95 research outputs found

    Micromechanical finite element modelling of thermo-mechanical fatigue for P91 steels

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    In this paper, the cyclic plasticity and fatigue crack initiation behaviour of a tempered martensite ferritic steel under thermo-mechanical fatigue conditions is examined by means of micromechanical finite element modelling. The crystal plasticity-based model explicitly reflects the microstructure of the material, measured by electronic backscatter diffraction. The predicted cyclic thermo-mechanical response agrees well with experiments under both in-phase and out-of-phase conditions. A thermo-mechanical fatigue indicator parameter, with stress triaxiality and temperature taken into account, is developed to predict fatigue crack initiation. In the fatigue crack initiation simulation, the out-of-phase thermo-mechanical response is identified to be more dangerous than in-phase response, which is consistent with experimental failure data. It is shown that the behaviour of thermo-mechanical fatigue can be effectively predicted at the microstructural level and this can lead to a more accurate assessment procedure for power plant components

    Genome-wide expression assay comparison across frozen and fixed postmortem brain tissue samples

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    <p>Abstract</p> <p>Background</p> <p>Gene expression assays have been shown to yield high quality genome-wide data from partially degraded RNA samples. However, these methods have not yet been applied to postmortem human brain tissue, despite their potential to overcome poor RNA quality and other technical limitations inherent in many assays. We compared cDNA-mediated annealing, selection, and ligation (DASL)- and <it>in vitro </it>transcription (IVT)-based genome-wide expression profiling assays on RNA samples from artificially degraded reference pools, frozen brain tissue, and formalin-fixed brain tissue.</p> <p>Results</p> <p>The DASL-based platform produced expression results of greater reliability than the IVT-based platform in artificially degraded reference brain RNA and RNA from frozen tissue-based samples. Although data associated with a small sample of formalin-fixed RNA samples were poor when obtained from both assays, the DASL-based platform exhibited greater reliability in a subset of probes and samples.</p> <p>Conclusions</p> <p>Our results suggest that the DASL-based gene expression-profiling platform may confer some advantages on mRNA assays of the brain over traditional IVT-based methods. We ultimately consider the implications of these results on investigations of neuropsychiatric disorders.</p

    Clinicopathologic and gene expression parameters predict liver cancer prognosis

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    <p>Abstract</p> <p>Background</p> <p>The prognosis of hepatocellular carcinoma (HCC) varies following surgical resection and the large variation remains largely unexplained. Studies have revealed the ability of clinicopathologic parameters and gene expression to predict HCC prognosis. However, there has been little systematic effort to compare the performance of these two types of predictors or combine them in a comprehensive model.</p> <p>Methods</p> <p>Tumor and adjacent non-tumor liver tissues were collected from 272 ethnic Chinese HCC patients who received curative surgery. We combined clinicopathologic parameters and gene expression data (from both tissue types) in predicting HCC prognosis. Cross-validation and independent studies were employed to assess prediction.</p> <p>Results</p> <p>HCC prognosis was significantly associated with six clinicopathologic parameters, which can partition the patients into good- and poor-prognosis groups. Within each group, gene expression data further divide patients into distinct prognostic subgroups. Our predictive genes significantly overlap with previously published gene sets predictive of prognosis. Moreover, the predictive genes were enriched for genes that underwent normal-to-tumor gene network transformation. Previously documented liver eSNPs underlying the HCC predictive gene signatures were enriched for SNPs that associated with HCC prognosis, providing support that these genes are involved in key processes of tumorigenesis.</p> <p>Conclusion</p> <p>When applied individually, clinicopathologic parameters and gene expression offered similar predictive power for HCC prognosis. In contrast, a combination of the two types of data dramatically improved the power to predict HCC prognosis. Our results also provided a framework for understanding the impact of gene expression on the processes of tumorigenesis and clinical outcome.</p

