6 research outputs found

    Prophylactic methylprednisolone to reduce inflammation and improve outcomes from one lung ventilation in children: a randomized clinical trial.

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    BACKGROUND: One lung ventilation (OLV) results in inflammatory and mechanical injury, leading to intraoperative and postoperative complications in children. No interventions have been studied in children to minimize such injury. OBJECTIVE: We hypothesized that a single 2-mg·kg(-1) dose of methylprednisolone given 45-60 min prior to lung collapse would minimize injury from OLV and improve physiological stability. METHODS: Twenty-eight children scheduled to undergo OLV were randomly assigned to receive 2 mg·kg(-1) methylprednisolone (MP) or normal saline (placebo group) prior to OLV. Anesthetic management was standardized, and data were collected for physiological stability (bronchospasm, respiratory resistance, and compliance). Plasma was assayed for inflammatory markers related to lung injury at timed intervals related to administration of methylprednisolone. RESULTS: Three children in the placebo group experienced clinically significant intraoperative and postoperative respiratory complications. Respiratory resistance was lower (P = 0.04) in the methylprednisolone group. Pro-inflammatory cytokine IL-6 was lower (P = 0.01), and anti-inflammatory cytokine IL-10 was higher (P = 0.001) in the methylprednisolone group. Tryptase, measured before and after OLV, was lower (P = 0.03) in the methylprednisolone group while increased levels of tryptase were seen in placebo group after OLV (did not achieve significance). There were no side effects observed that could be attributed to methylprednisolone in this study. CONCLUSIONS: Methylprednisolone at 2 mg·kg(-1) given as a single dose prior to OLV provides physiological stability to children undergoing OLV. In addition, methylprednisolone results in lower pro-inflammatory markers and higher anti-inflammatory markers in the children\u27s plasma

    Inferring Parametric Energy Consumption Functions at Different Software Levels:ISA vs. LLVM IR

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    The static estimation of the energy consumed by program executions is an important challenge, which has applications in program optimization and verification, and is instrumental in energy-aware software development. Our objective is to estimate such energy consumption in the form of functions on the input data sizes of programs. We have developed a tool for experimentation with static analysis which infers such energy functions at two levels, the instruction set architecture (ISA) and the intermediate code (LLVM IR) levels, and re ects it upwards to the higher source code level. This required the development of a translation from LLVM IR to an intermediate representation and its integration with existing components, a translation from ISA to the same representation, a resource analyzer, an ISA-level energy model, and a mapping from this model to LLVM IR. The approach has been applied to programs written in the XC language running on XCore architectures, but is general enough to be applied to other languages. Experimental results show that our LLVM IR level analysis is reasonably accurate (less than 6:4% average error vs. hardware measurements) and more powerful than analysis at the ISA level. This paper provides insights into the trade-off of precision versus analyzability at these levels

    Additional file 5: of Genomic copy number variation association study in Caucasian patients with nonsyndromic cryptorchidism

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    CNV calls for Group 1 cases passed sample QC. Each column in Additional file 5 represents CNV location, SNPs numbers contained within the CNV, the length of the CNV, copy number (cn) of the CNV call, sample id, the starting marker identifier and the ending marker identifier in the CNV, and confidence score in PennCNV calling. (XLSX 653 kb

    Additional file 7: of Genomic copy number variation association study in Caucasian patients with nonsyndromic cryptorchidism

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    CNV calls for Group 2 cases passed sample QC. Each column in Additional file 7 represents CNV location, SNPs numbers contained within the CNV, the length of the CNV, copy number (cn) of the CNV call, sample id, the starting marker identifier and the ending marker identifier in the CNV, and confidence score in PennCNV calling. (XLSX 534 kb
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