Autor según el artículo: Bao, HN; Hackshaw, KV; Castellvi, SD; Wu, YL; Gonzalez, CM; Nuguri, SM; Yao, SY; Goetzman, CM; Schultz, ZD; Yu, LB; Aziz, R; Osuna-Diaz, MM; Sebastian, KR; Giusti, MM; Rodriguez-Saona, L
Departamento: Enginyeria Química
Autor/es de la URV: De Lamo Castellvi, Silvia
Palabras clave: Surface-enhanced raman spectroscopy Rheumatic-diseases Metabolic fingerprinting In-clinic disease diagnostics Fibromyalgia Chemometrics Central sensitization syndrome Blood surface-enhanced raman spectroscopy spectra serum prevalence pain metabolic fingerprinting inventory in-clinic disease diagnostics criteria classification chemometrics central sensitization syndrome blood amino-acids american-college
Resumen: Fibromyalgia (FM) is a chronic muscle pain disorder that shares several clinical features with other related rheumatologic disorders. This study investigates the feasibility of using surface-enhanced Raman spectroscopy (SERS) with gold nanoparticles (AuNPs) as a fingerprinting approach to diagnose FM and other rheumatic diseases such as rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), osteoarthritis (OA), and chronic low back pain (CLBP). Blood samples were obtained on protein saver cards from FM (n = 83), non-FM (n = 54), and healthy (NC, n = 9) subjects. A semi-permeable membrane filtration method was used to obtain low-molecular-weight fraction (LMF) serum of the blood samples. SERS measurement conditions were standardized to enhance the LMF signal. An OPLS-DA algorithm created using the spectral region 750 to 1720 cm−1 enabled the classification of the spectra into their corresponding FM and non-FM classes (Rcv > 0.99) with 100% accuracy, sensitivity, and specificity. The OPLS-DA regression plot indicated that spectral regions associated with amino acids were responsible for discrimination patterns and can be potentially used as spectral biomarkers to differentiate FM and other rheumatic diseases. This exploratory work suggests that the AuNP SERS method in combination with OPLS-DA analysis has great potential for the label-free diagnosis of FM.
Áreas temáticas: Pharmacology & pharmacy Medicine, research & experimental Medicine (miscellaneous) General biochemistry,genetics and molecular biology Ciencias sociales Biochemistry, genetics and molecular biology (miscellaneous) Biochemistry, genetics and molecular biology (all) Biochemistry & molecular biology
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: silvia.delamo@urv.cat silvia.delamo@urv.cat
Identificador del autor: 0000-0002-5261-6806 0000-0002-5261-6806
Fecha de alta del registro: 2024-02-17
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2227-9059/12/1/133
Referencia al articulo segun fuente origial: Biomedicines. 12 (1):
Referencia de l'ítem segons les normes APA: Bao, HN; Hackshaw, KV; Castellvi, SD; Wu, YL; Gonzalez, CM; Nuguri, SM; Yao, SY; Goetzman, CM; Schultz, ZD; Yu, LB; Aziz, R; Osuna-Diaz, MM; Sebastian (2024). Early Diagnosis of Fibromyalgia Using Surface-Enhanced Raman Spectroscopy Combined with Chemometrics. Biomedicines, 12(1), -. DOI: 10.3390/biomedicines12010133
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.3390/biomedicines12010133
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2024
Tipo de publicación: Journal Publications
Repositori URV