Understanding the barriers and improving care in type 2 diabetes: Brazilian perspective in time to do more in diabetes

This is the final version of the article. Available from the publisher via the DOI in this record.BACKGROUND: Type 2 diabetes mellitus (T2DM) is a complex disease, particularly in a continental country like Brazil. We attempted to understand and evaluate the perceptions and routines of Brazilians with T2DM and physicians, compared with other countries. METHODS: We compared the results from a 20-min online survey in Brazil with simultaneously collated data from India, Japan, Spain, UK and USA. RESULTS: In total, 652 adults with T2DM and 337 treating physicians were enrolled, of whom 100 patients and 55 physicians were from Brazil. The numbers of primary care physicians from the five countries were 221 versus 43 in Brazil, diabetes specialists were 61 versus 12. There was disconnect between the opinions of physicians and people with diabetes globally. Further, there were differences between clinical practices in Brazil versus the rest of the world, in many areas Brazilians were performing better. CONCLUSIONS: Communication between patients and physicians should be clearer. There is an urgent need to identify the deficits in education, in order to address the clinical inertia within the diabetes management team. There is a necessity to understand the specific requirements of the Brazilian population in order to contextualise international guidelines and implement local changes in practice.The online survey was supported by Novarti

Periodontal disease and oral hygiene benefits in HIV seropositive and AIDS patients

Objectives: The frequency of gingival and periodontal disease in HIV-seropositive and AIDS patients was investigated in order to evaluate the oral hygiene benefits of using mechanical therapy. Study design: thirty-two consenting HIV-positive patients were examined. Their gingival and periodontal status were evaluated using the Gingival Index and the Simplified Oral Hygiene Index. The data were assessed at baseline, after three months and after six months. Subjects received mechanical therapy, which included calculus removal, scaling and root planning, tooth polishing and oral hygiene instructions. The maintenance of oral hygiene was performed weekly. HIV staging and CD4 counts were also investigated. Results: At the baseline, gingival and periodontal disease was present in 71.9% of all subjects. Chronic gingivitis (43.8%) was the most frequent in all subjects. A clear improvement in gingival health was registered in 78.2% of subjects after six months of mechanical therapy. No association was registered between CD4 count and gingival/periodontal status or attachment loss with HIV staging. Conclusions: Chronic gingivitis was the most frequent disease in HIV infected and AIDS patients. Oral hygiene using mechanical therapy improves the gingival condition, suggesting that it is an important step in the maintenance of periodontal health

Simcluster: clustering enumeration gene expression data on the simplex space

Transcript enumeration methods such as SAGE, MPSS, and sequencing-by-synthesis EST "digital northern", are important high-throughput techniques for digital gene expression measurement. As other counting or voting processes, these measurements constitute compositional data exhibiting properties particular to the simplex space where the summation of the components is constrained. These properties are not present on regular Euclidean spaces, on which hybridization-based microarray data is often modeled. Therefore, pattern recognition methods commonly used for microarray data analysis may be non-informative for the data generated by transcript enumeration techniques since they ignore certain fundamental properties of this space.

Here we present a software tool, Simcluster, designed to perform clustering analysis for data on the simplex space. We present Simcluster as a stand-alone command-line C package and as a user-friendly on-line tool. Both versions are available at: http://xerad.systemsbiology.net/simcluster.

Simcluster is designed in accordance with a well-established mathematical framework for compositional data analysis, which provides principled procedures for dealing with the simplex space, and is thus applicable in a number of contexts, including enumeration-based gene expression data

ProbCD: enrichment analysis accounting for categorization uncertainty

As in many other areas of science, systems biology makes extensive use of statistical association and significance estimates in contingency tables, a type of categorical data analysis known in this field as enrichment (also over-representation or enhancement) analysis. In spite of efforts to create probabilistic annotations, especially in the Gene Ontology context, or to deal with uncertainty in high throughput-based datasets, current enrichment methods largely ignore this probabilistic information since they are mainly based on variants of the Fisher Exact Test. We developed an open-source R package to deal with probabilistic categorical data analysis, ProbCD, that does not require a static contingency table. The contingency table for
the enrichment problem is built using the expectation of a Bernoulli Scheme stochastic process given the categorization probabilities. An on-line interface was created to allow usage by non-programmers and is available at: http://xerad.systemsbiology.net/ProbCD/. We present an analysis framework and software tools to address the issue of uncertainty in categorical data analysis. In particular, concerning the enrichment analysis, ProbCD can accommodate: (i) the stochastic nature of the high-throughput experimental techniques and (ii) probabilistic gene annotation

