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Description
Proton beam therapy (PBT) is a state-of-the-art radiotherapy treatment approach that uses focused proton beams for tumor ablation. A key advantage of this approach over conventional photon radiotherapy (XRT) is the unique dose deposition characteristics of protons, resulting in superior healthy tissue sparing. This results in fewer unwanted side effects and improved outcomes for patients. Current available dosimeters are intrinsic, complex and expensive; hence cannot be used to determine the dose delivered to the tumor routinely. Here, we report a hydrogel based plasmonic nanosensor for measurements of clinical doses in ranges between 2-4 GyRBE. In this nanosensor, gold ions, encapsulated in a hydrogel, are reduced to gold nanoparticles following irradiation with proton beams. Formation of gold nanoparticles renders a color change to the originally colorless hydrogel. The intensity of the color can be used to calibrate the hydrogel nanosensor in order to quantify different radiation doses employed during treatment. The potential of this nanosensor for clinical translation was demonstrated using an anthropomorphic phantom mimicking a clinical radiotherapy session. The simplicity of fabrication, detection range in the fractionated radiotherapy regime and ease of detection with translational potential makes this a first-in-kind plasmonic colorimetric nanosensor for applications in clinical proton beam therapy.
ContributorsInamdar, Sahil (Author) / Rege, Kaushal (Thesis advisor) / Anand, Aman (Committee member) / Nannenga, Brent (Committee member) / Arizona State University (Publisher)
Created2017
Description
Lignocellulosic biomass represents a renewable domestic feedstock that can support large-scale biochemical production processes for fuels and specialty chemicals. However, cost-effective conversion of lignocellulosic sugars into valuable chemicals by microorganisms still remains a challenge. Biomass recalcitrance to saccharification, microbial substrate utilization, bioproduct titer toxicity, and toxic chemicals associated with chemical pretreatments are at the center of the bottlenecks limiting further commercialization of lignocellulose conversion. Genetic and metabolic engineering has allowed researchers to manipulate microorganisms to overcome some of these challenges, but new innovative approaches are needed to make the process more commercially viable. Transport proteins represent an underexplored target in genetic engineering that can potentially help to control the input of lignocellulosic substrate and output of products/toxins in microbial biocatalysts. In this work, I characterize and explore the use of transport systems to increase substrate utilization, conserve energy, increase tolerance, and enhance biocatalyst performance.
ContributorsKurgan, Gavin (Author) / Wang, Xuan (Thesis advisor) / Nielsen, David (Committee member) / Misra, Rajeev (Committee member) / Nannenga, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Alzheimer’s disease is a major problem affecting over 5.7 million Americans. Although much is known about the effects of this neurogenerative disease, the exact pathogenesis is still unknown. One very important characteristic of Alzheimer’s is the accumulation of beta amyloid protein which often results in plaques. To understand these beta amyloid proteins better, antibody fragments may be used to bind to these oligomers and potentially reduce the effects of Alzheimer’s disease.
This thesis focused on the expression and crystallization the fragment antigen binding antibody fragment A4. A fragment antigen binding fragment was chosen to be worked with as it is more stable than many other antibody fragments. A4 is important in Alzheimer’s disease as it is able to identify toxic beta amyloid.
This thesis focused on the expression and crystallization the fragment antigen binding antibody fragment A4. A fragment antigen binding fragment was chosen to be worked with as it is more stable than many other antibody fragments. A4 is important in Alzheimer’s disease as it is able to identify toxic beta amyloid.
