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The objective of this work was to perform image-based classification of abdominal aortic aneurysms (AAA) based on their demographic, geometric, and biomechanical attributes. We retrospectively reviewed existing demographics and abdominal computed tomography angiography images of 100 asymptomatic and 50 symptomatic AAA patients who received an elective or emergent repair, respectively, within 1–6 months of their last follow up. An in-house script developed within the MATLAB computational platform was used to segment the clinical images, calculate 53 descriptors of AAA geometry, and generate volume meshes suitable for finite element analysis (FEA). Using a third party FEA solver, four biomechanical markers were calculated from the wall stress distributions. Eight machine learning algorithms (MLA) were used to develop classification models based on the discriminatory potential of the demographic, geometric, and biomechanical variables. The overall classification performance of the algorithms was assessed by the accuracy, area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and precision of their predictions. The generalized additive model (GAM) was found to have the highest accuracy (87%), AUC (89%), and sensitivity (78%), and the third highest specificity (92%), in classifying the individual AAA as either asymptomatic or symptomatic. The k-nearest neighbor classifier yielded the highest specificity (96%). GAM used seven markers (six geometric and one biomechanical) to develop the classifier. The maximum transverse dimension, the average wall thickness at the maximum diameter, and the spatially averaged wall stress were found to be the most influential markers in the classification analysis. A second classification analysis revealed that using maximum diameter alone results in a lower accuracy (79%) than using GAM with seven geometric and biomechanical markers. We infer from these results that biomechanical and geometric measures by themselves are not sufficient to discriminate adequately between population samples of asymptomatic and symptomatic AAA, whereas MLA offer a statistical approach to stratification of rupture risk by combining demographic, geometric, and biomechanical attributes of patient-specific AAA.

Introduction Spinal cord infarction is extremely rare in childhood and can result from a wide range of causes. Fibrocartilaginous embolism can give rise to spinal stroke and mimic non-vascular disease such as acute transverse myelitis. Case We report two children who suffered an asymmetrical spinal cord infarction due to fibrocartilaginous embolism. The clinical presentation, radiological findings, and pathophysiology of fibrocartilaginous embolism are described. Each patient demonstrated marked clinical improvement after receiving extensive physical therapy and rehabilitation. One child demonstrated complete clinical recovery. The other had persistent asymmetrical foot weakness and distal sensory deficits. Conclusion We outline the key clinical and radiographic features that enable spinal cord infarction to be differentiated from transverse myelitis. Prognosis depends on many factors such as extent and type of injury, level of the cord affected, and age at the time of spinal cord infarction.

The overall mortality rate due to rupture of an abdominal aortic aneurysm (AAA) is greater than 80%. The current clinical standard for assessing rupture risk of an AAA is a size-based approach solely based on measuring the maximum transverse diameter (Dmax) of the aneurysm. If Dmax is greater or equal than 5.0 cm (in the U.S.), repair is recommended; otherwise, the patient is placed in a surveillance program consisting of “watchful waiting” with periodic imaging follow ups. This approach is not always reliable as some small aneurysms rupture prior to reaching the aforementioned critical diameter. Likewise, some large AAAs remain stable and are not diagnosed until they have exceeded the critical size. Biomechanical measures such as peak wall stress and 99th percentile wall stress have been shown to be better predictors of AAA rupture compared to Dmax. AAA rupture is a mechanical event that occurs when the stress on the AAA wall exceeds the wall strength. Therefore, the rupture potential index (RPI) – the ratio of local wall stress to wall strength ‒, is a biomechanical metric that can be used as an AAA rupture risk predictor. The calculation of RPI is based on patient-specific finite element analysis (FEA), which requires prior knowledge of volume meshing, use of a FEA solver, and warrants patient-specific material properties. Moreover, FEA can be user-dependent and the outcomes vary with mesh density and the applied boundary conditions. Geometric markers such as area-averaged Gaussian curvature and minimum wall thickness, have been previously utilized as surrogates to predict spatially averaged wall stress (SAWS) in symptomatic and emergently repaired AAAs. Using these geometric markers, AAA wall stress can be predicted in lieu of FEA. Similarly, area-averaged Mean curvature and proximal neck diameter have been quantified to accurately predict SAWS in asymptomatic and electively repaired AAAs. In this study, we built a machine learning model for the prediction of RPI using geometric markers derived from 3D reconstructed computed tomography angiography images. The current protocol for AAA geometric quantification calculates a set of global markers representative of the shape, size, wall thickness, and curvature of the AAA sac. We quantified the spatial distribution of such geometric markers for a more comprehensive prediction of RPI. Further, the predicted RPI was utilized to identify and classify high-risk AAA. In summary, we developed and validated an algorithm that can predict RPI in AAA models using a machine learning approach to identify geometric markers from individual clinical images.

