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- All Subjects: Biomedical Engineering
- Creators: Nikkhah, Mehdi
- Creators: Wang, Xiao
- Resource Type: Text
Description
The object of this study is to charac terize the effect of focused ultrasound stimulation (FUS) on the rat ce rvix which has been observed to speed its ripening during pregnancy. Ce rvical ripening is required for successful fetal delivery. Timed-pregnant Sprague-Dawley rats (n=36) were used. On day 14 of gestation, the FUS system was placed on the body surface of the rat over the cervix and ultrasound energy was applied to cervix for variable times up to 1 hour in the control group, the FUS system was placed on rats but no energy was applied. Daily measurement of cervix light-induced florescence (LIF, photon counts of collagen x-bridge fluorescence) were made on days 16 of gestation and daily until spont-aneous delivery (day22) to estimate changes in cervical ripening. We found that pulses of 680 KHz ultrasound at 25 Hertz, 1 millisecond pulse duration at 1W/cm^2 applied for as little as 30 minutes would immediately afterwards show the cervix to hav e ripened to the degree seen just before delivery on day 22. Delivery times, fetal weights and viability were unaffected in the FUS-treated animals.
ContributorsLuo, Daishen (Author) / Towe, Bruce C (Thesis advisor) / Wang, Xiao (Committee member) / Caplan, Michael (Committee member) / Arizona State University (Publisher)
Created2012
Description
Sensitivity is a fundamental challenge for in vivo molecular magnetic resonance imaging (MRI). Here, I improve the sensitivity of metal nanoparticle contrast agents by strategically incorporating pure and doped metal oxides in the nanoparticle core, forming a soluble, monodisperse, contrast agent with adjustable T2 or T1 relaxivity (r2 or r1). I first developed a simplified technique to incorporate iron oxides in apoferritin to form "magnetoferritin" for nM-level detection with T2- and T2* weighting. I then explored whether the crystal could be chemically modified to form a particle with high r1. I first adsorbed Mn2+ ions to metal binding sites in the apoferritin pores. The strategic placement of metal ions near sites of water exchange and within the crystal oxide enhance r1, suggesting a mechanism for increasing relaxivity in porous nanoparticle agents. However, the Mn2+ addition was only possible when the particle was simultaneously filled with an iron oxide, resulting in a particle with a high r1 but also a high r2 and making them undetectable with conventional T1-weighting techniques. To solve this problem and decrease the particle r2 for more sensitive detection, I chemically doped the nanoparticles with tungsten to form a disordered W-Fe oxide composite in the apoferritin core. This configuration formed a particle with a r1 of 4,870mM-1s-1 and r2 of 9,076mM-1s-1. These relaxivities allowed the detection of concentrations ranging from 20nM - 400nM in vivo, both passively injected and targeted to the kidney glomerulus. I further developed an MRI acquisition technique to distinguish particles based on r2/r1, and show that three nanoparticles of similar size can be distinguished in vitro and in vivo with MRI. This work forms the basis for a new, highly flexible inorganic approach to design nanoparticle contrast agents for molecular MRI.