Time to do more

AbstractAimsClinical inertia, the tendency to maintain current treatment strategies despite results demanding escalation, is thought to substantially contribute to the disconnect between clinical aspirations for patients with diabetes and targets achieved. We wished to explore potential causes of clinical inertia among physicians and people with diabetes.MethodsA 20-min online survey of 652 adults with diabetes and 337 treating physicians in six countries explored opinions relating to clinical inertia from both perspectives, in order to correlate perceptions and expectations relating to diagnosis, treatment, diabetes complications and therapeutic escalation.ResultsPhysicians had low expectations for their patients, despite the belief that the importance of good glycaemic control through lifestyle and pharmacological interventions had been adequately conveyed. Conversely, people with diabetes had, at best, a rudimentary understanding of the risks of complications and the importance of good control; indeed, only a small proportion believed lifestyle changes were important and the majority did not intend to comply.ConclusionsThe principal findings of this survey suggest that impairments in communication are at the heart of clinical inertia. This manuscript lays out four key principles that we believe are achievable in all environments and can improve the lives of people with diabetes

Modeling SAGE tag formation and its effects on data interpretation within a Bayesian framework

<p>Abstract</p> <p>Background</p> <p>Serial Analysis of Gene Expression (SAGE) is a high-throughput method for inferring mRNA expression levels from the experimentally generated sequence based tags. Standard analyses of SAGE data, however, ignore the fact that the probability of generating an observable tag varies across genes and between experiments. As a consequence, these analyses result in biased estimators and posterior probability intervals for gene expression levels in the transcriptome.</p> <p>Results</p> <p>Using the yeast <it>Saccharomyces cerevisiae </it>as an example, we introduce a new Bayesian method of data analysis which is based on a model of SAGE tag formation. Our approach incorporates the variation in the probability of tag formation into the interpretation of SAGE data and allows us to derive exact joint and approximate marginal posterior distributions for the mRNA frequency of genes detectable using SAGE. Our analysis of these distributions indicates that the frequency of a gene in the tag pool is influenced by its mRNA frequency, the cleavage efficiency of the anchoring enzyme (AE), and the number of informative and uninformative AE cleavage sites within its mRNA.</p> <p>Conclusion</p> <p>With a mechanistic, model based approach for SAGE data analysis, we find that inter-genic variation in SAGE tag formation is large. However, this variation can be estimated and, importantly, accounted for using the methods we develop here. As a result, SAGE based estimates of mRNA frequencies can be adjusted to remove the bias introduced by the SAGE tag formation process.</p

Statistical analysis and significance testing of serial analysis of gene expression data using a Poisson mixture model

<p>Abstract</p> <p>Background</p> <p>Serial analysis of gene expression (SAGE) is used to obtain quantitative snapshots of the transcriptome. These profiles are count-based and are assumed to follow a Binomial or Poisson distribution. However, tag counts observed across multiple libraries (for example, one or more groups of biological replicates) have additional variance that cannot be accommodated by this assumption alone. Several models have been proposed to account for this effect, all of which utilize a continuous prior distribution to explain the excess variance. Here, a Poisson mixture model, which assumes excess variability arises from sampling a mixture of distinct components, is proposed and the merits of this model are discussed and evaluated.</p> <p>Results</p> <p>The goodness of fit of the Poisson mixture model on 15 sets of biological SAGE replicates is compared to the previously proposed hierarchical gamma-Poisson (negative binomial) model, and a substantial improvement is seen. In further support of the mixture model, there is observed: 1) an increase in the number of mixture components needed to fit the expression of tags representing more than one transcript; and 2) a tendency for components to cluster libraries into the same groups. A confidence score is presented that can identify tags that are differentially expressed between groups of SAGE libraries. Several examples where this test outperforms those previously proposed are highlighted.</p> <p>Conclusion</p> <p>The Poisson mixture model performs well as a) a method to represent SAGE data from biological replicates, and b) a basis to assign significance when testing for differential expression between multiple groups of replicates. Code for the R statistical software package is included to assist investigators in applying this model to their own data.</p

