Influence of traumatic impaction and pathological loading on knee menisci

Nearly half of the US population faces the risk of developing knee osteoarthritis (OA). Both in vitro and in vivo studies can aid in a better understanding of the etiology, progression, and advancement of this debilitating disorder. The knee menisci are fibrocartilagenous structures that aid in the distribution of load, attenuation of shock, alignment and lubrication of the knee. Little is known about the biochemical and morphological changes associated with knee menisci following altered loading and traumatic impaction, and investigations are needed to further elucidate how degradation of this soft tissue advances over time. The biochemical response of porcine meniscal explants was investigated following a single bout of dynamic compression with and without the treatment of the pharmaceutical drug, anakinra (IL-1RA). Dynamic loading led to a strain-dependent response in both anabolic and catabolic gene expression of meniscal explants. By inhibiting the Interleukin-1 pathway with IL-1RA, a marked decrease in several catabolic molecules was found. From these studies, future developments in OA treatments may be developed. The implementation of an in vivo animal model contributes to the understanding of how the knee joint behaves as a whole. A novel closed-joint in vivo model that induces anterior cruciate ligament (ACL) rupture has been developed to better understand how traumatic injury leads to OA. The menisci of knees from three different groups (healthy, ACL transected, and traumatically impacted) were characterized using histomorphometry. The acute and chronic changes within the knee following traumatic impaction were investigated. The works presented in this dissertation have focused on the characterization, implementation, and development of mechanically-induced changes to the knee menisci.

Skin Lesion Extraction And Its Application

In this thesis, I study skin lesion detection and its applications to skin cancer diagnosis. A skin lesion detection algorithm is proposed. The proposed algorithm is based color information and threshold. For the proposed algorithm, several color spaces are studied and the detection results are compared. Experimental results show that YUV color space can achieve the best performance. Besides, I develop a distance histogram based threshold selection method and the method is proven to be better than other adaptive threshold selection methods for color detection. Besides the detection algorithms, I also investigate GPU speed-up techniques for skin lesion extraction and the results show that GPU has potential applications in speeding-up skin lesion extraction. Based on the skin lesion detection algorithms proposed, I developed a mobile-based skin cancer diagnosis application. In this application, the user with an iPhone installed with the proposed application can use the iPhone as a diagnosis tool to find the potential skin lesions in a persons' skin and compare the skin lesions detected by the iPhone with the skin lesions stored in a database in a remote server.