The authors would like to recognize the Center for Advanced Vehicular Systems (CAVS) and the Agricultural and Biological Engineering Department at Mississippi State University for supporting this work. This material is based upon work supported by the U.S. Army TACOM Life Cycle Command under Contract No. W56HZV-08-C-0236, through a subcontract with Mississippi State University, and was performed for the Simulation Based Reliability and Safety (SimBRS) research program. Also, this material is based upon work supported by the National Nuclear Security Administration, (Department of Energy) under award number [DE-FC26-06NT42755]. Finally, the authors would like to thank Mr. David Adams, Mr. Michael McCollum and Ms. Erin Colebeck for their effort in this research.

NOTE: Ethics Statement: The current work is unique to the institution&#8217;s research policy, and strictly follows the appropriate bio-safety and Office of Regulatory Compliance (ORC) guidelines.
1. Biomaterial Specimen Procurement
<ol>
	<li>Wear personal protective equipment in accordance with standard biosafety protocols of the laboratory and/or institution. Wear closed-toed shoes, long pants, a lab coat, surgical gloves, a protective mask, and safety goggles while handling porcine tissue and testing.</li>
	<li>Obtain porcine tissue (head, abdomen, or hind leg) of healthy pigs from a local abattoir within 1-2 hr post mortem.</li>
	<li>Store porcine tissue in biohazard safety bags and then place them in an iced container (~5.56-7.22 &#176;C). 
	NOTE: Use a thermometer to check that the temperature in the porcine specimen does not drop below 7.22 &#176;C.</li>
	<li>Transport porcine tissue to the nearest laboratory (at the College of Veterinary Medicine in Mississippi State University) for dissection.</li>
	<li>Under the supervision of a veterinarian in the College of Veterinary Medicine, surgically extract porcine organ (brain, liver, muscle, fat, or tendon) and place them in containers filled with phosphate buffered saline (PBS) for temporary storage (pH 7.4).</li>
	<li>Store the PBS containers in an iced cooler (~5.56-7.22 &#176;C) and immediately transport them to the testing facility for sample preparation and SHPB testing.</li>
</ol>
2. Biomaterial Sample Preparation
<ol>
	<li>Remove the porcine organ from the PBS container and place it on a sterile surface.</li>
	<li>NOTE: Identify the primary fiber orientation and locations for each test sample. Use a cylindrical die with a 30 mm inner diameter to dissect the test sample from the porcine organ.</li>
	<li>If the test sample is wedged inside the cylindrical die, inject PBS through the opposite end of the dissection tool to allow the test sample to slide out intact. Place the extracted test sample on a separate area of the sterile surface.</li>
	<li>Use a scalpel to trim the sample to the prescribed thickness and aspect ratio. 
	NOTE: For SHPB testing of porcine samples, the thickness is 10-15 mm while the aspect ratio (thickness/diameter) is 0.33-0.50 (Figure 2).</li>
	<li>Use calipers to measure the thickness and diameter at three different locations.</li>
	<li>Store all test samples in fresh PBS until the SHPB device is ready for testing. 
	NOTE: Ensure that samples are tested within 4 hr after slaughter.</li>
	<li>Discard samples that are not cylindrical due to incision errors or variations in the cross-section. Place discarded samples in biohazard safety bags. Repeat steps 2.2-2.6 to obtain additional test samples.</li>
</ol>
3. Split-Hopkinson Pressure Bar Testing
<ol>
	<li>Place the striker bar, incident bar, and transmitted bar in the metal stanchions for SHPB testing. 
	NOTE: Ensure that bars are free moving to the touch and that their interfaces are aligned with each other. Provide a stopper for transmitted bar for safety.</li>
	<li>Connect the strain gages adhered to incident and transmitted bars to the signal amplifier. Turn on the signal conditioning amplifiers and the DAQ module computer.</li>
	<li>Initialize the high speed data capturing software.</li>
	<li>Verify the live capturing of the signals to see if they lie within the normal range, and nullify the noise signals by clicking the zero icon.</li>
	<li>Input the trigger level and the data rate (2 MHz).</li>
	<li>Initialize the software to record once the trigger level has been achieved.</li>
	<li>Load the striker bar adjacent to the pressure chamber. Fill the pressure chamber to a desired pressure. 
	NOTE: The typical pressure range is 5-25 psi.</li>
	<li>Zero out the laser speed meter by pressing the zero button and set it to read the striker bar velocity by setting the reflector strip on the striker bar behind the laser sensors.</li>
	<li>Place the sample confinement chamber such that it does not hinder the movement of incident and reflected bar. Place the incident bar in contact with the transmitted bar.</li>
	<li>For calibration purposes, run a test (without a sample) by turning on trigger switch for the pressure chamber on the striker bar.</li>
	<li>Once the data is acquired in the computer, save and analyze the SHPB strain gage data (which is discussed in the next section) to ensure that the testing procedure is functioning properly.</li>
	<li>Place the cylindrical sample between the incident and transmitted bar and then close the sample confinement chamber. 
	NOTE: Ensure that no pre-conditioning is performed on the sample.</li>
	<li>Perform Tasks 3.4-3.7 with the sample placed between the incident and transmitted bar. 
	NOTE: Ensure that the sample centerline is the same as the bar centerline. Before proceeding, also check that the sample is not compressed, but remains in the same geometry as previously extracted.</li>
	<li>After testing is complete, use disposable sanitary wipes to remove sample debris from the incident bar, transmitted bar, and sample confinement chamber. Dispose of all debris and wipes in biohazard safety bags.</li>
