Quantitative analysis of hindlimbs locomotion kinematics in spinalized rats treated with Tamoxifen plus treadmill exercise.

Locomotion recovery after a spinal cord injury (SCI) includes axon regeneration, myelin preservation and increased plasticity in propriospinal and descending spinal circuitries. The combined effects of tamoxifen and exercise after a SCI were analyzed in this study to determine whether the combination of both treatments induces the best outcome in locomotion recovery. In this study, the penetrating injury was provoked by a sharp projectile that penetrates through right dorsal and ventral portions of the T13-L1 spinal segments, affecting propriospinal and descending/ascending tracts. Intraperitoneal application of Tamoxifen and a treadmill exercise protocol, as rehabilitation therapies, separately or combined, were used. To evaluate the functional recovery, angular patterns of the hip, knee and ankle joints as well as the leg pendulum-like movement (PLM) were measured during the unrestricted gait of treated and untreated (UT) animals, previously and after the traumatic injury (15 and 30days post-injury (dpi)). A pattern (curve) comparison analysis was made by using a locally designed Matlab script that determines the Frechet dissimilarity. The SCI magnitude was assessed by qualitative and quantitative histological analysis of the injury site 30days after SCI. Our results showed that all treated groups had an improvement in hindlimbs kinematics compared to the UT group, which showed a poor gait locomotion recovery throughout the rehabilitation period. The group with the combined treatment (tamoxifen+exercise (TE)) presented the best outcome. In conclusion, tamoxifen and treadmill exercise treatments are complementary therapies for the functional recovery of gait locomotion in hemi-spinalized rats.

Towards cardiovascular risk stratification using imaging data.

In spite of the advancement and proliferation of cardiovascular imaging data, the rate of deaths due to unpredicted heart attack remains high. Thus, it becomes imperative to develop novel computational tools to mine quantitative parameters from imaging data for early detection and diagnosis of asymptomatic cardiovascular disease. In this paper, we present our progress towards developing a computational framework to mine cardiac imaging data and provide quantitative measures for developing a new risk assessment method. Specifically, we present computational methods developed for the detection of coronary calcification and segmentation of thoracic aorta in non-contrast cardiac computed tomography, and detection of neovessels in plaques in intravascular ultrasound imaging data.