Matching Items (102)
Description
Fluoroquinolone antibiotics have been known to cause severe, multisystem adverse side effects, termed fluoroquinolone toxicity (FQT). This toxicity syndrome can present with adverse effects that vary from individual to individual, including effects on the musculoskeletal and nervous systems, among others. The mechanism behind FQT in mammals is not known, although various possibilities have been investigated. Among the hypothesized FQT mechanisms, those that could potentially explain multisystem toxicity include off-target mammalian topoisomerase interactions, increased production of reactive oxygen species, oxidative stress, and oxidative damage, as well as metal chelating properties of FQs. This review presents relevant information on fluoroquinolone antibiotics and FQT and explores the mechanisms that have been proposed. A fluoroquinolone-induced increase in reactive oxygen species and subsequent oxidative stress and damage presents the strongest evidence to explain this multisystem toxicity syndrome. Understanding the mechanism of FQT in mammals is important to aid in the prevention and treatment of this condition.
ContributorsHall, Brooke Ashlyn (Author) / Redding, Kevin (Thesis director) / Wideman, Jeremy (Committee member) / Borges, Chad (Committee member) / School of Molecular Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2021-05
Description
The technology expansion seen in the last decade for genomics research has permitted the generation of large-scale data sources pertaining to molecular biological assays, genomics, proteomics, transcriptomics and other modern omics catalogs. New methods to analyze, integrate and visualize these data types are essential to unveil relevant disease mechanisms. Towards these objectives, this research focuses on data integration within two scenarios: (1) transcriptomic, proteomic and functional information and (2) real-time sensor-based measurements motivated by single-cell technology. To assess relationships between protein abundance, transcriptomic and functional data, a nonlinear model was explored at static and temporal levels. The successful integration of these heterogeneous data sources through the stochastic gradient boosted tree approach and its improved predictability are some highlights of this work. Through the development of an innovative validation subroutine based on a permutation approach and the use of external information (i.e., operons), lack of a priori knowledge for undetected proteins was overcome. The integrative methodologies allowed for the identification of undetected proteins for Desulfovibrio vulgaris and Shewanella oneidensis for further biological exploration in laboratories towards finding functional relationships. In an effort to better understand diseases such as cancer at different developmental stages, the Microscale Life Science Center headquartered at the Arizona State University is pursuing single-cell studies by developing novel technologies. This research arranged and applied a statistical framework that tackled the following challenges: random noise, heterogeneous dynamic systems with multiple states, and understanding cell behavior within and across different Barrett's esophageal epithelial cell lines using oxygen consumption curves. These curves were characterized with good empirical fit using nonlinear models with simple structures which allowed extraction of a large number of features. Application of a supervised classification model to these features and the integration of experimental factors allowed for identification of subtle patterns among different cell types visualized through multidimensional scaling. Motivated by the challenges of analyzing real-time measurements, we further explored a unique two-dimensional representation of multiple time series using a wavelet approach which showcased promising results towards less complex approximations. Also, the benefits of external information were explored to improve the image representation.
ContributorsTorres Garcia, Wandaliz (Author) / Meldrum, Deirdre R. (Thesis advisor) / Runger, George C. (Thesis advisor) / Gel, Esma S. (Committee member) / Li, Jing (Committee member) / Zhang, Weiwen (Committee member) / Arizona State University (Publisher)
Created2011
Description
Hepatocellular carcinoma (HCC) is a malignant tumor and seventh most common cancer in human. Every year there is a significant rise in the number of patients suffering from HCC. Most clinical research has focused on HCC early detection so that there are high chances of patient's survival. Emerging advancements in functional and structural imaging techniques have provided the ability to detect microscopic changes in tumor micro environment and micro structure. The prime focus of this thesis is to validate the applicability of advanced imaging modality, Magnetic Resonance Elastography (MRE), for HCC diagnosis. The research was carried out on three HCC patient's data and three sets of experiments were conducted. The main focus was on quantitative aspect of MRE in conjunction with Texture Analysis, an advanced imaging processing pipeline and multi-variate analysis machine learning method for accurate HCC diagnosis. We analyzed the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Along with this we studied different machine learning algorithms and developed models using them. Performance metrics such as Prediction Accuracy, Sensitivity and Specificity have been used for evaluation for the final developed model. We were able to identify the significant features in the dataset and also the selected classifier was robust in predicting the response class variable with high accuracy.
