Objective: The objective of this study is to evaluate the safety and efficacy of a tumor-specific apoptosis-inducing gene, apoptin, as delivered by the non-viral carrier, PAM-RG4, in an animal model of spinal cord tumor. Methods: Male Sprague-Dawley rats were given a 2.5-ml intramedullary injection of C6 glioma (100 000) cells and randomized into three groups (day 0). On day 5, animals received a 7.5-ml intramedullary injection of Dulbecco's modified Eagle's medium (Group 1; n ¼ 7), PAM-RG4/control gene polyplex (Group 2; n ¼ 7), or PAM-RG4/apoptin gene polyplex (Group 3; n ¼ 8). Hindlimb functional strength was assessed every other day for the duration of the study. The spinal cords of killed animals were collected and hematoxylineosin stained. Results: Following treatment, animals that received apoptin had significantly higher mean functional hindlimb scores than those of sham control animals, showing a level of preserved hindlimb function throughout the study. In addition, Group 1 (sham control) and Group 2 (control gene) animals had median survival scores lower than those of animals receiving apoptin. Histopathological analysis showed marked retardation of tumor progression in apoptin-treated animals compared with sham controls. Conclusion: Our study suggests that apoptin is safe for use in the mammalian spinal cord as well as effective in slowing the progression of tumor growth in the spinal cord. The significant slowing of tumor progression, as manifested by the preserved hindlimb function, coupled with the reduction in tumor volume, shows local non-viral delivery of apoptin could serve as an emerging therapy for the treatment of intramedullary spinal cord tumors.
In the field of Radiology, the Computer Aided Diagnosis is the technology which gives valuable information for surgical purpose. For its importance, several computer vison methods are processed to obtain useful information of images acquired from the imaging devices such as X-ray, Magnetic Resonance Imaging (MRI) and Computed Tomography (CT). These methods, called pattern recognition, extract features from images and feed them to some machine learning algorithm to find out meaningful patterns. Then the learned machine is then used for exploring patterns from unseen images. The radiologist can therefore easily find the information used for surgical planning or diagnosis of a patient through the Computer Aided Diagnosis. In this paper, we present a review on three widely-used methods applied to Computer Aided Diagnosis. The first one is the image processing methods which enhance meaningful information such as edge and remove the noise. Based on the improved image quality, we explain the second method called segmentation which separates the image into a set of regions. The separated regions such as bone, tissue, organs are then delivered to machine learning algorithms to extract representative information. We expect that this paper gives readers basic knowledges of the Computer Aided Diagnosis and intuition about computer vision methods applied in this area.
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