Peri-ampullary mixed acinar-endocrine carcinoma

Mixed acinar-endocrine carcinomas (MAEC) are rare tumors of the pancreas. We present the case of a patient with periampullary tumor that presented with painless jaundice and after investigation was found to have MAEC. He underwent pancreaticoduo-dunectomy with tumor free margins and negative lymph nodes. The patient presented with local recurrence and liver metastasis after 1 year and is on chemotherapy with stable lesions 30 months after the diagnosis

Comparative study of unsupervised dimension reduction techniques for the visualization of microarray gene expression data

<p>Abstract</p> <p>Background</p> <p>Visualization of DNA microarray data in two or three dimensional spaces is an important exploratory analysis step in order to detect quality issues or to generate new hypotheses. Principal Component Analysis (PCA) is a widely used linear method to define the mapping between the high-dimensional data and its low-dimensional representation. During the last decade, many new nonlinear methods for dimension reduction have been proposed, but it is still unclear how well these methods capture the underlying structure of microarray gene expression data. In this study, we assessed the performance of the PCA approach and of six nonlinear dimension reduction methods, namely Kernel PCA, Locally Linear Embedding, Isomap, Diffusion Maps, Laplacian Eigenmaps and Maximum Variance Unfolding, in terms of visualization of microarray data.</p> <p>Results</p> <p>A systematic benchmark, consisting of Support Vector Machine classification, cluster validation and noise evaluations was applied to ten microarray and several simulated datasets. Significant differences between PCA and most of the nonlinear methods were observed in two and three dimensional target spaces. With an increasing number of dimensions and an increasing number of differentially expressed genes, all methods showed similar performance. PCA and Diffusion Maps responded less sensitive to noise than the other nonlinear methods.</p> <p>Conclusions</p> <p>Locally Linear Embedding and Isomap showed a superior performance on all datasets. In very low-dimensional representations and with few differentially expressed genes, these two methods preserve more of the underlying structure of the data than PCA, and thus are favorable alternatives for the visualization of microarray data.</p

A Reliability-Generalization Study of Journal Peer Reviews: A Multilevel Meta-Analysis of Inter-Rater Reliability and Its Determinants

Background: This paper presents the first meta-analysis for the inter-rater reliability (IRR) of journal peer reviews. IRR is defined as the extent to which two or more independent reviews of the same scientific document agree. Methodology/Principal Findings: Altogether, 70 reliability coefficients (Cohen’s Kappa, intra-class correlation [ICC], and Pearson product-moment correlation [r]) from 48 studies were taken into account in the meta-analysis. The studies were based on a total of 19,443 manuscripts; on average, each study had a sample size of 311 manuscripts (minimum: 28, maximum: 1983). The results of the meta-analysis confirmed the findings of the narrative literature reviews published to date: The level of IRR (mean ICC/r 2 =.34, mean Cohen’s Kappa =.17) was low. To explain the study-to-study variation of the IRR coefficients, meta-regression analyses were calculated using seven covariates. Two covariates that emerged in the metaregression analyses as statistically significant to gain an approximate homogeneity of the intra-class correlations indicated that, firstly, the more manuscripts that a study is based on, the smaller the reported IRR coefficients are. Secondly, if the information of the rating system for reviewers was reported in a study, then this was associated with a smaller IRR coefficient than if the information was not conveyed. Conclusions/Significance: Studies that report a high level of IRR are to be considered less credible than those with a low level o

Analysis of ancestral and functionally relevant CD5 variants in systemic lupus erythematosus patients

OBJECTIVE: CD5 plays a crucial role in autoimmunity and is a well-established genetic risk factor of developing RA. Recently, evidence of positive selection has been provided for the CD5 Pro224-Val471 haplotype in East Asian populations. The aim of the present work was to further analyze the functional relevance of non-synonymous CD5 polymorphisms conforming the ancestral and the newly derived haplotypes (Pro224-Ala471 and Pro224-Val471, respectively) as well as to investigate the potential role of CD5 on the development of SLE and/or SLE nephritis. METHODS: The CD5 SNPs rs2241002 (C/T; Pro224Leu) and rs2229177 (C/T; Ala471Val) were genotyped using TaqMan allelic discrimination assays in a total of 1,324 controls and 681 SLE patients of Spanish origin. In vitro analysis of CD3-mediated T cell proliferative and cytokine response profiles of healthy volunteers homozygous for the above mentioned CD5 haplotypes were also analyzed. RESULTS: T-cell proliferation and cytokine release were significantly increased showing a bias towards to a Th2 profile after CD3 cross-linking of peripheral mononuclear cells from healthy individuals homozygous for the ancestral Pro224-Ala471 (CC) haplotype, compared to the more recently derived Pro224-Val471 (CT). The same allelic combination was statistically associated with Lupus nephritis. CONCLUSION: The ancestral Ala471 CD5 allele confers lymphocyte hyper-responsiveness to TCR/CD3 cross-linking and is associated with nephritis in SLE patients

Risky business: factor analysis of survey data – assessing the probability of incorrect dimensionalisation

This paper undertakes a systematic assessment of the extent to which factor analysis the correct number of latent dimensions (factors) when applied to ordered categorical survey items (so-called Likert items). We simulate 2400 data sets of uni-dimensional Likert items that vary systematically over a range of conditions such as the underlying population distribution, the number of items, the level of random error, and characteristics of items and item-sets. Each of these datasets is factor analysed in a variety of ways that are frequently used in the extant literature, or that are recommended in current methodological texts. These include exploratory factor retention heuristics such as Kaiser’s criterion, Parallel Analysis and a non-graphical scree test, and (for exploratory and confirmatory analyses) evaluations of model fit. These analyses are conducted on the basis of Pearson and polychoric correlations.We find that, irrespective of the particular mode of analysis, factor analysis applied to ordered-categorical survey data very often leads to over-dimensionalisation. The magnitude of this risk depends on the specific way in which factor analysis is conducted, the number of items, the properties of the set of items, and the underlying population distribution. The paper concludes with a discussion of the consequences of overdimensionalisation, and a brief mention of alternative modes of analysis that are much less prone to such problems