The effectiveness of the biannual application of silver nitrate solution followed by sodium fluoride varnish in arresting early childhood caries in preschool children: study protocol for a randomised controlled trial

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Treatment time for non-surgical endodontic therapy with or without a magnifying loupe

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Antibody-Dependent Cell-Mediated Cytotoxicity Epitopes on the Hemagglutinin Head Region of Pandemic H1N1 Influenza Virus Play Detrimental Roles in H1N1-Infected Mice

Engaging the antibody-dependent cell-mediated cytotoxicity (ADCC) for killing of virus-infected cells and secretion of antiviral cytokines and chemokines was incorporated as one of the important features in the design of universal influenza vaccines. However, investigation of the ADCC epitopes on the highly immunogenic influenza hemagglutinin (HA) head region has been rarely reported. In this study, we determined the ADCC and antiviral activities of two putative ADCC epitopes, designated E1 and E2, on the HA head of a pandemic H1N1 influenza virus in vitro and in a lethal mouse model. Our data demonstrated that sera from the E1-vaccinated mice could induce high ADCC activities. Importantly, the induction of ADCC response modestly decreased viral load in the lungs of H1N1-infected mice. However, the elevated ADCC significantly increased mouse alveolar damage and mortality than that of the PBS-vaccinated group (P < 0.0001). The phenotype was potentially due to an exaggerated inflammatory cell infiltration triggered by ADCC, as an upregulated release of cytotoxic granules (perforin) was observed in the lung tissue of E1-vaccinated mice after H1N1 influenza virus challenge. Overall, our data suggested that ADCC elicited by certain domains of HA head region might have a detrimental rather than protective effect during influenza virus infection. Thus, future design of universal influenza vaccine shall strike a balance between the induction of protective immunity and potential side effects of ADCC.published_or_final_versio

Differential maturation and subcellular localization of severe acute respiratory syndrome coronavirus surface proteins S, M and E

Post-translational modifications and correct subcellular localization of viral structural proteins are prerequisites for assembly and budding of enveloped viruses. Coronaviruses, like the severe acute respiratory syndrome-associated virus (SARS-CoV), bud from the endoplasmic reticulum-Golgi intermediate compartment. In this study, the subcellular distribution and maturation of SARS-CoV surface proteins S, M and E were analysed by using C-terminally tagged proteins. As early as 30 min post-entry into the endoplasmic reticulum, high-mannosylated S assembles into trimers prior to acquisition of complex N-glycans in the Golgi. Like S, M acquires high-mannose N-glycans that are subsequently modified into complex N-glycans in the Golgi. The N-glycosylation profile and the absence of O-glycosylation on M protein relate SARS-CoV to the previously described group 1 and 3 coronaviruses. Immunofluorescence analysis shows that S is detected in several compartments along the secretory pathway from the endoplasmic reticulum to the plasma membrane while M predominantly localizes in the Golgi, where it accumulates, and in trafficking vesicles. The E protein is not glycosylated. Pulse-chase labelling and confocal microscopy in the presence of protein translation inhibitor cycloheximide revealed that the E protein has a short half-life of 30 min. E protein is found in bright perinuclear patches colocalizing with endoplasmic reticulum markers. In conclusion, SARS-CoV surface proteins S, M and E show differential subcellular localizations when expressed alone suggesting that additional cellular or viral factors might be required for coordinated trafficking to the virus assembly site in the endoplasmic reticulum-Golgi intermediate compartment. © 2005 SGM.postprin

UNCLES: Method for the identification of genes differentially consistently co-expressed in a specific subset of datasets

