Advances in large-scale analysis are becoming very useful in understanding health and disease. Here, we used high-throughput mass spectrometry to identify differentially expressed proteins between early and advanced lesions. Carotid endarterectomy samples were collected and dissected into early and advanced atherosclerotic lesion portions. Proteins were extracted and subjected to liquid chromatography-tandem mass spectrometry (LC-MS/MS) analysis. Differentially expressed proteins were identified and verified using multiple reaction monitoring (MRM), on which advanced systems biology and enrichment analyses were performed. The identified proteins were further compared to the transcriptomic data of 32 paired samples obtained from early and advanced atherosclerotic lesions. A total of 95 proteins were upregulated, and 117 proteins were downregulated in advanced lesions compared to early atherosclerotic lesions (p < 0.05). The upregulated proteins were associated with proatherogenic processes, whereas downregulated proteins were involved in extracellular matrix organization and vascular smooth muscle cytoskeleton. Many of the identified proteins were linked to various "upstream regulators", among which TGFβ had the highest connections. Specifically, a total of 19 genes were commonly upregulated, and 30 genes were downregulated at the mRNA and protein levels. These genes were involved in vascular smooth muscle cell activity, for which enriched transcription factors were identified. This study deciphers altered pathways in atherosclerosis and identifies upstream regulators that could be candidate targets for treatment.
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http://dx.doi.org/10.1038/s41440-018-0192-4 | DOI Listing |
J Magn Reson Imaging
January 2025
Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Osteoarthritis (OA) is heterogeneous and involves structural changes in the whole joint, such as cartilage, meniscus/labrum, ligaments, and tendons, mainly with short T2 relaxation times. Detecting OA before the onset of irreversible changes is crucial for early proactive management and limit growing disease burden. The more recent advanced quantitative imaging techniques and deep learning (DL) algorithms in musculoskeletal imaging have shown great potential for visualizing "pre-OA.
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View Article and Find Full Text PDFVis Comput Ind Biomed Art
January 2025
School of Engineering Medicine and School of Biological Science and Medical Engineering, Beihang University, Beijing, 100191, China.
Fluorescence endoscopy technology utilizes a light source of a specific wavelength to excite the fluorescence signals of biological tissues. This capability is extremely valuable for the early detection and precise diagnosis of pathological changes. Identifying a suitable experimental approach and metric for objectively and quantitatively assessing the imaging quality of fluorescence endoscopy is imperative to enhance the image evaluation criteria of fluorescence imaging technology.
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Department of Research and Innovation, Medway NHS Foundation Trust, Gillingham ME7 5NY, United Kingdom; Faculty of Medicine, Health and Social Care, Canterbury Christ Church University, United Kingdom.
This narrative review examines the transformative role of Artificial Intelligence (AI) and Machine Learning (ML) in organ retrieval and transplantation. AI and ML technologies enhance donor-recipient matching by integrating and analyzing complex datasets encompassing clinical, genetic, and demographic information, leading to more precise organ allocation and improved transplant success rates. In surgical planning, AI-driven image analysis automates organ segmentation, identifies critical anatomical features, and predicts surgical outcomes, aiding pre-operative planning and reducing intraoperative risks.
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