Electronic Theses and Dissertations

Date

2022

Document Type

Thesis

Degree Name

Master of Science

Department

Biomedical Engineering

Committee Chair

Aaryani Tipirneni-Sajja

Committee Member

Amy de Jongh Curry

Committee Member

Melissa Puppa

Abstract

Nuclear magnetic resonance (NMR) spectroscopy is a quantitative, non-destructive analytical technique for identifying and quantifying metabolites. Conventional quantitative NMR analysis involves processing the recorded time-domain signal into a frequency-domain spectrum along with user-dependent processing and resonance characterization steps which may introduce bias and error. This study implements a Bayesian approach for data analysis in the time-domain to bypass these drawbacks and increase automation in metabolite quantification. The time-domain workflow is first assessed using metabolite reference standards to validate accurate resonance identification and quantification before profiling metabolites in liver, heart, and white adipose tissue of a diurnal animal model, the Nile grass rat. Animals on high-fat diet exhibited accumulation of lipids in the heart and liver. A time-restricted feeding diet was associated with decreased cardiac and hepatic lipid levels. This study demonstrates the validity of systematic Bayesian time-domain NMR data analysis and its application to understanding tissue-level responses to diet.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to ProQuest.

Notes

Open access

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