State-of-the-art treatment of hypertension: established and new drugs.

The treatment of essential hypertension is based essentially on the prescription of four major classes of antihypertensive drugs, i.e. blockers of the renin-angiotensin system, calcium channel blockers, diuretics and beta-blockers. In recent years, very few new drug therapies of hypertension have become available. Therefore, it is crucial for physicians to optimize their antihypertensive therapies with the drugs available on the market. In each of the classes of antihypertensive drugs, questions have recently been raised: are angiotensin-converting enzyme (ACE) inhibitors superior to angiotensin II receptor blockers (ARB)? Is it possible to reduce the incidence of peripheral oedema with calcium antagonists? Is hydrochlorothiazide really the good diuretic to use in combination therapies? The purpose of this review is to discuss these various questions in the light of the most recent clinical studies and meta-analyses. These latter suggest that ACE inhibitors and ARB are equivalent except for a better tolerability profile of ARB. Third generation calcium channel blockers enable to reduce the incidence of peripheral oedema and chlorthalidone is certainly more effective than hydrochlorothiazide in preventing cardiovascular events in hypertension. At last, studies suggest that drug adherence and long-term persistence under therapy is one of the major issues in the actual management of essential hypertension.

Swimming prevents vulnerable atherosclerotic plaque development in hypertensive 2-kidney, 1-clip mice by modulating angiotensin II type 1 receptor expression independently from hemodynamic changes.

Exercise is known to reduce cardiovascular risk. However, its role on atherosclerotic plaque stabilization is unknown. Apolipoprotein E(-/-) mice with vulnerable (2-kidney, 1-clip: angiotensin [Ang] II-dependent hypertension model) or stable atherosclerotic plaques (1-kidney, 1-clip: Ang II-independent hypertension model and normotensive shams) were used for experiments. Mice swam regularly for 5 weeks and were compared with sedentary controls. Exercised 2-kidney, 1-clip mice developed significantly more stable plaques (thinner fibrous cap, decreased media degeneration, layering, macrophage content, and increased smooth muscle cells) than sedentary controls. Exercise did not affect blood pressure. Conversely, swimming significantly reduced aortic Ang II type 1 receptor mRNA levels, whereas Ang II type 2 receptor expression remained unaffected. Sympathetic tone also significantly diminished in exercised 2-kidney, 1-clip mice compared with sedentary ones; renin and aldosterone levels tended to increase. Ang II type 1 downregulation was not accompanied by improved endothelial function, and no difference in balance among T-helper 1, T-helper 2, and T regulatory cells was observed between sedentary and exercised mice. These results show for the first time, in a mouse model of Ang II-mediated vulnerable plaques, that swimming prevents atherosclerosis progression and plaque vulnerability. This benefit is likely mediated by downregulating aortic Ang II type 1 receptor expression independent from any hemodynamic change. Ang II type 1 downregulation may protect the vessel wall from the Ang II proatherogenic effects. Moreover, data presented herein further emphasize the pivotal and blood pressure-independent role of Ang II in atherogenesis.

Brain tissue segmentation of fetal MR images

We present a segmentation method for fetal brain tissuesof T2w MR images, based on the well known ExpectationMaximization Markov Random Field (EM- MRF) scheme. Ourmain contribution is an intensity model composed of 7Gaussian distribution designed to deal with the largeintensity variability of fetal brain tissues. The secondmain contribution is a 3-steps MRF model that introducesboth local spatial and anatomical priors given by acortical distance map. Preliminary results on 4 subjectsare presented and evaluated in comparison to manualsegmentations showing that our methodology cansuccessfully be applied to such data, dealing with largeintensity variability within brain tissues and partialvolume (PV).

Kernel-based methods for change detection in remote sensing images

Nowadays, the joint exploitation of images acquired daily by remote sensing instruments and of images available from archives allows a detailed monitoring of the transitions occurring at the surface of the Earth. These modifications of the land cover generate spectral discrepancies that can be detected via the analysis of remote sensing images. Independently from the origin of the images and of type of surface change, a correct processing of such data implies the adoption of flexible, robust and possibly nonlinear method, to correctly account for the complex statistical relationships characterizing the pixels of the images. This Thesis deals with the development and the application of advanced statistical methods for multi-temporal optical remote sensing image processing tasks. Three different families of machine learning models have been explored and fundamental solutions for change detection problems are provided. In the first part, change detection with user supervision has been considered. In a first application, a nonlinear classifier has been applied with the intent of precisely delineating flooded regions from a pair of images. In a second case study, the spatial context of each pixel has been injected into another nonlinear classifier to obtain a precise mapping of new urban structures. In both cases, the user provides the classifier with examples of what he believes has changed or not. In the second part, a completely automatic and unsupervised method for precise binary detection of changes has been proposed. The technique allows a very accurate mapping without any user intervention, resulting particularly useful when readiness and reaction times of the system are a crucial constraint. In the third, the problem of statistical distributions shifting between acquisitions is studied. Two approaches to transform the couple of bi-temporal images and reduce their differences unrelated to changes in land cover are studied. The methods align the distributions of the images, so that the pixel-wise comparison could be carried out with higher accuracy. Furthermore, the second method can deal with images from different sensors, no matter the dimensionality of the data nor the spectral information content. This opens the doors to possible solutions for a crucial problem in the field: detecting changes when the images have been acquired by two different sensors.