Use of plasma Renin activity to monitor mineralocorticoid treatment in dogs with primary hypoadrenocorticism: desoxycorticosterone versus fludrocortisone.

BACKGROUND: Measurement of plasma renin activity (PRA) is the gold standard for monitoring mineralocorticoid treatment in humans with primary hypoadrenocorticism (PH). OBJECTIVES: To compare PRA in dogs with newly diagnosed PH, dogs with diseases mimicking PH, and healthy dogs, and evaluate measurement of PRA to monitor therapeutic effects in dogs with PH treated with different mineralocorticoids. ANIMALS: Eleven dogs with newly diagnosed PH (group 1), 10 dogs with diseases mimicking PH (group 2), 21 healthy dogs (group 3), 17 dogs with treated PH (group 4). METHODS: In group 1, PRA was measured before treatment and at different times after initiating treatment. In groups 2 and 3, PRA was measured at initial presentation only. In group 4, no baseline PRA was obtained but PRA was measured once or every 1-6 months during treatment. Mineralocorticoid treatment consisted of fludrocortisone acetate (FC) or desoxycorticosterone pivalate (DOCP). RESULTS: Plasma renin activity before treatment was increased in dogs with PH compared to normal dogs and dogs with diseases mimicking PH with median activity of 27, 0.8, and 1.0 ng/mL/h, respectively. In dogs with PH, PRA decreased and normalized with mineralocorticoid treatment using DOCP but not with FC. In dogs treated with DOCP, PRA was lower than in dogs treated with FC. Plasma sodium concentrations were higher and potassium concentrations were lower with DOCP treatment compared to FC treatment. CONCLUSION AND CLINICAL IMPORTANCE: Plasma renin activity is a reliable tool for monitoring mineralocorticoid treatment. DOCP treatment more effectively suppresses PRA compared to FC in dogs with PH

Cell shape analysis of random tessellations based on Minkowski tensors

To which degree are shape indices of individual cells of a tessellation characteristic for the stochastic process that generates them? Within the context of stochastic geometry and the physics of disordered materials, this corresponds to the question of relationships between different stochastic models. In the context of image analysis of synthetic and biological materials, this question is central to the problem of inferring information about formation processes from spatial measurements of resulting random structures. We address this question by a theory-based simulation study of shape indices derived from Minkowski tensors for a variety of tessellation models. We focus on the relationship between two indices: an isoperimetric ratio of the empirical averages of cell volume and area and the cell elongation quantified by eigenvalue ratios of interfacial Minkowski tensors. Simulation data for these quantities, as well as for distributions thereof and for correlations of cell shape and volume, are presented for Voronoi mosaics of the Poisson point process, determinantal and permanental point processes, and Gibbs hard-core and random sequential absorption processes as well as for Laguerre tessellations of polydisperse spheres and STIT- and Poisson hyperplane tessellations. These data are complemented by mechanically stable crystalline sphere and disordered ellipsoid packings and area-minimising foam models. We find that shape indices of individual cells are not sufficient to unambiguously identify the generating process even amongst this limited set of processes. However, we identify significant differences of the shape indices between many of these tessellation models. Given a realization of a tessellation, these shape indices can narrow the choice of possible generating processes, providing a powerful tool which can be further strengthened by density-resolved volume-shape correlations.Comment: Chapter of the forthcoming book "Tensor Valuations and their Applications in Stochastic Geometry and Imaging" in Lecture Notes in Mathematics edited by Markus Kiderlen and Eva B. Vedel Jense

Cell shape analysis of random tessellations based on Minkowski tensors

To which degree are shape indices of individual cells of a tessellation characteristic for the stochastic process that generates them? Within the context of stochastic geometry and the physics of disordered materials, this corresponds to the question of relationships between different stochastic processes and models. In the context of applied image analysis of structured synthetic and biological materials, this question is central to the problem of inferring information about the formation process from spatial measurements of the resulting random structure. This chapter addresses this question by a theory-based simulation study of cell shape indices derived from tensor-valued intrinsic volumes, or Minkowski tensors, for a variety of common tessellation models. We focus on the relationship between two indices: (1) the dimensionless ratio 〈V 〉2∕〈A〉3 of empirical average cell volumes to areas, and (2) the degree of cell elongation quantified by the eigenvalue ratio 〈β10,2〉 of the interface Minkowski tensors W10,2. Simulation data for these quantities, as well as for distributions thereof and for correlations of cell shape and cell volume, are presented for Voronoi mosaics of the Poisson point process, determinantal and permanental point processes, Gibbs hard-core processes of spheres, and random sequential absorption processes as well as for Laguerre tessellations of configurations of polydisperse spheres, STIT-tessellations, and Poisson hyperplane tessellations. These data are complemented by experimental 3D image data of mechanically stable ellipsoid configurations, area-minimising liquid foam models, and mechanically stable crystalline sphere configurations. We find that, not surprisingly, the indices 〈V 〉2∕〈A〉3 and 〈β10,2〉 are not sufficient to unambiguously identify the generating process even amongst this limited set of processes. However, we identify significant differences of these shape indices between many of the tessellation models listed above. Therefore, given a realization of a tessellation (e.g., an experimental image), these shape indices are able to narrow the choice of possible generating processes, providing a powerful tool which can be further strengthened by considering density-resolved volume-shape correlations