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- Genre: Masters Thesis
Description
Salmonella enterica is a gastrointestinal (GI) pathogen that can cause systemic diseases. It invades the host through the GI tract and can induce powerful immune responses in addition to disease. Thus, it is considered as a promising candidate to use as oral live vaccine vectors. Scientists have been making great efforts to get a properly attenuated Salmonella vaccine strain for a long time, but could not achieve a balance between attenuation and immunogenicity. So the regulated delayed attenuation/lysis Salmonella vaccine vectors were proposed as a design to seek this balance. The research work is progressing steadily, but more improvements need to be made. As one of the possible improvements, the cyclic adenosine monophosphate (cAMP) -independent cAMP receptor protein (Crp*) is expected to protect the Crp-dependent crucial regulator, araC PBAD, in these vaccine designs from interference by glucose, which decreases synthesis of cAMP, and enhance the colonizing ability by and immunogenicity of the vaccine strains. In this study, the cAMP-independent crp gene mutation, crp-70, with or without araC PBAD promoter cassette, was introduced into existing Salmonella vaccine strains. Then the plasmid stability, growth rate, resistance to catabolite repression, colonizing ability, immunogenicity and protection to challenge of these new strains were compared with wild-type crp or araC PBAD crp strains using western blots, enzyme-linked immunosorbent assays (ELISA) and animal studies, so as to evaluate the effects of the crp-70 mutation on the vaccine strains. The performances of the crp-70 strains in some aspects were closed to or even exceeded the crp+ strains, but generally they did not exhibit the expected advantages compared to their wild-type parents. Crp-70 rescued the expression of araC PBAD fur from catabolite repression. The strain harboring araC PBAD crp-70 was severely affected by its slow growth, and its colonizing ability and immunogenicity was much weaker than the other strains. The Pcrp crp-70 strain showed relatively good ability in colonization and immune stimulation. Both the araC PBAD crp-70 and the Pcrp crp-70 strains could provide certain levels of protection against the challenge with virulent pneumococci, which were a little lower than for the crp+ strains.
ContributorsShao, Shihuan (Author) / Curtiss, Roy (Thesis advisor) / Arizona State University (Publisher)
Created2012
Description
It is possible in a properly controlled environment, such as industrial metrology, to make significant headway into the non-industrial constraints on image-based position measurement using the techniques of image registration and achieve repeatable feature measurements on the order of 0.3% of a pixel, or about an order of magnitude improvement on conventional real-world performance. These measurements are then used as inputs for a model optimal, model agnostic, smoothing for calibration of a laser scribe and online tracking of velocimeter using video input. Using appropriate smooth interpolation to increase effective sample density can reduce uncertainty and improve estimates. Use of the proper negative offset of the template function has the result of creating a convolution with higher local curvature than either template of target function which allows improved center-finding. Using the Akaike Information Criterion with a smoothing spline function it is possible to perform a model-optimal smooth on scalar measurements without knowing the underlying model and to determine the function describing the uncertainty in that optimal smooth. An example of empiric derivation of the parameters for a rudimentary Kalman Filter from this is then provided, and tested. Using the techniques of Exploratory Data Analysis and the "Formulize" genetic algorithm tool to convert the spline models into more accessible analytic forms resulted in stable, properly generalized, KF with performance and simplicity that exceeds "textbook" implementations thereof. Validation of the measurement includes that, in analytic case, it led to arbitrary precision in measurement of feature; in reasonable test case using the methods proposed, a reasonable and consistent maximum error of around 0.3% the length of a pixel was achieved and in practice using pixels that were 700nm in size feature position was located to within ± 2 nm. Robust applicability is demonstrated by the measurement of indicator position for a King model 2-32-G-042 rotameter.
