Phyto-oestrogens and breast cancer chemoprevention

Phytoestrogens are polyphenol compounds of plant origin that exhibit a structural similarity to the mammalian steroid hormone 17β-oestradiol. In Asian nations the staple consumption of phyto-oestrogen-rich foodstuffs correlates with a reduced incidence of breast cancer. Human dietary intervention trials have noted a direct relationship between phyto-oestrogen ingestion and a favourable hormonal profile associated with decreased breast cancer risk. However, these studies failed to ascertain the precise effect of dietary phyto-oestrogens on the proliferation of mammary tissue. Epidemiological and rodent studies crucially suggest that breast cancer chemoprevention by dietary phyto-oestrogen compounds is dependent on ingestion before puberty, when the mammary gland is relatively immature. Phyto-oestrogen supplements are commercially marketed for use by postmenopausal women as natural and safe alternatives to hormone replacement therapy. Of current concern is the effect of phyto-oestrogen compounds on the growth of pre-existing breast tumours. Data are contradictory, with cell culture studies reporting both the oestrogenic stimulation of oestrogen receptor-positive breast cancer cell lines and the antagonism of tamoxifen activity at physiological phyto-oestrogen concentrations. Conversely, phyto-oestrogen ingestion by rodents is associated with the development of less aggressive breast tumours with reduced metastatic potential. Despite the present ambiguity, current data do suggest a potential benefit from use of phyto-oestrogens in breast cancer chemoprevention and therapy. These aspects are discussed

Non-Steroidal Anti-Inflammatory Drugs and Cognitive Function: Are Prostaglandins at the Heart of Cognitive Impairment in Dementia and Delirium ?

Studies of non-steroidal anti-inflammatory drugs (NSAIDs) in rheumatoid arthritis imply that inflammation is important in the development of Alzheimer’s disease (AD). However, these drugs have not alleviated the symptoms of AD in those who have already developed dementia. This suggests that the primary mediator targeted by these drugs, PGE2, is not actively suppressing memory function in AD. Amyloid-β oligomers appear to be important for the mild cognitive changes seen in AD transgenic mice, yet amyloid immunotherapy has also proven unsuccessful in clinical trials. Collectively, these findings indicate that NSAIDs may target a prodromal process in mice that has already passed in those diagnosed with AD, and that synaptic and neuronal loss are key determinants of cognitive dysfunction in AD. While the role of inflammation has not yet become clear, inflammatory processes definitely have a negative impact on cognitive function during episodes of delirium during dementia. Delirium is an acute and profound impairment of cognitive function frequently occurring in aged and demented patients exposed to systemic inflammatory insults, which is now recognised to contribute to long-term cognitive decline. Recent work in animal models is beginning to shed light on the interactions between systemic inflammation and CNS pathology in these acute exacerbations of dementia. This review will assess the role of prostaglandin synthesis in the memory impairments observed in dementia and delirium and will examine the relative contribution of amyloid, synaptic and neuronal loss. We will also discuss how understanding the role of inflammatory mediators in delirious episodes will have major implications for ameliorating the rate of decline in the demented population

Adaptive Tree Approximation with Finite Element Functions: A First Look

We provide an introduction to adaptive tree approximation with finite element functions over meshes that are generated by bisection. This approximation technique can be seen as a benchmark for adaptive finite element methods, but may be also used therein for the approximation of data and coarsening. Correspondingly, we focus on approximation problems related to adaptive finite element methods, the design and performance of algorithms, and the resulting convergence rates, together with the involved regularity. For simplicity and clarity, these issues are presented and discussed in detail in the univariate case. The additional technicalities and difficulties of the multivariate case are briefly outlined

Discrete Distortion for 3D Data Analysis

We investigate a morphological approach to the analysis and understanding of three-dimensional scalar fields, and we consider applications to 3D medical and molecular images as examples.We consider a discrete model of the scalar field obtained by discretizing its 3D domain into a tetrahedral mesh. In particular, our meshes correspond to approximations at uniform or variable resolution extracted from a multi-resolution model of the 3D scalar field, that we call a hierarchy of diamonds. We analyze the images based on the concept of discrete distortion, that we have introduced in [26], and on segmentations based on Morse theory. Discrete distortion is defined by considering the graph of the discrete 3D field, which is a tetrahedral hypersurface in R 4, and measuring the distortion of the transformation which maps the tetrahedral mesh discretizing the scalar field domain into the mesh representing its graph in R 4. We describe a segmentation algorithm to produce Morse decompositions of a 3D scalar field which uses a watershed approach and we apply it to 3D images by using as scalar field both intensity and discrete distortion. We present experimental results by considering the influence of resolution on distortion computation. In particular, we show that the salient features of the distortion field appear prominently in lower resolution approximations to the dataset

Primer of adaptive finite element methods

Adaptive finite element methods (AFEM) are a fundamental numerical instrument in science and engineering to approximate partial differential equations. In the 1980s and 1990s a great deal of effort was devoted to the design of a posteriori error estimators, following the pioneering work of Babuska. These are computable quantities, depending on the discrete solution(s) and data, that can be used to assess the approximation quality and improve it adaptively. Despite their practical success, adaptive processes have been shown to converge, and to exhibit optimal cardinality, only recently for dimension d > 1 and for linear elliptic PDE. These series of lectures presents an up-to-date discussion of AFEM encompassing the derivation of upper and lower a posteriori error bounds for residual-type estimators, including a critical look at the role of oscillation, the design of AFEM and its basic properties, as well as a complete discussion of convergence, contraction property and quasi-optimal cardinality of AFEM