ContributorsClavijo Jordan, Maria Veronica (Author) / Bennett, Kevin M (Thesis advisor) / Kodibagkar, Vikram (Committee member) / Sherry, A Dean (Committee member) / Wang, Xiao (Committee member) / Yarger, Jeffery (Committee member) / Arizona State University (Publisher)
Created2012
Description
There is a strong medical need and important therapeutic applications for improved wireless bioelectric interfaces to the nervous system. Multichannel devices are desired for neural control of robotic prosthetics that interface to remaining nerves in limb stumps of amputees and as alternatives to traditional wired arrays used in for some types of brain stimulation. This present work investigates a new approach to ultrasound-powering of implantable microelectronic devices within the tissue that may better support such applications. These devices are of ultra-miniature size that is enabled by a wireless technique. This study investigates two types of ultrasound-powered neural interfaces for multichannel sensory feedback in neurostimulation. The piezoceramics lead zirconate titanate (PZT) ceramic and polyvinylidene fluoride (PVDF) polymer were the primary materials used to build the devices. They convert ultrasound to electricity that when rectified by a diode produce a current output that is neuro stimulatory to peripheral nerve or the neurons in the brain. Multichannel devices employ a form of spatial multiplexing that directs focused ultrasound towards localized and segmented regions of PVDF or PZT that allows independent channels of nerve actuation. Different frequencies of ultrasound were evaluated for best results. Firstly, a 2.25 MHz frequency signal that is reasonably penetrating through body tissue to an implant several centimeters deep and also a 5 MHz frequency more suited to application for actuation of devices within a less than a centimeter of nerve. Results show multichannel device performance to have a complex inter-relationship with frequency, size and thickness, angular incidence, channel separations, and number of folds (layers connected in series and parallel). The output electrical port impedances of PVDF devices were examined in relationship to that of stimulating electrodes and tissue interfaces. Miniature multichannel devices were constructed using an unreported method of employing state of the art laser cutting systems. The results show that PVDF based devices have advantages over PZT, because of better acoustic coupling with tissue, known better biocompatibility, and better separation between multiple channels. However, the PZT devices proved to be better overall in terms of compactness and higher outputs for a given ultrasound power level.
ContributorsNanda Kumar, Yashwanth (Author) / Towe, Bruce (Thesis advisor) / Muthuswamy, Jitendran (Committee member) / Nikkhah, Mehdi (Committee member) / Arizona State University (Publisher)
Created2015
Description
While techniques for reading DNA in some capacity has been possible for decades,
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
the ability to accurately edit genomes at scale has remained elusive. Novel techniques
have been introduced recently to aid in the writing of DNA sequences. While writing
DNA is more accessible, it still remains expensive, justifying the increased interest in
in silico predictions of cell behavior. In order to accurately predict the behavior of
cells it is necessary to extensively model the cell environment, including gene-to-gene
interactions as completely as possible.
Significant algorithmic advances have been made for identifying these interactions,
but despite these improvements current techniques fail to infer some edges, and
fail to capture some complexities in the network. Much of this limitation is due to
heavily underdetermined problems, whereby tens of thousands of variables are to be
inferred using datasets with the power to resolve only a small fraction of the variables.
Additionally, failure to correctly resolve gene isoforms using short reads contributes
significantly to noise in gene quantification measures.
This dissertation introduces novel mathematical models, machine learning techniques,
and biological techniques to solve the problems described above. Mathematical
models are proposed for simulation of gene network motifs, and raw read simulation.
Machine learning techniques are shown for DNA sequence matching, and DNA
sequence correction.
Results provide novel insights into the low level functionality of gene networks. Also
shown is the ability to use normalization techniques to aggregate data for gene network
inference leading to larger data sets while minimizing increases in inter-experimental
noise. Results also demonstrate that high error rates experienced by third generation
sequencing are significantly different than previous error profiles, and that these errors can be modeled, simulated, and rectified. Finally, techniques are provided for amending this DNA error that preserve the benefits of third generation sequencing.
ContributorsFaucon, Philippe Christophe (Author) / Liu, Huan (Thesis advisor) / Wang, Xiao (Committee member) / Crook, Sharon M (Committee member) / Wang, Yalin (Committee member) / Sarjoughian, Hessam S. (Committee member) / Arizona State University (Publisher)
Created2017
Description
Recombinases are powerful tools for genome engineering and synthetic biology, however recombinases are limited by a lack of user-programmability and often require complex directed-evolution experiments to retarget specificity. Conversely, CRISPR systems have extreme versatility yet can induce off-target mutations and karyotypic destabilization. To address these constraints we developed an RNA-guided recombinase protein by fusing a hyperactive mutant resolvase from transposon TN3 to catalytically inactive Cas9. We validated recombinase-Cas9 (rCas9) function in model eukaryote Saccharomyces cerevisiae using a chromosomally integrated fluorescent reporter. Moreover, we demonstrated cooperative targeting by CRISPR RNAs at spacings of 22 or 40bps is necessary for directing recombination. Using PCR and Sanger sequencing, we confirmed rCas9 targets DNA recombination. With further development we envision rCas9 becoming useful in the development of RNA-programmed genetic circuitry as well as high-specificity genome engineering.
