Iron oxide nanoparticles (IONPs) have shown great potential in various biomedical applications. However, information on the interaction between IONPs and biological systems, especially the uptake and distribution of IONPs in cells and tissues, as well as the mechanism of biological action, is relatively limited. In the present study, multi-modal visualization methods, including confocal fluorescence microscopy, transmission electron microscopy, magnetic resonance imaging, and fluorescence optical imaging, were utilized to unveil the uptake and distribution of IONPs in macrophages, cancer cells, and xenograft models. Our results demonstrated that uptake of IONPs in RAW264.7 macrophages and SKOV-3 cancer cells were dose- and cell type-dependent. Cellular uptake of IONPs was an energy-dependent process, and caveolae-mediated endocytosis was the main uptake pathway. All the IONPs were primarily present in endocytic compartments (e.g., endosomes, lysosomes) inside the cells. At 48 hours after intravenous injection of IONPs in SKOV-3 tumor bearing mice, most of the IONPs was distributed in the liver and spleen, with obvious uptake in the tumor, less but significant amount in the kidney and brain. Taken together, multi-modal visualization approaches in our study provide detailed information on the cellular uptake and tissue distribution of IONPs from multiple levels and perspectives.
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http://dx.doi.org/10.1166/jbn.2019.2810 | DOI Listing |
Sci Rep
January 2025
ADAPT Research Centre, School of Computer Science, University of Galway, Galway, Ireland.
This study utilizes the Breast Ultrasound Image (BUSI) dataset to present a deep learning technique for breast tumor segmentation based on a modified UNet architecture. To improve segmentation accuracy, the model integrates attention mechanisms, such as the Convolutional Block Attention Module (CBAM) and Non-Local Attention, with advanced encoder architectures, including ResNet, DenseNet, and EfficientNet. These attention mechanisms enable the model to focus more effectively on relevant tumor areas, resulting in significant performance improvements.
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January 2025
Chongqing Vocational Institute of Tourism, Chongqing, China.
To enhance enterprises' interactive exploration capabilities for unstructured chart data, this paper proposes a multimodal chart question-answering method. Facing the challenge of recognizing curved and irregular text in charts, we introduce Gaussian heatmap encoding technology to achieve character-level precise text annotation. Additionally, we combine a key point detection algorithm to extract numerical information from the charts and convert it into structured table data.
View Article and Find Full Text PDFSoybean ( [L.] Merr.) production is susceptible to biotic and abiotic stresses, exacerbated by extreme weather events.
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January 2025
School of Intelligent Manufacturing Modern Industry, Xinjiang University, Urumqi, 830046, Xinjiang, China.
To achieve real-time monitoring and intelligent maintenance of transformers, a framework based on deep vision and digital twin has been developed. An enhanced visual detection model, DETR + X, is proposed, implementing multidimensional sample data augmentation through Swin2SR and GAN networks. This model converts one-dimensional DGA data into three-dimensional feature images based on Gram angle fields, facilitating the transformation and fusion of heterogeneous modal information.
View Article and Find Full Text PDFEnviron Sci Process Impacts
January 2025
College of Environment, Zhejiang University of Technology, Hangzhou, 310014, P. R. China.
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