Yttrium-90 used for therapy should be of very high radionuclidic (RN) purity (>99.998%) as the most probable contaminant, strontium-90, is a bone seeker with a maximum permissible body burden of 74 kBq (2 microCi) only. None of the current known methods of RN purity estimations is adequate to reliably measure the 90Sr RN impurity at such low levels. Our aim was to develop a reliable technique to accurately determine the amount of 90Sr in 90Y used for therapy. This new technique combines chelate-based extraction with paper chromatography using paper impregnated with 2-ethylhexyl, 2-ethylhexylphosphonic acid (KSM-17), which is a 90Y-specific chelator. A PC strip impregnated with KSM-17 at the point of spotting is used for chromatography. Upon development with normal saline, 90Sr moves to the solvent front leaving 90Y completely chelated and retained at the point of spotting. The activity at the solvent front (90Sr) is quantified by liquid scintillation counting, and the data are compared with the total applied activity to provide the RN purity of the test solution. The method has a sensitivity of > or =74 kBq (2 microCi) of 90Sr per 37 GBq (1 Ci) of 90Y. This novel, innovative, and simple technique offers a reliable solution to the unanswered problem of estimation of 90Sr content in 90Y used for cancer therapy.
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http://dx.doi.org/10.1021/ac701651u | DOI Listing |
BMC Med Educ
January 2025
First Affiliated Hospital of Kunming Medical University, No. 295, Xichang Road, Kunming, 650032, China.
Background: With the continuous development of educational methods, desktop virtual reality technology has gradually attracted widespread attention. Although current research has shown that this technology can promote learning among nursing students, the mechanism and intrinsic factors are not yet clear. This study aims to explore the mechanisms and factors of the application of desktop virtual reality technology in nursing students' education and discuss the possible outcomes.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Faculty of Medicine and Pharmacy of Rabat, Mohammed V University of Rabat, Rabat, 10000, Morocco.
Gastrointestinal (GI) disease examination presents significant challenges to doctors due to the intricate structure of the human digestive system. Colonoscopy and wireless capsule endoscopy are the most commonly used tools for GI examination. However, the large amount of data generated by these technologies requires the expertise and intervention of doctors for disease identification, making manual analysis a very time-consuming task.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronics and Information, Xijing University, Xi'an, 710123, China.
To enhance high-frequency perceptual information and texture details in remote sensing images and address the challenges of super-resolution reconstruction algorithms during training, particularly the issue of missing details, this paper proposes an improved remote sensing image super-resolution reconstruction model. The generator network of the model employs multi-scale convolutional kernels to extract image features and utilizes a multi-head self-attention mechanism to dynamically fuse these features, significantly improving the ability to capture both fine details and global information in remote sensing images. Additionally, the model introduces a multi-stage Hybrid Transformer structure, which processes features at different resolutions progressively, from low resolution to high resolution, substantially enhancing reconstruction quality and detail recovery.
View Article and Find Full Text PDFSci Rep
January 2025
University Institute of Computing, Chandigarh University, Punjab, India.
Automatic Sign Language Recognition Systems (ASLR) offers smooth communication between hearing-impaired and normal-hearing individuals, enhancing educational opportunities for impaired. However, it struggles with "curse of dimensionality" due to excessive features resulting in prolonged training time and exhaustive computational demand. This paper proposes technique that integrates machine learning and swarm intelligence to effectively address this issue.
View Article and Find Full Text PDFSci Rep
January 2025
Department of Theoretical Electrical Engineering and Diagnostics of Electrical Equipment, Institute of Electrodynamics, National Academy of Sciences of Ukraine, Beresteyskiy, 56, Kyiv-57, Kyiv, 03680, Ukraine.
Power quality (PQ) disturbances, such as voltage sags, are significant issues that can lead to damage in electrical equipment and system downtime. Detecting and classifying these disturbances accurately is essential for maintaining reliable power systems. This paper introduces a novel approach to voltage sag analysis by employing wavelet packet analysis combined with energy-based feature extraction to enhance PQ monitoring.
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