The Prognostic Role of Pulmonary Arterial Elastance in Patients Undergoing Left Ventricular Assist Device Implantation: A Pilot Study.

: Pulmonary arterial elastance (Ea) is a helpful parameter to predict the risk of acute postoperative right ventricular failure (RVF) after left ventricular assist device (LVAD) implantation. A new method for calculating Ea, obtained by the ratio between transpulmonary gradient and stroke volume (Ea), has been proposed as a more accurate measure than the Ea obtained as the ratio between pulmonary artery systolic pressure and stroke volume (Ea). However, the role of Ea in predicting acute RVF post-LVAD implantation remains unclear.

On convergence properties of the brain-state-in-a-convex-domain.

Convergence in the presence of multiple equilibrium points is one of the most fundamental dynamical properties of a neural network (NN). Goal of the paper is to investigate convergence for the classic Brain-State-in-a-Box (BSB) NN model and some of its relevant generalizations named Brain-State-in-a-Convex-Body (BSCB). In particular, BSCB is a class of discrete-time NNs obtained by projecting a linear system onto a convex body of R.

Complete Stability of Neural Networks With Extended Memristors.

The article considers a large class of delayed neural networks (NNs) with extended memristors obeying the Stanford model. This is a widely used and popular model that accurately describes the switching dynamics of real nonvolatile memristor devices implemented in nanotechnology. The article studies via the Lyapunov method complete stability (CS), i.

miR-181c targets Parkin and SMAD7 in human cardiac fibroblasts: Validation of differential microRNA expression in patients with diabetes and heart failure with preserved ejection fraction.

Background: Cardiac fibrosis represents a key element in the pathophysiology of heart failure with preserved ejection fraction (HFpEF), a condition highly prevalent amongst geriatric patients, especially if diabetic. The microRNA 181c (miR-181c) has been shown to be associated with the response to exercise training in HFpEF patients and has been also linked to diabetic cardiovascular complications. However, the underlying mechanisms have not been fully elucidated.

Acute coronary syndrome and renal impairment: a systematic review.

Background: Coronary artery disease (CAD) and chronic kidney disease (CKD) may reciprocally influence each other. Patients with CAD and CKD have an increased risk of both ischemic and hemorrhagic events.

Methods: In the present review, we summarize the existing literature focusing on the relationship between kidney dysfunction and acute coronary syndromes (ACS) in terms of risk factors, complications, and prognosis.

Memristor Circuits for Simulating Neuron Spiking and Burst Phenomena.

Since the introduction of memristors, it has been widely recognized that they can be successfully employed as synapses in neuromorphic circuits. This paper focuses on showing that memristor circuits can be also used for mimicking some features of the dynamics exhibited by neurons in response to an external stimulus. The proposed approach relies on exploiting multistability of memristor circuits, i.

Renal Involvement in COVID-19: A Review of the Literature.

Kidney injury may be a severe complication of acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and contributes to worsen the prognosis. Various pathophysiological mechanisms can contribute to organ damage and impair renal function, proving the complexity of the virus activity and the resulting immunity response. We summarized the evidence of the literature on the prevalence of kidney involvement, on the pathogenic pathways and on its management.

Repositioning of Immunomodulators: A Ray of Hope for Alzheimer's Disease?

Alzheimer's disease (AD) is the most common age-related neurodegenerative disorder characterized by cognitive decline and by the presence of amyloid β plaques and neurofibrillary tangles in the brain. Despite recent advances in understanding its pathophysiological mechanisms, to date, there are no disease-modifying therapeutic options, to slow or halt the evolution of neurodegenerative processes in AD. Current pharmacological treatments only transiently mitigate the severity of symptoms, with modest or null overall improvement.

Cardiovascular System in COVID-19: Simply a Viewer or a Leading Actor?

As of January 2020, a new pandemic has spread from Wuhan and caused thousands of deaths worldwide. Several studies have observed a relationship between coronavirus disease (COVID-19) infection and the cardiovascular system with the appearance of myocardial damage, myocarditis, pericarditis, heart failure and various arrhythmic manifestations, as well as an increase in thromboembolic risk. Cardiovascular manifestations have been highlighted especially in older and more fragile patients and in those with multiple cardiovascular risk factors such as cancer, diabetes, obesity and hypertension.

Anticoagulant treatment in COVID-19: a narrative review.

The actual Coronavirus Disease (COVID 19) pandemic is due to Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a member of the coronavirus family. Besides the respiratory involvement, COVID 19 patients frequently develop a pro-coagulative state caused by virus-induced endothelial dysfunction, cytokine storm and complement cascade hyperactivation. It is common to observe diffuse microvascular thrombi in multiple organs, mostly in pulmonary microvessels.

Memristor Neural Networks for Linear and Quadratic Programming Problems.

This article introduces a new class of memristor neural networks (NNs) for solving, in real-time, quadratic programming (QP) and linear programming (LP) problems. The networks, which are called memristor programming NNs (MPNNs), use a set of filamentary-type memristors with sharp memristance transitions for constraint satisfaction and an additional set of memristors with smooth memristance transitions for memorizing the result of a computation. The nonlinear dynamics and global optimization capabilities of MPNNs for QP and LP problems are thoroughly investigated via a recently introduced technique called the flux-charge analysis method.

Gut Microbiota in Alzheimer's Disease, Depression, and Type 2 Diabetes Mellitus: The Role of Oxidative Stress.

Gut microbiota consists of over 100 trillion microorganisms including at least 1000 different species of bacteria and is crucially involved in physiological and pathophysiological processes occurring in the host. An imbalanced gastrointestinal ecosystem (dysbiosis) seems to be a contributor to the development and maintenance of several diseases, such as Alzheimer's disease, depression, and type 2 diabetes mellitus. Interestingly, the three disorders are frequently associated as demonstrated by the high comorbidity rates.

