An imprinted polymeric matrix containing DNA for electrochemical sensing of 2,4-dichlorophenoxyacetic acid.

The authors describe an electrochemical method for the determination of herbicide 2,4-dichlorophenoxyacetic acid (2,4-D). It is based on the use of a molecularly imprinted polymer (MIP) and of dsDNA as a bio-specific substance. The modified electrode was prepared by electropolymerization of ortho-phenylenediamine (oPD) in the presence of DNA and of 2,4-D (the template). The imprinted MIP was placed on a pencil graphite electrode (PGE) modified with chitosan and multiwalled carbon nanotubes (MWCNTs). The template was removed with 0.4 M NaOH. The interaction of DNA with 2,4-D leads to its adsorption on the electrode, and this increases the sensitivity and selectivity of the method. After rebinding 2,4-D, the decrease in the peak current of oxidation of iron(II) acting as an electrochemical redox probe was measured by differential pulse voltammetry (DPV). The current, typically measured at around 0.5 V, increases linearly in the 0.01 to 10 pM 2,4-D concentration range, and the detection limit is 4.0 fM. The method is highly selective for 2,4-D. The modified electrode was applied to quantify 2,4-D in spiked environmental water and soil samples and gave absolute recoveries varying from 91.5 to 109.0%. Graphical abstractSchematic representation of the fabrication of an electrochemical sensor for determination of 2,4-dichlorophenoxyacetic acid (2,4-D). Initially, the electrode was modified with chitosan and MWCNTs and then a composite was formed on it consisting of ortho-phenylenediamine (oPD), DNA and 2,4-D.

Shuffling cross-validation-bee algorithm as a new descriptor selection method for retention studies of pesticides in biopartitioning micellar chromatography.

Bee algorithm (BA) is an optimization algorithm inspired by the natural foraging behaviour of honey bees to find the optimal solution which can be proposed to feature selection. In this paper, shuffling cross-validation-BA (CV-BA) was applied to select the best descriptors that could describe the retention factor (log k) in the biopartitioning micellar chromatography (BMC) of 79 heterogeneous pesticides. Six descriptors were obtained using BA and then the selected descriptors were applied for model development using multiple linear regression (MLR). The descriptor selection was also performed using stepwise, genetic algorithm and simulated annealing methods and MLR was applied to model development and then the results were compared with those obtained from shuffling CV-BA. The results showed that shuffling CV-BA can be applied as a powerful descriptor selection method. Support vector machine (SVM) was also applied for model development using six selected descriptors by BA. The obtained statistical results using SVM were better than those obtained using MLR, as the root mean square error (RMSE) and correlation coefficient (R) for whole data set (training and test), using shuffling CV-BA-MLR, were obtained as 0.1863 and 0.9426, respectively, while these amounts for the shuffling CV-BA-SVM method were obtained as 0.0704 and 0.9922, respectively.

Structurally responsive oligonucleotide-based single-probe lateral-flow test for detection of miRNA-21 mimics.

A single-probe strip test for the rapid and sensitive detection of miRNA-21 mimics is reported herein. Highly specific structurally responsive bi-functional, thiol and biotin, DNA/LNA oligonucleotide probes (molecular beacons-MB) were designed and conjugated with gold nanoparticles (AuNPs) (i.e. biotin-MB-AuNPs). The proposed design had the ability to modulate the accessibility of the biotin group as a function of the presence of a miRNA target allowing the interaction of the boilable with the streptavidin test zone only in the presence of the miRNA-21 mimics. For quantitative evaluation, images of the strip tests were recorded using a flatbed scanner (Epson Perfection V370 Photo). The colour intensities of the test zones of the strip tests were analysed with the ImageJ software (Scion Corp., USA) and quantified as a function of pixel intensity. The response of the strip test was linear over the range 0.5 to 20 nM miRNA-21 (limit of detection of 115 pM) and showed good reproducibility (intra and inter CVs below 8%); furthermore, the assay was shown to be highly selective, discriminating other interference miRNAs mimics (e.g. miRNA-221 and miRNA-205). Finally, the proposed strip test was used for detection of miRNA-21 mimics in spiked serum samples, demonstrating its potential for point-of-care clinical applications. Main advantages of the single-probe strip test design are its versatility, simplicity and robustness, which can be easily extended to other miRNA targets by tuning the sequence of the single probe. Furthermore, the use of the structurally responsive single probe is particularly relevant in the case of short-length targets, such as miRNA, whereas a conventional sandwich approach might require a careful control of assay conditions such as hybridization temperature and salt concentration.

