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Biomedical subjects

Z D Hu

Publications and source records attributed to Z D Hu.

At least 19 recordsLinked to original sources
PubMed2006

Synthesis and characterizations of amorphous carbon nanotubes by pyrolysis of ferrocene confined within AAM templates.

Amorphous carbon nanotubes (a-CNTs) are synthesized by pyrolysis of ferrocene confined in the nanopores of the anodic alumina membrane (AAM) and characterized by field emission scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDS), transmission electron microscopy (TEM), electron energy-loss spectroscopy (EELS), and Raman spectroscopy. It is shown that the a-CNT has an ultrathin amorphous wall (approximately 3 nm) and a relatively large diameter (approximately 50 nm), and is capsulated with iron oxide nanoparticles. It is found that the growth of the a-CNTs is governed mainly by the template limitation effect. Electrical transport measurements on individual a-CNTs demonstrate that the a-CNT may be connected with electrodes via either ohmic or Schottky contacts, and the resisitivity of the a-CNTs was measured to be 4.5 x 10(-3) Omega cm.

Journal Article↗
PubMed2006

Quantitative structure-toxicity relationships (QSTRs): a comparative study of various non linear methods. General regression neural network, radial basis function neural network and support vector machine in predicting toxicity of nitro- and cyano- aromatics to Tetrahymena pyriformis.

Prediction of toxicity of 203 nitro- and cyano-aromatic chemicals to Tetrahymena pyriformis was carried out by radial basis function neural network, general regression neural network and support vector machine, in non-linear response surface methodology. Toxicity was predicted from hydrophobicity parameter (log Kow) and maximum superdelocalizability (Amax). Special attention was drawn to prediction ability and robustness of the models, investigated both in a leave-one-out and 10-fold cross validation (CV) processes. The influence that the corresponding changes in the learning sets during these CV processes could have on a common external test set including 41 compounds was also examined. This allowed us to establish the stability of the models. The non linear results slightly outperform (as expected) multilinear relationships (MLR) and also favourably compete with various other non linear approaches recently proposed by Ren (J. Chem. Inf. Comput. Sci., 43 1679 (2003)).

Animals↗
PubMed2005

Prediction of the tissue/blood partition coefficients of organic compounds based on the molecular structure using least-squares support vector machines.

The accurate nonlinear model for predicting the tissue/blood partition coefficients (PC) of organic compounds in different tissues was firstly developed based on least-squares support vector machines (LS-SVM), as a novel machine learning technique, by using the compounds' molecular descriptors calculated from the structure alone and the composition features of tissues. The heuristic method (HM) was used to select the appropriate molecular descriptors and build the linear model. The prediction result of the LS-SVM model is much better than that obtained by HM method and the prediction values of tissue/blood partition coefficients based on the LS-SVM model are in good agreement with the experimental values, which proved that nonlinear model can simulate the relationship between the structural descriptors, the tissue composition and the tissue/blood partition coefficients more accurately as well as LS-SVM was a powerful and promising tool in the prediction of the tissue/blood partition behaviour of compounds. Furthermore, this paper provided a new and effective method for predicting the tissue/blood partition behaviour of the compounds in the different tissues from their structures and gave some insight into structural features related to the partition process of the organic compounds in different tissues.

Least-Squares Analysis↗
PubMed2005

Application of support vector machine (SVM) for prediction toxic activity of different data sets.

As a new method, support vector machine (SVM) were applied for prediction of toxicity of different data sets compared with other two common methods, multiple linear regression (MLR) and RBFNN. Quantitative structure-activity relationships (QSAR) models based on calculated molecular descriptors have been clearly established. Among them, SVM model gave the highest q(2) and correlation coefficient R. It indicates that the SVM performed better generalization ability than the MLR and RBFNN methods, especially in the test set and the whole data set. This eventually leads to better generalization than neural networks, which implement the empirical risk minimization principle and may not converge to global solutions. We would expect SVM method as a powerful tool for the prediction of molecular properties.

