Dental management considerations for the patient with an acquired coagulopathy. Part 1: Coagulopathies from systemic disease

Current teaching suggests that many patients are at risk for prolonged bleeding during and following invasive dental procedures, due to an acquired coagulopathy from systemic disease and/or from medications. However, treatment standards for these patients often are the result of long-standing dogma with little or no scientific basis. The medical history is critical for the identification of patients potentially at risk for prolonged bleeding from dental treatment. Some time-honoured laboratory tests have little or no use in community dental practice. Loss of functioning hepatic, renal, or bone marrow tissue predisposes to acquired coagulopathies through different mechanisms, but the relationship to oral haemostasis is poorly understood. Given the lack of established, science-based standards, proper dental management requires an understanding of certain principles of pathophysiology for these medical conditions and a few standard laboratory tests. Making changes in anticoagulant drug regimens are often unwarranted and/or expensive, and can put patients at far greater risk for morbidity and mortality than the unlikely outcome of postoperative bleeding. It should be recognised that prolonged bleeding is a rare event following invasive dental procedures, and therefore the vast majority of patients with suspected acquired coagulopathies are best managed in the community practice setting

How does study quality affect the results of a diagnostic meta-analysis?

Background: The use of systematic literature review to inform evidence based practice in diagnostics is rapidly expanding. Although the primary diagnostic literature is extensive, studies are often of low methodological quality or poorly reported. There has been no rigorously evaluated, evidence based tool to assess the methodological quality of diagnostic studies. The primary objective of this study was to determine the extent to which variations in the quality of primary studies impact the results of a diagnostic meta-analysis and whether this differs with diagnostic test type. A secondary objective was to contribute to the evaluation of QUADAS, an evidence-based tool for the assessment of quality in diagnostic accuracy studies. Methods: This study was conducted as part of large systematic review of tests used in the diagnosis and further investigation of urinary tract infection (UTI) in children. All studies included in this review were assessed using QUADAS, an evidence-based tool for the assessment of quality in systematic reviews of diagnostic accuracy studies. The impact of individual components of QUADAS on a summary measure of diagnostic accuracy was investigated using regression analysis. The review divided the diagnosis and further investigation of UTI into the following three clinical stages: diagnosis of UTI, localisation of infection, and further investigation of the UTI. Each stage used different types of diagnostic test, which were considered to involve different quality concerns. Results: Many of the studies included in our review were poorly reported. The proportion of QUADAS items fulfilled was similar for studies in different sections of the review. However, as might be expected, the individual items fulfilled differed between the three clinical stages. Regression analysis found that different items showed a strong association with test performance for the different tests evaluated. These differences were observed both within and between the three clinical stages assessed by the review. The results of regression analyses were also affected by whether or not a weighting (by sample size) was applied. Our analysis was severely limited by the completeness of reporting and the differences between the index tests evaluated and the reference standards used to confirm diagnoses in the primary studies. Few tests were evaluated by sufficient studies to allow meaningful use of meta-analytic pooling and investigation of heterogeneity. This meant that further analysis to investigate heterogeneity could only be undertaken using a subset of studies, and that the findings are open to various interpretations. Conclusion: Further work is needed to investigate the influence of methodological quality on the results of diagnostic meta-analyses. Large data sets of well-reported primary studies are needed to address this question. Without significant improvements in the completeness of reporting of primary studies, progress in this area will be limited