	<li>Sanitize the bars and sample confinement chamber using a 70% ethanol cleaning solution and sanitary wipes.</li>
</ol>
4. SHPB Data Post-processing
<ol>
	<li>Open the &#8220;MSU High Rate Software19&#8221; for analysis of Hopkinson Bar waves.</li>
	<li>Begin the software by examining the Settings window and choosing the &#8220;Tension/Compression&#8221; option in the Mode Tab for uniaxial testing. Also, select &#8220;2 Gages&#8221; in the Gages Tab and click &#8220;Continue.&#8221;</li>
	<li>In the Main Window, select the Open File 1 Tab, and navigate to the incident wave data from the strain gage record on the incident bar. Select the Open File 2 Tab for importing the transmitted bar strain gage record.</li>
	<li>Select the Parameters Tab in the Main Window and input the physical parameters of the test setup including: bar dimensions, voltage to strain factors, strain gage positions, and viscoelastic dispersion constants. Click &#8220;Continue.&#8221;</li>
	<li>Then select the Select Data Tab in the Main Window and use the cursor bars to reduce the dataset to only the amount of data containing the incident, reflected, and transmitted waves. Click &#8220;Continue.&#8221;</li>
	<li>Then select the Select Waves Tab in the Main Window and use the cursor bars to confine the incident wave in the Incident Wave Graph, the reflected wave in the Reflected Wave Graph, and the transmitted wave in the Transmitted Wave Graph. Click &#8220;Continue.&#8221;</li>
	<li>After that, select the Correct Tab in the Main Window to allow the software to correct for the viscoelastic dispersion20-21.</li>
	<li>Now select the Shift Tab in the Main Window. In the Wave Graph, use the cursor to drag the incident, reflected, and transmitted waves to the same initial position in time by selecting each one individually in the Wave Select Tab. View all of the waves in the Data Graph. Once completed, click &#8220;Continue.&#8221;</li>
	<li>In the results file, save the load, displacement, position, and velocity, profiles by clicking &#8220;Save As.&#8221;</li>
	<li>Use conventional methods in Microsoft Excel (or any other spreadsheet software) to calculate true stress and true strain using the specimen dimensions measured before the Hopkinson Bar test.</li>
</ol>
5. SHPB Finite Element Modeling
<ol>
	<li>Using commercial finite element (FE) software, create a FE model of the SHPB setup. 
	NOTE: Use the same geometries and material properties.</li>
	<li>Assign an initial velocity to the FE model of the striker bar to initialize the FE simulation. 
	NOTE: The velocity of the striker bar should correspond to that in the SHPB experiment for a particular strain rate9.</li>
	<li>Create a FE model of the SHPB setup without a sample placed between the incident and transmitted bars. Run the FE simulation. 
	NOTE: The simulated striker bar velocity should correspond to the experimental striker bar velocity under the &#8220;no-sample&#8221; condition. Assign material properties given in Table 1 for polymeric bars.</li>
	<li>Verify if the strain gage measurements (stain versus time) in the experiment and FE simulation are in good agreement.</li>
	<li>Incorporate the biomaterial sample into the FE model of the SHPB setup. Assign the three-dimensional implementation (in vumat file format22) of the ISV material model to the biomaterial sample11.</li>
	<li>Perform a mesh refinement study using three different mesh sizes and then analyze the results to determine if the solutions converge. 
	NOTE: The mesh size corresponds to the total number of hexahedral and/or tetrahedral elements that comprise the FE model. Select the FE model with the lowest mesh size that converges after further simulations9.</li>
	<li>Conduct the two-step FE model calibration. In the first step, upload the experimental data into the one-dimensional implement of the ISV material model.</li>
	<li>Calibrate the true stress-stain curve of the experiment with the model&#8217;s true stress-strain curve by adjusting the ISV material model&#8217;s parameters (see Table 1). 
	NOTE: Further iterations are needed because the experimental SHPB data is three-dimensional in nature while the material model is one-dimensional.</li>
	<li>Assign the ISV material constants to the biomaterial sample in the FE model of the SHPB setup.</li>
	<li>Run the FE simulation with the striker bar velocity and sample deformation strain rate corresponding to the SHPB tests at the same strain rate.</li>
	<li>Compare the strain gage measurements from experiment and FE simulation for good agreement (strain versus time). 
	NOTE: If there is good agreement between the FE simulations and experiment strain gage values, proceed to the second step of the model calibration process. If not, repeat Tasks 5.7-5.11.</li>
	<li>In the second step of the FE model calibration, run the FE simulation strain gage data SHPB experiment post-processing software, MSU High Rate software19-21. 
	NOTE: If the simulated true stress-strain response compares to the experimental true stress-strain response, then the two-step FE model calibration has been completed. If not, repeat Tasks 5.7-5.12.</li>
	<li>Perform a volume average of the loading direction (&#931;33) stress along the centerline elements of the FE model sample. 
	NOTE: If this stress is in good agreement with the stress-strain curve of the one-dimensional ISV material model result, then the results obtained through Tasks 5.7-5.12 are fully calibrated. If not, repeat Tasks 5.7-5.13. The true stress-strain response captured through the one-dimensional implementation of the ISV material model represents the uniaxial true stress-strain response of a biomaterial that was tested in a SHPB setup.</li>
</ol>

