Cervical cancer is the fourth most common cancer affecting women worldwide but mortality can be decreased by early detection of pre-malignant lesions. The Pap smear test is the most commonly used method in cervical cancer screening programmes. Although specificity is high for this test, it is widely acknowledged that sensitivity can be poor mainly due to the subjective nature of the test. There is a need for new objective tests for the early detection of pre-malignant cervical lesions. Over the past two decades, Raman spectroscopy has emerged as a promising new technology for cancer screening and diagnosis. The aim of this study was to evaluate the potential of Raman spectroscopy for cervical cancer screening using both Cervical Intraepithelial Neoplasia (CIN) and Squamous Intraepithelial Lesion (SIL) classification terminology. ThinPrep® Pap samples were recruited from a cervical screening population. Raman spectra were recorded from single cell nuclei and subjected to multivariate statistical analysis. Normal and abnormal ThinPrep® samples were discriminated based on the biochemical fingerprint of the cells using Principal Component Analysis (PCA). Principal Component Analysis - Linear Discriminant Analysis (PCA-LDA) was employed to build classification models based on either CIN or SIL terminology. This study has shown that Raman spectroscopy can be successfully applied to the study of routine cervical cytology samples from a cervical screening programme and that the use of CIN terminology resulted in improved sensitivity for high grade cases.
International audienceRaman spectroscopy has been used to identify the biochemical changes associated with the presence of the Hepatitis C virus (HCV) in infected human blood plasma samples as compared with healthy samples, as control. The aim of the study was to establish the Raman spectral markers of hepatitis infection, which could be used for diagnostic purposes. Moreover, multivariate data analysis techniques, including Principal Component Analysis (PCA), coupled with Linear Discriminant Analysis (LDA), and Partial Least Square Regression (PLSR) are employed to further demonstrate the diagnostic capability of the technique. The PLSR model is developed to predict the viral loads of the HCV infected plasma on the basis of the biochemical changes caused by the viral infection.Specific Raman spectral features are observed in the mean spectra of HCV plasma samples which are not observed in the control mean spectra. PCA differentiated the ‘normal’ and ‘HCV’ groups of the Raman spectra and PCA-LDA was employed to increase the efficiency of prediction of the presence of HCV infection, resulting in a sensitivity and specificity 98.8% and 98.6%, with corresponding Positive Predictive Value of 99.2%, and Negative Predictive Value of 98%. PLSR modelling was found to be 99% accurate in predicting the actual viral loads of the HCV samples, as determined clinically using the Polymerase Chain Reaction (PCR) technique, on the basis of the Raman spectral changes caused by the virus during the process of the development of Hepatitis C
a Although formalin fixed paraffin preserved (FFPP) tissues are a major resource for retrospective studies of disease progression, their use in vibrational spectroscopy studies has been undermined by issues of contributions of substrate and paraffin wax which persist in the spectra and can compromise spectral analysis. Recognising the microcrystalline nature of the wax in the tissue, which are inhomogeneously oriented with respect to the polarisation of the Raman source laser, in this study, we have developed a novel method for removing the paraffin wax contributions to the spectra using matrices of multiple wax spectra. FFPP tissue sections from the oral mucosca were obtained and, with no further chemical processing, the Raman spectral analysis of two regions, epithelium and connective tissue were compared. Matrices of multiple wax spectra were collected from different regions and subtracted from the epithelial and connective tissue spectra using a least squares analysis with non-negative constraints. Spectra of multiple cell components such as DNA and RNA were used in fitting the least squares model to reduce the residual error. The use of a data matrix of multiple wax spectra, as opposed to a single spectrum, results in a more accurate removal of the wax, hence reducing its contribution to spectral analysis. In unprocessed FFPP tissue sections, the contribution of the glass substrate is seen to be minimised in comparison to chemically dewaxed FFPP tissue sections. Contributions of the glass substrate were also successfully removed digitally using the same methodology. The combined results indicate that direct analysis of FFPP tissue sections is feasible using Raman spectroscopy, avoiding the need for chemical dewaxing. Additionally, the ability to use glass slides is very important in translation to the clinic.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.