This book offers comprehensive information on all aspects of ELISA, starting with the fundamentals of the immune system. It also reviews the history of analytical assays prior to the advent of ELISA (enzyme-linked immunosorbent assay) and addresses the materials of choice for the fabrication of the platforms, possible biomolecular interactions, different protocols, and evaluation parameters. The book guides readers through the respective steps of the analytical assay, while also familiarizing them with the possible sources of error in the assay. It offers detailed insights into the immobilization techniques used for protein attachment, as well as methods for evaluating the assay and calculating the key parameters, such as sensitivity, specificity, accuracy and limit of detection. In addition, the book explores the advantages and shortcomings of the conventional ELISA, as well as various approaches to improving its performance. In this regard, merging and integrating other technologies with widely known ELISAs have opened new avenues for the advancement of this immunoassay. Accordingly, the book provides cutting-edge information on integrated platforms such as ELISpot, plasmonic ELISAs, sphere-/bead-based ELISAs, paper-/fiber-based ELISAs and ELISA in micro-devices.
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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