Effects of natto extract on endothelial injury in a rat model

Vascular endothelial damage has been found to be associated with thrombus formation, which is considered to be a risk factor for cardiovascular disease. A diet of natto leads to a low prevalence of cardiovascular disease. The aim of the present study was to investigate the effects of natto extract on vascular endothelia damage with exposure to laser irradiation. Endothelial damage both in vitro and in vivo was induced by irradiation of rose bengal using a DPSS green laser. Cell viability was determined by MTS assay, and the intimal thickening was verified by a histological approach. The antioxidant content of natto extract was determined for the free radical scavenging activity. Endothelial cells were injured in the presence of rose bengal irradiated in a dose-dependent manner. Natto extract exhibits high levels of antioxidant activity compared with purified natto kinase. Apoptosis of laser-injured endothelial cells was significantly reduced in the presence of natto extract. Both the natto extract and natto kinase suppressed intimal thickening in rats with endothelial injury. The present findings suggest that natto extract suppresses vessel thickening as a synergic effect attributed to its antioxidant and anti-apoptosis properties

THE ACUTE EFFECT OF UPPER EXTREMITY PLYOMETRIC TRAINING

The purpose of this study was to probe the acute effect of the performance of upper extremity muscle groups after the plyometric training intervention. The participants were 13 healthy male college students. The force transducers (300kg, 200 Hz) and EMG sensor (1000 Hz) were taken to diagnose the acute effects of strength and muscle activation done by upper extremity pre and post plyometric training (load :24kg, 12 repetiiion times Iset, 3 set), and pair t-test was taken to test the significance(a=.05). The result showed that the strength after the upper extremity plyometric training intervention obviously had decreased 8% (

Using a Q Matrix to Assess Students’ Latent Skills in an Online Course

Online teaching and learning has become an increasingly important aspect of the educational mission of universities. In person, teachers have time-tested tools for assessing student ability, including a wealth of verbal and nonverbal communication. The online format provides a wealth of data, and promises—but may not yet deliver—useful tools for this sort of just-in-time assessment. Publisher homework websites and quizzes inside a learning management system like Canvas can theoretically provide up-to-the-minute performance data including scores, use of help features, access of resources, and more. Our setting (teaching introductory online quantitative classes in the College of Business at a large research university) makes these innovations particularly appealing. Publishers have correctly identified our interest in “knowing” our students better via their online performance, but we have not yet seen an off-the-shelf solution that gets at our need: the ability to quickly and effectively react to student data in real time. In this paper, we discuss a portion of our research conducted in an online quantitative methods class, a 200-level undergraduate course in the College of Business. This research included constructing a Q Matrix as part of a Cognitive Diagnosis Model for our quantitative methods class. A Q Matrix is a mathematical tool that creates a linkage between underlying concept development and students’ performance on test items. In order to create assessments of learning which are based on student responses to questions, we must first investigate whether these questions are actually testing the foundational concepts we wish to evaluate. The Q Matrix offers a more holistic view of student achievement, and allows better insight (in terms of specificity regarding particular skills and concepts) into student growth and accomplishment than traditional item response methods. Q Matrix analysis requires serious attention to questions about how students are learning material and what underlying skills are being assessed by test questions. The research is based on two main theoretical foundations: Item Response Theory and Cognitive Diagnosis Models

Estimating quality weights for EQ-5D (EuroQol-5 dimensions) health states with the time trade-off method in Taiwan

Background/PurposeEQ-5D (EuroQol-5 dimensions) is a preference-based measure of health, which is widely used in cost–utility analyses. It has been suggested that each country should develop its own value set. We therefore sought to develop the quality weights of the EQ-5D health states with the time trade-off (TTO) method in Taiwan.MethodsA total of 745 respondents consisting of employees and volunteers in 17 different hospitals were recruited and interviewed. Each of them valued 13 of 73 EQ-5D health states using the TTO method. Based on the three exclusion criteria for valuation data, only 456 (61.21%) respondents were considered eligible for data analysis. The quality weights for all EQ-5D health states were modeled by generalized estimating equations (GEEs).ResultsOver half of the responses were given negative values, and the medical personnel seemed to have a significantly higher TTO value (+0.1) than others after controlling for other predictors. The N3 model (level 3 occurred within at least 1 dimension) yielded an acceptable fit for the observed OTT data [mean absolute error (MAE) = 0.056, R2 = 0.35]. The magnitude of mean absolute differences (MADs) between Taiwan data and those from the UK, Japan, and South Korea ranged from 0.146 to 0.592, but the rank correlation coefficients were all above 0.811.ConclusionThis study reaffirms the differences in health-related preference values across countries. The high proportion of negative values might indicate that we have also partially measured the intensity of fear in addition to the utility of different health states

