FLUed: A Novel Four-Layer Model for Simulating Epidemic Dynamics and Assessing Intervention Policies

From the 2003 severe acute respiratory syndrome (SARS) epidemic, to the 2009 swine-origin influenza A (H1N1) pandemic, to the projected highly pathogenic avian influenza A event, emerging infectious diseases highlight the importance of computational epidemiology to assess potential intervention policies. Hence, an important and timely research goal is a general-purpose and extendable simulation model that integrates two major epidemiological factors—age group and population movement—and substantial amounts of demographic, geographic, and epidemiologic data. In this paper, we describe a model that we have named FLUed for Four-layer Universal Epidemic Dynamics that integrates complex daily commuting network data into multiple age-structured compartmental models. FLUed has four contact structures for simulating the epidemic dynamics of emerging infectious diseases, assessing the potential efficacies of various intervention policies, and identifying the potential impacts of spatial-temporal epidemic trends on specific populations. We used data from the seasonal influenza A and 2009 swine-origin influenza A (H1N1) epidemics to validate model reliability and suitability and to assess the potential impacts of intervention policies and variation in initial outbreak areas for novel/seasonal influenza A in Taiwan. We believe that the FLUed model represents an effective tool for public health agencies responsible for initiating early responses to potential pandemics

Identification of novel DNA methylation inhibitors via a two-component reporter gene system

<p>Abstract</p> <p>Background</p> <p>Targeting abnormal DNA methylation represents a therapeutically relevant strategy for cancer treatment as demonstrated by the US Food and Drug Administration approval of the DNA methyltransferase inhibitors azacytidine and 5-aza-2'-deoxycytidine for the treatment of myelodysplastic syndromes. But their use is associated with increased incidences of bone marrow suppression. Alternatively, procainamide has emerged as a potential DNA demethylating agent for clinical translation. While procainamide is much safer than 5-aza-2'-deoxycytidine, it requires high concentrations to be effective in DNA demethylation in suppressing cancer cell growth. Thus, our laboratories have embarked on the pharmacological exploitation of procainamide to develop potent DNA methylation inhibitors through lead optimization.</p> <p>Methods</p> <p>We report the use of a DNA methylation two-component enhanced green fluorescent protein reporter system as a screening platform to identify novel DNA methylation inhibitors from a compound library containing procainamide derivatives.</p> <p>Results</p> <p>A lead agent IM25, which exhibits substantially higher potency in <it>GSTp1 </it>DNA demethylation with lower cytotoxicity in MCF7 cells relative to procainamide and 5-aza-2'-deoxycytidine, was identified by the screening platform.</p> <p>Conclusions</p> <p>Our data provide a proof-of-concept that procainamide could be pharmacologically exploited to develop novel DNA methylation inhibitors, of which the translational potential in cancer therapy/prevention is currently under investigation.</p

Electrochemical Oxidation of Cysteine at a Film Gold Modified Carbon Fiber Microelectrode Its Application in a Flow—Through Voltammetric Sensor

A flow-electrolytical cell containing a strand of micro Au modified carbon fiber electrodes (CFE) has been designedand characterized for use in a voltammatric detector for detecting cysteine using high-performance liquid chromatography. Cysteine is more efficiently electrochemical oxidized on a Au /CFE than a bare gold and carbon fiber electrode. The possible reaction mechanism of the oxidation process is described from the relations to scan rate, peak potentials and currents. For the pulse mode, and measurements with suitable experimental parameters, a linear concentration from 0.5 to 5.0 mg·L−1 was found. The limit of quantification for cysteine was below 60 ng·mL−1

Sequence Conservation, Radial Distance and Packing Density in Spherical Viral Capsids

<div><p>The conservation level of a residue is a useful measure about the importance of that residue in protein structure and function. Much information about sequence conservation comes from aligning homologous sequences. Profiles showing the variation of the conservation level along the sequence are usually interpreted in evolutionary terms and dictated by site similarities of a proper set of homologous sequences. Here, we report that, of the viral icosahedral capsids, the sequence conservation profile can be determined by variations in the distances between residues and the centroid of the capsid – with a direct inverse proportionality between the conservation level and the centroid distance – as well as by the spatial variations in local packing density. Examining both the centroid and the packing density models against a dataset of 51 crystal structures of nonhomologous icosahedral capsids, we found that many global patterns and minor features derived from the viral structures are consistent with those present in the sequence conservation profiles. The quantitative link between the level of conservation and structural features like centroid-distance or packing density allows us to look at residue conservation from a structural viewpoint as well as from an evolutionary viewpoint.</p></div

The capsid crystal structure of the AAV serotype 8 (AAV8) in three surface representations.

<p>(<b>A</b>) the conservation-colored structure, on which the variable loop regions I-IX are labeled, (<b>B</b>) the c-distance-colored structure and (<b>C</b>) the WCN-colored structure.</p

Comparison of profiles.

<p>Each plot shows the radius distribution profile (solid line) and conservation profile (dotted line) (the top plot under the PDB ID) and the WCN profile (solid line) and the conservation profile (dotted line) (the bottom plot under the PDB ID). The Pearson’s correlation coefficient is shown on each plot.</p

Plots showing different structural features against conservation score.

<p><b>(A)</b> the scatter plot of conservation score vs. c-distance; (B) the scatter plot of conservation score vs. WCN score. Note that these quantities are all normalized (see <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132234#sec002" target="_blank">Methods</a>).</p