Algorithms↗
PubMed2005

Study of probabilistic neural networks to classify the active compounds in medicinal plants.

Probabilistic neural networks (PNNs) were utilized for the classifications of 102 active compounds from diverse medicinal plants with anticancer activity against human rhinopharyngocele cell line KB. Molecular descriptors calculated from structure alone were used to represent molecular structures. A subset of the calculated descriptors selected using factor correlation analysis and forward stepwise regression was used to construct the prediction models. Linear discriminant analysis (LDA) was also utilized to construct the classification model to compare the results with those obtained by PNNs. The accuracy of the training set, the cross-validation set, and the test set given by PNNs and LDA were 100, 92.3, 90.9% and 71.8, 92.3, 54.5%, respectively, which indicated that the results obtained by PNNs agree well with the experimental values of these compounds and also revealed the superiority of PNNs over LDA approach for the classification of anticancer activities of compounds. The models built in this work would be of potential help in the design of novel and more potent anticancer agents.

Algorithms↗
PubMed2005

The prediction of human oral absorption for diffusion rate-limited drugs based on heuristic method and support vector machine.

Support vector machine (SVM), as a novel machine learning technique, was used for the prediction of the human oral absorption for a large and diverse data set using the five descriptors calculated from the molecular structure alone. The molecular descriptors were selected by heuristic method (HM) implemented in CODESSA. At the same time, in order to show the influence of different molecular descriptors on absorption and to well understand the absorption mechanism, HM was used to build several multivariable linear models using different numbers of molecular descriptors. Both the linear and non-linear model can give satisfactory prediction results: the square of correlation coefficient R(2) was 0.78 and 0.86 for the training set, and 0.70 and 0.73 for the test set respectively. In addition, this paper provides a new and effective method for predicting the absorption of the drugs from their structures and gives some insight into structural features related to the absorption of the drugs.

Administration, Oral↗
PubMed2005

QSAR study of natural, synthetic and environmental endocrine disrupting compounds for binding to the androgen receptor.

A large data set of 146 natural, synthetic and environmental chemicals belonging to a broad range of structural classes have been tested for their relative binding affinity (expressed as log (RBA)) to the androgen receptor (AR). These chemicals commonly termed endocrine disrupting compounds (EDCs) present a variety of adverse effects in humans and animals. As assays for binding affinity remains a time-consuming task, it is important to develop predictive methods. In this work, quantitative structure-activity relationships (QSARs) were determined using three methods, multiple linear regression (MLR), radical basis function neural network (RBFNN) and support vector machine (SVM). Five descriptors, accounting for hydrogen-bonding interaction, distribution of atomic charges and molecular branching degree, were selected from a heuristic method to build predictive QSAR models. Comparison of the results obtained from three models showed that the SVM method exhibited the best overall performances, with a RMS error of 0.54 log (RBA) units for the training set, 0.59 for the test set, and 0.55 for the whole set. Moreover, six linear QSAR models were constructed for some specific families based on their chemical structures. These predictive toxicology models, should be useful to rapidly identify potential androgenic endocrine disrupting compounds.

Algorithms↗
PubMed2004

QSAR and classification models of a novel series of COX-2 selective inhibitors: 1,5-diarylimidazoles based on support vector machines.

The support vector machine, which is a novel algorithm from the machine learning community, was used to develop quantitation and classification models which can be used as a potential screening mechanism for a novel series of COX-2 selective inhibitors. Each compound was represented by calculated structural descriptors that encode constitutional, topological, geometrical, electrostatic, and quantum-chemical features. The heuristic method was then used to search the descriptor space and select the descriptors responsible for activity. Quantitative modelling results in a nonlinear, seven-descriptor model based on SVMs with root mean-square errors of 0.107 and 0.136 for training and prediction sets, respectively. The best classification results are found using SVMs: the accuracy for training and test sets is 91.2% and 88.2%, respectively. This paper proposes a new and effective method for drug design and screening.

Algorithms↗
PubMed2004

3D QSAR studies on antimalarial alkoxylated and hydroxylated chalcones by CoMFA and CoMSIA.

