The Experts below are selected from a list of 132 Experts worldwide ranked by ideXlab platform

John H. Zhang - One of the best experts on this subject based on the ideXlab platform.

  • crotalus atrox disintegrin reduces hemorrhagic transformation by attenuating matrix metalloproteinase 9 activity after middle cerebral artery occlusion in hyperglycemic male rats
    Journal of Neuroscience Research, 2020
    Co-Authors: Devin W. Mcbride, Eric C K Gren, Wayne Kelln, William K Hayes, John H. Zhang
    Abstract:

    Hemorrhagic transformation after ischemic stroke is an independent predictor for poor outcome and is characterized by Blood Vessel Rupture leading to brain edema. To date, no therapies for preventing hemorrhagic transformation exist. Disintegrins from the venom of Crotalus atrox have targets within the coagulation cascade, including receptors on platelets. We hypothesized that disintegrins from C. atrox venom can attenuate hemorrhagic transformation by preventing activation of matrix metalloproteinase after middle cerebral artery occlusion (MCAO) in hyperglycemic rats. We subjected 48 male Sprague-Dawley rats weighing 240-260 g to MCAO and hyperglycemia to induce hemorrhagic transformation of the infarction. At reperfusion, we administered either saline (vehicle), whole C. atrox venom (two doses were used), or fractionated C. atrox venom (HPLC Fraction 2). Rats were euthanized 24 hr post-ictus for measurement of infarction and hemoglobin volume. Reversed-phase HPLC was performed to fractionate the whole venom and peaks were combined to form Fraction 2, which contained the disintegrin Crotatroxin. Fraction 2 protected against hemorrhagic transformation after MCAO, and attenuated activation of matrix metalloproteinase-9. Administering matrix metalloproteinase antagonists prevented the protection by Fraction 2. The results of this study indicate that disintegrins found in C. atrox venom may have therapeutic potential for reducing hemorrhagic transformation after ischemic stroke. Moreover, the RP-HPLC fractions retained sufficient protein activity to suggest that gentler and less efficient orthogonal chromatographic methods may be unnecessary to isolate proteins and explore their function.

  • Maintaining Plasma Fibrinogen Levels and Fibrinogen Replacement Therapies for Treatment of Intracranial Hemorrhage
    Current drug targets, 2017
    Co-Authors: Devin W. Mcbride, Jiping Tang, John H. Zhang
    Abstract:

    Background Intracranial hemorrhage is characterized by the Blood Vessel Rupture and subsequent hematoma expansion. It is the least treatable stroke subtype, resulting in higher morbidity and mortality per incidence than ischemic stroke. Recent studies have observed lower than normal levels of plasma fibrinogen in patients of intracerebral hemorrhage. Furthermore, in other cases of severe hemorrhage, plasma fibrinogen levels have been identified as an indicator of prognosis. Current clinical management of cerebral hemorrhage includes adjunctive therapies and possible surgical evacuation. However, a possible therapeutic target for intracranial hemorrhage is fibrinogen. During intracranial hemorrhage with hematoma expansion, fibrinogen levels are rapidly depleted and thus are in need of replacement. Maintaining high levels of fibrinogen can promote rapid clotting and reduction of hematoma expansion. Objectives Within this review, we examine the role of fibrinogen in intracranial hemorrhage and evaluate the use of fibrinogen replacement therapies for maintaining normal levels of this key hemostatic protein. The pros and cons are discussed and an opinion of the most appropriate fibrinogen replacement therapy for intracranial hemorrhage is made. Conclusion It is concluded that fibrinogen concentrate seems to be the most suitable therapy for elevating plasma fibrinogen for the treatment of intracranial hemorrhage with hematoma expansion.

Devin W. Mcbride - One of the best experts on this subject based on the ideXlab platform.

