Reviews
| Rev Diabet Stud,
2010,
7(4):252-262 |
DOI 10.1900/RDS.2010.7.252 |
Computational Intelligence in Early Diabetes Diagnosis: A Review
Shankaracharya1, Devang Odedra1, Subir Samanta2, Ambarish S. Vidyarthi1
1Department of Biotechnology, Birla Institute of Technology, Mesra, Ranchi 835215, India
2Department of Pharmaceutical Sciences, Birla Institute of Technology, Mesra, Ranchi 835215, India
Address correspondence to: Shankaracharya, e-mail: shankaracharya@bitmesra.ac.in
Abstract
The development of an effective diabetes diagnosis system by taking advantage of computational intelligence is regarded as a primary goal nowadays. Many approaches based on artificial network and machine learning algorithms have been developed and tested against diabetes datasets, which were mostly related to individuals of Pima Indian origin. Yet, despite high accuracies of up to 99% in predicting the correct diabetes diagnosis, none of these approaches have reached clinical application so far. One reason for this failure may be that diabetologists or clinical investigators are sparsely informed about, or trained in the use of, computational diagnosis tools. Therefore, this article aims at sketching out an outline of the wide range of options, recent developments, and potentials in machine learning algorithms as diabetes diagnosis tools. One focus is on supervised and unsupervised methods, which have made significant impacts in the detection and diagnosis of diabetes at primary and advanced stages. Particular attention is paid to algorithms that show promise in improving diabetes diagnosis. A key advance has been the development of a more in-depth understanding and theoretical analysis of critical issues related to algorithmic construction and learning theory. These include trade-offs for maximizing generalization performance, use of physically realistic constraints, and incorporation of prior knowledge and uncertainty. The review presents and explains the most accurate algorithms, and discusses advantages and pitfalls of methodologies. This should provide a good resource for researchers from all backgrounds interested in computational intelligence-based diabetes diagnosis methods, and allows them to extend their knowledge into this kind of research.
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| Rev Diabet Stud,
2010,
7(4):263-274 |
DOI 10.1900/RDS.2010.7.263 |
Zinc and Zinc Transporter Regulation in Pancreatic Islets and the Potential Role of Zinc in Islet Transplantation
Mariea D. Bosco1, Daisy M. Mohanasundaram1, Chris J. Drogemuller1, Carol J. Lang2,3, Peter D. Zalewski2,3, P. Toby Coates1,2,4
1Central Northern Adelaide Renal and Transplantation Service, Renal and Transplantation Immunology Lab, Royal Adelaide Hospital, Adelaide, South Australia 5000
2School of Medicine, University of Adelaide, South Australia
3Chronic Inflammatory Disease Research Group, The Queen Elizabeth Hospital, South Australia
4Centre for Stem Cell Research, University of Adelaide, South Australia
Address correspondence to: P. Toby Coates, e-mail: toby.coates@health.sa.gov.au
Abstract
The critical trace element zinc is essential for normal insulin production, and plays a central role in cellular protection against apoptosis and oxidative stress. The regulation of zinc within the pancreas and β-cells is controlled by the zinc transporter families ZnT and ZIP. Pancreatic islets display wide variability in the occurrence of these molecules. The zinc transporter, ZnT8 is an important target for autoimmunity in type 1 diabetes. Gene polymorphisms of this transporter confer sensitivity for immunosuppressive drugs used in islet transplantation. Understanding the biology of zinc transport within pancreatic islets will provide insight into the mechanisms of β-cell death, and may well reveal new pathways for improvement of diabetes therapy, including islet transplantation. This review discusses the possible roles of zinc in β-cell physiology with a special focus on islet transplantation.
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