Spectroscopic Investigation and Nanoscale Characterization of Epinephrine Autooxidation under Alkaline Conditions

Single-Component Double-Emissive Ratiometric Probe: Toward Machine Learning Driven Detection and Discrimination of Neurological Biomarkers

This study presents an attractive single-component ratiometric fluorescent sensor that utilizes the oxidation of BSA-protected Au nanoclusters (BSA-Au NCs) by N-Bromosuccinimide (NBS) to detect catecholamine neurotransmitters and their metabolites, which are critical biomarkers for neurological diseases like neuroblastoma, pheochromocytomas, and paragangliomas. In this detailed sensing platform, NBS induces a noticeable fluorescence change in the emission of BSA- Au NCs, including the extinction of the emission peak at 650 nm and the simultaneous appearance of an emission peak at 450 nm. This shift represents a clear transition in the emission color of the probe from red to blue. The oxidation of Au NCs offers a promising approach for developing a ratiometric probe using a single fluorophore, eliminating the need to combine two individual fluorophores. The presence of neurogenic biomarkers inhibits the oxidation of BSA-Au NCs, varying with the concentration and identity of each analyte, making distinct changes in the spectral profiles along with vivid color variations. Spectral changes and RGB data derived from emission colors were analyzed using machine learning techniques, specifically linear discriminant analysis (LDA) for classification and partial least-squares regression (PLS-R) for multivariate calibration. Results from LDA and PLS-R highlighted the strong potential of the designed sensor for differentiating and quantifying these biomarkers. Furthermore, the successful application of this sensor in detecting and distinguishing these analytes in human urine provides valuable insights for clinical analysis in screening and diagnosing neurological disorders.

Experimental Methods to Get Polydopamine Films: A Comparative Review on the Synthesis Methods, the Films' Composition and Properties

In 2007, polydopamine (PDA) films were shown to be formed spontaneously on the surface of all known classes of materials by simply dipping those substrates in an aerated dopamine solution at pH = 8.5 in the presence of Tris(hydroxymethyl) amino methane buffer. This universal deposition method has raised a burst of interest in surface science, owing not only to the universality of this water based one pot deposition method but also to the ease of secondary modifications. Since then, PDA films and particles are shown to have applications in energy conversion, water remediation systems, and last but not least in bioscience. The deposition of PDA films from aerated dopamine solutions is however a slow and inefficient process at ambient temperature with most of the formed material being lost as a precipitate. This incited to explore the possibility to get PDA and related films based on other catecholamines, using other oxidants than dissolved oxygen and other deposition methods. Those alternatives to get PDA and related films are reviewed and compared in this paper. It will appear that many more investigations are required to get better insights in the relationships between the preparation method of PDA and the properties of the obtained coatings.

Machine-learning assisted multiplex detection of catecholamine neurotransmitters with a colorimetric sensor array

Catecholamine neurotransmitters (CNs), such as dopamine (DA), epinephrine (EP), norepinephrine (NEP), and levodopa (LD), are recognized as the primary biomarkers of a variety of neurological illnesses. Therefore, simultaneous monitoring of these biomarkers is highly recommended for clinical diagnosis and treatment. In this study, a high-performance colorimetric artificial tongue has been proposed for the multiplex detection of CNs. Different aggregation behaviors of gold nanoparticles in the presence of CNs under various buffering conditions generate unique fingerprint response patterns. Under various buffering conditions, the distinct acidity constants of CNs, and consequently their predominant species at a given pH, drive the aggregation of gold nanoparticles (AuNPs). The utilization of machine learning algorithms in this design enables classification and quantification of CNs in various samples. The response profile of the array was analyzed using the linear discriminant analysis algorithm for classification of CNs. This colorimetric sensor array is capable of accurately distinguishing between individual neurotransmitters and their combinations. Partial least squares regression was also applied for quantitation purposes. The obtained analytical figures of merit (FOMs) and linear ranges of 0.6-9 μM (R² = 0.99) for DA, 0.1-10 μM (R² = 0.99) for EP, 0.1-9 μM (R² = 0.99) for NEP and 1-70 μM (R² = 0.99) for LD demonstrated the potential applicability of the developed sensor array in precise and accurate determination of CNs. Finally, the feasibility of the array was validated in human urine samples as a complex biological fluid with LODs of 0.3, 0.5, 0.2, and 1.9 μM for DA, EP, NEP, and LD, respectively.