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Raman active components of skin cancer

机译:皮肤癌的拉曼活性成分

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摘要

Raman spectroscopy (RS) has shown great potential in noninvasive cancer screening. Statistically based algorithms, such as principal component analysis, are commonly employed to provide tissue classification; however, they are difficult to relate to the chemical and morphological basis of the spectroscopic features and underlying disease. As a result, we propose the first Raman biophysical model applied to in vivo skin cancer screening data. We expand upon previous models by utilizing in situ skin constituents as the building blocks, and validate the model using previous clinical screening data collected from a Raman optical fiber probe. We built an 830nm confocal Raman microscope integrated with a confocal laser-scanning microscope. Raman imaging was performed on skin sections spanning various disease states, and multivariate curve resolution (MCR) analysis was used to resolve the Raman spectra of individual in situ skin constituents. The basis spectra of the most relevant skin constituents were combined linearly to fit in vivo human skin spectra. Our results suggest collagen, elastin, keratin, cell nucleus, triolein, ceramide, melanin and water are the most important model components. We make available for download (see supplemental information) a database of Raman spectra for these eight components for others to use as a reference. Our model reveals the biochemical and structural makeup of normal, nonmelanoma and melanoma skin cancers, and precancers and paves the way for future development of this approach to noninvasive skin cancer diagnosis.
机译:拉曼光谱(RS)在非侵入性癌症筛查中显示出巨大潜力。通常采用基于统计的算法(例如主成分分析)来提供组织分类。但是,它们很难与光谱特征和潜在疾病的化学和形态基础联系起来。因此,我们提出了第一个应用于体内皮肤癌筛查数据的拉曼生物物理模型。我们通过利用原位皮肤成分作为构建模块来扩展以前的模型,并使用从拉曼光纤探针收集的先前的临床筛查数据来验证模型。我们构建了一个830nm共焦拉曼显微镜,并与共焦激光扫描显微镜集成在一起。在跨越各种疾病状态的皮肤切片上进行拉曼成像,并使用多元曲线分辨率(MCR)分析来解析各个原位皮肤成分的拉曼光谱。将最相关的皮肤成分的基本光谱线性组合以适合体内人体皮肤光谱。我们的结果表明胶原蛋白,弹性蛋白,角蛋白,细胞核,三油精,神经酰胺,黑色素和水是最重要的模型成分。我们提供了这八种成分的拉曼光谱数据库供下载(请参阅补充信息),以供其他参考。我们的模型揭示了正常,非黑色素瘤和黑色素瘤皮肤癌的生化和结构组成,以及前癌和铺平道路,为该方法用于非侵入性皮肤癌诊断的未来发展铺平了道路。

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