Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes

Shreya Madhav Nuguri; Kevin V. Hackshaw; Silvia de Lamo Castellvi; Yalan Wu; Celeste Matos Gonzalez; Chelsea M. Goetzman; Zachary D. Schultz; Lianbo Yu; Rija Aziz; Michelle Osuna Diaz; Katherine Sebastian; W. Michael Brode; M Monica Giusti; Luis Rodriguez-Saona

doi:10.20944/preprints202405.1749.v1

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preprints.org > medicine and pharmacology > internal medicine > doi: 10.20944/preprints202405.1749.v1

Preprint Article Version 1 Preserved in Portico This version is not peer-reviewed

Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes

Shreya Madhav Nuguri

Kevin V. Hackshaw

^* ,

Silvia de Lamo Castellvi

Yalan Wu

Celeste Matos Gonzalez

Version 1 : Received: 24 May 2024 / Approved: 27 May 2024 / Online: 27 May 2024 (11:28:23 CEST)

How to cite: Nuguri, S. M.; Hackshaw, K. V.; Castellvi, S. D. L.; Wu, Y.; Gonzalez, C. M.; Goetzman, C. M.; Schultz, Z. D.; Yu, L.; Aziz, R.; Osuna Diaz, M.; Sebastian, K.; Brode, W. M.; Giusti, M. M.; Rodriguez-Saona, L. Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes. Preprints 2024, 2024051749. https://doi.org/10.20944/preprints202405.1749.v1 Nuguri, S. M.; Hackshaw, K. V.; Castellvi, S. D. L.; Wu, Y.; Gonzalez, C. M.; Goetzman, C. M.; Schultz, Z. D.; Yu, L.; Aziz, R.; Osuna Diaz, M.; Sebastian, K.; Brode, W. M.; Giusti, M. M.; Rodriguez-Saona, L. Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes. Preprints 2024, 2024051749. https://doi.org/10.20944/preprints202405.1749.v1

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MDPI and ACS Style

Nuguri, S. M.; Hackshaw, K. V.; Castellvi, S. D. L.; Wu, Y.; Gonzalez, C. M.; Goetzman, C. M.; Schultz, Z. D.; Yu, L.; Aziz, R.; Osuna Diaz, M.; Sebastian, K.; Brode, W. M.; Giusti, M. M.; Rodriguez-Saona, L. Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes. Preprints 2024, 2024051749. https://doi.org/10.20944/preprints202405.1749.v1

APA Style

Nuguri, S. M., Hackshaw, K. V., Castellvi, S. D. L., Wu, Y., Gonzalez, C. M., Goetzman, C. M., Schultz, Z. D., Yu, L., Aziz, R., Osuna Diaz, M., Sebastian, K., Brode, W. M., Giusti, M. M., & Rodriguez-Saona, L. (2024). Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes. Preprints. https://doi.org/10.20944/preprints202405.1749.v1

Chicago/Turabian Style

Nuguri, S. M., M Monica Giusti and Luis Rodriguez-Saona. 2024 "Surface Enhanced Raman Spectroscopy Combined With Multivariate Analysis for Fingerprinting Clinically Similar, Fibromyalgia and Long-COVID Syndromes" Preprints. https://doi.org/10.20944/preprints202405.1749.v1

Abstract

Fibromyalgia (FM) is a chronic central sensitivity syndrome characterized by augmented pain processing at diffuse body sites and presents as a multimorbid clinical condition. Long COVID (LC) is a heterogenous clinical syndrome that affects 10-20% of individuals following COVID-19 infection. FM and LC shares similarities with regards to the pain and other clinical symptoms experienced, thereby posing a challenge for accurate diagnosis. This research explores the feasibility of using surface enhanced Raman spectroscopy (SERS) combined with soft independent modelling of class analogies (SIMCA) to develop classification models differentiating LC and FM. Venous blood samples were collected using two supports, dried bloodspot cards (DBS, n= 48 FM and n= 46 LC) and volumetric absorptive micro sampling tips (VAMS, n= 39 FM and n= 39 LC). A semi-permeable membrane (10 kDa) was used to extract low molecular fraction (LMF) from the blood samples, and Raman spectra was acquired using SERS with gold nanoparticles (AuNPs). Soft Independent Modelling of Class Analogy (SIMCA) models developed with spectral data of blood samples collected in VAMS tips showed superior performance with a validation performance of 100% accuracy, sensitivity, and specificity achieving an excellent classification accuracy of 0.86 area under the curve (AUC). Amide groups, aromatic and acidic amino acids were responsible for the discrimination patterns among FM and LC syndromes, emphasizing the findings from our previous studies. Overall, our results demonstrate the ability of AuNPs SERS to identify unique metabolites that can be potentially used as spectral biomarkers to differentiate FM and LC.

Keywords

Long COVID; Fibromyalgia; surface enhanced Raman spectroscopy; volumetric absorptive micro sampling; dried bloodspot cards

Subject

Medicine and Pharmacology, Internal Medicine

Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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