Flower-like WSe2 used as bio-matrix in ultrasensitive label-free electrochemical immunosensor for human immunoglobulin G determination.

The abnormal concentrations of human immunoglobulin G (hIgG) refers to many kinds of diseases. Analytical methods with the characteristics of rapid response, easy operation and high sensitivity should be designed to accurately determinate the hIgG levels in human serum. In this work, a label-free electrochemical immunosensor based on WSe2/rGO was developed to sensitively detect human immunoglobulin G. First, the flower-like transition metal dichalcogenides (TMDCs) Tungsten Diselenide (WSe2) with large effective specific surface area and porous structure was synthesized by hydrothermal synthesis. As a bio-matrix, the flower-like WSe2 efficiently increased the active sites for loading antibodies. Meanwhile, reduced graphene oxide (rGO) obtained by tannic acid reduction was used to improve the current response of the sensing interface. WSe2 was combined with rGO and the electrochemical active surface area (ECSA) of the sensing interface was enlarged to 2.1 times that of GCE. Finally, the combination of flower-like WSe2 and rGO broadened the detection range and reduced the detection limit of the sensing platform. The immunosensor exhibited a high sensitivity with a wide linear range of 0.01-1000 ng/mL and low detection limit of 4.72 pg/mL. The real sample analysis of hIgG were conducted under optimal conditions, and the spiked recovery rates were between 95.5 and 104.1%. Moreover, satisfactory results were obtained by testing the stability, specificity and reproducibility of the immunosensor. Therefore, it can be concluded that the as-proposed immunosensor has the application potential of clinical analyze of hIgG in human serum.

A Versatile Method for Quantitative Analysis of Total Iron Content in Iron Ore Using Laser-Induced Breakdown Spectroscopy.

Focus in quality assessment of iron ore is the content of total iron (TFe). Laser-induced breakdown spectroscopy (LIBS) technology possesses the merits of rapid, in situ, real-time multielement analysis for iron ore, but its application to quantitative TFe content is subject to interference of the iron matrix effect and the lack of suitable data mining tools. Here, a new method of LIBS-based variable importance back propagation artificial neural network (VI-BP-ANN) for quantitative TFe content in iron ore was first proposed. After the LIBS spectra of 80 representative iron samples were obtained, random forest (RF) was optimized by out-of-bag (OOB) error and then used to measure and rank variable importance. The variable importance thresholds and the number of neurons were optimized with five-fold cross-validation (CV) with correlation coefficient (R2) and root mean square error (RMSE). With using only 1.40% of full spectral variables to construct BP-ANN model, the resulted R2, the root mean squared error of prediction (RMSEP) and the modeling time of the final VI-BP-ANN model was 0.9450, 0.3174 wt%, and 24 s, respectively. Compared with full spectrum-based model, for example, BP-ANN, RF, support vector machine (SVM), and PLS and VI-RF model, the VI-BP-ANN model reduced overfitting and obtained the highest R2 and the lowest RMSE both for calibration and prediction. Meanwhile, the characteristics of variables selected by VI were analyzed. In addition to the elemental emission lines of Ca, Al, Na, K, Mn, Si, Mg, Ti, Zr, and Li, partial spectral baselines of 540-610 nm and 820-970 nm were also selected as characteristic variables, which indicated that VI can take into full consideration the elemental interactions and the spectral baselines. Our approach shows that LIBS combined with VI-BP-ANN is able to quantify TFe content rapidly and accurately in iron ore.

A specific identification platform based on biscuit-like bismuth nanosheets for label-free electrochemical immunosensor.

A label-free electrochemical immunosensor based on the biscuit-like bismuth nanosheets (BiNSs) and multi-wall carbon nanotubes-chitosan-gold nanoparticles (MWCNTs-Chit-AuNPs) was constructed for the detection of human immunoglobulin G (hIgG). The biscuit-like BiNSs prepared by one-step aqueous phase reduction method had a large electroactive surface area, 1.7 times that of bare glassy carbon electrode (GCE), which could load more antibodies and were used as a larger platform for the specific identification of antigens and antibodies. In addition, MWCNTs and AuNPs with good conductivity could be used to regulate the sensing interface, which promoted electron transfer greatly. Moreover, the AuNPs could stably anchor anti-hIgG by the affinity interaction between amine group of antibody and AuNPs, which greatly increased the number of anti-hIgG attached to the sensing platform. Under the optimal conditions, the proposed immunosensor exhibited excellent analytical performance for hIgG with a wide linear response of 0.01-1000 ng/mL and a low detection limit of 4.26 pg/mL. The electrochemical immunosensor exhibited favorable reproducibility, excellent specificity and high storage stability. Additionally, the immunosensor could be applied to determining hIgG in human serum samples as well. Considering these advantages, the electrochemical immunosensor has the potential application in clinical diagnosis.

