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Biomedical subjects

Y Xue

Publications and source records attributed to Y Xue.

At least 19 recordsLinked to original sources
PubMed2007

MODEL-molecular descriptor lab: a web-based server for computing structural and physicochemical features of compounds.

Molecular descriptors represent structural and physicochemical features of compounds. They have been extensively used for developing statistical models, such as quantitative structure activity relationship (QSAR) and artificial neural networks (NN), for computer prediction of the pharmacodynamic, pharmacokinetic, or toxicological properties of compounds from their structure. While computer programs have been developed for computing molecular descriptors, there is a lack of a freely accessible one. We have developed a web-based server, MODEL (Molecular Descriptor Lab), for computing a comprehensive set of 3,778 molecular descriptors, which is significantly more than the approximately 1,600 molecular descriptors computed by other software. Our computational algorithms have been extensively tested and the computed molecular descriptors have been used in a number of published works of statistical models for predicting variety of pharmacodynamic, pharmacokinetic, and toxicological properties of compounds. Several testing studies on the computed molecular descriptors are discussed. MODEL is accessible at http://jing.cz3.nus.edu.sg/cgi-bin/model/model.cgi free of charge for academic use.

Amino Acids↗
PubMed2006

Combination chemotherapy for high-risk gestational trophoblastic tumour.

BACKGROUND: Gestational trophoblastic disease (GTD) includes gestational trophoblastic tumour and hydatidiform mole. Many women of reproductive age are affected by this disease although its incidence differs by geographical location. A number of chemotherapy regimens are used for treating the disease, such as methotrexate, actinomycin D and cyclophosphamide (MAC), methotrexate, actinomycin D, cyclophosphamide, doxorubicin, melphalan, hydroxyurea and vincristine (CHAMOC), etoposide, methotrexate and actinomycin (EMA) plus cyclophosphamide and vincristine (CO) (EMA-CO), etoposide, methotrexate and actinomycin (EMA) plus etoposide and cisplatin(EP) (EMA-EP). The efficacy of these drugs has not been systematically reviewed. OBJECTIVES: To determine the efficacy and safety of combination chemotherapy in treating high-risk GTT. SEARCH STRATEGY: Electronic searches of MEDLINE, EMB, Cochrane Central Register of Controlled Trials (CENTRAL) and CBM were carried out. Four journals were handsearched and other searching methods were used for identifying more studies. SELECTION CRITERIA: The review included randomized controlled trials (RCTs) or quasi-RCTs of combination chemotherapy for treating high-risk GTT. Patients with placental-site trophoblastic tumour (PSTT), who had received chemotherapy in the previous two weeks, or patients with chemotherapy intolerance were excluded. DATA COLLECTION AND ANALYSIS: Two investigators independently collected data using a data extraction form. Meta-analysis was not performed and the review was conducted as a narrative review. MAIN RESULTS: One study with 42 participants was included in this review. It indicated that a MAC regimen was better than a CHAMOCA regimen for high-risk GTT because of lower toxicity. The quality of the study was unclear. AUTHORS' CONCLUSIONS: The methodological limitations of the included study prevent any firm conclusions about the best combination chemotherapy regimen for high-risk GTT. High quality studies are required.

Antineoplastic Combined Chemotherapy Protocols↗
PubMed2006

Spin-dependent transport through a magnetic carbon nanotube-molecule junction.

The electronic structure and spin-dependent conductance of a magnetic junction consisting of two Fe-doped carbon nanotubes and a C60 molecule are investigated using a first-principles approach that combines the density functional theory with the nonequilibrium Greens function technique. The tunneling magnetoresistance ratio is found to be 11%. The density of states and transmission coefficient through the molecular junction are analyzed and compared to layered magnetic tunneling junctions. Our findings suggest new possibilities for experiments and for future technology.

Journal Article↗
PubMed2006

Prediction of estrogen receptor agonists and characterization of associated molecular descriptors by statistical learning methods.

