Publications by authors named "T J O'Donnell"

Objectives: Endovascular aneurysm repair (EVAR) for large infrarenal abdominal aortic aneurysms (AAA) has been associated with worse outcomes compared to EVAR for smaller AAAs. Whether these findings apply to complex AAAs (cAAA) remains uncertain.

Methods: We identified all intact complex EVAR (cEVAR) from 2012-2024 in the Vascular Quality Initiative.

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Purpose: As the pancreas is a low contrast visibility organ, pancreatic ductal adenocarcinoma detection is challenging due to subtle attenuation differences between tumor and pancreatic parenchyma. Photon counting CT (PCCT) has superior iodine contrast-to-noise ratio than conventional CT and also affords the creation of low keV virtual monoenergetic images, both of which increase adenocarcinoma conspicuity. The purpose therefore was to identify the optimal virtual monoenergy for visualizing PDAC during the pancreatic parenchymal phase of enhancement at PCCT using both quantitative and qualitative analyses.

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The Cryogenic Underground Observatory for Rare Events (CUORE) is a detector array comprised by 988 5  cm×5  cm×5  cm TeO_{2} crystals held below 20 mK, primarily searching for neutrinoless double-beta decay in ^{130}Te. Unprecedented in size among cryogenic calorimetric experiments, CUORE provides a promising setting for the study of exotic throughgoing particles. Using the first tonne year of CUORE's exposure, we perform a search for hypothesized fractionally charged particles (FCPs), which are well-motivated by various standard model extensions and would have suppressed interactions with matter.

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Objective: Single-center studies have suggested that solid organ transplant recipients are at increased risk for arterial aneurysms. Moreover, they describe a more aggressive natural history with increased rates of expansion and rupture. In this exploratory analysis, we aim to assess the frequency of arterial aneurysms in solid organ transplant recipients using a large-scale national database.

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The adaptive immune system holds invaluable information on past and present immune responses in the form of B and T cell receptor sequences, but we are limited in our ability to decode this information. Machine learning approaches are under active investigation for a range of tasks relevant to understanding and manipulating the adaptive immune receptor repertoire, including matching receptors to the antigens they bind, generating antibodies or T cell receptors for use as therapeutics, and diagnosing disease based on patient repertoires. Progress on these tasks has the potential to substantially improve the development of vaccines, therapeutics, and diagnostics, as well as advance our understanding of fundamental immunological principles.

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