Yanfang Cui

Analysis of volatile aldehyde biomarkers in human blood by derivatization and dispersive liquid–liquid microextraction based on solidification of floating organic droplet method by high performance liquid chromatography

A new dispersive liquid-liquid microextraction based on solidification of floating organic droplet method (DLLME-SFO) was developed for the determination of volatile aldehyde biomarkers (hexanal and heptanal) in human blood samples. In the derivatization and extraction procedure, 2,4-dinitrophenylhydrazine (DNPH) as derivatization reagent and formic acid as catalyzer were injected into the sample solution for derivatization with aldehydes, then the formed hydrazones was rapidly extracted by dispersive liquid-liquid microextraction with 1-dodecanol as extraction solvent. After centrifugation, the floated droplet was solidified in an ice bath and was easily removed for analysis. The effects of various experimental parameters on derivatization and extraction conditions were studied, such as the kind and volume of extraction solvent and dispersive solvent, the amount of derivatization reagent, derivatization temperature and time, extraction time and salt effect. The limit of detections (LODs) for hexanal and heptanal were 7.90 and 2.34nmolL(-1), respectively. Good reproducibility and recovery of the method were also obtained. The proposed method is an alternative approach to the quantification of volatile aldehyde biomarkers in complex biological samples, being more rapid and simpler and providing higher sensitivity compared with the traditional dispersive liquid-liquid microextraction (DLLME) methods.

Improved liquid-liquid-liquid microextraction method and its application to analysis of four phenolic compounds in water samples

An improved liquid-liquid-liquid microextraction (LLLME) technique has been put forward based on the principle of single drop LLLME. In the technique, a vial insert was firstly utilized as acceptor phase container. Because the diameter of the bottom of the vial insert was small, the contact area between the acceptor phase and the vial insert was bigger than that between microsyringe and microdrop of acceptor phase in single drop LLLME, and the stability of microdrop was increased markedly. More acceptor phase could be held in the improved method than that in single drop LLLME, and the sensitivity of the method was increased. The sample vial and vial insert were horizontally placed so that the density of organic solvent has little effect on the selection of organic solvents. Aqueous ammonia and toluene were selected as the acceptor phase and the organic phase, respectively. The improved method was successfully applied to determine four phenolic compounds in real aqueous samples. Good recoveries that ranged from 82.2% to 117.2% were obtained. The intra-day and inter-day reproducibilities (RSD) were under 4.8% and 6.8%, respectively. The extraction efficiency of the improved method was 11-47 times higher than that of single drop LLLME method. The improved LLLME method is economical, rapid, simple, efficient, low organic solvent consumption and no cross-containment. This method is very suitable for the extraction of ionizable and chargeable analyte in complex environmental or biological samples.

Quantitative analysis of tea using ytterbium‐based internal standard near‐infrared spectroscopy coupled with boosting least‐squares support vector regression

The present study demonstrated the possibility of utilizing the ytterbium (Yb)‐based internal standard near‐infrared (NIR) spectroscopic measurement technique coupled with multivariate calibration for quantitative analysis of tea, including total free amino acids and total polyphenols in tea. Yb is a rare earth element aimed to compensate for the spectral variation induced by the alteration of sample quantity during the spectral measurement of the powdered samples. Boosting was invoked to be combined with least‐squares support vector regression (LS‐SVR), forming boosting least‐squares support vector regression (BLS‐SVR) for the multivariate calibration task. The results showed that the tea quality could be accurately and rapidly determined via the Yb‐based internal standard NIR spectroscopy combined with BLS‐SVR method. Moreover, the introduction of boosting drastically enhanced the performance of individual LS‐SVR, and BLS‐SVR compared favorably with partial least‐squares regression. Copyright

Tumor growth affects the metabonomic phenotypes of multiple mouse non-involved organs in an A549 lung cancer xenograft model

The effects of tumorigenesis and tumor growth on the non-involved organs remain poorly understood although many research efforts have already been made for understanding the metabolic phenotypes of various tumors. To better the situation, we systematically analyzed the metabolic phenotypes of multiple non-involved mouse organ tissues (heart, liver, spleen, lung and kidney) in an A549 lung cancer xenograft model at two different tumor-growth stages using the NMR-based metabonomics approaches. We found that tumor growth caused significant metabonomic changes in multiple non-involved organ tissues involving numerous metabolic pathways, including glycolysis, TCA cycle and metabolisms of amino acids, fatty acids, choline and nucleic acids. Amongst these, the common effects are enhanced glycolysis and nucleoside/nucleotide metabolisms. These findings provided essential biochemistry information about the effects of tumor growth on the non-involved organs.