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The authors hereby declare that there are no conflict of interests with all material related to this publication.

The reported methodology that couples the SHPB experiment and FE modeling of the SHPB offers a novel and unique technique to assess the uniaxial true stress-strain response of a biomaterial at high strain rates. In order to procure mechanical properties intrinsic to the native tissue, care must be taken to keep the biomaterial specimen between 5.56&#8211;7.22 &#176;C before SHPB testing. If the specimen is cooled below 5.56 &#176;C, water present in the tissue starts to crystalize into ice and subsequently changes the ti...

Motivation
The cardinal goal of the coupled Split &#8211; Hopkinson Pressure Bar (SHPB) experiment/finite element modeling of soft biomaterials (such as brain, liver, tendon, fat, etc.) was to extract their uniaxial mechanical behaviors for further implementation in human body FE simulations under injurious mechanical loads. The human body Finite Element (FE) model consists of a detailed human body mesh and a history dependent multiscale viscoelastic-viscoplastic Internal State Variable (ISV) material model for various human organs. This human body model can be used for a framework to build better standards for injury protection, to design innovative protective gear, and to enable occupant centric vehicular design.
Two modes of high rate injury have been widely observed in human trauma: explosive blast and blunt impact. Blast damage from explosive weaponry is the primary source of traumatic injury (TI) and the leading cause of death on the battlefield1. When detonated, these explosives form an outward propagating shock wave that produces large and abrupt accelerations and deformations. The resulting loads pose serious threats to those exposed. Although any part of the anatomy can be injured by shock waves, the prime areas of concern are (1) the lower extremity due to its close proximity to the ground, and (2) the head since injuries can inhibit normal brain function and survival2,3. These injuries can be categorized as primary, secondary, or tertiary injuries depending on the type of injury sustained. Because the strength of an explosive is characterized by its weight or size, standoff distance, positive pulse duration, and medium through which it travels, it can be difficult to adequately categorize these injuries3-6. Congressional reports indicate that military personnel have suffered nearly 179,000 traumatic injuries due to explosive weaponry and vehicle crashes in Iraq and Afghanistan from 2000 through March 20102. Due to the nature and locations of modern combat, head injuries are a leading concern for both the military and civilians3.
Aside from combat scenarios, TI has a variety of causes including automotive trauma; rodeo, motorcycle and domestic accidents; and sports injuries. For instance, despite improvements to safety equipment and protocols, mechanically induced traumatic brain injury (TBI) continues to be a leading source of mortality and lifelong morbidity in the U.S. The Center for Disease Control and Prevention (CDC) reports approximately 1.4 million TBI events each year, of which nearly 50,000 are fatal. American football alone accounts for more than 300,000 TBIs each year7. Survivors of such injuries are at risk for long-term neurological complications related to sensation, cognition, and communication. At this time there are approximately 5.3 million Americans living with these chronic disadvantages and disabilities. Direct and indirect U.S. medical costs from 2000 to 2010 totaled $60 billion8. However, these numbers do not account for non-medical costs and losses, or those incurred by the families and friends supporting TBI patients. Beyond purely economic analysis, TBI-induced disability creates a significant reduction in quality of life that can manifest as a significant burden on families and society.
The need for further understanding of the formation, characterization, and prevention of TI is clear. Biomechanical studies of the underlying mechanisms that cause TI provide insight and opportunity to reduce exposure or improve safety features for those at potential risk for TI. Furthermore, more advancement of the general understanding of TI formation may improve diagnostic methods and criteria, providing medical professionals who treat TI with better means of improving outcomes and saving lives.
A better knowledge of injury mechanisms and a better understanding of the biomechanics of injury development are needed to develop effective protective measures for the human body. Historically, simulations aimed at predicting injuries have been hampered by computational restrictions as well as the fidelity of the anatomical and material models employed. Full body simulations have focused on the overall loads on each body part, but the local stress, strain, and damage in each organ, muscle, bone, etc. has not been observed. For example, shoulder moment models use the dimensions of the arm, the load, and the applied angle to search for tabular values that specify whether or not a particular scenario is hazardous. A calculation of that nature is helpful for quick estimates but cannot capture what is happening locally from the hand all the way to the shoulder, especially when damage and injury are intrinsically local. Secondly, FE simulations have been used to capture the local response. The limitation in these efforts has not been FEA itself, but the material models that define each body part&#8217;s behavior under blast injury loads. Previously employed material models are adapted from simpler materials and have not endeavored to capture the myriad of complex mechanical behaviors exhibited by biological tissues. Therefore, high-fidelity computational models with ISV material models for organs in the human body represent the most realistic way to investigate the physics and biomechanics of TIs, to design innovative protective gear, and to establish better standards for injury metrics.
Background on Split-Hopkinson Pressure Bar (SHPB) and Internal State Variable (ISV) Material Model
