Simple, stable and sensitive electrogenerated chemiluminescence detector for high-performance liquid chromatography and its application in direct determination of multiple fluoroquinolone residues in milk

A simple, stable and sensitive electrogenerated chemiluminescence (ECL) detector was developed. It was based on tris(2,2-bipyridyl)ruthenium(II) (Ru(bpy)₃²⁺) immobilized on the surface of a Pt wire with Nepem-105D ion exchange solution. The detector was prepared by inserting a Pt wire with immobilized Ru(bpy)₃²⁺ (working electrode) into a capillary tube, followed by inserting another Pt wire (counter electrode) in this tube and sealing. ECL behavior was investigated using ofloxacin as an analyte. Under optimal conditions, stable ECL intensity was obtained. This detector has been used in HPLC-ECL for the determination of multiple target fluoroquinolone residues in milk. There is no post column reagent addition, which would dilute the analytes, potentially leading to chromatographic band-broadening. The system is very simple with low dead volume, low baseline and background noise, together with high sensitivity and stability. The as-prepared ECL detector, when was used for the determination of ofloxacin, pefloxacin, enrofloxacin and difloxacin in milk, demonstrated adequate sensitivity to allow quantification of trace FQ levels in commercial milk samples. One or more of the target FQ analytes were present at levels above the LOD of the new ECL detector in each and every one of the 22 milk samples analysed.     

Prediction of drug-target interaction by integrating diverse heterogeneous information source with multiple kernel learning and clustering methods

BackgroundIdentification of potential drug-target interaction pairs is very important for pharmaceutical innovation and drug discovery. Numerous machine learning-based and network-based algorithms have been developed for predicting drug-target interactions. However, large-scale pharmacological, genomic and chemical datum emerged recently provide new opportunity for further heightening the accuracy of drug-target interactions prediction.ResultsIn this work, based on the assumption that similar drugs tend to interact with similar proteins and vice versa, we developed a novel computational method (namely MKLC-BiRW) to predict new drug-target interactions. MKLC-BiRW integrates diverse drug-related and target-related heterogeneous information source by using the multiple kernel learning and clustering methods to generate the drug and target similarity matrices, in which the low similarity elements are set to zero to build the drug and target similarity correction networks. By incorporating these drug and target similarity correction networks with known drug-target interaction bipartite graph, MKLC-BiRW constructs the heterogeneous network on which Bi-random walk algorithm is adopted to infer the potential drug-target interactions.ConclusionsCompared with other existing state-of-the-art methods, MKLC-BiRW achieves the best performance in terms of AUC and AUPR. MKLC-BiRW can effectively predict the potential drug-target interactions.     

Identifying mutated driver pathways in cancer by integrating multi-omics data

Since the driver pathway in cancer plays a crucial role in the formation and progression of cancer, it is very imperative to identify driver pathways, which will offer important information for precision medicine or personalized medicine. In this paper, an improved maximum weight submatrix problem model is proposed by integrating such three kinds of omics data as somatic mutations, copy number variations, and gene expressions. The model tries to adjust coverage and mutual exclusivity with the average weight of genes in a pathway, and simultaneously considers the correlation among genes, so that the pathway having high coverage but moderate mutual exclusivity can be identified. By introducing a kind of short chromosome code and a greedy based recombination operator, a parthenogenetic algorithm PGA-MWS is presented to solve the model. Experimental comparisons among algorithms GA, MOGA, iMCMC and PGA-MWS were performed on biological and simulated data sets. The experimental results show that, compared with the other three algorithms, the PGA-MWS one based on the improved model can identify the gene sets with high coverage but moderate mutual exclusivity and scales well. Many of the identified gene sets are involved in known signaling pathways, most of the implicated genes are oncogenes or tumor suppressors previously reported in literatures. The experimental results indicate that the proposed approach may become a useful complementary tool for detecting cancer pathways.     

