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Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer

Edward Duckworth, Arti Hole, Atul Deshmukh, Pankaj Chaturvedi, Murali Krishna Chilakapati, Benjamin Mora Orcid Logo, Deb Roy Orcid Logo

Analytical Chemistry, Volume: 94, Issue: 40, Pages: 13642 - 13646

Swansea University Authors: Benjamin Mora Orcid Logo, Deb Roy Orcid Logo

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DOI (Published version): 10.1021/acs.analchem.2c02496

Abstract

We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers’ signals. A narrower range molecular w...

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Published in: Analytical Chemistry
ISSN: 0003-2700 1520-6882
Published: American Chemical Society (ACS) 2022
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URI: https://cronfa.swan.ac.uk/Record/cronfa60963
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first_indexed 2022-08-30T08:37:38Z
last_indexed 2023-01-13T19:21:28Z
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spelling 2022-12-22T13:31:22.1871274 v2 60963 2022-08-30 Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer 557f93dfae240600e5bd4398bf203821 0000-0002-2945-3519 Benjamin Mora Benjamin Mora true false a18d76438369122184e83fb683d8d787 0000-0002-7528-8649 Deb Roy Deb Roy true false 2022-08-30 SCS We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers’ signals. A narrower range molecular weight window of the serum was also investigated that yielded even higher accuracy on diagnosis. The most accurate results were produced in the serum’s 10–30 kDa molecular weight region to distinguish between the two hardest to discern classes, i.e., premalignant and cancer patients. This work promises an avenue for earlier diagnosis with high accuracy as well as greater insight into the molecular origins of these signals by identifying a key molecular weight region to focus on. Journal Article Analytical Chemistry 94 40 13642 13646 American Chemical Society (ACS) 0003-2700 1520-6882 11 10 2022 2022-10-11 10.1021/acs.analchem.2c02496 COLLEGE NANME Computer Science COLLEGE CODE SCS Swansea University SU Library paid the OA fee (TA Institutional Deal) E.D. and D.R. acknowledge financial support from CherishDE, EPSRC and Swansea University. The authors gratefully acknowledge PerkinElmer in kind support and instrument time for the study. 2022-12-22T13:31:22.1871274 2022-08-30T09:35:45.0176146 Faculty of Science and Engineering School of Engineering and Applied Sciences - Chemistry Edward Duckworth 1 Arti Hole 2 Atul Deshmukh 3 Pankaj Chaturvedi 4 Murali Krishna Chilakapati 5 Benjamin Mora 0000-0002-2945-3519 6 Deb Roy 0000-0002-7528-8649 7 60963__25317__0494ca6398cc4300b634aa087875fb7c.pdf 60963_VoR.pdf 2022-10-06T11:07:29.6087426 Output 1915925 application/pdf Version of Record true Released under the terms of a Creative Commons Attribution 4.0 International (CC BY 4.0) License true eng https://creativecommons.org/licenses/by/4.0/
title Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
spellingShingle Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
Benjamin Mora
Deb Roy
title_short Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
title_full Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
title_fullStr Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
title_full_unstemmed Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
title_sort Improving Vibrational Spectroscopy Prospects in Frontline Clinical Diagnosis: Fourier Transform Infrared on Buccal Mucosa Cancer
author_id_str_mv 557f93dfae240600e5bd4398bf203821
a18d76438369122184e83fb683d8d787
author_id_fullname_str_mv 557f93dfae240600e5bd4398bf203821_***_Benjamin Mora
a18d76438369122184e83fb683d8d787_***_Deb Roy
author Benjamin Mora
Deb Roy
author2 Edward Duckworth
Arti Hole
Atul Deshmukh
Pankaj Chaturvedi
Murali Krishna Chilakapati
Benjamin Mora
Deb Roy
format Journal article
container_title Analytical Chemistry
container_volume 94
container_issue 40
container_start_page 13642
publishDate 2022
institution Swansea University
issn 0003-2700
1520-6882
doi_str_mv 10.1021/acs.analchem.2c02496
publisher American Chemical Society (ACS)
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Engineering and Applied Sciences - Chemistry{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Engineering and Applied Sciences - Chemistry
document_store_str 1
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description We report a novel method with higher than 90% accuracy in diagnosing buccal mucosa cancer. We use Fourier transform infrared spectroscopic analysis of human serum by suppressing confounding high molecular weight signals, thus relatively enhancing the biomarkers’ signals. A narrower range molecular weight window of the serum was also investigated that yielded even higher accuracy on diagnosis. The most accurate results were produced in the serum’s 10–30 kDa molecular weight region to distinguish between the two hardest to discern classes, i.e., premalignant and cancer patients. This work promises an avenue for earlier diagnosis with high accuracy as well as greater insight into the molecular origins of these signals by identifying a key molecular weight region to focus on.
published_date 2022-10-11T04:19:30Z
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score 11.01353

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