This book offers a substantial examination of how contemporary authors deal with the complex legacies of authoritarian regimes in various Spanish-speaking countries. It does so by focusing on works that explore an under-studied aspect: the reliance of authoritarian power on medical notions for political purposes. From the Porfirian regime in Mexico to Castro’s Cuba, this book describes how such regimes have sought to seize medical knowledge to support propagandistic ideas and marginalize their opponents in ways that transcend specific pathologies, political ideologies, and geographical and temporal boundaries. Medicine, Power, and the Authoritarian Regime in Hispanic Literature brings together the work of literary scholars, cultural critics, and historians of medicine, arguing that contemporary authors have actively challenged authoritarian narratives of medicine and disease. In doing so, they continue to re-examine the place of these regimes in the collective memory of Latin America and Spain.
Neurocysticercosis (neural infection by larvae of Taenia solium) occurs when humans become intermediate hosts of the tapeworm Taenia solium after ingesting its eggs. The disease is now the most common helminthic infection of the nervous system in humans, and its prevalence has risen significantly even in countries where it was formerly considered exotic. The introduction of modern neuroimaging and serologic techniques has improved the diagnosis of neurocysticercosis; furthermore, the development of potent cysticidal drugs has changed the prognosis of most affected patients. Nevertheless, much remains to be learned about this parasitic disease. This book provides a comprehensive and up-to-date review of the various aspects of cysticercosis of the nervous system that will be of interest to all who are involved in the care of patients with this disease. Epidemiology, neuropathology, immunopathogenesis, clinical manifestations, diagnosis, and management are all thoroughly discussed based on current evidence and practice.
Bio-Inspired Strategies for Modeling and Detection in Diabetes Mellitus Treatment focuses on bio-inspired techniques such as modelling to generate control algorithms for the treatment of diabetes mellitus. The book addresses the identification of diabetes mellitus using a high-order recurrent neural network trained by the extended Kalman filter. The authors also describe the use of metaheuristic algorithms for the parametric identification of compartmental models of diabetes mellitus widely used in research works such as the Sorensen model and the Dallaman model. In addition, the book addresses the modelling of time series for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia using deep neural networks. The detection of diabetes mellitus in early stages or when current diagnostic techniques cannot detect glucose intolerance or prediabetes is proposed, carried out by means of deep neural networks in force in the literature. Readers will find leading-edge research in diabetes identification based on discrete high-order neural networks trained with the extended Kalman filter; parametric identification of compartmental models used to describe diabetes mellitus; modelling of data obtained by continuous glucose monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia; and screening for glucose intolerance using glucose tolerance test data and deep neural networks. Application of the proposed approaches is illustrated via simulation and real-time implementations for modelling, prediction, and classification. Addresses the online identification of diabetes mellitus using a high-order recurrent neural network trained online by an extended Kalman filter. Covers parametric identification of compartmental models used to describe diabetes mellitus. Provides modeling of data obtained by continuous glucose-monitoring sensors for the prediction of risk scenarios such as hyperglycaemia and hypoglycaemia.
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