Breast Cancer Detection by Means of Artificial Neural Networks

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

A neutron spectrum unfolding code based on generalized regression artificial neural networks

The most delicate part of neutron spectrometry, is the unfolding process. The derivation of the spectral information is not simple because the unknown is not given directly as a result of the measurements. Novel methods based on Artificial Neural Networks have been widely investigated. In prior works, back propagation neural networks (BPNN) have been used to solve the neutron spectrometry problem, however, some drawbacks still exist using this kind of neural nets, i.e. the optimum selection of the network topology and the long training time. Compared to BPNN, it's usually much faster to train a generalized regression neural network (GRNN). That's mainly because spread constant is the only parameter used in GRNN. Another feature is that the network will converge to a global minimum, provided that the optimal values of spread has been determined and that the dataset adequately represents the problem space. In addition, GRNN are often more accurate than BPNN in the prediction. These characteristics make GRNNs to be of great interest in the neutron spectrometry domain. This work presents a computational tool based on GRNN capable to solve the neutron spectrometry problem. This computational code, automates the pre-processing, training and testing stages using a k-fold cross validation of 3 folds, the statistical analysis and the post-processing of the information, using 7 Bonner spheres rate counts as only entrance data. The code was designed for a Bonner Spheres System based on a LiI(Eu) neutron detector and a response matrix expressed in 60 energy bins taken from an International Atomic Energy Agency compilation

Animal Models of Rheumatoid Arthritis

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

Differences in <i>Cx</i>. <i>quinquefasciatus</i> larvae exposed to different insecticides relative to unexposed.

<p>Differences in <i>Cx</i>. <i>quinquefasciatus</i> larvae exposed to different insecticides relative to unexposed.</p

Olfactomedin-like 2 A and B (OLFML2A and OLFML2B) expression profile in primates (human and baboon)

BACKGROUND: The olfactomedin-like domain (OLFML) is present in at least four families of proteins, including OLFML2A and OLFML2B, which are expressed in adult rat retina cells. However, no expression of their orthologous has ever been reported in human and baboon. OBJECTIVE: The aim of this study was to investigate the expression of OLFML2A and OLFML2B in ocular tissues of baboons (Papio hamadryas) and humans, as a key to elucidate OLFML function in eye physiology. METHODS: OLFML2A and OLFML2B cDNA detection in ocular tissues of these species was performed by RT-PCR. The amplicons were cloned and sequenced, phylogenetically analyzed and their proteins products were confirmed by immunofluorescence assays. RESULTS: OLFML2A and OLFML2B transcripts were found in human cornea, lens and retina and in baboon cornea, lens, iris and retina. The baboon OLFML2A and OLFML2B ORF sequences have 96% similarity with their human’s orthologous. OLFML2A and OLFML2B evolution fits the hypothesis of purifying selection. Phylogenetic analysis shows clear orthology in OLFML2A genes, while OLFML2B orthology is not clear. CONCLUSIONS: Expression of OLFML2A and OLFML2B in human and baboon ocular tissues, including their high similarity, make the baboon a powerful model to deduce the physiological and/or metabolic function of these proteins in the eye

Mean and observed measurements of identified metabolites.

<p>Arg, C0, and C2 from left to right. Each dot corresponds to a sample. Crossed circle represent the mean for each treatment group. The vertical axis shows the measurements. The horizontal axis represents samples and treatment groups.</p

Heat map representing concentration of metabolites analyzed.

<p>Columns to the left show the p-value showing statistical significance in red. Insecticides (chlorpyrifos, permethrin and temephos) and the control group (larvae unexposed) are shown in first columns. Acronyms on the right are the metabolites. In the heat map, blue, white, and red, indicates low, median and high concentration, respectively.</p