Partial least-squares discriminant analysis optimized by particle swarm optimization: application to 1H nuclear magnetic resonance analysis of lung cancer metabonomics

The complexity of metabolic profiles makes chemometric tools indispensable for extracting the most significant information. Partial least‐squares discriminant analysis (PLS‐DA) acts as one of the most effective strategies for data analysis in metabonomics. However, its actual efficacy in metabonomics is often weakened by the high similarity of metabolic profiles, which contain excessive variables. To rectify this situation, particle swarm optimization (PSO) was introduced to improve PLS‐DA by simultaneously selecting the optimal sample and variable subsets, the appropriate variable weights, and the best number of latent variables (SVWL) in PLS‐DA, forming a new algorithm named PSO‐SVWL‐PLSDA. Combined with 1H nuclear magnetic resonance‐based metabonomics, PSO‐SVWL‐PLSDA was applied to recognize the patients with lung cancer from the healthy controls. PLS‐DA was also investigated as a comparison. Relatively to the recognition rates of 86% and 65%, which were yielded by PLS‐DA, respectively, for the training and test sets, those of 98.3% and 90% were offered by PSO‐SVWL‐PLSDA. Moreover, several most discriminative metabolites were identified by PSO‐SVWL‐PLSDA to aid the diagnosis of lung cancer, including lactate, glucose (α‐glucose and β‐glucose), threonine, valine, taurine, trimethylamine, glutamine, glycoprotein, proline, and lipid. Copyright

Robust variable selection based on bagging classification tree for support vector machine in metabonomic data analysis

In metabonomics, metabolic profiles of high complexity bring out tremendous challenges to existing chemometric methods. Variable selection (ie, biomarker discovery) and pattern recognition (ie, classification) are two important tasks of chemometrics in metabonomics, especially biomarker discovery that can be potentially used for disease diagnosis and pathology discovery. Typically, the informative variables are elicited from a single classifier; however, it is often unreliable in practice. To rectify this, in the current study, bagging and classification tree (CT) were combined to form a general framework (ie, BAGCT) for robustly selecting the informative variables, based on the advantages of CT in automatically carrying out variable selection as well as measuring variable importance and the properties of bagging in improving the reliability and robustness of a single model. In BAGCT, a set of parallel CT models were established based on the idea of bagging, each CT providing some endowed information such as the splitting variables and their corresponding importance values. The informative variables can be successfully spied via inspecting the variable importance values over all CTs in BAGCT. Taking the promising properties of support vector machine (SVM) into account, we used the informative variables identified by BAGCT as the inputs of SVM, forming a new classification tool abbreviated as BAGCT‐SVM. A metabonomic dataset by hydrogen‐1 nuclear magnetic resonance from the patients with lung cancer and the healthy controls was used to validate BAGCT‐SVM with CT and SVM as comparisons. Results showed that BAGCT‐SVM with less number of variables can give better predictive ability than CT and SVM.

Site-Mutation of Hydrophobic Core Residues Synchronically Poise Super Interleukin 2 for Signaling: Identifying Distant Structural Effects through Affordable Computations

A superkine variant of interleukin-2 with six site mutations away from the binding interface developed from the yeast display technique has been previously characterized as undergoing a distal structure alteration which is responsible for its super-potency and provides an elegant case study with which to get insight about how to utilize allosteric effect to achieve desirable protein functions. By examining the dynamic network and the allosteric pathways related to those mutated residues using various computational approaches, we found that nanosecond time scale all-atom molecular dynamics simulations can identify the dynamic network as efficient as an ensemble algorithm. The differentiated pathways for the six core residues form a dynamic network that outlines the area of structure alteration. The results offer potentials of using affordable computing power to predict allosteric structure of mutants in knowledge-based mutagenesis.

Serum Metabolic Profile Alteration Reveals Response to Platinum-Based Combination Chemotherapy for Lung Cancer: Sensitive Patients Distinguished from Insensitive ones

Most lung cancers are diagnosed at fairly advanced stages due to limited clinical symptoms. Platinum-based chemotherapy, either as single regimen or in combination with radiation, is one of the major recommendations for the patients. Earlier evaluation of the effectiveness of the chemotherapies is critical for developing better treatment plan given the toxicity of the chemotherapeutic reagents. Drug efficacy could be reflected in the systemic metabolism characteristics though knowledge about which remains scarce. In this study, serum metabolism influence of three types of commonly used platinum-based combination chemotherapy regimens, namely cisplatin with gemcitabine, vinorelbine or docetaxel, were studied using pattern recognition coupled with nuclear magnetic resonance techniques. The treated patients were divided into sensitive or insensitive subgroups according to their response to the treatments. We found that insensitive subjects can be identified from the sensitive ones with up-regulation of glucose and taurine but reduced alanine and lactate concentrations in serum. The combination chemotherapy of lung cancer is accompanied by disturbances of multiple metabolic pathways such as energy metabolism, phosphatidylcholine biosynthesis, so that the treated patients were marginally discriminated from the untreated. Serum metabolic profile of patients shows potential as an indicator of their response to platinum-based combination chemotherapy.