Modernization Of The Biomedical Textile
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Modernization Of The Biomedical Textile

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    Modernization Of The Biomedical Textile Modernization Of The Biomedical Textile Presentation Transcript

    • Joint Department of Biomedical Engineering Challenges in Crossing Boundaries of Traditional Academic and Research Infrastructure North Carolina State University The University of North Carolina at Chapel Hill David S. Lalush Assistant Professor [email_address]
    • Joint Department of Biomedical Engineering
      • Who we are
      • Case studies of individual laboratories
      • Program-wide data issues
      • Ideas
    • Joint Department of Biomedical Engineering
      • Who we are
      • Case studies of individual laboratories
      • Program-wide data issues
      • Ideas
    • What is Biomedical Engineering?
      • 10 years ago: The application of engineering principles and technologies to solve problems in medicine and biology.
      • Living systems, cells, and biomolecules have become technologies themselves!
      • Now: The integration of engineering and life science disciplines to improve health care and better understand the biosphere.
    • Biomedical Engineering is Diverse
      • Engineering: Electrical, Chemical, Mechanical, Materials, Industrial, Nuclear, Textile, Computer Science
      • Physical Sciences: Chemistry, Physics
      • Life Sciences: Biology, Forestry, Physiology, Botany, Genetics
      • Clinical: Radiology, Radiation Oncology, Orthopaedics, Cardiology, Dentistry, Neurology, Surgery, Vet Med
      • Others: Pharmacy, Bioinformatics, Information Technology
    • BME Research
      • 32 core faculty; 60 affiliated faculty; ~110 grad students
      • Tissue Engineering: NCSU , UNC
      • Biomechanics: NCSU , UNC
      • Biomedical Imaging: UNC , NCSU
      • Metabolomics and Functional Genomics : UNC , NCSU
      • Medical Devices: NCSU , UNC
      • Systems Biology: UNC , NCSU
      • Medical Textiles: NCSU
      • Biomaterials: NCSU , UNC
      • Rehabilitation: NCSU , UNC
    • Our program crosses boundaries
      • BME is interdisciplinary, integrating research methods from
        • Life sciences
        • Physical sciences
        • Engineering
        • Medicine
    • Our program crosses boundaries
      • BME is a joint department of two universities
        • A joint graduate program
        • A BME undergraduate program at NCSU
        • A BME Applied Sciences undergraduate program at UNC
        • Possible joint undergraduate program in the future
    • Our program crosses boundaries
      • Our IT does not cross boundaries very well
        • Students and faculty have IDs and access to library and academic computing resources on both campuses.
        • But that’s all!
        • Individual researchers develop and maintain their own resources.
    • Joint Department of Biomedical Engineering
      • Who we are
      • Case studies of individual laboratories
      • Program-wide data issues
      • Ideas
    • Laboratory for Emerging Imaging Technologies
      • Novel in vivo imaging techniques using X-ray, gamma-ray, and optical methods
      • 3D and 4D (time-domain) imaging
      • Affiliated with UNC Biomedical Research Imaging Center (BRIC)
      David S. Lalush [email_address]
    • Dynamic X-ray Imaging
      • Q: How do we obtain high-resolution dynamic images in vivo?
      • Micro-CT using carbon nanotube X-ray sources
      • Microfluoroscopy, gating, and triggering from physiologic signals
    • Data Challenges
      • Images/ image sets and auxiliary files to process are quite large
        • 1000x1000x1000?
      • Integration of multimodal images (CT/SPECT/MRI)
      • Image storage formats are not standard
        • Floating-point, 3D or 4D images not supported by common formats
      • Students on two campuses use different systems
        • Maintaining program development on disjoint systems
      • Simulations are memory and storage-intensive
      • Integration of non-image data
    • Cochlear Implant Research
      • Assessing variability in outcomes for cochlear implant patients
      • Integrating experimental data with modeling and simulation
      Charles Finley UNC
    • Prediction of Neural Survival with Computational Models Understanding of Limits and Opportunities in Cochlear Interface Custom Processor Design Patient Outcome CNS ? General Study Approach Physio-anatomical Assessment EPs CT + =
      • Electrode Location:
      • Scala Tympani
      • Scala Vestibuli
      RW FN 0 ° Insertion Ref (Midmodiolar-RW) Insertion Marker Post-Op Pre-Op
    • Data Challenges
      • Integration of different image types
        • CT
        • microCT
        • Pathology
      • Integration of data types
        • Images
        • Signals
        • Computational models
        • Patient outcomes
    • Systems Biology Research
      • Spatiotemporal dynamics of cell/molecular signaling
      • Context dependence of gene expression and signaling network properties (e.g. tissue specificity, environment, etc.).
      Shawn Gomez UNC
    • Systems Biology: A few example challenges
      • Multiscale : Inferring and carrying information across scales (e.g. genes <=> proteins <=> cells <=> tissues <=> organs/organ systems <=> organisms <=> populations <=> ecosystems)
      • Multidata: Collection, standardization and integration of many types and qualities of data covering different biological scales.
      • Static vs. dynamic: Integration of static data (e.g. protein interaction maps) with dynamic data (e.g. movies of cell behavior under various stimuli).
      • Comparative genomics
      • Drug-related data
      • Incorporation of medical information
    • Large-Scale Data Storage Applications
      • Anything that helps with the previous!
      • Applications that can integrate and make inferences across data sets.
      • Deal with images, movies, expression data, species data, etc. and the associated meta-data.