Background: Collective analysis of the increasingly emerging gene expression datasets are required. The recently proposed binarisation of consensus partition matrices (Bi-CoPaM) method can combine clustering results from multiple datasets to identify the subsets of genes which are consistently co-expressed in all of the provided datasets in a tuneable manner. However, results validation and parameter setting are issues that complicate the design of such methods. Moreover, although it is a common practice to test methods by application to synthetic datasets, the mathematical models used to synthesise such datasets are usually based on approximations which may not always be sufficiently representative of real datasets. Results: Here, we propose an unsupervised method for the unification of clustering results from multiple datasets using external specifications (UNCLES). This method has the ability to identify the subsets of genes consistently co-expressed in a subset of datasets while being poorly co-expressed in another subset of datasets, and to identify the subsets of genes consistently co-expressed in all given datasets. We also propose the M-N scatter plots validation technique and adopt it to set the parameters of UNCLES, such as the number of clusters, automatically. Additionally, we propose an approach for the synthesis of gene expression datasets using real data profiles in a way which combines the ground-truth-knowledge of synthetic data and the realistic expression values of real data, and therefore overcomes the problem of faithfulness of synthetic expression data modelling. By application to those datasets, we validate UNCLES while comparing it with other conventional clustering methods, and of particular relevance, biclustering methods. We further validate UNCLES by application to a set of 14 real genome-wide yeast datasets as it produces focused clusters that conform well to known biological facts. Furthermore, in-silico-based hypotheses regarding the function of a few previously unknown genes in those focused clusters are drawn. Conclusions: The UNCLES method, the M-N scatter plots technique, and the expression data synthesis approach will have wide application for the comprehensive analysis of genomic and other sources of multiple complex biological datasets. Moreover, the derived in-silico-based biological hypotheses represent subjects for future functional studies.The National Institute for Health Research (NIHR) under its Programme Grants for Applied Research Programme (Grant Reference Number RP-PG-0310-1004)

pGQL: A probabilistic graphical query language for gene expression time courses

<p>Abstract</p> <p>Background</p> <p>Timeboxes are graphical user interface widgets that were proposed to specify queries on time course data. As queries can be very easily defined, an exploratory analysis of time course data is greatly facilitated. While timeboxes are effective, they have no provisions for dealing with noisy data or data with fluctuations along the time axis, which is very common in many applications. In particular, this is true for the analysis of gene expression time courses, which are mostly derived from noisy microarray measurements at few unevenly sampled time points. From a data mining point of view the robust handling of data through a sound statistical model is of great importance.</p> <p>Results</p> <p>We propose probabilistic timeboxes, which correspond to a specific class of Hidden Markov Models, that constitutes an established method in data mining. Since HMMs are a particular class of probabilistic graphical models we call our method Probabilistic Graphical Query Language. Its implementation was realized in the free software package pGQL. We evaluate its effectiveness in exploratory analysis on a yeast sporulation data set.</p> <p>Conclusions</p> <p>We introduce a new approach to define dynamic, statistical queries on time course data. It supports an interactive exploration of reasonably large amounts of data and enables users without expert knowledge to specify fairly complex statistical models with ease. The expressivity of our approach is by its statistical nature greater and more robust with respect to amplitude and frequency fluctuation than the prior, deterministic timeboxes.</p

Mycolactone-dependent depletion of endothelial cell thrombomodulin is strongly associated with fibrin deposition in Buruli ulcer lesions

