Patient Characteristics as Predictors of Image Quality and Diagnostic Accuracy of MDCT Compared With Conventional Coronary Angiography for Detecting Coronary Artery Stenoses: CORE-64 Multicenter International Trial

OBJECTIVE. The purpose of the study was to investigate patient characteristics associated with image quality and their impact on the diagnostic accuracy of MDCT for the detection of coronary artery stenosis. MATERIALS AND METHODS. Two hundred ninety-one patients with a coronary artery calcification (CAC) score of <= 600 Agatston units (214 men and 77 women; mean age, 59.3 +/- 10.0 years [SD]) were analyzed. An overall image quality score was derived using an ordinal scale. The accuracy of quantitative MDCT to detect significant (>= 50%) stenoses was assessed using quantitative coronary angiography (QCA) per patient and per vessel using a modified 19-segment model. The effect of CAC, obesity, heart rate, and heart rate variability on image quality and accuracy were evaluated by multiple logistic regression. Image quality and accuracy were further analyzed in subgroups of significant predictor variables. Diagnostic analysis was determined for image quality strata using receiver operating characteristic (ROC) curves. RESULTS. Increasing body mass index (BMI) (odds ratio [OR] = 0.89, p < 0.001), increasing heart rate (OR = 0.90, p < 0.001), and the presence of breathing artifact (OR = 4.97, p = 0.001) were associated with poorer image quality whereas sex, CAC score, and heart rate variability were not. Compared with examinations of white patients, studies of black patients had significantly poorer image quality (OR = 0.58, p = 0.04). At a vessel level, CAC score (10 Agatston units) (OR = 1.03, p = 0.012) and patient age (OR = 1.02, p = 0.04) were significantly associated with the diagnostic accuracy of quantitative MDCT compared with QCA. A trend was observed in differences in the areas under the ROC curves across image quality strata at the vessel level (p = 0.08). CONCLUSION. Image quality is significantly associated with patient ethnicity, BMI, mean scan heart rate, and the presence of breathing artifact but not with CAC score at a patient level. At a vessel level, CAC score and age were associated with reduced diagnostic accuracy.

In silico analysis of microRNAS targeting the HLA-G 3 ` untranslated region alleles and haplotypes

It has been reported that microRNAs (miRNA) may have allele-specific targeting for the 3` untranslated region (3` UTR) of the HLA-G locus. In a previous study, we reported 11 3`UTR haplotypes encompassing the 14-bp insertion/deletion polymorphism and seven SNPs (+3003 T/C, +3010 C/G, +3027 C/A, +3035 C/T, +3142 C/G, +3187A/G,and +3196 C/G), of which only the +3142 C/G SNP has been reported to influence the binding of miRNAs. Using bioinformatics analyses, we identified putative miRNA-binding sites considering the haplotypes encompassing these eight polymorphic sites, and we ranked the lowest free energies that could potentially lead to an mRNA degradation or translational repression. When a specific haplotype or a particular SNP was associated with a miRNA-binding site, we defined a free energy difference of 4 kcal/mol between alleles to classify them energetically distant. The best results were obtained for the miR-513a-5p, miR-518c*, miR-1262 and miR-92a-1*, miR-92a-2*, miR-661, miR-1224-5p, and miR-433 miRNAs, all influencing one or more of the +3003, +3010, +3027, and +3035 SNPs. The miR-2110, miR-93, miR-508-5p, miR-331-5p, miR-616, miR-513b, and miR-589* miRNAs targeted the 14-bp fragment region, and miR-148a, miR-19a*, miR-152, mir-148b,and miR-218-2 also influenced the +3142C/G polymorphism. These results suggest that these miRNAs might play a relevant role on the HLA-G expression pattern. (C) 2009 Published by Elsevier Inc. on behalf of American Society for Histocompatibility and Immunogenetics.

Supporting content-based image retrieval and computer-aided diagnosis systems with association rule-based techniques

In this work, we take advantage of association rule mining to support two types of medical systems: the Content-based Image Retrieval (CBIR) systems and the Computer-Aided Diagnosis (CAD) systems. For content-based retrieval, association rules are employed to reduce the dimensionality of the feature vectors that represent the images and to improve the precision of the similarity queries. We refer to the association rule-based method to improve CBIR systems proposed here as Feature selection through Association Rules (FAR). To improve CAD systems, we propose the Image Diagnosis Enhancement through Association rules (IDEA) method. Association rules are employed to suggest a second opinion to the radiologist or a preliminary diagnosis of a new image. A second opinion automatically obtained can either accelerate the process of diagnosing or to strengthen a hypothesis, increasing the probability of a prescribed treatment be successful. Two new algorithms are proposed to support the IDEA method: to pre-process low-level features and to propose a preliminary diagnosis based on association rules. We performed several experiments to validate the proposed methods. The results indicate that association rules can be successfully applied to improve CBIR and CAD systems, empowering the arsenal of techniques to support medical image analysis in medical systems. (C) 2009 Elsevier B.V. All rights reserved.