Impact of first-line protease inhibitors on predicted resistance to tipranavir in HIV-1-infected patients with virological failure

<p>Abstract</p> <p>Background</p> <p>Tipranavir (TPV) is a recently approved nonpeptidic protease inhibitor (PI) of HIV-1 and has been indicated for those infected with PIs-resistant HIV-1. However, in clinical practice, whether the HIV-1 from the patients with virological failure to the regimens containing first-line PIs remains susceptible to TPV/r may be questionable.</p> <p>Methods</p> <p>To assess the resistance levels to TPV of HIV-1 from patients with treatment failure to first-line PIs, patients who experienced virological failure were tested for genotypic resistance of HIV-1 since August 2006 in National Taiwan University Hospital. Patients were enrolled for this analysis if their failed regimens contained > 12 weeks of atazanavir or lopinavir/ritonavir (defined as ATV group and LPV/r group, respectively), but were excluded if they experienced both or other PIs. The levels of genotypic resistance to TPV/r were determined by TPV mutation score.</p> <p>Results</p> <p>Till May 2008, 21 subjects in ATV group and 20 subjects in LPV/r group were enrolled. The TPV mutation scores in subjects in LPV/r group were significantly higher than these in ATV group (median, 3 vs 1, P = 0.007). 95.2% subjects in ATV group and only 45% subjects in LPV/r group had an estimated maximal virological response to TPV/r (P < 0.001). The resistance levels to TPV/r correlated with the duration of exposure to first-line PIs, whether in ATV or LPV/r group.</p> <p>Conclusion</p> <p>Cross-resistance from first-line PIs may impede the effectiveness of TPV/r-containing salvage therapy. TPV/r should be used cautiously for patients with virological failure to LPV/r especially long duration of exposure.</p

An Analysis System for Integrating High-Throughput Transcript Abundance Data with Metabolic Pathways in Green Algae

As the most important non-vascular plants, algae have many research applications, including high species diversity, biofuel sources, adsorption of heavy metals and, following processing, health supplements. With the increasing availability of next-generation sequencing (NGS) data for algae genomes and transcriptomes, an integrated resource for retrieving gene expression data and metabolic pathway is essential for functional analysis and systems biology in algae. However, gene expression profiles and biological pathways are displayed separately in current resources, and making it impossible to search current databases directly to identify the cellular response mechanisms. Therefore, this work develops a novel AlgaePath database to retrieve gene expression profiles efficiently under various conditions in numerous metabolic pathways. AlgaePath, a web-based database, integrates gene information, biological pathways, and next-generation sequencing (NGS) datasets in Chlamydomonasreinhardtii and Neodesmus sp. UTEX 2219-4. Users can identify gene expression profiles and pathway information by using five query pages (i.e. Gene Search, Pathway Search, Differentially Expressed Genes (DEGs) Search, Gene Group Analysis, and Co-Expression Analysis). The gene expression data of 45 and 4 samples can be obtained directly on pathway maps in C. reinhardtii and Neodesmus sp. UTEX 2219-4, respectively. Genes that are differentially expressed between two conditions can be identified in Folds Search. Furthermore, the Gene Group Analysis of AlgaePath includes pathway enrichment analysis, and can easily compare the gene expression profiles of functionally related genes in a map. Finally, Co-Expression Analysis provides co-expressed transcripts of a target gene. The analysis results provide a valuable reference for designing further experiments and elucidating critical mechanisms from high-throughput data. More than an effective interface to clarify the transcript response mechanisms in different metabolic pathways under various conditions, AlgaePath is also a data mining system to identify critical mechanisms based on high-throughput sequencing

Advanced glycation end products (AGEs) in relation to atherosclerotic lipid profiles in middle-aged and elderly diabetic patients

