A study of age as a risk factor in ischemic stroke of elderly

Background: The aim of the study was to determine the effect of age as a risk factor and a determinant of outcome in elderly ischemic stroke patients.Methods: This is an observational study. One hundred, successive elderly patients aged 60 years and above, admitted with acute ischemic stroke in PESIMSR over a period of 18 months were prospectively studied. Patients with hemorrhagic stroke, neurological deficits following trauma or following infection were excluded. Demographics, risk factors, stroke severity at admission were estimated by NIHSS. Risk factors and clinical profile were noted and compared among male and female patients. Outcome at discharge was measured by-mRS-modified ranking score.Results: Patients in age group 60-75 years presented with less severe stroke and better mRS when compared to >75 years age group. Complications were significantly higher among the older age group.Conclusions: The risk factors identified for ischemic stroke in the present study are diabetes, hypertension, dyslipidaemia, obesity, smoking, and alcohol. Severity of stroke at presentation, clinical outcome and complication rate during the in-hospital stay were all significantly affected by the age, more so in ischemic stroke. Age specific factors of stroke prevention are crucial for successful prevention and implementation of well-organized stroke care

Machine Learning based Early Prediction of End-stage Renal Disease in Patients with Diabetic Kidney Disease using Clinical Trials Data

AimTo predict end‐stage renal disease (ESRD) in patients with type 2 diabetes by using machine‐learning models with multiple baseline demographic and clinical characteristics.Materials and methodsIn total, 11 789 patients with type 2 diabetes and nephropathy from three clinical trials, RENAAL (n = 1513), IDNT (n = 1715) and ALTITUDE (n = 8561), were used in this study. Eighteen baseline demographic and clinical characteristics were used as predictors to train machine‐learning models to predict ESRD (doubling of serum creatinine and/or ESRD). We used the area under the receiver operator curve (AUC) to assess the prediction performance of models and compared this with traditional Cox proportional hazard regression and kidney failure risk equation models.ResultsThe feed forward neural network model predicted ESRD with an AUC of 0.82 (0.76‐0.87), 0.81 (0.75‐0.86) and 0.84 (0.79‐0.90) in the RENAAL, IDNT and ALTITUDE trials, respectively. The feed forward neural network model selected urinary albumin to creatinine ratio, serum albumin, uric acid and serum creatinine as important predictors and obtained a state‐of‐the‐art performance for predicting long‐term ESRD.ConclusionsDespite large inter‐patient variability, non‐linear machine‐learning models can be used to predict long‐term ESRD in patients with type 2 diabetes and nephropathy using baseline demographic and clinical characteristics. The proposed method has the potential to create accurate and multiple outcome prediction automated models to identify high‐risk patients who could benefit from therapy in clinical practice.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/163629/2/dom14178.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/163629/1/dom14178_am.pd

PEACE: Parallel Environment for Assembly and Clustering of Gene Expression

We present PEACE, a stand-alone tool for high-throughput ab initio clustering of transcript fragment sequences produced by Next Generation or Sanger Sequencing technologies. It is freely available from www.peace-tools.org. Installed and managed through a downloadable user-friendly graphical user interface (GUI), PEACE can process large data sets of transcript fragments of length 50 bases or greater, grouping the fragments by gene associations with a sensitivity comparable to leading clustering tools. Once clustered, the user can employ the GUI's analysis functions, facilitating the easy collection of statistics and allowing them to single out specific clusters for more comprehensive study or assembly. Using a novel minimum spanning tree-based clustering method, PEACE is the equal of leading tools in the literature, with an interface making it accessible to any user. It produces results of quality virtually identical to those of the WCD tool when applied to Sanger sequences, significantly improved results over WCD and TGICL when applied to the products of Next Generation Sequencing Technology and significantly improved results over Cap3 in both cases. In short, PEACE provides an intuitive GUI and a feature-rich, parallel clustering engine that proves to be a valuable addition to the leading cDNA clustering tools

