Matching Items (82)
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (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
The goal of this thesis is to test whether Alzheimer's disease (AD) is associated with distinctive humoral immune changes that can be detected in plasma and tracked across time. This is relevant because AD is the principal cause of dementia, and yet, no specific diagnostic tests are universally employed in clinical practice to predict, diagnose or monitor disease progression. In particular, I describe herein a proteomic platform developed at the Center for Innovations in Medicine (CIM) consisting of a slide with 10.000 random-sequence peptides printed on its surface, which is used as the solid phase of an immunoassay where antibodies of interest are allowed to react and subsequently detected with a labeled secondary antibody. The pattern of antibody binding to the microarray is unique for each individual animal or person. This thesis will evaluate the versatility of the microarray platform and how it can be used to detect and characterize the binding patterns of antibodies relevant to the pathophysiology of AD as well as the plasma samples of animal models of AD and elderly humans with or without dementia. My specific aims were to evaluate the emergence and stability of immunosignature in mice with cerebral amyloidosis, and characterize the immunosignature of humans with AD. Plasma samples from APPswe/PSEN1-dE9 transgenic mice were evaluated longitudinally from 2 to 15 months of age to compare the evolving immunosignature with non-transgenic control mice. Immunological variation across different time-points was assessed, with particular emphasis on time of emergence of a characteristic pattern. In addition, plasma samples from AD patients and age-matched individuals without dementia were assayed on the peptide microarray and binding patterns were compared. It is hoped that these experiments will be the basis for a larger study of the diagnostic merits of the microarray-based immunoassay in dementia clinics.
ContributorsRestrepo Jimenez, Lucas (Author) / Johnston, Stephen A. (Thesis advisor) / Chang, Yung (Committee member) / Reiman, Eric (Committee member) / Sierks, Michael (Committee member) / Arizona State University (Publisher)
Created2011
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
Mathematical modeling and decision-making within the healthcare industry have given means to quantitatively evaluate the impact of decisions into diagnosis, screening, and treatment of diseases. In this work, we look into a specific, yet very important disease, the Alzheimer. In the United States, Alzheimer’s Disease (AD) is the 6th leading cause of death. Diagnosis of AD cannot be confidently confirmed until after death. This has prompted the importance of early diagnosis of AD, based upon symptoms of cognitive decline. A symptom of early cognitive decline and indicator of AD is Mild Cognitive Impairment (MCI). In addition to this qualitative test, Biomarker tests have been proposed in the medical field including p-Tau, FDG-PET, and hippocampal. These tests can be administered to patients as early detectors of AD thus improving patients’ life quality and potentially reducing the costs of the health structure. Preliminary work has been conducted in the development of a Sequential Tree Based Classifier (STC), which helps medical providers predict if a patient will contract AD or not, by sequentially testing these biomarker tests. The STC model, however, has its limitations and the need for a more complex, robust model is needed. In fact, STC assumes a general linear model as the status of the patient based upon the tests results. We take a simulation perspective and try to define a more complex model that represents the patient evolution in time.
Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.
The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
Specifically, this thesis focuses on the formulation of a Markov Chain model that is complex and robust. This Markov Chain model emulates the evolution of MCI patients based upon doctor visits and the sequential administration of biomarker tests. Data provided to create this Markov Chain model were collected by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. The data lacked detailed information of the sequential administration of the biomarker tests and therefore, different analytical approaches were tried and conducted in order to calibrate the model. The resulting Markov Chain model provided the capability to conduct experiments regarding different parameters of the Markov Chain and yielded different results of patients that contracted AD and those that did not, leading to important insights into effect of thresholds and sequence on patient prediction capability as well as health costs reduction.
The data in this thesis was provided from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). ADNI investigators did not contribute to any analysis or writing of this thesis. A list of the ADNI investigators can be found at: http://adni.loni.usc.edu/about/governance/principal-investigators/ .