A chronic fatigue syndrome – related proteome in human cerebrospinal fluid

BACKGROUND: Chronic Fatigue Syndrome (CFS), Persian Gulf War Illness (PGI), and fibromyalgia are overlapping symptom complexes without objective markers or known pathophysiology. Neurological dysfunction is common. We assessed cerebrospinal fluid to find proteins that were differentially expressed in this CFS-spectrum of illnesses compared to control subjects. METHODS: Cerebrospinal fluid specimens from 10 CFS, 10 PGI, and 10 control subjects (50 μl/subject) were pooled into one sample per group (cohort 1). Cohort 2 of 12 control and 9 CFS subjects had their fluids (200 μl/subject) assessed individually. After trypsin digestion, peptides were analyzed by capillary chromatography, quadrupole-time-of-flight mass spectrometry, peptide sequencing, bioinformatic protein identification, and statistical analysis. RESULTS: Pooled CFS and PGI samples shared 20 proteins that were not detectable in the pooled control sample (cohort 1 CFS-related proteome). Multilogistic regression analysis (GLM) of cohort 2 detected 10 proteins that were shared by CFS individuals and the cohort 1 CFS-related proteome, but were not detected in control samples. Detection of ≥1 of a select set of 5 CFS-related proteins predicted CFS status with 80% concordance (logistic model). The proteins were α-1-macroglobulin, amyloid precursor-like protein 1, keratin 16, orosomucoid 2 and pigment epithelium-derived factor. Overall, 62 of 115 proteins were newly described. CONCLUSION: This pilot study detected an identical set of central nervous system, innate immune and amyloidogenic proteins in cerebrospinal fluids from two independent cohorts of subjects with overlapping CFS, PGI and fibromyalgia. Although syndrome names and definitions were different, the proteome and presumed pathological mechanism(s) may be shared

The evolution of photosynthesis in chromist algae through serial endosymbioses

Chromist algae include diverse photosynthetic organisms of great ecological and social importance. Despite vigorous research efforts, a clear understanding of how various chromists acquired photosynthetic organelles has been complicated by conflicting phylogenetic results, along with an undetermined number and pattern of endosymbioses, and the horizontal movement of genes that accompany them. We apply novel statistical approaches to assess impacts of endosymbiotic gene transfer on three principal chromist groups at the heart of long-standing controversies. Our results provide robust support for acquisitions of photosynthesis through serial endosymbioses, beginning with the adoption of a red alga by cryptophytes, then a cryptophyte by the ancestor of ochrophytes, and finally an ochrophyte by the ancestor of haptophytes. Resolution of how chromist algae are related through endosymbioses provides a framework for unravelling the further reticulate history of red algal-derived plastids, and for clarifying evolutionary processes that gave rise to eukaryotic photosynthetic diversity

Meta-analysis of muscle transcriptome data using the MADMuscle database reveals biologically relevant gene patterns

<p>Abstract</p> <p>Background</p> <p>DNA microarray technology has had a great impact on muscle research and microarray gene expression data has been widely used to identify gene signatures characteristic of the studied conditions. With the rapid accumulation of muscle microarray data, it is of great interest to understand how to compare and combine data across multiple studies. Meta-analysis of transcriptome data is a valuable method to achieve it. It enables to highlight conserved gene signatures between multiple independent studies. However, using it is made difficult by the diversity of the available data: different microarray platforms, different gene nomenclature, different species studied, etc.</p> <p>Description</p> <p>We have developed a system tool dedicated to muscle transcriptome data. This system comprises a collection of microarray data as well as a query tool. This latter allows the user to extract similar clusters of co-expressed genes from the database, using an input gene list. Common and relevant gene signatures can thus be searched more easily. The dedicated database consists in a large compendium of public data (more than 500 data sets) related to muscle (skeletal and heart). These studies included seven different animal species from invertebrates (<it>Drosophila melanogaster, Caenorhabditis elegans</it>) and vertebrates (<it>Homo sapiens, Mus musculus, Rattus norvegicus, Canis familiaris, Gallus gallus</it>). After a renormalization step, clusters of co-expressed genes were identified in each dataset. The lists of co-expressed genes were annotated using a unified re-annotation procedure. These gene lists were compared to find significant overlaps between studies.</p> <p>Conclusions</p> <p>Applied to this large compendium of data sets, meta-analyses demonstrated that conserved patterns between species could be identified. Focusing on a specific pathology (Duchenne Muscular Dystrophy) we validated results across independent studies and revealed robust biomarkers and new pathways of interest. The meta-analyses performed with MADMuscle show the usefulness of this approach. Our method can be applied to all public transcriptome data.</p

A new method for class prediction based on signed-rank algorithms applied to Affymetrix® microarray experiments

