Springer Science and Business Media LLC

In this article, a “bedside to bench and back” approach for developing tissue engineered medical products (TEMPs) for clinical applications is reviewed. The driving force behind this approach is unmet clinical needs. Preclinical research, both in vitro and in vivo using small and large animal models, will help find solutions to key research questions. In clinical research, ethical issues regarding the use of cells and tissues, their sources, donor consent, as well as clinical trials are important considerations. Regulatory issues, at both institutional and government levels, must be addressed prior to the translation of TEMPs to clinical practice. TEMPs are regulated as drugs, biologics, devices, or combination products by the U.S. Food and Drug Administration (FDA). Depending on the mode of regulation, applications for TEMP introduction must be filed with the FDA to demonstrate safety and effectiveness in premarket clinical studies, followed by 510(k) premarket clearance or premarket approval (for medical devices), biologics license application approval (for biologics), or new drug application approval (for drugs). A case study on nerve cuffs is presented to illustrate the regulatory process. Finally, perspectives on commercialization such as finding a company partner and funding issues, as well as physician culture change, are presented.

This article is based on a lecture I presented as the recipient of the 2009 Pritzker Distinguished Lecturer Award at the Biomedical Engineering Society annual meeting in October 2009. Here, I review more than thirty years of research from my laboratory, beginning with studies designed to test the theory that cells use tensegrity (tensional integrity) architecture to stabilize their shape and sense mechanical signals, which I believed to be critical for control of cell function and tissue development. Although I was trained as a cell biologist, I found that the tools I had at my disposal were insufficient to experimentally test these theories, and thus I ventured into engineering to find critical solutions. This path has been extremely fruitful as it has led to confirmation of the critical role that physical forces play in developmental control, as well as how cells sense and respond to mechanical signals at the molecular level through a process known as cellular mechanotransduction. Many of the predictions of the cellular tensegrity model relating to cell mechanical behaviors have been shown to be valid, and this vision of cell structure led to discovery of the central role that transmembrane adhesion receptors, such as integrins, and the cytoskeleton play in mechanosensing and mechanochemical conversion. In addition, these fundamental studies have led to significant unexpected technology fallout, including development of micromagnetic actuators for non-invasive control of cellular signaling, microfluidic systems as therapeutic extracorporeal devices for sepsis therapy, and new DNA-based nanobiotechnology approaches that permit construction of artificial tensegrities that mimic properties of living materials for applications in tissue engineering and regenerative medicine.

Studying the mechanics of the knee joint has direct implications in understanding the state of human health and disease and can aid in treatment of injuries. In this work, we developed an axisymmetric model of the human knee joint using finite element method, which consisted of separate parts representing tibia, meniscus and femoral, and tibial articular cartilages. The articular cartilages were modeled as three separate layers with different material characteristics: top superficial layer, middle layer, and calcified layer. The biphasic characteristic of both meniscus and cartilage layers were included in the computational model. The developed model was employed to investigate several aspects of mechanical response of the knee joint under external loading associated with the standing posture. Specifically, we studied the role of the material characteristic of the articular cartilage and meniscus on the distribution of the shear stresses in the healthy knee joint and the knee joint after meniscectomy. We further employed the proposed computational model to study the mechanics of the knee joint with an artificial meniscus. Our calculations suggested an optimal elastic modulus of about 110 MPa for the artificial meniscus which was modeled as a linear isotropic material. The suggested optimum stiffness of the artificial meniscus corresponds to the stiffness of the physiological meniscus in the circumferential direction.

This paper presents a method for respiratory rate estimation using the camera of a smartphone, an MP3 player or a tablet. The iPhone 4S, iPad 2, iPod 5, and Galaxy S3 were used to estimate respiratory rates from the pulse signal derived from a finger placed on the camera lens of these devices. Prior to estimation of respiratory rates, we systematically investigated the optimal signal quality of these 4 devices by dividing the video camera’s resolution into 12 different pixel regions. We also investigated the optimal signal quality among the red, green and blue color bands for each of these 12 pixel regions for all four devices. It was found that the green color band provided the best signal quality for all 4 devices and that the left half VGA pixel region was found to be the best choice only for iPhone 4S. For the other three devices, smaller 50 × 50 pixel regions were found to provide better or equally good signal quality than the larger pixel regions. Using the green signal and the optimal pixel regions derived from the four devices, we then investigated the suitability of the smartphones, the iPod 5 and the tablet for respiratory rate estimation using three different computational methods: the autoregressive (AR) model, variable-frequency complex demodulation (VFCDM), and continuous wavelet transform (CWT) approaches. Specifically, these time-varying spectral techniques were used to identify the frequency and amplitude modulations as they contain respiratory rate information. To evaluate the performance of the three computational methods and the pixel regions for the optimal signal quality, data were collected from 10 healthy subjects. It was found that the VFCDM method provided good estimates of breathing rates that were in the normal range (12–24 breaths/min). Both CWT and VFCDM methods provided reasonably good estimates for breathing rates that were higher than 26 breaths/min but their accuracy degraded concomitantly with increased respiratory rates. Overall, the VFCDM method provided the best results for accuracy (smaller median error), consistency (smaller interquartile range of the median value), and computational efficiency (less than 0.5 s on 1 min of data using a MATLAB implementation) to extract breathing rates that varied from 12 to 36 breaths/min. The AR method provided the least accurate respiratory rate estimation among the three methods. This work illustrates that both heart rates and normal breathing rates can be accurately derived from a video signal obtained from smartphones, an MP3 player and tablets with or without a flashlight.

In recent years, development of rheumatoid arthritis (RA) drug therapy has been more directly targeted to counteract specific mechanisms of inflammation, and it is now believed that early aggressive treatment with disease modifying drugs is important to inhibit future structural joint damage. The development of these new treatments has increased the need for methodologies to assess disease activity in RA and monitor the effectiveness of drug therapy. Unlike X-ray, which shows only structural bone damage, magnetic resonance imaging (MRI) can depict soft tissue damage and synovitis, the primary pathology of RA. Recent studies have also indicated that MRI is sensitive to pathophysiologic changes that may predate radiographic erosions and may predict future joint damage. In this study, we have developed a computer automated analysis technique for MR wrist images that provides an objective measure of RA synovitis. This method applies a two-compartment pharmacokinetic model to every voxel of a dynamic contrast-enhanced MRI (DCE-MRI) dataset and outputs resulting parametric images. The aim of this technique is to not only objectively quantify the severity of rheumatoid synovitis, but to also locally determine where areas of serious disease activity are situated through kinetic modeling of blood-tissue exchange. Preliminary results show good correlation to early enhancement rate, which has previously been shown to be a useful clinical marker of RA activity. However, the use of tracer kinetic modeling methods potentially provides more specific information regarding underlying RA physiology. This approach could provide a useful new tool in RA patient management and could substantially improve RA therapeutic studies by calculating objective biomarkers of the disease state.