Modernization Of The Biomedical Textile

Joint Department of Biomedical Engineering

Challenges in Crossing Boundaries of Traditional Academic and Research

Infrastructure

North Carolina State UniversityThe University of North Carolina at Chapel Hill

David S. LalushAssistant Professor

[email protected]


• Who we are

• Case studies of individual laboratories

• Program-wide data issues

• Ideas


• Who we are



• 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


• 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 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.


• Who we are



• Ideas

Laboratory for Emerging Imaging Technologies

Laboratory for EmergingImaging TechnologiesNCSU/UNC Biomedical Engineering

• 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. [email protected]

Dynamic X-ray Imaging

Laboratory for EmergingImaging TechnologiesNCSU/UNC Biomedical Engineering

• 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 FinleyUNC

Prediction of Neural Survival with Computational Models

Understanding of Limits and Opportunities in Cochlear Interface

Custom Processor Design

Patient Outcome

Physio-anatomical Assessment

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General Study Approach

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Scala Tympani

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Insertion Marker

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 GomezUNC

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


• Who we are



• 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: UniversitiesPrivate hospitalsOther government entitiesIndustrial partners

Medical Devices and Textiles

• FDA critical path opportunities include:• Better evaluation tools

• Streamlining clinical trials

• Harnessing bioinformatics

• Moving manufacturing into the 21st 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 McCordNCSU

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


• Who we are



• 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?