Design of the Prevention of Adult Caries Study (PACS): A randomized clinical trial assessing the effect of a chlorhexidine dental coating for the prevention of adult caries

<p>Abstract</p> <p>Background</p> <p>Dental caries is one of the primary causes of tooth loss among adults. It is estimated to affect a majority of Americans aged 55 and older, with a disproportionately higher burden in disadvantaged populations. Although a number of treatments are currently in use for caries prevention in adults, evidence for their efficacy and effectiveness is limited.</p> <p>Methods/Design</p> <p>The Prevention of Adult Caries Study (PACS) is a multicenter, placebo-controlled, double-blind, randomized clinical trial of the efficacy of a chlorhexidine (10% w/v) dental coating in preventing adult caries. Participants (n = 983) were recruited from four different dental delivery systems serving four diverse communities, including one American Indian population, and were randomized to receive either chlorhexidine or a placebo treatment. The primary outcome is the net caries increment (including non-cavitated lesions) from baseline to 13 months of follow-up. A cost-effectiveness analysis also will be considered.</p> <p>Discussion</p> <p>This new dental treatment, if efficacious and approved for use by the Food and Drug Administration (FDA), would become a new in-office, anti-microbial agent for the prevention of adult caries in the United States.</p> <p>Trial Registration Number</p> <p>NCT00357877</p

Interactome and Gene Ontology provide congruent yet subtly different views of a eukaryotic cell

15 pages, 6 figures.-- 19604360 [PubMed]BACKGROUND: The characterization of the global functional structure of a cell is a major goal in bioinformatics and systems biology. Gene Ontology (GO) and the protein-protein interaction network offer alternative views of that structure. RESULTS: This study presents a comparison of the global structures of the Gene Ontology and the interactome of Saccharomyces cerevisiae. Sensitive, unsupervised methods of clustering applied to a large fraction of the proteome led to establish a GO-interactome correlation value of +0.47 for a general dataset that contains both high and low-confidence interactions and +0.58 for a smaller, high-confidence dataset. CONCLUSION: The structures of the yeast cell deduced from GO and interactome are substantially congruent. However, some significant differences were also detected, which may contribute to a better understanding of cell function and also to a refinement of the current ontologiesResearch supported by grant BIO2008-05067 (Programa Nacional de Biotecnología; Ministerio de Ciencia e Innovación. Spain), awarded to IM. AM was a FPI fellow from Ministerio de Educación y Ciencia (Spain).Peer reviewe

Accounting for Redundancy when Integrating Gene Interaction Databases

During the last years gene interaction networks are increasingly being used for the assessment and interpretation of biological measurements. Knowledge of the interaction partners of an unknown protein allows scientists to understand the complex relationships between genetic products, helps to reveal unknown biological functions and pathways, and get a more detailed picture of an organism's complexity. Being able to measure all protein interactions under all relevant conditions is virtually impossible. Hence, computational methods integrating different datasets for predicting gene interactions are needed. However, when integrating different sources one has to account for the fact that some parts of the information may be redundant, which may lead to an overestimation of the true likelihood of an interaction. Our method integrates information derived from three different databases (Bioverse, HiMAP and STRING) for predicting human gene interactions. A Bayesian approach was implemented in order to integrate the different data sources on a common quantitative scale. An important assumption of the Bayesian integration is independence of the input data (features). Our study shows that the conditional dependency cannot be ignored when combining gene interaction databases that rely on partially overlapping input data. In addition, we show how the correlation structure between the databases can be detected and we propose a linear model to correct for this bias. Benchmarking the results against two independent reference data sets shows that the integrated model outperforms the individual datasets. Our method provides an intuitive strategy for weighting the different features while accounting for their conditional dependencies

Air–liquid interface cultures enhance the oxygen supply and trigger the structural and functional differentiation of intestinal porcine epithelial cells (IPEC)

The specific function of the epithelium as critical barrier between the intestinal lumen and the organism’s internal microenvironment is reflected by permanent maintenance of intercellular junctions and cellular polarity. The intestinal epithelial cells are responsible for absorption of nutritional components, facing mechanical stress and a changing oxygen supplementation via blood stream. Oxygen itself can regulate the barrier and the absorptive function of the epithelium. Therefore, we compared the dish cell culture, the transwell-like membrane culture and the oxygen enriched air–liquid interface (ALI) culture. We demonstrated strong influence of the different culture conditions on morphology and function of intestinal porcine epithelial cell lines in vitro. ALI culture resulted in a significant increase in cell number, epithelial cell layer thickness and expression as well as apical localisation of the microvilli-associated protein villin. Remarkable similarities regarding the morphological parameters were observed between ALI cultures and intestinal epithelial cells in vivo. Furthermore, the functional analysis of protein uptake and degradation by the epithelial cells demonstrated the necessity of sufficient oxygen supply as achieved in ALI cultures. Our study is the first report providing marked evidence that optimised oxygen supply using ALI cultures directly affects the morphological differentiation and functional properties of intestinal epithelial cells in vitro

