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Full-Text Articles in Medicine and Health Sciences
Multiple Sclerosis Outcomes After Cancer Immunotherapy, Catherine R. Garcia, Rani Jayswal, Val R. Adams, Lowell B. Anthony, John L. Villano
Multiple Sclerosis Outcomes After Cancer Immunotherapy, Catherine R. Garcia, Rani Jayswal, Val R. Adams, Lowell B. Anthony, John L. Villano
Markey Cancer Center Faculty Publications
INTRODUCTION: Neurological immune-related adverse events are a rare but potentially deadly complication after immune checkpoint inhibitor (ICI) treatment. As multiple sclerosis (MS) is an immune-mediated disease, it is unknown how ICI treatment may affect outcomes.
METHODS: We analyzed the United States Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) database for pembrolizumab, atezolizumab, nivolumab, ipilimumab, avelumab, and durvalumab 2 years prior their FDA approval until December 31, 2017, to include all cases with confirmed diagnosis/relapse of MS. We also included cases reported in the literature and a patient from our institution.
RESULTS: We identified 14 cases of MS …
Imaging Of Glucose Metabolism By 13c-Mri Distinguishes Pancreatic Cancer Subtypes In Mice, Shun Kishimoto, Jeffrey R. Brender, Daniel R. Crooks, Shingo Matsumoto, Tomohiro Seki, Nobu Oshima, Hellmut Merkle, Penghui Lin, Galen Reed, Albert P. Chen, Jan Henrik Ardenkjaer-Larsen, Jeeva Munasinghe, Keita Saito, Kazutoshi Yamamoto, Peter L. Choyke, James Mitchell, Andrew N. Lane, Teresa W. M. Fan, W. Marston Linehan, Murali C. Krishna
Imaging Of Glucose Metabolism By 13c-Mri Distinguishes Pancreatic Cancer Subtypes In Mice, Shun Kishimoto, Jeffrey R. Brender, Daniel R. Crooks, Shingo Matsumoto, Tomohiro Seki, Nobu Oshima, Hellmut Merkle, Penghui Lin, Galen Reed, Albert P. Chen, Jan Henrik Ardenkjaer-Larsen, Jeeva Munasinghe, Keita Saito, Kazutoshi Yamamoto, Peter L. Choyke, James Mitchell, Andrew N. Lane, Teresa W. M. Fan, W. Marston Linehan, Murali C. Krishna
Center for Environmental and Systems Biochemistry Faculty Publications
Metabolic differences among and within tumors can be an important determinant in cancer treatment outcome. However, methods for determining these differences non-invasively in vivo is lacking. Using pancreatic ductal adenocarcinoma as a model, we demonstrate that tumor xenografts with a similar genetic background can be distinguished by their differing rates of the metabolism of 13C labeled glucose tracers, which can be imaged without hyperpolarization by using newly developed techniques for noise suppression. Using this method, cancer subtypes that appeared to have similar metabolic profiles based on steady state metabolic measurement can be distinguished from each other. The metabolic maps from …