Calcium-channel blockers for the prevention of stroke: from scientific evidences to the clinical practice

AIM OF THE REVIEW The present review aims to analyze the role of calcium-channel blockers, and particularly newer molecules, as first-line therapy for cerebrovascular disease. BACKGROUND Stroke is the leading cause of disability in the general population. Among traditional cardiovascular risk factors, hypertension has a key role in the genesis of both hemorrhagic and ischemic stroke and a direct correlation exists between blood pressure values and the risk of stroke. Moreover, blood pressure reduction has been demonstrated to be the most important route to reduce stroke incidence and recurrence. However, the mere reduction of blood pressure values does not normalize the cardiovascular risk of the hypertensive patient. It is therefore necessary to use drug classes that beyond their blood pressure-lowering effect have also an additional effect in terms of organ protection. Among these, calcium-channel blockers have a crucial profile. Firstly, they are effective in inducing left ventricular hypertrophy regression, with a strength at least equal to that of ACE-inhibitors. Secondly, they have an antithrombotic and an endothelium-protecting effect, mediated by their antioxidant activity. Finally, calcium-channel blockers are the most powerful drugs in preventing vascular remodeling. For these reasons this drug class has probably the strongest antiatherosclerotic effect, and it is the first-choice treatment mainly for cerebrovascular disease. Among different available calcium-channel blockers, the newer ones seem to possess pharmacokinetic characteristics allowing a more homogeneous 24 hours coverage as compared to older molecules, and preliminary data seem to suggest a greater beneficial effect also on left ventricular hypertrophy and lower incidence of side effects. CONCLUSIONS Although blood pressure reduction is the main tool to reduce cerebrovascular risk in hypertensive patients, some drug classes, such as calciumchannel blockers, seem to provide a protective action beyond the mere antihypertensive effect, and represent a key element in the prevention of atherosclerosis

Strictly localized bounding functions and Floquet boundary value problems

Semilinear multivalued equations are considered, in separable Banach spaces with the Radon-Nikodym property. An effective criterion for the existence of solutions to the associated Floquet boundary value problem is showed. Its proof is obtained combining a continuation principle with a Liapunov-like technique and a Scorza-Dragoni type theorem. A strictly localized transversality condition is assumed. The employed method enables to localize the solution values in a not necessarily invariant set; it allows also to introduce nonlinearities with superlinear growth in the state variable

Strictly localized bounding functions and Floquet boundary value problems

Semilinear multivalued equations are considered, in separable Ba-nach spaces with the Radon-Nikodym property. An effective criterion for the existence of solutions to the associated Floquet boundary value problem is showed. Its proof is obtained combining a continuation principle with a Liapunov-like technique and a Scorza-Dragoni type theorem. A strictly localized transversality condition is assumed. The employed method enables to localize the solution values in a not necessarily invariant set; it allows also to introduce nonlinearities with superlinear growth in the state variable

A Machine Learning-Based Method for Modelling a Proprietary SO2 Removal System in the Oil and Gas Sector

The aim of this study is to develop a model for a proprietary SO2 removal technology by using machine learning techniques and, more specifically, by exploiting the potentialities of artificial neural networks (ANNs). This technology is employed at the Eni oil and gas treatment plant in southern Italy. The amine circulating in this unit, that allows for a reduction in the SO2 concentration in the flue gases and to be compliant with the required specifications, is a proprietary solvent; thus, its composition is not publicly available. This has led to the idea of developing a machine learning (ML) algorithm for the unit description, with the objective of becoming independent from the licensor and more flexible in unit modelling. The model was developed in MatLab® by implementing ANNs and the aim was to predict three targets, namely the flow rate of SO2 that goes to the Claus unit, the emissions of SO2, and the flow rate of steam sent to the regenerator reboiler. These represent, respectively, the two physical outputs of the unit and a proxy variable of the amine quality. Three different models were developed, one for each target, that employed the Levenberg–Marquardt optimization algorithm. In addition, the ANN topology was optimized case by case. From the analysis of the results, it emerged that with a purely data-driven technique, the targets can be predicted with good accuracy. Therefore, this model can be employed to better manage the SO2 removal system, since it allows for the definition of an optimal control strategy and the maximization of the plant’s productivity by not exceeding the process constraints