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Search Results: 1 - 10 of 35385 matches for " HuiXiao Hong "
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Next generation sequencing for profiling expression of miRNAs: technical progress and applications in drug development  [PDF]
Jie Liu, Steven F. Jennings, Weida Tong, Huixiao Hong
Journal of Biomedical Science and Engineering (JBiSE) , 2011, DOI: 10.4236/jbise.2011.410083
Abstract: miRNAs are non-coding RNAs that play a regulatory role in expression of genes and are associated with diseases. Quantitatively measuring expression levels of miRNAs can help understanding the mechanisms of human diseases and discovering new drug targets. There are three major methods that have been used to measure the expression levels of miRNAs: real-time reverse transcription PCR (qRT-PCR), microarray, and the newly introduced next-generation sequencing (NGS). NGS is not only suitable for profiling of known miRNAs that qRT-PCR and microarray can do too but also able to detect unknown miRNAs that the other two methods are incapable. Profiling of miRNAs by NGS has been progressed rapidly and is a promising field for applications in drug development. This paper will review the technical advancement of NGS for profiling miRNAs, including comparative analyses between different platforms and software packages for analyzing NGS data. Examples and future perspectives of applications of NGS profiling miRNAs in drug development will be discussed.
Pitfall of genome-wide association studies: Sources of inconsistency in genotypes and their effects  [PDF]
Huixiao Hong, Lei Xu, Zhenqiang Su, Jie Liu, Weigong Ge, Jie Shen, Hong Fang, Roger Perkins, Leming Shi, Weida Tong
Journal of Biomedical Science and Engineering (JBiSE) , 2012, DOI: 10.4236/jbise.2012.510069
Abstract: Personalized medicine will improve heath outcomes and patient satisfaction. However, implementing personalized medicine based on individuals’ biological information is far from simple, requiring genetic biomarkers that are mainly developed and used by the pharmaceutical companies for selecting those patients who benefit more, or have less risk of adverse drug reactions, from a particular drug. Genome-wide Association Studies (GWAS) aim to identify genetic variants across the human genome that might be utilized as genetic biomarkers for diagnosis and prognosis. During the last several years, high-density genotyping SNP arrays have facilitated GWAS that successfully identified common genetic variants associated with a variety of phenotypes. However, each of the identified genetic variants only explains a very small fraction of the underlying genetic contribution to the studied phenotypic trait. The replication studies demonstrated that only a small portion of associated loci in the initial GWAS can be replicated, even within the same populations. Given the complexity of GWAS, multiple sources of Type I (false positive) and Type II (false negative) errors exist. The inconsistency in genotypes that caused either by the genotypeing experiment or by genotype calling process is a major source of the false GWAS findings. Accurate and reproducible genotypes are paramount as inconsistency in genotypes can lead to an inflation of false associations. This article will review the sources of inconsistency in genotypes and discuss its effect in GWAS findings.
Homology Model and Ligand Binding Interactions of the Extracellular Domain of the Human α4β2 Nicotinic Acetylcholine Receptor  [PDF]
Shu Mao, Hui Wen Ng, Michael Orr, Heng Luo, Hao Ye, Weigong Ge, Weida Tong, Huixiao Hong
Journal of Biomedical Science and Engineering (JBiSE) , 2016, DOI: 10.4236/jbise.2016.91005
Abstract: Addiction to nicotine, and possibly other tobacco constituents, is a major factor that contributes to the difficulties smokers face when attempting to quit smoking. Amongst the various subtypes of nicotinic acetylcholine receptors (nAChRs), the α4β2 subtype plays an important role in mediating the addiction process. The characterization of human α4β2-ligand binding interactions provides a molecular framework for understanding ligand-receptor interactions, rendering insights into mechanisms of nicotine addiction and may furnish a tool for efficiently identifying ligands that can bind the nicotine receptor. Therefore, we constructed a homology model of human α4β2 nAChR and performed molecular docking and molecular dynamics (MD) simulations to elucidate the potential human α4β2-ligand binding modes for eleven compounds known to bind to this receptor. Residues V96, L97 and F151 of the α4 subunit and L111, F119 and F121 of the β2 subunit were found to be involved in hydrophobic interactions while residues S153 and W154 of the α4 subunit were involved in the formation of hydrogen bonds between the receptor and respective ligands. The homology model and its eleven ligand-bound structures will be used to develop a virtual screening program for identifying tobacco constituents that are potentially addictive.
