Biomedical subjects

Ying Liu

Publications and source records attributed to Ying Liu.

441 records · Page 25Linked to original sources

PubMed1999

Evaluation of dot immunogold filtration assay for anti-HAV IgM antibody.

AIM:To detect hepatitis A virus-specific immunoglobulin M (IgM) antibody rapidly.METHODS:Colloidal gold with an average diameter of 15nm was prepared by controlled reduction of a boiling solution of 0.2g/L chloroauric acid with 10g/L sodium citrate and labeled with anti-HAVIgG as gold probe. Dot immunogold filtration assay (DIGFA) has been developed by coating anti-human &mgr; chain on nitrocellulose membrane (NCM) for capturing the anti-HAV IgM in serum, then using cultured hepatitis A antigen as a bridge , connecting anti-HAV IgM in sample and anti-HAV IgG labeled colloidal gold. If there was anti-HAV IgM in sample, gold probes would concentrate on NCM, which will appear a pink dot.RESULTS:A total of 264 serum samples were comparatively detected with both DIGFA and ELISA by blind method. Among them, 88 were positive and 146 were negative with the two methods. The sensitivity and the specificity of DIGFA were 86.27% and 90.12%, respectively. Fifteen negative serum samples and 15 positive serum samples were detected 3 times repeatedly, the results were the same.CONCLUSION:DIGFA is a simple, rapid, sensitive, specific and reliable method without expensive equipment and is not interfered with rheumatoid factor (RF) in serum. It is suitable for basic medical laboratories. The test could be applied for diagnosis and epidemiological survey of hepatitis A. It has a broad prospect in application.

Journal Article↗

PubMedDate not supplied

Effects of protein kinase C on M-phase promoting factor in early development of fertilized mouse eggs.

The mechanism of development of mouse fertilized eggs from the one-cell stage to the two-cell stage remains unclear to date. In the present study, we have evaluated protein kinase C (PKC) and M-phase promoting factor (MPF) kinase activity in fertilized mouse eggs treated with a PKC modulator. PKC and MPF activity have similar activity. The two subunits of MPF, p34(cdc2) and cyclin B, were shown to be included in the substrates phosphorylated by PKC in fertilized mouse eggs, while PKC modulator affected the electrophoretic mobility shift of cdc2 and cdc25C by dephosphorylation and phosphorylation. These results clearly indicate that PKC may affect the progression of the cell cycle through post-translational modification of MPF activity.

Amanitins↗

PubMedDate not supplied

Virtual screening of novel noncovalent inhibitors for SARS-CoV 3C-like proteinase.

The SARS coronavirus 3C-like proteinase is considered as a potential drug design target for the treatment of severe acute respiratory syndrome (SARS). Owing to the lack of available drugs for the treatment of SARS, the discovery of inhibitors for SARS coronavirus 3C-like proteinase that can potentially be optimized as drugs appears to be highly desirable. We have built a "flexible" three-dimensional model for SARS 3C-like proteinase by homology modeling and multicanonical molecular dynamics method and used the model for virtual screening of chemical databases. After Dock procedures, strategies including pharmocophore model, consensus scoring, and "drug-like" filters were applied in order to accelerate the process and improve the success rate of virtual docking screening hit lists. Forty compounds were purchased and tested by HPLC and colorimetric assay against SARS 3C-like proteinase. Three of them including calmidazolium, a well-known antagonist of calmodulin, were found to inhibit the enzyme with an apparent K(i) from 61 to 178 microM. These active compounds and their binding modes provide useful information for understanding the binding sites and for further selective drug design against SARS and other coronavirus.

Binding Sites↗

PubMedDate not supplied

Active learning with support vector machine applied to gene expression data for cancer classification.