含芳香基团的梳状聚合物型降凝剂与沥青质协同改善合成蜡油的流变性

采用流变学实验、 差示扫描量热(DSC)分析、 显微观察及沥青质沉淀实验研究了聚丙烯酸十八酯-马来酸酐(POM)、 聚丙烯酸十八酯-马来酸酐-苯胺(POMA)及聚丙烯酸十八酯-马来酸酐-萘胺(POMN)梳状聚合物对合成蜡油的流变性能的影响规律. 实验结果表明, 这3种梳状聚合物降凝剂均能在一定程度上改善不含沥青质合成蜡油(MO-1)的低温流变性, 其中POM对MO-1蜡油的流变性改善效果最佳. 添加500 mg/kg POM后, MO-1的凝点从29 ℃降至23 ℃, 屈服值从627.20 Pa降至83.35 Pa. 而POM, POMA和POMN可以显著改善含0.3%(质量分数)沥青质的合成蜡油(MO-2)的低温流变性, 且添加500 mg/kg POMA后MO-2蜡油的流变改善效果最佳: 凝点降至3 ℃, 屈服值降至1.27 Pa. 可见, 本文制备的梳状聚合物降凝剂均能与沥青质协同改善MO-2蜡油的流变性, 并且向POM中引入芳香基团能进一步促进沥青质与降凝剂分子的相互作用, 进而增强梳状聚合物降凝剂与沥青质的协同作用.     

One-dimensional and two-dimensional nanomaterials for the detection of multiple biomolecules

The complexity of biological samples determines that the detection of a single biomolecule is unable to satisfy actual needs. Moreover, the “false positives” results caused by a single biomolecule detections easily leads to erroneous clinical diagnosis and treatment. Thus, it is important for the homogenous quantification of multiple biomolecules in not only basic research but also practical application. As a consequent, a large number of literatures have been exploited to monitor multiple biomolecules in homogenous solution, enabling facilitating the development of the disease diagnosis, treatment as well as drug discovery. One-dimensional nanomaterials and two-dimensional nanomaterials have special physical and chemical properties, such as good electrochemical properties, stable structure, large specific surface area, and biocompatibility, which are widely used in electrochemical and fluorescent detection of biomolecules. This tutorial review highlights the recent development for the detection of multiple biomolecules by using nanomaterials including one-dimensional materials (1DMs) as well as two-dimensional materials (2DMs).     

Physicochemical and Graph Theoretical Descriptors in Developmental Toxicity SAR: A Comparative Study

Abstract

Chemical insults to the developing fetus can lead to growth retardation, malformation, death, and functional deficits. The present study seeks to determine if physicochemical and/or graph theoretical parameters can be used to determine a structure-activity relationship (SAR) for developmental toxicity, and if consistency is observed among the selected features. The biological data utilized consists of a diverse series of compounds evaluated within the Chernoff-Kavlock in vivo mouse assay. Physicochemical parameters calculated correspond to electronic, steric, and transport properties. Graph theoretical parameters calculated include the simple, valence, and kappa indices. Both sets of parameters were independently applied to derive SARs in order to compare the quality of the respective models. Multiple random sampling, without replacement, was utilized to obtain ten training/test partitions. Models were built by linear discriminant analysis, decision trees, and neural networks respectively. Comparisons on identical sets of data were carried out to determine if any of the model building procedures had a significant advantage in terms of predictive performance. Furthermore, comparison of the features selected within and across the model building processes led to the determination of model consistency. Our results indicate that consistent features related to developmental toxicity are observed and that both physicochemical and graph theoretical parameters have equal utility.     

