A 49-year-old woman was transferred to our hospital with acute-onset chest pain. Her electrocardiogram showed complete atrioventricular block and bradycardia with ST-segment elevation in the inferior leads, and she presented with cardiogenic shock. She was diagnosed with inferior acute myocardial infarction (AMI), and subsequent emergency cardiac catheterization was performed. Selective coronary angiography showed neither stenosis nor obstruction in any of the coronary arteries. Left ventriculography showed a large floating object located on the ascending aortic wall above the ostium of the right coronary artery (RCA). Chest enhanced computed tomography confirmed the floating object in the ascending aorta. These findings suggested that the floating object was associated with the RCA occlusion. To remove the floating object, emergency surgery was performed. The floating object was a large thrombus derived from the localized atheromatous plaque in the ascending aorta. Specialized immunostaining for surface antigen CD34 revealed that regenerated endothelial cells were present on the erosion, along the stalk, and on the floating thrombus. These findings indicate that the CD34-positive endothelial precursor cells strayed into the surface and/or inside of the thrombus, and consequently the floating thrombus supported by these regenerated endothelial cells occluded the RCA, causing AMI. < A free floating thrombus formed in the ascending aorta can cause obstruction of the coronary artery ostium, leading to AMI. This unusual cause of AMI mostly occurs in females, and shows high mortality rates. Although the risk factors are known to be current smoking, oral hormone therapy, and hypercoagulable state such as pregnancy, the underlying mechanism of thrombus formation is still unclear. This report describes a possible role of CD-34 positive regenerated endothelial cells in thrombus formation.>.
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http://dx.doi.org/10.1016/j.jccase.2013.04.003 | DOI Listing |
Mar Pollut Bull
January 2025
ISPRA Italian National Institute for Environmental Protection and Research, Roma, Italy.
The EU Marine Strategy Framework Directive (MSFD, 2008/56/EC) requires Member States to establish monitoring programs for Descriptor 10-Marine Litter, to track progress towards achieving a marine Good Environmental Status (GES). Italy conducted systematic monitoring of Floating Marine Macro Litter (FMML) in three Marine Reporting Units: Western, Central Mediterranean, and Adriatic (2018-2022, 534 surveys, 2719 km across all seasons). This study assessed baseline values for FMML amount and composition, giving indication for tracking GES progress.
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December 2024
Department of Automation, North China Electric Power University, Baoding 071003, China.
To address the difficulty in detecting workers' violation behaviors in electric power construction scenarios, this paper proposes an innovative method that integrates knowledge reasoning and progressive multi-level distillation techniques. First, standards, norms, and guidelines in the field of electric power construction are collected to build a comprehensive knowledge graph, aiming to provide accurate knowledge representation and normative analysis. Then, the knowledge graph is combined with the object-detection model in the form of triplets, where detected objects and their interactions are represented as subject-predicate-object relationship.
View Article and Find Full Text PDFJ Vis
January 2025
Department of Psychology, New York University, New York, NY, USA.
Active object recognition, fundamental to tasks like reading and driving, relies on the ability to make time-sensitive decisions. People exhibit a flexible tradeoff between speed and accuracy, a crucial human skill. However, current computational models struggle to incorporate time.
View Article and Find Full Text PDFSci Rep
December 2024
School of Naval Architecture and Ocean Engineering, Huazhong University of Science and Technology, Wuhan, 430074, China.
With the advancement of artificial intelligence technology, unmanned boats utilizing deep learning models have shown significant potential in water surface garbage classification. This study employs Convolutional Neural Network (CNN) to extract features of water surface floating objects and constructs the VGG16-15 model based on the VGG-16 architecture, capable of identifying 15 common types of water surface floatables. A garbage classification dataset was curated to obtain 5707 images belonging to 15 categories, which were then split into training and validation sets in a 4:1 ratio.
View Article and Find Full Text PDFFront Plant Sci
December 2024
The Key Laboratory for Crop Production and Smart Agriculture of Yunnan Province, Yunnan Agricultural University, Kunming, Yunnan, China.
Tea leaf diseases are significant causes of reduced quality and yield in tea production. In the Yunnan region, where the climate is suitable for tea cultivation, tea leaf diseases are small, scattered, and vary in scale, making their detection challenging due to complex backgrounds and issues such as occlusion, overlap, and lighting variations. Existing object detection models often struggle to achieve high accuracy in detecting tea leaf diseases.
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