Correction: Accuracy improvement on quantitative analysis of the total iron content in branded iron ores by laser-induced breakdown spectroscopy combined with the double back propagation artificial neural network.

Correction for 'Accuracy improvement on quantitative analysis of the total iron content in branded iron ores by laser-induced breakdown spectroscopy combined with the double back propagation artificial neural network' by Piao Su et al., Anal. Methods, 2022, 14, 427-437, DOI: 10.1039/D1AY01881G.

Accuracy improvement on quantitative analysis of the total iron content in branded iron ores by laser-induced breakdown spectroscopy combined with the double back propagation artificial neural network.

The rapid and accurate quantitative analysis of the total iron (TFe) content in iron ores is extremely important in global iron ore trade. Due to the matrix effect among iron ores from different origins, it is a major challenge to accurately determine the TFe content of iron ores by laser-induced breakdown spectroscopy (LIBS). The double back propagation artificial neural network (DBP-ANN) proposed in this paper provides a solution to improve the accuracy of LIBS in determining the TFe content of branded iron ores, which is a combination of pattern recognition and regression analysis based on BP-ANN. In this study, LIBS spectra of 80 batches of representative iron ore samples from 4 brands were collected. The univariate regression methods based on brand-independent and brand-hybrid were analyzed and compared for determining the TFe content of branded iron ores, and the multivariate model based on DBP-ANN was constructed for the first time. BP-ANN was employed to establish different quantitative models of the TFe content of each type of brand after brand classification of iron ores based on the BP-ANN algorithm. Compared with the brand-hybrid BP-ANN, the coefficient of determination (R2) of the test samples using DBP-ANN increased from 0.972 to 0.996, and the root mean square error of prediction (RMSEP) and the average relative error of prediction (AREP) were reduced from 0.456 wt% and 0.584% to 0.177 wt% and 0.228% respectively. Moreover, the prediction error based on the DBP-ANN model was within the error range (<0.275 wt%) accepted by the traditional chemical analysis method GB/T 6730.5-2009. Meanwhile, the established DBP-ANN method was also compared with the common multivariate method, and it showed better analytical performance. The results showed that LIBS combined with DBP-ANN has the potential to achieve rapid and accurate analysis of the TFe content of branded iron ores.

Interpretable deep learning-assisted laser-induced breakdown spectroscopy for brand classification of iron ores.

Brand classification of iron ores using laser-induced breakdown spectroscopy (LIBS) combined with artificial neural networks can quickly realize the compliance verification and guarantee the interests of both trading partners. However, its practical application is impeded by complex pretreatments and unexplained feature learning problems. According to the LIBS data characteristics of iron ores, a convolutional neural network (CNN) is designed to predict 16 types of brand iron ores from Australia, Brazil, and South Africa. The accuracies of the calibration set and the prediction set with five-fold cross-validation (5-CV) were 99.86% and 99.88%, and the value of loss function was 0.0356. Meanwhile, the established CNN method was also compared with common machine learning methods using raw spectra as input variables, and it outperformed other methods. For the first time, this work interprets the CNN's effectiveness layer by layer in self-adaptively extracting LIBS features through t-distributed stochastic neighbor embedding (t-SNE) and the quantitative data of major chemical components in iron ores. Our approach shows that deep learning assisted LIBS is able to significantly reduce manual factors in preprocessing and feature selection and has broad application prospects in the brand classification of iron ores.

Size-controllable carbon spheres doped Ni (II) for enhancing the catalytic oxidation of methanol.

