Autor según el artículo: Hackshaw KV; Yao S; Bao H; de Lamo Castellvi S; Aziz R; Nuguri SM; Yu L; Osuna-Diaz MM; Brode WM; Sebastian KR; Giusti MM; Rodriguez-Saona L
Departamento: Enginyeria Química
Autor/es de la URV: De Lamo Castellvi, Silvia
Palabras clave: Post-acute sequelae of sars-cov-2 (pasc)/long covid Metabolic fingerprinting In-clinic disease diagnostics Fibromyalgia Chemometrics Blood
Resumen: Post Acute Sequelae of SARS-CoV-2 infection (PASC or Long COVID) is characterized by lingering symptomatology post-initial COVID-19 illness that is often debilitating. It is seen in up to 30–40% of individuals post-infection. Patients with Long COVID (LC) suffer from dysautonomia, malaise, fatigue, and pain, amongst a multitude of other symptoms. Fibromyalgia (FM) is a chronic musculoskeletal pain disorder that often leads to functional disability and severe impairment of quality of life. LC and FM share several clinical features, including pain that often makes them indistinguishable. The aim of this study is to develop a metabolic fingerprinting approach using portable Fourier-transform mid-infrared (FT-MIR) spectroscopic techniques to diagnose clinically similar LC and FM. Blood samples were obtained from LC (n = 50) and FM (n = 50) patients and stored on conventional bloodspot protein saver cards. A semi-permeable membrane filtration approach was used to extract the blood samples, and spectral data were collected using a portable FT-MIR spectrometer. Through the deconvolution analysis of the spectral data, a distinct spectral marker at 1565 cm−1 was identified based on a statistically significant analysis, only present in FM patients. This IR band has been linked to the presence of side chains of glutamate. An OPLS-DA algorithm created using the spectral region 1500 to 1700 cm−1 enabled the classification of the spectra into their corresponding classes (Rcv > 0.96) with 100% accuracy and specificity. This high-throughput approach allows unique metabolic signatures associated with LC and FM to be identified, allowing these conditions to be distinguished and implemented for in-clinic diagnostics, which is crucial to guide future therapeutic approaches.
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
Identificador del autor: 0000-0002-5261-6806
Fecha de alta del registro: 2023-11-18
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.mdpi.com/2227-9059/11/10/2704
Referencia al articulo segun fuente origial: Biomedicines. 11 (10):
Referencia de l'ítem segons les normes APA: Hackshaw KV; Yao S; Bao H; de Lamo Castellvi S; Aziz R; Nuguri SM; Yu L; Osuna-Diaz MM; Brode WM; Sebastian KR; Giusti MM; Rodriguez-Saona L (2023). Metabolic Fingerprinting for the Diagnosis of Clinically Similar Long COVID and Fibromyalgia Using a Portable FT-MIR Spectroscopic Combined with Chemometrics. Biomedicines, 11(10), -. DOI: 10.3390/biomedicines11102704
URL Documento de licencia: http://repositori.urv.cat/ca/proteccio-de-dades/
DOI del artículo: 10.3390/biomedicines11102704
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications
Repositori URV