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Description
Relapse after tumor dormancy is one of the leading causes of cancer recurrence that ultimately leads to patient mortality. Upon relapse, cancer manifests as metastases that are linked to almost 90% cancer related deaths. Capture of the dormant and relapsed tumor phenotypes in high-throughput will allow for rapid targeted drug discovery, development and validation. Ablation of dormant cancer will not only completely remove the cancer disease, but also will prevent any future recurrence. A novel hydrogel, Amikagel, was developed by crosslinking of aminoglycoside amikacin with a polyethylene glycol crosslinker. Aminoglycosides contain abundant amount of easily conjugable groups such as amino and hydroxyl moieties that were crosslinked to generate the hydrogel. Cancer cells formed 3D spheroidal structures that underwent near complete dormancy on Amikagel high-throughput drug discovery platform. Due to their dormant status, conventional anticancer drugs such as mitoxantrone and docetaxel that target the actively dividing tumor phenotype were found to be ineffective. Hypothesis driven rational drug discovery approaches were used to identify novel pathways that could sensitize dormant cancer cells to death. Strategies were used to further accelerate the dormant cancer cell death to save time required for the therapeutic outcome.
Amikagel’s properties were chemo-mechanically tunable and directly impacted the outcome of tumor dormancy or relapse. Exposure of dormant spheroids to weakly stiff and adhesive formulation of Amikagel resulted in significant relapse, mimicking the response to changes in extracellular matrix around dormant tumors. Relapsed cells showed significant differences in their metastatic potential compared to the cells that remained dormant after the induction of relapse. Further, the dissertation discusses the use of Amikagels as novel pDNA binding resins in microbead and monolithic formats for potential use in chromatographic purifications. High abundance of amino groups allowed their utilization as novel anion-exchange pDNA binding resins. This dissertation discusses Amikagel formulations for pDNA binding, metastatic cancer cell separation and novel drug discovery against tumor dormancy and relapse.
Amikagel’s properties were chemo-mechanically tunable and directly impacted the outcome of tumor dormancy or relapse. Exposure of dormant spheroids to weakly stiff and adhesive formulation of Amikagel resulted in significant relapse, mimicking the response to changes in extracellular matrix around dormant tumors. Relapsed cells showed significant differences in their metastatic potential compared to the cells that remained dormant after the induction of relapse. Further, the dissertation discusses the use of Amikagels as novel pDNA binding resins in microbead and monolithic formats for potential use in chromatographic purifications. High abundance of amino groups allowed their utilization as novel anion-exchange pDNA binding resins. This dissertation discusses Amikagel formulations for pDNA binding, metastatic cancer cell separation and novel drug discovery against tumor dormancy and relapse.
ContributorsGrandhi, Taraka Sai Pavan (Author) / Rege, Kaushal (Thesis advisor) / Meldrum, Deirdre R (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Caplan, Michael (Committee member) / Tian, Yanqing (Committee member) / Arizona State University (Publisher)
Created2016
Description
In brain imaging study, 3D surface-based algorithms may provide more advantages over volume-based methods, due to their sub-voxel accuracy to represent subtle subregional changes and solid mathematical foundations on which global shape analyses can be achieved on complicated topological structures, such as the convoluted cortical surfaces. On the other hand, given the enormous amount of data being generated daily, it is still challenging to develop effective and efficient surface-based methods to analyze brain shape morphometry. There are two major problems in surface-based shape analysis research: correspondence and similarity. This dissertation covers both topics by proposing novel surface registration and indexing algorithms based on conformal geometry for brain morphometry analysis.
First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).
Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.
Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
First, I propose a surface fluid registration system, which extends the traditional image fluid registration to surfaces. With surface conformal parameterization, the complexity of the proposed registration formula has been greatly reduced, compared to prior methods. Inverse consistency is also incorporated to drive a symmetric correspondence between surfaces. After registration, the multivariate tensor-based morphometry (mTBM) is computed to measure local shape deformations. The algorithm was applied to study hippocampal atrophy associated with Alzheimer's disease (AD).
Next, I propose a ventricular surface registration algorithm based on hyperbolic Ricci flow, which computes a global conformal parameterization for each ventricular surface without introducing any singularity. Furthermore, in the parameter space, unique hyperbolic geodesic curves are introduced to guide consistent correspondences across subjects, a technique called geodesic curve lifting. Tensor-based morphometry (TBM) statistic is computed from the registration to measure shape changes. This algorithm was applied to study ventricular enlargement in mild cognitive impatient (MCI) converters.
