Breast cancer is a fatal disease causing high mortality in women. Constant efforts are being made for creating more efficient techniques for early and accurate diagnosis. Classical methods require oncologists to examine the breast lesions for detection and classification of various stages of cancer. Such manual attempts are time consuming and inefficient in many cases. Hence, there is a need for efficient methods that diagnoses the cancerous cells without human involvement with high accuracies. In this research, image processing techniques were used to develop imaging biomarkers through mammography analysis and based on artificial intelligence technology aiming to detect breast cancer in early stages to support diagnosis and prioritization of high-risk patients. For automatic classification of breast cancer on mammograms, a generalized regression artificial neural network was trained and tested to separate malignant and benign tumors reaching an accuracy of 95.83%. With the biomarker and trained neural net, a computer-aided diagnosis system is being designed. The results obtained show that generalized regression artificial neural network is a promising and robust system for breast cancer detection. The Laboratorio de Innovacion y Desarrollo Tecnologico en Inteligencia Artificial is seeking collaboration with research groups interested in validating the technology being developed.
Part of the book: Advanced Applications for Artificial Neural Networks
Inadequate metabolic control predisposes diabetic patient to a series of complications on account of diabetes mellitus (DM). Among the most common complications of DM is neuropathy, which causes microvascular damage by hyperglycemia in the lower extremities which arrives characterized by a delayed closing. The global prevalence of diabetic neuropathy (DN) was 66% of people with diabetes in 2015, representing the principal cause of total or partial lower extremities amputation, with 22.6% of the patients with DN. Matrix metalloproteinases (MMPs) are involved in healing. The function that these mainly play is the degradation during inflammation that has as consequence the elimination of the extracellular matrix (ECM), the disintegration of the capillary membrane to give way to angiogenesis and cellular migration for the remodeling of damaged tissue. The imbalance in MMPs may increase the chronicity of a wound, what leads to chronic foot ulcers and amputation. This chapter focuses on the role of MMPs in diabetic wound healing.
Part of the book: The Role of Matrix Metalloproteinase in Human Body Pathologies
Autoimmunity is a condition in which the host organizes an immune response against its own antigens. Rheumatoid arthritis (RA) is an autoimmune disease of unknown etiology, characterized by the presence of chronic inflammatory infiltrates, the development of destructive arthropathy, bone erosion, and degradation of the articular cartilage and subchondral bone. There is currently no treatment that resolves the disease, only the use of palliatives, and not all patients respond to pharmacologic therapy. According to RA multifactorial origin, several in vivo models have been used to evaluate its pathophysiology as well as to identify the usefulness of biomarkers to predict, to diagnose, or to evaluate the prognosis of the disease. This chapter focuses on the most common in vivo models used for the study of RA, including those related with genetic, immunological, hormonal, and environmental interactions. Similarly, the potential of these models to understand RA pathogenesis and to test preventive and therapeutic strategies of autoimmune disorder is also highlighted. In conclusion, of all the animal models discussed, the CIA model could be considered the most successful by generating arthritis using type II collagen and adjuvants and evaluating therapeutic compounds both intra-articularly and systemically.
Part of the book: Experimental Animal Models of Human Diseases