ContributorsCamarena, Raquel (Author) / Pedrielli, Giulia (Thesis advisor) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Arizona State University (Publisher)
Created2018
Description
Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) are the leading causes of early onset dementia. There are currently no ways to slow down progression, to prevent or cure AD and FTD. Both AD and FTD share a lot of the symptoms and pathology. Initial symptoms such as confusion, memory loss, mood swings and behavioral changes are common in both these dementia subtypes. Neurofibrillary tau tangles and intraneuronal aggregates of TAR DNA Binding Protein 43 (TDP-43) are also observed in both AD and FTD. Hence, FTD cases are often misdiagnosed as AD due to a lack of accurate diagnostics. Prior to the formation of tau tangles and TDP-43 aggregates, tau and TDP-43 exist as intermediate protein variants which correlate with cognitive decline and progression of these neurodegenerative diseases. Effective diagnostic and therapeutic agents would selectively recognize these toxic, disease-specific variants. Antibodies or antibody fragments such as single chain antibody variable domain fragments (scFvs), with their diverse binding capabilities, can aid in developing reagents that can selectively bind these protein variants. A combination of phage display library and Atomic Force Microscopy (AFM)-based panning was employed to identify antibody fragments against immunoprecipitated tau and immunoprecipitated TDP-43 from human postmortem AD and FTD brain tissue respectively. Five anti-TDP scFvs and five anti-tau scFvs were selected for characterization by Enzyme Linked Immunosorbent Assays (ELISAs) and Immunohistochemistry (IHC). The panel of scFvs, together, were able to identify distinct protein variants present in AD but not in FTD, and vice versa. Generating protein variant profiles for individuals, using the panel of scFvs, aids in developing targeted diagnostic and therapeutic plans, gearing towards personalized medicine.
ContributorsVenkataraman, Lalitha (Author) / Sierks, Michael R (Thesis advisor) / Dunckley, Travis (Committee member) / Oddo, Salvatore (Committee member) / Stabenfeldt, Sarah (Committee member) / Arizona State University (Publisher)
Created2018
Description
Alzheimer’s disease (AD) is characterized by the degeneration of cholinergic basal forebrain (CBF) neurons in the nucleus basalis of Meynert (nbM), which provides the majority of cholinergic input to the cortical mantle and together form the basocortical cholinergic system. Histone deacetylase (HDAC) dysregulation in the temporal lobe has been associated with neuronal degeneration during AD progression. However, whether HDAC alterations play a role in cortical and cortically-projecting cholinergic nbM neuronal degeneration during AD onset is unknown. In an effort to characterize alterations in the basocortical epigenome semi-quantitative western blotting and immunohistochemistry were utilized to evaluate HDAC and sirtuin (SIRT) levels in individuals that died with a premortem clinical diagnosis of no cognitive impairment (NCI), mild cognitive impairment (MCI), mild/moderate AD (mAD), or severe AD (sAD). In the frontal cortex, immunoblots revealed significant increases in HDAC1 and HDAC3 in MCI and mAD, followed by a decrease in sAD. Cortical HDAC2 levels remained stable across clinical groups. HDAC4 was significantly increased in prodromal and mild AD compared to aged cognitively normal controls. HDAC6 significantly increased during disease progression, while SIRT1 decreased in MCI, mAD, and sAD compared to controls. Basal forebrain levels of HDAC1, 3, 4, 6 and SIRT1 were stable across disease progression, while HDAC2 levels were significantly decreased in sAD. Quantitative immunohistochemistry was used to identify HDAC2 protein levels in individual cholinergic nbM nuclei immunoreactive for the early phosphorylated tau marker AT8, the late-stage apoptotic tau marker TauC3, and Thioflavin-S, a marker of mature neurofibrillary tangles (NFTs). HDAC2 nuclear immunoreactivity was reduced in individual cholinergic nbM neurons across disease stages, and was exacerbated in tangle-bearing cholinergic nbM neurons. HDAC2 nuclear reactivity correlated with multiple cognitive domains and with NFT formation. These findings identify global HDAC and SIRT alterations in the cortex while HDAC2 dysregulation contributes to cholinergic nbM neuronal dysfunction and NFT pathology during the progression of AD.