ContributorsBansal, Gaurav (Author) / Wu, Teresa (Thesis advisor) / Mitchell, Ross (Thesis advisor) / Li, Jing (Committee member) / Arizona State University (Publisher)
Created2013
Description
A P-value based method is proposed for statistical monitoring of various types of profiles in phase II. The performance of the proposed method is evaluated by the average run length criterion under various shifts in the intercept, slope and error standard deviation of the model. In our proposed approach, P-values are computed at each level within a sample. If at least one of the P-values is less than a pre-specified significance level, the chart signals out-of-control. The primary advantage of our approach is that only one control chart is required to monitor several parameters simultaneously: the intercept, slope(s), and the error standard deviation. A comprehensive comparison of the proposed method and the existing KMW-Shewhart method for monitoring linear profiles is conducted. In addition, the effect that the number of observations within a sample has on the performance of the proposed method is investigated. The proposed method was also compared to the T^2 method discussed in Kang and Albin (2000) for multivariate, polynomial, and nonlinear profiles. A simulation study shows that overall the proposed P-value method performs satisfactorily for different profile types.
ContributorsAdibi, Azadeh (Author) / Montgomery, Douglas C. (Thesis advisor) / Borror, Connie (Thesis advisor) / Li, Jing (Committee member) / Zhang, Muhong (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cancer claims hundreds of thousands of lives every year in US alone. Finding ways for early detection of cancer onset is crucial for better management and treatment of cancer. Thus, biomarkers especially protein biomarkers, being the functional units which reflect dynamic physiological changes, need to be discovered. Though important, there are only a few approved protein cancer biomarkers till date. To accelerate this process, fast, comprehensive and affordable assays are required which can be applied to large population studies. For this, these assays should be able to comprehensively characterize and explore the molecular diversity of nominally "single" proteins across populations. This information is usually unavailable with commonly used immunoassays such as ELISA (enzyme linked immunosorbent assay) which either ignore protein microheterogeneity, or are confounded by it. To this end, mass spectrometric immuno assays (MSIA) for three different human plasma proteins have been developed. These proteins viz. IGF-1, hemopexin and tetranectin have been found in reported literature to show correlations with many diseases along with several carcinomas. Developed assays were used to extract entire proteins from plasma samples and subsequently analyzed on mass spectrometric platforms. Matrix assisted laser desorption ionization (MALDI) and electrospray ionization (ESI) mass spectrometric techniques where used due to their availability and suitability for the analysis. This resulted in visibility of different structural forms of these proteins showing their structural micro-heterogeneity which is invisible to commonly used immunoassays. These assays are fast, comprehensive and can be applied in large sample studies to analyze proteins for biomarker discovery.
ContributorsRai, Samita (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Borges, Chad (Committee member) / Ros, Alexandra (Committee member) / Arizona State University (Publisher)
Created2012
Description
Rapid advance in sensor and information technology has resulted in both spatially and temporally data-rich environment, which creates a pressing need for us to develop novel statistical methods and the associated computational tools to extract intelligent knowledge and informative patterns from these massive datasets. The statistical challenges for addressing these massive datasets lay in their complex structures, such as high-dimensionality, hierarchy, multi-modality, heterogeneity and data uncertainty. Besides the statistical challenges, the associated computational approaches are also considered essential in achieving efficiency, effectiveness, as well as the numerical stability in practice. On the other hand, some recent developments in statistics and machine learning, such as sparse learning, transfer learning, and some traditional methodologies which still hold potential, such as multi-level models, all shed lights on addressing these complex datasets in a statistically powerful and computationally efficient way. In this dissertation, we identify four kinds of general complex datasets, including "high-dimensional datasets", "hierarchically-structured datasets", "multimodality datasets" and "data uncertainties", which are ubiquitous in many domains, such as biology, medicine, neuroscience, health care delivery, manufacturing, etc. We depict the development of novel statistical models to analyze complex datasets which fall under these four categories, and we show how these models can be applied to some real-world applications, such as Alzheimer's disease research, nursing care process, and manufacturing.
ContributorsHuang, Shuai (Author) / Li, Jing (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
Bacteria play a vital role in the world ecosystem, more importantly human health and disease. The capability to differentiate and identify these microorganisms serves as an important research objective. In past years, separations-based approaches have served as a way to observe and identify bacteria based on their characteristics. Gradient insulator dielectrophoresis (g-iDEP) provides benefits in identifying serotypes of a single species with precise separation. Separation of Staphylococcus epidermidis in a single g-iDEP microchannel is conducted exploiting their electrophoretic and electrokinetic properties. The cells were captured and concentrated at gates with interacting forces within the microchannel to clearly distinguish between the two strains. These results provide support for g-iDEP serving as a separating method and, furthermore, future clinical applications.
