Endoscopic and Percutaneous Preoperative Biliary Drainage in Patients with Suspected Hilar Cholangiocarcinoma

INTRODUCTION: Controversy exists over the preferred technique of preoperative biliary drainage (PBD) in patients with hilar cholangiocarcinoma (HCCA) requiring major liver resection. The current study compared outcomes of endoscopic biliary drainage (EBD) and percutaneous transhepatic biliary drainage (PTBD) in patients with resectable HCCA. METHODS: One hundred fifteen consecutive patients were explored for HCCA between 2001 and July 2008 and assigned by initial PBD procedure to either EBD or PTBD. RESULTS: Of these patients, 101 (88%) underwent PBD; 90 patients underwent EBD as primary procedure, and 11 PTBD. The technical success rate of initial drainage was 81% in the EBD versus 100% in the PTBD group (P = 0.20). Stent dislocation was similar in the EBD and PTBD groups (23% vs. 20%, P = 0.70). Infectious complications were significantly more common in the endoscopic group (48% vs. 9%, P < 0.05). Patients in the EBD group underwent more drainage procedures (2.8 vs. 1.4, P < 0.01) and had a significantly longer drainage period until laparotomy (mean 15 weeks vs. 11 weeks in the PTBD group; P < 0.05). In 30 patients, EBD was converted to PTBD due to failure of the endoscopic approach. CONCLUSIONS: Preoperative percutaneous drainage could outperform endoscopic stent placement in patients with resectable HCCA, showing fewer infectious complications, using less procedure

Genetic factors associated with intestinal metaplasia in a high risk Singapore-Chinese population: a cohort study

BMC Gastroenterology97

Supply Chain Intelligence

This chapter provides on overall picture of business intelligence (BI) and supply chain analytics (SCA) as a means to support supply chain management (SCM) and decision-making. Based on the literature review, we clarify the needs of BI and performance measurement in the SCM sphere, and discuss its potential to enhance decision-making in strategic, tactical and operational levels. We also make a closer look in to SCA in different areas and functions of SCM. Our findings indicate that the main challenge for harnessing the full potential of SCA is the lack of holistic and integrated BI approaches that originates from the fact that each functional area is using its own IT applications without necessary integration in to the company’s overall BI system. Following this examination, we construct a holistic framework that illustrates how an integrated, managerially planned BI system can be developed. Finally, we discuss the main competency requirements, as well as the challenges still prohibiting the great majority of firms from building smart and comprehensive BI systems for SCM.fi=vertaisarvioitu|en=peerReviewed

Factors Influencing the Statistical Power of Complex Data Analysis Protocols for Molecular Signature Development from Microarray Data

Critical to the development of molecular signatures from microarray and other high-throughput data is testing the statistical significance of the produced signature in order to ensure its statistical reproducibility. While current best practices emphasize sufficiently powered univariate tests of differential expression, little is known about the factors that affect the statistical power of complex multivariate analysis protocols for high-dimensional molecular signature development.We show that choices of specific components of the analysis (i.e., error metric, classifier, error estimator and event balancing) have large and compounding effects on statistical power. The effects are demonstrated empirically by an analysis of 7 of the largest microarray cancer outcome prediction datasets and supplementary simulations, and by contrasting them to prior analyses of the same data.THE FINDINGS OF THE PRESENT STUDY HAVE TWO IMPORTANT PRACTICAL IMPLICATIONS: First, high-throughput studies by avoiding under-powered data analysis protocols, can achieve substantial economies in sample required to demonstrate statistical significance of predictive signal. Factors that affect power are identified and studied. Much less sample than previously thought may be sufficient for exploratory studies as long as these factors are taken into consideration when designing and executing the analysis. Second, previous highly-cited claims that microarray assays may not be able to predict disease outcomes better than chance are shown by our experiments to be due to under-powered data analysis combined with inappropriate statistical tests

Very Important Pool (VIP) genes – an application for microarray-based molecular signatures