A new human chromogranin A (CgA) immunoradiometric assay involving monoclonal antibodies raised against the unprocessed central domain (145-245)

Chromogranin A (CgA), a major protein of chromaffin granules, has been described as a potential marker for neuroendocrine tumours. Because of an extensive proteolysis which leads to a large heterogeneity of circulating fragments, its presence in blood has been assessed in most cases either by competitive immunoassays or with polyclonal antibodies. In the present study, 24 monoclonal antibodies were raised against native or recombinant human CgA. Their mapping with proteolytic peptides showed that they defined eight distinct epitopic groups which spanned two-thirds of the C-terminal part of human CgA. All monoclonal antibodies were tested by pair and compared with a reference radioimmunoassay (RIA) involving CGS06, one of the monoclonal antibodies against the 198–245 sequence. It appears that CgA C-terminal end seems to be highly affected by proteolysis and the association of C-terminal and median-part monoclonal antibodies is inadequate for total CgA assessment. Our new immunoradiometric assay involves two monoclonal antibodies, whose contiguous epitopes lie within the median 145–245 sequence. This assay allows a sensitive detection of total human CgA and correlates well with RIA because dibasic cleavage sites present in the central domain do not seem to be affected by degradation. It has been proved to be efficient in measuring CgA levels in patients with neuroendocrine tumours. © 1999 Cancer Research Campaig

Practices participating in a dental PBRN have substantial and advantageous diversity even though as a group they have much in common with dentists at large

<p>Abstract</p> <p>Background</p> <p>Practice-based research networks offer important opportunities to move recent advances into routine clinical practice. If their findings are not only generalizable to dental practices at large, but can also elucidate how practice characteristics are related to treatment outcome, their importance is even further elevated. Our objective was to determine whether we met a key objective for The Dental Practice-Based Research Network (DPBRN): to recruit a diverse range of practitioner-investigators interested in doing DPBRN studies.</p> <p>Methods</p> <p>DPBRN participants completed an enrollment questionnaire about their practices and themselves. To date, more than 1100 practitioners from the five participating regions have completed the questionnaire. The regions consist of: Alabama/Mississippi, Florida/Georgia, Minnesota, Permanente Dental Associates, and Scandinavia (Denmark, Norway, and Sweden). We tested the hypothesis that there are statistically significant differences in key characteristics among DPBRN practices, based on responses from dentists who participated in DPBRN's first network-wide study (n = 546).</p> <p>Results</p> <p>There were statistically significant, substantive regional differences among DPBRN-participating dentists, their practices, and their patient populations.</p> <p>Conclusion</p> <p>Although as a group, participants have much in common with practices at large; their substantial diversity offers important advantages, such as being able to evaluate how practice differences may affect treatment outcomes, while simultaneously offering generalizability to dentists at large. This should help foster knowledge transfer in both the research-to-practice and practice-to-research directions.</p

Bayesian Inference for Genomic Data Integration Reduces Misclassification Rate in Predicting Protein-Protein Interactions

Protein-protein interactions (PPIs) are essential to most fundamental cellular processes. There has been increasing interest in reconstructing PPIs networks. However, several critical difficulties exist in obtaining reliable predictions. Noticeably, false positive rates can be as high as >80%. Error correction from each generating source can be both time-consuming and inefficient due to the difficulty of covering the errors from multiple levels of data processing procedures within a single test. We propose a novel Bayesian integration method, deemed nonparametric Bayes ensemble learning (NBEL), to lower the misclassification rate (both false positives and negatives) through automatically up-weighting data sources that are most informative, while down-weighting less informative and biased sources. Extensive studies indicate that NBEL is significantly more robust than the classic naïve Bayes to unreliable, error-prone and contaminated data. On a large human data set our NBEL approach predicts many more PPIs than naïve Bayes. This suggests that previous studies may have large numbers of not only false positives but also false negatives. The validation on two human PPIs datasets having high quality supports our observations. Our experiments demonstrate that it is feasible to predict high-throughput PPIs computationally with substantially reduced false positives and false negatives. The ability of predicting large numbers of PPIs both reliably and automatically may inspire people to use computational approaches to correct data errors in general, and may speed up PPIs prediction with high quality. Such a reliable prediction may provide a solid platform to other studies such as protein functions prediction and roles of PPIs in disease susceptibility

