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Sodium Intake Differences and its Relationship to the Gut Microbiome in Female Individuals with Irritable Bowel Syndrome and Healthy Controls
No full textThesis (Master's)--University of Washington, 2025Irritable bowel syndrome (IBS) is a disorder of gut-brain interaction characterized by abdominal pain and changes in bowel movements. Although IBS pathophysiology is still unclear, research has suggested that IBS may be associated with diet and the gut microbiome. Sodium is a nutrient that may be associated with bloating, gut motility, and gut microbial composition. This study investigated the relationship between sodium intake, symptoms, and the gut microbiome in females with IBS and healthy controls. 113 participants provided 3-day food records as well as 28-day symptom diaries. Participants also provided a stool sample for microbiome analysis where 16S rRNA gene sequencing was used for bacterial genus identification. We compared differences in sodium intake and abundances of Lactobacillus and Bifidobacterium between 67 females with IBS and 46 healthy controls. Results showed no significant differences in sodium intake between females with IBS and healthy controls. There was also no significant correlation between sodium intake and the symptoms of interest: abdominal pain, bloating, and intestinal gas. Sodium density (mg sodium /1000 kcal) did not significantly correlate with Lactobacillus and Bifidobacterium relative abundances, but our analysis showed significant associations in abundance of other genera that were not selected a priori. We also found significant associations between sodium, energy, and macronutrient intake. Because sodium is such an integral component in daily diet, our study indicates the need for further, more focused nutrition studies on how sodium intake is associated with gut microbial composition and IBS
Fundamental Investigations of Single-entity Electrochemistry towards Ultrasensitive Biosensing
Get PDFThesis (Ph.D.)--University of Washington, 2025This dissertation explores novel advances in single-entity electrochemistry, with a focus on understanding nanoscale electrochemical processes and developing innovative sensing platforms. Chapter 2 investigates the electrocatalytic behavior of individual Pt nanoparticles on electrode-solution interface using a single-nanoparticle collision approach. The study reveals that molecular adsorption and dynamic changes in the local chemical environment critically influence catalytic responses. Furthermore, the observed steady-state currents are found to be governed by either chemical kinetics or mass transport limitations, providing key mechanistic insights into single-nanoparticle electrochemistry. In Chapter 3, a glass microbulb (GMB) nanopore is employed to study the transient bipolar electrochemical behavior of single metal nanoparticles. The design of GMB enables high-throughput recording of translocation events with minimal clogging. The ionic current response exhibits biphasic signals at low voltages and oscillatory behavior at higher potentials, suggesting the formation of transient nanobubbles on moving nanoparticles. These findings advance the development of ultrasensitive biosensors based on single-entity bipolar electrochemistry. Chapters 4 and 5 focus on the development of optical imaging techniques for single-entity electrochemical detection. Chapter 4 explores the use of electrogenerated chemiluminescence (ECL) to image gold nanoparticle translocations, identifying key challenges such as nanoparticle residence time, faradaic efficiency, and optical sensitivity. Chapter 5 introduces a novel dark-field microscopy (DFM) platform based on closed-bipolar electrochemistry, enabling real-time optical monitoring of electrochemical reactions. This system demonstrates quantitative detection capabilities, serving as a promising optical reporter for transient single-entity electrochemistry. Collectively, this work advances fundamental understanding and practical applications of single-entity electrochemistry, paving the way for next-generation biosensing, nanoscale electroanalysis, and high-throughput single-particle studies
Inter-leg Coordination and Static Stability of Walking Drosophila in Courtship
No full textThesis (Master's)--University of Washington, 2025Animals use different gaits (leg coordination patterns) to move at different speeds. Abundant research examines the step characteristics and leg coordination of walking Drosophila under controlled conditions, where individual flies exhibit straight forward walking. It remains unclear what walking patterns flies choose under a more complex, naturalistic behavior paradigm, such as courtship. During courtship, male flies exhibit a repertoire of walking behaviors, including following, turning, and circling. These behaviors require precise, flexible temporal and spatial coordination of limb movements. Here I explore how male flies position their legs in courtship context. Using high-speed videography and pose estimation, we tracked male Drosophila during engaged (active courtship) and disengaged walking. We found that while stepping kinematics (stance duration and step length) were broadly similar across social contexts, static stability of the leg coordination diverged. Specifically, engaged males exhibited greater use of more stable coordination patterns, particularly at low to intermediate speeds. These findings indicate that while locomotor parameters remain stereotyped, static stability is flexibly modulated during social interactions. We conclude that social goals, such as tracking or signaling to a mate, shape locomotor strategy. This study highlights how internal and external context shape motor output and raises the question of how neural circuits integrate social cues to influence movement in natural behaviors
Mechanisms of contractile dysfunction of the G256E HCM-associated mutation
No full textThesis (Ph.D.)--University of Washington, 2025Hypertrophic cardiomyopathy (HCM) affects approximately 1 in 500 individuals in the U.S. and is associated with adverse outcomes conditions such as atrial fibrillation, heart failure, and sudden cardiac death. About 60% of HCM cases result from inherited mutations with the MYH7 gene, encoding β-myosin, the molecular motor of the sarcomere, is the second most common mutational hotspot. Despite advancements in genetic sequencing, connecting specific mutations to clinical outcomes remains challenging, with many identified variants having unknown significance. Current therapeutic strategies for HCM are geared towards managing symptoms rather than targeting the underlying molecular causes of the disease. Our goal is to elucidate how HCM mutations in myosin lead to alterations at the protein and contractile organelle level, and how these alterations manifest at the cell and tissue level in order to find direct, druggable targets for more effective therapies.In the first body of work, we collaborated with academic groups from Stanford, UC Santa Barbara, Institut Curie, and others at UW to form a multi-institutional, multidisciplinary group to study how single missense mutations in myosin lead to HCM. We collaborated with the Allen Institute for Cell Science to engineer a CRISPR/Cas9-edited human induced pluripotent stem cell line (hiPSC) with the MYH7 G256E mutation as a HCM disease model. We (the Regnier Lab) focused on the contractile organelle, or myofibril, and molecular scale. In myofibrils isolated from hiPSC-cardiomyocytes (CMs), we saw greater and faster force development accompanied by impairment of the slow, early phase of relaxation. In molecular dynamics (MD) simulations of post-rigor human cardiac myosin (M.ATP), the G256E mutation caused instability in the transducer region, possibly altering signal transduction from the nucleotide pocket and the actin-binding cleft. Altogether, these data suggest that G256E leads to hypercontractility through impairment of relaxation resulting in a greater population of force generating myosin.
