Dynamic segmental kinematics of the lumbar spine during diagnostic movements

Background:In vivo measurements of segmental-level kinematics are a promising avenue for better understanding the relationship between pain and its underlying, multi-factorial basis. To date, the bulk of the reported segmental-level motion has been restricted to single plane motions.Methods: The present work implemented a novel marker set used with an optical motion capture system to non-invasively measure dynamic, 3D in vivo segmental kinematics of the lower spine in a laboratory setting. Lumbar spinal kinematics were measured for 28 subjects during 17 diagnostic movements.Results: Overall regional range of motion data and lumbar angular velocity measurement were consistent with previously published studies. Key findings from the work included measurement of differences in ascending versus descending segmental velocities during functional movements and observations of motion coupling paradigms in the lumbar spinal segments.Conclusion: The work contributes to the task of establishing a baseline of segmental lumbar movement patterns in an asymptomatic cohort, which serves as a necessary pre-requisite for identifying pathological and symptomatic deviations from the baseline

Decreased hippocampal translocator protein (18 kDa) expression in alcohol dependence: a [11C]PBR28 PET study

Repeated withdrawal from alcohol is clinically associated with progressive cognitive impairment. Microglial activation occurring during pre-clinical models of alcohol withdrawal is associated with learning deficits. We investigated whether there was microglial activation in recently detoxified alcohol-dependent patients (ADP), using [11C]PBR28 positron emission tomography (PET), selective for the 18kDa translocator protein (TSPO) highly expressed in activated microglia and astrocytes. We investigated the relationship between microglial activation and cognitive performance. Twenty healthy control (HC) subjects (45±13; M:F 14:6) and nine ADP (45±6, M:F 9:0) were evaluated. Dynamic PET data were acquired for 90 min following an injection of 331±15 MBq [11C]PBR28. Regional volumes of distribution (VT) for regions of interest (ROIs) identified a priori were estimated using a two-tissue compartmental model with metabolite-corrected arterial plasma input function. ADP had an ~20% lower [11C]PBR28 VT, in the hippocampus (F(1,24) 5.694; P=0.025), but no difference in VT in other ROIs. Hippocampal [11C]PBR28 VT was positively correlated with verbal memory performance in a combined group of HC and ADP (r=0.720, P<0.001), an effect seen in HC alone (r=0.738; P=0.001) but not in ADP. We did not find evidence for increased microglial activation in ADP, as seen pre-clinically. Instead, our findings suggest lower glial density or an altered activation state with lower TSPO expression. The correlation between verbal memory and [11C]PBR28 VT, raises the possibility that abnormalities of glial function may contribute to cognitive impairment in ADP

Growth conditions for carbon-infiltrated carbon nanotubes induce corrosion sensitization in 316L stainless steel

Carbon Infiltrated Carbon Nanotubes (CICNTs) show promise as a surface modification for medical devices and implants due to their potential structural resistance to bacterial colonization. However, when 316L stainless steel is used as the substrate for CICNT growth, the steel loses its passivating layer and experiences oxidative corrosion when placed in an aqueous physiological environment. This effect, confirmed by both energy dispersive x-ray analysis and electrochemical potentiokinetic reactivation, may be attributed to carburization of the alloy during CICNT production. One potential solution to this problem was investigated by employing an indirect CICNT growth method that utilized protective thin films under the CICNT surface and a nitrocellulose-based coating on other exposed edges. Samples that had been thus treated exhibited no significant corrosion over a 48 h testing period

Accounting for Viscoelasticity When Interpreting Nano-Composite High-Deflection Strain Gauges

High-deflection strain gauges show potential as economical and user-friendly sensors for capturing large deformations. The interpretation of these sensors is much more complex than that of conventional strain gauges due to the viscoelastic nature of strain gauges. This research endeavor developed and tested a model for interpreting sensor outputs that includes the time-dependent nature of strain gauges. A model that captures the effect of quasi-static strains was determined by using a conventional approach of fitting an equation to observed data. The dynamic relationship between the strain and the resistance was incorporated by superimposing dynamic components onto the quasi-static model to account for spikes in resistances that accompany each change in sensor strain and subsequent exponential decays. It was shown that the model can be calibrated for a given sensor by taking two data points at known strains. The resulting sensor-specific model was able to interpret strain-gauge electrical signals during a cyclical load to predict strain with an average mean absolute error (MAE) of 1.4% strain, and to determine the strain rate with an average MAE of 0.036 mm/s. The resulting model and tuning procedure may be used in a wide range of applications, such as biomechanical monitoring and analysis

Use Of Medical Image Data To Compute Strain Fields In Biological Tissue

INTRODUCTION Medical images, such as magnetic resonace (MR) or computed tomography (CT), provide measures of the three dimensional distribution of physical properties within tissues. Under the action of applied loads, the distribution is transformed such that images interrogated from the deformed tissues are not identical to those from the reference state. Part of this transformation is due to convection of material through fixed Eularian observation points of the imager and part is due to Lagrangian changes in the physical properties of the material. In the case of CT, for example, image intensity data is closely related to tissue density. During deformation, material points of differing density convect through image voxels while simultaneously changing density values according to the volume ratio. This type of data thereby provides a powerful constraint that can be employed to reconstruct three-dimensional strain fields in tissues. In the present work a time sequence of MR i

Biomechanical analysis of the camelid cervical intervertebral disc

Chronic low back pain (LBP) is a prevalent global problem, which is often correlated with degenerative disc disease. The development and use of good, relevant animal models of the spine may improve treatment options for this condition. While no animal model is capable of reproducing the exact biology, anatomy, and biomechanics of the human spine, the quality of a particular animal model increases with the number of shared characteristics that are relevant to the human condition. The purpose of this study was to investigate the camelid (specifically, alpaca and llama) cervical spine as a model of the human lumbar spine. Cervical spines were obtained from four alpacas and four llamas and individual segments were used for segmental flexibility/biomechanics and/or morphology/anatomy studies. Qualitative and quantitative data were compared for the alpaca and llama cervical spines, and human lumbar specimens in addition to other published large animal data. Results indicate that a camelid cervical intervertebral disc (IVD) closely approximates the human lumbar disc with regard to size, spinal posture, and biomechanical flexibility. Specifically, compared with the human lumbar disc, the alpaca and llama cervical disc size are approximately 62%, 83%, and 75% with regard to area, depth, and width, respectively, and the disc flexibility is approximately 133%, 173%, and 254%, with regard to range of motion (ROM) in axial-rotation, flexion-extension, and lateral-bending, respectively. These results, combined with the clinical report of disc degeneration in the llama lower cervical spine, suggest that the camelid cervical spine is potentially well suited for use as an animal model in biomechanical studies of the human lumbar spine

Inverse Piezoresistive Nanocomposite Sensors for Identifying Human Sitting Posture

Sitting posture is the position in which one holds his/her body upright against gravity while sitting. Poor sitting posture is regarded as an aggravating factor for various diseases. In this paper, we present an inverse piezoresistive nanocomposite sensor, and related deciphering neural network, as a new tool to identify human sitting postures accurately. As a low power consumption device, the proposed tool has simple structure, and is easy to use. The strain gauge is attached to the back of the user to acquire sitting data. A three-layer BP neural network is employed to distinguish normal sitting posture, slight hunchback and severe hunchback according to the acquired data. Experimental results show that our method is both realizable and effective, achieving 98.75% posture identification accuracy. This successful application of inverse piezoresistive nanocomposite sensors reveals that the method could potentially be used for monitoring of diverse physiological parameters in the future