257 research outputs found

    Analysis of clinical and dosimetric factors associated with severe acute radiation pneumonitis in patients with locally advanced non-small cell lung cancer treated with concurrent chemotherapy and intensity-modulated radiotherapy

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    <p>Abstract</p> <p>Background</p> <p>To evaluate the association between the clinical, dosimetric factors and severe acute radiation pneumonitis (SARP) in patients with locally advanced non-small cell lung cancer (LANSCLC) treated with concurrent chemotherapy and intensity-modulated radiotherapy (IMRT).</p> <p>Methods</p> <p>We analyzed 94 LANSCLC patients treated with concurrent chemotherapy and IMRT between May 2005 and September 2006. SARP was defined as greater than or equal 3 side effects and graded according to Common Terminology Criteria for Adverse Events (CTCAE) version 3.0.</p> <p>The clinical and dosimetric factors were analyzed. Univariate and multivariate logistic regression analyses were performed to evaluate the relationship between clinical, dosimetric factors and SARP.</p> <p>Results</p> <p>Median follow-up was 10.5 months (range 6.5-24). Of 94 patients, 11 (11.7%) developed SARP. Univariate analyses showed that the normal tissue complication probability (NTCP), mean lung dose (MLD), relative volumes of lung receiving more than a threshold dose of 5-60 Gy at increments of 5 Gy (V5-V60), chronic obstructive pulmonary disease (COPD) and Forced Expiratory Volume in the first second (FEV1) were associated with SARP (<it>p </it>< 0.05). In multivariate analysis, NTCP value (<it>p </it>= 0.001) and V10 (<it>p </it>= 0.015) were the most significant factors associated with SARP. The incidences of SARP in the group with NTCP > 4.2% and NTCP ≤4.2% were 43.5% and 1.4%, respectively (<it>p </it>< 0.01). The incidences of SARP in the group with V10 ≤50% and V10 >50% were 5.7% and 29.2%, respectively (<it>p </it>< 0.01).</p> <p>Conclusions</p> <p>NTCP value and V10 are the useful indicators for predicting SARP in NSCLC patients treated with concurrent chemotherapy and IMRT.</p

    Conditional random pattern model for copy number aberration detection

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    <p>Abstract</p> <p>Background</p> <p>DNA copy number aberration (CNA) is very important in the pathogenesis of tumors and other diseases. For example, CNAs may result in suppression of anti-oncogenes and activation of oncogenes, which would cause certain types of cancers. High density single nucleotide polymorphism (SNP) array data is widely used for the CNA detection. However, it is nontrivial to detect the CNA automatically because the signals obtained from high density SNP arrays often have low signal-to-noise ratio (SNR), which might be caused by whole genome amplification, mixtures of normal and tumor cells, experimental noise or other technical limitations. With the reduction in SNR, many false CNA regions are often detected and the true CNA regions are missed. Thus, more sophisticated statistical models are needed to make the CNAs detection, using the low SNR signals, more robust and reliable.</p> <p>Results</p> <p>This paper presents a conditional random pattern (CRP) model for CNA detection where much contextual cues are explored to suppress the noise and improve CNA detection accuracy. Both simulated and the real data are used to evaluate the proposed model, and the validation results show that the CRP model is more robust and reliable in the presence of noise for CNA detection using high density SNP array data, compared to a number of widely used software packages.</p> <p>Conclusions</p> <p>The proposed conditional random pattern (CRP) model could effectively detect the CNA regions in the presence of noise.</p

    Assessing the Role of Inward Foreign Direct Investment in Chinese Economic Development, 1990-2007: Towards a Synthesis of Alternative Views

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    In the context of globalization, China has been widely regarded as the most successful country in the world in utilizing inward foreign direct investment (FDI) for economic development. The mainstream of the literature has produced a wide range of studies that are largely within the theoretical framework of neoclassical economics, and they tend to conclude that FDI has contributed significantly to Chinese economic development – through capital formation, export expansion, technology transfer, and the transformation of the economic structures and institutions. The objective of this paper is to assess the role of FDI in Chinese economic development with reference to the broader theoretical literature on FDI and late development, which encompasses structuralism and radical political economy along with neoclassical economics. From the perspectives of the broader literature, the analyses of the paper find that FDI in China has indeed promoted economic development in one respect (improving allocative efficiency), but has also had unfavourable effect in another respect (worsening productive efficiency), resulting in an overall impact that tends to be on the negative side. The mainstream story of China is thus judged to be partial, and the lessons to be drawn from the experience are arguably far more complex than have been hitherto perceived

    High content image analysis for human H4 neuroglioma cells exposed to CuO nanoparticles

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    <p>Abstract</p> <p>Background</p> <p>High content screening (HCS)-based image analysis is becoming an important and widely used research tool. Capitalizing this technology, ample cellular information can be extracted from the high content cellular images. In this study, an automated, reliable and quantitative cellular image analysis system developed in house has been employed to quantify the toxic responses of human H4 neuroglioma cells exposed to metal oxide nanoparticles. This system has been proved to be an essential tool in our study.</p> <p>Results</p> <p>The cellular images of H4 neuroglioma cells exposed to different concentrations of CuO nanoparticles were sampled using IN Cell Analyzer 1000. A fully automated cellular image analysis system has been developed to perform the image analysis for cell viability. A multiple adaptive thresholding method was used to classify the pixels of the nuclei image into three classes: bright nuclei, dark nuclei, and background. During the development of our image analysis methodology, we have achieved the followings: (1) The Gaussian filtering with proper scale has been applied to the cellular images for generation of a local intensity maximum inside each nucleus; (2) a novel local intensity maxima detection method based on the gradient vector field has been established; and (3) a statistical model based splitting method was proposed to overcome the under segmentation problem. Computational results indicate that 95.9% nuclei can be detected and segmented correctly by the proposed image analysis system.</p> <p>Conclusion</p> <p>The proposed automated image analysis system can effectively segment the images of human H4 neuroglioma cells exposed to CuO nanoparticles. The computational results confirmed our biological finding that human H4 neuroglioma cells had a dose-dependent toxic response to the insult of CuO nanoparticles.</p