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- Creators: Harrington Bioengineering Program
- Resource Type: Text
Description
The main objective of this research is to develop and characterize a targeted contrast agent that will recognize acute neural injury pathology (i.e. fibrin) after traumatic brain injury (TBI). Single chain fragment variable antibodies (scFv) that bind specifically to fibrin have been produced and purified. DSPE-PEG micelles have been produced and the scFv has been conjugated to the surface of the micelles; this nanoparticle system will be used to overcome limitations in diagnosing TBI. The binding and imaging properties will be analyzed in the future to determine functionality of the nanoparticle system in vivo.
ContributorsRumbo, Kailey Michelle (Author) / Stabenfeldt, Sarah (Thesis director) / Kodibagkar, Vikram (Committee member) / Barrett, The Honors College (Contributor) / Harrington Bioengineering Program (Contributor)
Created2014-05
Description
The growth of the medical diagnostic industry in the past several decades has largely been due to the creation and iterative optimization of bio sensors. Recent pushes towards value added as well as preventative health care has made point of care devices more attractive to health care providers. Rapid detection for diseases and cancers is done with a bio sensor, which a broad term used to describe an instrument which uses a bio chemical reaction to detect a chemical compound with the use of a bio recognition event in addition to a signal detection event. The bio sensors which are presented in this work are known as ion-sensitive field effects transistors (ISFETs) and are similar in function to a metal oxide field effect transistor (MOSFET). These ISFETs can be used to sense pH or the concentration of protons on the surface of the gate channel. These ISFETs can be used for certain bio recognition events and this work presents the application of these transistors for the quantification of tumor cell proliferation. This includes the development of a signal processing and acquisition system for the long term assessment of cellular metabolism and optimizing the system for use in an incubator. This thesis presents work done towards the optimization and implementation of complementary metal\u2014oxide\u2014semiconductor (CMOS) ISFETs as well as remote gate ISFETs for the continuous assessment of tumor cell extracellular pH. The work addresses the challenges faced with the fabrication and optimization of these sensors, which includes the mitigation of current drift with the use of pulse width modulation in addition to issues encountered with fabrication of electrodes on a quartz substrate. This work culminates in the testing of an autonomous system with mammary tumor cells as well as the assessment of cell viability in an incubator over extended periods. Future applications of this work include the creation of a remote gate ISFET array for multiplexed detection as well as the implementation of ISFETs for bio marker detection via an immunoassay.
ContributorsArafa, Hany Mohamed (Author) / Blain Christen, Jennifer (Thesis director) / LaBelle, Jeffrey (Committee member) / Harrington Bioengineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2017-05
Description
This thesis project is the result of close collaboration with the Arizona State University Biodesign Clinical Testing Laboratory (ABCTL) to document the characteristics of saliva as a test sample, preanalytical considerations, and how the ABCTL utilized saliva testing to develop swift COVID-19 diagnostic tests for the Arizona community. As of April 2021, there have been over 130 million recorded cases of COVID-19 globally, with the United States taking the lead with approximately 31.5 million cases. Developing highly accurate and timely diagnostics has been an important need of our country that the ABCTL has had tremendous success in delivering. Near the start of the pandemic, the ABCTL utilized saliva as a testing sample rather than nasopharyngeal (NP) swabs that were limited in supply, required highly trained medical personnel, and were generally uncomfortable for participants. Results from literature across the globe showed how saliva performed just as well as the NP swabs (the golden standard) while being an easier test to collect and analyze. Going forward, the ABCTL will continue to develop high quality diagnostic tools and adapt to the ever-evolving needs our communities face regarding the COVID-19 pandemic.
ContributorsSmetanick, Jennifer (Author) / Compton, Carolyn (Thesis director) / Magee, Mitch (Committee member) / School of Life Sciences (Contributor) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
Cancer researchers have traditionally used a handful of markers to understand the origin of tumors and to predict therapeutic response. Additionally, performing machine learning activities on disparate data sources of varying quality is fraught with inherent bias. The Caris Life Sciences Molecular Database (CMD) is an immense resource for discovery as it contains over 215,000 molecular profiles of tumors with consistently gathered clinical grade molecular data along with immense amounts of clinical outcomes data. This resource was leveraged to generate two artificial intelligence algorithms aiding in diagnosis and one for therapy selection.
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
The Molecular Disease Classifier (MDC) was trained on 34,352 cases and tested on 15,473 unambiguously diagnosed cases. The MDC predicted the correct tumor type out of thirteen possibilities in the labeled data set with sensitivity, specificity, PPV, and NPV of 90.5%, 99.2%, 90.5% and 99.2% respectively when considering up to 5 predictions for a case.
The availability of whole transcriptome data in the CMD prompted its inclusion into a new platform called MI GPSai (MI Genomic Prevalence Score). The algorithm trained on genomic data from 34,352 cases and genomic and transcriptomic data from 23,137 cases and was validated on 19,555 cases. MI GPSai can predict the correct tumor type out of 21 possibilities on 93% of cases with 94% accuracy. When considering the top two predictions for a case, the accuracy increases to 97%.
Finally, a 67 gene molecular signature predictive of efficacy of oxaliplatin-based chemotherapy in patients with metastatic colorectal cancer was developed - FOLFOXai. The signature was predictive of survival in an independent real-world evidence (RWE) dataset of 412 patients who had received FOLFOX/BV in 1st line and inversely predictive of survival in RWE data from 55 patients who had received 1st line FOLFIRI. Blinded analysis of TRIBE2 samples confirmed that FOLFOXai was predictive of OS in both oxaliplatin-containing arms (FOLFOX HR=0.629, p=0.04 and FOLFOXIRI HR=0.483, p=0.02).
ContributorsAbraham, Jim (Author) / Spetzler, David (Thesis advisor) / Frasch, Wayne (Thesis advisor) / Lake, Douglas (Committee member) / Compton, Carolyn (Committee member) / Arizona State University (Publisher)
Created2020
Description
Markov Chain Monte-Carlo methods are a Bayesian approach to predictive statistics, which takes advantage of prior beliefs and conditions as well as the existing data to produce posterior distributions of relevant parameters. This approach, implementable through the JAGS packaging in R, is promising for its impact on the diagnostics space, which is a critical bottleneck for pandemic planning and rapid response. Specifically, these methods provide the means to optimize diagnostic testing, for example, by determining whether it is best to test individuals in a certain locale once or multiple times. This study compares the expected accuracy of single and double testing under two specific conditions, a general and Icelandic test case, in order to ascertain the validity of MCMC methods in this space and inform decisionmakers and future research in the space. Models based on this platform may eventually be tailored to the priors of specific locales. Additionally, the ability to test multiple regimes of real or simulated data while maintaining uncertainty widens the pool of researchers that can impact the space. In future studies, ensemble methods investigating the full range of parameters and their combinations can be studied.
ContributorsSuresh, Tarun (Author) / Naufel, Mark (Thesis director) / Panchanathan, Sethuraman (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05