ContributorsMahady, Laura Jean (Author) / Mufson, Elliott J (Thesis advisor) / Bimonte-Nelson, Heather A. (Thesis advisor) / Coleman, Paul (Committee member) / Bowser, Robert (Committee member) / Arizona State University (Publisher)
Created2018
Description
Alzheimer’s Disease (AD) affects over 5 million individuals in the U.S. and has a direct cost estimated in excess of $200 billion per year. Broadly speaking, there are two forms of AD—early-onset, familial AD (FAD) and late-onset-sporadic AD (SAD). Animal models of AD, which rely on the overexpression of FAD-related mutations, have provided important insights into the disease. However, these models do not display important disease-related pathologies and have been limited in their ability to model the complex genetics associated with SAD.
Advances in cellular reprogramming, have enabled the generation of in vitro disease models that can be used to dissect disease mechanisms and evaluate potential therapeutics. To that end, efforts by many groups, including the Brafman laboratory, to generated patient-specific hiPSCs have demonstrated the promise of studying AD in a simplified and accessible system. However, neurons generated from these hiPSCs have shown some, but not all, of the early molecular and cellular hallmarks associated with the disease. Additionally, phenotypes and pathological hallmarks associated with later stages of the human disease have not been observed with current hiPSC-based systems. Further, disease relevant phenotypes in neurons generated from SAD hiPSCs have been highly variable or largely absent. Finally, the reprogramming process erases phenotypes associated with cellular aging and, as a result, iPSC-derived neurons more closely resemble fetal brain rather than adult brain.
It is well-established that in vivo cells reside within a complex 3-D microenvironment that plays a significant role in regulating cell behavior. Signaling and other cellular functions, such as gene expression and differentiation potential, differ in 3-D cultures compared with 2-D substrates. Nonetheless, previous studies using AD hiPSCs have relied on 2-D neuronal culture models that do not reflect the 3-D complexity of native brain tissue, and therefore, are unable to replicate all aspects of AD pathogenesis. Further, the reprogramming process erases cellular aging phenotypes. To address these limitations, this project aimed to develop bioengineering methods for the generation of 3-D organoid-based cultures that mimic in vivo cortical tissue, and to generate an inducible gene repression system to recapitulate cellular aging hallmarks.
Advances in cellular reprogramming, have enabled the generation of in vitro disease models that can be used to dissect disease mechanisms and evaluate potential therapeutics. To that end, efforts by many groups, including the Brafman laboratory, to generated patient-specific hiPSCs have demonstrated the promise of studying AD in a simplified and accessible system. However, neurons generated from these hiPSCs have shown some, but not all, of the early molecular and cellular hallmarks associated with the disease. Additionally, phenotypes and pathological hallmarks associated with later stages of the human disease have not been observed with current hiPSC-based systems. Further, disease relevant phenotypes in neurons generated from SAD hiPSCs have been highly variable or largely absent. Finally, the reprogramming process erases phenotypes associated with cellular aging and, as a result, iPSC-derived neurons more closely resemble fetal brain rather than adult brain.
It is well-established that in vivo cells reside within a complex 3-D microenvironment that plays a significant role in regulating cell behavior. Signaling and other cellular functions, such as gene expression and differentiation potential, differ in 3-D cultures compared with 2-D substrates. Nonetheless, previous studies using AD hiPSCs have relied on 2-D neuronal culture models that do not reflect the 3-D complexity of native brain tissue, and therefore, are unable to replicate all aspects of AD pathogenesis. Further, the reprogramming process erases cellular aging phenotypes. To address these limitations, this project aimed to develop bioengineering methods for the generation of 3-D organoid-based cultures that mimic in vivo cortical tissue, and to generate an inducible gene repression system to recapitulate cellular aging hallmarks.