Age-related changes in relative expression stability of commonly used housekeeping genes in selected porcine tissues

<p>Abstract</p> <p>Background</p> <p>Gene expression analysis using real-time RT-PCR (qRT-PCR) is increasingly important in biological research due to the high-throughput and accuracy of qRT-PCR. For accurate and reliable gene expression analysis, normalization of gene expression data against housekeeping genes or internal control genes is required. The stability of reference genes has a tremendous effect on the results of relative quantification of gene expression by qRT-PCR. The expression stability of reference genes could vary according to tissues, age of individuals and experimental conditions. In the pig however, very little information is available on the expression stability of reference genes. The aim of this research was therefore to develop a new set of reference genes which can be used for normalization of mRNA expression data of genes expressed in varieties of porcine tissues at different ages.</p> <p>Results</p> <p>The mRNA expression stability of nine commonly used reference genes (<it>B2M, BLM, GAPDH, HPRT1, PPIA, RPL4, SDHA, TBP </it>and <it>YWHAZ</it>) was determined in varieties of tissues collected from newborn, young and adult pigs. geNorm, NormFinder and BestKeeper software were used to rank the genes according to their stability. geNorm software revealed that <it>RPL4, PPIA </it>and <it>YWHAZ </it>showed high stability in newborn and adult pigs, while <it>B2M, YWHAZ </it>and <it>SDHA </it>showed high stability in young pigs. In all cases, <it>GAPDH </it>showed the least stability in geNorm. NormFinder revealed that <it>TBP </it>was the most stable gene in newborn and young pigs, while <it>PPIA </it>was most stable in adult pigs. Moreover, geNorm software suggested that the geometric mean of three most stable gene would be the suitable combination for accurate normalization of gene expression study.</p> <p>Conclusions</p> <p>Although, there was discrepancy in the ranking order of reference genes obtained by different analysing software methods, the geometric mean of the <it>RPL4, PPIA </it>and <it>YWHAZ </it>seems to be the most appropriate combination of housekeeping genes for accurate normalization of gene expression data in different porcine tissues at different ages.</p

Mining expressed sequence tags identifies cancer markers of clinical interest

BACKGROUND: Gene expression data are a rich source of information about the transcriptional dis-regulation of genes in cancer. Genes that display differential regulation in cancer are a subtype of cancer biomarkers. RESULTS: We present an approach to mine expressed sequence tags to discover cancer biomarkers. A false discovery rate analysis suggests that the approach generates less than 22% false discoveries when applied to combined human and mouse whole genome screens. With this approach, we identify the 200 genes most consistently differentially expressed in cancer (called HM200) and proceed to characterize these genes. When used for prediction in a variety of cancer classification tasks (in 24 independent cancer microarray datasets, 59 classifications total), we show that HM200 and the shorter gene list HM100 are very competitive cancer biomarker sets. Indeed, when compared to 13 published cancer marker gene lists, HM200 achieves the best or second best classification performance in 79% of the classifications considered. CONCLUSION: These results indicate the existence of at least one general cancer marker set whose predictive value spans several tumor types and classification types. Our comparison with other marker gene lists shows that HM200 markers are mostly novel cancer markers. We also identify the previously published Pomeroy-400 list as another general cancer marker set. Strikingly, Pomeroy-400 has 27 genes in common with HM200. Our data suggest that a core set of genes are responsive to the deregulation of pathways involved in tumorigenesis in a variety of tumor types and that these genes could serve as transcriptional cancer markers in applications of clinical interest. Finally, our study suggests new strategies to select and evaluate cancer biomarkers in microarray studies

HIPPIE: Integrating Protein Interaction Networks with Experiment Based Quality Scores

Protein function is often modulated by protein-protein interactions (PPIs) and therefore defining the partners of a protein helps to understand its activity. PPIs can be detected through different experimental approaches and are collected in several expert curated databases. These databases are used by researchers interested in examining detailed information on particular proteins. In many analyses the reliability of the characterization of the interactions becomes important and it might be necessary to select sets of PPIs of different confidence levels. To this goal, we generated HIPPIE (Human Integrated Protein-Protein Interaction rEference), a human PPI dataset with a normalized scoring scheme that integrates multiple experimental PPI datasets. HIPPIE's scoring scheme has been optimized by human experts and a computer algorithm to reflect the amount and quality of evidence for a given PPI and we show that these scores correlate to the quality of the experimental characterization. The HIPPIE web tool (available at http://cbdm.mdc-berlin.de/tools/hippie) allows researchers to do network analyses focused on likely true PPI sets by generating subnetworks around proteins of interest at a specified confidence level