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Academic Events

  • Machine Learning Enhanced Translational Research Methods Refine Novel Biomarker for Human Epilepsy

    Thursday, December 14, 2017

    4:00 PM-6:00 PM

    Bossone Research Center, Room 709, located at 32nd and Market Streets.

    • Undergraduate Students
    • Graduate Students
    • Faculty
    • Staff

    BIOMED PhD Thesis Defense

    Title:
    Machine Learning Enhanced Translational Research Methods Refine Novel Biomarker for Human Epilepsy

    Speaker:
    Walter Hinds, PhD Candidate, School of Biomedical Engineering, Science and Health Systems, Drexel University

    Advisor:
    Karen A. Moxon, PhD, Professor, University of California, Davis; and Research Professor, School of Biomedical Engineering, Science and Health Systems, Drexel University

    Abstract:
    One in three cases of epilepsy do not respond to drug treatment. If left untreated, status epilepticus can have severe symptoms and even lead to death. In some cases, it is possible to identify the location of brain tissue that is generating seizures and surgical removal of this area may result in reduced or even complete stoppage of seizures. Unfortunately, localization of epileptic brain tissue is difficult and complex cases require advanced techniques. Currently, electrocorticography (ECoG) is the ideal tool for identifying epileptic tissue because it has good temporal and spatial resolution. A recently proposed electrophysiological biomarker for epileptogenic tissue is the high frequency oscillation (HFO). In this thesis dissertation, translational research is applied to add greater capabilities for using ECoG to detect HFO’s, primarily as a surgical tool for identifying epileptogenic tissue.

    Identifying the epileptogenic zone, i.e., the brain tissue causing seizures, is not straightforward due to multiple reasons. The spatial resolution for ECoG is limited to 2 millimeters by multi-modal co-registration and detecting HFO’s is confounded by frequent false detections (10 - 60%). These issues can affect the localization of epileptogenic zone because HFO biomarkers occur in relatively small numbers and in sparse areas of the brain. Therefore, improving spatial and temporal localizations of the HFO biomarker will aid surgical removal and improve outcomes for cases of intractable epilepsy.

    To account for the multi-modal co-registration errors, a novel method was developed using the patient’s post-implant magnetic resonance image (MRI). Although the MRI-MRI intra-modality allows for better registration, typically this scan is not used due to limited visibility of electrode imprints. However, after applying an iterative closest point (ICP) matching algorithm, the electrode artifacts extracted from brain-boundary registered post-implant MRI's can be utilized to enhance the fusion of multi-modal co-registrations. Additionally, this semi-automated method can be applied seamlessly to multiple types of electrodes, i.e., depths, strips, and grids.

    To reduce the false detections of HFO’s a machine learning classification algorithm was developed from gold-standard, manually identified samples. First the initial detection procedure was used to find as many HFO candidate events as possible with 97% sensitivity. From this, a pool of ~2,000 hand-labeled events (~1,000 HFO and ~1,000 Noise) was created to train the algorithm and test its performance. The algorithm operates as a multiple classifier system (MCS) created from nine patient’s individually trained, artificial neural network (ANN) classifiers. The MCS was able to classify the events with 89% sensitivity, i.e. preserving almost 9 out of 10 true HFO events, while having the lowest reported false detection rate 5% of all available methods.

    The channel-wise false detection rate of seizure onset zones was also reduced 20% by the MCS, while maintaining the same sensitivity. Analysis using patient resections and outcomes found HFO rates detect the epileptogenic zone with a sensitivity of 100% and negative predictive value of 100%. Therefore, the clinical relevance of automatically detected HFO's is promising, especially as a tool for surgical brain mapping. However, extensive analysis of this novel biomarker is still necessary to completely realize its potential diagnostic utility.

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  • CCI Information Science Departmental Talk Series: "Revealing the fine-grained structure of science"

    Thursday, December 14, 2017

    4:00 PM-5:00 PM

    Rush Building, Room 014 (basement) 30 N. 33rd Street Philadelphia, PA 19104

    • Undergraduate Students
    • Graduate Students
    • Faculty
    • Staff
    Abstract: Dr. Ludo Waltman will provide an overview of his research on studying the fine-grained structure of science. He will discuss the clustering techniques that he has developed for identifying research topics at a highly detailed level of granularity. These techniques clusters scientific publications based on data from large-scale bibliometric databases. Recent algorithmic advances make it possible to identify many thousands of research topics throughout all fields of science in computing times of less than an hour. Dr. Waltman will also show practical examples illustrating how a fine-grained analysis of the structure of science allows us to address policy relevant questions, for instance related to the identification of emerging research topics and the comparison of the research profiles of universities.
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  • Pediatric Grand Rounds

    Friday, December 15, 2017

    8:00 AM-9:00 AM

    Angelo M. DiGeorge Teaching Center Lower Level St. Christopher's Hospital for Children

    • Graduate Students
    • Faculty
    • Staff
    • Medical Residents/Fellows

    Topic
    Multidisciplinary Case Conference: 6-Week-Old Infant With Right Upper Extremity Weakness

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  • Neurosciences Grand Rounds

    Friday, December 15, 2017

    8:00 AM-9:00 AM

    New College Building Geary Auditorium A 245 North 15th Street

    • Graduate Students
    • Faculty
    • Staff
    • Alumni
    • Medical Residents/Fellows

    Speaker:
    Dr. Erol Veznedaroglu

    Topic:
    TBA

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  • Novel Therapeutic Intervention for Myocardial Infarction in Large Animal Model

    Friday, December 15, 2017

    12:00 PM-2:00 PM

    Bossone Research Center, Room 709, located at 32nd and Market Streets.

    • Undergraduate Students
    • Graduate Students
    • Faculty
    • Staff

    BIOMED Master's Thesis Defense

    Title:
    Novel Therapeutic Intervention for Myocardial Infarction in Large Animal Model

    Speaker:
    Maria Mercedes, MS Candidate, School of Biomedical Engineering, Science and Health Systems, Drexel University

    Advisors:
    John Gearhart, PhD, Professor, Department of Cell and Developmental Biology, Perelman School of Medicine, University of Pennsylvania

    Fred Allen, PhD, Associate Teaching Professor, School of Biomedical Engineering, Science and Health Systems, Drexel University

    Adrian Shieh, PhD, Associate Teaching Professor, School of Biomedical Engineering, Science and Health Systems, Drexel University

    Abstract:
    Heart failure (HF) represents an enormous clinical problem that imposes a significant burden to both
    society and survivors. During a myocardial infarction, cardiomyocytes are damaged due to a lack of oxygen to the left ventricle and as a result these cells die. The dead cells are then replaced by fibrotic scar tissue that inhibits the heart from properly pumping blood to the rest of the heart.

    The current treatments of HF do not reverse the damage that the left ventricle experiences, and as a result, many people experience recurrent heart failure and sometimes cardiac arrest. The field of reprogramming investigates the process of direct reprogramming of endogenous cardiac fibroblasts to cardiomyocytes as a novel approach to help the heart pump blood properly after a myocardial infarction.

    In this project, we used a biodegradable hyaluronic acid (HA) hydrogel to encapsulate Adeno-associated virus (AAV) type 9 that was engineered to deliver a green fluorescent protein (GFP) reporter gene to a pig's heart post-myocardial. Our method represents a unique approach to transfect cardiomyocyte cells in vivo post-myocardial infarction in the pig. This study also demonstrates a cardiac functional improvement with the injection of hydrogel gel, AAV9, and selected transcription factors in the compromised area of the left ventricle.

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