The heterogeneity of the primary mechanical mechanism of spinal cord injury (SCI) is not currently used to tailor treatment strategies because the effects of these distinct patterns of acute mechanical damage on long-term neuropathology have not been fully investigated. A computational model of SCI enables the dynamic analysis of mechanical forces and deformations within the spinal cord tissue that would otherwise not be visible from histological tissue sections. We created a dynamic, three-dimensional finite element (FE) model of the rat cervical spine and simulated contusion and dislocation SCI mechanisms. We investigated the relationship between maximum principal strain and tissue damage, and compared primary injury patterns between mechanisms. The model incorporated the spinal cord white and gray matter, the dura mater, cerebrospinal fluid, spinal ligaments, intervertebral discs, a rigid indenter and vertebrae, and failure criteria for ligaments and vertebral endplates. High-speed (∼ 1 m/sec) contusion and dislocation injuries were simulated between vertebral levels C3 and C6 to match previous animal experiments, and average peak maximum principal strains were calculated for several regions at the injury epicenter and at 1-mm intervals from +5 mm rostral to -5 mm caudal to the lesion. Average peak principal strains were compared to tissue damage measured previously in the same regions via axonal permeability to 10-kD fluorescein-dextran. Linear regression of tissue damage against peak maximum principal strain for pooled data within all white matter regions yielded similar and significant (p<0.0001) correlations for both contusion (R(2)=0.86) and dislocation (R(2)=0.52). The model enhances our understanding of the differences in injury patterns between SCI mechanisms, and provides further evidence for the link between principal strain and tissue damage.
Falls are a major cause of death and morbidity in older adults. In recent years many researchers have examined the role of wearable inertial sensors (accelerometers and/or gyroscopes) to automatically detect falls. The primary goal of such fall monitors is to alert care providers of the fall event, who can then commence earlier treatment. Although such fall detection systems may reduce time until the arrival of medical assistance, they cannot help to prevent or reduce the severity of traumatic injury caused by the fall. In the current study, we extend the application of wearable inertial sensors beyond post-impact fall detection, by developing and evaluating the accuracy of a sensor system for detecting falls prior to the fall impact. We used support vector machine (SVM) analysis to classify 7 fall and 8 non-fall events. In particular, we focused on the effect of data window size and lead time on the accuracy of our pre-impact fall detection system using signals from a single waist sensor. We found that our system was able to detect fall events at between 0.0625-0.1875 s prior to the impact with at least 95% sensitivity and at least 90% specificity for window sizes between 0.125-1 s.
Individuals with healthy cartilage and posterior meniscal horn lesions have increased relaxation times when compared to matched controls, increased relaxation time changes over 2 years, and consistently report a lower KOOS QOL, yet show no difference in gait biomechanics.
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