<p>Abstract</p> <p>Background</p> <p>The huge amount of data generated by DNA chips is a powerful basis to classify various pathologies. However, constant evolution of microarray technology makes it difficult to mix data from different chip types for class prediction of limited sample populations. Affymetrix^®technology provides both a quantitative fluorescence signal and a decision (<it>detection call</it>: absent or present) based on signed-rank algorithms applied to several hybridization repeats of each gene, with a per-chip normalization. We developed a new prediction method for class belonging based on the detection call only from recent Affymetrix chip type. Biological data were obtained by hybridization on U133A, U133B and U133Plus 2.0 microarrays of purified normal B cells and cells from three independent groups of multiple myeloma (MM) patients.</p> <p>Results</p> <p>After a call-based data reduction step to filter out non class-discriminative probe sets, the gene list obtained was reduced to a predictor with correction for multiple testing by iterative deletion of probe sets that sequentially improve inter-class comparisons and their significance. The error rate of the method was determined using leave-one-out and 5-fold cross-validation. It was successfully applied to (i) determine a sex predictor with the normal donor group classifying gender with no error in all patient groups except for male MM samples with a Y chromosome deletion, (ii) predict the immunoglobulin light and heavy chains expressed by the malignant myeloma clones of the validation group and (iii) predict sex, light and heavy chain nature for every new patient. Finally, this method was shown powerful when compared to the popular classification method Prediction Analysis of Microarray (PAM).</p> <p>Conclusion</p> <p>This normalization-free method is routinely used for quality control and correction of collection errors in patient reports to clinicians. It can be easily extended to multiple class prediction suitable with clinical groups, and looks particularly promising through international cooperative projects like the "Microarray Quality Control project of US FDA" MAQC as a predictive classifier for diagnostic, prognostic and response to treatment. Finally, it can be used as a powerful tool to mine published data generated on Affymetrix systems and more generally classify samples with binary feature values.</p

Cryptococcal meningitis: A neglected NTD?

Although HIV/AIDS has been anything but neglected over the last decade, opportunistic infections (OIs) are increasingly overlooked as large scale donors shift their focus from acute care to prevention and earlier antiretroviral treatment (ART) initiation. Of these OIs, cryptococcal meningitis, a deadly invasive fungal infection, continues to affect hundreds of thousands of HIV patients with advanced disease each year and is responsible for an estimated 15%-20% of all AIDS-related deaths [1,2]. Yet cryptococcal meningitis ranks amongst the most poorly funded “neglected” diseases in the world, receiving 0.2% of available relevant research and development (RandD) funding according to Policy Cures’ 2016 G-Finder Report [3,4]

Gene Expression Patterns in Peripheral Blood Correlate with the Extent of Coronary Artery Disease

Systemic and local inflammation plays a prominent role in the pathogenesis of atherosclerotic coronary artery disease, but the relationship of whole blood gene expression changes with coronary disease remains unclear. We have investigated whether gene expression patterns in peripheral blood correlate with the severity of coronary disease and whether these patterns correlate with the extent of atherosclerosis in the vascular wall

Haemophilus influenzae Infection Drives IL-17-Mediated Neutrophilic Allergic Airways Disease

A subset of patients with stable asthma has prominent neutrophilic and reduced eosinophilic inflammation, which is associated with attenuated airways hyper-responsiveness (AHR). Haemophilus influenzae has been isolated from the airways of neutrophilic asthmatics; however, the nature of the association between infection and the development of neutrophilic asthma is not understood. Our aim was to investigate the effects of H. influenzae respiratory infection on the development of hallmark features of asthma in a mouse model of allergic airways disease (AAD). BALB/c mice were intraperitoneally sensitized to ovalbumin (OVA) and intranasally challenged with OVA 12–15 days later to induce AAD. Mice were infected with non-typeable H. influenzae during or 10 days after sensitization, and the effects of infection on the development of key features of AAD were assessed on day 16. T-helper 17 cells were enumerated by fluorescent-activated cell sorting and depleted with anti-IL-17 neutralizing antibody. We show that infection in AAD significantly reduced eosinophilic inflammation, OVA-induced IL-5, IL-13 and IFN-γ responses and AHR; however, infection increased airway neutrophil influx in response to OVA challenge. Augmented neutrophilic inflammation correlated with increased IL-17 responses and IL-17 expressing macrophages and neutrophils (early, innate) and T lymphocytes (late, adaptive) in the lung. Significantly, depletion of IL-17 completely abrogated infection-induced neutrophilic inflammation during AAD. In conclusion, H. influenzae infection synergizes with AAD to induce Th17 immune responses that drive the development of neutrophilic and suppress eosinophilic inflammation during AAD. This results in a phenotype that is similar to neutrophilic asthma. Infection-induced neutrophilic inflammation in AAD is mediated by IL-17 responses