ContributorsStandage-Beier, Kylie S (Author) / Wang, Xiao (Thesis advisor) / Brafman, David A (Committee member) / Tian, Xiao-jun (Committee member) / Arizona State University (Publisher)
Created2018
Description
Synthetic biology is an emerging field which melds genetics, molecular biology, network theory, and mathematical systems to understand, build, and predict gene network behavior. As an engineering discipline, developing a mathematical understanding of the genetic circuits being studied is of fundamental importance. In this dissertation, mathematical concepts for understanding, predicting, and controlling gene transcriptional networks are presented and applied to two synthetic gene network contexts. First, this engineering approach is used to improve the function of the guide ribonucleic acid (gRNA)-targeted, dCas9-regulated transcriptional cascades through analysis and targeted modification of the RNA transcript. In so doing, a fluorescent guide RNA (fgRNA) is developed to more clearly observe gRNA dynamics and aid design. It is shown that through careful optimization, RNA Polymerase II (Pol II) driven gRNA transcripts can be strong enough to exhibit measurable cascading behavior, previously only shown in RNA Polymerase III (Pol III) circuits. Second, inherent gene expression noise is used to achieve precise fractional differentiation of a population. Mathematical methods are employed to predict and understand the observed behavior, and metrics for analyzing and quantifying similar differentiation kinetics are presented. Through careful mathematical analysis and simulation, coupled with experimental data, two methods for achieving ratio control are presented, with the optimal schema for any application being dependent on the noisiness of the system under study. Together, these studies push the boundaries of gene network control, with potential applications in stem cell differentiation, therapeutics, and bio-production.
ContributorsMenn, David J (Author) / Wang, Xiao (Thesis advisor) / Kiani, Samira (Committee member) / Haynes, Karmella (Committee member) / Nielsen, David (Committee member) / Marshall, Pamela (Committee member) / Arizona State University (Publisher)
Created2018
Description
Severe cases of congenital heart defect (CHD) require surgeries to fix the structural problem, in which artificial grafts are often used. Although outcome of surgeries has improved over the past decades, there remains to be patients who require re-operations due to graft-related complications and the growth of patients which results in a mismatch in size between the patient’s anatomy and the implanted graft. A graft in which cells of the patient could infiltrate, facilitating transformation of the graft to a native-like tissue, and allow the graft to grow with the patient heart would be ideal. Cardiac tissue engineering (CTE) technologies, including extracellular matrix (ECM)-based hydrogels has emerged as a promising approach for the repair of cardiac damage. However, most of the previous studies have mainly focused on treatments for ischemic heart disease and related heart failure in adults, therefore the potential of CTE for CHD treatment is underexplored. In this study, a hybrid hydrogel was developed by combining the ECM derived from cardiac tissue of pediatric CHD patients and gelatin methacrylate (GelMA). In addition, the influence of incorporating gold nanorods (GNRs) within the hybrid hydrogels was studied. The functionalities of the ECM-GelMA-GNR hydrogels as a CTE scaffold were assessed by culturing neonatal rat cardiomyocytes on the hydrogel. After 8 days of cell culture, highly organized sarcomeric alpha-actinin structures and connexin 43 expression were evident in ECM- and GNR-incorporated hydrogels compared to pristine GelMA hydrogel, indicating cell maturation and formation of cardiac tissue. The findings of this study indicate the promising potential of ECM-GelMA-GNR hybrid hydrogels as a CTE approach for CHD treatment.
