Synergistic actions between angiotensin-converting enzyme inhibitors and statins in atherosclerosis

Aims: Hypertension and hypercholesterolemia are independent risk factors for atherosclerotic cardiovascular disease (ASCVD) by acting directly on the endothelium and activating the renin-angiotensin aldosterone system (RAAS) and mevalonate pathways. This review examines how the severity and duration of these risk factors may influence the cardiovascular risk through a reciprocal interplay leading to oxidative stress and pro-inflammatory response. Data synthesis: The review highlights the clinical evidence supporting the benefits of statins and angiotensin-converting enzyme (ACE) inhibitors for hypertension, lipid disorders and ASCVD management, both individually and combined, at all stages of the cardiovascular continuum. Conclusion: Drug strategies incorporating an ACE-inhibitor and a statin, and in particular perindopril and atorvastatin, have consistently demonstrated reductions in the rate of ASCVD events in patients with hypertension and lipid disorders, cementing their position as first-line therapies for the management of atherosclerosis complications

Anomalous Transport in Sketched Nanostructures at the LaAlO3/SrTiO3 Interface

The oxide heterostructure LaAlO3/SrTiO3 supports a two-dimensional electron liquid with a variety of competing phases including magnetism, superconductivity and weak antilocalization due to Rashba spin-orbit coupling. Further confinement of this 2D electron liquid to the quasi-one-dimensional regime can provide insight into the underlying physics of this system and reveal new behavior. Here we describe magnetotransport experiments on narrow LaAlO3/SrTiO3 structures created by a conductive atomic force microscope lithography technique. Four-terminal local transport measurements on ~10-nm-wide Hall bar structures yield longitudinal resistances that are comparable to the resistance quantum h/e2 and independent of the channel length. Large nonlocal resistances (as large as 10^4 ohms) are observed in some but not all structures with separations between current and voltage that are large compared to the 2D mean-free path. The nonlocal transport is strongly suppressed by the onset of superconductivity below ~200 mK. The origin of these anomalous transport signatures is not understood, but may arise from coherent transport defined by strong spin-orbit coupling and/or magnetic interactions

"Water-cycle" mechanism for writing and erasing nanostructures at the LaAlO3/SrTiO3 interface

Nanoscale control of the metal-insulator transition in LaAlO3/ SrTiO3 heterostructures can be achieved using local voltages applied by a conductive atomic-force microscope probe. One proposed mechanism for the writing and erasing process involves an adsorbed H2O layer at the top LaAlO3 surface. In this picture, water molecules dissociates into OH- and H+ which are then selectively removed by a biased AFM probe. To test this mechanism, writing and erasing experiments are performed in a vacuum AFM using various gas mixtures. Writing ability is suppressed in those environments where H2O is not present. The stability of written nanostructures is found to be strongly associated with the ambient environment. The self-erasure process in air can be strongly suppressed by creating a modest vacuum or replacing the humid air with dry inert gas. These experiments provide strong constraints for theories of both the writing process as well as the origin of interfacial conductance.Comment: 11 pages, 3 figure

Spiking Neural Networks for Inference and Learning: A Memristor-based Design Perspective

On metrics of density and power efficiency, neuromorphic technologies have the potential to surpass mainstream computing technologies in tasks where real-time functionality, adaptability, and autonomy are essential. While algorithmic advances in neuromorphic computing are proceeding successfully, the potential of memristors to improve neuromorphic computing have not yet born fruit, primarily because they are often used as a drop-in replacement to conventional memory. However, interdisciplinary approaches anchored in machine learning theory suggest that multifactor plasticity rules matching neural and synaptic dynamics to the device capabilities can take better advantage of memristor dynamics and its stochasticity. Furthermore, such plasticity rules generally show much higher performance than that of classical Spike Time Dependent Plasticity (STDP) rules. This chapter reviews the recent development in learning with spiking neural network models and their possible implementation with memristor-based hardware

Graphene-Complex-oxide Nanoscale Device Concepts

The integration of graphene with complex-oxide heterostructures such as LaAlO$_3$/SrTiO$_3$ offers the opportunity to combine the multifunctional properties of an oxide interface with the electronic properties of graphene. The ability to control interface conduction through graphene and understanding how it affects the intrinsic properties of an oxide interface are critical to the technological development of novel multifunctional devices. Here we demonstrate several device archetypes in which electron transport at an oxide interface is modulated using a patterned graphene top gate. Nanoscale devices are fabricated at the oxide interface by conductive atomic force microscope (c-AFM) lithography, and transport measurements are performed as a function of the graphene gate voltage. Experiments are performed with devices written adjacent to or directly underneath the graphene gate. Unique capabilities of this approach include the ability to create highly flexible device configurations, the ability to modulate carrier density at the oxide interface, and the ability to control electron transport up to the single-electron-tunneling regime, while maintaining intrinsic transport properties of the oxide interface. Our results facilitate the design of a variety of nanoscale devices that combine unique transport properties of these two intimately coupled two-dimensional electron systems.Comment: 27 pages, 10 figure

Analysis of the intraspinal calcium dynamics and its implications on the plasticity of spiking neurons

The influx of calcium ions into the dendritic spines through the N-metyl-D-aspartate (NMDA) channels is believed to be the primary trigger for various forms of synaptic plasticity. In this paper, the authors calculate analytically the mean values of the calcium transients elicited by a spiking neuron undergoing a simple model of ionic currents and back-propagating action potentials. The relative variability of these transients, due to the stochastic nature of synaptic transmission, is further considered using a simple Markov model of NMDA receptos. One finds that both the mean value and the variability depend on the timing between pre- and postsynaptic action-potentials. These results could have implications on the expected form of synaptic-plasticity curve and can form a basis for a unified theory of spike time-dependent, and rate based plasticity.Comment: 14 pages, 10 figures. A few changes in section IV and addition of a new figur