Elastic constant of dielectric nano-thin films using three-layer resonance studied by picosecond ultrasonics

Elastic constants and sound velocities of nm order thin films are essential for designing acoustic filters. However, it is difficult to measure them for dielectric thin films. In this study, we use a three-layer structure where a dielectric nano-thin film is sandwiched between thicker metallic films to measure the longitudinal elastic constant of the dielectric film. We propose an efficiency function to estimate the optimal thicknesses of the components. We use Pt/NiO/Pt three-layer films for confirming our proposed method. The determined elastic constant of NiO deposited at room temperature is smaller than the bulk value by ∼40%. However, it approaches the bulk value as the deposition temperature increases. We also reveal that the uncertainty of the elastic constant of the Pt film insignificantly affects the accuracy of the determined elastic constant of NiO in this structure.This is the Accepted Manuscript version of an article accepted for publication in Japanese Journal of Applied Physics. IOP Publishing Ltd are not responsible for any errors or omissions in this version of the manuscript or any version derived from it. The Version of Record is available online at https://doi.org/10.35848/1347-4065/abec5a

Clinically Feasible and Accurate View Classification of Echocardiographic Images Using Deep Learning

A proper echocardiographic study requires several video clips recorded from different acquisition angles for observation of the complex cardiac anatomy. However, these video clips are not necessarily labeled in a database. Identification of the acquired view becomes the first step of analyzing an echocardiogram. Currently, there is no consensus whether the mislabeled samples can be used to create a feasible clinical prediction model of ejection fraction (EF). The aim of this study was to test two types of input methods for the classification of images, and to test the accuracy of the prediction model for EF in a learning database containing mislabeled images that were not checked by observers. We enrolled 340 patients with five standard views (long axis, short axis, 3-chamber view, 4-chamber view and 2-chamber view) and 10 images in a cycle, used for training a convolutional neural network to classify views (total 17,000 labeled images). All DICOM images were rigidly registered and rescaled into a reference image to fit the size of echocardiographic images. We employed 5-fold cross validation to examine model performance. We tested models trained by two types of data, averaged images and 10 selected images. Our best model (from 10 selected images) classified video views with 98.1% overall test accuracy in the independent cohort. In our view classification model, 1.9% of the images were mislabeled. To determine if this 98.1% accuracy was acceptable for creating the clinical prediction model using echocardiographic data, we tested the prediction model for EF using learning data with a 1.9% error rate. The accuracy of the prediction model for EF was warranted, even with training data containing 1.9% mislabeled images. The CNN algorithm can classify images into five standard views in a clinical setting. Our results suggest that this approach may provide a clinically feasible accuracy level of view classification for the analysis of echocardiographic data

Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids

This paper solves a long-standing problem in resonant ultrasound spectroscopy (RUS) for cubic solids through the use of two-dimensional deep learning (DL). By means of inverse methods, conventional RUS can determine all independent elastic constants of a crystalline solid specimen by comparing measured and calculated free-vibration resonance frequencies. However, to avoid invalid local minima in the inverse process, good initial guesses of the elastic constants must be available. Here, we propose a DL scheme to remedy this problem, which utilizes an input elasticity image composed of three layers obtained from resonance frequency data. After network training, this scheme is executed in two steps: DL processing by a neural network to output elastic constants in a Blackman diagram classification, followed by the use of a regression network around the classified point in this diagram for more accurate determination. By means of simulations, we demonstrate that this DL scheme yields the principal elastic constants within an error of approximately 5% without any inverse processing even in the unfavorable case of five missing modes for 111 existing cubic crystals.Fukuda Hiroki, Nagakubo Akira, Wright Oliver B., et al. Deep-Learning-Assisted Resonant Ultrasound Spectroscopy for Cubic Solids. Physical Review Applied 20, 228 (2023); https://doi.org/10.1103/physrevapplied.20.034048

