Contribution of the vasculature to the Alzheimer's disease process

One of the pathological hallmarks of Alzheimer's disease (AD) brain is extracellular β-amyloid (Aβ) plaques containing 39-42 amino acid Aβ peptides. The deposition of Aβ around blood vessels, known as Cerebral amyloid angiopathy (CAA), is also a common feature in AD brain. Vascular density and cerebral blood flow are reduced in AD brains, and vascular risk factors such as hypertension and diabetes are also risk factors for AD. We have shown previously that Aβ peptides can potently inhibit angiogenesis both in-vitro and in-vivo, but the mechanism of action for this effect is not known. Therefore, my first hypothesis was that particular amino acid sequence(s) within the Aβ peptide are required for inhibition of angiogenesis. From this aim, I found a peptide sequence which was critical for anti-angiogenic activity (HHQKLVFF). This sequence contains a heparan sulfate proteoglycan growth factor binding domain implying that Aβ can interfere with growth factor signaling. Leading on from this, my second hypothesis was that Aβ can inhibit angiogenesis by binding to growth factor receptors. I found that Aβ can bind to Vascular Endothelial Growth Factor Receptor-2 (VEGFR-2), and showed that this is one mechanism by which Aβ can inhibit angiogenesis. Since the vasculature is disrupted in AD brains, I investigated whether a strategy to increase brain vascularization would be beneficial against AD pathology. Therefore, my third hypothesis was that voluntary exercise (which is known to increase brain vascularization in rodents) can ameliorate Aβ pathology, increase brain vascularization, and improve behavioral deficits in a transgenic mouse model of AD. I found that exercise has no effect on Aβ pathology, brain vascularization or behavioral deficits. Therefore, in the transgenic mouse model that I used, exercise is an ineffective therapeutic strategy against AD pathology and symptoms.

A novel 3-D segmentation algorithm for anatomic liver and tumor volume calculations for liver cancer treatment planning

Three-Dimensional (3-D) imaging is vital in computer-assisted surgical planning including minimal invasive surgery, targeted drug delivery, and tumor resection. Selective Internal Radiation Therapy (SIRT) is a liver directed radiation therapy for the treatment of liver cancer. Accurate calculation of anatomical liver and tumor volumes are essential for the determination of the tumor to normal liver ratio and for the calculation of the dose of Y-90 microspheres that will result in high concentration of the radiation in the tumor region as compared to nearby healthy tissue. Present manual techniques for segmentation of the liver from Computed Tomography (CT) tend to be tedious and greatly dependent on the skill of the technician/doctor performing the task. ^ This dissertation presents the development and implementation of a fully integrated algorithm for 3-D liver and tumor segmentation from tri-phase CT that yield highly accurate estimations of the respective volumes of the liver and tumor(s). The algorithm as designed requires minimal human intervention without compromising the accuracy of the segmentation results. Embedded within this algorithm is an effective method for extracting blood vessels that feed the tumor(s) in order to plan effectively the appropriate treatment. ^ Segmentation of the liver led to an accuracy in excess of 95% in estimating liver volumes in 20 datasets in comparison to the manual gold standard volumes. In a similar comparison, tumor segmentation exhibited an accuracy of 86% in estimating tumor(s) volume(s). Qualitative results of the blood vessel segmentation algorithm demonstrated the effectiveness of the algorithm in extracting and rendering the vasculature structure of the liver. Results of the parallel computing process, using a single workstation, showed a 78% gain. Also, statistical analysis carried out to determine if the manual initialization has any impact on the accuracy showed user initialization independence in the results. ^ The dissertation thus provides a complete 3-D solution towards liver cancer treatment planning with the opportunity to extract, visualize and quantify the needed statistics for liver cancer treatment. Since SIRT requires highly accurate calculation of the liver and tumor volumes, this new method provides an effective and computationally efficient process required of such challenging clinical requirements.^