  • crotalus atrox disintegrin reduces hemorrhagic transformation by attenuating matrix metalloproteinase 9 activity after middle cerebral artery occlusion in hyperglycemic male rats
    Journal of Neuroscience Research, 2020
    Co-Authors: Devin W. Mcbride, Eric C K Gren, Wayne Kelln, William K Hayes, John H. Zhang
    Abstract:

    Hemorrhagic transformation after ischemic stroke is an independent predictor for poor outcome and is characterized by Blood Vessel Rupture leading to brain edema. To date, no therapies for preventing hemorrhagic transformation exist. Disintegrins from the venom of Crotalus atrox have targets within the coagulation cascade, including receptors on platelets. We hypothesized that disintegrins from C. atrox venom can attenuate hemorrhagic transformation by preventing activation of matrix metalloproteinase after middle cerebral artery occlusion (MCAO) in hyperglycemic rats. We subjected 48 male Sprague-Dawley rats weighing 240-260 g to MCAO and hyperglycemia to induce hemorrhagic transformation of the infarction. At reperfusion, we administered either saline (vehicle), whole C. atrox venom (two doses were used), or fractionated C. atrox venom (HPLC Fraction 2). Rats were euthanized 24 hr post-ictus for measurement of infarction and hemoglobin volume. Reversed-phase HPLC was performed to fractionate the whole venom and peaks were combined to form Fraction 2, which contained the disintegrin Crotatroxin. Fraction 2 protected against hemorrhagic transformation after MCAO, and attenuated activation of matrix metalloproteinase-9. Administering matrix metalloproteinase antagonists prevented the protection by Fraction 2. The results of this study indicate that disintegrins found in C. atrox venom may have therapeutic potential for reducing hemorrhagic transformation after ischemic stroke. Moreover, the RP-HPLC fractions retained sufficient protein activity to suggest that gentler and less efficient orthogonal chromatographic methods may be unnecessary to isolate proteins and explore their function.

  • Maintaining Plasma Fibrinogen Levels and Fibrinogen Replacement Therapies for Treatment of Intracranial Hemorrhage
    Current drug targets, 2017
    Co-Authors: Devin W. Mcbride, Jiping Tang, John H. Zhang
    Abstract:

    Background Intracranial hemorrhage is characterized by the Blood Vessel Rupture and subsequent hematoma expansion. It is the least treatable stroke subtype, resulting in higher morbidity and mortality per incidence than ischemic stroke. Recent studies have observed lower than normal levels of plasma fibrinogen in patients of intracerebral hemorrhage. Furthermore, in other cases of severe hemorrhage, plasma fibrinogen levels have been identified as an indicator of prognosis. Current clinical management of cerebral hemorrhage includes adjunctive therapies and possible surgical evacuation. However, a possible therapeutic target for intracranial hemorrhage is fibrinogen. During intracranial hemorrhage with hematoma expansion, fibrinogen levels are rapidly depleted and thus are in need of replacement. Maintaining high levels of fibrinogen can promote rapid clotting and reduction of hematoma expansion. Objectives Within this review, we examine the role of fibrinogen in intracranial hemorrhage and evaluate the use of fibrinogen replacement therapies for maintaining normal levels of this key hemostatic protein. The pros and cons are discussed and an opinion of the most appropriate fibrinogen replacement therapy for intracranial hemorrhage is made. Conclusion It is concluded that fibrinogen concentrate seems to be the most suitable therapy for elevating plasma fibrinogen for the treatment of intracranial hemorrhage with hematoma expansion.

Cheng Kiang Lee - One of the best experts on this subject based on the ideXlab platform.

