A simple method that relies only on an electrochemical workstation has been investigated to fabricate a highly sensitive glutamate microbiosensor for potential neuroscience applications. In this study, in order to develop the highly sensitive glutamate electrode, a 100 µm platinum wire was modified by the electrochemical deposition of gold nanoparticles, Prussian blue nanocubes, and reduced graphene oxide sheets, which increased the electroactive surface area; and the chitosan layer, which provided a suitable environment to bond the glutamate oxidase. The optimization of the fabrication procedure and analytical conditions is described. The modified electrode was characterized using field emission scanning electron microscopy, impedance spectroscopy, and cyclic voltammetry. The results exhibited its excellent sensitivity for glutamate detection (LOD = 41.33 nM), adequate linearity (50 nM–40 µM), ascendant reproducibility (RSD = 4.44%), and prolonged stability (more than 30 repetitive potential sweeps, two-week lifespan). Because of the important role of glutamate in neurotransmission and brain function, this small-dimension, high-sensitivity glutamate electrode is a promising tool in neuroscience research.
Background: Currently, Parkinson’s disease
(PD) diagnosis is mainly based on medical history and physical examination,
and there is no objective and consistent basis. By the time of diagnosis,
the disease would have progressed to the middle and late stages. Pilot
studies have shown that a unique smell was present in the skin sebum
of PD patients. This increases the possibility of a noninvasive diagnosis
of PD using an odor profile. Methods: Fast gas chromatography
(GC) combined with a surface acoustic wave sensor with embedded machine
learning (ML) algorithms was proposed to establish an artificial intelligent
olfactory (AIO) system for the diagnosis of Parkinson’s through
smell. Sebum samples of 43 PD patients and 44 healthy controls (HCs)
from Fourth Affiliated Hospital of Zhejiang University School of Medicine,
China, were smelled by the AIO system. Univariate and multivariate
methods were used to identify the significant volatile organic compound
(VOC) features in the chromatograms. ML algorithms, including support
vector machine, random forest (RF), k nearest neighbor
(KNN), AdaBoost (AB), and Naive Bayes (NB), were used to distinguish
PD patients from HC based on the VOC peaks in the chromatograms of
sebum samples. Results: VOC peaks with average retention
times of 5.7, 6.0, and 10.6 s, respectively, corresponding to octanal,
hexyl acetate, and perillic aldehyde, were significantly different
in PD and HC. The accuracy of the classification based on the significant
features was 70.8%. Based on the odor profile, the classification
had the highest accuracy and F1 of the five models with 0.855 from
NB and 0.846 from AB, respectively, in the process of model establishing.
The highest specificity and sensitivity of the five classifiers were
91.6% from NB and 91.7% from RF and KNN, respectively, in the evaluating
set. Conclusions: The proposed AIO system can be
used to diagnose PD through the odor profile of sebum. Using the AIO
system is helpful for the screening and diagnosis of PD and is conducive
to further tracking and frequent monitoring of the PD treatment process.
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