Studies on abacavir-induced hypersensitivity reaction: a successful example of translation of pharmacogenetics to personalized medicine
YongLi Guo,LeMing Shi,HuiXiao Hong,ZhenQiang Su,James Fuscoe,BaiTang Ning
Science China Life Sciences , 2013, DOI: 10.1007/s11427-013-4438-8
Abstract: Abacavir is an effective nucleoside analog reverse transcriptase inhibitor used to treat human immunodeficiency virus (HIV) infected patients. Its main side effect is hypersensitivity reaction (HSR). The incidence of the HSR is associated with ethnicity among patients exposed to abacavir, and retrospective and prospective studies show a significantly increased risk of abacavir-induced HSR in human leukocyte antigen (HLA)-B*57:01-carrying patients. Immunological studies indicated that abacavir interacts specifically with HLA-B*57:01 and changed the binding specificity between the HLA molecule and the HLA-presented endogenous peptide repertoire, leading to a systemic autoimmune reaction. HLA-B*57:01 screening, combined with patch testing, had clinically predictive value and cost-effective impact in reducing the incidence of abacavir-induced HSR regardless of the HLA-B*57:01 prevalence in the population. Therefore, the US Food and Drug Administration (FDA) and international HIV treatment guidelines recommend a routine HLA-B*57:01 screening prior to abacavir treatment to decrease false positive diagnosis and prevent abacavir-induced HSR. The studies of abacavir-induced HSR and the implementation of the HLA-B*57:01 screening in the clinic represent a successful example of the use of pharmacogenetics for personalized diagnosis and therapy.
SNPTrackTM : an integrated bioinformatics system for genetic association studies
Joshua Xu, Reagan Kelly, Guangxu Zhou, Steven A Turner, Don Ding, Stephen C Harris, Huixiao Hong, Hong Fang, Weida Tong
Human Genomics , 2012, DOI: 10.1186/1479-7364-6-5
Abstract:
Critical role of bioinformatics in translating huge amounts of next-generation sequencing data into personalized medicine
HuiXiao Hong,WenQian Zhang,Jie Shen,ZhenQiang Su,BaiTang Ning,Tao Han,Roger Perkins,LeMing Shi,WeiDa Tong
Science China Life Sciences , 2013, DOI: 10.1007/s11427-013-4439-7
Abstract: Realizing personalized medicine requires integrating diverse data types with bioinformatics. The most vital data are genomic information for individuals that are from advanced next-generation sequencing (NGS) technologies at present. The technologies continue to advance in terms of both decreasing cost and sequencing speed with concomitant increase in the amount and complexity of the data. The prodigious data together with the requisite computational pipelines for data analysis and interpretation are stressors to IT infrastructure and the scientists conducting the work alike. Bioinformatics is increasingly becoming the rate-limiting step with numerous challenges to be overcome for translating NGS data for personalized medicine. We review some key bioinformatics tasks, issues, and challenges in contexts of IT requirements, data quality, analysis tools and pipelines, and validation of biomarkers.
Controlled Synthesis and Characterization of Nobel Metal Nanoparticles  [PDF]
Huixiao Hei, Rui Wang, Xiaojun Liu, Long He, Guizhen Zhang
Soft Nanoscience Letters (SNL) , 2012, DOI: 10.4236/snl.2012.23007
Abstract: In this work, monodispersed, well-shaped platinum (3.2 - 6.4 nm), rhodium (2.4 - 5.1 nm), palladium (3.2 - 5.3 nm) nanoparticles capped with poly(vinylpyrrolidone) were synthesized by a polyol reduction method in an ethylene glycol solution at temperature of 190℃. The influences of synthetic parameters on the size and morphology of the noble metal nanoparticles have been systematically investigated. The noble metal nanoparticles were characterized by means of UV-vis, laser scattering particle size distribution analysis (LSPSDA) and transmission electron microscopy (TEM). The experimental results showed that the particle size of metals nanoparticles, the morphology of which was spherical, increased with the raise of metal precursor concentration as well as the amount of PVP. The optimal molar ratio of PVP/metal and metal precursor concentration for the fabrication of Pt, Rh, and Pd nanoparticles with uniform distribution were 10 and 0.1 mM, respectively. The morphologies of the Rh nanoparticles with the size of 5.1 nm were polygons, including hexagons, pentagons, and triangles.