      • Collaborative sharing and manipulation.
    • One simple example:
      • Protein interaction networks from:
        • “ wet” experiments (Y2H, MS, …)
        • “ dry” experiments (computational predictions)
        • Interactions mined from literature (Natural Language Processing)
        • Secondary evidence of functional interaction (e.g. correlated gene expression)
        • Inference through comparative genomics (data from other species)
      • We would like to integrate this data and make inferences for genome annotation, understanding signal transduction, etc.
    • Spatiotemporal dynamics of signaling
      • Collaboration w/ Klaus Hahn & Gary Johnson
      • Biosensors - RhoA activity (red) in space and time. Can use two biosensors simultaneously (e.g. RhoA and Cdc42).
      • Integrate dynamic and static network data.
    • Data Challenges
      • Integration of image and non-image data
      • Integration of acquired and simulated data
      • Multiple analysis applications
      • Common access for collaborators at other universities
    • Joint Department of Biomedical Engineering
      • Who we are
      • Case studies of individual laboratories
      • Program-wide data issues
      • Ideas
    • Research Issues
      • Department-wide collaborative research initiatives require common access to data and applications across labs and universities
        • Tissue engineering
        • Medical textiles and devices
    • Tissue Engineering Cell Mechanics Lab In vivo imaging microscopy microarray Tissue Mechanics Lab Metabolomics Lab Tissue Engineering Lab Tissue Systems Lab Biomaterials research Implant simulation
    • Tissue Engineering Molecular biology data Multimodal image data Microscope images Microarray data Mechanical testing Spectroscopy data Cell biology data Tissue biology data Materials testing Implant Simulation data
    • Medical Textiles and Devices Biocompatibility testing Preclinical testing Clinical trials FDA approval Partners: Universities Private hospitals Other government entities Industrial partners
    • Medical Devices and Textiles
      • FDA critical path opportunities include:
        • Better evaluation tools
        • Streamlining clinical trials
        • Harnessing bioinformatics
        • Moving manufacturing into the 21 st century
        • Developing products to address urgent public health needs – rapid response
        • At-risk populations - pediatrics
    • A Dream
      • Develop a structure for sharing testing data that can facilitate getting medical devices approved and to market
        • Biomarker data
        • Biocompatibility data
        • Preclinical (animal) data
        • Clinical trials (?)
        • Security: Protect IP
    • Proposal: Biomedical Textiles and Devices Innovation Consortium
      • Vision: Become the premier national research and educational center for critical path acceleration and modernization of the biomedical textile and devices product development process by fostering collaboration across science, medical, engineering, social science and design disciplines .
      Marian McCord NCSU
    • Proposal: Biomedical Textiles and Devices Innovation Consortium
      • Virtual Control Groups in Clinical Trials . Databases, models, and/or imaging collections could be used by multiple sponsors across different product types as historical controls to reduce the necessary size of control groups in clinical trials .
      • Identification and Qualification of Safety Biomarkers. Collaborative efforts to pool and mine existing safety and toxicology data would create new sources for identification and qualification of safety biomarkers.
      • Development of a Biocompatibility Database. A publicly accessible database of the biocompatibility profile of materials used in the design and manufacture of implanted medical devices would facilitate continuous improvement in design of these products.
      • Multiple Complex Therapies. Pooled data on the effects of combined use of complex technologies — for example, multiple implanted devices, microwave therapy to coronary vessels followed by a stent, or radiation therapy in a person with an implanted device—would create information that would improve both patient safety and new product development.
      • Failure Analysis. Development of a public database of information from trials of unsuccessful products could allow identification of patterns associated with failure and help sponsors avoid repeating past mistakes.
    • Academic Issues
      • Joint graduate and undergraduate programs need
        • Equal access to course materials from both campuses
        • Effective integration of multiple forms of data
        • Opportunities for (cooperative) student application development
    • Joint Department of Biomedical Engineering
      • Who we are
      • Case studies of individual laboratories
      • Program-wide data issues
      • Ideas
    • Data Integration
      • A general platform for linking different types of data
        • Image sets
        • Molecular biology data (gels, PCR, etc)
        • Signals
        • Circuit designs
        • Simulations
        • Papers/Manuscripts/Presentations
        • AND their exploratory/visualization applications
    • Data Integration
      • A general platform for linking different types of data
        • Must be easy for researchers who have little IT skill to curate
        • Must have access control
    • Application Access
      • A platform for common storage and access of researcher-developed applications
        • Repository for executables and libraries
        • Source code for GUI-based applications (Matlab, IDL, AVS, etc)
        • Maintenance and level-control
        • Ability to bring application code down to local systems for execution via web or other interface
    • Academic Access
      • A database of materials used by our two-campus classes
        • Datasets
        • Analysis applications
        • Reference materials
    • Medical Device Development
      • A platform for sharing of data among researchers working on device development with or without industrial partnerships
        • Materials biocompatibility data
        • Preclinical testing
        • Papers/presentations/manuscripts
        • Designs and plans
        • Marketing data(?)
    • Conclusion
      • What we need
        • Crossing boundaries of data types : Flexibility to store and associate many types of data
        • Crossing disciplinary boundaries : Accessible applications to explore and integrate the data
        • Crossing organizational boundaries : Collaborative project-oriented environments
        • Crossing academic boundaries : Access for undergraduates and graduates at both universities, as well as external collaborators.
    • The End
      • What now?