Due to ethical issues involved with in&#160;vivo testing of human organs and the logistical issues associated with broad-scale human cadaveric testing, the current research effort involves mechanical experiments in&#160;vitro using specimens prepared from organs extracted from animal surrogates (e.g., pig as a most frequently used surrogate). Polymeric SHPB has been the preferred method for in-vitro testing soft biomaterials at high strain rates. The relevant deformational behaviors from SHPB testing and corresponding tissue damage-related information from the microstructural features of the tissue are incorporated into our ISV material models for organ mechanical descriptions9-10. These material models are then implemented into our virtual human body model to conduct FEA of various injuries. This process enables us to move towards the goal of accurately predicting the physics and nature of an injury for a given organ under diverse mechanical loading conditions (e.g. blast-induced, car crash and blunt impact) without the need for further physical experimentation. In order to accurately describe the phenomenological mechanical properties, particularly the higher level strain-rate dependency, of the biomaterials used in the FE simulations of the human body, SHPB experiments were performed on the biomaterials to obtain dynamic mechanical responses at strain rates pertaining to human TIs. An overview of the SHPB setup at the Center for Advanced Vehicular Systems (CAVS), Mississippi State University (MSU) is presented in Figure 1.
Previous studies have shown that SHPB testing has three major flaws associated with it12-18. The first and most significant one is the material inertial effect, which shows up in the high strain rate mechanical response of a biomaterial specimen as an initial spike. In order to overcome this issue, previous research efforts suggested modifying the geometry of the specimen from cylindrical in shape to cuboidal or annular in shape. The resultant mechanical behaviors from such studies were different from each other because the geometry of the specimen affected the wave propagation, wave interactions, and the mechanical response. This type of modification to the specimen geometry has led to erroneous representations of the mechanical response (multiaxial and non-uniform stress state) of the biomaterial. The second major flaw was the inability to maintain dynamic force equilibrium during a test. Researchers overcame this issue by reducing the sample thickness-to-diameter ratio and/or freezing the tissue prior to testing. While reducing the sample thickness-to-diameter ratio addressed the issue of dynamic force equilibrium, freezing the tissue further complicated the testing procedure as it changed the material properties due to crystallization of water present in the tissue. A number of studies completely abandoned the SHPB to avoid the above mentioned flaws and used shock tubes to obtain the pressure-time response in various animal models (rats, pigs, etc.). However, these animal models do not give one-dimensional uniaxial stress-strain behaviors necessary for material models used in FE simulations. The third flaw was the failure of the SHPB to give one dimensional stress-strain results because of the specimen barreling due to the material softness and the amount of water content in the specimen.
Hence, the SHPB presents a viable testing apparatus to garner high strain rate data . For soft materials, however, the SHPB induces bulging that produces a three-dimensional stress state mainly from hydrostatic pressure, yet the one dimensional stress-strain data is desired. We show here how one can still use the SHPB to garner the one-dimensional uniaxial true stress-strain curve for material model calibration; however, the process involved in obtaining the uniaxial true stress-strain curve is complicated. This process includes both the multi-axial experimental data and FE simulation results, and it requires iterative recalibration of the material model constants. The one-dimensional implementation of the ISV material model in MATLAB, also known as material point simulator, requires one-dimensional experimental data for the calibration. So, the ISV material model was optimized using a systematic calibration process. Here, experimental data from SHPB tests was considered in the context of wave theory formulation and dynamic force equilibrium (MSU High Rate Software). In order to account for the viscoelastic dispersion of the polymeric SHPB, viscoelastic dispersion equations, as reported by Zhao et al. (2007), were implemented in MSU High Rate Software. The viscoelastic dispersion equations helped in ensuring dynamic force equilibrium while testing. The one-dimensional material point simulator was then adjusted in the context of a couple experiment-FE modeling methodology until the two processes were considered to be appropriately compatible, that is, the data from both were in good agreement. These data were used to adjust the ISV model material constants by comparing the MATLAB material response simulator&#8217;s (one-dimensional) mechanical response and the SHPB FE model&#8217;s (one-dimensional) specimen centerline stress. Here the FE model&#8217;s specimen stress component was along the wave loading direction. Then the three-dimensional behavior of the FE model specimen was calibrated by iteratively performing FE simulations and adjusting ISV constants so that volume-averaged loading direction stress correlated well with the experimental true stress-strain response. Thus, a process of iterative optimization between the experimental data, FE results, and one-dimensional ISV material model was conducted. Table 1 gives a summary of the variables of ISV material model (MSU TP Ver. 1.1)11.
The most important element to this methodology is obtaining the one-dimensional mechanical response of the biomaterial and its material parameters for the ISV material model, which circumvents the SHPB testing issues of the stress-state non-uniformity. It also separates out the initial nonlinear response of the biomaterial arising from inertial effects and renders a mechanical response that is intrinsic to the material. The coupled methodology also showed that a change in the specimen geometry completely changes the Boundary Value Problem (BVP) and the loading direction true stress-strain of the specimen. As such, the above mentioned methodology can be used with any material model (phenomenological or microstructural-based) for calibrating and then simulating high strain rate behaviors of human organs under injurious mechanical loads.