The 3D QSAR analyses of antimalarial alkoxylated and hydroxylated chalcones were first conducted by Comparative molecular field analysis (CoMFA) and Comparative similarity indices analysis (CoMSIA) to determine the factors required for the activity of these compounds. Satisfactory results were obtained after performing a leave-one-out (LOO) cross-validation study with cross-validation q(2) and conventional r(2) values of 0.740 and 0.972 by the CoMFA model, 0.714 and 0.976 by the CoMSIA model, respectively. The results provided the tools for predicting the affinity of related compounds, and for guiding the design and synthesis of novel and more potent antimalarial agents.

Antimalarials↗
PubMed2001

Microdetermination of proteins with the arsenazo-DBN-Al(III) complex by Rayleigh light-scattering technique and application of the method.

The determination of proteins with arsenazo-DBN and Al3+ by Rayleigh light-scattering (RLS) is described. The weak RLS of arsenazo-DBN and BSA can be enhanced greatly by addition of Al3+ in the pH range 5.3-7.0; this resulted in two enhanced RLS signals at 420-440 nm and 460-480 nm. The reaction between arsenazo-DBN, Al3+, and proteins was studied and a new method was developed for quantitative determination of proteins. This method is very sensitive (0.34-41.71 microg mL(-1) for bovine serum albumin, BSA, and 0.29-53.41 microg mL(-1) for human serum albumin, HSA), rapid (< 2 min), simple (one step), and tolerant of most interfering substances. The effects of different surfactants were also examined. When these proteins were determined in four human serum samples the maximum relative error was not more than 2% and the recovery was between 97 and 103%.

Aluminum↗
PubMed2001

[Determination of enantiomeric purity for lactic acid in fermentation broth by Rhizopus oryzae with high performance liquid chromatography].

A procedure for the resolution of DL-lactic acid and the determination of D-isomer ratio in L-lactic acid fermentation broth by Rhizopus oryzae is described. The effects of pH of mobile phase and concentration of chiral mobile phase additives on resolution of DL-lactic acid were investigated. The optical isomers of lactic acid were resolved by RP-HPLC with 2,3,6-tri-O-beta-cyclodextrin(TM-beta-CD) as a chiral mobile phase additive, and C18 column as stationary phase, and detected at wavelength 210 nm. The results showed that a correction factor should be introduced into the equation for calculation of the percentage of D-lactic acid, because the UV absorption of D-lactic acid and L-lactic acid might not be the same when TM-beta-CD was present. Quantitation was achieved with external standard method, the average recovery was 100.4%, and the relative standard deviation was 0.82%. This method can be used for the determination of the percentage of D-isomer in L-lactic acid fermentation broth by Rhizopus oryzae, and it is simple, rapid and accurate. The results showed that the mass fraction of D-isomer in the fermented broth increased during the period of storage.

Chromatography, High Pressure Liquid↗
PubMed2000

Electrophoretic behavior study and determination of some active components in Chinese medicinal preparations by capillary electrophoresis.

The determination of icariin (IC), rhein (RH), chrysophanol (CH), physcion (PHY), glycyrrhetic acid (GE), and glycyrrhizic acid (GI), in traditional Chinese preparations, Anshen Bunao oral liquid and Maren pill, has been investigated by micellar electrokinetic capillary electrophoresis. With borate buffer (10 mM), SDS (20 mM) and acetonitrile (10%) as background electrolyte (pH 9.55), 20 kV applied voltage and 254 nm UV detection, the six active compounds were completely separated within 10 min. The effects of buffer pH, concentration of borate, SDS and modifier on electrophoretic behavior and separation are discussed. Regression equations revealed linear relationships (correlation coefficients: 0.9960-0.9999) between the peak-area of each component and the content. In addition, the levels of the six active compounds in two kinds of traditional Chinese medicinal preparations were easily determined with recoveries of from 94.7% to 106.4%.

Chromatography, Micellar Electrokinetic Capillary↗
PubMed2000

Prediction of programmed-temperature retention values of naphthas by artificial neural networks.

