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Anal Chem ; 2020 Feb 13.
Artigo em Inglês | MEDLINE | ID: mdl-32054267


As an important chiral molecule for the preparation of levofloxacin, the optical purity of L-aminopropanol has a crucial effect on the pharmacology and pharmacodynamics of levofloxacin. Therefore, it is of great significance to discriminate D-aminopropanol in L-aminopropanol. In this paper, an effective aminopropanol enantiomer recognition method was established based on the chiral fluorescent silicon nanoparticles (SiNPs) probe. The chiral fluorescent SiNPs were fabricated via one-step aqueous solution synthesis strategy, which avoided multistep, pressurizing operation, and time-consuming post-modified procedures. Significantly, D-aminopropanol could significantly enhance the fluorescence of the chiral SiNPs, while L-aminopropanol could not affect the fluorescence of the chiral SiNPs. It may be due to the stronger interaction between the chiral SiNPs and D-aminopropanol than that of L-aminopropanol. Thus, the rapid and selective recognization of the aminopropanol enantiomer was ideally realized. The mechanism of the chiral SiNPs recognizing aminopropanol was simulated by density functional theory quantum mechanical calculations. Interestingly, this was also proved by the separation of aminopropanol enantiomer using this chiral SiNPs-modified silica column in normal phase liquid chromatography. To the best of our knowledge, this is the first time that the chiral fluorescent SiNPs were synthesized and used to detect the aminopropanol enantiomer successfully. This work will inspire more synthesis of chiral silicon nanomaterials and other nanomaterials with marvellous properties, and apply chiral nanomaterials to other fields.

J Biomol Struct Dyn ; : 1-9, 2020 Jan 25.
Artigo em Inglês | MEDLINE | ID: mdl-31918625


In recent years, deep neural networks have begun to receive much attention, which has obvious advantages in feature extraction and modeling. However, in the using of deep neural networks for the QSAR modeling process, the selection of various parameters (number of neurons, hidden layers, transfer functions, data set partitioning, number of iterations, etc.) becomes difficult. Thus, we proposed a new and easy method for optimizing the model and selecting Deep Neural Networks (DNN) parameters through uniform design ideas and orthogonal design methods. By using this approach, 222 chloroquine (CQ) derivatives with half maximal inhibitory concentration values reported in different kinds of literature were selected to establish DNN models and a total number of 128,000 DNN models were built to determine the optimized parameters for selecting the better models. Comparing with linear and Artificial Neural Network (ANN) models, we found that DNN models showed better performance.Communicated by Ramaswamy H. Sarma.

Arch Toxicol ; 93(11): 3207-3218, 2019 Nov.
Artigo em Inglês | MEDLINE | ID: mdl-31552475


Prediction of pEC50 values of dioxins binding with the aryl hydrocarbon receptor (AhR) is of great significance for exploring how dioxins induce toxicity in human body and evaluating their environmental behaviors and risks. To reveal the factors that influence the toxicity of dioxins, provide more accurate mathematical models for predicting the pEC50 values of dioxins, and supplement the toxicity database of persistent organic pollutants, qualitative structure-activity relationship (SAR) and two-dimensional quantitative structure-activity relationship (2D-QSAR) were used in this study. The research objects in this study were 60 organic compounds with pEC50 values and 162 compounds without pEC50 values, which included polychlorinated dibenzofurans (PCDFs), polychlorinated dibenzo-p-dioxins (PCDDs), and polybrominated dibenzo-p-dioxins (PBDDs). The qualitative structure-activity relationship (SAR) was performed first and concluded that halogen substitutions at any of the 2, 3, 7, and 8 sites increased the pEC50 value of the compound. Moreover, two-dimensional quantitative structure-activity relationship (2D-QSAR) models were established by employing multiple linear regression (MLR) method and artificial neural network (ANN) algorithm to investigate the factors affecting the pEC50 values of dioxins molecules. MLR was used to establish the well-understood linear model and ANN was used to establish a more accurate non-linear model. Both models have good fitting, robustness, and predictive ability. Importantly, the ability of dioxins binding to AhR is mainly determined by molecular descriptors including E1m, SM09_AEA (dm), RDF065u, F05 [Cl-Cl], and Neoplastic-80. In addition, the pEC50 values of the 162 dioxins without toxicity data were predicted by MLR and ANN models, respectively.

J Chromatogr A ; 1532: 223-231, 2018 Jan 12.
Artigo em Inglês | MEDLINE | ID: mdl-29203115


The popularity of novel nanoparticles coated capillary column has aroused widespread attention of researchers. Metal organic frameworks (MOFs) with special structure and chemical properties have received great interest in separation sciences. This work presents the investigation of HKUST-1 (Hong Kong University of Science and Technology-1, called Cu3(BTC)2 or MOF-199) nanoparticles as a new type of coating material for capillary electrochromatography. For the first time, three layers coating (3-LC), five layers coating (5-LC), ten layers coating (10-LC), fifteen layers coating (15-LC), twenty layers coating(20-LC) and twenty-five layers coating (25-LC) capillary columns coated with HKUST-1 nanoparticles were synthesized by covalent bond with in situ, layer-by-layer self-assembly approach. The results of scanning electron microscopy (SEM), X-ray diffraction (XRD) and plasma atomic emission spectrometry (ICP-AES) indicated that HKUST-1 was successfully grafted on the inner wall of the capillary. The separating performances of 3-LC, 5-LC, 10-LC, 15-LC, 20-LC and 25-LC open tubular (OT) capillary columns were studied with some neutral small organic molecules. The results indicated that the neutral small organic molecules were separated successfully with 10-LC, 15-LC and 20-LC OT capillary columns because of the size selectivity of lattice aperture and hydrophobicity of organic ligands. In addition, 10-LC and 15-LC OT capillary columns showed better performance for the separation of certain phenolic compounds. Furthermore, 10-LC, 15-LC and 20-LC OT capillary columns exhibited good intra-day repeatability with the relative standard deviations (RSDs; %) of migration time and peak areas lying in the range of 0.3-1.2% and 0.5-4.2%, respectively. For inter-day reproducibility, the RSDs of the three OT capillary columns were found to be lying in the range of 0.3-5.5% and 0.3-4.5% for migration time and peak area, respectively. The RSDs of retention times for column-to-column for three batches of 10-LC, 15-LC and 20-LC OT capillary columns were in the range from 2.3% to 7.2%. Moreover, the fabricated 10-LC, 15-LC and 20-LC OT capillary columns exhibited good repeatability and stability for separation, which could be used successively for more than 120 runs with no observable changes on the separation efficiency.

Eletrocromatografia Capilar/métodos , Compostos Orgânicos/isolamento & purificação , Compostos Organometálicos/química , Tampões (Química) , Etanol/análise , Concentração de Íons de Hidrogênio , Microscopia Eletrônica de Varredura , Nanopartículas/química , Nanopartículas/ultraestrutura , Fenóis/análise , Reprodutibilidade dos Testes , Soluções , Difração de Raios X