Search

Real-time Raman spectroscopy can be used to assist in assessing skin lesions suspicious for cancer. Most of the diagnostic algorithms are based on full band of the Raman spectra, either in the fingerprint region or the high wavenumber region. In this paper we explored wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Wavenumber selection was implemented using windows of wavenumber and leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected windows of wavenumber using multivariate statistical analyses, including principal component and general discriminate analysis (PC-GDA) and partial least squares (PLS). In total a combined cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included, which were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. It was found that the area under the receiver operating characteristic curve (ROC) was improved from 0.861–0.891 to 0.891–0.911 and the diagnostic specificity for fixed sensitivity 0.99–0.90 was improved from 0.17–0.65 to 0.20–0.75 with wavenumber selection based analysis. [ABSTRACT FROM AUTHOR]

Real-time Raman spectroscopy can be used to assist in assessing skin lesions suspicious for cancer. Most of the diagnostic algorithms are based on full band of the Raman spectra, either in the fingerprint region or the high wavenumber region. In this paper we explored wavenumber selection based analysis in Raman spectroscopy for skin cancer diagnosis. Wavenumber selection was implemented using windows of wavenumber and leave-one-out cross-validated stepwise regression or least and shrinkage selection operator (LASSO). The diagnostic algorithms were then generated from the selected windows of wavenumber using multivariate statistical analyses, including principal component and general discriminate analysis (PC-GDA) and partial least squares (PLS). In total a combined cohort of 645 confirmed lesions from 573 patients encompassing skin cancers, precancers and benign skin lesions were included, which were divided into training cohort (n = 518) and testing cohort (n = 127) according to the measurement time. It was found that the area under the receiver operating characteristic curve (ROC) was improved from 0.861–0.891 to 0.891–0.911 and the diagnostic specificity for fixed sensitivity 0.99–0.90 was improved from 0.17–0.65 to 0.20–0.75 with wavenumber selection based analysis. [ABSTRACT FROM AUTHOR]

Stem cells offer tremendous opportunities for regenerative medicine. Over the past decade considerable research has taken place to identify and characterize the differentiation states of stem cells in culture. Raman micro-spectroscopy has emerged as an ideal technology since it is fast, nondestructive, and does not require potentially toxic dyes. Raman spectroscopy systems can also be incorporated into confocal microscope imaging systems allowing spectra to be obtained from below the tissue surface. Thus there is significant potential for monitoring stem cells in living tissue. Stem cells that reside in hair follicles are suitable for testing this possibility since they are close to the skin surface, and typically clustered around the bulge area. One of the first steps needed would be to obtain Raman micro-spectra from stem cells located in thin sections of tissue, and then see whether these spectra are clearly different from those of the surrounding differentiated cells. To facilitate this test, standard 5 μm thick sections of murine skin tissue were stained to identify the location of hair follicle stem cells and their progeny. Raman spectra were then obtained from adjacent cells in a subsequent unstained 10 μm thick section. The spectra revealed significant differences in peak intensities associated with nucleic acids, proteins, lipids and amino acids. Statistical analyses of the Raman micro-spectra identified stem cells with 98% sensitivity and 94% specificity, as compared with a CD34 immunostaining gold standard. Furthermore analyses of the spectral variance indicated differences in cellular dynamics between the two cell groups. This study shows that Raman micro-spectroscopy has a potential role in identifying adult follicle stem cells, laying the groundwork for future applications of hair follicle stem cells and other somatic stem cells in situ. [ABSTRACT FROM AUTHOR]