Specific estrogen receptor (ER) agonists have been used for hormone replacement therapy, contraception, osteoporosis prevention, and prostate cancer treatment. Some ER agonists and partial-agonists induce cancer and endocrine function disruption. Methods for predicting ER agonists are useful for facilitating drug discovery and chemical safety evaluation. Structure-activity relationships and rule-based decision forest models have been derived for predicting ER binders at impressive accuracies of 87.1-97.6% for ER binders and 80.2-96.0% for ER non-binders. However, these are not designed for identifying ER agonists and they were developed from a subset of known ER binders. This work explored several statistical learning methods (support vector machines, k-nearest neighbor, probabilistic neural network and C4.5 decision tree) for predicting ER agonists from comprehensive set of known ER agonists and other compounds. The corresponding prediction systems were developed and tested by using 243 ER agonists and 463 ER non-agonists, respectively, which are significantly larger in number and structural diversity than those in previous studies. A feature selection method was used for selecting molecular descriptors responsible for distinguishing ER agonists from non-agonists, some of which are consistent with those used in other studies and the findings from X-ray crystallography data. The prediction accuracies of these methods are comparable to those of earlier studies despite the use of significantly more diverse range of compounds. SVM gives the best accuracy of 88.9% for ER agonists and 98.1% for non-agonists. Our study suggests that statistical learning methods such as SVM are potentially useful for facilitating the prediction of ER agonists and for characterizing the molecular descriptors associated with ER agonists.

Forecasting↗
PubMed2006

Classification of a diverse set of Tetrahymena pyriformis toxicity chemical compounds from molecular descriptors by statistical learning methods.

Toxicity of various compounds has been measured in many studies by their toxic effects against Tetrahymena pyriformis. Efforts have also been made to use computational quantitative structure-activity relationship (QSAR) and statistical learning methods (SLMs) for predicting Tetrahymena pyriformis toxicity (TPT) at impressive accuracies. Because of the diversity of compounds and toxicity mechanisms, it is desirable to explore additional methods and to examine if these methods are applicable to more diverse sets of compounds. We tested several SLMs (logistic regression, C4.5 decision tree, k-nearest neighbor, probabilistic neural network, support vector machines) for their capability in predicting TPT by using 1129 compounds (841 TPT and 288 non-TPT agents) which are more diverse than those in other studies. A feature selection method was used for improving prediction performance and selecting molecular descriptors responsible for distinguishing TPT and non-TPT agents. The prediction accuracies are 86.9% approximately 94.2% for TPT and 71.2% approximately 87.5% for non-TPT agents based on 5-fold cross-validation studies, which are comparable to some of earlier studies despite the use of more diverse sets of compounds. The selected molecular descriptors are consistent with those used in other studies and experimental findings. These suggest that SLMs are useful for predicting TPT potential of diverse sets of compounds and for characterizing the molecular descriptors associated with TPT.

Animals↗
PubMed2006

Halostagnicola larsenii gen. nov., sp. nov., an extremely halophilic archaeon from a saline lake in Inner Mongolia, China.

Strain XH-48(T) was isolated from the sediment of Lake Xilinhot, a saline lake in Inner Mongolia (China). The organism is pleomorphic, neutrophilic and requires at least 2.5 M (15 %) NaCl, but not MgCl(2), for growth; it exhibits optimal growth at 3.4 M (20 %) NaCl. The G+C content of its DNA is 61 mol%. 16S rRNA gene sequence analysis revealed that strain XH-48(T) is a member of the family Halobacteriaceae, but there were low levels of similarity with other members of this family. The highest sequence similarity values (94.5 and 93.3 %) were obtained with the 16S rRNA genes of Natrialba aegyptiaca and Natrialba asiatica, respectively. Polar lipid analyses revealed that strain XH-48(T) contains phosphatidylglycerol and phosphatidylglyceromethylphosphate, derived from both C(20)C(20) and C(20)C(25) glycerol diethers, and two unidentified glycolipids. On the basis of the data obtained, the novel isolate cannot be classified within any recognized genus. Strain XH-48(T) should be placed within a novel genus and species within the family Halobacteriaceae, order Halobacteriales, for which the name Halostagnicola larsenii gen. nov., sp. nov. is proposed. The type strain of Halostagnicola larsenii is strain XH-48(T) (=DSM 17691(T)=CGMCC 1.5338(T)=JCM 13463(T)=CECT 7116(T)).

Aerobiosis↗
PubMed2006

Halorubrum orientale sp. nov., a halophilic archaeon isolated from Lake Ejinor, Inner Mongolia, China.

