Design and baseline characteristics of the finerenone in reducing cardiovascular mortality and morbidity in diabetic kidney disease trial

Background: Among people with diabetes, those with kidney disease have exceptionally high rates of cardiovascular (CV) morbidity and mortality and progression of their underlying kidney disease. Finerenone is a novel, nonsteroidal, selective mineralocorticoid receptor antagonist that has shown to reduce albuminuria in type 2 diabetes (T2D) patients with chronic kidney disease (CKD) while revealing only a low risk of hyperkalemia. However, the effect of finerenone on CV and renal outcomes has not yet been investigated in long-term trials. Patients and Methods: The Finerenone in Reducing CV Mortality and Morbidity in Diabetic Kidney Disease (FIGARO-DKD) trial aims to assess the efficacy and safety of finerenone compared to placebo at reducing clinically important CV and renal outcomes in T2D patients with CKD. FIGARO-DKD is a randomized, double-blind, placebo-controlled, parallel-group, event-driven trial running in 47 countries with an expected duration of approximately 6 years. FIGARO-DKD randomized 7,437 patients with an estimated glomerular filtration rate >= 25 mL/min/1.73 m(2) and albuminuria (urinary albumin-to-creatinine ratio >= 30 to <= 5,000 mg/g). The study has at least 90% power to detect a 20% reduction in the risk of the primary outcome (overall two-sided significance level alpha = 0.05), the composite of time to first occurrence of CV death, nonfatal myocardial infarction, nonfatal stroke, or hospitalization for heart failure. Conclusions: FIGARO-DKD will determine whether an optimally treated cohort of T2D patients with CKD at high risk of CV and renal events will experience cardiorenal benefits with the addition of finerenone to their treatment regimen. Trial Registration: EudraCT number: 2015-000950-39; ClinicalTrials.gov identifier: NCT02545049

An Attention-Based Residual U-Net for Tumour Segmentation Using Multi-Modal MRI Brain Images

Detecting brain tumours is challenging due to the complex brain anatomy and wide range of tumour sizes, shapes, and locations. A crucial stage in diagnosing and treating brain tumours is automatically segmenting the tumour area from brain MRI. It involves the precise delineation of tumour boundaries within MRI scans, which helps to understand the tumour&#x2019;s extent, monitor its growth, plan treatment strategies, and assess treatment response over time. Hence, this research proposes a novel automated deep-learning approach based on U-Net for segmenting Glioma tumours. The basic U-Net model is enhanced with several components to improve its performance in the proposed model. The U-Net&#x2019;s encoder has an improved MCA (Multi-scale Context Attention) module designed to extract and collect rich spatial contextual information from the input image. The proposed U-Net&#x2019;s decoder uses a Squeeze and Excitation module and residual blocks. The residual blocks help reduce network degradation and gradient disappearance, enabling the model to retain important information during decoding. The Squeeze and Excitation module allows the model to retrieve high-level semantic properties and a high level of spatial context, which have been collected from the encoder module and IMCA-Block. The performance of proposed model is evaluated on two datasets BraTS 2020 and BraTS 2018. The experiments on both datasets demonstrate that the proposed framework enhances multi-modal MRI brain tumour segmentation performance on all metrics evaluated. For BraTS 2020 it achieved Dice Coefficient of 0.9978, 0.9378 and 0.9478 for WT (Whole tumour), TC (Tumour core), and ET (Enhancing Tumour) respectively and for BraTS 2018 it achieved Dice Coefficient 98.32, 93.32 and 92.32 for WT (Whole tumour), TC (Tumour core), and ET (Enhancing Tumour) respectively

Non-equilibrium phase and entanglement entropy in 2D holographic superconductors via Gauge-String duality

An alternative method of developing the theory of non-equilibrium two-dimensional holographic superconductor is to start from the definition of a time-dependent AdS3 background. As originally proposed, many of these formulae were cast in exponential form, but the adoption of the numeric method of expression throughout the bulk serves to show more clearly the relationship between the various parameters. The time dependence behavior of the scalar condensation and Maxwell fields are fitted numerically. A usual value for Maxwell field on AdS horizon is exp(–bt), and the exponential log ratio is therefore 10−8 s−1. The coefficient b of the time in the exponential term exp(–bt) can be interpreted as a tool to measure the degree of dynamical instability; its reciprocal 1/b is the time in which the disturbance is multiplied in the ratio. A discussion of some of the exponential formulae is given by the scalar field ψ(z, t) near the AdS boundary. It may be possible that a long interval would elapse in the system, which tends to the equilibrium state, where the normal mass and conformal dimensions emerged. A somewhat curious calculation has been made to illustrate the holographic entanglement entropy for this system. The foundation of all this calculation is, of course, a knowledge of multiple (connected and disconnected) extremal surfaces. There are several cases in which exact and approximate solutions are jointly used; a variable numerical quantity is represented by a graph, and the principles of approximation are then applied to determine related numerical quantities. In the case of the disconnected phase with a finite extremal area, we find a discontinuity in the first derivative of the entanglement entropy as the conserved charge J is increased.The accepted manuscript in pdf format is listed with the files at the bottom of this page. The presentation of the authors' names and (or) special characters in the title of the manuscript may differ slightly between what is listed on this page and what is listed in the pdf file of the accepted manuscript; that in the pdf file of the accepted manuscript is what was submitted by the author