Effects of soil temperature,water stress and sowing depth on germination and emergence of maize
土壤温度、水分胁迫和播种浓度对玉米种子萌发出苗的影响

Wang Huixiao,
王会肖

中国生态农业学报 , 1995,
Abstract: 萌发试验同时在实验室和田间进行。试验结果表明,当土壤含水量低于10%时,玉米种子将不能顺利萌发。温度变化可以加速种子萌发,田间条件下,50%种子萌发所需的积温范围为18.6-23.8℃,决定于昼夜的温差。播种深度影响出苗速率,50%出苗所需的时间播深8厘米比播深5厘米推迟半天(相当于积温3-5℃),80%出苗所需的时间播深8厘米比播深5厘米推迟1天(相当于积温7-10℃)。
Measurement and simulation of evaporation from a bare soil
Wang Huixiao,Lester P.Simmonds
环境科学学报(英文版) , 1997,
Abstract: MeasurementandsimulationofevaporationfromabaresoilWangHuixiaoInstituteofGeography,ChineseAcademyofSciences,Beijing100101,Chin...
Modeling Chemical Interaction Profiles: I. Spectral Data-Activity Relationship and Structure-Activity Relationship Models for Inhibitors and Non-inhibitors of Cytochrome P450 CYP3A4 and CYP2D6 Isozymes
Brooks McPhail,Yunfeng Tie,Huixiao Hong,Bruce A. Pearce,Laura K. Schnackenberg,Weigong Ge,Luis G. Valerio,James C. Fuscoe,Weida Tong,Dan A. Buzatu,Jon G. Wilkes,Bruce A. Fowler,Eugene Demchuk,Richard D. Beger
Molecules , 2012, DOI: 10.3390/molecules17033383
Abstract: An interagency collaboration was established to model chemical interactions that may cause adverse health effects when an exposure to a mixture of chemicals occurs. Many of these chemicals—drugs, pesticides, and environmental pollutants—interact at the level of metabolic biotransformations mediated by cytochrome P450 (CYP) enzymes. In the present work, spectral data-activity relationship (SDAR) and structure-activity relationship (SAR) approaches were used to develop machine-learning classifiers of inhibitors and non-inhibitors of the CYP3A4 and CYP2D6 isozymes. The models were built upon 602 reference pharmaceutical compounds whose interactions have been deduced from clinical data, and 100 additional chemicals that were used to evaluate model performance in an external validation (EV) test. SDAR is an innovative modeling approach that relies on discriminant analysis applied to binned nuclear magnetic resonance (NMR) spectral descriptors. In the present work, both 1D 13C and 1D 15N-NMR spectra were used together in a novel implementation of the SDAR technique. It was found that increasing the binning size of 1D 13C-NMR and 15N-NMR spectra caused an increase in the tenfold cross-validation (CV) performance in terms of both the rate of correct classification and sensitivity. The results of SDAR modeling were verified using SAR. For SAR modeling, a decision forest approach involving from 6 to 17 Mold2 descriptors in a tree was used. Average rates of correct classification of SDAR and SAR models in a hundred CV tests were 60% and 61% for CYP3A4, and 62% and 70% for CYP2D6, respectively. The rates of correct classification of SDAR and SAR models in the EV test were 73% and 86% for CYP3A4, and 76% and 90% for CYP2D6, respectively. Thus, both SDAR and SAR methods demonstrated a comparable performance in modeling a large set of structurally diverse data. Based on unique NMR structural descriptors, the new SDAR modeling method complements the existing SAR techniques, providing an independent estimator that can increase confidence in a structure-activity assessment. When modeling was applied to hazardous environmental chemicals, it was found that up to 20% of them may be substrates and up to 10% of them may be inhibitors of the CYP3A4 and CYP2D6 isoforms. The developed models provide a rare opportunity for the environmental health branch of the public health service to extrapolate to hazardous chemicals directly from human clinical data. Therefore, the pharmacological and environmental health branches are both expected to benefit from these reported models.
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