Nonlinear Networks With Mem-Elements: Complex Dynamics via Flux-Charge Analysis Method.

Nonlinear dynamic memory elements, as memristors, memcapacitors, and meminductors (also known as mem-elements), are of paramount importance in conceiving the neural networks, mem-computing machines, and reservoir computing systems with advanced computational primitives. This paper aims to develop a systematic methodology for analyzing complex dynamics in nonlinear networks with such emerging nanoscale mem-elements. The technique extends the flux-charge analysis method (FCAM) for nonlinear circuits with memristors to a broader class of nonlinear networks N containing also memcapacitors and meminductors.

A Quartz Crystal Microbalance Immunosensor for Stem Cell Selection and Extraction.

A cost-effective immunosensor for the detection and isolation of dental pulp stem cells (DPSCs) based on a quartz crystal microbalance (QCM) has been developed. The recognition mechanism relies on anti-CD34 antibodies, DPSC-specific monoclonal antibodies that are anchored on the surface of the quartz crystals. Due to its high specificity, real time detection, and low cost, the proposed technology has a promising potential in the field of cell biology, for the simultaneous detection and sorting of stem cells from heterogeneous cell samples.

Memristor standard cellular neural networks computing in the flux-charge domain.

The paper introduces a class of memristor neural networks (NNs) that are characterized by the following salient features. (a) The processing of signals takes place in the flux-charge domain and is based on the time evolution of memristor charges. The processing result is given by the constant asymptotic values of charges that are stored in the memristors acting as non-volatile memories in steady state.

New Conditions for Global Asymptotic Stability of Memristor Neural Networks.

Recent papers in the literature introduced a class of neural networks (NNs) with memristors, named dynamic-memristor (DM) NNs, such that the analog processing takes place in the charge-flux domain, instead of the typical current-voltage domain as it happens for Hopfield NNs and standard cellular NNs. One key advantage is that, when a steady state is reached, all currents, voltages, and power of a DM-NN drop off, whereas the memristors act as nonvolatile memories that store the processing result. Previous work in the literature addressed multistability of DM-NNs, i.

Convergence and Multistability of Nonsymmetric Cellular Neural Networks With Memristors.

Recent work has considered a class of cellular neural networks (CNNs) where each cell contains an ideal capacitor and an ideal flux-controlled memristor. One main feature is that during the analog computation the memristor is assumed to be a dynamic element, hence each cell is second-order with state variables given by the capacitor voltage and the memristor flux. Such CNNs, named dynamic memristor (DM)-CNNs, were proved to be convergent when a symmetry condition for the cell interconnections is satisfied.

Unilateral paravertebral block compared with subarachnoid anesthesia for the management of postoperative pain syndrome after inguinal herniorrhaphy: a randomized controlled clinical trial.

Inguinal herniorrhaphy is a common surgical procedure. The aim of this investigation was to determine whether unilateral paravertebral block could provide better control of postoperative pain syndrome compared with unilateral subarachnoid block (SAB). A randomized controlled study was conducted using 50 patients with unilateral inguinal hernias.

Discontinuous Neural Networks for Finite-Time Solution of Time-Dependent Linear Equations.

This paper considers a class of nonsmooth neural networks with discontinuous hard-limiter (signum) neuron activations for solving time-dependent (TD) systems of algebraic linear equations (ALEs). The networks are defined by the subdifferential with respect to the state variables of an energy function given by the L norm of the error between the state and the TD-ALE solution. It is shown that when the penalty parameter exceeds a quantitatively estimated threshold the networks are able to reach in finite time, and exactly track thereafter, the target solution of the TD-ALE.

Nonsmooth Neural Network for Convex Time-Dependent Constraint Satisfaction Problems.

This paper introduces a nonsmooth (NS) neural network that is able to operate in a time-dependent (TD) context and is potentially useful for solving some classes of NS-TD problems. The proposed network is named nonsmooth time-dependent network (NTN) and is an extension to a TD setting of a previous NS neural network for programming problems. Suppose C(t), t ≥ 0, is a nonempty TD convex feasibility set defined by TD inequality constraints.

Regio- and enantioselective friedel-crafts reactions of indoles to epoxides catalyzed by graphene oxide: a green approach.

Graphene oxide efficiently promotes high regio- and enantioselective ring opening reactions of aromatic epoxides by indoles addition, in solvent- and metal-free conditions. The Friedel-Crafts products were obtained with enantioselectivity up to 99 % ee. The complete inversion of stereochemistry indicates the occurrence of SN 2-type reaction, which assures high level of enantioselectivity.

Necessary and sufficient condition for multistability of neural networks evolving on a closed hypercube.

The paper considers nonsmooth neural networks described by a class of differential inclusions termed differential variational inequalities (DVIs). The DVIs include the relevant class of neural networks, introduced by Li, Michel and Porod, described by linear systems evolving in a closed hypercube of R(n). The main result in the paper is a necessary and sufficient condition for multistability of DVIs with nonsymmetric and cooperative (nonnegative) interconnections between neurons.

Experimental evidence of a buoyant mass difference between bovine spermatozoa bearing X- and Y-chromosomes using a micromechanical resonator.

Flow cytometry is to date the only commercially viable technique for sex preselection of mammalian spermatozoa, measuring the different DNA content in X- and Y-chromosome bearing spermatozoa. Here we present experimental evidence of a measurable difference between bovine spermatozoa bearing X- and Y-chromosomes based on their buoyant mass. Single cells of two populations of flow-cytometrically sorted spermatozoa were analyzed by means of a micromechanical resonator, consisting of a suspended doubly-clamped microcapillary.