Bee algorithm and adaptive neuro-fuzzy inference system as tools for QSAR study toxicity of substituted benzenes to Tetrahymena pyriformis.

A quantitative structure-activity relationship (QSAR) was developed to predict the toxicity of substituted benzenes to Tetrahymena pyriformis. A set of 1,497 zero- to three-dimensional descriptors were used for each molecule in the data set. A major problem of QSAR is the high dimensionality of the descriptor space; therefore, descriptor selection is one of the most important steps. In this paper, bee algorithm was used to select the best descriptors. Three descriptors were selected and used as inputs for adaptive neuro-fuzzy inference system (ANFIS). Then the model was corrected for unstable compounds (the compounds that can be ionized in the aqueous solutions or can easily metabolize under some conditions). Finally squared correlation coefficients were obtained as 0.8769, 0.8649 and 0.8301 for training, test and validation sets, respectively. The results showed bee-ANFIS can be used as a powerful model for prediction of toxicity of substituted benzenes to T. pyriformis.

Ti3C2 MXene-based nanozyme as coreaction accelerator for enhancing electrochemiluminescence of glucose biosensing.

Delamination of the exfoliated multilayer MXenes with electro-catalysts, not only leads to increasing surface area for high electrochemiluminescent (ECL) signal tracer loading but also provides highly sensitive achievements in a coreaction accelerator manner. To this end, herein, we used bromophenol blue (BPB)-delaminated multilayer Ti3C2 MXene as both a coreaction accelerator to promote the electrochemiluminescent (ECL) reaction rate of luminol (LUM) and the co-reactant H2O2 and a substrate for retaining high loading of glucose oxidase (GOx)-conjugated polyethylene imine (PEI) along with luminophore species into more open structure of Ti3C2 MXene for sensitive detection of glucose. In the presence of glucose, in situ generating H2O2 product through a GOx-catalyzed process could produce abundant •OH radicals via the peroxidase-like activity of the BPB@Ti3C2 in the LUM ECL reaction. Moreover, decreasing the distance between the high-content LUM into the BPB@Ti3C2 and the generated •OH, minimizes the decomposition of highly active •OH, providing a superb ECL signal. Last, the proximity of incorporated GOx into the delaminated Ti3C2 MXene near the electrode allows efficient electron transfer between the electrode and enzyme. The integration of such amplifying effects endowed high sensitivity and excellent selectivity for glucose with a low limit of detection of 0.02 µM in the wide range of 0.01 µM-40,000 µM, enabling the feasibility of the glucose analysis in human serum samples. Overall, the enhanced ECL based on the BPB@Ti3C2 opens a new horizon to develop highly sensitive MXene-based ECL toward the field of biosensors.

An electrochemical sensor for attomolar determination of mercury(II) using DNA/poly-L-methionine-gold nanoparticles/pencil graphite electrode.

The present work describes an ultrasensitive electrochemical sensor for determination of mercury(II) using deoxyribonucleic acid/poly-L-methionine-gold nanoparticles/pencil graphite electrode (DNA/PMET-AuNPs/PGE). To fabricate this biosensor, L-methionine (L-MET) was electropolymerized on the PGE surface followed by simultaneous electrochemical entrapment of AuNPs. Next, DNA was immobilized on the PMET-AuNPs/PGE by applying a 0.5 V potential. The surface area of modified and unmodified electrodes was determined by chronocoulometric technique. Hg2+ was detected in the linear dynamic range of 0.1 aM to 0.1 nM, and the detection limit was determined as 0.004 aM using square wave anodic stripping voltammetry (SWASV) under optimized conditions. The DNA/PMET-AuNPs/PGE demonstrated good selectivity toward Hg2+ against other metal ions such as V4+, Pb2+, Cr3+, Cd2+, Cu2+, Zn2+, Sn2+, In3+, Ge4+, and Fe3+. Real samples studies were carried out in sea water and fish samples.

QSAR Studies of Halogenated Pyrimidine Derivatives as Inhibitors of Human Dihydroorotate Dehydrogenase Using Modified Bee Algorithm.

