Diagnostic raman spectroscopy : from liquid phase bacterial identification to co-incubation free antibiotic susceptibility testing
Abstract/Contents
- Abstract
- Bacterial bloodstream infections account for over 40% of deaths in hospitals and are one of the most expensive medical conditions in the US. Globally, a child dies every 20 seconds due to infections. Current diagnostic methods are slow and costly, due to the long bacterial culturing step required for detection, identification, and antibiotic susceptibility testing. My work utilized Raman spectroscopy for rapid culture-free, sensitive, and specific bacterial identification and antibiotic susceptibility testing. Despite such promise, Raman's clinical translation has lagged due to reproducibility issues, bulky spectroscopy equipment needs and challenges in clinically suited sample preparation. This thesis demonstrates three major milestones to address these issues by using machine learning and nanophotonics. First, we develop a novel and robust liquid well setup for clinical sample handling with uniform Raman spectral enhancement using gold nanorods. Second, we demonstrate promising antibiotic susceptibility prediction on 100 patient derived E. coli samples with diverse response profiles to 15 major antibiotics. Third, we enable design of lower resolution more affordable spectrometers by implementing feature recognition approaches to isolate bands of spectra that are key for the accurate classifications. This work opens the door for clinical translation of novel spectroscopy based diagnostic tools for identifying bacterial infections, viral infections such as the current COVID-19 virus, early cancer detection and drug susceptibility testing by merging machine learning and nanophotonics.
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 | 2021; ©2021 |
| Publication date | 2021; 2021 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Tadesse, Loza Fekadu |
|---|---|
| Degree supervisor | Dionne, Jennifer Anne |
| Thesis advisor | Dionne, Jennifer Anne |
| Thesis advisor | Brongersma, Mark L |
| Thesis advisor | Prakash, Manu |
| Degree committee member | Brongersma, Mark L |
| Degree committee member | Prakash, Manu |
| Associated with | Stanford University, Department of Bioengineering |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Loza F. Tadesse. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2021. |
| Location | https://purl.stanford.edu/ht121tz9241 |
Access conditions
- Copyright
- © 2021 by Loza Fekadu Tadesse
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
Also listed in
Loading usage metrics...