    Generation of a non-small cell lung cancer transcriptome microarray

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    <p>Abstract</p> <p>Background</p> <p>Non-small cell lung cancer (NSCLC) is the leading cause of cancer mortality worldwide. At present no reliable biomarkers are available to guide the management of this condition. Microarray technology may allow appropriate biomarkers to be identified but present platforms are lacking disease focus and are thus likely to miss potentially vital information contained in patient tissue samples.</p> <p>Methods</p> <p>A combination of large-scale in-house sequencing, gene expression profiling and public sequence and gene expression data mining were used to characterise the transcriptome of NSCLC and the data used to generate a disease-focused microarray – the Lung Cancer DSA research tool.</p> <p>Results</p> <p>Built on the Affymetrix GeneChip platform, the Lung Cancer DSA research tool allows for interrogation of ~60,000 transcripts relevant to Lung Cancer, tens of thousands of which are unavailable on leading commercial microarrays.</p> <p>Conclusion</p> <p>We have developed the first high-density disease specific transcriptome microarray. We present the array design process and the results of experiments carried out to demonstrate the array's utility. This approach serves as a template for the development of other disease transcriptome microarrays, including non-neoplastic diseases.</p

    Expression analysis of genes associated with human osteosarcoma tumors shows correlation of RUNX2 overexpression with poor response to chemotherapy

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    Background: Human osteosarcoma is the most common pediatric bone tumor. There is limited understanding of the molecular mechanisms underlying osteosarcoma oncogenesis, and a lack of good diagnostic as well as prognostic clinical markers for this disease. Recent discoveries have highlighted a potential role of a number of genes including: RECQL4, DOCK5, SPP1, RUNX2, RB1, CDKN1A, P53, IBSP, LSAMP, MYC, TNFRSF1B, BMP2, HISTH2BE, FOS, CCNB1, and CDC5L. Methods: Our objective was to assess relative expression levels of these 16 genes as potential biomarkers of osteosarcoma oncogenesis and chemotherapy response in human tumors. We performed quantitative expression analysis in a panel of 22 human osteosarcoma tumors with differential response to chemotherapy, and 5 normal human osteoblasts.Results: RECQL4, SPP1, RUNX2, and IBSP were significantly overexpressed, and DOCK5, CDKN1A, RB1, P53, and LSAMP showed significant loss of expression relative to normal osteoblasts. In addition to being overexpressed in osteosarcoma tumor samples relative to normal osteoblasts, RUNX2 was the only gene of the 16 to show significant overexpression in tumors that had a poor response to chemotherapy relative to good responders. Conclusion: These data underscore the loss of tumor suppressive pathways and activation of specific oncogenic mechanisms associated with osteosarcoma oncogenesis, while drawing attention to the role of RUNX2 expression as a potential biomarker of chemotherapy failure in osteosarcoma. © 2010 Sadikovic et al; licensee BioMed Central Ltd

    One life ends, another begins: Management of a brain-dead pregnant mother - A systematic review -

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    Background: An accident or a catastrophic disease may occasionally lead to brain death (BD) during pregnancy. Management of brain-dead pregnant patients needs to follow special strategies to support the mother in a way that she can deliver a viable and healthy child and, whenever possible, also be an organ donor. This review discusses the management of brain-dead mothers and gives an overview of recommendations concerning the organ supporting therapy. Methods: To obtain information on brain-dead pregnant women, we performed a systematic review of Medline, EMBASE and the Cochrane Central Register of Controlled Trials (CENTRAL). The collected data included the age of the mother, the cause of brain death, maternal medical complications, gestational age at BD, duration of extended life support, gestational age at delivery, indication of delivery, neonatal outcome, organ donation of the mothers and patient and graft outcome. Results: In our search of the literature, we found 30 cases reported between1982 and 2010. A nontraumatic brain injury was the cause of BD in 26 of 30 mothers. The maternal mean age at the time of BD was 26.5 years. The mean gestational age at the time of BD and the mean gestational age at delivery were 22 and 29.5 weeks, respectively. Twelve viable infants were born and survived the neonatal period. Conclusion: The management of a brain-dead pregnant woman requires a multidisciplinary team which should follow available standards, guidelines and recommendations both for a nontraumatic therapy of the fetus and for an organ-preserving treatment of the potential donor
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