[vasopressin Intravenous Infusion Causes Dose Dependent Adverse Cardiovascular Effects In Anesthetized Dogs].

BACKGROUND: Arginine vasopressin (AVP) has been broadly used in the management of vasodilatory shock. However, there are many concerns regarding its clinical use, especially in high doses, as it can be associated with adverse cardiovascular events. OBJECTIVE: To investigate the cardiovascular effects of AVP in continuous IV infusion on hemodynamic parameters in dogs. METHODS: Sixteen healthy mongrel dogs, anesthetized with pentobarbital were intravascularly catheterized, and randomly assigned to: control (saline-placebo; n=8) and AVP (n=8) groups. The study group was infused with AVP for three consecutive 10-minute periods at logarithmically increasing doses (0.01; 0.1 and 1.0 U/kg/min), at them 20-min intervals. Heart rate (HR) and intravascular pressures were continuously recorded. Cardiac output was measured by the thermodilution method. RESULTS: No significant hemodynamic effects were observed during 0.01 U/kg/min of AVP infusion, but at higher doses (0.1 and 1.0 U/kg/min) a progressive increase in mean arterial pressure (MAP) and systemic vascular resistance index (SVRI) were observed, with a significant decrease in HR and the cardiac index (CI). A significant increase in the pulmonary vascular resistance index (PVRI) was also observed with the 1.0 U/kg/min dose, mainly due to the decrease in the CI. CONCLUSION: AVP, when administered at doses between 0.1 and 1.0 U/kg/min, induced significant increases in MAP and SVRI, with negative inotropic and chronotropic effects in healthy animals. Although these doses are ten to thousand times greater than those routinely used for the management of vasodilatory shock, our data confirm that AVP might be used carefully and under strict hemodynamic monitoring in clinical practice, especially if doses higher than 0.01 U/kg/min are needed.942213218, 229-234, 216-22

Automatic diagnosis of the 12-lead ECG using a deep neural network

The role of automatic electrocardiogram (ECG) analysis in clinical practice is limited by the accuracy of existing models. Deep Neural Networks (DNNs) are models composed of stacked transformations that learn tasks by examples. This technology has recently achieved striking success in a variety of task and there are great expectations on how it might improve clinical practice. Here we present a DNN model trained in a dataset with more than 2 million labeled exams analyzed by the Telehealth Network of Minas Gerais and collected under the scope of the CODE (Clinical Outcomes in Digital Electrocardiology) study. The DNN outperform cardiology resident medical doctors in recognizing 6 types of abnormalities in 12-lead ECG recordings, with F1 scores above 80% and specificity over 99%. These results indicate ECG analysis based on DNNs, previously studied in a single-lead setup, generalizes well to 12-lead exams, taking the technology closer to the standard clinical practice

On the influence of a Coulomb-like potential induced by the Lorentz symmetry breaking effects on the Harmonic Oscillator

In this work, we obtain bound states for a nonrelativistic spin-half neutral particle under the influence of a Coulomb-like potential induced by the Lorentz symmetry breaking effects. We present a new possible scenario of studying the Lorentz symmetry breaking effects on a nonrelativistic quantum system defined by a fixed space-like vector field parallel to the radial direction interacting with a uniform magnetic field along the z-axis. Furthermore, we also discuss the influence of a Coulomb-like potential induced by Lorentz symmetry violation effects on the two-dimensional harmonic oscillator.Comment: 14 pages, no figure, this work has been accepted for publication in The European Physical Journal Plu