Methods to study splicing from high-throughput RNA Sequencing data

The development of novel high-throughput sequencing (HTS) methods for RNA (RNA-Seq) has provided a very powerful mean to study splicing under multiple conditions at unprecedented depth. However, the complexity of the information to be analyzed has turned this into a challenging task. In the last few years, a plethora of tools have been developed, allowing researchers to process RNA-Seq data to study the expression of isoforms and splicing events, and their relative changes under different conditions. We provide an overview of the methods available to study splicing from short RNA-Seq data. We group the methods according to the different questions they address: 1) Assignment of the sequencing reads to their likely gene of origin. This is addressed by methods that map reads to the genome and/or to the available gene annotations. 2) Recovering the sequence of splicing events and isoforms. This is addressed by transcript reconstruction and de novo assembly methods. 3) Quantification of events and isoforms. Either after reconstructing transcripts or using an annotation, many methods estimate the expression level or the relative usage of isoforms and/or events. 4) Providing an isoform or event view of differential splicing or expression. These include methods that compare relative event/isoform abundance or isoform expression across two or more conditions. 5) Visualizing splicing regulation. Various tools facilitate the visualization of the RNA-Seq data in the context of alternative splicing. In this review, we do not describe the specific mathematical models behind each method. Our aim is rather to provide an overview that could serve as an entry point for users who need to decide on a suitable tool for a specific analysis. We also attempt to propose a classification of the tools according to the operations they do, to facilitate the comparison and choice of methods.Comment: 31 pages, 1 figure, 9 tables. Small corrections adde

MaxDIA enables library-based and library-free data-independent acquisition proteomics

MaxDIA is a software platform for analyzing data-independent acquisition (DIA) proteomics data within the MaxQuant software environment. Using spectral libraries, MaxDIA achieves deep proteome coverage with substantially better coefficients of variation in protein quantification than other software. MaxDIA is equipped with accurate false discovery rate (FDR) estimates on both library-to-DIA match and protein levels, including when using whole-proteome predicted spectral libraries. This is the foundation of discovery DIA-hypothesis-free analysis of DIA samples without library and with reliable FDR control. MaxDIA performs three- or four-dimensional feature detection of fragment data, and scoring of matches is augmented by machine learning on the features of an identification. MaxDIA's bootstrap DIA workflow performs multiple rounds of matching with increasing quality of recalibration and stringency of matching to the library. Combining MaxDIA with two new technologies-BoxCar acquisition and trapped ion mobility spectrometry-both lead to deep and accurate proteome quantification. The software platform MaxDIA streamlines analysis of data-independent acquisition proteomics

Performance of Indian Manufacturing in the Post Reform Period

Many emerging countries in recent decades have relied on a development strategy that focused primarily on promoting the manufacturing sector and the exports of manufactured goods. However, an acceleration of growth of output and employment in manufacturing has eluded India. This is despite the fact that the central focus of the reforms in the 1980s and 1990s was to unshackle the manufacturing sector. Instead it is the services sector which has grown rapidly, contributing about two-third of GDP growth in recent years. This paper discusses the reasons behind the modest performance of the manufacturing sector in India post reforms. It argues that there are many factors that have inhibited the growth of industrial sector in India. One major factor is the rigid and strict labor laws which have affected the industrial performance in a number of ways, by keeping the size of the establishments small, by not encouraging the production of labor intensive goods, by pushing activities to the unorganized sector, and by keeping the Indian industry uncompetitive. Besides the labor laws other factors that are responsible for the modest performance of the manufacturing sector include difficulty in the acquisition of land for industrial use, inadequate financing and infrastructure, and cumbersome business climate. The paper presents arguments and evidence which shows the importance of these factors

Double-Stranded RNA Attenuates the Barrier Function of Human Pulmonary Artery Endothelial Cells

Circulating RNA may result from excessive cell damage or acute viral infection and can interact with vascular endothelial cells. Despite the obvious clinical implications associated with the presence of circulating RNA, its pathological effects on endothelial cells and the governing molecular mechanisms are still not fully elucidated. We analyzed the effects of double stranded RNA on primary human pulmonary artery endothelial cells (hPAECs). The effect of natural and synthetic double-stranded RNA (dsRNA) on hPAECs was investigated using trans-endothelial electric resistance, molecule trafficking, calcium (Ca2+) homeostasis, gene expression and proliferation studies. Furthermore, the morphology and mechanical changes of the cells caused by synthetic dsRNA was followed by in-situ atomic force microscopy, by vascular-endothelial cadherin and F-actin staining. Our results indicated that exposure of hPAECs to synthetic dsRNA led to functional deficits. This was reflected by morphological and mechanical changes and an increase in the permeability of the endothelial monolayer. hPAECs treated with synthetic dsRNA accumulated in the G1 phase of the cell cycle. Additionally, the proliferation rate of the cells in the presence of synthetic dsRNA was significantly decreased. Furthermore, we found that natural and synthetic dsRNA modulated Ca2+ signaling in hPAECs by inhibiting the sarco-endoplasmic Ca2+-ATPase (SERCA) which is involved in the regulation of the intracellular Ca2+ homeostasis and thus cell growth. Even upon synthetic dsRNA stimulation silencing of SERCA3 preserved the endothelial monolayer integrity. Our data identify novel mechanisms by which dsRNA can disrupt endothelial barrier function and these may be relevant in inflammatory processes

