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Description
Falls are the leading cause of fatal and non-fatal injuries in the older adult population with more than 27,000 fall related deaths reported every year[1]. Adults suffering from lower extremity arthritis have more than twice the likelihood of experiencing multiple falls resulting in increased fall-related injuries compared to healthy adults. People with lower extremity end-stage osteoarthritis(KOA), experience a number of fall risk factors such as knee instability, poor mobility, and knee pain/stiffness. At end-stage knee OA, the space between the bones in the joint of the knee is significantly reduced, resulting in bone to bone frictional wearing causing bone deformation. In addition, an impaired stepping response during a postural perturbation is seen in people with OA related knee instability. The most common treatment for end-stage knee osteoarthritis is a surgical procedure called, total knee replacement (TKR). It is known that TKR significantly reduces pain, knee stiffness, and restores musculoskeletal functions such as range of motion. Despite studies concluding that knee OA increases fall-risk, it remains unknown if standard treatments, such as TKR, can effectively decrease fall-risk. Analyzing the compensatory step response during a fall is a significant indicator of whether a fall or a recovery will occur in the event of a postural disturbance and is key to determining fall risk among people. Studies have shown reduced trunk stability and step length, as well as increased trunk velocities, correspond to an impaired compensatory step. This study looks at these populations to determine whether TKR significantly enhances compensatory stepping response by analyzing trunk velocities and flexions among other kinematic/kinetic variable analysis during treadmill induced perturbations and clinical assessments.
ContributorsMeza, Estefania (Author) / Honeycutt, Claire (Thesis advisor) / Lockhart, Thurmon E (Committee member) / Hodge, William A (Committee member) / Arizona State University (Publisher)
Created2019
Description
Calcium imaging is a well-established, non-invasive or minimally technique designed to study the electrical signaling neurons. Calcium regulates the release of gliotransmitters in astrocytes. Analyzing astrocytic calcium transients can provide significant insights into mechanisms such as neuroplasticity and neural signal modulation.
In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes.
The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height.
In the past decade, numerous methods have been developed to analyze in-vivo calcium imaging data that involves complex techniques such as overlapping signals segregation and motion artifact correction. The hypothesis used to detect calcium signal is the spatiotemporal sparsity of calcium signal, and these methods are unable to identify the passive cells that are not actively firing during the time frame in the video. Statistics regarding the percentage of cells in each frame of view can be critical for the analysis of calcium imaging data for human induced pluripotent stem cells derived neurons and astrocytes.
The objective of this research is to develop a simple and efficient semi-automated pipeline for analysis of in-vitro calcium imaging data. The region of interest (ROI) based image segmentation is used to extract the data regarding intensity fluctuation caused by calcium concentration changes in each cell. It is achieved by using two approaches: basic image segmentation approach and a machine learning approach. The intensity data is evaluated using a custom-made MATLAB that generates statistical information and graphical representation of the number of spiking cells in each field of view, the number of spikes per cell and spike height.
ContributorsBhandarkar, Siddhi Umesh (Author) / Brafman, David (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Tian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2019
Description
Semi-supervised learning (SSL) is sub-field of statistical machine learning that is useful for problems that involve having only a few labeled instances with predictor (X) and target (Y) information, and abundance of unlabeled instances that only have predictor (X) information. SSL harnesses the target information available in the limited labeled data, as well as the information in the abundant unlabeled data to build strong predictive models. However, not all the included information is useful. For example, some features may correspond to noise and including them will hurt the predictive model performance. Additionally, some instances may not be as relevant to model building and their inclusion will increase training time and potentially hurt the model performance. The objective of this research is to develop novel SSL models to balance data inclusivity and usability. My dissertation research focuses on applications of SSL in healthcare, driven by problems in brain cancer radiomics, migraine imaging, and Parkinson’s Disease telemonitoring.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.
The first topic introduces an integration of machine learning (ML) and a mechanistic model (PI) to develop an SSL model applied to predicting cell density of glioblastoma brain cancer using multi-parametric medical images. The proposed ML-PI hybrid model integrates imaging information from unbiopsied regions of the brain as well as underlying biological knowledge from the mechanistic model to predict spatial tumor density in the brain.
The second topic develops a multi-modality imaging-based diagnostic decision support system (MMI-DDS). MMI-DDS consists of modality-wise principal components analysis to incorporate imaging features at different aggregation levels (e.g., voxel-wise, connectivity-based, etc.), a constrained particle swarm optimization (cPSO) feature selection algorithm, and a clinical utility engine that utilizes inverse operators on chosen principal components for white-box classification models.
The final topic develops a new SSL regression model with integrated feature and instance selection called s2SSL (with “s2” referring to selection in two different ways: feature and instance). s2SSL integrates cPSO feature selection and graph-based instance selection to simultaneously choose the optimal features and instances and build accurate models for continuous prediction. s2SSL was applied to smartphone-based telemonitoring of Parkinson’s Disease patients.
