Diagnosis of pericardial cysts using diffusion weighted magnetic resonance imaging: A case series

<p>Abstract</p> <p>Introduction</p> <p>Congenital pericardial cysts are benign lesions that arise from the pericardium during embryonic development. The diagnosis is based on typical imaging features, but atypical locations and signal magnetic resonance imaging sequences make it difficult to exclude other lesions. Diffusion-weighted magnetic resonance imaging is a novel method that can be used to differentiate tissues based on their restriction to proton diffusion. Its use in differentiating pericardial cysts from other pericardial lesions has not yet been described.</p> <p>Case presentation</p> <p>We present three cases (a 51-year-old Caucasian woman, a 66-year-old Caucasian woman and a 77-year-old Caucasian woman) with pericardial cysts evaluated with diffusion-weighted imaging using cardiac magnetic resonance imaging. Each lesion demonstrated a high apparent diffusion coefficient similar to that of free water.</p> <p>Conclusion</p> <p>This case series is the first attempt to investigate the utility of diffusion-weighted magnetic resonance imaging in the assessment of pericardial cysts. Diffusion-weighted imaging may be a useful noninvasive diagnostic tool for pericardial cysts when conventional imaging findings are inconclusive.</p

Circular pattern matching with k mismatches

The k-mismatch problem consists in computing the Hamming distance between a pattern P of length m and every length-m substring of a text T of length n, if this distance is no more than k. In many real-world applications, any cyclic shift of P is a relevant pattern, and thus one is interested in computing the minimal distance of every length-m substring of T and any cyclic shift of P. This is the circular pattern m

Novel n-3 Docosapentaneoic Acid-Derived Pro-resolving Mediators Are Vasculoprotective and Mediate the Actions of Statins in Controlling Inflammation

“This is a post-peer-review, pre-copyedit version of a chapter published in Advances in Experimental Medicine and Biology book series (AEMB, volume 1161). The final publication is available athttps://doi.org/10.1007/978-3-030-21735-8_7

Gene-Centric Characteristics of Genome-Wide Association Studies

BACKGROUND: The high-throughput genotyping chips have contributed greatly to genome-wide association (GWA) studies to identify novel disease susceptibility single nucleotide polymorphisms (SNPs). The high-density chips are designed using two different SNP selection approaches, the direct gene-centric approach, and the indirect quasi-random SNPs or linkage disequilibrium (LD)-based tagSNPs approaches. Although all these approaches can provide high genome coverage and ascertain variants in genes, it is not clear to which extent these approaches could capture the common genic variants. It is also important to characterize and compare the differences between these approaches. METHODOLOGY/PRINCIPAL FINDINGS: In our study, by using both the Phase II HapMap data and the disease variants extracted from OMIM, a gene-centric evaluation was first performed to evaluate the ability of the approaches in capturing the disease variants in Caucasian population. Then the distribution patterns of SNPs were also characterized in genic regions, evolutionarily conserved introns and nongenic regions, ontologies and pathways. The results show that, no mater which SNP selection approach is used, the current high-density SNP chips provide very high coverage in genic regions and can capture most of known common disease variants under HapMap frame. The results also show that the differences between the direct and the indirect approaches are relatively small. Both have similar SNP distribution patterns in these gene-centric characteristics. CONCLUSIONS/SIGNIFICANCE: This study suggests that the indirect approaches not only have the advantage of high coverage but also are useful for studies focusing on various functional SNPs either in genes or in the conserved regions that the direct approach supports. The study and the annotation of characteristics will be helpful for designing and analyzing GWA studies that aim to identify genetic risk factors involved in common diseases, especially variants in genes and conserved regions