Intracellular pH in pheochromocytoma (PC12) cells was manipulated by 'acid loading' the cells and the effect of such a change on radioactive zinc uptake was studied. It was found that zinc uptake was stimulated in cells loaded with protons without causing any measurable change in the intracellular pH. To confirm our assumption that the proton flux due to zinc entry is too small to be measured, we calculated the pH change that one would expect because of zinc influx. The intrinsic buffer capacity of PC12 cells was determined to be 8.03 mM/pH unit and was used in these calculations. It was found that at the five-minute incubation, zinc uptake occurring under our experimental conditions could cause a pH change of 0.000277 pH units per minute (assuming a 1:2 zinc:proton stoichiometry). This study adds a new dimension towards understanding the role played by intracellular pH in causing zinc entry into cells.

Recent evidence for the involvement of zinc in the formation of beta-amyloid plaques in the brain in Alzheimer's disease has led to the establishment of new therapeutic strategies for the degenerative disorder based on metal chelation. The present experiment was conducted on a membrane-permeable zinc chelator, clioquinol (CQ), that has shown potential in initial studies on a mouse model of Alzheimer's disease [1]. The degree of chelatable zinc in mice treated with CQ, delivered by two different routes, was measured using complementary protocols for identifying chelatable zinc: 6-methoxy-8-quinolyl- p-toluenesulfonamide (TSQ) histofluorescence, and selenite autometalography. Mice injected intraperitoneally with CQ showed a dramatic reduction in chelatable zinc in brain, testis, and pancreas. In contrast, mice given CQ orally showed no significant change in levels of chelatable zinc in these tissues. This suggests that CQ administered orally to patients with Alzheimer's disease should not significantly perturb chelatable zinc levels in key organs and may be used over long periods without adverse endocrinological and reproductive effects related to zinc deficiency. In contrast, CQ injected intraperitoneally may be used not only as a tool for investigating chelatable zinc pools but also in a clinical context. For example, injected CQ could be employed in situations requiring the rapid buffering of excessive chelatable zinc following ischemic episodes or brain trauma. Thus, our findings indicate that CQ has considerable potential as a versatile scientific and clinical tool used for selective modulation of zinc pools.

Abdominal aortic aneurysm (AAA) is a vascular disease characterized by the enlargement of the infrarenal segment of the aorta. A ruptured AAA can cause internal bleeding and carries a high mortality rate, which is why the clinical management of the disease is focused on preventing aneurysm rupture. AAA rupture risk is estimated by the change in maximum diameter over time (i.e., growth rate) or if the diameter reaches a prescribed threshold. The latter is typically 5.5 cm in most clinical centers, at which time surgical intervention is recommended. While a size-based criterion is suitable for most patients who are diagnosed at an early stage of the disease, it is well known that some small AAA rupture or patients become symptomatic prior to a maximum diameter of 5.5 cm. Consequently, the mechanical stress in the aortic wall can also be used as an integral component of a biomechanics-based rupture risk assessment strategy. In this work, we seek to identify geometric characteristics that correlate strongly with wall stress using a sample space of 100 asymptomatic, unruptured, electively repaired AAA models. The segmentation of the clinical images, volume meshing, and quantification of up to 45 geometric measures of each AAA were done using in-house Matlab scripts. Finite element analysis was performed to compute the first principal stress distributions from which three global biomechanical parameters were calculated: peak wall stress, 99th percentile wall stress and spatially averaged wall stress. Following a feature reduction approach consisting of Pearson’s correlation matrices with Bonferroni correction and linear regressions, a multivariate stepwise regression analysis was conducted to find the geometric measures most highly correlated with each of the biomechanical parameters. Our findings indicate that wall stress can be predicted by geometric indices with an accuracy of up to 94% when AAA models are generated with uniform wall thickness and up to 67% for patient specific, non-uniform wall thickness AAA. These geometric predictors of wall stress could be used in lieu of complex finite element models as part of a geometry-based protocol for rupture risk assessment.

