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Your search keyword '"Kimberly Argueta"' showing total 3 results
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3 results on '"Kimberly Argueta"'

1. Proteomic characterization of acute kidney injury in patients hospitalized with SARS-CoV2 infection

Author

Ishan Paranjpe, Pushkala Jayaraman, Chen-Yang Su, Sirui Zhou, Steven Chen, Ryan Thompson, Diane Marie Del Valle, Ephraim Kenigsberg, Shan Zhao, Suraj Jaladanki, Kumardeep Chaudhary, Steven Ascolillo, Akhil Vaid, Edgar Gonzalez-Kozlova, Justin Kauffman, Arvind Kumar, Manish Paranjpe, Ross O. Hagan, Samir Kamat, Faris F. Gulamali, Hui Xie, Joceyln Harris, Manishkumar Patel, Kimberly Argueta, Craig Batchelor, Kai Nie, Sergio Dellepiane, Leisha Scott, Matthew A. Levin, John Cijiang He, Mayte Suarez-Farinas, Steven G. Coca, Lili Chan, Evren U. Azeloglu, Eric Schadt, Noam Beckmann, Sacha Gnjatic, Miram Merad, Seunghee Kim-Schulze, Brent Richards, Benjamin S. Glicksberg, Alexander W. Charney, and Girish N. Nadkarni

Subjects
Medicine
Abstract

Abstract Background Acute kidney injury (AKI) is a known complication of COVID-19 and is associated with an increased risk of in-hospital mortality. Unbiased proteomics using biological specimens can lead to improved risk stratification and discover pathophysiological mechanisms. Methods Using measurements of ~4000 plasma proteins in two cohorts of patients hospitalized with COVID-19, we discovered and validated markers of COVID-associated AKI (stage 2 or 3) and long-term kidney dysfunction. In the discovery cohort (N = 437), we identified 413 higher plasma abundances of protein targets and 30 lower plasma abundances of protein targets associated with COVID-AKI (adjusted p

Published
2023
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2. Circulating proteins to predict COVID-19 severity

Author

Chen-Yang Su, Sirui Zhou, Edgar Gonzalez-Kozlova, Guillaume Butler-Laporte, Elsa Brunet-Ratnasingham, Tomoko Nakanishi, Wonseok Jeon, David R. Morrison, Laetitia Laurent, Jonathan Afilalo, Marc Afilalo, Danielle Henry, Yiheng Chen, Julia Carrasco-Zanini, Yossi Farjoun, Maik Pietzner, Nofar Kimchi, Zaman Afrasiabi, Nardin Rezk, Meriem Bouab, Louis Petitjean, Charlotte Guzman, Xiaoqing Xue, Chris Tselios, Branka Vulesevic, Olumide Adeleye, Tala Abdullah, Noor Almamlouk, Yara Moussa, Chantal DeLuca, Naomi Duggan, Erwin Schurr, Nathalie Brassard, Madeleine Durand, Diane Marie Del Valle, Ryan Thompson, Mario A. Cedillo, Eric Schadt, Kai Nie, Nicole W. Simons, Konstantinos Mouskas, Nicolas Zaki, Manishkumar Patel, Hui Xie, Jocelyn Harris, Robert Marvin, Esther Cheng, Kevin Tuballes, Kimberly Argueta, Ieisha Scott, The Mount Sinai COVID-19 Biobank Team, Celia M. T. Greenwood, Clare Paterson, Michael A. Hinterberg, Claudia Langenberg, Vincenzo Forgetta, Joelle Pineau, Vincent Mooser, Thomas Marron, Noam D. Beckmann, Seunghee Kim-schulze, Alexander W. Charney, Sacha Gnjatic, Daniel E. Kaufmann, Miriam Merad, and J. Brent Richards

Subjects
Medicine, Science
Abstract

Abstract Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.

Published
2023
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3. Downregulation of exhausted cytotoxic T cells in gene expression networks of multisystem inflammatory syndrome in children

Author

Noam D. Beckmann, Phillip H. Comella, Esther Cheng, Lauren Lepow, Aviva G. Beckmann, Scott R. Tyler, Konstantinos Mouskas, Nicole W. Simons, Gabriel E. Hoffman, Nancy J. Francoeur, Diane Marie Del Valle, Gurpawan Kang, Anh Do, Emily Moya, Lillian Wilkins, Jessica Le Berichel, Christie Chang, Robert Marvin, Sharlene Calorossi, Alona Lansky, Laura Walker, Nancy Yi, Alex Yu, Jonathan Chung, Matthew Hartnett, Melody Eaton, Sandra Hatem, Hajra Jamal, Alara Akyatan, Alexandra Tabachnikova, Lora E. Liharska, Liam Cotter, Brian Fennessy, Akhil Vaid, Guillermo Barturen, Hardik Shah, Ying-chih Wang, Shwetha Hara Sridhar, Juan Soto, Swaroop Bose, Kent Madrid, Ethan Ellis, Elyze Merzier, Konstantinos Vlachos, Nataly Fishman, Manying Tin, Melissa Smith, Hui Xie, Manishkumar Patel, Kai Nie, Kimberly Argueta, Jocelyn Harris, Neha Karekar, Craig Batchelor, Jose Lacunza, Mahlet Yishak, Kevin Tuballes, Ieisha Scott, Arvind Kumar, Suraj Jaladanki, Charuta Agashe, Ryan Thompson, Evan Clark, Bojan Losic, Lauren Peters, The Mount Sinai COVID-19 Biobank Team, Panagiotis Roussos, Jun Zhu, Wenhui Wang, Andrew Kasarskis, Benjamin S. Glicksberg, Girish Nadkarni, Dusan Bogunovic, Cordelia Elaiho, Sandeep Gangadharan, George Ofori-Amanfo, Kasey Alesso-Carra, Kenan Onel, Karen M. Wilson, Carmen Argmann, Supinda Bunyavanich, Marta E. Alarcón-Riquelme, Thomas U. Marron, Adeeb Rahman, Seunghee Kim-Schulze, Sacha Gnjatic, Bruce D. Gelb, Miriam Merad, Robert Sebra, Eric E. Schadt, and Alexander W. Charney

Subjects
Science
Abstract

Multisystem inflammatory syndrome in children (MIS-C) onsets in COVID-19 patients with manifestations similar to Kawasaki disease (KD). Here the author probe the peripheral blood transcriptome of MIS-C patients to find signatures related to natural killer (NK) cell activation and CD8+ T cell exhaustion that are shared with KD patients.

Published
2021
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