Spectroscopic Technologies and Computational Approaches for Real-Time Detection of FMN as Biomarker during Machine Perfusion

Organ transplant waiting lists are progressively lengthening worldwide due to the shortage of suitable organs, resulting in a high mortality rate among patients. The traditional preservation method is static cold storage, but in recent years a new technique has emerged: machine perfusion. This approach consists of perfusing the organ with an oxygenated solution, either acellular or blood-based, with the aim of reactivating aerobic metabolism prior to transplantation and enabling the assessment of organ viability through the analysis of metabolites released into the perfusion fluid. This doctoral thesis focuses on the use of technologies for the real-time detection of Flavin Mononucleotide (FMN), considered a biomarker of ischemia-reperfusion injury, during organ perfusion. The methodologies employed are based on spectrophotometric measurements, UV-visible absorption, and fluorescence, using both standard laboratory instruments, such as benchtop spectrophotometers, and an innovative stand-alone device, specifically developed by the company funding the doctoral research, capable of detecting fluorescence signals directly within the perfusion circuit. A measurement protocol was proposed for the real-time monitoring of FMN and NADH concentrations using benchtop spectrophotometers, with potential clinical application. The stand-alone device was calibrated to detect FMN in the range of 12–237 ng/mL according to the developed protocol. Furthermore, dedicated software was implemented to identify the FMN spectrum and distinguish it from potential interferents, while a machine learning algorithm allows automatic, real-time quantification of FMN concentration during organ perfusion. The prototype was validated in preclinical settings, both on animal and human organs, demonstrating excellent performance in biomarker detection. This thesis demonstrates the high potential of this technology for large-scale application in clinical settings.

Grantor dc:publisher

Università degli Studi di Cagliari

Year dc:date

2026

Author dc:creator

• CADINU, LORENZO AGOSTINO

Contributors dc:contributor

• RICCI, PIER CARLO
• BARBARO, MASSIMO

• Settore ING-INF/01 - Elettronica

Statement dc:rights

• info:eu-repo/semantics/openAccess

Language dc:language

eng

Handle dc:identifier

https://hdl.handle.net/11584/471685

OAI identifier oai:identifier

oai:iris.unica.it:11584/471685