Issue 7, 2023

Mobile multi-configuration clinical translational Raman system for oral cancer application

Abstract

Early diagnosis of oral cancer is critical to improve the survival rate of patients. Raman spectroscopy, a non-invasive spectroscopic technique, has shown potential in identifying early-stage oral cancer biomarkers in the oral cavity environment. However, inherently weak signals necessitate highly sensitive detectors, which restricts widespread usage due to high setup costs. In this research, the fabrication and assembly of a customised Raman system that can adapt three different configurations for the in vivo and ex vivo analysis is reported. This novel design will help in reducing the cost required to have multiple Raman instruments specific for a given application. First, we demonstrated the capability of a customized microscope for acquiring Raman signals from a single cell with high signal-to-noise ratio. Generally, when working with liquid samples with low concentration of analytes (such as saliva) under a microscope, excitation light interacts with a small sample volume, which may not be representative of whole sample. To address this issue, we have designed a novel long-path transmission set-up, which was found to be sensitive towards low concentration of analytes in aqueous solution. We further demonstrated that the same Raman system can be incorporated with the multimodal fibre optical probe to collect in vivo data from oral tissues. In summary, this flexible, portable, multi-configuration Raman system has the potential to provide a cost-effective solution for complete screening of precancer oral lesions.

Graphical abstract: Mobile multi-configuration clinical translational Raman system for oral cancer application

Article information

Article type
Paper
Submitted
23 Nov 2022
Accepted
22 Feb 2023
First published
06 Mar 2023
This article is Open Access
Creative Commons BY-NC license

Analyst, 2023,148, 1514-1523

Mobile multi-configuration clinical translational Raman system for oral cancer application

S. Maryam, S. Konugolu Venkata Sekar, M. D. Ghauri, E. Fahy, M. S. Nogueira, H. Lu, F. Beffara, G. Humbert, R. Ni Riordain, P. Sheahan, R. Burke, K. Wei Kho, R. Gautam and S. Andersson-Engels, Analyst, 2023, 148, 1514 DOI: 10.1039/D2AN01921C

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