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a coupled experiment-finite element modeling methodology for assessing high strain rate mechanical response of soft biomaterials

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. brain, liver, tendon, fat, etc.) when exposed to high strain rates. This study utilized a Split-Hopkinson Pressure Bar (SHPB) to generate strain rates of 100-1,500 sec-1. The SHPB employed a striker bar consisting of a viscoelastic material (polycarbonate). A sample of the biomaterial was obtained shortly postmortem and prepared for SHPB testing. The specimen was interposed between the incident and transmitted bars, and the pneumatic components of the SHPB were activated to drive the striker bar toward the incident bar. The resulting impact generated a compressive stress wave (i.e. incident wave) that traveled through the incident bar. When the compressive stress wave reached the end of the incident bar, a portion continued forward through the sample and transmitted bar (i.e. transmitted wave) while another portion reversed through the incident bar as a tensile wave (i.e. reflected wave). These waves were measured using strain gages mounted on the incident and transmitted bars. The true stress-strain behavior of the sample was determined from equations based on wave propagation and dynamic force equilibrium. The experimental stress-strain response was three dimensional in nature because the specimen bulged. As such, the hydrostatic stress (first invariant) was used to generate the stress-strain response. In order to extract the uniaxial (one-dimensional) mechanical response of the tissue, an iterative coupled optimization was performed using experimental results and Finite Element Analysis (FEA), which contained an Internal State Variable (ISV) material model used for the tissue. The ISV material model used in the FE simulations of the experimental setup was iteratively calibrated (i.e. optimized) to the experimental data such that the experiment and FEA strain gage values and first invariant of stresses were in good agreement.

The effectiveness of the coupled methodology is exemplified in Figure 3. Here the SHPB experimental stress-strain response for the brain is at a lower stress state (with a peak stress of 0.32 MPa) in comparison to the stress state of the one-dimensional material point simulator (with a peak value of 0.74 MPa), which is akin to the FE sample center line (element) average. This is due to the nature of deformation that soft biomaterials exhibit. Because the strain rates are high, and the wave speed and stre...

Watch this Scientific Journal Video about A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials at JoVE.com

A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

The current study prescribes a coupled experiment-finite element simulation methodology to obtain the uniaxial dynamic mechanical response of soft biomaterials (brain, liver, tendon, fat, etc.). The multiaxial experimental results that arose because of specimen bulging obtained from Split-Hopkinson Pressure Bar testing were rendered to a uniaxial true stress-strain behavior when simulated through iterative optimization of the finite element analysis of the biomaterial.

This study offers a combined experimental and finite element (FE) simulation approach for examining the mechanical behavior of soft biomaterials (e.g. ...

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Research

JoVE Journal

Bioengineering

12.4K Views.  Mississippi State University.  L'attuale studio prevede una metodologia di simulazione agli elementi finiti esperimento accoppiato per ottenere la monoassiale risposta meccanica dinamica di biomateriali molli (cervello, fegato, tendine, grassi, ecc.) I risultati sperimentali multiassiali sorti a causa del campione sporgenti ottenuti da Split-Hopkinson test di pressione Bar sono stati resi ad un monoassiale vero comportamento sforzo-deformazione quando simulato attraverso l'ottimizzazi...

