Bovine-serum-albumin-containing receptor phase better predicts transdermal absorption parameters for lipophilic compounds

Based on the hypothesis that limited receptor solubility of lipophilic compounds may result in lower observed permeability parameters, the aim of this study was to determine the in vitro human epidermal permeability coefficients and membrane retention of a series of aliphatic alcohols (C1-C10, log p -0.72 to 4.06) using two different receptor solutions (water and 4% bovine serum albumin in phosphate-buffered saline). Aqueous solutions of radiolabeled alcohols were dosed into the stratum corneum side of membranes mounted in side-by-side glass diffusion cells. Appearance of alcohol in the receptor compartment filled with either of the two solutions was monitored over a 7 h period when both stratum corneum (assessed by tape stripping) and the remaining epidermis levels of radioactivity were determined. In a separate study the degree of binding of alcohols to 4% bovine serum albumin was determined. The data showed increased receptor phase solubility in the bovine serum albumin solution and higher permeability coefficients for the more lipophilic alcohols in the series. No changes were seen in the partitioning of the alcohols from the vehicle into either the stratum corneum or tape-stripped epidermis with the two receptor phases; however, a decrease in the amount of the more lipophilic alcohols partitioning into the water receptor phase from the tape-stripped epidermis was observed. We conclude that bovine serum albumin receptor phase allows better estimation of real permeability parameters for lipophilic compounds due to its increased solubility capacity and we question whether permeability parameters for lipophilic solutes from older data sets based on aqueous receptor phases are completely reliable.

On the induced electric field gradients in the human body for magnetic stimulation by gradient coils in MRI

Prior theoretical studies indicate that the negative spatial derivative of the electric field induced by magnetic stimulation may he one of the main factors contributing to depolarization of the nerve fiber. This paper studies this parameter for peripheral nerve stimulation (PNS) induced by time.-varying gradient fields during MRI scans. The numerical calculations are based on an efficient, quasi-static, finite-difference scheme and an anatomically realistic human, full-body model. Whole-body cylindrical and planar gradient sets in MRI systems and various input signals have been explored. The spatial distributions of the induced electric field and their gradients are calculated and attempts are made to correlate these areas with reported experimental stimulation data. The induced electrical field pattern is similar for both the planar coils and cylindrical coils. This study provides some insight into the spatial characteristics of the induced field gradients for PNS in MRI, which may be used to further evaluate the sites where magnetic stimulation is likely to occur and to optimize gradient coil design.

Influence of magnetically-induced E-fields on cardiac electric activity during MRI: A modeling study

In modern magnetic resonance imaging (MRI), patients are exposed to strong, time-varying gradient magnetic fields that may be able to induce electric fields (E-fields)/currents in tissues approaching the level of physiological significance. In this work we present theoretical investigations into induced E-fields in the thorax, and evaluate their potential influence on cardiac electric activity under the assumption that the sites of maximum E-field correspond to the myocardial stimulation threshold (an abnormal circumstance). Whole-body cylindrical and planar gradient coils were included in the model. The calculations of the induced fields are based on an efficient, quasi-static, finite-difference scheme and an anatomically realistic, whole-body model. The potential for cardiac stimulation was evaluated using an electrical model of the heart. Twelve-lead electrocardiogram (ECG) signals were simulated and inspected for arrhythmias caused by the applied fields for both healthy and diseased hearts. The simulations show that the shape of the thorax and the conductive paths significantly influence induced E-fields. In healthy patients, these fields are not sufficient to elicit serious arrhythmias with the use of contemporary gradient sets. However, raising the strength and number of repeated switching episodes of gradients, as is certainly possible in local chest gradient sets, could expose patients to increased risk. For patients with cardiac disease, the risk factors are elevated. By the use of this model, the sensitivity of cardiac pathologies, such as abnormal conductive pathways, to the induced fields generated by an MRI sequence can be investigated. (C) 2003 Wiley-Liss, Inc.

Methodologies for calibration and predictive analysis of a watershed model

The use of a fitted parameter watershed model to address water quantity and quality management issues requires that it be calibrated under a wide range of hydrologic conditions. However, rarely does model calibration result in a unique parameter set. Parameter nonuniqueness can lead to predictive nonuniqueness. The extent of model predictive uncertainty should be investigated if management decisions are to be based on model projections. Using models built for four neighboring watersheds in the Neuse River Basin of North Carolina, the application of the automated parameter optimization software PEST in conjunction with the Hydrologic Simulation Program Fortran (HSPF) is demonstrated. Parameter nonuniqueness is illustrated, and a method is presented for calculating many different sets of parameters, all of which acceptably calibrate a watershed model. A regularization methodology is discussed in which models for similar watersheds can be calibrated simultaneously. Using this method, parameter differences between watershed models can be minimized while maintaining fit between model outputs and field observations. In recognition of the fact that parameter nonuniqueness and predictive uncertainty are inherent to the modeling process, PEST's nonlinear predictive analysis functionality is then used to explore the extent of model predictive uncertainty.