Use of complementary and alternative medicine in cancer patients: a European survey

Background: The aim of this study was to explore the use of complementary and alternative medicine (CAM) in cancer patients across a number of European countries. Methods: A descriptive survey design was developed. Fourteen countries participated in the study and data was collected through a descriptive questionnaire from 956 patients. Results: Data suggest that CAM is popular among cancer patients with 35.9% using some form of CAM (range among countries 14.8% to 73.1%). A heterogeneous group of 58 therapies were identified as being used. Herbal medicines and remedies were the most commonly used CAM therapies, together with homeopathy, vitamins/minerals, medicinal teas, spiritual therapies and relaxation techniques. Herbal medicine use tripled from use before diagnosis to use since diagnosis with cancer. Multivariate analysis suggested that the profile of the CAM user was that of younger people, female and with higher educational level. The source of information was mainly from friends/family and the media, while physicians and nurses played a small part in providing CAM-related information. The majority used CAM to increase the body's ability to fight cancer or improve physical and emotional well-being, and many seemed to have benefited from using CAM (even though the benefits were not necessarily related to the initial reason for using CAM). Some 4.4% of patients, however, reported side-effects, mostly transient. Conclusions: It is imperative that health professionals explore the use of CAM with their cancer patients, educate them about potentially beneficial therapies in light of the limited available evidence of effectiveness, and work towards an integrated model of health-care provisio

A Genome-Scale DNA Repair RNAi Screen Identifies SPG48 as a Novel Gene Associated with Hereditary Spastic Paraplegia

We have identified a novel gene in a genome-wide, double-strand break DNA repair RNAi screen and show that is involved in the neurological disease hereditary spastic paraplegia

Flexible mapping of homology onto structure with Homolmapper

<p>Abstract</p> <p>Background</p> <p>Over the past decade, a number of tools have emerged for the examination of homology relationships among protein sequences in a structural context. Most recent software implementations for such analysis are tied to specific molecular viewing programs, which can be problematic for collaborations involving multiple viewing environments. Incorporation into larger packages also adds complications for users interested in adding their own scoring schemes or in analyzing proteins incorporating unusual amino acid residues such as selenocysteine.</p> <p>Results</p> <p>We describe homolmapper, a command-line application for mapping information from a multiple protein sequence alignment onto a protein structure for analysis in the viewing software of the user's choice. Homolmapper is small (under 250 K for the application itself) and is written in Python to ensure portability. It is released for non-commercial use under a modified University of California BSD license. Homolmapper permits facile import of additional scoring schemes and can incorporate arbitrary additional amino acids to allow handling of residues such as selenocysteine or pyrrolysine. Homolmapper also provides tools for defining and analyzing subfamilies relative to a larger alignment, for mutual information analysis, and for rapidly visualizing the locations of mutations and multi-residue motifs.</p> <p>Conclusion</p> <p>Homolmapper is a useful tool for analysis of homology relationships among proteins in a structural context. There is also extensive, example-driven documentation available. More information about homolmapper is available at <url>http://www.mcb.ucdavis.edu/faculty-labs/lagarias/homolmapper_home/homolmapper%20web%20page.htm</url>.</p

Attenuated T Cell Responses to a High-Potency Ligand In Vivo

According to this study, the strongest T cell receptor ligands in vitro do not necessarily induce the strongest T cell responses in vivo, suggesting that vaccine designers may need to reconsider their strategies

Primed T Cell Responses to Chemokines Are Regulated by the Immunoglobulin-Like Molecule CD31

CD31, an immunoglobulin-like molecule expressed by leukocytes and endothelial cells, is thought to contribute to the physiological regulation T cell homeostasis due to the presence of two immunotyrosine-based inhibitory motifs in its cytoplasmic tail. Indeed, loss of CD31 expression leads to uncontrolled T cell-mediated inflammation in a variety of experimental models of disease and certain CD31 polymorphisms correlate with increased disease severity in human graft-versus-host disease and atherosclerosis. The molecular mechanisms underlying CD31-mediated regulation of T cell responses have not yet been clarified. We here show that CD31-mediated signals attenuate T cell chemokinesis both in vitro and in vivo. This effect selectively affects activated/memory T lymphocytes, in which CD31 is clustered on the cell membrane where it segregates to the leading edge. We provide evidence that this molecular segregation, which does not occur in naïve T lymphocytes, might lead to cis-CD31 engagement on the same membrane and subsequent interference with the chemokine-induced PI3K/Akt signalling pathway. We propose that CD31-mediated modulation of memory T cell chemokinesis is a key mechanism by which this molecule contributes to the homeostatic regulation of effector T cell immunity

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments