Quantitative pharmacokinetic analysis of DCE-MRI data without an arterial input function: a reference region model

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can assess tumor perfusion, microvascular vessel wall permeability and extravascular-extracellular volume fraction. Analysis of DCE-MRI data is usually based on indicator dilution theory that requires knowledge of the concentration of the contrast agent in the blood plasma, the arterial input function (AIF). A method is presented that compares the tissues of interest (TOI) curve shape to that of a reference region (RR), thereby eliminating the need for direct AIF measurement. By assigning literature values for Ktrans (the blood perfusion-vessel permeability product) and v(e) (extravascular-extracellular volume fraction) in a reference tissue, it is possible to extract the Ktrans and v(e) values for a TOI without knowledge of the AIF. The operational RR equation for DCE-MRI analysis is derived, and its sensitivity to noise and incorrect assignment of the RR parameters is tested via simulations. The method is robust at noise levels of 10%, returning accurate (+/-20% in the worst case) and precise (+/-15% in the worst case) values. Errors in the TOI Ktrans and v(e) values scale approximately linearly with the errors in the assigned RR Ktrans and v(e) values. The methodology is then applied to a Lewis Lung Carcinoma mouse tumor model. A slowly enhancing TOI yielded Ktrans=0.039+/-0.002 min-1 and v(e)=0.46+/-0.01, while a rapidly enhancing region yielded Ktrans=0.35+/-0.05 min-1 and v(e)=0.31+/-0.01. Parametric Ktrans and v(e) mappings manifested a tumor periphery with elevated Ktrans (>0.30 min-1) and v(e) (>0.30) values. The main advantage of the RR approach is that it allows for quantitative assessment of tissue properties without having to obtain high temporal resolution images to characterize an AIF. This allows for acquiring images with higher spatial resolution and/or SNR, and therefore, increased ability to probe tissue heterogeneity.     

Aufbewahrung der Schwefelwasserstoffl?sungen

Ohne Zusammenfassung     

Investigating the relationship between cell cycle stage and diosgenin-induced megakaryocytic differentiation of HEL cells using sedimentation field-flow fractionation

Differentiation therapy could be one strategy for stopping cancer cell proliferation. A plant steroid, diosgenin, is known to induce megakaryocytic differentiation in human erythroleukemia (HEL) cells. In recent studies, the use of sedimentation field-flow fractionation (SdFFF) allowed the preparation of subpopulations that may differ in regard to sensitivity to differentiation induction. The specific goal of this study was to determine the relationship between cell cycle stage and sensitivity to megakaryocytic differentiation induction of HEL cells. After first confirming the capacity of diosgenin to specifically select targets, hyperlayer SdFFF cell sorting was used to prepare fractions according to cell cycle position from crude HEL cells. The sensitivities of these fractions to diosgenin-induced differentiation were then tested. The coupling of SdFFF cell separation to imaging flow cytometry showed that G1-phase cells were more sensitive to differentiation induction than S/G2M-phase cells, confirming the relationship between cell status at the start of induction, the extent of the biological event, and the potential of SdFFF in cancer research.     

The Scheduling of Continuous Flow Prodcution: A Separable Programming Approach

Zero-one integer programming formulations have been described in the literature to solve a wide range of problems in areas as diverse as capital budgeting, allocation, machine sequencing, etc., but as yet large-scale realistic problems can be very expensive to compute using the standard branch-and-bound extensions to linear programming packages. Many authors, noting that there are many situations in which low-cost approximate solutions may be very acceptable, suggest the use of approximating methods, such as the corresponding linear programme or "effective gradients". This paper describes a complex production scheduling problem for which a near-optimal solution is obtained using a separable programming algorithm.By utilizing the special features of separable programming, the model is able to include considerations of setup times in the optimization. Computational experience suggests that the method is a considerable improvement on heuristic attempts, giving improvements in throughput of approximately 30 per cent.     

Atomic XAFS as a tool to probe the electronic properties of supported noble metal nanoclusters

Atomic XAFS is a very attractive technique for probing electronic properties of supported metal nanoclusters. For platinum nanoparticles on different supports, the technique is found to be in good agreement with infrared CO adsorption measurements. The advantages of AXAFS, however, are that no probe molecule is required and that real-time measurements under reaction conditions are possible.