A motile, pleomorphic, red-pigmented archaeon, strain EJ-52T, was isolated from water from Lake Ejinor, a saline lake in Inner Mongolia, China. Analysis of the almost-complete 16S rRNA gene sequence showed that the isolate was phylogenetically related to species of the genus Halorubrum, being most closely related to Halorubrum saccharovorum ATCC 29252T (96.1% sequence similarity), Halorubrum lacusprofundi JCM 8891T (95.9%), Halorubrum tibetense AS 1.3239T (95.2%), Halorubrum alcaliphilum AS 1.3528T (95.2%) and Halorubrum vacuolatum JCM 9060T (95.1%). The polar lipids of strain EJ-52T were C20C20 derivatives of phosphatidylglycerol phosphate and phosphatidylglycerol phosphate methyl ester and a sulfated diglycosyl diether. Strain EJ-52T requires at least 2.5 M NaCl for growth and grows optimally at 3.4 M NaCl. The strain grows at 25-50 degrees C, with optimal growth occurring at 35-45 degrees C. Mg2+ is not required. The DNA G+C content is 64.2 mol%. On the basis of the data obtained in this study, strain EJ52T represents a novel species, for which the name Halorubrum orientale sp. nov. is proposed. The type strain is EJ-52T (=CECT 7145T=JCM 13889T=CGMCC 1.6295T).

Base Composition↗
PubMed2006

Natrinema ejinorense sp. nov., isolated from a saline lake in Inner Mongolia, China.

A Gram-negative, non-motile, neutrophilic, pleomorphic and extremely halophilic archaeon, strain EJ-57T, was isolated from saline Lake Ejinor in Inner Mongolia, China. Strain EJ-57T was able to grow at 25-50 degrees C, required at least 1.8 M NaCl for growth (optimum at 3.4 M NaCl) and grew over a pH range from 6.0 to 8.5 (optimum at pH 7.0). Hypotonic treatment with less than 1.5 M NaCl caused cell lysis. Analysis of the almost complete 16S rRNA gene sequence indicated that the isolate represented a member of the genus Natrinema in the family Halobacteriaceae. Strain EJ-57T was most closely related to Natrinema versiforme JCM 10478T (96.2% sequence similarity), Natrinema pallidum NCIMB 777T (95.9% sequence similarity), Natrinema altunense JCM 12890T (95.8% sequence similarity) and Natrinema pellirubrum NCIMB 786T (95.5 % sequence similarity). However, DNA-DNA hybridization experiments showed that strain EJ-57T was not related to these species, with levels of DNA-DNA relatedness equal to or below 39%. The major polar lipids of the isolate were C20C20 and C20C25 derivatives of phosphatidylglycerol, phosphatidylglycerol phosphate methyl ester and the disulfated glycolipid S2-DGA-1. The G+C content of the genomic DNA was 64.7 mol%. Comparative analysis of phenotypic characteristics between strain EJ-57T and recognized Natrinema species supported the conclusion that EJ-57T represents a novel species within this genus, for which the name Natrinema ejinorense sp. nov. is proposed. The type strain is EJ-57T (=CECT 7144T=JCM 13890T=CGMCC 1.6202T).

Base Composition↗
PubMed2006

One-step purification of mammalian deoxyribonucleases I and differences among pancreas, parotid, and pancreas-parotid (mixed) types based on species- and organ-specific N-linked glycosylation.

Mammalian deoxyribonucleases I (DNase I) are classified into three types, namely, pancreas, parotid, and pancreas-parotid (mixed), based on differences in their tissue concentrations. In this study, DNase I purification by concanavalin A-wheat germ agglutinin mixture-agarose column from rat (parotid type), rabbit (mixed type), and pig (pancreas type) is described. This method permits a relatively easy one-step purification of DNase I from rat and rabbit parotid glands, the rat submaxillary gland, and porcine pancreas. To elucidate differences among the three types, these DNases I were subjected to enzymatic deglycosylation either by peptide N-glycosidase F (PNGase F) or endoglycosidase H (Endo H). Following deglycosylation, digests were separated on DNA-casting polyacrylamide gel electrophoresis. PNGase F produced a single lower mobility product in all samples. Endo H produced a double band in rat and rabbit parotid glands and porcine pancreas, and a single band in the rabbit pancreas corresponding with the PNGase F product. DNase I activity of the porcine pancreas was completely extinguished by deglycosylation, while that of the parotid glands and rabbit pancreas was unaffected. Our results suggest that the distinct properties of DNase I exhibited by the three types may be attributed to differences in the extent of post-translational N-linked glycosylation of the enzyme.

Animals↗
PubMed2006

CYP2A6 polymorphism reveals differences in Japan and the existence of a specific variant in Ovambo and Turk populations.