ContributorsGaw, Nathan (Author) / Li, Jing (Thesis advisor) / Wu, Teresa (Committee member) / Yan, Hao (Committee member) / Hu, Leland (Committee member) / Arizona State University (Publisher)
Created2019
Description
Tissue approximation and repair have been performed with sutures and staples for centuries, but these means are inherently traumatic. Tissue repair using laser-responsive nanomaterials can lead to rapid tissue sealing and repair and is an attractive alternative to existing clinical methods. Laser tissue welding is a sutureless technique for sealing incised or wounded tissue, where chromophores convert laser light to heat to induce in tissue sealing. Introducing chromophores that absorb near-infrared light creates differential laser absorption and allows for laser wavelengths that minimizes tissue damage.
In this work, plasmonic nanocomposites have been synthesized and used in laser tissue welding for ruptured porcine intestine ex vivo and incised murine skin in vivo. These laser-responsive nanocomposites improved tissue strength and healing, respectively. Additionally, a spatiotemporal model has been developed for laser tissue welding of porcine and mouse cadaver intestine sections using near-infrared laser irradiation. This mathematical model can be employed to identify optimal conditions for minimizing healthy cell death while still achieving a strong seal of the ruptured tissue using laser welding. Finally, in a model of surgical site infection, laser-responsive nanomaterials were shown to be efficacious in inhibiting bacterial growth. By incorporating an anti-microbial functionality to laser-responsive nanocomposites, these materials will serve as a treatment modality in sealing tissue, healing tissue, and protecting tissue in surgery.
In this work, plasmonic nanocomposites have been synthesized and used in laser tissue welding for ruptured porcine intestine ex vivo and incised murine skin in vivo. These laser-responsive nanocomposites improved tissue strength and healing, respectively. Additionally, a spatiotemporal model has been developed for laser tissue welding of porcine and mouse cadaver intestine sections using near-infrared laser irradiation. This mathematical model can be employed to identify optimal conditions for minimizing healthy cell death while still achieving a strong seal of the ruptured tissue using laser welding. Finally, in a model of surgical site infection, laser-responsive nanomaterials were shown to be efficacious in inhibiting bacterial growth. By incorporating an anti-microbial functionality to laser-responsive nanocomposites, these materials will serve as a treatment modality in sealing tissue, healing tissue, and protecting tissue in surgery.
ContributorsUrie, Russell Ricks (Author) / Rege, Kaushal (Thesis advisor) / Acharya, Abhinav (Committee member) / DeNardo, Dale (Committee member) / Holloway, Julianne (Committee member) / Thomas, Marylaura (Committee member) / Arizona State University (Publisher)
Created2019
Description
Ideas from coding theory are employed to theoretically demonstrate the engineering of mutation-tolerant genes, genes that can sustain up to some arbitrarily chosen number of mutations and still express the originally intended protein. Attention is restricted to tolerating substitution mutations. Future advances in genomic engineering will make possible the ability to synthesize entire genomes from scratch. This presents an opportunity to embed desirable capabilities like mutation-tolerance, which will be useful in preventing cell deaths in organisms intended for research or industrial applications in highly mutagenic environments. In the extreme case, mutation-tolerant genes (mutols) can make organisms resistant to retroviral infections.
An algebraic representation of the nucleotide bases is developed. This algebraic representation makes it possible to convert nucleotide sequences into algebraic sequences, apply mathematical ideas and convert results back into nucleotide terms. Using the algebra developed, a mapping is found from the naturally-occurring codons to an alternative set of codons which makes genes constructed from them mutation-tolerant, provided no more than one substitution mutation occurs per codon. The ideas discussed naturally extend to finding codons that can tolerate t arbitrarily chosen number of mutations per codon. Finally, random substitution events are simulated in both a wild-type green fluorescent protein (GFP) gene and its mutol variant and the amino acid sequence expressed from each post-mutation is compared with the amino acid sequence pre-mutation.
This work assumes the existence of synthetic protein-assembling entities that function like tRNAs but can read k nucleotides at a time, with k greater than or equal to 5. The realization of this assumption is presented as a challenge to the research community.
An algebraic representation of the nucleotide bases is developed. This algebraic representation makes it possible to convert nucleotide sequences into algebraic sequences, apply mathematical ideas and convert results back into nucleotide terms. Using the algebra developed, a mapping is found from the naturally-occurring codons to an alternative set of codons which makes genes constructed from them mutation-tolerant, provided no more than one substitution mutation occurs per codon. The ideas discussed naturally extend to finding codons that can tolerate t arbitrarily chosen number of mutations per codon. Finally, random substitution events are simulated in both a wild-type green fluorescent protein (GFP) gene and its mutol variant and the amino acid sequence expressed from each post-mutation is compared with the amino acid sequence pre-mutation.
