Development of an LC-MS/MS method for the quantitation of 55 compounds prescribed in combined cardiovascular therapy

This paper reports an LC-MS/MS method with positive electrospray ionization for the screening of commonly prescribed cardiovascular drugs in human plasma, including compounds with antihypertensive (57), antidiabetic (12), hypolipemiant (5), anticoagulant (2) and platelet anti-aggregation (2) effects. Sample treatment consisted of a simple protein precipitation with MeOH/0.1 M ZnSO₄ (4:1, v/v) solution after the addition of internal standard, followed by evaporation and reconstitution. Analytes separation was performed on a Polar-RP column (150 m x 2 mm, 4 μm) using a gradient elution of 15 min. The MS system was operated in MRM mode, monitoring one quantitation and one confirmation transition for each analyte. The recovery of the protein precipitation step ranged from 50 to 70% for most of the compounds, while some were considerably affected by matrix effects. Since several analytes fulfilled the linearity, accuracy and precision values required by the ICH guidelines, the method proved to be suitable for their quantitative analysis. The limits of quantitation varied from 0.38 to 9.1 μg/L and the limits of detection from 0.12 to 5.34 μg/L. The method showed to be suitable for the detection of plasma samples of patients under cardiovascular treatment with the studied drugs, and for 55 compounds reliable quantitative results could be obtained.

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.