Video: A Coupled Experiment-finite Element Modeling Methodology for Assessing High Strain Rate Mechanical Response of Soft Biomaterials

Microfabricated Platforms for Mechanically Dynamic Cell Culture

The ability to systematically probe in vitro cellular response to combinations of mechanobiological stimuli for tissue engineering, drug discovery or fundamental cell biology studies is limited by current bioreactor technologies, which cannot simultaneously apply a variety of mechanical stimuli to cultured cells. In order to address this issue, we have developed a series of microfabricated platforms designed to screen for the effects of mechanical stimuli in a high-throughput format. In this protocol, we demonstrate the fabrication of a microactuator array of vertically displaced posts on which the technology is based, and further demonstrate how this base technology can be modified to conduct high-throughput mechanically dynamic cell culture in both two-dimensional and three-dimensional culture paradigms.

In this protocol, we demonstrate the fabrication of a microactuator array of vertically displaced posts on which the technology is based, and how this base technology can be modified to conduct high-throughput mechanically dynamic cell culture in both two-dimensional and three-dimensional culture paradigms.

The ability to systematically probe in vitro cellular response to combinations of mechanobiological stimuli for tissue engineering, drug discovery or ...

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

An Improved Mechanical Testing Method to Assess Bone-implant Anchorage

Recent advances in material science have led to a substantial increase in the topographical complexity of implant surfaces, both on a micro- and a nano-scale. As such, traditional methods of describing implant surfaces -&#160;namely numerical determinants of surface roughness -&#160;are inadequate for predicting in vivo performance. Biomechanical testing provides an accurate and comparative platform to analyze the performance of biomaterial surfaces. An improved mechanical testing method to test the anchorage of bone to candidate implant surfaces is presented. The method is applicable to both early and later stages of healing and can be employed for any range of chemically or mechanically modified surfaces -&#160;but not smooth surfaces. Custom rectangular implants are placed bilaterally in the distal femora of male Wistar rats and collected with the surrounding bone. Test specimens are prepared and potted using a novel breakaway mold and the disruption test is conducted using a mechanical testing machine. This method allows for alignment of the disruption force exactly perpendicular, or parallel, to the plane of the implant surface, and provides an accurate and reproducible means for isolating an exact peri-implant region for testing.

An improved method to mechanically test bone anchorage to candidate implant surfaces is presented. This method allows for alignment of the disruption force exactly perpendicular, or parallel, to the plane of the implant surface, and provides an accurate means to direct the disruption forces to an exact peri-implant region.

Recent advances in material science have led to a substantial increase in the topographical complexity of implant surfaces, both on a micro- and a ...

Fabrication and Implantation of Miniature Dual-element Strain Gages for Measuring In Vivo Gastrointestinal Contractions in Rodents.

Gastrointestinal dysfunction remains a major cause of morbidity and mortality. Indeed, gastrointestinal (GI) motility in health and disease remains an area of productive research with over 1,400 published animal studies in just the last 5 years. Numerous techniques have been developed for quantifying smooth muscle activity of the stomach, small intestine, and colon. In vitro and ex vivo techniques offer powerful tools for mechanistic studies of GI function, but outside the context of the integrated systems inherent to an intact organism. Typically, measuring in vivo smooth muscle contractions of the stomach has involved an anesthetized preparation coupled with the introduction of a surgically placed pressure sensor, a static pressure load such as a mildly inflated balloon or by distending the stomach with fluid under barostatically-controlled feedback. Yet many of these approaches present unique disadvantages regarding both the interpretation of results as well as applicability for in vivo use in conscious experimental animal models. The use of dual element strain gages that have been affixed to the serosal surface of the GI tract has offered numerous experimental advantages, which may continue to outweigh the disadvantages. Since these gages are not commercially available, this video presentation provides a detailed, step-by-step guide to the fabrication of the current design of these gages. The strain gage described in this protocol is a design for recording gastric motility in rats. This design has been modified for recording smooth muscle activity along the entire GI tract and requires only subtle variation in the overall fabrication. Representative data from the entire GI tract are included as well as discussion of analysis methods, data interpretation and presentation.

Fabrication and Implantation of Miniature Dual-element Strain Gages for Measuring In Vivo Gastrointestinal Contractions in Rodents.

The in vivo measurement of smooth muscle contractions along the gastrointestinal tract of laboratory animals remains a powerful, though underutilized, technique. Flexible, dual element strain gages are not commercially available and require fabrication. This protocol describes the construction of reliable, inexpensive strain gages for acute or chronic implantation in rodents.

Gastrointestinal dysfunction remains a major cause of morbidity and mortality. Indeed, gastrointestinal (GI) motility in health and disease remains an ...

An Experimental and Finite Element Protocol to Investigate the Transport of Neutral and Charged Solutes across Articular Cartilage

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular cartilage impairs its load-bearing function substantially as it experiences tremendous chemical degradation, i.e. proteoglycan loss and collagen fibril disruption. One promising way to investigate chemical damage mechanisms during OA is to expose the cartilage specimens to an external solute and monitor the diffusion of the molecules. The degree of cartilage damage (i.e. concentration and configuration of essential macromolecules) is associated with collisional energy loss of external solutes while moving across articular cartilage creates different diffusion characteristics compared to healthy cartilage. In this study, we introduce a protocol, which consists of several steps and is based on previously developed experimental micro-Computed Tomography (micro-CT) and finite element modeling. The transport of charged and uncharged iodinated molecules is first recorded using micro-CT, which is followed by applying biphasic-solute and multiphasic finite element models to obtain diffusion coefficients and fixed charge densities across cartilage zones.