As another approach to improve CHD treatment, this study sought the possibility of performing a proteomic analysis on cardiac ECM of pediatric CHD patient tissue. As the ECM play important roles in regulating cell signaling, there is an increasing interest in studying the ECM proteome and the influences caused by diseases. Proteomics on ECM is challenging due to the insoluble nature of ECM proteins which makes protein extraction and digestion difficult. In this study, as a first step to perform proteomics, optimization on sample preparation procedure was attempted.
As another approach to improve CHD treatment, this study sought the possibility of performing a proteomic analysis on cardiac ECM of pediatric CHD patient tissue. As the ECM play important roles in regulating cell signaling, there is an increasing interest in studying the ECM proteome and the influences caused by diseases. Proteomics on ECM is challenging due to the insoluble nature of ECM proteins which makes protein extraction and digestion difficult. In this study, as a first step to perform proteomics, optimization on sample preparation procedure was attempted.
ContributorsSugamura, Yuka (Author) / Nikkhah, Mehdi (Thesis advisor) / Smith, Barbara (Committee member) / Willis, Brigham (Committee member) / Arizona State University (Publisher)
Created2018
Description
With an increased demand for more enzyme-sensitive, bioresorbable and more biodegradable polymers, various studies of copolymers have been developed. Polymers are widely used in various applications of biomedical engineering such as in tissue engineering, drug delivery and wound healing. Depending on the conditions in which polymers are used, they are modified to accommodate a specific need. For instance, polymers used in drug delivery are more efficient if they are biodegradable. This ensures that the delivery system does not remain in the body after releasing the drug. It is therefore crucial that the polymer used in the drug system possess biodegradable properties. Such modification can be done in different ways including the use of peptides to make copolymers that will degrade in the presence of enzymes. In this work, we studied the effect of a polypeptide GAPGLL on the polymer NIPAAm and compare with the previously studied Poly(NIPAAm-co-GAPGLF). Both copolymers Poly(NIPAAm-co-GAPGLL) were first synthesized from Poly(NIPAAm-co-NASI) through nucleophilic substitution by the two peptides. The synthesis of these copolymers was confirmed by 1H NMR spectra and through cloud point measurement, the corresponding LCST was determined. Both copolymers were degraded by collagenase enzyme at 25 ° C and their 1H NMR spectra confirmed this process. Both copolymers were cleaved by collagenase, leading to an increase in solubility which yielded a higher LCST compared to before enzyme degradation. Future studies will focus on evaluating other peptides and also using other techniques such as Differential Scanning Microcalorimetry (DSC) to better observe the LCST behavior. Moreover, enzyme kinetics studies is also crucial to evaluate how fast the enzyme degrades each of the copolymers.
ContributorsUwiringiyimana, Mahoro Marie Chantal (Author) / Vernon, Brent (Thesis director) / Nikkhah, Mehdi (Committee member) / Harrington Bioengineering Program (Contributor) / Barrett, The Honors College (Contributor)
Created2018-05
Description
Several debilitating neurological disorders, such as Alzheimer's disease, stroke, and spinal cord injury, are characterized by the damage or loss of neuronal cell types in the central nervous system (CNS). Human neural progenitor cells (hNPCs) derived from human pluripotent stem cells (hPSCs) can proliferate extensively and differentiate into the various neuronal subtypes and supporting cells that comprise the CNS. As such, hNPCs have tremendous potential for disease modeling, drug screening, and regenerative medicine applications. However, the use hNPCs for the study and treatment of neurological diseases requires the development of defined, robust, and scalable methods for their expansion and neuronal differentiation. To that end a rational design process was used to develop a vitronectin-derived peptide (VDP)-based substrate to support the growth and neuronal differentiation of hNPCs in conventional two-dimensional (2-D) culture and large-scale microcarrier (MC)-based suspension culture. Compared to hNPCs cultured on ECMP-based substrates, hNPCs grown on VDP-coated surfaces displayed similar morphologies, growth rates, and high expression levels of hNPC multipotency markers. Furthermore, VDP surfaces supported the directed differentiation of hNPCs to neurons at similar levels to cells differentiated on ECMP substrates. Here it has been demonstrated that VDP is a robust growth and differentiation matrix, as demonstrated by its ability to support the expansions and neuronal differentiation of hNPCs derived from three hESC (H9, HUES9, and HSF4) and one hiPSC (RiPSC) cell lines. Finally, it has been shown that VDP allows for the expansion or neuronal differentiation of hNPCs to quantities (>1010) necessary for drug screening or regenerative medicine purposes. In the future, the use of VDP as a defined culture substrate will significantly advance the clinical application of hNPCs and their derivatives as it will enable the large-scale expansion and neuronal differentiation of hNPCs in quantities necessary for disease modeling, drug screening, and regenerative medicine applications.
