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Brain Hemorrhage

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Eng H Lo – 1st expert on this subject based on the ideXlab platform

  • astrocytic induction of matrix metalloproteinase 9 and edema in Brain Hemorrhage
    Journal of Cerebral Blood Flow and Metabolism, 2007
    Co-Authors: Emiri Tejima, Bing-qiao Zhao, Kiyoshi Tsuji, Anna Rosell, Klaus Van Leyen, Gilberto R Gonzalez, Joan Montaner, Xiaoying Wang, Eng H Lo

    Abstract:

    We tested the hypothesis that astrocytic matrix metalloproteinase-9 (MMP-9) mediates hemorrhagic Brain edema. In a clinical case of hemorrhagic stroke, MMP-9 co-localized with astrocytes and neurons in peri-hematoma areas. In a mouse model where blood was injected into striatum, MMP-9 was colocalized with astrocytes surrounding the hemorrhagic lesion. Because MMP-9 is present in blood as well as Brain, we compared four groups of wild type (WT) and MMP-9 knockout (KO) mice: WT blood injected into WT Brain, KO blood into KO Brain, WT blood into KO Brain, and KO blood into WT Brain. Gel zymography showed that MMP-9 was elevated in WT hemorrhagic Brain tissue but absent from KO hemorrhagic Brain tissue. Edematous water content was elevated when WT blood was injected into WT Brain. However, edema was ameliorated when MMP-9 was absent in either blood or Brain or both. To further assess the mechanisms involved in astrocytic induction of MMP-9, we next examined primary mouse astrocyte cultures. Exposure to hemoglobin rapidly upregulated MMP-9 in conditioned media within 1 to 24 h. Hemoglobin-induced MMP-9 was reduced by the free radical scavenger U83836E. Taken together, these data suggest that although there are large amounts of MMP-9 in blood, hemoglobin-induced oxidative stress can trigger MMP-9 in astrocytes and these parenchymal sources of matrix degradation may also be an important factor in the pathogenesis of hemorrhagic Brain edema.

Shinji Nagahiro – 2nd expert on this subject based on the ideXlab platform

  • edaravone a free radical scavenger inhibits mmp 9 related Brain Hemorrhage in rats treated with tissue plasminogen activator
    Stroke, 2009
    Co-Authors: Kenji Yagi, Keiko T Kitazato, Yoshiteru Tada, Tomoya Kinouchi, Kenji Shimada, Shinji Nagahiro

    Abstract:

    Background and Purpose— Intracerebral Hemorrhage, induced by recombinant tissue plasminogen activator (rtPA) in ischemic stroke, is attributable to the increased activity of matrix metalloproteinase-9 (MMP-9). Patients with acute infarct benefit from the neuroprotective drug edaravone, a free radical scavenger. We examined the mechanisms by which edaravone may help to suppress rtPA-induced Brain Hemorrhage. Methods— Male Wistar rats weighing 250 to 280 g were subjected to 3-hour transient middle cerebral artery occlusion (MCAO) and divided randomly into 3 groups. Immediately after reperfusion, 1 group was intravenously injected with 10 mg/kg rtPA, another with rtPA plus 3 mg/kg edaravone, and the 3rd group received no treatment. We assessed the Hemorrhage volume and the activity of MMP-9 in the Brain 24 hours postischemia. We also studied the activity of MMP-9, its mRNA expression, and nuclear factor-kappa B (NF-κB) activity in rtPA-stimulated human microvascular endothelial cells (HBECs). Results— The de…

M. U. S Perera – 3rd expert on this subject based on the ideXlab platform

  • Intelligent Brain Hemorrhage diagnosis using artificial neural networks
    BEIAC 2012 – 2012 IEEE Business Engineering and Industrial Applications Colloquium, 2012
    Co-Authors: Ushani Balasooriya, M. U. S Perera

    Abstract:

    Brain Hemorrhage is a type of stroke which is caused by an artery in the Brain bursting and causing bleeding in the surrounded tissues. Diagnosing Brain Hemorrhage, which is mainly through the examination of a CT scan enables the accurate prediction of disease and the extraction of reliable and robust measurement for patients in order to describe the morphological changes in the Brain as the recovery progresses. Though a lot of research on medical image processing has been done, still there is opportunity for further research in the area of Brain Hemorrhage diagnosis due to the low accuracy level in the current methods and algorithms, coding complexity of the developed approaches, impracticability in the real environment, and lack of other enhancements which may make the system more interactive and useful. Additionally many of the existing approaches address the diagnosis of a limited no of Brain Hemorrhage types. This project investigates the possibility of diagnosing Brain Hemorrhage using an image segmentation of CT scan images using watershed method and feeding of the appropriate inputs extracted from the Brain CT image to an artificial neural network for classification. The output generated as the type of Brain Hemorrhages, can be used to verify expert diagnosis and also as a learning tool for trainee radiologists to minimize errors in current methods. The prototype developed using Matlab can help medical students to practice the related concepts they learn using an image guide with examples for surgeries and surgical simulation. System was evaluated by the domain experts, like radiologists, intended users such as medical students as well as by technical experts. The prototype developed was successful since it was being evaluated as credible, innovative and useful software for the students in the field of radiology while 100% of the evaluators mentioned the diagnosis accuracy is acceptable. © 2012 IEEE.

  • Intelligent Brain Hemorrhage diagnosis system
    2011 IEEE International Symposium on IT in Medicine and Education, 2011
    Co-Authors: Ushani Balasooriya, M. U. S Perera

    Abstract:

    Diagnosing Brain Hemorrhage, which is a condition caused by a Brain artery busting and causing bleeding is currently done by medical experts using a CT scan. Periodic examination of scans enable the accurate prediction of disease and the extraction of reliable and robust measurement for patients in order to describe the morphological changes in the Brain as the recovery progresses. Prior attempts to use medical image processing techniques to extract relevant information from a CT scan has shortcoming due to the low accuracy level in the current methods and algorithms, coding complexity of the developed approaches, impracticability in the real environment, and lack of other enhancements which may make the system more interactive and useful. This research investigates the possibility of diagnosing Brain Hemorrhage using an image segmentation of CT scan images using watershed method, feeding of the appropriate inputs extracted from the Brain CT image to an artificial neural network for classification. The output generated as the type of Brain Hemorrhages, can be used to verify expert diagnosis and also as learning tool for trainee radiologists to narrow down the errors in current methods.