A novel biomarker for prediction of atrial fibrillation susceptibility in patients with celiac disease - Fig 1

<p><b>Atrial electromechanical coupling (PA’)</b>; the time interval from the onset of the P-wave on the surface electrocardiogram to the beginning of the late diastolic wave A’ [in a patient with CD (A) and normal individual (B).</p

Atrial electrical activity parameters of the study populations.

<p>Atrial electrical activity parameters of the study populations.</p

Demographic characteristics of the study populations.

<p>Demographic characteristics of the study populations.</p

Atrial electrical activity parameters of the study populations.

<p>Atrial electrical activity parameters of the study populations.</p

Conventional echocardiographic parameters of the study populations.

<p>Conventional echocardiographic parameters of the study populations.</p

Relationship between atrial EMD values and fibrosis.

<p>(<b>A</b>) Differences between each EMDs in NFP and FP groups have been clearly shown in the box and whisker diagram. (<b>B</b>) Correlation analysis between atrial EMD values and AST/ALT ratio (AAR).</p

Multi‐domain convolutional neural network (MD‐CNN) for radial reconstruction of dynamic cardiac MRI

Purpose Cardiac MR cine imaging allows accurate and reproducible assessment of cardiac function. However, its long scan time not only limits the spatial and temporal resolutions but is challenging in patients with breath‐holding difficulty or non‐sinus rhythms. To reduce scan time, we propose a multi‐domain convolutional neural network (MD‐CNN) for fast reconstruction of highly undersampled radial cine images. Methods MD‐CNN is a complex‐valued network that processes MR data in k‐space and image domains via k‐space interpolation and image‐domain subnetworks for residual artifact suppression. MD‐CNN exploits spatio‐temporal correlations across timeframes and multi‐coil redundancies to enable high acceleration. Radial cine data were prospectively collected in 108 subjects (50 ± 17 y, 72 males) using retrospective‐gated acquisition with 80%:20% split for training/testing. Images were reconstructed by MD‐CNN and k‐t Radial Sparse‐Sense(kt‐RASPS) using an undersampled dataset (14 of 196 acquired views; relative acceleration rate = 14). MD‐CNN images were evaluated quantitatively using mean‐squared‐error (MSE) and structural similarity index (SSIM) relative to reference images, and qualitatively by three independent readers for left ventricular (LV) border sharpness and temporal fidelity using 5‐point Likert‐scale (1‐non‐diagnostic, 2‐poor, 3‐fair, 4‐good, and 5‐excellent). Results MD‐CNN showed improved MSE and SSIM compared to kt‐RASPS (0.11 ± 0.10 vs. 0.61 ± 0.51, and 0.87 ± 0.07 vs. 0.72 ± 0.07, respectively; P < .01). Qualitatively, MD‐CCN significantly outperformed kt‐RASPS in LV border sharpness (3.87 ± 0.66 vs. 2.71 ± 0.58 at end‐diastole, and 3.57 ± 0.6 vs. 2.56 ± 0.6 at end‐systole, respectively; P < .01) and temporal fidelity (3.27 ± 0.65 vs. 2.59 ± 0.59; P < .01). Conclusion MD‐CNN reduces the scan time of cine imaging by a factor of 23.3 and provides superior image quality compared to kt‐RASPS

Decrease of Urotensin II activity can impact on the volume status in predialysis chronic kidney disease

© 2015 Informa Healthcare USA, Inc. All rights reserved: reproduction in whole or part not permitted.Urotensin II (U-II) was thought to be one of the mediators of primary renal sodium retention due to effects on renal sodium excretion. For this purpose, the relationship between U-II and overhydration was investigated. A total of 107 patients were enrolled in the study. According to body compositor monitor analysis, fluid overload up to 1.1 L, was considered normohydration. Patients were divided according to hydration status; overhydrate (n = 42) and normohydrate (n = 65) were studied in both groups. Pulse waveform velocity propagation for arterial stiffness and blood pressure analysis and echocardiographic left ventricular and left atrial indices were performed with known fluid overload-related parameters. U-II levels were measured by using Human ELISA kit. In overhydrated group, U-II levels were significantly lower. All parameters (blood pressure, arterial stiffness parameters, echocardiographic data, age, gender, diabetes, U-II, hemoglobin) correlated with overhydration, were determined by linear regression model (method = enter), when considered together, U-II was found to be an independent predictor from other conventional overhydration-related parameters. Male sex, left ventricular mass index, left atrial volume index, hemoglobin value were found to be independent predictors for overhydration. Considering the association of low U-II levels with adverse cardiovascular events and its role in sodium retention, we think that low U-II levels can be accepted as a potential therapeutic target in patients with hypervolemic cardio-renal syndrome