ContributorsColasurd, Paige (Author) / Nannenga, Brent (Thesis advisor) / Mills, Jeremy (Committee member) / Varman, Arul (Committee member) / Arizona State University (Publisher)
Created2018
Description
In the United States, 12% of women are typically diagnosed with breast cancer, where 20-30% of these cases are identified as Triple Negative Breast Cancer (TNBC). In the state of Arizona, 810 deaths occur due to breast cancer and more than 4,600 cases are diagnosed every year (American Cancer Society). The lack of estrogen, progesterone, and HER2 receptors in TNBC makes discovery of targeted therapies further challenging. To tackle this issue, a novel multi-component drug vehicle is presented. Previously, we have shown that mitoxantrone, a DNA damaging drug, can sensitize TNBC cells to TRAIL, which is a protein that can selectively kill cancer cells. In this current study, we have formulated aminoglycoside-derived nanoparticles (liposomes) loaded with mitoxantrone, PARP inhibitors, for delivery to cancer cells. PARP inhibitors are helpful in preventing cancer cells from repairing their DNA following damage with other drugs (e.g. mitoxantrone). Various treatment liposome groups, consisting of lipid-containing polymers (lipopolymers) synthesized in our laboratory, were formulated and characterized for their size, surface charge, and stability. PARP inhibitors and treatment of cells for in-vitro and in-vivo experiments with these liposomes resulted in synergistic death of cancer cells. Finally, studies to evaluate the pre-clinical efficacy of these approaches using immuno-deficient mouse models of TNBC disease have been initiated.
ContributorsMuralikrishnan, Harini (Author) / Rege, Kaushal (Thesis advisor) / Holechek, Susan (Committee member) / Nannenga, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Rapid development of new technology has significantly disrupted the way radiotherapy is planned and delivered. These processes involve delivering high radiation doses to the target tumor while minimizing dose to the surrounding healthy tissue. However, with rapid implementation of these new technologies, there is a need for the detection of prescribed ionizing radiation for radioprotection of the patient and quality assurance of the technique employed. Most available clinical sensors are subjected to various limitations including requirement of extensive training, loss of readout with sequential measurements, sensitivity to light and post-irradiation wait time prior to analysis. Considering these disadvantages, there is still a need for a sensor that can be fabricated with ease and still operate effectively in predicting the delivered radiation dose.
The dissertation discusses the development of a sensor that changes color upon exposure to therapeutic levels of ionizing radiation used during routine radiotherapy. The underlying principle behind the sensor is based on the formation of gold nanoparticles from its colorless precursor salt solution upon exposure to ionizing radiation. Exposure to ionizing radiation generates free radicals which reduce ionic gold to its zerovalent gold form which further nucleate and mature into nanoparticles. The generation of these nanoparticles render a change in color from colorless to a maroon/pink depending on the intensity of incident ionizing radiation. The shade and the intensity of the color developed is used to quantitatively and qualitatively predict the prescribed radiation dose.
The dissertation further describes the applicability of sensor to detect a wide range of ionizing radiation including high energy photons, protons, electrons and emissions from radioactive isotopes while remaining insensitive to non-ionizing radiation. The sensor was further augmented with a capability to differentiate regions that are irradiated and non-irradiated in two dimensions. The dissertation further describes the ability of the sensor to predict dose deposition in all three dimensions. The efficacy of the sensor to predict the prescribed dose delivered to canine patients undergoing radiotherapy was also demonstrated. All these taken together demonstrate the potential of this technology to be translatable to the clinic to ensure patient safety during routine radiotherapy.
The dissertation discusses the development of a sensor that changes color upon exposure to therapeutic levels of ionizing radiation used during routine radiotherapy. The underlying principle behind the sensor is based on the formation of gold nanoparticles from its colorless precursor salt solution upon exposure to ionizing radiation. Exposure to ionizing radiation generates free radicals which reduce ionic gold to its zerovalent gold form which further nucleate and mature into nanoparticles. The generation of these nanoparticles render a change in color from colorless to a maroon/pink depending on the intensity of incident ionizing radiation. The shade and the intensity of the color developed is used to quantitatively and qualitatively predict the prescribed radiation dose.
The dissertation further describes the applicability of sensor to detect a wide range of ionizing radiation including high energy photons, protons, electrons and emissions from radioactive isotopes while remaining insensitive to non-ionizing radiation. The sensor was further augmented with a capability to differentiate regions that are irradiated and non-irradiated in two dimensions. The dissertation further describes the ability of the sensor to predict dose deposition in all three dimensions. The efficacy of the sensor to predict the prescribed dose delivered to canine patients undergoing radiotherapy was also demonstrated. All these taken together demonstrate the potential of this technology to be translatable to the clinic to ensure patient safety during routine radiotherapy.
