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Towards a Bioartificial Kidney: Validating Nanoporous Filtration Membranes Jacob Bumpus, BME/EE 2014...

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Towards a Bioartificial Kidney: Validating Nanoporous Filtration Membranes Jacob Bumpus, BME/EE 2014 Casey Fitzgerald, BME 2014 Michael Schultis, BME/EE 2014
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  • Slide 1
  • Towards a Bioartificial Kidney: Validating Nanoporous Filtration Membranes Jacob Bumpus, BME/EE 2014 Casey Fitzgerald, BME 2014 Michael Schultis, BME/EE 2014
  • Slide 2
  • Background In 2010, 600,000 patients were treated for end stage renal disease (ESRD) in the US alone Treatment Options: Kidney transplant Donor Shortage Dialysis Costly and time consuming Concept illustration of an implantable bioartificial kidney. Courtesy of Shuvo Roy Image Citation: Fissell, William H., Shuvo Roy, and Andrew Davenport. "Achieving more frequent and longer dialysis for the majority: wearable dialysis and implantable artificial kidney devices." Kidney international 84.2 (2013): 256-264.
  • Slide 3
  • Background Dr. Fissell is working to develop an implantable bioartificial kidney using nanoporous silicon membranes as biological filters These chips feature nanometer- scale pore arrays, invisible to optical characterization methods Screenshots courtesy UCSF School of Pharmacy http://pharmacy.ucsf.edu/kidney-project/
  • Slide 4
  • Problem Statement These membranes must be thoroughly tested to verify their filtration characteristics These experiments are manually monitored, and data is collected by hand Current experiments are unable to simulate physiologically relevant fluid flow profiles, and are limited to constant flow rates There are no failsafes to protect these expensive and fragile membranes
  • Slide 5
  • Needs Statement To design an integrated hardware/software suite that will streamline verification of nanoporous silicon filtration membranes while maximizing experimental control and minimizing user involvement
  • Slide 6
  • Goals Develop an intuitive graphical user interface (GUI) that allows the user to easily control the system Automate the experimental protocol and data collection Allow user-defined hardware setup so that numerous experiments can be run simultaneously Add programatic flow rate control to allow for pulsatility Include failsafes and shutdown procedures to protect these membranes
  • Slide 7
  • Clinical Relevance Ease of Use Efficiency Control Relevance Risk Lost Time Lost Money Effort Increase Reduce
  • Slide 8
  • Experiments The solution must automate three modes of experimentation Hydraulic Permeability Mode Measures filtration rate as a function of pressure (uL/min/psi) Filtration Mode Collect filtrate samples for further analysis Dialysis Mode Collect samples in a closed blood/dialysate system Filtration and Dialysis Mode should include an option to run with constant flow or a pulsatile waveform
  • Slide 9
  • Experimental Setup Hydraulic Permeability PSI Peristaltic Pump Water Pressure Transducer Air Regulator To House Air Air Filtration Membrane Implantable Device Zero 0.015 0.010 0.005 g 0.000 0.020
  • Slide 10
  • Feedback Control Diagram Arduino/LabVIEW Pressure Regulator PID Loop Pressure Transducer Convert VI Setpoint Pressure Pressure Voltage Signal ADC Voltage ErrorV Pump VI Peristaltic Pump Setpoint Flow or Waveform RS-232 Signal Pressure Hagen- Poiseuille Flow Rate PP Filtration Membrane Mass Balance Filtration Rate Comparison VI (Actual > Setpoint?) Setpoint Mass Sample Mass Yes/No Shutdown? V
  • Slide 11
  • Modified from Zhang, Guanqun, Jin-Oh Hahn, and Ramakrishna Mukkamala. "Tube-load model parameter estimation for monitoring arterial hemodynamics." Engineering Approaches to Study Cardiovascular Physiology: Modeling, Estimation, and Signal Processing (2011): 20. Pulsatility: Replicating Arterial Pressure Waveforms ex vivo
  • Slide 12
  • Control Box Concept Pressure Transducers 124 3 6 5 8 7 General Purpose USB 12 3 45 67 141312111098 Pressure Regulators 12 3 456 7 8 Power Supply AC Power Line USB Hubs and Female Connector Ports H N G 24 12 5 -12 Through Hole Board Control Box: Front View Control Box: Top View C R
  • Slide 13
  • Hydraulic Permeability Filtration Dialysis Quadrant 1 Quadrant 2 Quadrant 4 Quadrant 3 Top Level Menu Software Architecture Diagram
  • Slide 14
  • Hardware select (Pump, Transducer/Regulator, Balance) Hardware select (Pump, Transducer/Regulator, Balance) Hardware select (Pump, Transducer/Regulator, Balance) Hardware select (Pump, Transducer/Regulator, Balance) Hardware select (2x Pump, 2x Transducer/Regulator, Balance, Syringe Pump) Hardware select (2x Pump, 2x Transducer/Regulator, Balance, Syringe Pump) Experimental Runtime GUI Experimental Runtime GUI Pressure Transducer Air Regulator Mass Balance Peristaltic Pump Syringe Pump Experiment Overview Calibration
  • Slide 15
  • Experiment Overview Experimental Runtime GUI Pressure Transducer Transducer Calibration
  • Slide 16
  • Mass Balance Experimental Runtime GUI Peristaltic Pump Syringe Pump
  • Slide 17
  • Hydraulic Permeability Experiment Load from File
  • Slide 18
  • Hydraulic Permeability Results Experiment ResultsData Log
  • Slide 19
  • Fail Safes Set point = 0 Overrides the PID controller Record Max/Min Pressure Alert user of potential errors Next: Automatic shut-down Error Handling What to do if something goes wrong?