ContributorsBounds, Lexi Rose (Author) / Brafman, David (Thesis director) / Wang, Xiao (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Background: Noninvasive MRI methods that can accurately detect subtle brain changes are highly desirable when studying disease-modifying interventions. Texture analysis is a novel imaging technique which utilizes the extraction of a large number of image features with high specificity and predictive power. In this investigation, we use texture analysis to assess and classify age-related changes in the right and left hippocampal regions, the areas known to show some of the earliest change in Alzheimer's disease (AD). Apolipoprotein E (APOE)'s e4 allele confers an increased risk for AD, so studying differences in APOE e4 carriers may help to ascertain subtle brain changes before there has been an obvious change in behavior. We examined texture analysis measures that predict age-related changes, which reflect atrophy in a group of cognitively normal individuals. We hypothesized that the APOE e4 carriers would exhibit significant age-related differences in texture features compared to non-carriers, so that the predictive texture features hold promise for early assessment of AD. Methods: 120 normal adults between the ages of 32 and 90 were recruited for this neuroimaging study from a larger parent study at Mayo Clinic Arizona studying longitudinal cognitive functioning (Caselli et al., 2009). As part of the parent study, the participants were genotyped for APOE genetic polymorphisms and received comprehensive cognitive testing every two years, on average. Neuroimaging was done at Barrow Neurological Institute and a 3D T1-weighted magnetic resonance image was obtained during scanning that allowed for subsequent texture analysis processing. Voxel-based features of the appearance, structure, and arrangement of these regions of interest were extracted utilizing the Mayo Clinic Python Texture Analysis Pipeline (pyTAP). Algorithms applied in feature extraction included Grey-Level Co-Occurrence Matrix (GLCM), Gabor Filter Banks (GFB), Local Binary Patterns (LBP), Discrete Orthogonal Stockwell Transform (DOST), and Laplacian-of-Gaussian Histograms (LoGH). Principal component (PC) analysis was used to reduce the dimensionality of the algorithmically selected features to 13 PCs. A stepwise forward regression model was used to determine the effect of APOE status (APOE e4 carriers vs. noncarriers), and the texture feature principal components on age (as a continuous variable). After identification of 5 significant predictors of age in the model, the individual feature coefficients of those principal components were examined to determine which features contributed most significantly to the prediction of an aging brain. Results: 70 texture features were extracted for the two regions of interest in each participant's scan. The texture features were coded as 70 initial components andwere rotated to generate 13 principal components (PC) that contributed 75% of the variance in the dataset by scree plot analysis. The forward stepwise regression model used in this exploratory study significantly predicted age, accounting for approximately 40% of the variance in the data. The regression model revealed 5 significant regressors (2 right PC's, APOE status, and 2 left PC by APOE interactions). Finally, the specific texture features that contributed to each significant PCs were identified. Conclusion: Analysis of image texture features resulted in a statistical model that was able to detect subtle changes in brain integrity associated with age in a group of participants who are cognitively normal, but have an increased risk of developing AD based on the presence of the APOE e4 phenotype. This is an important finding, given that detecting subtle changes in regions vulnerable to the effects of AD in patients could allow certain texture features to serve as noninvasive, sensitive biomarkers predictive of AD. Even with only a small number of patients, the ability for us to determine sensitive imaging biomarkers could facilitate great improvement in speed of detection and effectiveness of AD interventions..
ContributorsSilva, Annelise Michelle (Author) / Baxter, Leslie (Thesis director) / McBeath, Michael (Committee member) / Presson, Clark (Committee member) / School of Life Sciences (Contributor) / Department of Psychology (Contributor) / Barrett, The Honors College (Contributor)
Created2016-05
Description
The research objective is to maintain the A4 nanobody stability during dialysis. Various dialysis buffers were tested and compared, including PBS with varying amounts of the detergent, Tween: low, high, none. Furthermore, PBS, Tris, and HEPES, were tested and compared. PBS without Tween was the worst for preserving A4 stability. PBS was determined to be a better dialysis buffer than Tris or HEPES. To find the optimum buffer, other buffers will be tested and compared with PBS; methods such as gravity filtration and lyophilization will be considered as alternatives to dialysis.
ContributorsTao, Kevin Huang (Author) / Sierks, Michael (Thesis director) / Williams, Stephanie (Committee member) / Barrett, The Honors College (Contributor) / Chemical Engineering Program (Contributor)
Created2015-05