Deep Learning for Echocardiography

Objectives: The aim of this study was to evaluate whether a deep convolutional neural network (DCNN) could detect regional wall motion abnormalities (RWMAs) and differentiate groups of coronary infarction territories from conventional 2-dimensional echocardiographic images compared with cardiologist/sonographer or resident readers. Background: An effective intervention for reduction of misreading of RWMAs is needed. We hypothesized that a DCNN trained with echocardiographic images may provide improved detection of RWMAs in the clinical setting. Methods: A total of 300 patients with history of myocardial infarction were enrolled. In this cohort, 100 each had infarctions of the left anterior descending branch (LAD), left circumflex branch (LCX), and right coronary artery (RCA). The age-matched 100 control patients with normal wall motion were selected from our database. Each case contained cardiac ultrasound images from short axis views at end-diastolic, mid-systolic and end-systolic phases. After 100 steps of training, diagnostic accuracies were calculated on the test set. We independently trained 10 versions of the same model, and performed ensemble predictions with them. Results: For detection of the presence of wall motion abnormality, the area under the receiver-operating characteristic curve (AUC) by deep learning algorithm was similar to that by cardiologist/sonographer readers (0.99 vs. 0.98, p =0.15), and significantly higher than the AUC by resident readers (0.99 vs. 0.90, p =0.002). For detection of territories of wall motion abnormality, the AUC by the deep learning algorithm was similar to the AUC by cardiologist/sonographer readers (0.97 vs. 0.95, p =0.61) and significantly higher than the AUC by resident readers (0.97 vs. 0.83, p =0.003). In a validation group from an independent site (n=40), the AUC by the DL algorithm was 0.90. Conclusions: Our results support the possibility of DCNN use for automated diagnosis of RWMAs in the field of echocardiography

Cor Triatriatum in the Adult with Aortic Stenosis and Mitral Stenosis

Background：Cor triatriatum is a rare congenital cardiac anomaly, in which the left atrium or right atrium is separated by an abnormal fibromuscular membrane with one or more restrictive orifices. This condition typically presents in infancy or early childhood and can be associated with other cardiac anomalies.Case presentation：A 75-year-old woman was admitted for exertional dyspnea with moderate aortic and mitral stenosis. As cor triatriatum was revealed by a computed tomography and echocardiography, she was referred to our department for surgery. Aortic valve replacement, mitral valve replacement and excision of the membranous septum in the left atrium was performed. This report presents an incidental findings of cor triatriatum with aortic stenosis, moderate mitral stenosis in septuagenarian.Conclusion：We encountered a rare case of cor triatriatum with aortic stenosis and mitral stenosis in septuagenarian. She was incidentally diagnosed by rheumatic aortic and mitral stenosis which had advanced to moderate level

特異的活性化第X因子阻害薬であるリバーロキサバンは、糖尿病マウス大動脈の血管内皮依存性弛緩反応を改善した

Activated factor X (FXa) plays a central role in the coagulation cascade, while it also mediates vascular function through activation of protease-activated receptors (PARs). Here, we examined whether inhibition of FXa by rivaroxaban, a direct FXa inhibitor, attenuates endothelial dysfunction in streptozotocin (STZ)-induced diabetic mice. Induction of diabetes increased the expression of a major FXa receptor, PAR2, in the aorta (P < 0.05). Administration of rivaroxaban (10 mg/kg/day) to diabetic wild-type (WT) mice for 3 weeks attenuated endothelial dysfunction as determined by acetylcholine-dependent vasodilation compared with the control (P < 0.001), without alteration of blood glucose level. Rivaroxaban promoted eNOSSer1177 phosphorylation in the aorta (P < 0.001). Induction of diabetes to PAR2-deficient (PAR2−/−) mice did not affect endothelial function and eNOSSer1177 phosphorylation in the aorta compared with non-diabetic PAR2−/− mice. FXa or a PAR2 agonist significantly impaired endothelial function in aortic rings obtained from WT mice, but not in those from PAR2−/− mice. FXa promoted JNK phosphorylation (P < 0.01) and reduced eNOSSer1177 phosphorylation (P < 0.05) in human coronary artery endothelial cells (HCAEC). FXa-induced endothelial dysfunction in aortic rings (P < 0.001) and eNOSSer1177 phosphorylation (P < 0.05) in HCAEC were partially ameliorated by a JNK inhibitor. Rivaroxaban ameliorated diabetes-induced endothelial dysfunction. Our results suggest that FXa or PAR2 is a potential therapeutic target