CYP2A6 is a polymorphic enzyme, and CYP2A6 genotype has been shown to be associated with smoking habits and lung cancer. We investigated CYP2A6 polymorphism in Japanese from four different geographic areas of Japan and in the Ovambo and Turk populations. Using two polymerase chain reaction restriction fragment length polymorphisms (PCR-RFLPs), we identified the functionally important variants of CYP2A6: *1A, *1B, *1F, *1G, *4A, and *4D. In the Japanese population the highest frequencies of the CYP2A6*1A allele were observed in subjects from the Fukuoka (Kyushu Island) and Ehime (Shikoku Island) prefectures, whereas subjects in Shimane and Tottori (both located on the Japan Sea side of Honshu Island) showed the highest frequencies of the CYP2A6*1B allele. In the Tottori and Shimane groups no subject was homozygous for the CYP2A6*4A allele, a whole gene deletion type that is prevalent among Asians. In the Ovambo and Turk populations the CYP2A6*1A allele was predominant. Furthermore, two alleles undetected in the Japanese were observed in these latter two ethnic groups: CYP2A6*1G was found solely in the Ovambos, and CYP2A6*1F was found solely in the Turks. The present study is the first to show interprefecture differences in CYP2A6 polymorphism in Japanese who live in relatively close but distinct geographic areas; this is also the first study to evaluate CYP2A6 variations among these Japanese and the Ovambo and Turk populations. The distribution results of these alleles could help to define the true significance of CYP2A6 polymorphism as a genetic susceptibility marker in worldwide populations.

Alleles↗
PubMed2006

Prediction of compounds with specific pharmacodynamic, pharmacokinetic or toxicological property by statistical learning methods.

Computational methods for predicting compounds of specific pharmacodynamic, pharmacokinetic, or toxicological property are useful for facilitating drug discovery and drug safety evaluation. The quantitative structure-activity relationship (QSAR) and quantitative structure-property relationship (QSPR) methods are the most successfully used statistical learning methods for predicting compounds of specific property. More recently, other statistical learning methods such as neural networks and support vector machines have been explored for predicting compounds of higher structural diversity than those covered by QSAR and QSPR. These methods have shown promising potential in a number of studies. This article is intended to review the strategies, current progresses and underlying difficulties in using statistical learning methods for predicting compounds of specific property. It also evaluates algorithms commonly used for representing structural and physicochemical properties of compounds.

Pharmacokinetics↗
PubMed2006

Application of support vector machines to in silico prediction of cytochrome p450 enzyme substrates and inhibitors.

Cytochrome P450 enzymes are responsible for phase I metabolism of the majority of drugs and xenobiotics. Identification of the substrates and inhibitors of these enzymes is important for the analysis of drug metabolism, prediction of drug-drug interactions and drug toxicity, and the design of drugs that modulate cytochrome P450 mediated metabolism. The substrates and inhibitors of these enzymes are structurally diverse. It is thus desirable to explore methods capable of predicting compounds of diverse structures without over-fitting. Support vector machine is an attractive method with these qualities, which has been employed for predicting the substrates and inhibitors of several cytochrome P450 isoenzymes as well as compounds of various other pharmacodynamic, pharmacokinetic, and toxicological properties. This article introduces the methodology, evaluates the performance, and discusses the underlying difficulties and future prospects of the application of support vector machines to in silico prediction of cytochrome P450 substrates and inhibitors.

Animals↗
PubMed2005

Stabilization analysis and modified Korteweg-de Vries equation in a cooperative driving system.

Two lattice traffic models are proposed by incorporating a cooperative driving system. The lattice versions of the hydrodynamic model of traffic flow are described by the differential-difference equation and difference-difference equation, respectively. The stability conditions for the two models are obtained using the linear stability theory. The results show that considering more than one site ahead in vehicle motion leads to the stabilization of the system. The modified Korteweg-de Vries equation (the mKdV equation, for short) near the critical point is derived by using the reductive perturbation method to show the traffic jam which is proved to be described by kink-anti-kink soliton solutions obtained from the mKdV equations.

Journal Article↗
PubMed2005

Prediction of genotoxicity of chemical compounds by statistical learning methods.

