Citations

Periocular tumors are common in infancy. The most common periocular tumors are capillary hemangiomas, which are present in 1-2% of newborns and develop in 10%–12% of children by the age of 1 year old. Deep capillary hemangiomas may be more challenging to diagnose than superficial capillary hemangiomas and can be confused with other orbital lesions. Deep orbital hemangiomas can mimic teratoma, lymphangioma, rhabdomyosarcoma, metastatic neuroblastoma, and granulocytic sarcoma. In this paper, we describe 2 pediatric cases where previously diagnosed dermoid cyst and dacrocystocele were found to be capillary hemangiomas upon biopsy. Approaches to distinguish capillary hemangiomas from other periocular tumors are further discussed. To our knowledge, this is the first case report of periocular hemangiomas imitating a dermoid cyst and a dacrocystocele. These cases emphasize the importance of including infantile hemangiomas in the differential diagnosis of subcutaneous periocular abnormalities.

SummaryFollowing the discovery of the Janus kinase (JAK) 2 V617F mutation in 2005 the explosion of research and drug development activity has not only advanced our understanding of the pathogenesis of myeloproliferative neoplasms (MPNs) but also triggered debate about classification, allowed revised diagnostic and response criteria, provided a target for treatment and a mode of monitoring its success. These changes and the resultant clinical research are discussed in this article where we argue that discovery of the JAK2 V617F mutation has signalled the much delayed change in therapeutic paradigm for myelofibrosis and possibly other MPNs from palliation and allowing us to move closer to, but not yet attain, a cure.

With new technologies, multiple types of genomic data are commonly collected on a single set of samples. However, standard analysis methods concentrate on a single data type at a time and ignore the relationships between genes, proteins, and biochemical reactions that give rise to complex phenotypes. In this paper, we propose a novel integrative model to incorporate multiple types of genomic data into an association analysis with a complex phenotype. The method combines path analysis and stochastic search variable selection into a Bayesian hierarchical model that simultaneously identifies both direct and indirect genomic effects on the phenotype. Results from a simulation study and application of the Bayesian model to a pharmacogenomic study of the drug gemcitabine demonstrate greater sensitivity to detect genomic effects in some simulation scenarios, when compared to the standard single data type analysis. Further research is required to extend and modify this integrative modeling framework to increase computational efficiency to best capitalize on the wealth of information available across multiple genomic data types. Genet. Epidemiol. 36:352–359, 2012. © 2012 Wiley Periodicals, Inc.

The mechanisms by which common genetic variation predisposes to type 2 diabetes remain unclear. The disease-associated variants in TCF7L2 (rs7903146) and WFS1 (rs10010131) have been shown to affect response to exogenous glucagon-like peptide 1 (GLP-1), while variants in KCNQ1 (rs151290, rs2237892, and rs2237895) alter endogenous GLP-1 secretion. We set out to validate these observations using a model of GLP-1–induced insulin secretion. We studied healthy individuals using a hyperglycemic clamp and GLP-1 infusion. In addition, we measured active and total GLP-1 in response to an oral challenge in nondiabetic subjects. After genotyping the relevant single nucleotide polymorphisms, generalized linear regression models and repeated-measures ANCOVA models incorporating potential confounders, such as age and BMI, were used to assess the associations, if any, of response with genotype. These variants did not alter GLP-1 concentrations in response to oral intake. No effects on β-cell responsiveness to hyperglycemia and GLP-1 infusion were apparent. Diabetes-associated variation (T allele at rs7903146) in TCF7L2 may impair the ability of hyperglycemia to suppress glucagon (45 ± 2 vs. 47 ± 2 vs. 60 ± 5 ng/L for CC, CT, and TT, respectively, P = 0.02). In nondiabetic subjects, diabetes-associated genetic variation does not alter GLP-1 concentrations after an oral challenge or its effect on insulin secretion.