AIM AND OBJECTIVE: Human dihydroorotate dehydrogenase (DHODH) catalyzes the fourth stage of the biosynthesis of pyrimidines in cells. Hence it is important to identify suitable inhibitors of DHODH to prevent virus replication. In this study, a quantitative structure-activity relationship was performed to predict the activity of one group of newly synthesized halogenated pyrimidine derivatives as inhibitors of DHODH. MATERIALS AND METHODS: Molecular structures of halogenated pyrimidine derivatives were drawn in the HyperChem and then molecular descriptors were calculated by DRAGON software. Finally, the most effective descriptors for 32 halogenated pyrimidine derivatives were selected using bee algorithm. RESULTS: The selected descriptors using bee algorithm were applied for modeling. The mean relative error and correlation coefficient were obtained as 2.86% and 0.9627, respectively, while these amounts for the leave one out-cross validation method were calculated as 4.18% and 0.9297, respectively. The external validation was also conducted using two training and test sets. The correlation coefficients for the training and test sets were obtained as 0.9596 and 0.9185, respectively. CONCLUSION: The results of modeling of present work showed that bee algorithm has good performance for variable selection in QSAR studies and its results were better than the constructed model with the selected descriptors using the genetic algorithm method.

Quantitative structure-property relationship study of the solvent polarity using wavelet neural networks.

Quantitative structure-property relationship (QSPR) studies based on artificial neural network (ANN) and wavelet neural network (WNN) techniques were carried out for the prediction of solvent polarity. Experimental S' values for 69 solvents were assembled. This set included saturated and unsaturated hydrocarbons, solvents containing halogen, cyano, nitro, amide, sulfide, mercapto, sulfone, phosphate, ester, ether, etc. Semi-empirical quantum chemical calculations at AM1 level were used to find the optimum 3D geometry of the studied molecules and different quantum-chemical descriptors were calculated by the HyperChem software. A stepwise MLR method was used to select the best descriptors and the selected descriptors were used as input neurons in neural network models. The results obtained by the two methods were compared and it was shown that in WNN, the convergence speed was faster and the root mean square error of prediction set was also smaller than ANN. The average relative error in WNN was 7.9 and 6.8% for calibration and prediction set, respectively, and the results showed the ability of the WNN developed here to predict solvent polarity.

Simultaneous spectrophotometric determination of phosphate and silicate by using principal component artificial neural network.

A very sensitive, simple and selective spectrophotometric method for simultaneous determination of phosphate and silicate based on formation of phospho- and silicomolybdenum blue complexes in the presence of ascorbic acid is described. Although the complexes of phosphate and silicate with reagent in the presence of ascorbic acid show a spectral overlap, they have been simultaneously determined by principal component artificial neural network (PC-ANN). The PC-ANN architectures were different for phosphate and silicate. The output of phosphate PC-ANN architecture was used as an input for silicate PC-ANN architecture. This modification improves the capability of silicate PC-ANN model for prediction of silicate concentrations. The linear range was 0.01-3.00 microg mL(-1) for phosphate and 0.01-5.00 microg mL(-1) for silicate. Interference effects of common anions and cations were studied and the proposed method was also applied satisfactorily to the determination of phosphate and silicate in detergents.

Development and characterization of an electrochemical sensor for furosemide detection based on electropolymerized molecularly imprinted polymer.

A novel electrochemical sensor based on a molecularly imprinted polymer, poly(o-phenylenediamine) (PoPD), has been developed for selective and sensitive detection of furosemide. The sensor was prepared by incorporating of furosemide as template molecules during the electropolymerization of o-phenylenediamine on a gold electrode. To develop the molecularly imprinted polymer (MIP), the template molecules were removed from the modified electrode's surface by washing it with 0.25 mol L(-1) NaOH solution. The imprinted layer was characterized by cyclic voltammetry (CV), electrochemical impedance spectroscopy (EIS) and atomic force microscopy (AFM). The sensor's preparation conditions including furosemide concentration, the number of CV cycles in the electropolymerization process, extraction solution of the template from the imprinted film, the incubation time and the pH level were optimized. The incubation of the MIP-modified electrode, with respect to furosemide concentration, resulted in a suppression of the K4[Fe(CN)6] oxidation process. Under the optimal experimental conditions, the response of the imprinted sensor was linear in the range of 1.0×10(-7)-7.0×10(-6) mol L(-1) of furosemide. The detection limit was obtained as 7.0×10(-8) mol L(-1) for furosemide by using this sensor. The sensor was successfully used to determine the furosemide amount in the tablet and in human urine samples with satisfactory results.