  • Slide 20
  • Recent Progress LabVIEW Control of Pressure transducer (COMPLETE) Pressure Regulator (COMPLETE) Peristaltic Pump (COMPLETE) Mass balance (COMPLETE) Syringe Pump (TBD) Initial iterations of pulsatile flow Abstract submission to American Society for Artificial Internal Organs (ASAIO) Student Design Competition Fully Automated Hydraulic Permeability and Filtration Experiments Primary Fail-safes and Error handling Parts have arrived
  • Slide 21
  • Next Steps Revisit pulsatility using known pressure profiles Evaluate syringe pump functionality/feasibility Compile individual components into a single, unified system Order and assemble box
  • Slide 22
  • Gantt Chart
  • Slide 23
  • Special Thanks To: Vanderbilt University Medical Center Vanderbilt School of Engineering Vanderbilt Renal Nanotechnology Lab Dr. William Fissell Joey Groszek Dr. Amanda Buck Dr. Tim Holman Dr. A.B. Bonds Dr. Matthew Walker III JustMyPACE Peer Senior Design Group
  • Slide 24
  • Questions?
  • Slide 25
  • Feedback Control Diagram Arduino/LabVIEW Pressure Regulator 1 PID Loop Pressure Regulator 2 Pressure Transducer 2 Pressure Transducer 1 Conversion VI Setpoint Pressure Pressure (Blood) Pressure (Dialysate) Voltage Signal 1 ADC Voltage Signal 2 ADC VV Voltage Error Voltage Pump VI Peristaltic Pumps Setpoint Flows or Waveforms RS-232 Signals Pressure (Blood) HP Flow Rate Pressure (Dialysate) PP
  • Slide 26
  • Experimental Setup Dialysis Mode PSI To House Air Peristaltic Pump Pressure Transducer Dialysate Side Blood Side Pressure Transducer Air Regulator To House Air Air Filtration Membrane Syringe Pump
  • Slide 27
  • Hydraulic Permeability Mode Fissell, William H., et al. "High-performance silicon nanopore hemofiltration membranes." Journal of membrane science 326.1 (2009): 58-63.
  • Slide 28
  • Filtration/Dialysis Mode 0 Ideal Filtration Example 1 psi Pressure Example 2 psi Pressure Filtrate Mass/ Original Mass () Size (arbitrary units)
  • Slide 29
  • Previous System
  • Slide 30
  • Previous Interface
  • Slide 31
  • Appendix: Feedback Control Simplified Arduino/LabVIEW Pressure Regulator 1 PID Loop Pressure Regulator 2 Pressure Transducer 2 Pressure Transducer 1 Conversion VI Setpoint Pressure Pressure (Dialysate) Voltage Signal 1 ADC Voltage Signal 2 ADC VV Voltage Error Voltage Pressure (Blood)
  • Slide 32
  • Appendix: Feedback Control Diagram Arduino/LabVIEW Pressure Regulator 1 PID Loop Pressure Regulator 2 Pressure Transducer 2 Pressure Transducer 1 Conversion VI Setpoint Pressure Pressure (Blood) Pressure (Dialysate) Voltage Signal 1 ADC Voltage Signal 2 ADC VV Voltage Error Voltage Pump VI Peristaltic Pump Setpoint Flow or Waveform RS-232 Signal Flow Rate Pressure (Blood)
  • Slide 33
  • Top Level Menu
  • Slide 34
  • Hardware Select
  • Slide 35
  • Design Factors Software Platform LabVIEW more $ / much less development time Software concurrency More fewer programs running but internals are more complex Hardware connections Fewer cheaper in size and $ but more technically challenging
  • Slide 36
  • Slide 37
  • Slide 38
  • Slide 39

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