The Ohio University College of Osteopathic Medicine in 2005 created an innovative competitive grant program aimed at stimulating faculty to submit more and better NIH research proposals, thereby increasing the probability of success. In this internal competition, three experienced external reviewers critique each proposal and assign a priority score, mirroring the NIH review process. An internal panel then selects the two to three best proposals to receive $20,000 awards, contingent upon submission to NIH of a revised proposal that incorporates the comments and suggestions of the reviewers. Thus, the awardees receive additional resources to move their project forward. Moreover, all participants benefit from the constructive reviews, the \"free\" review cycle (in addition to the NIH \"three-strike\" system), and the excellent learning experience in grant preparation, revision and submission of competitive proposals. Academic researchers and administrators, particularly at smaller, less research-intensive institutions, today face a challenging environment with increased competition for a limited funding pool. Under such circumstances, an internal grant development program may be a great avenue for mentoring and education for faculty, and also serve as a cost-effective investment for research officers to increase external research funding as well as enhance the research skills of faculty. [PUBLICATION ABSTRACT]

The Ohio University College of Osteopathic Medicine in 2005 created an innovative competitive grant program aimed at stimulating faculty to submit more and better NIH research proposals, thereby increasing the probability of success. In this internal competition, three experienced external reviewers critique each proposal and assign a priority score, mirroring the NIH review process. An internal panel then selects the two to three best proposals to receive $20,000 awards, contingent upon submission to NIH of a revised proposal that incorporates the comments and suggestions of the reviewers. Thus, the awardees receive additional resources to move their project forward. Moreover, all participants benefit from the constructive reviews, the \"free\" review cycle (in addition to the NIH \"three-strike\" system), and the excellent learning experience in grant preparation, revision and submission of competitive proposals, Academic researchers and administrators, particularly at smaller, less research-intensive institutions, today face a challenging environment with increased competition for a limited funding pool. Under such circumstances, an internal grant development program may be a great avenue for mentoring and education for faculty, and also serve as a cost-effective investment for research officers to increase external research funding as well as enhance the research skills of faculty. Key Words: Grant, NIH, research administration, mentoring program, seed money

This dissertation investigates how to optimize flow-level performance in interference dominated wireless networks serving dynamic traffic loads. The schemes presented in this dissertation adapt to long-term (hours) spatial load variations, and the main metrics of interest are the file transfer delay or average flow throughput and the mean power expended by the transmitters. The first part presents a system level approach to interference management in an infrastructure based wireless network with full frequency reuse. The key idea is to use loose base station coordination that is tailored to the spatial load distribution and the propagation environment to exploit the diversity in a user population's sensitivity to interference. System architecture and abstractions to enable such coordination are developed for both the downlink and the uplink cases, which present differing interference characteristics. The basis for the approach is clustering and aggregation of traffic loads into classes of users with similar interference sensitivities that enable coarse grained information exchange among base stations with greatly reduced communication overheads. The dissertation explores ways to model and optimize the system under dynamic traffic loads where users come and go resulting in interference induced performance coupling across base stations. Based on extensive system-level simulations, I demonstrate load-dependent reductions in file transfer delay ranging from 20-80% as compared to a simple baseline not unlike systems used in the field today, while simultaneously providing more uniform coverage. Average savings in user power consumption of up to 75% are achieved. Performance results under heterogeneous spatial loads illustrate the importance of being traffic and environment aware. The second part studies the impact of policies to associate users with base stations/access points on flow-level performance in interference limited wireless networks. Most research in this area has used static interference models (i.e., neighboring base stations are always active) and resorted to intuitive objectives such as load balancing. In this dissertation, it is shown that this can be counter productive, and that asymmetries in load can lead to significantly better performance in the presence of dynamic interference which couples the transmission rates experienced by users at various base stations. A methodology that can be used to optimize the performance of a class of coupled systems is proposed, and applied to study the user association problem. It is demonstrated that by properly inducing load asymmetries, substantial performance gains can be achieved relative to a load balancing policy (e.g., 15 times reduction in mean delay). A novel measurement based, interference-aware association policy is presented that infers the degree of interference induced coupling and adapts to it. Systematic simulations establish that both the optimized static and interference-sensitive, adaptive association policies substantially outperform various proposed dynamic policies and that these results are robust to changes in file size distributions, channel parameters, and spatial load distributions.

Specialty rubbers are high-performance materials. As elastomers, they require a low modulus and high reversible elongation, but in all other properties, they must meet stringent requirements that vary depending on the application. They require properties ranging from high ultimate strength, high break strain, low permanent set, toughness, durability to cyclic strain, abrasion resistance, thermal resistance, and solvent or corrosive chemical resistance. Low hysteresis and high damping are alternatives that may be required. Fillers are added to contribute to many of these properties and nanofillers can be most effective because of large surface-area-to-volume ratio. The nanosized fillers contribute to the performance via specialized mechanisms such as resisting crack propagation, forming agglomerates, or edge bridging; having fibrous, platelet, or particulate shape; and maybe forming specific bonds with the polymer. Specialty elastomers consist of higher performing polymers each with their own particular characteristics: fluoroelastomers, polysiloxanes, and polyurethanes are typical examples. However, the use of nanofillers can increase the performance of commodity polymers for them to be considered specialty polymers as they open new applications.