Multi-omic characterization of segmental graft dysfunction in liver transplant patients

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Abstract/Contents

Abstract
Liver transplantation was pioneered in canine models in the 1950s and extended to humans in the 1960s. Since then, improvements in recipient-donor matching, organ allocation policies, and immunosuppression have made transplantation a viable intervention for patients with end-stage liver disease. Nowadays, the 5-year survival rate is 75%, with the majority of transplant failures occurring within 90 days after surgery. The success of liver transplantation is overshadowed by the significant organ shortage which motivated the inclusion of living donors. Within the first week after receiving a partial liver graft, patients could experience segmental graft dysfunction (SGD), a form of early allograft dysfunction (EAD). Although the majority of patients recover, an SGD diagnosis is still predictive of graft failure. This thesis focuses on determining the molecular signatures underpinning the development of SGD and developing predictive models of graft failure to identify high-risk patients.

Description

Type of resource text
Form electronic resource; remote; computer; online resource
Extent 1 online resource.
Place California
Place [Stanford, California]
Publisher [Stanford University]
Copyright date 2022; ©2022
Publication date 2022; 2022
Issuance monographic
Language English

Creators/Contributors

Author Erazo Castillo, Kevin Paul
Degree supervisor Snyder, Michael, Ph. D.
Thesis advisor Snyder, Michael, Ph. D.
Thesis advisor Bertozzi, Carolyn R, 1966-
Thesis advisor Khosla, Chaitan, 1964-
Degree committee member Bertozzi, Carolyn R, 1966-
Degree committee member Khosla, Chaitan, 1964-
Associated with Stanford University, Department of Chemistry

Subjects

Genre Theses
Genre Text

Bibliographic information

Statement of responsibility Kevin Paul Erazo Castillo.
Note Submitted to the Department of Chemistry.
Thesis Thesis Ph.D. Stanford University 2022.
Location https://purl.stanford.edu/fr977vj0087

Access conditions

Copyright
© 2022 by Kevin Paul Erazo Castillo
License
This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).

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