Type 2 diabetes is contemporarily a major social and epidemiological problem and among others is a strong risk factor for cardiovascular diseases. Pentraxin 3 a potential early biomarker of the atherrosclerosis, is an acute phase reactant produced by the peripheral tissues where the inflammation takes place. to assess PTX3 plasma levels in metabolic syndrome patients compared to control subjects and their potential associations with anthropometric and clinical components of the syndrome as well as with carotid artery intima-media thickness (cIMT), a marker of subclinical atherosclerosis. This study was conducted on 58 type 2 diabetic patients(34 men and 24 women ) as well as 30 age and sex matched non diabetic control subjects (12 men and 18 women ) the following tests were done FBS , lipid profile (Total serum cholesterol, triglyceride level and HDL cholesterol level). The LDL cholesterol level was estimated .In addition, we performed basic biochemical blood analysis (ALT,AST,CBC and TSH), Hemoglobin A1c and Plasma PTX3. Mean plasma PTX3concentration turned out to be significantly higher in patients with history of myocardial infarction,CABG and PCI (p=0.01) . PTX3 was associated with outcomes in acute MI patients. PTX3 was associated with fatal events, and adverse clinical outcomes in heart failure patients.
With the widespread of multicore systems, automatic parallelization becomes more pronounced, particularly for legacy programs, where the source code is not generally available. An essential operation in any parallelization system is detecting data dependence among parallelization candidate instructions. Conducting dependence analysis at the binary-level is more challenging than that at the source-level due to the much lower semantics of the binary code. In this paper, we consider using the elaborate 'static' analysis of abstract interpretation, for the first time, at runtime for data dependence detection. Specifically, our system interprets instructions at a hot region, while at the same time, collect programs semantics for seen program points, thereby conducting abstract interpretation analysis dynamically. The analysis is guaranteed to be correct as long as execution does not exit the region prematurely. Moreover, successive hot region re-entries will resume previous analysis, albeit much faster in case no major change in the program semantics. Such approach provides for more rigorous analysis than other simple dynamic analysis which would typically miss parallelization opportunities. The proposed approach also does not require any hardware support, availability of the source code, as well as any code re-compilation. To study the performance and accuracy of our approach, we have extended the Padrone dynamic code modification framework, and conduct an initial study on a set of PolyBench kernels and selected programs from SPEC CPU. Experimental results show accurate dependence detection with low overhead.
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