A multidisciplinary group of scientists from the University of Rzeszów, including Bohdan Mahlovanyi, Yaroslav Shpotyuk, Grzegorz Gruzeł, and Józef Cebulski from the Institute of Physics; Andriy Lopushansky from the Institute of Computer Science; Nikola Król, Kornelia Łach, Aneta Kowal, and Agnieszka Gala Błądzinska from the Institute of Medical Sciences; and Kamil Szmuc from the Institute of Materials Engineering, in collaboration with the University of Rennes (Catherine Boussard-Pledel and Bruno Bureau) and Austin Peay State University (Michael Truax and Roman Golovchak), have published an article in the Biosensing and Bioelectronics journal (Impact factor: 10.7).

The work, titled "Diagnostic and Prognostic Perspectives of Fabry Disease via Fiber Evanescent Wave Spectroscopy Advanced by Machine Learning" presents an innovative approach offering a promising, cost-effective diagnostic tool sensitive enough for the early detection and monitoring of Fabry disease.

Fabry disease is a rare genetic kidney disorder linked to the X chromosome, caused by deficient or absent activity of the enzyme α-galactosidase (α-Gal). This deficiency leads to the accumulation of sphingolipids in the lysosomes of various cell types, including those in the heart, kidneys, skin, eyes, and central nervous system, resulting in organ failure and premature death.

Currently, Fabry disease diagnosis relies on expensive, time-consuming genetic tests and enzymatic analysis, often leading to delayed or inaccurate diagnoses, which contribute to the rapid progression of the disease. However, if this condition is diagnosed early enough, it can be effectively treated causally. In this research, the authors demonstrate that mid-infrared Fiber Evanescent Wave Spectroscopy (FEWS), combined with statistical analysis and machine learning algorithms, is an innovative and reliable method for detecting globotriaosylsphingosine (Lyso-Gb3)—a Fabry disease biomarker—in urine and serum samples by analyzing infrared spectra. This method enables highly sensitive, cost-effective, and rapid diagnostic tests.

More information:

https://doi.org/10.1016/j.bios.2025.117139

https://www.sciencedirect.com/science/article/pii/S0956566325000132