Hydroimidazolone Modification of the Conserved Arg12 in Small Heat Shock Proteins: Studies on the Structure and Chaperone Function Using Mutant Mimics

Methylglyoxal (MGO) is an α-dicarbonyl compound present ubiquitously in the human body. MGO reacts with arginine residues in proteins and forms adducts such as hydroimidazolone and argpyrimidine in vivo. Previously, we showed that MGO-mediated modification of αA-crystallin increased its chaperone function. We identified MGO-modified arginine residues in αA-crystallin and found that replacing such arginine residues with alanine residues mimicked the effects of MGO on the chaperone function. Arginine 12 (R12) is a conserved amino acid residue in Hsp27 as well as αA- and αB-crystallin. When treated with MGO at or near physiological concentrations (2–10 µM), R12 was modified to hydroimidazolone in all three small heat shock proteins. In this study, we determined the effect of arginine substitution with alanine at position 12 (R12A to mimic MGO modification) on the structure and chaperone function of these proteins. Among the three proteins, the R12A mutation improved the chaperone function of only αA-crystallin. This enhancement in the chaperone function was accompanied by subtle changes in the tertiary structure, which increased the thermodynamic stability of αA-crystallin. This mutation induced the exposure of additional client protein binding sites on αA-crystallin. Altogether, our data suggest that MGO-modification of the conserved R12 in αA-crystallin to hydroimidazolone may play an important role in reducing protein aggregation in the lens during aging and cataract formation

EasyCluster: a fast and efficient gene-oriented clustering tool for large-scale transcriptome data

<p>Abstract</p> <p>Background</p> <p>ESTs and full-length cDNAs represent an invaluable source of evidence for inferring reliable gene structures and discovering potential alternative splicing events. In newly sequenced genomes, these tasks may not be practicable owing to the lack of appropriate training sets. However, when expression data are available, they can be used to build EST clusters related to specific genomic transcribed <it>loci</it>. Common strategies recently employed to this end are based on sequence similarity between transcripts and can lead, in specific conditions, to inconsistent and erroneous clustering. In order to improve the cluster building and facilitate all downstream annotation analyses, we developed a simple genome-based methodology to generate gene-oriented clusters of ESTs when a genomic sequence and a pool of related expressed sequences are provided. Our procedure has been implemented in the software EasyCluster and takes into account the spliced nature of ESTs after an <it>ad hoc </it>genomic mapping.</p> <p>Methods</p> <p>EasyCluster uses the well-known GMAP program in order to perform a very quick EST-to-genome mapping in addition to the detection of reliable splice sites. Given a genomic sequence and a pool of ESTs/FL-cDNAs, EasyCluster starts building genomic and EST local databases and runs GMAP. Subsequently, it parses results creating an initial collection of pseudo-clusters by grouping ESTs according to the overlap of their genomic coordinates on the same strand. In the final step, EasyCluster refines the clustering by again running GMAP on each pseudo-cluster and groups together ESTs sharing at least one splice site.</p> <p>Results</p> <p>The higher accuracy of EasyCluster with respect to other clustering tools has been verified by means of a manually cured benchmark of human EST clusters. Additional datasets including the Unigene cluster Hs.122986 and ESTs related to the human <it>HOXA </it>gene family have also been used to demonstrate the better clustering capability of EasyCluster over current genome-based web service tools such as ASmodeler and BIPASS. EasyCluster has also been used to provide a first compilation of gene-oriented clusters in the <it>Ricinus communis </it>oilseed plant for which no Unigene clusters are yet available, as well as an evaluation of the alternative splicing in this plant species.</p