A well-known histopathological feature of diseased skin in Buruli ulcer (BU) is coagulative necrosis caused by the Mycobacterium ulcerans macrolide exotoxin mycolactone. Since the underlying mechanism is not known, we have investigated the effect of mycolactone on endothelial cells, focussing on the expression of surface anticoagulant molecules involved in the protein C anticoagulant pathway. Congenital deficiencies in this natural anticoagulant pathway are known to induce thrombotic complications such as purpura fulimans and spontaneous necrosis. Mycolactone profoundly decreased thrombomodulin (TM) expression on the surface of human dermal microvascular endothelial cells (HDMVEC) at doses as low as 2ng/ml and as early as 8hrs after exposure. TM activates protein C by altering thrombin's substrate specificity, and exposure of HDMVEC to mycolactone for 24 hours resulted in an almost complete loss of the cells' ability to produce activated protein C. Loss of TM was shown to be due to a previously described mechanism involving mycolactone-dependent blockade of Sec61 translocation that results in proteasome-dependent degradation of newly synthesised ER-transiting proteins. Indeed, depletion from cells determined by live-cell imaging of cells stably expressing a recombinant TM-GFP fusion protein occurred at the known turnover rate. In order to determine the relevance of these findings to BU disease, immunohistochemistry of punch biopsies from 40 BU lesions (31 ulcers, nine plaques) was performed. TM abundance was profoundly reduced in the subcutis of 78% of biopsies. Furthermore, it was confirmed that fibrin deposition is a common feature of BU lesions, particularly in the necrotic areas. These findings indicate that there is decreased ability to control thrombin generation in BU skin. Mycolactone's effects on normal endothelial cell function, including its ability to activate the protein C anticoagulant pathway are strongly associated with this. Fibrin-driven tisischemia could contribute to the development of the tissue necrosis seen in BU lesions

Anodization of nanoporous alumina on impurity-induced hemisphere curved surface of aluminum at room temperature

Nanoporous alumina which was produced by a conventional direct current anodization [DCA] process at low temperatures has received much attention in various applications such as nanomaterial synthesis, sensors, and photonics. In this article, we employed a newly developed hybrid pulse anodization [HPA] method to fabricate the nanoporous alumina on a flat and curved surface of an aluminum [Al] foil at room temperature [RT]. We fabricate the nanopores to grow on a hemisphere curved surface and characterize their behavior along the normal vectors of the hemisphere curve. In a conventional DCA approach, the structures of branched nanopores were grown on a photolithography-and-etched low-curvature curved surface with large interpore distances. However, a high-curvature hemisphere curved surface can be obtained by the HPA technique. Such a curved surface by HPA is intrinsically induced by the high-resistivity impurities in the aluminum foil and leads to branching and bending of nanopore growth via the electric field mechanism rather than the interpore distance in conventional approaches. It is noted that by the HPA technique, the Joule heat during the RT process has been significantly suppressed globally on the material, and nanopores have been grown along the normal vectors of a hemisphere curve. The curvature is much larger than that in other literatures due to different fabrication methods. In theory, the number of nanopores on the hemisphere surface is two times of the conventional flat plane, which is potentially useful for photocatalyst or other applications

Accounting for centre-effects in multicentre trials with a binary outcome - when, why, and how?

BACKGROUND: It is often desirable to account for centre-effects in the analysis of multicentre randomised trials, however it is unclear which analysis methods are best in trials with a binary outcome. METHODS: We compared the performance of four methods of analysis (fixed-effects models, random-effects models, generalised estimating equations (GEE), and Mantel-Haenszel) using a re-analysis of a previously reported randomised trial (MIST2) and a large simulation study. RESULTS: The re-analysis of MIST2 found that fixed-effects and Mantel-Haenszel led to many patients being dropped from the analysis due to over-stratification (up to 69% dropped for Mantel-Haenszel, and up to 33% dropped for fixed-effects). Conversely, random-effects and GEE included all patients in the analysis, however GEE did not reach convergence. Estimated treatment effects and p-values were highly variable across different analysis methods. The simulation study found that most methods of analysis performed well with a small number of centres. With a large number of centres, fixed-effects led to biased estimates and inflated type I error rates in many situations, and Mantel-Haenszel lost power compared to other analysis methods in some situations. Conversely, both random-effects and GEE gave nominal type I error rates and good power across all scenarios, and were usually as good as or better than either fixed-effects or Mantel-Haenszel. However, this was only true for GEEs with non-robust standard errors (SEs); using a robust ‘sandwich’ estimator led to inflated type I error rates across most scenarios. CONCLUSIONS: With a small number of centres, we recommend the use of fixed-effects, random-effects, or GEE with non-robust SEs. Random-effects and GEE with non-robust SEs should be used with a moderate or large number of centres