NSAIDs: How they Work and their Prospects as Therapeutics in Alzheimer's Disease

There is significant epidemiological evidence to suggest that there are beneficial effects of treatment with non-steroidal anti-inflammatory drugs (NSAIDs) in Alzheimer's disease, although these effects have not been reproduced in clinical trials. The failure of the clinical trials may be attributed to several possible facts: (1) NSAIDS may have been delivered too late to patients, as they may only be effective in early stages of the disease and possibly counterproductive in the late stages; (2) the beneficial effect may depend on the drug, because different NSAIDs may have different molecular targets; (3) the NSAID concentration reaching the brain and the duration of the treatment could also be critical, so increasing drug penetration is important in order to improve the efficacy and avoid secondary gastro-intestinal effects of the NSAIDs. In this report we analyze these different factors, with special emphasis on the role of NSAIDs in microglia activation over time

Interactions between APP secretases and inflammatory mediators

There is now a large body of evidence linking inflammation to Alzheimer's disease (AD). This association manifests itself neuropathologically in the presence of activated microglia and astrocytes around neuritic plaques and increased levels of inflammatory mediators in the brains of AD patients. It is considered that amyloid-β peptide (Aβ), which is derived from the processing of the longer amyloid precursor protein (APP), could be the most important stimulator of this response, and therefore determining the role of the different secretases involved in its generation is essential for a better understanding of the regulation of inflammation in AD. The finding that certain non-steroidal anti-inflammatory drugs (NSAIDs) can affect the processing of APP by inhibiting β- and γ-secretases, together with recent revelations that these enzymes may be regulated by inflammation, suggest that they could be an interesting target for anti-inflammatory drugs. In this review we will discuss some of these issues and the role of the secretases in inflammation, independent of their effect on Aβ formation

rtracklayer: an R package for interfacing with genome browsers

Summary: The rtracklayer package supports the integration of existing genome browsers with experimental data analyses performed in R. The user may (i) transfer annotation tracks to and from a genome browser and (ii) create and manipulate browser views to focus on a particular set of annotations in a specific genomic region. Currently, the UCSC genome browser is supported

ShortRead: a bioconductor package for input, quality assessment and exploration of high-throughput sequence data

Summary: ShortRead is a package for input, quality assessment, manipulation and output of high-throughput sequencing data. ShortRead is provided in the R and Bioconductor environments, allowing ready access to additional facilities for advanced statistical analysis, data transformation, visualization and integration with diverse genomic resources

Moments of spectral functions: Monte Carlo evaluation and verification

The subject of the present study is the Monte Carlo path-integral evaluation of the moments of spectral functions. Such moments can be computed by formal differentiation of certain estimating functionals that are infinitely-differentiable against time whenever the potential function is arbitrarily smooth. Here, I demonstrate that the numerical differentiation of the estimating functionals can be more successfully implemented by means of pseudospectral methods (e.g., exact differentiation of a Chebyshev polynomial interpolant), which utilize information from the entire interval $(-\beta \hbar / 2, \beta \hbar/2)$. The algorithmic detail that leads to robust numerical approximations is the fact that the path integral action and not the actual estimating functional are interpolated. Although the resulting approximation to the estimating functional is non-linear, the derivatives can be computed from it in a fast and stable way by contour integration in the complex plane, with the help of the Cauchy integral formula (e.g., by Lyness' method). An interesting aspect of the present development is that Hamburger's conditions for a finite sequence of numbers to be a moment sequence provide the necessary and sufficient criteria for the computed data to be compatible with the existence of an inversion algorithm. Finally, the issue of appearance of the sign problem in the computation of moments, albeit in a milder form than for other quantities, is addressed.Comment: 13 pages, 2 figure

A Hydrogel-Integrated Culture Device to Interrogate T Cell Activation with Physicochemical Cues