<p>Abstract</p> <p>Background</p> <p>Advances in DNA microarray technology portend that molecular signatures from which microarray will eventually be used in clinical environments and personalized medicine. Derivation of biomarkers is a large step beyond hypothesis generation and imposes considerably more stringency for accuracy in identifying informative gene subsets to differentiate phenotypes. The inherent nature of microarray data, with fewer samples and replicates compared to the large number of genes, requires identifying informative genes prior to classifier construction. However, improving the ability to identify differentiating genes remains a challenge in bioinformatics.</p> <p>Results</p> <p>A new hybrid gene selection approach was investigated and tested with nine publicly available microarray datasets. The new method identifies a Very Important Pool (VIP) of genes from the broad patterns of gene expression data. The method uses a bagging sampling principle, where the re-sampled arrays are used to identify the most informative genes. Frequency of selection is used in a repetitive process to identify the VIP genes. The putative informative genes are selected using two methods, t-statistic and discriminatory analysis. In the t-statistic, the informative genes are identified based on p-values. In the discriminatory analysis, disjoint Principal Component Analyses (PCAs) are conducted for each class of samples, and genes with high discrimination power (DP) are identified. The VIP gene selection approach was compared with the p-value ranking approach. The genes identified by the VIP method but not by the p-value ranking approach are also related to the disease investigated. More importantly, these genes are part of the pathways derived from the common genes shared by both the VIP and p-ranking methods. Moreover, the binary classifiers built from these genes are statistically equivalent to those built from the top 50 p-value ranked genes in distinguishing different types of samples.</p> <p>Conclusion</p> <p>The VIP gene selection approach could identify additional subsets of informative genes that would not always be selected by the p-value ranking method. These genes are likely to be additional true positives since they are a part of pathways identified by the p-value ranking method and expected to be related to the relevant biology. Therefore, these additional genes derived from the VIP method potentially provide valuable biological insights.</p

Creative aspiration and the betrayal of promise? The experience of new creative workers

The promise of ‘doing what you love’ continues to attract new aspirants to creative work, yet most creative industries are so characterised by low investment, shifting foci and ongoing technological innovation that all promises must be unreliable. Some would-be creative workers negotiate their own pathways from the outset, ‘following their dream’ as they attempt to convert personal enthusiasms and amateur activities into income-earning careers. Others look to the proliferation of available training and education options, including higher education courses, as possible pathways into creative work. This chapter reviews recent research from the USA, Australia and the UK on the effectiveness – or otherwise – of higher education as preparation for a creative career. The chapter discusses the obstacles that many creative workers, including graduates, encounter on their creative pathways, for instance, as a result of informal work practices and self-employment. The chapter also looks at sources of advantage and disadvantage, such as those associated with particular geographic locations or personal identities. The chapter concludes by introducing the subsequent chapters in the collection. These critically explore the experience of new creative workers in a wide range of national contexts including Australia, Belgium, China, Ireland, Italy, Finland, the Netherlands, Russia and the United Kingdom

Google Goes Cancer: Improving Outcome Prediction for Cancer Patients by Network-Based Ranking of Marker Genes

Predicting the clinical outcome of cancer patients based on the expression of marker genes in their tumors has received increasing interest in the past decade. Accurate predictors of outcome and response to therapy could be used to personalize and thereby improve therapy. However, state of the art methods used so far often found marker genes with limited prediction accuracy, limited reproducibility, and unclear biological relevance. To address this problem, we developed a novel computational approach to identify genes prognostic for outcome that couples gene expression measurements from primary tumor samples with a network of known relationships between the genes. Our approach ranks genes according to their prognostic relevance using both expression and network information in a manner similar to Google's PageRank. We applied this method to gene expression profiles which we obtained from 30 patients with pancreatic cancer, and identified seven candidate marker genes prognostic for outcome. Compared to genes found with state of the art methods, such as Pearson correlation of gene expression with survival time, we improve the prediction accuracy by up to 7%. Accuracies were assessed using support vector machine classifiers and Monte Carlo cross-validation. We then validated the prognostic value of our seven candidate markers using immunohistochemistry on an independent set of 412 pancreatic cancer samples. Notably, signatures derived from our candidate markers were independently predictive of outcome and superior to established clinical prognostic factors such as grade, tumor size, and nodal status. As the amount of genomic data of individual tumors grows rapidly, our algorithm meets the need for powerful computational approaches that are key to exploit these data for personalized cancer therapies in clinical practice