Ni(II)/CSs were prepared using a simple two-step hydrothermal method. The morphology and composition of the catalysts were studied with scanning electron microscope, transmission electron microscope, and X-ray diffraction. Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy showed that the surface of the prepared carbon spheres was rich in hydroxyl groups, which was beneficial to remove CO intermediates, and therefore, improving the catalytic efficiency and the antipoisoning ability of the catalysts. The results of cyclic voltammetry and chronoamperometry showed that the electrocatalytic activity and stability of Ni(II)/CSs were higher than that of unloaded NiAc under alkaline environment. When the nickel content was 5 wt.%, the peak oxidation current density of methanol on Ni(II)/CSs electrocatalyst reached the maximum of 34.54 mA/cm2, which was about 1.8 times that of unloaded NiAc. These results indicate that Ni(II)/CSs has potential applications in the electrocatalytic oxidation of methanol.

Highly sensitive electrochemical immunoassay integrated with polymeric nanocomposites and enhanced SiO2@Au core-shell nanobioprobes for SirT1 determination.

An ultra-highly sensitive electrochemical immunosensor for SirT1 (a key protein in age-related diseases) evaluation has been designed, employing polymeric nanocomposites as sensing platform and core-shell SiO2@Au to immobilize HRP-Ab2 as nanobioprobes. The approach includes chemical synthesis of PAMAM-Au-MWCNT nanocomposites (PNCs) with abundant PAMAM-Au nanoparticles immobilized on the MWCNT matrix, and biochemically synthesis of nanobioprobes with highly dispersed SiO2@Au tracing tags for successive efficient load functionalized enzyme-antibodies (HRP-Ab2). The PNCs nanocomposites could improve the efficiency of immune response via abundant capture antibodies (Ab1#1) on the electrode surface, and accelerate electron transfer through MWCNT and Au nanoparticles. Besides, the SiO2@Au was employed as tracing tags to label numerous HRP-Ab2 to further enhance signal readout during HRP-thionine-H2O2 system. Under optimal conditions, the signal intensity was linearly related to the concentration of SirT1 in the range of 20 pg mL-1 to 500 ng ml-1, and the limit of detection was 12.5 pg mL-1. It is noteworthy that the proposed immunoassay protocol has been successfully applied to evaluate SirT1 expression in cells by different treatment with high sensitivity and accuracy.

Sirtuin 1 evaluation with a novel immunoassay approach based on TiO2-Au label and hyperbranched polymer hybrid.

Accurate and highly sensitive evaluation of the sirtuin 1 (SirT1) level is becoming increasingly important for understanding the contribution of SirT1 in metabolism pathways. Here, a novel electrochemical immunoassay of SirT1 based on crosslinked hyperbranched azo-polymer decorated with gold colloids (Au-HAP) as sensing platform and titanium dioxide (TiO2)-Au nanocomposites to immobilize secondary antibody-horseradish peroxidase (Ab2-HRP) as electrochemical labels has been designed. Greatly enhanced sensitivity was achieved by exploiting the excellent conductivity of Au nanoparticle, the amplification effect of Au-HAP and TiO2-Au, and the favorable catalytic ability of HRP. The nanocomposites of Au-HAP and TiO2-Au could attach numerous capture antibodies on the surface for significant immune recognition efficiency. Meanwhile, the TiO2-Au-labeled Ab2-HRP using an HRP-thionine-H2O2 (hydrogen peroxide) detection system could further induce signal readout. Under optimal conditions, the signal intensity was linearly related to the concentration of SirT1 in the range of 1-500 ng ml(-1), and the limit of detection was 0.28 ng ml(-1). The developed biosensor exhibits attractive performance for the analysis of SirT1, with rapid response, high sensitivity, and high accuracy, and could become a promising technique for protein detection.

A TiO2-Au-polymer hybrid system for the photoelectrochemical immunoassay of SirT1.

Human sirtuin1 (SirT1), which is a member of the sirtuin family, plays an important role in a wide range of cellular processes. Here we demonstrate a new strategy for the photoelectrochemical assay of SirT1 in different cell lines based on a semiconductor-polymer hybrid system consisting of Au-polymer and TiO2-Au nanocomposites. Au-polymer (GC-HBAP) hybrids were synthesized from crosslinked hyperbranched azo-polymer and gold colloids and then used as an immobilization platform for SirT1 antibody. Gold-doped TiO2 (TiO2-Au) nanocomposites were prepared as the photoelectrochemical labels for signal readout in the sandwiched immunoassay. The integration of GC-HBAP with TiO2-Au facilitated the electron transfer and the photoelectrocatalytic reaction, resulting in good analytical performance with high sensitivity, selectivity and rapid response for the analysis of SirT1 levels in different cell lines. This proposed semiconductor-polymer system might open a new perspective for the development of a highly sensitive photoelectrochemical immunosensor, and have potentially promising applications in assays of other proteins.

