Prognostic impact of human epidermal growth factor-like receptor 2 and hormone receptor status in inflammatory breast cancer (IBC): analysis of 2,014 IBC patient cases from the California Cancer Registry

IntroductionInflammatory breast cancer (IBC) is an aggressive form of breast cancer associated with overexpression of Her2/Neu (human epidermal growth factor-like receptor 2 (HER2)) and poor survival. We investigated survival differences for IBC patient cases based on hormone receptor status and HER2 receptor status using data from the California Cancer Registry, as contrasted with locally advanced breast cancer (LABC), metastatic breast cancer (MBC) and non-T4 breast cancer.MethodsA case-only analysis of 80,099 incident female breast cancer patient cases in the California Cancer Registry during 1999 to 2003 was performed, with follow-up through March 2007. Overall survival (OS) and breast cancer-specific survival (BC-SS) were analyzed using Kaplan-Meier methods and Cox proportional hazards ratios.ResultsA total of 2,014 IBC, 1,268 LABC, 3,059 MBC, and 73,758 non-T4 breast cancer patient cases were identified. HER2+ was associated with advanced tumor stage (P < 0.0001). IBC patient cases were more likely to be HER2+ (40%) and less likely to be hormone receptor-positive (HmR+) (59%) compared with LABC (35% and 69%, respectively), MBC (35% and 74%), and non-T4 patient cases (22% and 82%). HmR+ status was associated with improved OS and BC-SS for each breast cancer subtype after adjustment for clinically relevant factors. In multivariate analysis, HER2+ (versus HER2-) status was associated with poor BC-SS for non-T4 patient cases (hazards ratio = 1.16, 95% confidence interval 1.05 to 1.28) and had a borderline significant association with improved BC-SS for IBC (hazards ratio = 0.82, 95% confidence interval = 0.68 to 0.99).ConclusionsDespite an association with advanced tumor stage, HER2+ status is not an independent adverse prognostic factor for survival among IBC patient cases

Case-control vaccine effectiveness studies: data collection, analysis and reporting results

The case-control methodology is frequently used to evaluate vaccine effectiveness post-licensure. The results of such studies provide important insight into the level of protection afforded by vaccines in a 'real world' context, and are commonly used to guide vaccine policy decisions. However, the potential for bias and confounding are important limitations to this method, and the results of a poorly conducted or incorrectly interpreted case-control study can mislead policies. In 2012, a group of experts met to review recent experience with case-control studies evaluating vaccine effectiveness; we summarize the recommendations of that group regarding best practices for data collection, analysis, and presentation of the results of case-control vaccine effectiveness studies. Vaccination status is the primary exposure of interest, but can be challenging to assess accurately and with minimal bias. Investigators should understand factors associated with vaccination as well as the availability of documented vaccination status in the study context; case-control studies may not be a valid method for evaluating vaccine effectiveness in settings where many children lack a documented immunization history. To avoid bias, it is essential to use the same methods and effort gathering vaccination data from cases and controls. Variables that may confound the association between illness and vaccination are also important to capture as completely as possible, and where relevant, adjust for in the analysis according to the analytic plan. In presenting results from case-control vaccine effectiveness studies, investigators should describe enrollment among eligible cases and controls as well as the proportion with no documented vaccine history. Emphasis should be placed on confidence intervals, rather than point estimates, of vaccine effectiveness. Case-control studies are a useful approach for evaluating vaccine effectiveness; however careful attention must be paid to the collection, analysis and presentation of the data in order to best inform evidence-based vaccine policies

Identifying Hallmark Symptoms of Developmental Prosopagnosia for Non-Experts

Developmental prosopagnosia (DP) is characterised by a severe and relatively selective deficit in face recognition, in the absence of neurological injury. Because public and professional awareness of DP is low, many adults and children are not identified for formal testing. This may partly result from the lack of appropriate screening tools that can be used by non-experts in either professional or personal settings. To address this issue, the current study sought to (a) explore when DP can first be detected in oneself and another, and (b) identify a list of the condition’s everyday behavioural manifestations. Questionnaires and interviews were administered to large samples of adult DPs, their unaffected significant others, and parents of children with the condition; and data were analysed using inductive content analysis. It was found that DPs have limited insight into their difficulties, with most only achieving realisation in adulthood. Nevertheless, the DPs’ reflections on their childhood experiences, together with the parental responses, revealed specific indicators that can potentially be used to spot the condition in early childhood. These everyday hallmark symptoms may aid the detection of individuals who would benefit from objective testing, in oneself (in adults) or another person (for both adults and children)

Climate Change and the Geographic Distribution of Infectious Diseases

Our ability to predict the effects of climate change on the spread of infectious diseases is in its infancy. Numerous, and in some cases conflicting, predictions have been developed, principally based on models of biological processes or mapping of current and historical disease statistics. Current debates on whether climate change, relative to socioeconomic determinants, will be a major influence on human disease distributions are useful to help identify research needs but are probably artificially polarized. We have at least identified many of the critical geophysical constraints, transport opportunities, biotic requirements for some disease systems, and some of the socioeconomic factors that govern the process of migration and establishment of parasites and pathogens. Furthermore, we are beginning to develop a mechanistic understanding of many of these variables at specific sites. Better predictive understanding will emerge in the coming years from analyses regarding how these variables interact with each other

Linking the Epigenome to the Genome: Correlation of Different Features to DNA Methylation of CpG Islands

