Clay Minerals and Clay Mineral Water Dispersions — Properties and Applications

This chapter deals with the properties and applications of clay mineral water dispersions and clay minerals as flame retardant additives for polymers. Clay minerals, such as kaolinites, micas, and smectites, are the basic constituents of clay raw materials, which are classically employed in the ceramic industry to produce porcelain, fine ceramics, coarse ceramics, cements, electro-ceramics, tiles and refractories. These products are mainly used in sectors of economic importance, such as agriculture, civil engineering, and environment. A direct method to prepare clay mineral polymer composites is through dispersion in water. Water dispersions of clay exhibit some interesting flow phenomena such as yield stress; i.e., the material behaves as a solid until a critical force applied on the material forces it to flow. Water dispersions of clay have also been reported to be used to prepare materials with enhanced flame-retardant properties such as leather. On the other hand, direct melt compounding of clay mineral with different polymers as the composite matrix (HIPS, PP, and HDPE) to prepare a number of polymer composites with flame-retardant properties has also been reported

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

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

Generalized Regression Neural Networks with Application in Neutron Spectrometry

The aim of this research was to apply a generalized regression neural network (GRNN) to predict neutron spectrum using the rates count coming from a Bonner spheres system as the only piece of information. In the training and testing stages, a data set of 251 different types of neutron spectra, taken from the International Atomic Energy Agency compilation, were used. Fifty-one predicted spectra were analyzed at testing stage. Training and testing of GRNN were carried out in the MATLAB environment by means of a scientific and technological tool designed based on GRNN technology, which is capable of solving the neutron spectrometry problem with high performance and generalization capability. This computational tool automates the pre-processing of information, the training and testing stages, the statistical analysis, and the post-processing of the information. In this work, the performance of feed-forward backpropagation neural networks (FFBPNN) and GRNN was compared in the solution of the neutron spectrometry problem. From the results obtained, it can be observed that despite very similar results, GRNN performs better than FFBPNN because the former could be used as an alternative procedure in neutron spectrum unfolding methodologies with high performance and accuracy

Assessment of extrusion-sonication process on flame retardant polypropylene by rheological characterization

In this work, the rheological behavior of flame retardant polypropylene composites produced by two methods: 1) twin-screw extrusion and 2) ultrasound application combined with a static mixer die single-screw extrusion is analyzed in detail; results are related to the morphology of the composites. The flame retardant polymer composites are composed of a polypropylene matrix, an intumescent flame retardant system and functionalized clay. Scanning electron microscopy revealed that the combination of the static mixer die and on-line sonication reduced particle size and improved the dispersion and distribution of the intumescent additives in the polypropylene matrix at the micrometric level. From linear viscoelastic properties, the Han, Cole-Cole and van Gurp-Palmen diagrams characterized the improved particle dispersion of the flame retardant additives. Two well-defined rheological behaviors were observed in these diagrams. These behaviors are independent on clay presence and concentration. In fact, the ultrasound device generates a 3D highly interconnected structure similar to a co-continuous pattern observed in polymer blends as evidenced by rheological measurements. This improvement in the dispersion and distribution of the additives is attributed to the combined effect of the static mixer die and on-line sonication that allowed reducing the additive content while achieving the optimum classification UL94-V0

Global economic burden of unmet surgical need for appendicitis

Background There is a substantial gap in provision of adequate surgical care in many low- and middle-income countries. This study aimed to identify the economic burden of unmet surgical need for the common condition of appendicitis. Methods Data on the incidence of appendicitis from 170 countries and two different approaches were used to estimate numbers of patients who do not receive surgery: as a fixed proportion of the total unmet surgical need per country (approach 1); and based on country income status (approach 2). Indirect costs with current levels of access and local quality, and those if quality were at the standards of high-income countries, were estimated. A human capital approach was applied, focusing on the economic burden resulting from premature death and absenteeism. Results Excess mortality was 4185 per 100 000 cases of appendicitis using approach 1 and 3448 per 100 000 using approach 2. The economic burden of continuing current levels of access and local quality was US $92 492 million using approach 1 and$73 141 million using approach 2. The economic burden of not providing surgical care to the standards of high-income countries was $95 004 million using approach 1 and$75 666 million using approach 2. The largest share of these costs resulted from premature death (97.7 per cent) and lack of access (97.0 per cent) in contrast to lack of quality. Conclusion For a comparatively non-complex emergency condition such as appendicitis, increasing access to care should be prioritized. Although improving quality of care should not be neglected, increasing provision of care at current standards could reduce societal costs substantially