Sensitive electrochemical immunosensor for α-synuclein based on dual signal amplification using PAMAM dendrimer-encapsulated Au and enhanced gold nanoparticle labels.

A novel electrochemical immunosensor for sensitive detection of α-synuclein (α-SYN), a very important neuronal protein, has been developed based on dual signal amplification strategy. Herein, G4-polyamidoamine dendrimer-encapsulated Au nanoparticles (PAMAM-Au nanocomposites) were covalently bound on the poly-o-aminobenzoic acid (poly-o-ABA), which was initially electropolymerized on the electrode surface to perform abundant carboxyl groups. The formed immunosensor platform, PAMAM-Au, was proved to provide numerous amino groups to allow highly dense immobilization of antigen, and facilitate the improvement of electrochemical responses as well. Subsequently, the enhanced gold nanoparticle labels ({HRP-Ab(2)-GNPs}) were fabricated by immobilizing horseradish peroxidase-secondary antibody (HRP-Ab(2)) on the surface of gold nanoparticles (GNPs). After an immunoassay process, the {HRP-Ab(2)-GNPs} labels were introduced onto the electrode surface, and produced an electrocatalytic response by reduction of hydrogen peroxide (H(2)O(2)) in the presence of enzymatically oxidized thionine. On the basis of the dual signal amplification of PAMAM-Au and {HRP-Ab(2)-GNPs} labels, the designed immunosensor displayed an excellent analytical performance with high sensitivity and stability. This developed strategy was successfully proved as a simple, cost-effective method, and could be easily extended to other protein analysis schemes.

A dual enzymatic-biosensor for simultaneous determination of glucose and cholesterol in serum and peritoneal macrophages of diabetic mice: evaluation of the diabetes-accelerated atherosclerosis risk.

In this paper, a novel dual enzymatic-biosensor is described for simultaneous determination of glucose and cholesterol in serum and peritoneal macrophages (PMs) of diabetic mice to evaluate the risk of diabetes-accelerated atherosclerosis. The biosensor was constructed by a three-step method. First, a poly-thionine (PTH) film was assembled on the surface of glassy carbon electrode by cyclic voltammetric electropolymerization of thionine, which serves as an electron transfer mediator (ETM). Second, gold nanoparticles (GNPs) were covered on the surface of PTH facilitating the electron transfer between glucose oxidase (GOx), cholesterol oxidase (ChOx) and electrode. Finally, the enzymes, GOx, cholesterol esterase (ChE), and ChOx, were covalently attached to the PTH layer through a chitosan (CH) linker. The PTH coupled with GNPs provides good selectivity, high sensitivity and little crosstalk for the dual enzymatic-biosensor. The developed biosensor had good electrocatalytic activity toward the oxidations of glucose and cholesterol, exhibiting a linear range from 0.008 mM to 6.0 mM for glucose with a detection limit of 2.0 µM, and a linear range from 0.002 mM to 1.0 mM for cholesterol with a detection limit of 0.6 µM. The results of the diabetic mice demonstrated that the cholesterol level did not change obviously with the increase of glucose level in serum, while the cholesterol level was induced with the increase of the glucose level in PMs. Previous studies have shown that the large accumulation of cholesterol in macrophage could lead to macrophage foam cell formation, which is the hallmark of early atherosclerosis. This study provides useful further evidences for the development of diabetes-accelerated atherosclerosis.

A photoelectrochemical immunosensor based on Au-doped TiO2 nanotube arrays for the detection of α-synuclein.