DNA methylation of CpG islands plays a crucial role in the regulation of gene expression. More than half of all human promoters contain CpG islands with a tissue-specific methylation pattern in differentiated cells. Still today, the whole process of how DNA methyltransferases determine which region should be methylated is not completely revealed. There are many hypotheses of which genomic features are correlated to the epigenome that have not yet been evaluated. Furthermore, many explorative approaches of measuring DNA methylation are limited to a subset of the genome and thus, cannot be employed, e.g., for genome-wide biomarker prediction methods. In this study, we evaluated the correlation of genetic, epigenetic and hypothesis-driven features to DNA methylation of CpG islands. To this end, various binary classifiers were trained and evaluated by cross-validation on a dataset comprising DNA methylation data for 190 CpG islands in HEPG2, HEK293, fibroblasts and leukocytes. We achieved an accuracy of up to 91% with an MCC of 0.8 using ten-fold cross-validation and ten repetitions. With these models, we extended the existing dataset to the whole genome and thus, predicted the methylation landscape for the given cell types. The method used for these predictions is also validated on another external whole-genome dataset. Our results reveal features correlated to DNA methylation and confirm or disprove various hypotheses of DNA methylation related features. This study confirms correlations between DNA methylation and histone modifications, DNA structure, DNA sequence, genomic attributes and CpG island properties. Furthermore, the method has been validated on a genome-wide dataset from the ENCODE consortium. The developed software, as well as the predicted datasets and a web-service to compare methylation states of CpG islands are available at http://www.cogsys.cs.uni-tuebingen.de/software/dna-methylation/

High-spin structures in Xe 132 and Xe 133 and evidence for isomers along the N=79 isotones

The transitional nuclei Xe132 and Xe133 are investigated after multinucleon-transfer (MNT) and fusion-evaporation reactions. Both nuclei are populated (i) in Xe136+Pb208 MNT reactions employing the high-resolution Advanced GAmma Tracking Array (AGATA) coupled to the magnetic spectrometer PRISMA, (ii) in the Xe136+Pt198 MNT reaction employing the GAMMASPHERE spectrometer in combination with the gas-detector array CHICO, and (iii) as an evaporation residue after a Te130(α,xn)Xe134-xn fusion-evaporation reaction employing the HORUS γ-ray array at the University of Cologne. The high-spin level schemes are considerably extended above the Jπ=(7-) and (10+) isomers in Xe132 and above the 11/2- isomer in Xe133. The results are compared to the high-spin systematics of the Z=54 as well as the N=78 and N=79 chains. Furthermore, evidence is found for a long-lived (T1/2â‰1μs) isomer in Xe133 which closes a gap along the N=79 isotones. Shell-model calculations employing the SN100PN and PQM130 effective interactions reproduce the experimental findings and provide guidance to the interpretation of the observed high-spin features

Case-control vaccine effectiveness studies: data collection, analysis and reporting results

The case-control methodology is frequently used to evaluate vaccine effectiveness post-licensure. The results of such studies provide important insight into the level of protection afforded by vaccines in a 'real world' context, and are commonly used to guide vaccine policy decisions. However, the potential for bias and confounding are important limitations to this method, and the results of a poorly conducted or incorrectly interpreted case-control study can mislead policies. In 2012, a group of experts met to review recent experience with case-control studies evaluating vaccine effectiveness; we summarize the recommendations of that group regarding best practices for data collection, analysis, and presentation of the results of case-control vaccine effectiveness studies. Vaccination status is the primary exposure of interest, but can be challenging to assess accurately and with minimal bias. Investigators should understand factors associated with vaccination as well as the availability of documented vaccination status in the study context; case-control studies may not be a valid method for evaluating vaccine effectiveness in settings where many children lack a documented immunization history. To avoid bias, it is essential to use the same methods and effort gathering vaccination data from cases and controls. Variables that may confound the association between illness and vaccination are also important to capture as completely as possible, and where relevant, adjust for in the analysis according to the analytic plan. In presenting results from case-control vaccine effectiveness studies, investigators should describe enrollment among eligible cases and controls as well as the proportion with no documented vaccine history. Emphasis should be placed on confidence intervals, rather than point estimates, of vaccine effectiveness. Case-control studies are a useful approach for evaluating vaccine effectiveness; however careful attention must be paid to the collection, analysis and presentation of the data in order to best inform evidence-based vaccine policies

Case-control vaccine effectiveness studies: Preparation, design, and enrollment of cases and controls.

Case-control studies are commonly used to evaluate effectiveness of licensed vaccines after deployment in public health programs. Such studies can provide policy-relevant data on vaccine performance under 'real world' conditions, contributing to the evidence base to support and sustain introduction of new vaccines. However, case-control studies do not measure the impact of vaccine introduction on disease at a population level, and are subject to bias and confounding, which may lead to inaccurate results that can misinform policy decisions. In 2012, a group of experts met to review recent experience with case-control studies evaluating the effectiveness of several vaccines; here we summarize the recommendations of that group regarding best practices for planning, design and enrollment of cases and controls. Rigorous planning and preparation should focus on understanding the study context including healthcare-seeking and vaccination practices. Case-control vaccine effectiveness studies are best carried out soon after vaccine introduction because high coverage creates strong potential for confounding. Endpoints specific to the vaccine target are preferable to non-specific clinical syndromes since the proportion of non-specific outcomes preventable through vaccination may vary over time and place, leading to potentially confusing results. Controls should be representative of the source population from which cases arise, and are generally recruited from the community or health facilities where cases are enrolled. Matching of controls to cases for potential confounding factors is commonly used, although should be reserved for a limited number of key variables believed to be linked to both vaccination and disease. Case-control vaccine effectiveness studies can provide information useful to guide policy decisions and vaccine development, however rigorous preparation and design is essential