There is growing interest in the application of machine learning techniques in bioinformatics. The supervised machine learning approach has been widely applied to bioinformatics and gained a lot of success in this research area. With this learning approach researchers first develop a large training set, which is a time-consuming and costly process. Moreover, the proportion of the positive examples and negative examples in the training set may not represent the real-world data distribution, which causes concept drift. Active learning avoids these problems. Unlike most conventional learning methods where the training set used to derive the model remains static, the classifier can actively choose the training data and the size of training set increases. We introduced an algorithm for performing active learning with support vector machine and applied the algorithm to gene expression profiles of colon cancer, lung cancer, and prostate cancer samples. We compared the classification performance of active learning with that of passive learning. The results showed that employing the active learning method can achieve high accuracy and significantly reduce the need for labeled training instances. For lung cancer classification, to achieve 96% of the total positives, only 31 labeled examples were needed in active learning whereas in passive learning 174 labeled examples were required. That meant over 82% reduction was realized by active learning. In active learning the areas under the receiver operating characteristic (ROC) curves were over 0.81, while in passive learning the areas under the ROC curves were below 0.50.

Artificial Intelligence↗

PubMedDate not supplied

A comparative study on feature selection methods for drug discovery.

Feature selection is frequently used as a preprocessing step to machine learning. The removal of irrelevant and redundant information often improves the performance of learning algorithms. This paper is a comparative study of feature selection in drug discovery. The focus is on aggressive dimensionality reduction. Five methods were evaluated, including information gain, mutual information, a chi2-test, odds ratio, and GSS coefficient. Two well-known classification algorithms, Naïve Bayesian and Support Vector Machine (SVM), were used to classify the chemical compounds. The results showed that Naïve Bayesian benefited significantly from the feature selection, while SVM performed better when all features were used. In this experiment, information gain and chi2-test were most effective feature selection methods. Using information gain with a Naïve Bayesian classifier, removal of up to 96% of the features yielded an improved classification accuracy measured by sensitivity. When information gain was used to select the features, SVM was much less sensitive to the reduction of feature space. The feature set size was reduced by 99%, while losing only a few percent in terms of sensitivity (from 58.7% to 52.5%) and specificity (from 98.4% to 97.2%). In contrast to information gain and chi2-test, mutual information had relatively poor performance due to its bias toward favoring rare features and its sensitivity to probability estimation errors.

Algorithms↗

PubMedDate not supplied

Text mining biomedical literature for discovering gene-to-gene relationships: a comparative study of algorithms.

Partitioning closely related genes into clusters has become an important element of practically all statistical analyses of microarray data. A number of computer algorithms have been developed for this task. Although these algorithms have demonstrated their usefulness for gene clustering, some basic problems remain. This paper describes our work on extracting functional keywords from MEDLINE for a set of genes that are isolated for further study from microarray experiments based on their differential expression patterns. The sharing of functional keywords among genes is used as a basis for clustering in a new approach called BEA-PARTITION in this paper. Functional keywords associated with genes were extracted from MEDLINE abstracts. We modified the Bond Energy Algorithm (BEA), which is widely accepted in psychology and database design but is virtually unknown in bioinformatics, to cluster genes by functional keyword associations. The results showed that BEA-PARTITION and hierarchical clustering algorithm outperformed k-means clustering and self-organizing map by correctly assigning 25 of 26 genes in a test set of four known gene groups. To evaluate the effectiveness of BEA-PARTITION for clustering genes identified by microarray profiles, 44 yeast genes that are differentially expressed during the cell cycle and have been widely studied in the literature were used as a second test set. Using established measures of cluster quality, the results produced by BEA-PARTITION had higher purity, lower entropy, and higher mutual information than those produced by k-means and self-organizing map. Whereas BEA-PARTITION and the hierarchical clustering produced similar quality of clusters, BEA-PARTITION provides clear cluster boundaries compared to the hierarchical clustering. BEA-PARTITION is simple to implement and provides a powerful approach to clustering genes or to any clustering problem where starting matrices are available from experimental observations.

Abstracting and Indexing↗

PubMedDate not supplied

Effects of cataractogenesis on the CDP-choline pathway: changes in ATP concentration and phosphocholine synthesis during and after exposure of rat lenses to sugars in vitro and in vivo.