α-Synuclein (α-SYN) is a very important neuronal protein that is associated with Parkinson's disease. In this paper, we utilized Au-doped TiO(2) nanotube arrays to design a photoelectrochemical immunosensor for the detection of α-SYN. The highly ordered TiO(2) nanotubes were fabricated by using an electrochemical anodization technique on pure Ti foil. After that, a photoelectrochemical deposition method was exploited to modify the resulting nanotubes with Au nanoparticles, which have been demonstrated to facilitate the improvement of photocurrent responses. Moreover, the Au-doped TiO(2) nanotubes formed effective antibody immobilization arrays and immobilized primary antibodies (Ab(1)) with high stability and bioactivity to bind target α-SYN. The enhanced sensitivity was obtained by using {Ab(2)-Au-GOx} bioconjugates, which featured secondary antibody (Ab(2)) and glucose oxidase (GOx) labels linked to Au nanoparticles for signal amplification. The GOx enzyme immobilized on the prepared immunosensor could catalyze glucose in the detection solution to produce H(2)O(2), which acted as a sacrificial electron donor to scavenge the photogenerated holes in the valence band of TiO(2) nanotubes upon irradiation of the other side of the Ti foil and led to a prompt photocurrent. The photocurrents were proportional to the α-SYN concentrations, and the linear range of the developed immunosensor was from 50 pg mL(-1) to 100 ng mL(-1) with a detection limit of 34 pg mL(-1). The proposed method showed high sensitivity, stability, reproducibility, and could become a promising technique for protein detection.

Study on acetylcholinesterase inhibition induced by endogenous neurotoxin with an enzyme-semiconductor photoelectrochemical system.

The integration of Au-doped TiO(2) nanotubes with biomolecule acetylcholinesterase (AChE) yields a novel AChE-Au-TiO(2) hybrid system, which provides a new rapid and valid photoelectrochemical approach to the determination of AChE inhibition induced by endogenous neurotoxin.

A new microdialysis-electrochemical device for in vivo simultaneous determination of acetylcholine and choline in rat brain treated with N-methyl-(R)-salsolinol.

Acetylcholine (ACh) and choline (Ch) play a critical role in cholinergic neurotransmission and the abnormalities in their concentrations are related to several neural diseases. Therefore, the in vivo determination of ACh and Ch is important to the research on neurodegenerative disorders. In this work, electrochemical biosensors based on poly(m-(1,3)-phenylenediamine) (pmPD) and polytyramine (PTy) modified enzyme electrodes were fabricated. The electropolymerized pmPD polymer was used to exclude interfering substances and the PTy layer facilitated the immobilization of acetylcholinesterase (AChE) and choline oxidase (ChOx). Then, ACh/Ch sensor and Ch sensor were coupled with microdialysis to produce a novel device, which provides a sensitive and selective method for simultaneous determination of ACh and Ch. This method has detection limits of 63.0+/-3.4 nM for ACh and 25.0+/-1.2 nM for Ch. The integrated device was successfully applied to assessing the impact of endogenous neurotoxin N-methyl-(R)-salsolinol [1(R),2-dimethyl-6,7-dihydroxy-1,2,3,4-tetrahydroisoquinoline, (R)-NMSal] on ACh and Ch concentration, which is of great benefit to understand the pathogenesis of Parkinson's disease.

Effect of (R)-salsolinol and N-methyl-(R)-salsolinol on the balance impairment between dopamine and acetylcholine in rat brain: involvement in pathogenesis of Parkinson disease.

BACKGROUND: Parkinson disease (PD), a progressive neurodegenerative disease, affects at least 1% of population above the age of 65. Although the specific etiology of PD remains unclear, recently the endogenous neurotoxins such as (R)-salsolinol [(R)-Sal] and N-methyl-(R)-salsolinol [(R)-NMSal] have been thought to play a major role in PD. Much interest is focused on the degeneration of dopamine neurons induced by these neurotoxins. However, little literature is available on the impact of endogenous neurotoxins on the balance between dopamine (DA) and acetylcholine (ACh). METHODS: After injection of (R)-Sal or (R)-NMSal into the rat brain striatum, the concentrations of DA and its metabolites were detected by HPLC with electrochemical detection. We assessed the influence of neurotoxins on acetylcholinesterase (AChE) activity and developed a microdialysis-electrochemical device to measure ACh concentrations with enzyme-modified electrodes. RESULTS: (R)-Sal and (R)-NMSal led to concentration-dependent decreases in the activity of AChE. ACh concentrations in striatum treated with (R)-Sal or (R)-NMSal were increased to 131.7% and 239.8% of control, respectively. As to the dopaminergic system, (R)-NMSal caused a significant decrease in DA concentrations and (R)-Sal reduced the concentrations of DA metabolites in the striatum. CONCLUSIONS: (R)-Sal and (R)-NMSal exerted a considerable effect on the balance between DA and ACh by impairing the cholinergic system as well as the dopaminergic system. It is likely that the disruption of balance between DA and ACh plays a critical role in the pathogenesis of neurotoxin-induced PD.