Finally, a new shape index, the hyperbolic Wasserstein distance, is introduced. This algorithm computes the Wasserstein distance between general topological surfaces as a shape similarity measure of different surfaces. It is based on hyperbolic Ricci flow, hyperbolic harmonic map, and optimal mass transportation map, which is extended to hyperbolic space. This method fills a gap in the Wasserstein distance study, where prior work only dealt with images or genus-0 closed surfaces. The algorithm was applied in an AD vs. control cortical shape classification study and achieved promising accuracy rate.
ContributorsShi, Jie, Ph.D (Author) / Wang, Yalin (Thesis advisor) / Caselli, Richard (Committee member) / Li, Baoxin (Committee member) / Xue, Guoliang (Committee member) / Arizona State University (Publisher)
Created2016
Description
The WNT signaling pathway plays numerous roles in development and maintenance of adult homeostasis. In concordance with it’s numerous roles, dysfunction of WNT signaling leads to a variety of human diseases ranging from developmental disorders to cancer. WNT signaling is composed of a family of 19 WNT soluble secreted glycoproteins, which are evolutionarily conserved across all phyla of the animal kingdom. WNT ligands interact most commonly with a family of receptors known as frizzled (FZ) receptors, composed of 10 independent genes. Specific interactions between WNT proteins and FZ receptors are not well characterized and are known to be promiscuous, Traditionally canonical WNT signaling is described as a binary system in which WNT signaling is either off or on. In the ‘off’ state, in the absence of a WNT ligand, cytoplasmic β-catenin is continuously degraded by the action of the APC/Axin/GSK-3β destruction complex. In the ‘on’ state, when WNT binds to its Frizzled (Fz) receptor and LRP coreceptor, this protein destruction complex is disrupted, allowing β-catenin to translocate into the nucleus where it interacts with the DNA-bound T cell factor/lymphoid factor (TCF/LEF) family of proteins to regulate target gene expression. However in a variety of systems in development and disease canonical WNT signaling acts in a gradient fashion, suggesting more complex regulation of β-catenin transcriptional activity. As such, the traditional ‘binary’ view of WNT signaling does not clearly explain how this graded signal is transmitted intracellularly to control concentration-dependent changes in gene expression and cell identity. I have developed an in vitro human pluripotent stem cell (hPSC)-based model that recapitulates the same in vivo developmental effects of the WNT signaling gradient on the anterior-posterior (A/P) patterning of the neural tube observed during early development. Using RNA-seq and ChIP-seq I have characterized β-catenin binding at different levels of WNT signaling and identified different classes of β-catenin peaks that bind cis-regulatory elements to influence neural cell fate. This work expands the traditional binary view of canonical WNT signaling and illuminates WNT/β-catenin activity in other developmental and diseased contexts.
ContributorsCutts, Joshua Patrick (Author) / Brafman, David A (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Nikkhah, Mehdi (Committee member) / Wang, Xiao (Committee member) / Plaisier, Christopher (Committee member) / Arizona State University (Publisher)
Created2019
Description
Previously accomplished research examined sensory integration between upper limb proprioception and tactile sensation. The active proprioceptive-tactile relationship points towards an opportunity to examine neuromodulation effects on sensory integration with respect to proprioceptive error magnitude and direction. Efforts to improve focus and attention during upper limb proprioceptive tasks results in a decrease of proprioceptive error magnitudes and greater endpoint accuracy. Increased focus and attention can also be correlated to neurophysiological activity in the Locus Coeruleus (LC) during a variety of mental tasks. Through non-invasive trigeminal nerve stimulation, it may be possible to affect the activity of the LC and induce improvements in arousal and attention that would assist in proprioceptive estimation. The trigeminal nerve projects to the LC through the mesencephalic nucleus of the trigeminal complex, providing a pathway similar to the effects seen from vagus nerve stimulation. In this experiment, the effect of trigeminal nerve stimulation (TNS) on proprioceptive ability is evaluated by the proprioceptive estimation error magnitude and direction, while LC activation via autonomic pathways is indirectly measured using pupil diameter, pupil recovery time, and pupil velocity. TNS decreases proprioceptive error magnitude in 59% of subjects, while having no measurable impact on proprioceptive strategy. Autonomic nervous system changes were observed in 88% of subjects, with mostly parasympathetic activation and a mixed sympathetic effect.
ContributorsOrthlieb, Gerrit Chi Luk (Author) / Helms-Tillery, Stephen (Thesis advisor) / Tanner, Justin (Committee member) / Buneo, Christopher (Committee member) / Arizona State University (Publisher)
Created2019
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