30 research outputs found

    Quantitative analysis of CT-perfusion parameters in the evaluation of brain gliomas and metastases

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    <p>Abstract</p> <p>Background</p> <p>The paper reports a quantitative analysis of the perfusion maps of 22 patients, affected by gliomas or by metastasis, with the aim of characterizing the malignant tissue with respect to the normal tissue. The gold standard was obtained by histological exam or nuclear medicine techniques. The perfusion scan provided 11 parametric maps, including Cerebral Blood Volume (CBV), Cerebral Blood Flow (CBF), Average Perfusion (P<sub>mean</sub>) and Permeability-surface area product (PS).</p> <p>Methods</p> <p>The perfusion scans were performed after the injection of 40 ml of non-ionic contrast agent, at an injection rate of 8 ml/s, and a 40 s cine scan with 1 s interval was acquired. An expert radiologist outlined the region of interest (ROI) on the unenhanced CT scan, by using a home-made routine. The mean values with their standard deviations inside the outlined ROIs and the contralateral ROIs were calculated on each map. Statistical analyses were used to investigate significant differences between diseased and normal regions. Receiving Operating Characteristic (ROC) curves were also generated.</p> <p>Results</p> <p>Tumors are characterized by higher values of all the perfusion parameters, but after the statistical analysis, only the <it>PS</it>, <it>Pat</it><sub><it>Rsq </it></sub>(Patlak Rsquare) and <it>T</it><sub><it>peak </it></sub>(Time to Peak) resulted significant. ROC curves, confirmed both <it>Pat</it><sub><it>Rsq </it></sub>and <it>PS </it>as equally reliable metrics for discriminating between malignant and normal tissues, with areas under curves (AUCs) of 0.82 and 0.81, respectively.</p> <p>Conclusion</p> <p>CT perfusion is a useful and non invasive technique for evaluating brain neoplasms. Malignant and normal tissues can be accurately differentiated using perfusion map, with the aim of performing tumor diagnosis and grading, and follow-up analysis.</p

    Analysis of organics in 643-G groundwaters by GC/MS

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    Twenty-three of the 63 monitoring wells in the 643-G burial ground consistently contain measurable (> 1 ppm) amounts of total organic carbon, TOC. Of these 23 wells, 10 that contain elevated (2--400 ppm) TOC were chosen for in-depth analysis of semivolatile organics by gas chromatography/mass spectrometry, GC/MS. A well located near the site of previous decontamination operations was also chosen for analysis. About 40% of the organic compounds detected in these well waters have been identified. Many of these compounds are indicative of liquid scintillation wastes, spent solvent wastes, and solvent degradation products. Four priority pollutants were present at low levels. Some of the organics identified are probably degradation products from humic substances. Organic compounds of unknown origin are also present. No strong chelators capable of increasing radionuclide mobility have been identified. Preliminary dialysis work indicates that up to 30--40% of the TOC may be present as nonvolatile humic substances that cannot be analyzed by GC/MS

    Modeling mechanisms of persisting and resolving delay in language development

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    - Purpose: This study employed neural network modeling to investigate the possible mechanistic basis of developmental language delay and test the viability of the hypotheses that persisting delay (PD) and resolving delay (RD) lie on a mechanistic continuum with normal development. - Method: A population modeling approach was used to study individual rates of development in 1000 simulated individuals acquiring a notional language domain (here represented by English past tense). Variation was caused by differences in internal neurocomputational learning parameters, as well as the richness of the language environment. An early language delay group was diagnosed and individual trajectories then traced. - Results: Quantitative variations in learning mechanisms were sufficient to produce PD and RD subgroups in similar proportions to empirical observations. In the model, persistent language delay was caused by limitations in processing capacity, while resolving delay was caused by low plasticity. Richness of the language environment did not predict the emergence of PD, but did predict the final ability levels of individuals with RD. - Conclusion: Mechanistically, it is viable that PD and RD are only quantitatively different. There may be an interaction between environmental factors and outcome groups, with individuals with RD influenced more by the richness of the language environment
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