Citations

Recording body accelerations has been investigated previously to monitor health, metabolic energy expenditure, postural sway and falls [1]. Activity monitoring and the subsequent analysis of the accelerometry data have also proven useful in detecting physical activity levels among Parkinson’s [2] and osteoarthritis patients [3].

Characterization of the viscoelastic mechanical properties of skin is important for improving accurate medical examination and diagnosis of disorders involving cutaneous and subcutaneous tissues; more thoroughly understanding skin biophysical and physiological properties; identifying normal and abnormal skin aging and disease processes; developing and applying improved pharmacologic and other therapeutic interventions; as well as cosmetic applications. Several noninvasive methods have been developed for measurement of mechanical properties of skin [1]. However, most methods measure a stiffness parameter but not the material properties of skin. For example, the suction method measures the displacement of skin in response to suction. The measurement is dependent on the aperture and fixation of probes and also difficult to interpret.

Intramuscular pressure (IMP) has been put forth as a surrogate measure for muscle force. As technological advancements have lead to the creation of smaller IMP microsensors, obtaining IMP readings in the clinic has come closer to becoming a minimally-invasive reality. However, appropriate use of data from these sensors relies upon an understanding of the mechanism of pressure changes within skeletal muscle. To that end, a constitutive model, representing muscle as a transversely isotropic, hyperelastic, and isovolumetric was created [1] for implementation in a finite element simulation. The purpose of this study was to validate this constitutive muscle model with passive elongation tests of skeletal muscle tissue from New Zealand White (NZW) rabbits. Reaction forces and hydrostatic pressures resulting from applied deformations were determined with the finite element modeling (FEM) approach and were compared with previously published experimental data [2].

Knee menisci are semi-lunar, fibrocartilaginous structures that convert applied compressive loads to circumferential hoop stresses which are attenuated at the tibial plateau via the meniscal attachments [1]. These specialized interfaces, located at the lateral anterior (LA), lateral posterior (LP), medial anterior (MA), and medial posterior (MP) horns, are crucial to maintaining mechanical functionality of menisci, thereby preventing osteoarthritis [2]. Soft-tissues subjected to compression and shear loads are known to possess proteoglycans, which aid in resisting these applied stresses by retaining interstitial fluid [1]. Interestingly, proteoglycans are also known to be present at the meniscus to bone interface [3]. Despite the presence of these biphasic attachments and their important role in joint load transfer, there have been no quantitative investigations of the mechanical environment within these attachments under physiological and pathological loads. Recent development of a novel pressure microsensor now allows direct observation of fluid pressure, which will aid in understanding the internal mechanical environment within the attachment sites. Previous work indicates that the attachment sites are geometrically, mechanically, and histologically different [4], thus it is hypothesized that the fluid pressures would likewise differ.

The Modeling and Analysis group in the Division of Engineering at Mayo Clinic, Rochester works in the area of numerical modeling and experimental validation of cerebral aneurysms. In our modeling we use patient-specific geometries from imaging studies provided by Dr. Kallmes. The CFD analysis is performed with commercial packages Mimics (Materialise, Leuven, Belgium), for segmentation and ANSYS (ANSYS Inc. Canonsburg, PA) for meshing, simulation and post-processing. The experiments are conducted using a tomographic Particle Image Velocimetry system from LaVision Inc. The group is led by Dan Dragomir-Daescu, PhD and David F. Kallmes, MD and consists of two postdoctoral fellows and three engineers. For this project, CFD simulations were performed by postdoctoral fellow Simona Hodis, using the finite volume based solver Fluent.

Gait analysis is an important tool in assessing the health and activity levels of patients and regular physical activity has been associated with health improvements in a number of populations. Step counting is one of the most commonly used measures of physical activity [1] and many studies have investigated the use of wearable sensors for step counts [2–4]. Their small size and light weight mean that they may be used in a free living environment and are suitable for home deployment. One of the main issues associated with step counts as a measure of physical activity is that a very high level of accuracy in step detection is needed.

Many single-phase, solid mechanics computational models of skeletal muscle have been created based on the assumptions that skeletal muscle is hyperelastic, transversely isotropic,1,2 and isovolumic. However, muscle has also been observed to have non-linear time-dependent behaviors that can be modeled using viscoelasticity or poroelasticity. The inherent viscoelasticity of muscle may not fully explain the time-dependent nature that may arise from extracellular fluid filtration3 and redistribution4 within the tissue. Further, intramuscular pressure (IMP) has been shown to have a heterogeneous distribution in muscle. This may be attributable to the movement of extracellular fluid, but the mechanisms of fluid redistribution are not completely understood. While a viscoelastic model may predict time-dependent responses of skeletal muscle, viscoelastic theory does not explain underlying causative tissue behaviors.5

Transaortic valve implantation via direct aortic puncture has been shown to be useful alternative to more traditional access routes. In patients with previously placed ascending aorta saphenous vein grafts, access to the aortic valve may be limited. We therefore sought to detail the utility of a new approach for accessing the ascending aorta during transcatheter aortic valve implantation via the hood of previously placed saphenous vein grafts. © 2013 Wiley Periodicals, Inc.

ABSTRACT Parkinson's disease (PD) biomarkers are needed to enhance therapeutics research and to understand PD pathogenesis. Methods that simultaneously measure hundreds of small molecular‐weight compounds—metabolomic analysis–“fingerprint” disease‐specific alterations in individual compounds or metabolic pathways. Beyond a nontargeted search for PD biomarkers, we hypothesized that PD cerebrospinal fluid would show increased formation of the excitotoxin 3‐hydroxykynurenine and diminished concentration of the antioxidant glutathione. Cerebrospinal fluid was collected at <4 hours postmortem from 48 pathologically‐verified PD subjects and 57 comparably‐aged controls. Assays involved ultra‐high‐performance liquid and gas chromatography linked to mass spectrometry. We used univariate techniques to determine fold‐changes in concentrations of biochemicals; false‐discovery rates were calculated to exclude spurious findings. Data was modeled using a Support Vector Machine for analyzing compounds selected by Welch's t test. Classification accuracy was determined by cross‐validation. Of 243 structurally‐identified biochemicals,19 compounds differentiated PD from controls at a 20% false‐discovery level. In PD, mean 3‐hydroxykynurenine concentration was increased by one‐third, and mean oxidized glutathione was decreased by 40% (for each, P < .01). Four of the 19 compounds differentiating PD from controls were N‐acetylated amino acids, suggesting a generalized alteration in N‐acetylation activity. The Support Vector Machine classification model distinguished between groups at 83% sensitivity and 91% specificity for the learning data, and at 65% and 79% from cross‐validation. In this study, the first for metabolomic profiling of PD cerebrospinal fluid, we found several novel biomarkers and offer new directions for recognizing disease‐specific biochemical indicators. The findings support involvement of excitotoxicity and oxidative stress in the pathogenesis of PD. © 2013 International Parkinson and Movement Disorder Society