ContributorsVarun, Divya (Author) / Brafman, David (Thesis advisor) / Nikkhah, Mehdi (Committee member) / Stabenfeldt, Sarah (Committee member) / Arizona State University (Publisher)
Created2016
Description
Synthetic biology is a novel method that reengineers functional parts of natural genes of interest to build new biomolecular devices able to express as designed. There is increasing interest in synthetic biology due to wide potential applications in various fields such as clinics and fuel production. However, there are still many challenges in synthetic biology. For example, many natural biological processes are poorly understood, and these could be more thoroughly studied through model synthetic gene networks. Additionally, since synthetic biology applications may have numerous design constraints, more inducer systems should be developed to satisfy different requirements for genetic design.
This thesis covers two topics. First, I attempt to generate stochastic resonance (SR) in a biological system. Synthetic bistable systems were chosen because the inducer range in which they exhibit bistability can satisfy one of the three requirements of SR: a weak periodic force is unable to make the transition between states happen. I synthesized several different bistable systems, including toggle switches and self-activators, to select systems matching another requirement: the system has a clear threshold between the two energy states. Their bistability was verified and characterized. At the same time, I attempted to figure out the third requirement for SR – an effective noise serving as the stochastic force – through one of the most widespread toggles, the mutual inhibition toggle, in both yeast and E. coli. A mathematic model for SR was written and adjusted.
Secondly, I began work on designing a new genetic system capable of responding to pulsed magnetic fields. The operators responding to pulsed magnetic stimuli in the rpoH promoter were extracted and reorganized. Different versions of the rpoH promoter were generated and tested, and their varying responsiveness to magnetic fields was recorded. In order to improve efficiency and produce better operators, a directed evolution method was applied with the help of a CRISPR-dCas9 nicking system. The best performing promoters thus far show a five-fold difference in gene expression between trials with and without the magnetic field.
This thesis covers two topics. First, I attempt to generate stochastic resonance (SR) in a biological system. Synthetic bistable systems were chosen because the inducer range in which they exhibit bistability can satisfy one of the three requirements of SR: a weak periodic force is unable to make the transition between states happen. I synthesized several different bistable systems, including toggle switches and self-activators, to select systems matching another requirement: the system has a clear threshold between the two energy states. Their bistability was verified and characterized. At the same time, I attempted to figure out the third requirement for SR – an effective noise serving as the stochastic force – through one of the most widespread toggles, the mutual inhibition toggle, in both yeast and E. coli. A mathematic model for SR was written and adjusted.
Secondly, I began work on designing a new genetic system capable of responding to pulsed magnetic fields. The operators responding to pulsed magnetic stimuli in the rpoH promoter were extracted and reorganized. Different versions of the rpoH promoter were generated and tested, and their varying responsiveness to magnetic fields was recorded. In order to improve efficiency and produce better operators, a directed evolution method was applied with the help of a CRISPR-dCas9 nicking system. The best performing promoters thus far show a five-fold difference in gene expression between trials with and without the magnetic field.
ContributorsHu, Hao (Author) / Wang, Xiao (Thesis advisor) / Stabenfeldt, Sarah (Committee member) / Brafman, David (Committee member) / Arizona State University (Publisher)
Created2016