Reactivity profiles of ligands of mammalian retinoic acid receptors: A preliminary COREPA analysis

Retinoic acid and associated derivatives comprise a class of endogenous hormones that bind to and activate different families of retinoic acid receptors (RARs, RXRs), and control many aspects of vertebrate development. Identification of potential RAR and RXR ligands is of interest both from a pharmaceutical and toxicological perspective. The recently developed COREPA (COmmon REactivity PAttern) algorithm was used to establish reactivity profiles for a limited data set of retinoid receptor ligands in terms of activation of three RARs ( f , g , n ) and an RXR ( f ). Conformational analysis of a training set of retinoids and related analogues in terms of thermodynamic stability of conformers and rotational barriers showed that these chemicals tend to be quite flexible. This flexibility, and the observation that relatively small energy differences between conformers can result in significant variations in electronic structure, highlighted the necessity of considering all energetically reasonable conformers in defining common reactivity profiles. The derived reactivity patterns for three different subclasses of the RAR ( f , g , n ) were similar in terms of their global electrophilicity (nucleophilicity) and steric parameters. However, the profile of active chemicals with respect to interaction with the RXR- f differed qualitatively from that of the RARs. Variations in reactivity profiles for the RAR versus RXR families would be consistent with established differences in their affinity for endogenous retinoids, likely reflecting functional differences in the receptors.     

Quantitative prediction of biodegradability,metabolite distribution and toxicity of stable metabolites

An evaluation of the capability of organic chemicals to mineralize is an important factor to consider when assessing their fate in the environment. Microbial degradation can convert a toxic chemical into an innocuous one, and vice versa , or alter the toxicity of a chemical. Moreover, primary biodegradation can convert chemicals into stable products that can be difficult to mineralize. In this paper, we present some new results obtained on the basis of a recently developed probabilistic approach to modeling biodegradation based on microbial transformation pathways. The metabolic transformations and their hierarchy were calibrated by making use of the ready biodegradability data from the MITI-I test and expert knowledge for the most probable transformation pathways. A model was developed and integrated into an expert software system named CATABOL that is able to predict the probability of biodegradation of organic chemicals directly from their structure. CATABOL simulates the effects of microbial enzyme systems, generates the most plausible transformation pathways, and quantitatively predicts the persistence and toxicity of the biodegradation products. A subset of 300 organic chemicals were selected from Canada's Domestic Substances List and subjected to CATABOL to compare predicted properties of the parent chemicals with their respective first stable metabolite. The results show that most of the stable metabolites have a lower acute toxicity to fish and a lower bioaccumulation potential compared to the parent chemicals. In contrast, the metabolites appear to be generally more estrogenic than the parent chemicals.     

A model validation and consensus building environment

Over half of the failures in drug development are due to problems with the absorption, distribution, metabolism, excretion, and toxicity, or ADME/Tox properties of a candidate compound. The utilization of in silico tools to predict ADME/Tox and physicochemical properties holds great potential for reducing the attrition rate in drug research and development, as this technology can prioritize candidate compounds in the pharmaceutical R&D pipeline. However, a major concern surrounding the use of in silico ADME/Tox technology is the reliability of the property predictions. Bio-Rad Laboratories, Inc. has created a computational environment that addresses these concerns. This environment is referred to as KnowItAll®. Within this platform are encoded a number of ADME/Tox predictors, the ability to validate these predictors with/without in-house data and models, as well as build a ‘consensus’ model that may be a much better model than any of the individual predictive model. The KnowItAll® system can handle two types of predictions: real number and categorical classification.     

5,6—二亚甲基双环[2.2.1]庚—2—醇及其衍生物的旋光与构型

从（－）－（１Ｓ，２Ｒ，４Ｓ）－５，６－二亚甲基双环［２．２．１］庚－２－醇（Ｉ）和（－）－（１Ｓ，２Ｓ，４Ｓ）－５，６－二亚甲基双环［２．２．１］庚－２－醇（Ⅱ）出发，合成了它们的醚类和取代苯甲酸酯类光学纯的衍生物。通过［α］Ｄ测定和构型分析，发现它们是含有３个手性碳（Ｃ１，Ｃ２和Ｃ４）的桥环化合物，其旋光性与构型的关系均符合可极化度多级圆球不对称性模型。