Various toxicological profiles, such as genotoxic potential, need to be studied in drug discovery processes and submitted to the drug regulatory authorities for drug safety evaluation. As part of the effort for developing low cost and efficient adverse drug reaction testing tools, several statistical learning methods have been used for developing genotoxicity prediction systems with an accuracy of up to 73.8% for genotoxic (GT+) and 92.8% for nongenotoxic (GT-) agents. These systems have been developed and tested by using less than 400 known GT+ and GT- agents, which is significantly less in number and diversity than the 860 GT+ and GT- agents known at present. There is a need to examine if a similar level of accuracy can be achieved for the more diverse set of molecules and to evaluate other statistical learning methods not yet applied to genotoxicity prediction. This work is intended for testing several statistical learning methods by using 860 GT+ and GT- agents, which include support vector machines (SVM), probabilistic neural network (PNN), k-nearest neighbor (k-NN), and C4.5 decision tree (DT). A feature selection method, recursive feature elimination, is used for selecting molecular descriptors relevant to genotoxicity study. The overall accuracies of SVM, k-NN, and PNN are comparable to and those of DT lower than the results from earlier studies, with SVM giving the highest accuracies of 77.8% for GT+ and 92.7% for GT- agents. Our study suggests that statistical learning methods, particularly SVM, k-NN, and PNN, are useful for facilitating the prediction of genotoxic potential of a diverse set of molecules.

Computational Biology↗
PubMed2005

OsAGAP, an ARF-GAP from rice, regulates root development mediated by auxin in Arabidopsis.

Arf (ADP-ribosylation factor) proteins, which mediate vesicular transport, have little or no intrinsic GTPase activity. They rely on the action of GTPase-activating proteins (GAPs) and guanine nucleotide exchange factors (GEFs) for their function. In the present study the OsAGAP gene in rice, which encoded a protein with predicted structure similar to ArfGAP, was identified. The purified OsAGAP-GST fusion protein was able to stimulate the GTPase activity of rice Arf. Furthermore, OsAGAP can rescue the defect of vesicular transport in the yeast gcs1 delta glo3 delta double-mutant cells. Transgenic Arabidopsis with OsAGAP constitutively expression showed reduced apical dominance, shorter primary roots, increasing number of longer adventitious roots. Many of the phenotypes can be phenocopied by treatment of exogenous indoleacetic acid level (IAA) in wild-type plants. Determination of whole-plant IAA level showed that there is a sharp increase of free IAA in OsAGAP transgenic Arabidopsis seedlings. In addition, removal of the 4-day-old shoot apex could inhibit the adventitious root formation in the transgenic seedlings. These results suggest OsAGAP, an ARF-GAP of rice, maybe involved in the mediation of plant root development by regulating auxin level.

ADP-Ribosylation Factors↗
PubMed2004

Stabilization effect of traffic flow in an extended car-following model based on an intelligent transportation system application.

An extended car following model is proposed by incorporating an intelligent transportation system in traffic. The stability condition of this model is obtained by using the linear stability theory. The results show that anticipating the behavior of more vehicles ahead leads to the stabilization of traffic systems. The modified Korteweg-de Vries equation (the mKdV equation, for short) near the critical point is derived by applying the reductive perturbation method. The traffic jam could be thus described by the kink-antikink soliton solution for the mKdV equation. From the simulation of space-time evolution of the vehicle headway, it is shown that the traffic jam is suppressed efficiently with taking into account the information about the motion of more vehicles in front, and the analytical result is consonant with the simulation one.

Journal Article↗
PubMed2004

MoViES: molecular vibrations evaluation server for analysis of fluctuational dynamics of proteins and nucleic acids.

Analysis of vibrational motions and thermal fluctuational dynamics is a widely used approach for studying structural, dynamic and functional properties of proteins and nucleic acids. Development of a freely accessible web server for computation of vibrational and thermal fluctuational dynamics of biomolecules is thus useful for facilitating the relevant studies. We have developed a computer program for computing vibrational normal modes and thermal fluctuational properties of proteins and nucleic acids and applied it in several studies. In our program, vibrational normal modes are computed by using modified AMBER molecular mechanics force fields, and thermal fluctuational properties are computed by means of a self-consistent harmonic approximation method. A web version of our program, MoViES (Molecular Vibrations Evaluation Server), was set up to facilitate the use of our program to study vibrational dynamics of proteins and nucleic acids. This software was tested on selected proteins, which show that the computed normal modes and thermal fluctuational bond disruption probabilities are consistent with experimental findings and other normal mode computations. MoViES can be accessed at http://ang.cz3.nus.edu.sg/cgi-bin/prog/norm.pl.

Computational Biology↗