Andrographolide, isolated from Andrographis paniculata, induces apoptosis in monocytic leukemia and multiple myeloma cells via augmentation of reactive oxygen species production

藤田医科大学博士(医療科学)doctoral thesi

Administrative Alternative Dispute Resolution for Environment in Japan: Achievements and Challenges of 50 Years of Activities

departmental bulletin pape

drummondiana

Salix drummondiana Barratt ex HookerDrummond's willow;blue willowsaule de DrummondRuby Lakecreek bank near outlet of lake6800 feetStudies in Northern American Salix. Shrub, 5 ft

Conceptual metaphors

This chapter offers an overview of conceptual metaphor research with particular attention to specialised language. It surveys the growing body of research showing the role of metaphor both as a tool to generate and develop scientific thinking and as a resource for specialised knowledge popularisation. This chapter also addresses specialised language studies that explore not only the embodied but also the socio-cultural dimension of metaphor in terminology, thus crucially refining conceptual metaphor theories and typologies in all spheres of communication

Chasm of semantic despair

<p>The chasm of semantic despair. Data across the biological and medical spectrum is difficult to use across modalities and translational stages. The NCATS Data Translator project aims to bridge this divide for mechanistic discovery. It features key "TOADS" components (Tools, Ontologies, Algorithms, Data and Standards) from the Monarch Initiative.<br></p

IFNβ-Induced Changes in Gene Expression over Time

<div><p>(A) An unsupervised hierarchical clustering representation of the weighted difference in gene expression at each time point versus baseline. For each gene, the obtained differences were log normalized and multiplied by the <i>F</i>-statistic from an ANOVA (time effect) run previously (shown in [B]). The “heat” colored bar represents the absolute value of this difference. With the exception of <i>IFNAR1</i> (arrow), all genes showing a significantly different expression in at least one time point with respect to baseline were arranged in the same cluster (framed in blue).</p> <p>(B) List of all genes showing a significant time effect along with their <i>F</i>-statistic and <i>p</i>-values. Genes that were part of any triplet showing more than 80% prediction accuracy at <i>T</i> = 0 are in bold.</p> <p>(C) A continuous representation of the longitudinal average expression of two representative genes over all samples. <i>MX1</i> (̂) shows a marked departure from <i>T</i> = 0 and remains elevated for the rest of the observed period. This correlates well with the shading (#) displayed in the clustering shown in (A). In contrast, <i>IRF6</i> (•) displays an almost flat curve, consistent with its color in the clustering (*).</p></div

Accuracy Ranges of the Three-Gene Predictive Model of IFNβ Response

<p>After the initial data split into training and test sets, using IBIS on the training set only, nine best-performing triplets were identified. The triplet of <i>Caspase 2, Caspase 10,</i> and <i>FLIP</i> resulted in an accuracy rate of 86% correct prediction on the blind test set resulting from the original split. To minimize the effect of fortuitous initial data division in the accuracy outcome, an extra 100 data splits were performed as a coarse approximation of the possible ranges of accuracies in which this gene triplet could result. A histogram of prediction accuracy over the 100 trials for the gene triplet composed of <i>Caspase 2, Caspase 10,</i> and <i>FLIP</i> is shown as an example of classification and prediction of response to IFNβ at <i>T</i> = 0. A red Gaussian curve encompasses the distribution, where the mean prediction accuracy was 87.9%, with a maximum of 100% (in 11 cases) and a minimum of 64.3% (in two cases). The broken blue line indicates the tenth percentile (78.6%). No major differences were found when we performed the same classification/prediction strategy in 500 random splits of the data.</p

Transcription-Based Prediction of Response to IFNβ Using Supervised Computational Methods

<div><p>Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNβ) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNβ to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNβ engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects.</p> </div

Test Dataset Performance of the Top Three-Gene Predictive Model of IFNβ Response

<div><p>The same probability model generated from the training dataset (see <a href="http://www.plosbiology.org/article/info:doi/10.1371/journal.pbio.0030002#pbio-0030002-g004" target="_blank">Figure 4</a>G) provides the background shading of volumes predictive of good response (red) and poor response (blue). Three samples are identified with arrows and followed along different graphical representations.</p> <p>(A and B) The two rotations of the full 3D model show that all good responder samples are correctly classified.</p> <p>(C) Projection of full model onto one of the possible 2D surfaces is provided as an aid to visualization.</p> <p>(D–F) Two-dimensional IBIS predictive models. Three samples are identified with arrows and followed along different graphical representations. If prediction was performed in only two dimensions, a higher number of misclassifications would have occurred. For example, the 2D model built using only <i>Caspase 2/FLIP</i> (D), could not resolve the good responding sample identified by a cyan arrow, whereas it correctly resolves the good responding sample shown by the orange arrow. The model built using <i>Caspase 10/FLIP</i> (E), in contrast, acts oppositely and can resolve the good responding sample shown by the cyan arrow and not the sample shown by the orange arrow. Both these sample are correctly resolved the 2D model built using <i>Caspase 2/Caspase 10</i> (F); however, this model is unable to resolve the poor responding sample identified by the yellow arrow, whereas one of the previous models (E) was able to do this. As demonstrated in the full 3D model view from (A) and (B), as well as the projection of model (C), all the labeled poor and good responding patients are correctly classified. Although 2D models show high predictive capabilities, all three genes are needed to increase the classification accuracy of the IBIS model.</p></div