Greve Pape (wealthlentil8)

This study chronologically evaluated the expression of the intensity and distribution of the sigma-1 receptor (σ1R) demonstrated by radiolabeled 2-[4-(2-iodophenyl)piperidino]cyclopentanol (OI5V) in a rat model of myocardial ischemia and reperfusion.Methods and ResultsThe left coronary artery was occluded for 30 min, followed by reperfusion. Dual-tracer autoradiography with I-OI5V and Tc-MIBI was performed to assess the spatiotemporal changes in I-OI5V uptake (n=5-6). Significant and peaked I-OI5V uptake in the ischemic area was observed at 3 days after reperfusion, and the I-OI5V uptake ratio of ischemic area to normally perfused left ventricular area decreased gradually from 3 to 28 days (mean value±SD; 0.90±0.12 at 1 day, 1.89±0.19 at 3 days, 1.52±0.17 at 7 days, 1.34±0.13 at 14 days, and 1.16±0.14 at 28 days, respectively). Triple-tracer autoradiography with I-OI5V, Tc-MIBI, and TlCl was performed to evaluate I-OI5V uptake in the ischemic area in relation to the residual perfusion at 7 days (n=4). The I-OI5V uptake ratio of the non-salvaged area was higher compared to that of the salvaged area in the ischemic area. I-OI5V and Tc-MIBI SPECT/CT was performed 3 days after reperfusion (n=3), and the in vivo images showed clear uptake of I-OI5V in the perfusion defect area. The present study confirmed the spatiotemporal expression pattern of σ1R expression. Non-invasive σ1R imaging with I or I-OI5V was feasible to monitor the expression of σ1R after myocardial ischemia and reperfusion. The present study confirmed the spatiotemporal expression pattern of σ1R expression. Non-invasive σ1R imaging with 123I or 125I-OI5V was feasible to monitor the expression of σ1R after myocardial ischemia and reperfusion. Because the early diagnosis of subclinical cardiac sarcoidosis (CS) remains difficult, we developed a deep learning algorithm to distinguish CS patients from healthy subjects using echocardiographic movies.Methods and ResultsAmong the patients who underwent echocardiography from January 2015 to December 2019, we chose 151 echocardiographic movies from 50 CS patients and 151 from 149 healthy subjects. We trained two 3D convolutional neural networks (3D-CNN) to identify CS patients using a dataset of 212 echocardiographic movies with and without a transfer learning method (Pretrained algorithm and Non-pretrained algorithm). On an independent set of 41 echocardiographic movies, the area under the receiver-operating characteristic curve (AUC) of the Pretrained algorithm was greater than that of Non-pretrained algorithm (0.842, 95% confidence interval (CI) 0.722-0.962 vs. 0.724, 95% CI 0.566-0.882, P=0.253). The AUC from the interpretation of the same set of 41 echocardiographic movies by 5 cardiologists was not significantly different from that of the Pretrained algorithm (0.855, 95% CI 0.735-0.975 vs. 0.842, 95% CI 0.722-0.962, P=0.885). A sensitivity map demonstrated that the Pretrained algorithm focused on the area of the mitral valve. A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie. A 3D-CNN with a transfer learning method may be a promising tool for detecting CS using an echocardiographic movie.4D flow MRI allows time-resolved 3D velocity-encoded phase-contrast imaging for 3D visualization and quantification of aortic and intracardiac flow. Radiologists should be familiar with the principles of 4D flow MRI and methods for evaluating blood flow qualitatively and quantitatively. The most substantial benefits of 4D flow MRI are that it enables the simultaneous comprehensive assessment of different vessels, and that retrospective analysis can be achieved in all vessels in any direction in the field of view, which is especially beneficial for patients with complicated congenital heart disease (CHD). For aortic valvular dis