The Role of S1P2 in Atherogenesis

Aim: The bioactive lipid, sphingosine-1-phosphate (S1P), has various roles in the physiology and pathophysiology of many diseases. There are five S1P receptors; however, the role of each S1P receptor in atherogenesis is still obscure. Here we investigated the contribution of S1P receptor 2 (S1P2) to atherogenesis by using a specific S1P2 antagonist, ONO-5430514, in apolipoprotein E-deficient (Apoe−/− ) mice. Methods: Apoe−/− mice fed with a western-type diet (WTD) received ONO-5430514 (30 mg/kg/day) or vehicle. To examine the effect on atherogenesis, Sudan IV staining, histological analysis, qPCR, and vascular reactivity assay was performed. Human umbilical vein endothelial cells (HUVEC) were used for in vitro experiments. Results: WTD-fed Apoe−/− mice had significantly higher S1P2 expression in the aorta compared with wild-type mice. S1P2 antagonist treatment for 20 weeks reduced atherosclerotic lesion development (p＜0.05). S1P2 antagonist treatment for 8 weeks ameliorated endothelial dysfunction (p＜0.05) accompanied with significant reduction of lipid deposition, macrophage accumulation, and inflammatory molecule expression in the aorta compared with vehicle. S1P2 antagonist attenuated the phosphorylation of JNK in the abdominal aorta compared with vehicle (p＜0.05). In HUVEC, S1P promoted inflammatory molecule expression such as MCP-1 and VCAM-1 (p＜0.001), which was attenuated by S1P2 antagonist or a JNK inhibitor (p＜0.01). S1P2 antagonist also inhibited S1P-induced JNK phosphorylation in HUVEC (p＜0.05). Conclusions: Our results suggested that an S1P2 antagonist attenuates endothelial dysfunction and prevents atherogenesis. S1P2, which promotes inflammatory activation of endothelial cells, might be a therapeutic target for atherosclerosis

Effects of dabigatran on diabetic endothelial dysfunction

Diabetic patients have coagulation abnormalities, in which thrombin plays a key role. Whereas accumulating evidence suggests that it also contributes to the development of vascular dysfunction through the activation of protease-activated receptors (PARs). Here we investigated whether the blockade of thrombin attenuates endothelial dysfunction in diabetic mice. Induction of diabetes by streptozotocin (STZ) increased the expression of PAR1, PAR3, and PAR4 in the aorta. STZ-induced diabetic mice showed impairment of endothelial function, while the administration of dabigatran etexilate, a direct thrombin inhibitor, significantly attenuated endothelial dysfunction in diabetic mice with no alteration of metabolic parameters including blood glucose level. Dabigatran did not affect endothelium-independent vasodilation. Dabigatran decreased the expression of inflammatory molecules (e.g., MCP-1 and ICAM-1) in the aorta of diabetic mice. Thrombin increased the expression of these inflammatory molecules and the phosphorylation of IκBα, and decreased the phosphorylation of eNOSSer1177 in human umbilical endothelial cells (HUVEC). Thrombin significantly impaired the endothelium-dependent vascular response of aortic rings obtained from wild-type mice. Inhibition of NF-κB attenuated thrombin-induced inflammatory molecule expression in HUVEC and ameliorated thrombin-induced endothelial dysfunction in aortic rings. Dabigatran attenuated the development of diabetes-induced endothelial dysfunction. Thrombin signaling may serve as a potential therapeutic target in diabetic condition

Decision-making using preload stress echocardiography

Aims Abnormal left ventricular diastolic response to preload stress can be an early marker of heart failure (HF). The aim of this study was to assess clinical course in patients with HF with preserved ejection fraction (HFpEF) who underwent preload stress echocardiography. In the subgroup analysis, we assessed the prognosis of patients with unstable signs during preload stress classified by treatment strategies. Methods and results We prospectively conducted preload stress echocardiographic studies between January 2006 and December 2013 in 211 patients with HFpEF. Fifty-eight patients had abnormal diastolic reserve during preload stress (unstable impaired relaxation: unstable IR). Of 58 patients with unstable IR, 19 patients were assigned to additional therapy by increased or additional therapy and 39 patients were assigned to standard therapy. Composite outcomes were prespecified as the primary endpoint of death and hospitalization for deteriorating HF. During a median period of 6.9 years, 19 patients (33%) reached the composite outcome. Unstable group with standard therapy had significantly shorter event-free survival than stable group. Patients with uptitration of therapy had longer event-free survival than those with standard therapy group after adjustment of laboratory data (hazard ratio, 0.20, 95% confidence interval, 0.05–0.90; P = 0.036); the 10 year event-free survival in patients with and without uptitration of therapy was 93% and 51%, respectively (P = 0.023). Conclusions Patients with unstable sign had significantly shorter event-free survival than patients with stable sign. After additional therapy, the prognosis of patients with unstable signs improved. This technique may impact decision-making for improving their prognosis