We propose a protocol to investigate the transport of charged and uncharged molecules across articular cartilage with the aid of recently developed experimental and numerical methods.

Osteoarthritis (OA) is a debilitating disease that is associated with degeneration of articular cartilage and subchondral bone. Degeneration of articular ...

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for specialized equipment and trained personnel require that many diagnostic tests be performed at remote, centralized clinical laboratories. Magnetic levitation is a simple yet powerful technique and can be applied to levitate cells, which are suspended in a paramagnetic solution and placed in a magnetic field, at a position determined by equilibrium between a magnetic force and a buoyancy force. Here, we present a versatile platform technology designed for point-of-care diagnostics which uses magnetic levitation coupled to microscopic imaging and automated analysis to determine the density distribution of a patient's cells as a useful diagnostic indicator. We present two platforms operating on this principle: (i) a smartphone-compatible version of the technology, where the built-in smartphone camera is used to image cells in the magnetic field and a smartphone application processes the images and to measures the density distribution of the cells and (ii) a self-contained version where a camera board is used to capture images and an embedded processing unit with attached thin-film-transistor (TFT) screen measures and displays the results. Demonstrated applications include: (i) measuring the altered distribution of a cell population with a disease phenotype compared to a healthy phenotype, which is applied to sickle cell disease diagnosis, and (ii) separation of different cell types based on their characteristic densities, which is applied to separate white blood cells from red blood cells for white blood cell cytometry. These applications, as well as future extensions of the essential density-based measurements enabled by this portable, user-friendly platform technology, will significantly enhance disease diagnostic capabilities at the point of care.

We present a magnetic levitation technique coupled with automated imaging and analysis in both a smartphone-compatible device and a device with embedded imaging and processing. This is applied to measure the density distribution of cells with two demonstrated biomedical applications: sickle cell disease diagnosis and separating white and red blood cells.

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for ...

Quantification of Strain in a Porcine Model of Skin Expansion Using Multi-View Stereo and Isogeometric Kinematics

Tissue expansion is a popular technique in plastic and reconstructive surgery that grows skin in vivo for correction of large defects such as burns and giant congenital nevi. Despite its widespread use, planning and executing an expansion protocol is challenging due to the difficulty in measuring the deformation imposed at each inflation step and over the length of the procedure. Quantifying the deformation fields is crucial, as the distribution of stretch over time determines the rate and amount of skin grown at the end of the treatment. In this manuscript, we present a method to study tissue expansion in order to gain quantitative knowledge of the deformations induced during an expansion process. This experimental protocol incorporates multi-view stereo and isogeometric kinematic analysis in a porcine model of tissue expansion. Multi-view stereo allows three-dimensional geometric reconstruction from uncalibrated sequences of images. The isogeometric kinematic analysis uses splines to describe the regional deformations between smooth surfaces with few mesh points. Our protocol has the potential to bridge the gap between basic scientific inquiry regarding the mechanics of skin expansion and the clinical setting. Eventually, we expect that the knowledge gained with our methodology will enable treatment planning using computational simulations of skin deformation in a personalized manner.

This protocol uses multi-view stereo to generate three-dimensional (3D) models out of uncalibrated sequences of photographs, making it affordable and adjustable to a surgical setting. Strain maps between the 3D models are quantified with spline-based isogeometric kinematics, which facilitate representation of smooth surfaces over coarse meshes sharing the same parameterization.

Tissue expansion is a popular technique in plastic and reconstructive surgery that grows skin in vivo for correction of large defects such as burns and ...

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

Photoreceptor degenerative diseases cause irreparable blindness through the progressive loss of photoreceptor cells in the retina. Retinal prostheses are an emerging treatment for photoreceptor degenerative diseases that seek to restore vision by artificially stimulating the surviving retinal neurons in the hope of eliciting comprehensible visual perception in patients. Current retinal prostheses have demonstrated success in restoring limited vision to patients using an array of electrodes to electrically stimulate the retina but face substantial physical barriers in restoring high acuity, natural vision to patients. Chemical neurostimulation using native neurotransmitters is a biomimetic alternative to electrical stimulation and could bypass the fundamental limitations associated with retinal prostheses using electrical neurostimulation. Specifically, chemical neurostimulation has the potential to restore more natural vision with comparable or better visual acuities to patients by injecting very small quantities of neurotransmitters, the same natural agents of communication used by retinal chemical synapses, at much finer resolution than current electrical prostheses. However, as a relatively unexplored stimulation paradigm, there is no established protocol for achieving chemical stimulation of the retina in vitro. The purpose of this work is to provide a detailed framework for accomplishing chemical stimulation of the retina for investigators who wish to study the potential of chemical neuromodulation of the retina or similar neural tissues in vitro. In this work, we describe the experimental setup and methodology for eliciting retinal ganglion cell (RGC) spike responses similar to visual light responses in wild-type and photoreceptor-degenerated wholemount rat retinas by injecting controlled volumes of the neurotransmitter glutamate into the subretinal space using glass micropipettes and a custom multiport microfluidic device. This methodology and protocol are general enough to be adapted for neuromodulation using other neurotransmitters or even other neural tissues.