ContributorsSubramaniam Pushpavanam, Karthik (Author) / Rege, Kaushal (Thesis advisor) / Sapareto, Stephen (Committee member) / Nannenga, Brent (Committee member) / Green, Matthew (Committee member) / Mu, Bin (Committee member) / Arizona State University (Publisher)
Created2019
Description
Engineering is a multidisciplinary field with a variety of applications. However, since there are so many disciplines of engineering, it is often challenging to find the discipline that best suits an individual interested in engineering. Not knowing which area of engineering most aligns to one’s interests is challenging when deciding on a major and a career. With the development of the Engineering Interest Quiz (EIQ), the goal was to help individuals find the field of engineering that is most similar to their interests. Initially, an Engineering Faculty Survey (EFS) was created to gather information from engineering faculty at Arizona State University (ASU) and to determine keywords that describe each field of engineering. With this list of keywords, the EIQ was developed. Data from the EIQ compared the engineering students’ top three results for the best engineering discipline for them with their current engineering major of study. The data analysis showed that 70% of the respondents had their major listed as one of the top three results they were given and 30% of the respondents did not have their major listed. Of that 70%, 64% had their current major listed as the highest or tied for the highest percentage and 36% had their major listed as the second or third highest percentage. Furthermore, the EIQ data was compared between genders. Only 33% of the male students had their current major listed as their highest percentage, but 55% had their major as one of their top three results. Women had higher percentages with 63% listing their current major as their highest percentage and 81% listing it in the top three of their final results.
ContributorsWagner, Avery Rose (Co-author) / Lucca, Claudia (Co-author) / Taylor, David (Thesis director) / Miller, Cindy (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
The impact of physical/chemical properties of gray water on microbial inactivation in gray water using chlorine was investigated through creating artificial gray water in lab, varying specific components, and then measuring microbial inactivation. Gray water was made through taking autoclaved nanopure water, and increasing the concentration of surfacants, the turbidity, the concentration of organic content, and spiking E. coli grown in tryptic soy broth (TSB); chlorine was introduced using Clorox Disinfecting Bleach2. Bacteria was detected using tryptic soy agar (TSA), and E. coli was specifically detected using the selective media, brilliance. The log inactivation of bacteria detected using TSA was shown to be inversely related to the turbidity of the solution. Complete inactivation of E. coli concentrations between 104-105 CFU/100 ml in gray water with turbidities between 10-100 NTU, 0.1-0.5 mg/L of humic acid, and 0.1 ml of Dawn Ultra, was shown to occur, as detected by brilliance, at chlorine concentrations of 1-2 mg/L within 30 seconds. These result in concentration time (CT) values between 0.5-1 mg/L·min. Under the same gray water conditions, and an E. coli concentration of 104 CFU/100 ml and a chlorine concentration of 0.01 mg/L, complete inactivation was shown to occur in all trials within two minutes. These result in CT values ranging from 0.005 to 0.02. The turbidity and humic acid concentration were shown to be inversely related to the log inactivation and directly related to the CT value. This study shows that chlorination is a valid method of treatment of gray water for certain irrigation reuses.
ContributorsGreenberg, Samuel Gabe (Author) / Abbaszadegan, Morteza (Thesis director) / Schoepf, Jared (Committee member) / Alum, Absar (Committee member) / Chemical Engineering Program (Contributor, Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
Increasing energy and environmental problems describe the need to develop renewable chemicals and fuels. Global research has been targeting using microbial systems on a commercial scale for synthesis of valuable compounds. The goal of this project was to refactor and overexpress b6-f complex proteins in cyanobacteria to improve photosynthesis under dynamic light conditions. Improvement in the photosynthetic system can directly relate to higher yields of valuable compounds such as carotenoids and higher yields of biomass which can be used as energy molecules. Four engineered strains of cyanobacteria were successfully constructed and overexpressed the corresponding four large subunits in the cytochrome b6-f complex. No significant changes were found in cell growth or pigment titer in the modified strains compared to the wild type. The growth assay will be performed at higher and/or dynamic light intensities including natural light conditions for further analysis.
