Opt Lett. 2023 Feb 15;48(4):936-939. doi: 10.1364/OL.483076.
The Stokes shift spectra (S3) of human cancerous and normal prostate tissues were collected label free at a selected wavelength interval of 40 nm to investigate the efficacy of the approach based on three key molecules-tryptophan, collagen, and reduced nicotinamide adenine dinucleotide (NADH)-as cancer biomarkers. S3 combines both fluorescence and absorption spectra in one scan. The S3 spectra were analyzed using machine learning (ML) algorithms, including principal component analysis (PCA), nonnegative matrix factorization (NMF), and support vector machines (SVMs). The components retrieved from the S3 spectra were considered principal biomarkers. The differences in the weights of the components between the two types of tissues were found to be significant. Sensitivity, specificity, and accuracy were calculated to evaluate the performance of SVM classification. This research demonstrates that S3 spectroscopy is effective for detecting the changes in the relative concentrations of the endogenous fluorophores in tissues due to the development of cancer label free.
采集了人类癌变和正常前列腺组织的斯托克斯位移光谱（S3），在选定的 40nm 波长间隔内进行无标记检测，以研究基于色氨酸、胶原蛋白和还原型烟酰胺腺嘌呤二核苷酸（NADH）这三种关键分子作为癌症生物标志物的方法的效果。S3 在一次扫描中结合了荧光和吸收光谱。使用机器学习（ML）算法（包括主成分分析（PCA）、非负矩阵分解（NMF）和支持向量机（SVM））对 S3 光谱进行了分析。从 S3 光谱中提取的成分被认为是主要的生物标志物。发现两种类型组织之间成分权重的差异具有统计学意义。计算了灵敏度、特异性和准确性来评估 SVM 分类的性能。这项研究表明，S3 光谱学可有效检测由于癌症的发生而导致组织内内源性荧光团相对浓度变化，无需标记。