MediPlEx - a tool to combine in silico & experimental gene expression profiles of the model legume Medicago truncatula

Henckel K, Küster H, Stutz L, Goesmann A. MediPlEx - a tool to combine in silico and experimental gene expression profiles of the model legume Medicago truncatula. BMC Research Notes. 2010;3(1): 262.BACKGROUND:Expressed Sequence Tags (ESTs) are in general used to gain a first insight into gene activities from a species of interest. Subsequently, and typically based on a combination of EST and genome sequences, microarray-based expression analyses are performed for a variety of conditions. In some cases, a multitude of EST and microarray experiments are conducted for one species, covering different tissues, cell states, and cell types. Under these circumstances, the challenge arises to combine results derived from the different expression profiling strategies, with the goal to uncover novel information on the basis of the integrated datasets.FINDINGS:Using our new application, MediPlEx (MEDIcago truncatula multiPLe EXpression analysis), expression data from EST experiments, oligonucleotide microarrays and Affymetrix GeneChips can be combined and analyzed, leading to a novel approach to integrated transcriptome analysis. We have validated our tool via the identification of a set of well-characterized AM-specific and AM-induced marker genes, identified by MediPlEx on the basis of in silico and experimental gene expression profiles from roots colonized with AM fungi.CONCLUSIONS:MediPlEx offers an integrated analysis pipeline for different sets of expression data generated for the model legume Medicago truncatula. As expected, in silico and experimental gene expression data that cover the same biological condition correlate well. The collection of differentially expressed genes identified via MediPlEx provides a starting point for functional studies in plant mutants. MediPlEx can freely be used at http://www.cebitec.uni-bielefeld.de/mediplex

Deep RNA Sequencing Reveals Novel Cardiac Transcriptomic Signatures for Physiological and Pathological Hypertrophy

Although both physiological hypertrophy (PHH) and pathological hypertrophy (PAH) of the heart have similar morphological appearances, only PAH leads to fatal heart failure. In the present study, we used RNA sequencing (RNA-Seq) to determine the transcriptomic signatures for both PHH and PAH. Approximately 13–20 million reads were obtained for both models, among which PAH showed more differentially expressed genes (DEGs) (2,041) than PHH (245). The expression of 417 genes was barely detectable in the normal heart but was suddenly activated in PAH. Among them, Foxm1 and Plk1 are of particular interest, since Ingenuity Pathway Analysis (IPA) using DEGs and upstream motif analysis showed that they are essential hub proteins that regulate the expression of downstream proteins associated with PAH. Meanwhile, 52 genes related to collagen, chemokines, and actin showed opposite expression patterns between PHH and PAH. MAZ-binding motifs were enriched in the upstream region of the participating genes. Alternative splicing (AS) of exon variants was also examined using RNA-Seq data for PAH and PHH. We found 317 and 196 exon inclusions and exon exclusions, respectively, for PAH, and 242 and 172 exon inclusions and exclusions, respectively for PHH. The AS pattern was mostly related to gains or losses of domains, changes in activity, and localization of the encoded proteins. The splicing variants of 8 genes (i.e., Fhl1, Rcan1, Ndrg2, Synpo, Ttll1, Cxxc5, Egfl7, and Tmpo) were experimentally confirmed. Multilateral pathway analysis showed that the patterns of quantitative (DEG) and qualitative (AS) changes differ depending on the type of pathway in PAH and PHH. One of the most significant changes in PHH is the severe downregulation of autoimmune pathways accompanied by significant AS. These findings revealed the unique transcriptomic signatures of PAH and PHH and also provided a more comprehensive understanding at both the quantitative and qualitative levels