The recent rise of adoptive T cell therapy (ATCT) as a promising cancer immunotherapy has triggered increased interest in therapeutic T cell bioprocessing. T cell activation is a critical processing step and is known to be modulated by physical parameters, such as substrate stiffness. Nevertheless, relatively little is known about how biophysical factors regulate immune cells, such as T cells. Understanding how T cell activation is modulated by physical and biochemical cues may offer novel methods to control cell behavior for therapeutic cell processing. Inspired by T cell mechanosensitivity, we developed a multiwell, reusable, customizable, two-dimensional (2D) polyacrylamide (PA) hydrogel-integrated culture device to study the physicochemical stimulation of Jurkat T cells. Substrate stiffness and ligand density were tuned by concentrations of the hydrogel cross-linker and antibody in the coating solution, respectively. We cultured Jurkat T cells on 2D hydrogels of different stiffnesses that presented surface-immobilized stimulatory antibodies against CD3 and CD28 and demonstrated that Jurkat T cells stimulated by stiff hydrogels (50.6 ± 15.1 kPa) exhibited significantly higher interleukin-2 (IL-2) secretion, but lower proliferation, than those stimulated by softer hydrogels (7.1 ± 0.4 kPa). In addition, we found that increasing anti-CD3 concentration from 10 to 30 μg/mL led to a significant increase in IL-2 secretion from cells stimulated on 7.1 ± 0.4 and 9.3 ± 2.4 kPa gels. Simultaneous tuning of substrate stiffness and stimulatory ligand density showed that the two parameters synergize (two-way ANOVA interaction effect: p < 0.001) to enhance IL-2 secretion. Our results demonstrate the importance of physical parameters in immune cell stimulation and highlight the potential of designing future immunostimulatory biomaterials that are mechanically tailored to balance stimulatory strength and downstream proliferative capacity of therapeutic T cells

Properties of continuous Fourier extension of the discrete cosine transform and its multidimensional generalization

A versatile method is described for the practical computation of the discrete Fourier transforms (DFT) of a continuous function $g(t)$ given by its values $g_{j}$ at the points of a uniform grid $F_{N}$ generated by conjugacy classes of elements of finite adjoint order $N$ in the fundamental region $F$ of compact semisimple Lie groups. The present implementation of the method is for the groups SU(2), when $F$ is reduced to a one-dimensional segment, and for $SU(2)\times ... \times SU(2)$ in multidimensional cases. This simplest case turns out to result in a transform known as discrete cosine transform (DCT), which is often considered to be simply a specific type of the standard DFT. Here we show that the DCT is very different from the standard DFT when the properties of the continuous extensions of these two discrete transforms from the discrete grid points $t_j; j=0,1, ... N$ to all points $t \in F$ are considered. (A) Unlike the continuous extension of the DFT, the continuous extension of (the inverse) DCT, called CEDCT, closely approximates $g(t)$ between the grid points $t_j$. (B) For increasing $N$, the derivative of CEDCT converges to the derivative of $g(t)$. And (C), for CEDCT the principle of locality is valid. Finally, we use the continuous extension of 2-dimensional DCT to illustrate its potential for interpolation, as well as for the data compression of 2D images.Comment: submitted to JMP on April 3, 2003; still waiting for the referee's Repor

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data

Modelling background intensity in Affymetrix Genechips

DNA microarrays are devices that are able, in principle, to detect and quantify the presence of specific nucleic acid sequences in complex biological mixtures. The measurement consists in detecting fluorescence signals from several spots on the microarray surface onto which different probe sequences are grafted. One of the problems of the data analysis is that the signal contains a noisy background component due to non-specific binding. This paper presents a physical model for background estimation in Affymetrix Genechips. It combines two different approaches. The first is based on the sequence composition, specifically its sequence dependent hybridization affinity. The second is based on the strong correlation of intensities from locations which are the physical neighbors of a specific spot on the chip. Both effects are incorporated in a background functional which contains 24 free parameters, fixed by minimization on a training data set. In all data analyzed the sequence specific parameters, obtained by minimization, are found to strongly correlate with empirically determined stacking free energies for RNA/DNA hybridization in solution. Moreover, there is an overall agreement with experimental background data and we show that the physics-based model proposed in this paper performs on average better than purely statistical approaches for background calculations. The model thus provides an interesting alternative method for background subtraction schemes in Affymetrix Genechips.Comment: 8 pages, 4 figure