The regulatory mechanism of fungal elicitor-induced secondary metabolite biosynthesis in medical plants.

A wide range of external stress stimuli trigger plant cells to undergo complex network of reactions that ultimately lead to the synthesis and accumulation of secondary metabolites. Accumulation of such metabolites often occurs in plants subjected to stresses including various elicitors or signal molecules. Throughout evolution, endophytic fungi, an important constituent in the environment of medicinal plants, have known to form long-term stable and mutually beneficial symbiosis with medicinal plants. The endophytic fungal elicitor can rapidly and specifically induce the expression of specific genes in medicinal plants which can result in the activation of a series of specific secondary metabolic pathways resulting in the significant accumulation of active ingredients. Here we summarize the progress made on the mechanisms of fungal elicitor including elicitor signal recognition, signal transduction, gene expression and activation of the key enzymes and its application. This review provides guidance on studies which may be conducted to promote the efficient synthesis and accumulation of active ingredients by the endogenous fungal elicitor in medicinal plant cells, and provides new ideas and methods of studying the regulation of secondary metabolism in medicinal plants

AtHKT1;1 Mediates Nernstian Sodium Channel Transport Properties in Arabidopsis Root Stelar Cells

The Arabidopsis AtHKT1;1 protein was identified as a sodium (Na+) transporter by heterologous expression in Xenopus laevis oocytes and Saccharomyces cerevisiae. However, direct comparative in vivo electrophysiological analyses of a plant HKT transporter in wild-type and hkt loss-of-function mutants has not yet been reported and it has been recently argued that heterologous expression systems may alter properties of plant transporters, including HKT transporters. In this report, we analyze several key functions of AtHKT1;1-mediated ion currents in their native root stelar cells, including Na+ and K+ conductances, AtHKT1;1-mediated outward currents, and shifts in reversal potentials in the presence of defined intracellular and extracellular salt concentrations. Enhancer trap Arabidopsis plants with GFP-labeled root stelar cells were used to investigate AtHKT1;1-dependent ion transport properties using patch clamp electrophysiology in wild-type and athkt1;1 mutant plants. AtHKT1;1-dependent currents were carried by sodium ions and these currents were not observed in athkt1;1 mutant stelar cells. However, K+ currents in wild-type and athkt1;1 root stelar cell protoplasts were indistinguishable correlating with the Na+ over K+ selectivity of AtHKT1;1-mediated transport. Moreover, AtHKT1;1-mediated currents did not show a strong voltage dependence in vivo. Unexpectedly, removal of extracellular Na+ caused a reduction in AtHKT1;1-mediated outward currents in Columbia root stelar cells and Xenopus oocytes, indicating a role for external Na+ in regulation of AtHKT1;1 activity. Shifting the NaCl gradient in root stelar cells showed a Nernstian shift in the reversal potential providing biophysical evidence for the model that AtHKT1;1 mediates passive Na+ channel transport properties

A systematic review of the safety of topical therapies for atopic dermatitis

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75613/1/j.1365-2133.2006.07538.x.pd

Identification of Single- and Multiple-Class Specific Signature Genes from Gene Expression Profiles by Group Marker Index

Informative genes from microarray data can be used to construct prediction model and investigate biological mechanisms. Differentially expressed genes, the main targets of most gene selection methods, can be classified as single- and multiple-class specific signature genes. Here, we present a novel gene selection algorithm based on a Group Marker Index (GMI), which is intuitive, of low-computational complexity, and efficient in identification of both types of genes. Most gene selection methods identify only single-class specific signature genes and cannot identify multiple-class specific signature genes easily. Our algorithm can detect de novo certain conditions of multiple-class specificity of a gene and makes use of a novel non-parametric indicator to assess the discrimination ability between classes. Our method is effective even when the sample size is small as well as when the class sizes are significantly different. To compare the effectiveness and robustness we formulate an intuitive template-based method and use four well-known datasets. We demonstrate that our algorithm outperforms the template-based method in difficult cases with unbalanced distribution. Moreover, the multiple-class specific genes are good biomarkers and play important roles in biological pathways. Our literature survey supports that the proposed method identifies unique multiple-class specific marker genes (not reported earlier to be related to cancer) in the Central Nervous System data. It also discovers unique biomarkers indicating the intrinsic difference between subtypes of lung cancer. We also associate the pathway information with the multiple-class specific signature genes and cross-reference to published studies. We find that the identified genes participate in the pathways directly involved in cancer development in leukemia data. Our method gives a promising way to find genes that can involve in pathways of multiple diseases and hence opens up the possibility of using an existing drug on other diseases as well as designing a single drug for multiple diseases

A922 Sequential measurement of 1 hour creatinine clearance (1-CRCL) in critically ill patients at risk of acute kidney injury (AKI)

Meeting abstrac

Improving the performance of outbreak detection algorithms by classifying the levels of disease incidence

We evaluated a novel strategy to improve the performance of outbreak detection algorithms, namely setting the alerting threshold separately in each region according to the disease incidence in that region. By using data on hand, foot and mouth disease in Shandong province, China, we evaluated the impact of disease incidence on the performance of outbreak detection algorithms (EARS-C1, C2 and C3). Compared to applying the same algorithm and threshold to the whole region, setting the optimal threshold in each region according to the level of disease incidence (i.e., high, middle, and low) enhanced sensitivity (C1: from 94.4% to 99.1%, C2: from 93.5% to 95.4%, C3: from 91.7% to 95.4%) and reduced the number of alert signals (the percentage of reduction is C1?4.3%, C2?11.9%, C3?10.3%). Our findings illustrate a general method for improving the accuracy of detection algorithms that is potentially applicable broadly to other diseases and regions