This work assumes the existence of synthetic protein-assembling entities that function like tRNAs but can read k nucleotides at a time, with k greater than or equal to 5. The realization of this assumption is presented as a challenge to the research community.
ContributorsAmpofo, Prince Kwame (Author) / Tian, Xiaojun (Thesis advisor) / Kiani, Samira (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2019
Description
Wearable assistive devices have been greatly improved thanks to advancements made in soft robotics, even creation soft extra arms for paralyzed patients. Grasping remains an active area of research of soft extra limbs. Soft robotics allow the creation of grippers that due to their inherit compliance making them lightweight, safer for human interactions, more robust in unknown environments and simpler to control than their rigid counterparts. A current problem in soft robotics is the lack of seamless integration of soft grippers into wearable devices, which is in part due to the use of elastomeric materials used for the creation of most of these grippers. This work introduces fabric-reinforced textile actuators (FRTA). The selection of materials, design logic of the fabric reinforcement layer and fabrication method are discussed. The relationship between the fabric reinforcement characteristics and the actuator deformation is studied and experimentally verified. The FRTA are made of a combination of a hyper-elastic fabric material with a stiffer fabric reinforcement on top. In this thesis, the design, fabrication, and evaluation of FRTAs are explored. It is shown that by varying the geometry of the reinforcement layer, a variety of motion can be achieve such as axial extension, radial expansion, bending, and twisting along its central axis. Multi-segmented actuators can be created by tailoring different sections of fabric-reinforcements together in order to generate a combination of motions to perform specific tasks. The applicability of this actuators for soft grippers is demonstrated by designing and providing preliminary evaluation of an anthropomorphic soft robotic hand capable of grasping daily living objects of various size and shapes.
ContributorsLopez Arellano, Francisco Javier (Author) / Santello, Marco (Thesis advisor) / Zhang, Wenlong (Thesis advisor) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2019
Description
According to the World Health Organization, cancer is one of the leading causes of death around the world. Although early diagnostics using biomarkers and improved treatments with targeted therapy have reduced the rate of cancer related mortalities, there remain many unknowns regarding the contributions of the tumor microenvironment to cancer progression and therapeutic resistance. The tumor microenvironment plays a significant role by manipulating the progression of cancer cells through biochemical and biophysical signals from the surrounding stromal cells along with the extracellular matrix. As such, there is a critical need to understand how the tumor microenvironment influences the molecular mechanisms underlying cancer metastasis to facilitate the discovery of better therapies. This thesis described the development of microfluidic technologies to study the interplay of cancer cells with their surrounding microenvironment. The microfluidic model was used to assess how exposure to chemoattractant, epidermal growth factor (EGF), impacted 3D breast cancer cell invasion and enhanced cell motility speed was noted in the presence of EGF validating physiological cell behavior. Additionally, breast cancer and patient-derived cancer-associated fibroblast (CAF) cells were co-cultured to study cell-cell crosstalk and how it affected cancer invasion. GPNMB was identified as a novel gene of interest and it was shown that CAFs enhanced breast cancer invasion by up-regulating the expression of GPNMB on breast cancer cells resulting in increased migration speed. Lastly, this thesis described the design, biological validation, and use of this microfluidic platform as a new in vitro 3D organotypic model to study mechanisms of glioma stem cell (GSC) invasion in the context of a vascular niche. It was confirmed that CXCL12-CXCR4 signaling is involved in promoting GSC invasion in a 3D vascular microenvironment, while also demonstrating the effectiveness of the microfluidic as a drug screening assay. Taken together, the broader impacts of the microfluidic model developed in this dissertation include, a possible alternative platform to animal testing that is focused on mimicking human physiology, a potential ex vivo platform using patient-derived cells for studying the interplay of cancer cells with its surrounding microenvironment, and development of future therapeutic strategies tailored toward disrupting key molecular pathways involved in regulatory mechanisms of cancer invasion.
ContributorsTruong, Danh, Ph.D (Author) / Nikkhah, Mehdi (Thesis advisor) / LaBaer, Joshua (Committee member) / Smith, Barbara (Committee member) / Mouneimne, Ghassan (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2018
Description
Stroke remains a leading cause of adult disability in the United States. In recent studies, chronic vagus nerve stimulation (VNS) has been proven to enhance functional recovery when paired with motor rehabilitation training after stroke. Other studies have also demonstrated that delivering VNS during the onset of a stroke may elicit some neuroprotective effects as observed in remaining neural tissue and motor function. While these studies have demonstrated the benefits of VNS as a treatment or therapy in combatting stroke damage, the mechanisms responsible for these effects are still not well understood or known. The aim of this research was to further investigate the mechanisms underlying the efficacy of acute VNS treatment of stroke by observing the effect of VNS when applied after the onset of stroke. Animals were randomly assigned to three groups: Stroke animals received cortical ischemia (ET-1 injection), VNS+Stroke animals received acute VNS starting within 48 hours after cortical ischemia and continuing once per day for three days, or Control animals which received neither the injury nor stimulation. Results showed that stroke animals receiving acute VNS had smaller lesion volumes and larger motor cortical maps than those in the Stroke group. The results suggest VNS may confer neuroprotective effects when delivered within the first 96 hours of stroke.