Methodology for Biomimetic Chemical Neuromodulation of Rat Retinas with the Neurotransmitter Glutamate In Vitro

This protocol describes a novel method for investigating a form of chemical neurostimulation of wholemount rat retinas in vitro with the neurotransmitter glutamate. Chemical neurostimulation is a promising alternative to the conventional electrical neurostimulation of retinal neurons for treating irreversible blindness caused by photoreceptor degenerative diseases.

Photoreceptor degenerative diseases cause irreparable blindness through the progressive loss of photoreceptor cells in the retina. Retinal prostheses are ...

Stiffness Measurement of Soft Silicone Substrates for Mechanobiology Studies Using a Widefield Fluorescence Microscope

Soft tissues in the human body typically have stiffness in the kilopascal (kPa) range. Accordingly, silicone and hydrogel flexible substrates have been proven to be useful substrates for culturing cells in a physical microenvironment that partially mimics in vivo conditions. Here, we present a simple protocol for characterizing the Young's moduli of isotropic linear elastic substrates typically used for mechanobiology studies. The protocol consists of preparing a soft silicone substrate on a Petri dish or stiff silicone, coating the top surface of the silicone substrate with fluorescent beads, using a millimeter-scale sphere to indent the top surface (by gravity), imaging the fluorescent beads on the indented silicone surface using a fluorescence microscope, and analyzing the resultant images to calculate the Young's modulus of the silicone substrate. Coupling the substrate's top surface with a moduli extracellular matrix protein (in addition to the fluorescent beads) allows the silicone substrate to be readily used for cell plating and subsequent studies using traction force microscopy experiments. The use of stiff silicone, instead of a Petri dish, as the base of the soft silicone, enables the use of mechanobiology studies involving external stretch. A specific advantage of this protocol is that a widefield fluorescence microscope, which is commonly available in many labs, is the major equipment necessary for this procedure. We demonstrate this protocol by measuring the Young's modulus of soft silicone substrates of different elastic moduli.

Substrates with stiffness in the kilopascal-range are useful to study the response of cells to physiologically relevant micro-environment stiffness. Using just a widefield fluorescence microscope, the Young's modulus of soft silicone gels can be determined using an indentation with a suitable sphere.

Soft tissues in the human body typically have stiffness in the kilopascal (kPa) range. Accordingly, silicone and hydrogel flexible substrates have been ...

Biaxial Mechanical Characterizations of Atrioventricular Heart Valves

Extensive biaxial mechanical testing of the atrioventricular heart valve leaflets can be utilized to derive&#160;optimal parameters used in constitutive models, which provide a mathematical representation of the mechanical function of those structures. This presented biaxial mechanical testing protocol involves (i) tissue acquisition, (ii) the preparation of tissue specimens, (iii) biaxial mechanical testing, and (iv) postprocessing of the acquired data. First, tissue acquisition requires obtaining porcine or ovine hearts from a local Food and Drug Administration-approved abattoir for later dissection to retrieve the valve leaflets. Second, tissue preparation requires using tissue specimen cutters on the leaflet tissue to extract a clear zone for testing. Third, biaxial mechanical testing of the leaflet specimen requires the use of a commercial biaxial mechanical tester, which consists of force-controlled, displacement-controlled, and stress-relaxation testing protocols to characterize the leaflet tissue&#39;s mechanical properties. Finally, post-processing requires the use of data image correlation techniques and force and displacement readings to summarize the tissue&#39;s mechanical behaviors in response to external loading. In general, results from biaxial testing demonstrate that the leaflet tissues yield a nonlinear, anisotropic mechanical response. The presented biaxial testing procedure is advantageous to other methods since the method presented here allows for a more comprehensive characterization of the valve leaflet tissue under one unified testing scheme, as opposed to separate testing protocols on different tissue specimens. The proposed testing method has its limitations in that shear stress is potentially present in the tissue sample. However, any potential shear is presumed negligible.

This protocol involves characterizations of atrioventricular valve leaflets with force-controlled, displacement-controlled, and stress-relaxation biaxial mechanical testing procedures. Results acquired with this protocol can be used for constitutive model development to simulate the mechanical behavior of functioning valves under a finite element simulation framework.

Extensive biaxial mechanical testing of the atrioventricular heart valve leaflets can be utilized to derive&#160;optimal parameters used in constitutive ...