  • finding distinctive shape features for automatic hematoma classification in head ct images from traumatic brain injuries
    International Conference on Tools with Artificial Intelligence, 2013
    Co-Authors: Tianxia Gong, Nengli Lim, Li Cheng, Hwee Kuan Lee, Chew Lim Tan, C Tchoyoson C Lim, Boon Chuan Pang, Cheng Kiang Lee
    Abstract:

    Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by Blood Vessel Rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

  • ICTAI - Finding Distinctive Shape Features for Automatic Hematoma Classification in Head CT Images from Traumatic Brain Injuries
    2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013
    Co-Authors: Tianxia Gong, Nengli Lim, Li Cheng, Hwee Kuan Lee, Chew Lim Tan, Boon Chuan Pang, C. C. Tchoyoson Lim, Cheng Kiang Lee
    Abstract:

    Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by Blood Vessel Rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

Tianxia Gong - One of the best experts on this subject based on the ideXlab platform.

  • finding distinctive shape features for automatic hematoma classification in head ct images from traumatic brain injuries
    International Conference on Tools with Artificial Intelligence, 2013
    Co-Authors: Tianxia Gong, Nengli Lim, Li Cheng, Hwee Kuan Lee, Chew Lim Tan, C Tchoyoson C Lim, Boon Chuan Pang, Cheng Kiang Lee
    Abstract:

    Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by Blood Vessel Rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

  • ICTAI - Finding Distinctive Shape Features for Automatic Hematoma Classification in Head CT Images from Traumatic Brain Injuries
    2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013
    Co-Authors: Tianxia Gong, Nengli Lim, Li Cheng, Hwee Kuan Lee, Chew Lim Tan, Boon Chuan Pang, C. C. Tchoyoson Lim, Cheng Kiang Lee
    Abstract:

    Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by Blood Vessel Rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

Chew Lim Tan - One of the best experts on this subject based on the ideXlab platform.

  • finding distinctive shape features for automatic hematoma classification in head ct images from traumatic brain injuries
    International Conference on Tools with Artificial Intelligence, 2013
    Co-Authors: Tianxia Gong, Nengli Lim, Li Cheng, Hwee Kuan Lee, Chew Lim Tan, C Tchoyoson C Lim, Boon Chuan Pang, Cheng Kiang Lee
    Abstract:

    Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by Blood Vessel Rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.

  • ICTAI - Finding Distinctive Shape Features for Automatic Hematoma Classification in Head CT Images from Traumatic Brain Injuries
    2013 IEEE 25th International Conference on Tools with Artificial Intelligence, 2013
    Co-Authors: Tianxia Gong, Nengli Lim, Li Cheng, Hwee Kuan Lee, Chew Lim Tan, Boon Chuan Pang, C. C. Tchoyoson Lim, Cheng Kiang Lee
    Abstract:

    Computer aided diagnosis (CAD) in medical imaging is of growing interest in recent years. Our proposed CAD system aims to enhance diagnosis and prognosis of traumatic brain injury (TBI) patients with hematomas. Hematoma caused by Blood Vessel Rupture is the major lesion in TBI cases and is usually assessed using head computed tomography (CT). In our CAD system, we segment the hematoma region from each slice of a CT series, extract features from the hematoma segments, and automatically classify the hematoma types using machine learning methods. We propose two sets of shape based features for each segmented hematoma region. The first set contains primitive features describing the overall shape of a hematoma region. The features in the second set are based on the dissimilarities of the shapes of hematoma regions measured by geodesic distances. After feature extraction, we classify the hematoma regions into three types -- epidural hematoma, sub-dural hematoma, and intracerebral hematoma, using random forest. Each tree of the random forest votes one class for each hematoma, and the random forest takes the class label with the majority votes for the hematoma. As hematomas are volumetric in nature, some hematomas are observed across several consecutive slices in the same CT series. For each class, we add the votes from each hematoma slice that comprises the volumetric hematoma in that class, then we take the class with the majority of the summed votes as the class label for that volumetric hematoma. The overall classification accuracies for hematoma region from each CT slice are 80.7%, 81.3%, and 81.1% using primitive features only, geodesic distance features only, or both sets of features, respectively. For volumetric hematoma classification, the overall accuracies are 80.9%, 81.5%, and 81.5% respectively. The results are promising to radiologists and neurosurgeons specialized in this field of research.