CD55 Deficiency Protects against Atherosclerosis in ApoE-Deficient Mice via C3a Modulation of Lipid Metabolism

Atherosclerosis, the leading cause of death in the Western world, is driven by chronic inflammation within the artery wall. Elements of the complement cascade are implicated in the pathogenesis, because complement proteins and their activation products are found in the atherosclerotic plaque. We examined the role of CD55, a membrane inhibitor of the complement component 3 (C3) convertase, which converts C3 into C3a and C3b, in atherosclerosis. CD55-deficient (CD55−/−) mice were crossed onto the atherosclerosis-prone apolipoprotein E (apoE)-deficient (apoE−/−) background. High fat–fed male apoE−/−/CD55−/− mice were strongly protected from developing atherosclerosis compared with apoE−/− controls. Lipid profiling showed significantly lower levels of triglycerides, nonesterified fatty acids, and cholesterol in apoE−/−/CD55−/− mice than that in controls after high-fat feeding, whereas body fat in apoE−/−/CD55−/− mice content was increased. Plasma levels of C3 fell, whereas concentrations of C3adesArg (alias acylation stimulating protein; ASP), produced by serum carboxypeptidase N–mediated desargination of C3a, increased in nonfasted high fat–fed apoE−/−/CD55−/− mice, indicating complement activation. Thus, complement dysregulation in the absence of CD55 provoked increased C3adesArg production that, in turn, caused altered lipid handling, resulting in atheroprotection and increased adiposity. Interventions that target complement activation in adipose tissue should be explored as lipid-decreasing strategies

Anti-oncogenic and pro-differentiation effects of clorgyline, a monoamine oxidase A inhibitor, on high grade prostate cancer cells

<p>Abstract</p> <p>Background</p> <p>Monoamine oxidase A (MAO-A), a mitochondrial enzyme that degrades monoamines including neurotransmitters, is highly expressed in basal cells of the normal human prostatic epithelium and in poorly differentiated (Gleason grades 4 and 5), aggressive prostate cancer (PCa). Clorgyline, an MAO-A inhibitor, induces secretory differentiation of normal prostate cells. We examined the effects of clorgyline on the transcriptional program of epithelial cells cultured from high grade PCa (E-CA).</p> <p>Methods</p> <p>We systematically assessed gene expression changes induced by clorgyline in E-CA cells using high-density oligonucleotide microarrays. Genes differentially expressed in treated and control cells were identified by Significance Analysis of Microarrays. Expression of genes of interest was validated by quantitative real-time polymerase chain reaction.</p> <p>Results</p> <p>The expression of 156 genes was significantly increased by clorgyline at all time points over the time course of 6 – 96 hr identified by Significance Analysis of Microarrays (SAM). The list is enriched with genes repressed in 7 of 12 oncogenic pathway signatures compiled from the literature. In addition, genes downregulated ≥ 2-fold by clorgyline were significantly enriched with those upregulated by key oncogenes including beta-catenin and ERBB2, indicating an anti-oncogenic effect of clorgyline. Another striking effect of clorgyline was the induction of androgen receptor (AR) and classic AR target genes such as prostate-specific antigen together with other secretory epithelial cell-specific genes, suggesting that clorgyline promotes differentiation of cancer cells. Moreover, clorgyline downregulated EZH2, a critical component of the Polycomb Group (PcG) complex that represses the expression of differentiation-related genes. Indeed, many genes in the PcG repression signature that predicts PCa outcome were upregulated by clorgyline, suggesting that the differentiation-promoting effect of clorgyline may be mediated by its downregulation of EZH2.</p> <p>Conclusion</p> <p>Our results suggest that inhibitors of MAO-A, already in clinical use to treat depression, may have potential application as therapeutic PCa drugs by inhibiting oncogenic pathway activity and promoting differentiation.</p

Progress and pitfalls in underrepresented minority recruitment: perspectives from the medical schools.

PURPOSE: To assess current initiatives at U.S. medical schools to recruit underrepresented minorities (URM) and to identify perceived barriers to enrollment of URM students. METHODS: We developed a survey that was mailed to the dean of Student Affairs of all U.S. allopathic and osteopathic medical schools in 2002. Respondents were asked to list their schools' URM recruitment programs and rate the effectiveness of these programs. They were also asked to indicate barriers to URM recruitment from a list of 37 potential barriers and rate their overall success with URM recruitment. RESULTS: The study had a 59% response rate. All schools reported a wide variety of initiatives for URM recruitment with > or =50% of all schools using each of the 11 strategies. The three most commonly listed barriers to URM recruitment were MCAT scores of applicants (90%), lack of minority faculty (71%) and lack of minority role models (71%). Most schools rated their recruitment efforts highly; on a scale of 1 to 10 (10 being very successful), the average score was an 8. CONCLUSION: While schools continue to invest tremendous efforts in recruiting minority applicants, admissions criteria, lack of URM faculty and the need for external evaluation remain important barriers to achieving a diverse physician workforce

A Hybrid 3D Learning-and-Interaction-based Segmentation Approach Applied on CT Liver Volumes

Medical volume segmentation in various imaging modalities using real 3D approaches (in contrast to slice-by-slice segmentation) represents an actual trend. The increase in the acquisition resolution leads to large amount of data, requiring solutions to reduce the dimensionality of the segmentation problem. In this context, the real-time interaction with the large medical data volume represents another milestone. This paper addresses the twofold problem of the 3D segmentation applied to large data sets and also describes an intuitive neuro-fuzzy trained interaction method. We present a new hybrid semi-supervised 3D segmentation, for liver volumes obtained from computer tomography scans. This is a challenging medical volume segmentation task, due to the acquisition and inter-patient variability of the liver parenchyma. The proposed solution combines a learning-based segmentation stage (employing 3D discrete cosine transform and a probabilistic support vector machine classifier) with a post-processing stage (automatic and manual segmentation refinement). Optionally, an optimization of the segmentation can be achieved by level sets, using as initialization the segmentation provided by the learning-based solution. The supervised segmentation is applied on elementary cubes in which the CT volume is decomposed by tilling, thus ensuring a significant reduction of the data to be classified by the support vector machine into liver/not liver. On real volumes, the proposed approach provides good segmentation accuracy, with a significant reduction in the computational complexity