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VIS-NIR spectral signature and quantitative analysis of HeLa and DU145 cell line

机译:VIS-NIR光谱拟计和HELA和DU145细胞定量分析

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Cancer is increasing in incidence and the leading cause of death worldwide. Controlling and reducing cancer requires early detection and technique to accurately detect and quantify predictive biomarkers. Optical spectroscopy has shown promising non-destructive ability to display distinctive spectral characteristics between cancerous and normal tissues from different part of human organ. Nonetheless, not many information is available on spectroscopic properties of cancer cell lines. In this research, the visible-near infrared (VIS-NIR) absorbance spectroscopy measurement of cultured cervical cancer (HeLa) and prostate cancer cells (DU145) lines has been performed to develop spectral signature of cancer cells and to generate algorithm to quantify cancer cells. Spectroscopic measurement on mouse skin fibroblast (L929) was also taken for comparative purposes. In visible region, the raw cells' spectra do not produce any noticeable peak absorbance that provides information on color because the medium used for cells is colorless and transparent. NIR wavelength between 950 and 975 nm exhibit significant peak due to water absorbance by the medium. Development of spectral signature for the cells through the application of regression technique significantly enhances the diverse characteristics between L929, HeLa and DU145. The application of multiple linear regression allows high measurement accuracy of the cells with coefficient of determination above 0.94. (C) 2019 Elsevier B.V. All rights reserved.
机译:癌症在全世界的发病率和死亡原因增加。控制和还原癌症需要早期检测和技术来准确地检测和量化预测性生物标志物。光学光谱表明,有前途的非破坏性能力在来自人体器官的不同部分的癌性和正常组织之间显示出独特的光谱特性。尽管如此,没有许多信息可用于癌细胞系的光谱性质。在本研究中,已经进行了近近红外(Vis-NIR)培养的宫颈癌(HELA)和前列腺癌细胞(DU145)系的吸光光度光谱测量,以开发癌细胞的光谱特征,并产生算法来量化癌细胞。对小鼠皮肤成纤维(L929)光谱测定也采取用于比较目的。在可见区域中,原始细胞的光谱不会产生任何明显的峰值吸光度,这提供了颜色的信息,因为用于细胞的介质是无色和透明的。由于培养基的吸水性,NIR波长在950和975nm之间表现出显着的峰。通过应用回归技术的电池的光谱特征的开发显着提高了L929,Hela和Du145之间的不同特性。多元线性回归的应用允许具有高于0.94以上的测定系数的电池的高测量精度。 (c)2019 Elsevier B.v.保留所有权利。

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