ContributorsMunroe, Michael R (Author) / Phelan, Patrick (Thesis advisor) / Kostelich, Eric (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2012
Description
Based on past studies, urinary glycan biomarkers have the potential to be used as diagnostic and prognostic markers for treatment purposes. This study brought into play the bottom-up glycan node analysis approach to analyze 39 urine samples from COVID-19 positive and negative individuals using gas chromatography-mass spectrometry (GC-MS) to determine potential urinary glycan biomarkers of COVID-19. Glycan node analysis involves chemically breaking down glycans in whole biospecimens in a way that conserves both monosaccharide identity and linkage information that facilitates the capture of unique glycan features as single analytical signals. Following data acquisition, the student t-test was done on all the nodes, but only four prominent nodes (t-Deoxyhexopyranose, 2,3-Gal, t-GlcNAc, and 3,6-GalNAc with respective p-values 0.03027, 0.03973, 0.0224, and 0.0004) were below the threshold p-value of 0.05 and showed some differences in the mean between both groups. To eliminate the probability of having false positive p-values, Bonferroni correction was done on the four nodes but only the 3,6-GalNAc node emerged as the only node that was below the newly adjusted p-value. Because sample analyses were done in batches, the Kruskal Wallis test was done to know if the batch effect was responsible for the observed lower relative concentration of 3,6-GalNAc in COVID-19 positive patients than in negative patients. A receiver operating characteristic curve (ROC) was plotted for the 3,6-GalNAc node and the area under the curve (AUC) was calculated to be 0.84, casting the 3,6-GalNAc node was a potential biomarker of COVID-19. 3,6-GalNAc largely arises from branched O-glycan core structures, which are abundant in mucin glycoproteins that line the urogenital tract. Lowered relative concentrations of 3,6-GalNAc in the urine of COVID-19 positive patients may be explained by compromised kidney function that allows non-mucinous glycoproteins from the blood to contribute a greater proportion of the relative glycan node signals than in COVID-19 negative patients. Future prospective clinical studies will be needed to validate both the biomarker findings and this hypothesis.
ContributorsEyonghebi Tanyi, Agbor (Author) / Borges, Chad R (Thesis advisor) / Mills, Jeremy H (Committee member) / Guo, Jia (Committee member) / Arizona State University (Publisher)
Created2023
Description
Alzheimer’s Disease (AD) is the most common form of dementia affecting the population over the age of 65. AD is characterized clinically by increasing difficulty with memory and language, resulting in a loss of independence. This is due to the presence of two characteristic protein aggregates in the brain: extracellular amyloid plaques and intracellular neurofibrillary tangles (NFTs). Utilizing multiplexed immunofluorescence and dimensional reduction analysis the types of cells present in the hippocampus, the region of the brain most affected by AD, can be explored. Understanding the kinds of cell subtypes present, the mechanism behind how AD develops can be explored. Multiplexed IF was performed on human hippocampus FFPE tissues to detect a total of 37 proteins. Dimensional reduction analysis was performed to identify the four major cell types in the brain: neurons, oligodendrocytes, astrocytes, and microglia. After identifying each cell type, further dimensional reduction analysis was performed within each cell type to identify cell subtypes. A total of 21 neuron, 41 oligodendrocyte, 20 astrocyte, and 22 microglia subtypes were identified. The location of cell subtypes in each region of the hippocampal formation was found to match previous reports, further validating the findings of this project.
ContributorsEllison, Mischa A (Author) / Guo, Jia (Thesis advisor) / Borges, Chad (Committee member) / Mastroeni, Diego (Committee member) / Arizona State University (Publisher)
Created2024
Description
In this thesis, a breadboard Integrated Microarray Printing and Detection System (IMPDS) was proposed to address key limitations of traditional microarrays. IMPDS integrated two core components of a high-resolution surface plasmon resonance imaging (SPRi) system and a piezoelectric dispensing system that can print ultra-low volume droplets. To avoid evaporation of droplets in the microarray, a 100 μm thick oil layer (dodecane) was used to cover the chip surface. The interaction between BSA (Bovine serum albumin) and Anti-BSA was used to evaluate the capability of IMPDS. The alignment variability of printing, stability of droplets array and quantification of protein-protein interactions based on nanodroplet array were evaluated through a 10 x 10 microarray on SPR sensor chip. Binding kinetic constants obtained from IMPDS are close with results from commercial SPR setup (BI-3000), which indicates that IMPDS is capable to measure kinetic constants accurately. The IMPDS setup has following advantages: 1) nanoliter scale sample consumption, 2) high-throughput detection with real-time kinetic information for biomolecular interactions, 3) real-time information during printing and spot-on-spot detection of biomolecular interactions 4) flexible selection of probes and receptors (M x N interactions). Since IMPDS studies biomolecular interactions with low cost and high flexibility in real-time manner, it has great potential in applications such as drug discovery, food safety and disease diagnostics, etc.
ContributorsXiao, Feng (Author) / Tao, Nongjian (Thesis advisor) / Borges, Chad (Committee member) / Guo, Jia (Committee member) / Arizona State University (Publisher)
Created2017
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020