We measured choline influx and phosphorylation, ATP concentration ([ATP]), choline kinase activity and lens swelling during formation and partial reversal of sugar cataracts in rat lenses incubated with xylose or galactose and in lenses of galactosemic rats. [ATP] and phosphocholine (P-Cho) synthesis decreased about 60 and 40% after 4 h in normal rat lenses incubated up to 24 h in medium containing 30 mM xylose and partially recovered when the lenses were then removed from the xylose. Incubation with the somewhat less cataractogenic sugar galactose decreased P-Cho synthesis but had little effect on [ATP]. P-Cho synthesis decreased rapidly in the lenses of rats fed a 50% galactose diet, but began recovery by the third day on this diet. [ATP] decreased for at least 10 days during the galactose diet and did not recover, even with resumption of the control diet (50% starch) after 4 or 7 days. The results of in vitro and in vivo sugar cataractogenesis differed from each other in several respects, including effects on choline influx and the degree to which the changes were reversible. The in vitro and in vivo sugar cataracts, however, could both produce swelling and opacification of the lens and decreased P-Cho synthesis and [ATP]. Neither model caused a substantial change in the choline kinase activity (as measured in cell-free assays). The data did not generally support the hypothesis that decreased [ATP] causes decreased P-Cho synthesis.

Adenosine Triphosphate↗

PubMedDate not supplied

Early oncogene mRNA expression in HT-29 cells treated with the endogenous colon mitosis inhibitor pyroglutamyl-histidyl-glycine.

BACKGROUND: The tripeptide pyroGlu-His-GlyOH (pEHG), isolated from intestinal extracts (1), suppresses growth of human colonic epithelial cells and human colorectal adenocarcinoma cells (HT-29) both in vitro and in vivo. The present study represents the first attempt to relate alterations in relevant gene expression to the effect on cell growth. MATERIALS AND METHODS: Northern blot with RNA, extracted from HT-29 cells exposed to pEHG, was hybridised with c-fos and c-myc probes. RESULTS: c-fos gene expression was doubled in HT-29 cells after 20 minutes of incubation with pEHG and decreased to half the initial value after 2 hours. The increase at 20 minutes was concentration-dependent at a peptide concentration range from 10(-6)-10(-12) M. The expression of the oncogene c-myc showed only marginal alternations at the concentrations and times tested. CONCLUSION: The colon mitosis-inhibiting peptide pEHG increases gene expression of c-fos, but not that of c-myc.

Antineoplastic Agents↗

PubMedDate not supplied

The effects of a growth-inhibiting tripeptide, acetylGlu-Ser-GlyNH2 (Ac-ESG), on gene expression and cell cycle progression of two lymphoma cell lines.

BACKGROUND: We recently isolated and characterized a tripeptide, acetylGlu-Ser-GlyNH2 (Ac-ESG), which inhibits proliferation of lymphoid cells. In this paper we describe the effects of Ac-ESG on growth-related gene expression and cell cycle progression in two lymphoma cell lines, Ramos and Molt, representing B and T lymphocytes, respectively. MATERIALS AND METHODS: RNA was extracted from Molt and Ramos cells with or without the tripeptide treatment. Gene expression was examined by semi-quantitative RT-PCR and Northern blot hybridization, and cell cycle progression was detected by flow cytometry. RESULTS: In the Molt cells, p53 gene expression was increased following treatment while c-myc was decreased after treatment shorter than 24 hours; N-ras expression was significantly reduced at picomolar concentration for 24-hour treatment; cyclinD1 and cdk4 expression did not show any change; DNA flow cytometry demonstrated that Molt cells were arrested or delayed predominantly in G2-M. Untreated Ramos cells had higher gene expression levels of c-myc, N-Ras and cdk4 than Molt cells, but lower p53 expression. These cells were not sensitive to Ac-ESG. CONCLUSION: The tripeptide Ac-ESG alters the expression of several growth-related genes in the Molt cell line and brings about an arrest or delay of cells in G2-M.

Blotting, Northern↗