ContributorsOkada, Kristen Yuri (Author) / Kleim, Jeffrey A (Thesis advisor) / Si, Jennie (Thesis advisor) / Helms Tillery, Stephen (Committee member) / Arizona State University (Publisher)
Created2019
Description
Extracellular Vesicles (EVs), particularly exosomes, are of considerable interest as tumor biomarkers since tumor-derived EVs contain a broad array of information about tumor pathophysiology including its metabolic and metastatic status. However, current EV based assays cannot distinguish between EV biomarker changes by altered secretion of EVs during diseased conditions like cancer, inflammation, etc. that express a constant level of a given biomarker, stable secretion of EVs with altered biomarker expression, or a combination of these two factors. This issue was addressed by developing a nanoparticle and dye-based fluorescent immunoassay that can distinguish among these possibilities by normalizing EV biomarker level(s) to EV abundance, revealing average expression levels of EV biomarker under observation. In this approach, EVs are captured from complex samples (e.g. serum), stained with a lipophilic dye and hybridized with antibody-conjugated quantum dot probes for specific EV surface biomarkers. EV dye signal is used to quantify EV abundance and normalize EV surface biomarker expression levels. EVs from malignant (PANC-1) and nonmalignant pancreatic cell lines (HPNE) exhibited similar staining, and probe-to-dye ratios did not change with EV abundance, allowing direct analysis of normalized EV biomarker expression without a separate EV quantification step. This EV biomarker normalization approach markedly improved the ability of serum levels of two pancreatic cancer biomarkers, EV EpCAM, and EV EphA2, to discriminate pancreatic cancer patients from nonmalignant control subjects. The streamlined workflow and robust results of this assay are suitable for rapid translation to clinical applications and its flexible design permits it to be rapidly adapted to quantitate other EV biomarkers by the simple swapping of the antibody-conjugated quantum dot probes for those that recognize a different disease-specific EV biomarker utilizing a workflow that is suitable for rapid clinical translation.
ContributorsRodrigues, Meryl (Author) / Hu, Tony (Thesis advisor) / Nikkhah, Mehdi (Committee member) / Kiani, Samira (Committee member) / Smith, Barbara (Committee member) / Han, Haiyong (Committee member) / Arizona State University (Publisher)
Created2019
Description
Alzheimer’s disease (AD) affects over 5 million individuals each year in the United States. Furthermore, most cases of AD are sporadic, making it extremely difficult to model and study in vitro. CRISPR/Cas9 and base editing technologies have been of recent interest because of their ability to create single nucleotide edits at nearly any genomic sequence using a Cas9 protein and a guide RNA (sgRNA). Currently, there is no available phenotype to differentiate edited cells from unedited cells. Past research has employed fluorescent proteins bound to Cas9 proteins to attempt to enrich for edited cells, however, these methods are only reporters of transfection (RoT) and are no indicative of actual base-editing occurring. Thus, this study proposes a transient reporter for editing enrichment (TREE) and Cas9-mediated adenosine TREE (CasMasTREE) which use plasmids to co-transfect with CRISPR/Cas9 technologies to serve as an indicator of base-editing. Specifically, TREE features a blue fluorescent protein (BFP) mutant that, upon a C-T conversion, changes the emission spectrum to a green fluorescent protein (GFP). CasMasTREE features a mCherry and GFP protein separated by a stop codon which can be negated using an A-G conversion. By employing a sgRNA that targets one of the TREE plasmids and at least one genomic site, cells can be sorted for GFP(+) cells. Using these methods, base-edited isogenic hiPSC line generation using TREE (BIG-TREE) was created to generate isogenic hiPSC lines with AD-relevant edits. For example, BIG-TREE demonstrates the capability of converting Apolipoprotein E (APOE), a gene associated with AD-risk development, wildtype (3/3) into another isoform, APOE2/2, to create isogenic hiPSC lines. The capabilities of TREE are vast and can be applied to generate various models of diseases with specific genomic edits.
ContributorsNguyen, Toan Thai Tran (Author) / Brafman, David (Thesis advisor) / Wang, Xiao (Committee member) / Tian, Xiaojun (Committee member) / Arizona State University (Publisher)
Created2020