ContributorsDavis, Paige Elizabeth (Author) / Hayes, Mark (Thesis director) / Borges, Chad (Committee member) / Jones, Paul (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / T. Denny Sanford School of Social and Family Dynamics (Contributor)
Created2015-05
Description
The Department of Defense (DoD) acquisition system is a complex system riddled with cost and schedule overruns. These cost and schedule overruns are very serious issues as the acquisition system is responsible for aiding U.S. warfighters. Hence, if the acquisition process is failing that could be a potential threat to our nation's security. Furthermore, the DoD acquisition system is responsible for proper allocation of billions of taxpayer's dollars and employs many civilians and military personnel. Much research has been done in the past on the acquisition system with little impact or success. One reason for this lack of success in improving the system is the lack of accurate models to test theories. This research is a continuation of the effort on the Enterprise Requirements and Acquisition Model (ERAM), a discrete event simulation modeling research on DoD acquisition system. We propose to extend ERAM using agent-based simulation principles due to the many interactions among the subsystems of the acquisition system. We initially identify ten sub models needed to simulate the acquisition system. This research focuses on three sub models related to the budget of acquisition programs. In this thesis, we present the data collection, data analysis, initial implementation, and initial validation needed to facilitate these sub models and lay the groundwork for a full agent-based simulation of the DoD acquisition system.
ContributorsBucknell, Sophia Robin (Author) / Wu, Teresa (Thesis director) / Li, Jing (Committee member) / Colombi, John (Committee member) / Industrial, Systems (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Background: High risk types of human papillomavirus (HPV) are known to cause cancer, including cervical (99%) and oropharyngeal cancer (70%). HPV type 16 is the most common subtype. Three antigens that are critical for integration or tumor progression are E2, E6 and E7. In this study, we developed a systematic approach to identify naturally-processed HPV16-derived HLA class I epitopes for immunotherapy development. Methods: K562 cells, which lack HLA expression, were transduced with each HPV16 antigen using lentivirus and supertransfected with HLA-A2 by nucleofection. Stable cell lines expressing each antigen were selected for and maintained throughout the investigation. In order to establish a Gateway-compatible vector for robust transient gene expression, a Gateway recombination expression cloning cassette was inserted into the commercial Lonza pMAX GFP backbone, which has been experimentally shown to display high transfection expression efficiency. GFP was cloned into the vector and plain K562 cells were transfected with the plasmid by nucleofection. Results: Expression of K562-A2 was tested at various time points by flow cytometry and A2 expression was confirmed. Protein expression was shown for the transduced K562 E7 by Western blot analysis. High transfection efficiency of the pMAX_GFP_Dest vector (up to 97% GFP+ cells) was obtained 48 hours post transfection, comparable to the commercial GFP-plasmid. Conclusion: We have established a rapid system for target viral antigen co-expression with single HLA molecules for analysis of antigen presentation. Using HPV as a model system, our goal is to identify specific antigenic peptide sequences to develop immunotherapeutic treatments for HPV-associated cancers.
ContributorsVarda, Bianca Marie (Author) / Anderson, Karen (Thesis director) / Borges, Chad (Committee member) / Krishna, Sri (Committee member) / School of Life Sciences (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
Protein AMPylation is a recently discovered and relatively unstudied post-translational modification (PTM). AMPylation has previously been shown to play an important role in metabolic regulation and host pathogenesis in bacteria, but the recent identification of potential AMPylators across many species in every domain of life has supported the possibility that AMPylation could be a more fundamental and physiologically significant regulatory PTM. For the first time, we characterized the auto-AMPylation capability of the human protein SOS1 through in vitro AMPylation experiments using full-length protein and whole-domain truncation mutants. We found that SOS1 can become AMPylated at a tyrosine residue possibly within the Cdc25 domain of the protein, the Dbl homology domain is vital for efficient auto-AMPylation activity, and the C-terminal proline-rich domain exhibits a complex regulatory function. The proline-rich domain alone also appears to be capable of catalyzing a separate, unidentified covalent self-modification using a fluorescent ATP analogue. Finally, SOS1 was shown to be capable of catalyzing the AMPylation of two endogenous human protein substrates: a ubiquitous, unidentified protein of ~49kDa and another breast-cancer specific, unidentified protein of ~28kDa.
ContributorsOber-Reynolds, Benjamin John (Author) / LaBaer, Joshua (Thesis director) / Borges, Chad (Committee member) / Barrett, The Honors College (Contributor) / Department of Chemistry and Biochemistry (Contributor) / School of Life Sciences (Contributor)
Created2014-05