An association rule-based method to support medical image diagnosis with efficiency

In this paper, we propose a method based on association rule-mining to enhance the diagnosis of medical images (mammograms). It combines low-level features automatically extracted from images and high-level knowledge from specialists to search for patterns. Our method analyzes medical images and automatically generates suggestions of diagnoses employing mining of association rules. The suggestions of diagnosis are used to accelerate the image analysis performed by specialists as well as to provide them an alternative to work on. The proposed method uses two new algorithms, PreSAGe and HiCARe. The PreSAGe algorithm combines, in a single step, feature selection and discretization, and reduces the mining complexity. Experiments performed on PreSAGe show that this algorithm is highly suitable to perform feature selection and discretization in medical images. HiCARe is a new associative classifier. The HiCARe algorithm has an important property that makes it unique: it assigns multiple keywords per image to suggest a diagnosis with high values of accuracy. Our method was applied to real datasets, and the results show high sensitivity (up to 95%) and accuracy (up to 92%), allowing us to claim that the use of association rules is a powerful means to assist in the diagnosing task.

Targeting the Acute Myeloid Leukemia Stem Cells

The idea that within the bulk of leukemic cells there are immature progenitors which are intrinsically resistant to chemotherapy and able to repopulate the tumor after treatment is not recent. Nevertheless, the term leukemia stem cells (LSCs) has been adopted recently to describe these immature progenitors based on the fact that they share the most relevant features of the normal hematopoetic stem cells (HSCs), i.e. the self-renewal potential and quiescent status. LSCs differ from their normal counterparts and from the more differentiated leukemic cells regarding the default status of pathways regulating apoptosis, cell cycle, telomere maintenance and transport pumps activity. In addition, unique features regarding the interaction of these cells with the microenvironment have been characterized. Therapeutic strategies targeting these unique features are at different stages of development but the reported results are promising. The aim of this review is, by taking acute myeloid leukemia (AML) as a bona fide example, to discuss some of the mechanisms used by the LSCs to survive and the strategies which could be used to eradicate these cells.

Targeting endothelin ET(A) and ET(B) receptors inhibits antigen-induced neutrophil migration and mechanical hypernociception in mice

Endothelin may contribute to the development of inflammatory events such as leukocyte recruitment and nociception. Herein, we investigated whether endothelin-mediated mechanical hypernociception (decreased nociceptive threshold, evaluated by electronic pressure-meter) and neutrophil migration (myeloperoxidase activity) are inter-dependent in antigen challenge-induced Th1-driven hind-paw inflammation. In antigen challenge-induced inflammation, endothelin (ET) ET(A) and ET(B) receptor antagonism inhibited both hypernociception and neutrophil migration. Interestingly, ET-1 peptide-induced hypernociception was not altered by inhibiting neutrophil migration or endothelin ET(B) receptor antagonism, but rather by endothelin ET(A) receptor antagonism. Furthermore, endothelin ET(A), but not ET(B), receptor antagonism inhibited antigen-induced PGE(2) production, whereas either selective or combined blockade of endothelin ET(A) and/or ET(B) receptors reduced hypernociception and neutrophil recruitment caused by antigen challenge. Concluding, this study advances knowledge into the role for endothelin in inflammatory mechanisms and further supports the potential of endothelin receptor antagonists in controlling inflammation.

Effect of image analysis software on neurofunctional activation during processing of emotional human faces

Functional brain imaging techniques such as functional MRI (fMRI) that allow the in vivo investigation of the human brain have been exponentially employed to address the neurophysiological substrates of emotional processing. Despite the growing number of fMRI studies in the field, when taken separately these individual imaging studies demonstrate contrasting findings and variable pictures, and are unable to definitively characterize the neural networks underlying each specific emotional condition. Different imaging packages, as well as the statistical approaches for image processing and analysis, probably have a detrimental role by increasing the heterogeneity of findings. In particular, it is unclear to what extent the observed neurofunctional response of the brain cortex during emotional processing depends on the fMRI package used in the analysis. In this pilot study, we performed a double analysis of an fMRI dataset using emotional faces. The Statistical Parametric Mapping (SPM) version 2.6 (Wellcome Department of Cognitive Neurology, London, UK) and the XBAM 3.4 (Brain Imaging Analysis Unit, Institute of Psychiatry, Kings College London, UK) programs, which use parametric and non-parametric analysis, respectively, were used to assess our results. Both packages revealed that processing of emotional faces was associated with an increased activation in the brain`s visual areas (occipital, fusiform and lingual gyri), in the cerebellum, in the parietal cortex, in the cingulate cortex (anterior and posterior cingulate), and in the dorsolateral and ventrolateral prefrontal cortex. However, blood oxygenation level-dependent (BOLD) response in the temporal regions, insula and putamen was evident in the XBAM analysis but not in the SPM analysis. Overall, SPM and XBAM analyses revealed comparable whole-group brain responses. Further Studies are needed to explore the between-group compatibility of the different imaging packages in other cognitive and emotional processing domains. (C) 2009 Elsevier Ltd. All rights reserved.