Fe3O4@PDA@PANI core-shell nanocomposites as a new adsorbent for simultaneous preconcentration of Tartrazine and Sunset Yellow by ultrasonic-assisted dispersive micro solid-phase extraction.

In this research, novel magnetic Fe3O4@PDA@PANI core-shell nanoparticles were designed and fabricated as an efficient adsorbent in the service of ultrasound-assisted dispersive micro-solid phase extraction for simultaneous preconcentration of Sunset Yellow (SY) and Tartrazine (Tar) before UV-Vis spectrophotometric detection. This adsorbent was fully characterized by Fourier Transform Infrared (FT-IR) spectroscopy, Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Energy Dispersive X-ray (EDX) analysis. To overcome the spectral overlapping of SY and Tar dyes, the derivative spectrophotometric method was successfully used for the simultaneous detection of dyes in their binary solutions. The operating parameters affecting preconcentration efficiency and spectrophotometric determination were optimized. Under optimal conditions, the limit of detections (LOD) was obtained 0.2 and 0.5 ng mL-1 for SY and Tar, respectively. The adsorption capacity and reusability of core-shell nanoparticles were significant. The satisfactory results of analysis of a few real samples indicate that the method is very favored in the analysis of various complex matrices.

In Silico Rational Design and Virtual Screening of Bioactive Peptides Based on QSAR Modeling.

Predicting the bioactivity of peptides is an important challenge in drug development and peptide research. In this study, numerical descriptive vectors (NDVs) for peptide sequences were calculated based on the physicochemical properties of amino acids (AAs) and principal component analysis (PCA). The resulted NDV had the same length as the peptide sequence, so that each entry of NDV corresponded to one AA in the sequence. They were then applied to quantitative structure-activity relationship (QSAR) analysis of angiotensin-converting enzyme (ACE) inhibitor dipeptides, bitter-tasting dipeptides, and nonameric binding peptides of the human leukocyte antigens (HLA-A*0201). Multiple linear regression was used to construct the QSAR models. For each peptide set, a proper subset of physicochemical properties was chosen by the ant colony optimization algorithm. The leave-one-out cross-validation (q loo 2) values were 0.855, 0.936, and 0.642 and the root-mean-square errors (RMSEs) were 0.450, 0.149, and 0.461. Our results revealed that the new numerical descriptive vector can afford extensive characterization of peptide sequence so that it can be easily employed in peptide QSAR studies. Moreover, the proposed numerical descriptive vectors were able to determine hot spot residues in the peptides under study.

Application of ultrasonic-assisted inclusion complex formation with α-cyclodextrin for simultaneous spectrophotometric determination of gallic acid and vanillic acids in fruit samples.

There are various methodologies to generate second-order data. Spectrophotometric method owing to its high sensitivity continues to be of wide interest to analytical chemists. Spectra recording at different interval time while the reaction is proceeded, or at different pHs, or at different concentrations of complexing agent are examples of strategies by which one can generate second-order data by the spectrophotometric method. In this work, for the first time, we employed α-CD as an inclusion complexing agent under ultrasonic irradiation for simultaneous determination of gallic acid (GA) and vanillic acid (VA). UV spectra of a mixture of two analytes were recorded as a function of α-CD concentration. A calibration set containing 9 reference samples was used to construct model using bilinear least squares/residual bilinearization (BLLS/RBL) as second-order calibration method. The predictive ability of the resulting model was validated by a test set including 5 samples. Finally, the proposed model was successfully applied to simultaneously determine the content of GA and VA in five fruit juice samples. Satisfactory results in terms of the recovery yields were obtained. In a word, UV-spectroscopy coupled with a strategy to yield a second-order data in combination to second-order calibration methods has high potential to be a simple, quick and accurate analysis procedure for the determination of various types of chemical structures in practice.

A carbon paste electrode modified with poly(methylene disulfide) nanoparticles for anodic stripping voltammetric determination of silver(I).

Poly(methylene disulfide) nanoparticles (PMDSNPs) were synthesized and characterized by FTIR, FESEM, EDX, and TGA. The nanomaterial was used to modify a carbon paste electrode to obtain a sensor for differential pulse anodic stripping voltammetric analysis of silver ion. The silver ions are accumulated on the modified electrode by reduction at a potential of -0.3 V. This is followed by the quantitation of adsorbed Ag(I) by differential pulse anodic stripping voltammetry. Under optimized conditions, the electrode has a dynamic range in the 3.0 × 10-12 to 1.0 × 10-9 mol L-1 Ag(I) concentration range, and the detection limit is 1.0 × 10-13 mol L-1. The relative standard deviation (for n = 6) is 1.8%, this showing good reproducibility. The method was successfully applied to the determination of Ag(I) in spiked tap and river waters and tea leaves. The results were in good agreement with those obtained by inductively coupled plasma optical emission spectrometry. Graphical abstract Graphical presentation of synthesis of poly(methylene disulfide) nanoparticles (PMDSNPs) and their use as a modifier in a carbon paste electrode (MCPE). Differential pulse anodic stripping voltammograms of the MCPE for silver ion are compared with those of the bare CPE.

QSAR Study of Artemisinin Analogues as Antimalarial Drugs by Neural Network and Replacement Method.

Quantitative structure-activity relationship (QSAR) models were derived for 179 analogues of artemisinin, a potent antimalarial agent. Molecular descriptors derived solely from molecular structure were used to represent molecular structure. Utilizing replacement method, a subset of 11 descriptors was selected. General regression neural network (GRNN) was used to construct the nonlinear QSAR models in all stages of study. The relative standard error percent in antimalarial activity predictions for the training set by the application of cross-validation (RMSE-CV) was 0.43, and for test set (RMSEtest) was 0.51. GRNN analysis yielded predicted activities in the excellent agreement with the experimentally obtained values (R2training = 0.967 and R2test = 0.918). The mean absolute error for the test set was computed as 0.4115.

In silico prediction of toxicity of phenols to Tetrahymena pyriformis by using genetic algorithm and decision tree-based modeling approach.

Risk assessment of chemicals is an important issue in environmental protection; however, there is a huge lack of experimental data for a large number of end-points. The experimental determination of toxicity of chemicals involves high costs and time-consuming process. In silico tools such as quantitative structure-toxicity relationship (QSTR) models, which are constructed on the basis of computational molecular descriptors, can predict missing data for toxic end-points for existing or even not yet synthesized chemicals. Phenol derivatives are known to be aquatic pollutants. With this background, we aimed to develop an accurate and reliable QSTR model for the prediction of toxicity of 206 phenols to Tetrahymena pyriformis. A multiple linear regression (MLR)-based QSTR was obtained using a powerful descriptor selection tool named Memorized_ACO algorithm. Statistical parameters of the model were 0.72 and 0.68 for Rtraining2 and Rtest2, respectively. To develop a high-quality QSTR model, classification and regression tree (CART) was employed. Two approaches were considered: (1) phenols were classified into different modes of action using CART and (2) the phenols in the training set were partitioned to several subsets by a tree in such a manner that in each subset, a high-quality MLR could be developed. For the first approach, the statistical parameters of the resultant QSTR model were improved to 0.83 and 0.75 for Rtraining2 and Rtest2, respectively. Genetic algorithm was employed in the second approach to obtain an optimal tree, and it was shown that the final QSTR model provided excellent prediction accuracy for the training and test sets (Rtraining2 and Rtest2 were 0.91 and 0.93, respectively). The mean absolute error for the test set was computed as 0.1615.