalization, baseline correction, multivariate scattering correction, standard normal variable transformation, detrending algorithm, first derivative and second derivative, were adopted in this paper. The spectral prediction model of fatty acid content in camellia seed was established by combining 4 modeling methods: principal components regression (PCR), partial least square regression (PLSR), back propagation neural network (BP), radial basis function neural network (RBF). The optimal prediction model was selected by comparing the coefficient of determination (R2) and root mean square error (RMSE) of various models. The results showed that the spectral sensitive bands with high correlation coefficients (r) were 410-420 nm, 450-460 nm, 490-510 nm, 545-580 nm, 845-870 nm and 905-925 nm, respectively. The r obtained by MSC pretreatment of spectral data was the largest. The data obtained by 8 different pretreatment methods combined with RBF neural network model was the best, in which the average value of coefficient of determination (RC2) in the calibration set was 0.8654, and the root mean square error of calibration (RMSEC) was 0.0777; the average value of coefficient of determination (R2P) and root mean square error of prediction (RMSEP) in the prediction set model were 0.8437 and 0.0827, respectively. It could be seen that the best accuracy could be achieved by MSC pretreatment combined with RBF neural network modeling. This paper can provide reference for rapid nondestructive detection of fatty acid content in camellia seed by hyperspectral technology.