Cancer development is driven by diverse processes, and metabolic alterations are among the primary characteristics. Multiscale imaging of aberrant metabolites in cancer is critical to understand the pathology and identify new targets for treatment. While peroxynitrite (ONOO) is reported being enriched in some tumors and plays important tumorigenic roles, whether it is upregulated in gliomas remains unexplored. To determine the levels and roles of ONOO in gliomas, efficient tools especially those with desirable blood-brain barrier (BBB) permeability and can realize the in situ imaging of ONOO in multiscale glioma-related samples are indispensable. Herein, we proposed a strategy of physicochemical property-guided probe design, which resulted in the development of a fluorogenic probe NOSTracker for smartly tracking ONOO. The probe showed sufficient BBB permeability. ONOO triggered oxidation of its arylboronate group was automatically followed by a self-immolative cleavage of a fluorescence-masking group, liberating its fluorescence signal. The probe was not only highly sensitive and selective towards ONOO, but its fluorescence favored desirable stability in various complex biological milieus. Guaranteed by these properties, multiscale imaging of ONOO was realized in vitro in patient-derived primary glioma cells, ex vivo in clinical glioma slices, and in vivo in the glioma of live mice. The results showed the upregulation of ONOO in gliomas. Furthermore, a specific ONOO scavenger uric acid (UA) was pharmaceutically used to downregulate ONOO in glioma cell lines, and an anti-proliferative effect was observed. These results taken together imply the potential of ONOO as a biomarker and target for glioma treatment, and propose NOSTracker as a reliable tool to further explore the role of ONOO in glioma development.
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http://dx.doi.org/10.1016/j.bios.2023.115415 | DOI Listing |
Front Neurorobot
January 2025
The College of Artificial Intelligence, Shenyang Aerospace University, Shenyang, China.
U-Net and its variants have been widely used in the field of image segmentation. In this paper, a lightweight multi-scale Ghost U-Net (MSGU-Net) network architecture is proposed. This can efficiently and quickly process image segmentation tasks while generating high-quality object masks for each object.
View Article and Find Full Text PDFSci Rep
January 2025
School of Electronic Information and Engineering, Changchun University of Science and Technology, Changchun, China.
Detecting ship targets in remote sensing images within complex scenarios faces numerous challenges. The limited feature information of small-scale targets and their random orientation angles often result in missed and false detections. To address these issues, this paper proposes a Multi-Scale Rotated Detection Network (MSRO-Net) for detecting rotated ship targets in remote sensing images.
View Article and Find Full Text PDFSci Rep
January 2025
School of Computer Science, Hunan University of Technology, Tianyuan District, Zhuzhou, 412007, China.
The railway track extraction using unmanned aerial vehicle (UAV) aerial images suffers from issues such as low extraction accuracy and high time consumption. In response to these problems, this paper presents a lightweight algorithm DA-DeepLabv3 + based on densely connected and attention mechanisms. Firstly, the lightweight MobileNetV2 network is employed to replace the Xception feature extraction network, thereby reducing the number of model parameters.
View Article and Find Full Text PDFLight Sci Appl
January 2025
Department of Electrical and Computer Engineering, Boston University, Boston, MA, 02215, USA.
A major challenge in neuroscience is visualizing the structure of the human brain at different scales. Traditional histology reveals micro- and meso-scale brain features but suffers from staining variability, tissue damage, and distortion, which impedes accurate 3D reconstructions. The emerging label-free serial sectioning optical coherence tomography (S-OCT) technique offers uniform 3D imaging capability across samples but has poor histological interpretability despite its sensitivity to cortical features.
View Article and Find Full Text PDFRadiol Med
January 2025
Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.
Background: Accurate differentiation between benign and malignant pancreatic lesions is critical for effective patient management. This study aimed to develop and validate a novel deep learning network using baseline computed tomography (CT) images to predict the classification of pancreatic lesions.
Methods: This retrospective study included 864 patients (422 men, 442 women) with confirmed histopathological results across three medical centers, forming a training cohort, internal testing cohort, and external validation cohort.
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