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An amperometric biosensor using a substrate recycling principle was realized for the detection of low adrenaline concentrations (1 nM) by measurements in phosphate buffer and Ringer’s solution at pH 6.5 and pH 7.4, respectively. In proof-of-concept experiments, a Boolean logic-gate principle has been applied to develop a digital adrenaline biosensor based on an enzyme AND logic gate. The obtained results demonstrate that the developed digital biosensor is capable for a rapid qualitative determination of the presence/absence of adrenaline in a YES/NO statement. Such digital biosensor could be used in clinical diagnostics for the control of a correct insertion of a catheter in the adrenal veins during adrenal venous-sampling procedure.
Human-induced pluripotent stem cell-derived cardiomyocytes (hiPS-CM) today are widely used for the investigation of normal electromechanical cardiac function, of cardiac medication and of mutations. Computational models are thus established that simulate the behavior of this kind of cells. This section first motivates the modeling of hiPS-CM and then presents and discusses several modeling approaches of microscopic and macroscopic constituents of human-induced pluripotent stem cell-derived and mature human cardiac tissue. The focus is led on the mapping of the computational results one can achieve with these models onto mature human cardiomyocyte models, the latter being the real matter of interest. Model adaptivity is the key feature that is discussed because it opens the way for modeling various biological effects like biological variability, medication, mutation and phenotypical expression. We compare the computational with experimental results with respect to normal cardiac function and with respect to inotropic and chronotropic drug effects. The section closes with a discussion on the status quo of the specificity of computational models and on what challenges have to be solved to reach patient-specificity.
Fields of asymmetric tensors play an important role in many applications such as medical imaging (diffusion tensor magnetic resonance imaging), physics, and civil engineering (for example Cauchy-Green-deformation tensor, strain tensor with local rotations, etc.). However, such asymmetric tensors are usually symmetrized and then further processed. Using this procedure results in a loss of information. A new method for the processing of asymmetric tensor fields is proposed restricting our attention to tensors of second-order given by a 2x2 array or matrix with real entries. This is achieved by a transformation resulting in Hermitian matrices that have an eigendecomposition similar to symmetric matrices. With this new idea numerical results for real-world data arising from a deformation of an object by external forces are given. It is shown that the asymmetric part indeed contains valuable information.