It is proposed for the first time a method of prediction of the programmed-temperature retention times of components of naphthas in capillary gas chromatography using artificial neural networks. People are used to predict the programmed-temperature retention time using many formulas such as the integral formula, which requires that four parameters must be determined by calculation or experiments. However the results obtained by the formula are not so good to meet the demand of industry. In order to predict retention time accurately and conveniently, artificial neural networks using five-fold cross-validation and leave-20%-out methods have been applied. Only two parameters: density and isothermal retention index were used as input vectors. The average RMS error for predicted values of five different networks was 0.18, whereas the RMS error of predictions by the integral formula was 0.69. Obviously, the predictions by neural networks were much better than predictions by the formula, and neural networks need fewer parameters than the formula. So neural networks can successfully and conveniently solve the problem of predictions of programmed-temperature retention times, and provide useful data for analysis of naphthas in petrochemical industry.

Chemical Industry↗
PubMed2000

[Study on taxol release in the two-liquid-phase cultures of Taxus cuspidata].

Effects of rare earth compound (ammonium sulphate), organic solvents(oleic acid and dibutylphthalate) and the integrated function of the rare earth compound and organic solvents were studied on taxol release in the Taxus cuspidata suspension cultures. And then effects of different organic solvents(paraffin, organic acid, alcohol and ester), their volumetric fraction and phase toxicity were studied on taxol release in the two-liquid-phase cultures of Taxus cuspidata. The results showed that the addition of the rare earth compound or the organic solvents could strengthen obviously taxol release, especially the organic solvents. But the addition of the rare earth compound could not strengthen further taxol release in the twoliquid-phase cultures of Taxus cuspidata. Therefore the organic solvents were very good permeabilizing reagents, which could enhance obviously secondary metabolite in the twoliquid-phase cultures of plant cells. Release percentage of taxol was increased into more than 75% from 40% of the control.

Antineoplastic Agents, Phytogenic↗
PubMed2000

[The medium optimization of xylitol fermentation based on neural networks and genetic algorithms].

Using genetic algorithms(GA) for medium optimization of xylitol fermentation, and coupling neural networks model for predicting xylitol concentration is introduced. The medium compose determined by GA is as input data of the neural networks, while the output data predicted by neural networks is as suitable value of GA for predicting. The optimum medium is further validated by experimentation. The good result, which save the experimental workload and charge, enhance the level of xylitol fermentation as well as reduced the medium consume is obtained.

Algorithms↗
PubMed2000

[Determination of lactic acid in fermentation broth of Rhizopus oryzae by reversed-phase high performance liquid chromatography (RP-HPLC)].

A method for determining lactic acid in fermentation broth of Rhizopus oryzae by RP-HPLC is described. The operating conditions were Wakosil-II 5 C18 RS column(4.6 mm i.d. x 150 mm, 5 microns) at room temperature, 0.01 mol/L phosphoric acid solution (pH 2.5) as mobile phase with a flow rate of 1.0 mL/min and UV detection at 210 nm. The retention time of lactic acid was 3.820 min. This method is simple, rapid and accurate. The results will not be affected by other components in the broth. The relative standard deviation was 0.22% (n = 5), and the recovery was over 99%.

Chromatography, High Pressure Liquid↗
PubMed2000

[The effect of artesunate in preventing the populations from Schistosoma japonicum infection during flood-control].

OBJECTIVE: To evaluate the prophylactic effect of artesunate in high-risk populations who had contacted the infested water. METHODS: From the 7th day post-exposure to infested water, a total of 17,031 people who had contacted the infested water for more than 20 days had been treated with artesunate at a dose of 300 mg once a week for three successive weeks. On day 17 after the last medication, they were examined for schistosomiasis and followed-up 60 days later. RESULTS: No acute schistosomiasis case was found. ELISA-positive was found in 204(1.20%) and COPT > 3% was found in 195 cases (1.14%). The side-effects were slight. CONCLUSION: Artesunate is highly effective and safe for the prevention of schistosomiasis.

Adolescent↗