<p>Abstract</p> <p>Objectives</p> <p>To evaluate the association between AGEs and atherosclerotic lipid profiles among aging diabetic patients in Taiwan.</p> <p>Design and Methods</p> <p>After age and gender matching, we selected 207 diabetic subjects and 174 diabetic subjects with proteinuria. Lipid profiles, including total cholesterol (TC), triglycerides (TG), high density cholesterol-lipoprotein (HDL-C) and low density lipoprotein-cholesterol (LDL-C) were measured using standard methods. AGEs were measured with the immunoassay method.</p> <p>Results</p> <p>In general, males were heavier; however, females had higher AGEs, fasting glucose (GLU), TC, HDL-C and LDL-C levels than males, and had higher TC/HDL-C, LDL-C/HDL-C, and TG/HDL-C ratios compared to males. AGEs were more strongly correlated with TG levels and TCL/LDL-C, LDL-C/HDL-C and TG/HDL-C ratios when compared to glucose or hemoglobin A1c. Subjects had higher AGEs levels (≧ 2.0 AU) with more adverse lipid profiles.</p> <p>Conclusion</p> <p>AGEs seem to be a good biomarker to evaluate the association between diabetes and atherosclerotic disorders in aging diabetes.</p

Predicting microRNA precursors with a generalized Gaussian components based density estimation algorithm

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are short non-coding RNA molecules, which play an important role in post-transcriptional regulation of gene expression. There have been many efforts to discover miRNA precursors (pre-miRNAs) over the years. Recently, <it>ab initio </it>approaches have attracted more attention because they do not depend on homology information and provide broader applications than comparative approaches. Kernel based classifiers such as support vector machine (SVM) are extensively adopted in these <it>ab initio </it>approaches due to the prediction performance they achieved. On the other hand, logic based classifiers such as decision tree, of which the constructed model is interpretable, have attracted less attention.</p> <p>Results</p> <p>This article reports the design of a predictor of pre-miRNAs with a novel kernel based classifier named the generalized Gaussian density estimator (G²DE) based classifier. The G²DE is a kernel based algorithm designed to provide interpretability by utilizing a few but representative kernels for constructing the classification model. The performance of the proposed predictor has been evaluated with 692 human pre-miRNAs and has been compared with two kernel based and two logic based classifiers. The experimental results show that the proposed predictor is capable of achieving prediction performance comparable to those delivered by the prevailing kernel based classification algorithms, while providing the user with an overall picture of the distribution of the data set.</p> <p>Conclusion</p> <p>Software predictors that identify pre-miRNAs in genomic sequences have been exploited by biologists to facilitate molecular biology research in recent years. The G²DE employed in this study can deliver prediction accuracy comparable with the state-of-the-art kernel based machine learning algorithms. Furthermore, biologists can obtain valuable insights about the different characteristics of the sequences of pre-miRNAs with the models generated by the G²DE based predictor.</p

γ\gamma-SUP: A clustering algorithm for cryo-electron microscopy images of asymmetric particles

Cryo-electron microscopy (cryo-EM) has recently emerged as a powerful tool for obtaining three-dimensional (3D) structures of biological macromolecules in native states. A minimum cryo-EM image data set for deriving a meaningful reconstruction is comprised of thousands of randomly orientated projections of identical particles photographed with a small number of electrons. The computation of 3D structure from 2D projections requires clustering, which aims to enhance the signal to noise ratio in each view by grouping similarly oriented images. Nevertheless, the prevailing clustering techniques are often compromised by three characteristics of cryo-EM data: high noise content, high dimensionality and large number of clusters. Moreover, since clustering requires registering images of similar orientation into the same pixel coordinates by 2D alignment, it is desired that the clustering algorithm can label misaligned images as outliers. Herein, we introduce a clustering algorithm $\gamma$-SUP to model the data with a $q$-Gaussian mixture and adopt the minimum $\gamma$-divergence for estimation, and then use a self-updating procedure to obtain the numerical solution. We apply $\gamma$-SUP to the cryo-EM images of two benchmark macromolecules, RNA polymerase II and ribosome. In the former case, simulated images were chosen to decouple clustering from alignment to demonstrate $\gamma$-SUP is more robust to misalignment outliers than the existing clustering methods used in the cryo-EM community. In the latter case, the clustering of real cryo-EM data by our $\gamma$-SUP method eliminates noise in many views to reveal true structure features of ribosome at the projection level.Comment: Published in at http://dx.doi.org/10.1214/13-AOAS680 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org