ContributorsNauroth, Benjamin (Author) / Varman, Arul (Thesis director) / Singharoy, Abhishek (Committee member) / Li, Han (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2020-05
Description
This research investigated deionized water contact angle measurement reliability with alumina powder using the Washburn method. This method relates the capillary rise of a liquid through a column of packed powder to the contact angle of the system. A reference liquid that is assumed to be perfectly wetting, such as hexane due to the low surface energy, must be used to compare to the tested liquid. Consistency was hypothesized to be achieved with more powder structure and consistency of packing between reference and test trials. The three types of packing structures explored in this study were unstructured, visually-structured (user tapped), and machine-structured tapping. It was also hypothesized that similar contact angle results would be found for different packing methods of the same powder and liquid. However, the average contact angle for unstructured packing was found to be 32.9°, while the angle for the tapped structure was only 11.7°. This large deviation between types of packing shows that there are more inconsistencies with the use of this method than just the regulation of the packing structure. There were two similar glass chromatography columns used, but the second column experienced an unknown interference that led to a delay in the hexane uptake into the powder, which then led to invalid contact angle calculations. There was no discernible relationship between the packing structure and the standard deviation between trials, so the more structured packing does not seem to affect the consistency of results. It is recommended to perform more experiments on a single packing type with different apparatuses and a narrower particle size range.
ContributorsConvery, Brittany Alexis (Author) / Emady, Heather (Thesis director) / Vajrala, Spandana (Committee member) / Chemical Engineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2017-12
Description
The Experimental Data Processing (EDP) software is a C++ GUI-based application to streamline the process of creating a model for structural systems based on experimental data. EDP is designed to process raw data, filter the data for noise and outliers, create a fitted model to describe that data, complete a probabilistic analysis to describe the variation between replicates of the experimental process, and analyze reliability of a structural system based on that model. In order to help design the EDP software to perform the full analysis, the probabilistic and regression modeling aspects of this analysis have been explored. The focus has been on creating and analyzing probabilistic models for the data, adding multivariate and nonparametric fits to raw data, and developing computational techniques that allow for these methods to be properly implemented within EDP. For creating a probabilistic model of replicate data, the normal, lognormal, gamma, Weibull, and generalized exponential distributions have been explored. Goodness-of-fit tests, including the chi-squared, Anderson-Darling, and Kolmogorov-Smirnoff tests, have been used in order to analyze the effectiveness of any of these probabilistic models in describing the variation of parameters between replicates of an experimental test. An example using Young's modulus data for a Kevlar-49 Swath stress-strain test was used in order to demonstrate how this analysis is performed within EDP. In order to implement the distributions, numerical solutions for the gamma, beta, and hypergeometric functions were implemented, along with an arbitrary precision library to store numbers that exceed the maximum size of double-precision floating point digits. To create a multivariate fit, the multilinear solution was created as the simplest solution to the multivariate regression problem. This solution was then extended to solve nonlinear problems that can be linearized into multiple separable terms. These problems were solved analytically with the closed-form solution for the multilinear regression, and then by using a QR decomposition to solve numerically while avoiding numerical instabilities associated with matrix inversion. For nonparametric regression, or smoothing, the loess method was developed as a robust technique for filtering noise while maintaining the general structure of the data points. The loess solution was created by addressing concerns associated with simpler smoothing methods, including the running mean, running line, and kernel smoothing techniques, and combining the ability of each of these methods to resolve those issues. The loess smoothing method involves weighting each point in a partition of the data set, and then adding either a line or a polynomial fit within that partition. Both linear and quadratic methods were applied to a carbon fiber compression test, showing that the quadratic model was more accurate but the linear model had a shape that was more effective for analyzing the experimental data. Finally, the EDP program itself was explored to consider its current functionalities for processing data, as described by shear tests on carbon fiber data, and the future functionalities to be developed. The probabilistic and raw data processing capabilities were demonstrated within EDP, and the multivariate and loess analysis was demonstrated using R. As the functionality and relevant considerations for these methods have been developed, the immediate goal is to finish implementing and integrating these additional features into a version of EDP that performs a full streamlined structural analysis on experimental data.
ContributorsMarkov, Elan Richard (Author) / Rajan, Subramaniam (Thesis director) / Khaled, Bilal (Committee member) / Chemical Engineering Program (Contributor) / School of Mathematical and Statistical Sciences (Contributor) / Ira A. Fulton School of Engineering (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05