Colorimetry and SERS dual-mode sensing of serotonin based on functionalized gold nanoparticles

Recent advances of electrochemical and optical point-of-care biosensors for detecting neurotransmitter serotonin biomarkers

Since its discovery in 1984, the monoamine serotonin (5-HT) has been recognized for its critical role as a neuromodulator in both the central and peripheral nervous systems. Recent research reveals that serotonin also significantly influences various neuronal activities. Historically, it was believed that peripheral serotonin, produced by tryptophan hydroxylase in intestinal cells, functioned primarily as a hormone. However, new insights have expanded its known roles, necessitating advanced detection methods. Biosensors have emerged as indispensable tools in biomedical diagnostics, enabling the rapid and minimally invasive detection of target analytes with high spatial and temporal resolution. This review summarizes the progress made in the past decade in developing optical and electrochemical biosensors for serotonin detection. We evaluate various sensing strategies that optimize performance in terms of detection limits, sensitivity, and specificity. The study also explores recent innovations in biosensing technologies utilizing surface-modified electrodes with nanomaterials, including gold, graphite, carbon nanotubes, and metal oxide particles. Applications range from in vivo studies to chemical imaging and diagnostics, highlighting future prospects in the field.

Sialic Acid-Functionalized Gold Nanoparticles for Sensitive and Selective Colorimetric Determination of Serotonin

We present a novel colorimetric method inspired by nature's complex mechanisms, capable of selectively determining serotonin with high sensitivity. This method exploits the inherent binding affinity of serotonin with sialic acid (SA) molecules anchored to gold nanoparticles (SA-AuNPs). Upon serotonin binding, SA-AuNPs aggregate, and a characteristic red shift in the absorbance of SA-AuNPs accompanied by a dramatic color change (red to blue) occurs, readily observable even without instrumentation. The proposed method effectively eliminates interventions from potential interfering species such as dopamine, epinephrine, l-tyrosine, glucosamine, galactose, mannose, and oxalic acid. The absence of a color change with l-tryptophan, a structurally related precursor of serotonin, further confirms the high selectivity of this approach for serotonin detection. The colorimetric method has a wide linear dynamic range (0.05-1.0 μM), low limit of detection (0.02 μM), and fast response time (5 min). The limit of detection of the method is lower than other colorimetric serotonin sensors reported so far. The possible use of the proposed method in biological sample analysis was evaluated by employing a serotonin recovery assay in processed human plasma. The recoveries ranged from 90.5 to 104.2%, showing promising potential for clinical applications.

Breaking Barriers: Exploring Neurotransmitters through In Vivo vs. In Vitro Rivalry

Neurotransmitter analysis plays a pivotal role in diagnosing and managing neurodegenerative diseases, often characterized by disturbances in neurotransmitter systems. However, prevailing methods for quantifying neurotransmitters involve invasive procedures or require bulky imaging equipment, therefore restricting accessibility and posing potential risks to patients. The innovation of compact, in vivo instruments for neurotransmission analysis holds the potential to reshape disease management. This innovation can facilitate non-invasive and uninterrupted monitoring of neurotransmitter levels and their activity. Recent strides in microfabrication have led to the emergence of diminutive instruments that also find applicability in in vitro investigations. By harnessing the synergistic potential of microfluidics, micro-optics, and microelectronics, this nascent realm of research holds substantial promise. This review offers an overarching view of the current neurotransmitter sensing techniques, the advances towards in vitro microsensors tailored for monitoring neurotransmission, and the state-of-the-art fabrication techniques that can be used to fabricate those microsensors.

Quantification of tramadol and serotonin by cobalt nickel tungstate in real biological samples to evaluate the effect of analgesic drugs on neurotransmitters

In this work, CoNiWO4 nanocomposite was used as an electrochemical sensor for the simultaneous electrochemical detection of tramadol and serotonin. The nanocomposite was synthesized using a hydrothermal method and characterized via XRD, SEM, TGA, Zeta, UV, and FTIR. The sensor was developed by depositing CoNiWO4-NPs onto the glassy carbon electrode surface. Tramadol and serotonin were detected by employing cyclic voltammetry (CV), differential pulse voltammetry (DPV), electrochemical impedance spectroscopy (EIS), and chronoamperometry. Analytes were detected at different pH, concentrations, and scan rates. The prepared sensor showed a 0-60 µM linear range, with a LOD of 0.71 µM and 4.29 µM and LOQ of 14.3 µM and 2.3 µM for serotonin and tramadol, respectively. Finally, the modified electrode (CoNiWO4-GCE) was applied to determine tramadol and serotonin in biological samples.

Research on the difference between patients with coronary heart disease and healthy controls by surface enhanced Raman spectroscopy

Coronary heart disease (CHD) is one of the primary causes of death globally. There are several diagnostic techniques for CHD at present, but they are invasive and with limited accuracy. In the work, measurement of human urine based on surface-enhanced Raman spectroscopy (SERS) was proposed to diagnose CHD. Urine samples of 157 CHD patients and 63 healthy controls (HC) were investigated by SERS. Statistical analysis of the measured data was then performed. It was found that there were intensity differences in nine Raman peaks (1223/1243/1272/1463/1481/1516/1536/1541/1550 cm^-1) between CHD and HC in their average SERS spectrum. Furthermore, principal component analysis (PCA)-linear discriminant analysis (LDA) was then utilized to establish a prediction model to classify CHD and HC. It revealed that the accuracy, specificity and sensitivity of the prediction model validated by leave-one-patient-out cross validation (LOPOCV) were 84.09%, 92.06% and 80.89%, respectively. Therefore, the proposed method can be employed as a non-invasive, rapid and accurate tool for CHD diagnosis in clinical application.