Conference Agenda

Hemoglobin (Hb), a key iron-containing biomolecule responsible for oxygen transport, serves as a critical biomarker for diagnosing disorders such as β-thalassemia and sickle cell anemia. Traditional blood assays for Hb analysis often suffer from high cost, long processing times, and limited accessibility. Addressing these challenges, we present a novel nano-biosensing platform based on Surface-Enhanced Raman Spectroscopy (SERS) for rapid, specific, and reproducible detection of Hb from less than 10 µL sample volume.

The sensor substrate comprises of gold and silver thin films fabricated via pulsed laser ablation and electrochemical deposition, optimized for excitation with 532 nm and 633 nm lasers. These nanofilms were functionalized with a synthesized heteroaromatic ligand (L), derived from α-lipoic acid and 2-(2-pyridine)imidazo[4,5-f]-1,10-phenanthroline. The lipoic acid moiety anchors the ligand to the nanometallic surface, while the phenanthroline unit exhibits strong affinity to the iron center in the heme group, enabling high specificity toward Hb.

The developed sensor demonstrates high stability with consistent performance. Detection is based on the appearance of a characteristic SERS band at 1390 cm⁻¹, associated with the porphyrin methine bridge in Hb. This signal scales reliably with Hb concentration and enables differentiation of Fe²⁺/Fe³⁺ redox states, corresponding to oxyHb and deoxyHb forms, offering insight into the oxygen-carrying capacity of blood. Density Functional Theory-Molecular Dynamics (DFT-MD) simulations supported experimental spectral assignments, validating vibrational features of ligand L and Hb interactions.

Infrared spectroscopy, coupled with multivariate analysis, has become a valuable tool for biofluid analysis and particularly urine. However, it often requires extensive calibration with real samples or artificial urine spiked with standards. The prediction of UACR (Urinary Albumin to Creatinine Ratio), a key diagnostic marker for Diabetic Kidney Disease (DKD), requires the optimization the experimental conditions to obtain the best prediction for both albumin and creatinine. To reduce experimental time and resource consumption, we propose using in-silico simulated spectra to contribute to design experiment and optimization. We follow a bottom-up approach and simulate urine spectra based on linear combinations of the pure infrared spectra of the most abundant urine components. Resulting simulated spectra are comparable to the artificial and real samples except for minor contaminants from the preconcentration membrane filters. Different experimental conditions, such as preconcentration factors are tested, and obtained calibration models are almost identical for simulated and artificial datasets. Once experimental conditions are optimized, we process the real urine samples accordingly. Resulting machine learning models yield similar quantification and classification prediction errors when compared to models calibrated with in silico datasets. These findings highlight that simulated spectra are effective in guiding experimental design by helping to discard suboptimal pretreatment conditions, ultimately reducing resource use.

Epidemiological surveys indicate that Autism Spectrum Disorder (ASD) cases are on an increasing trend worldwide. The etiology of this multifactorial neurodevelopmental disorder is still unclear, but it is thought to be linked to both genetic and environmental factors [1]. Among the latter, exposure to neurotoxicants, and particularly to metallic pollutants, is increasingly thought to play a significant role in the onset of ASD in pediatric patients. While early diagnosis is crucial, ASD nowadays is still largely diagnosed based on behavioral evaluation and developmental history, with two typical pitfalls: 1) different disorders can manifest with similar symptoms; 2) behavioral changes may go unnoticed and the disorder undiagnosed for extended periods of time.

In this work, we present our first results on the development of a spectroscopic method to obtain a metallomic profile linked to ASD with the goal of improving the accuracy of its diagnosis and, in the medium-long term, its treatment and prevention. To this end, we employed Laser-Induced Breakdown Spectroscopy (LIBS) to interrogate aqueous solutions and microdrops of blood serum deposited and dried on solid substrates and withdrawn from ASD subjects and healthy controls.

Moreover, we will discuss the effect on the intensity of LIBS spectra intensity, and on the consequent classification ability, of two substrate-modification methods for the analysis of trace elements, the first based on the generation of Laser-Induced Periodic Surface Structures (LIPSS), the second based on the substrate functionalization with noble-metal nanoparticles, prior to the LIBS analysis of the deposited fluids.