The second body of work addresses how the G256E mutation alters ADP nucleotide handling. We challenged isolated hiPSC-CM myofibrils with elevated ADP to assess their response to product inhibition. G256E myofibrils demonstrated reduced sensitivity to ADP inhibition of the slow, early phase of relaxation, suggesting that ADP release is already impaired by the G256E mutation. In MD simulations of post-powerstroke myosin (A.M.ADP), we saw changes in ADP coordination in the nucleotide pocket due to the G256E mutation. Furthermore, we performed steered MD simulations to determine that G256E myosin requires on average 1.5x as much work to displace ADP compared to WT due to alterations in the molecular release pathway. This finding was validated through stopped-flow biochemistry with an observed 25% increase in ADP affinity with G256E sS1 compared to WT.
In summary, by combining experimental approaches with molecular dynamics simulations, we uncovered the molecular structural alterations resulting from the G256E HCM mutation and related them to functional findings, pinpointing a specific step in the cross-bridge cycle that is impacted by this mutation–ADP release. This work serves as a framework for how identifying the structural basis of disease provides insights into genotype-specific therapeutic targets, paving the way for more precise and effective treatments for HCM
Associations of Social Media Use Frequency with Sadness or Hopelessness among U.S. Adolescents: A Cross-Sectional Analysis of the 2023 Youth Risk Behavior Surveillance System
Get PDFThesis (Master's)--University of Washington, 2025Background: Adolescent mental health problems, including persistent sadness or hopelessness, are major public health concerns in the United States. Recent increases in adolescent mental health problems coincide with the near-ubiquitous adoption of social media. The relationship between social media use frequency and adolescent mental well-being is complex, with varying existing theories, such as the "Goldilocks hypothesis" (suggesting that moderate use is optimal). However, comprehensive evidence is lacking requiring further research that offers empirical validation. Sleep duration, often compromised in adolescents and potentially influenced by social media, is another critical factor for mental health. However, its precise role in the social media-mental health relationship remains underexplored.Objectives: This study aimed to quantify associations of social media use frequency (low, moderate, high) with feelings of sadness or hopelessness among U.S. high school students. It also examined whether sleep duration mediates or modifies the relationship between social media use frequency and sadness or hopelessness.
Methods: A cross-sectional analysis was conducted using data from 12,675 U.S. high school students (grades 9-12) participating in the 2023 Youth Risk Behavior Surveillance System (YRBSS), a study representative of the national population of high school students. The primary exposure was social media use frequency (Low: no use to few times per week; Moderate: about once per day; High: several times per day to >once per hour). The primary outcome was self-reported sadness or hopelessness. Sleep duration was dichotomized (Sufficient: 8-9 hours; Insufficient/Excessive: <8 or ≥10 hours). In complete case analyses, survey-weighted logistic regression models (adjusted for age, sex, race/ethnicity, household adult support and academic performance, with or without sleep duration) were used to estimate adjusted odds ratios (AORs) and 95% confidence intervals (CIs). Effect modification by sleep duration was assessed using stratified adjusted models and testing sleep duration by social media use frequency interaction terms.
Results: Social media use was reported as low by 6.0 % of students, moderate by 13.8%, and high by 80.2%. Overall, 40% of students reported sadness or hopelessness, and 78.9% reported insufficient or excessive sleep. Compared to moderate social media use, low social media use was not associated with lower or higher odds of sadness or hopelessness (AOR = 0.96; 95% CI: 0.75, 1.23; p = 0.734), while high social media use was significantly associated with higher odds of sadness or hopelessness (AOR 1.62; 95% CI 1.29-2.05; p < 0.001). In stratified analyses, among students with sufficient sleep (8-9 hours), neither low social media use (AOR = 0.69; 95% CI: 0.34, 1.39; p = 0.290) nor high social media use (AOR = 1.67; 95% CI: 0.93, 3.00; p = 0.082) were significantly associated with sadness or hopelessness compared to moderate social media use. Among students with insufficient or excessive sleep (<8 or ≥10 hours), high social media use was significantly associated with increased odds of sadness or hopelessness (AOR = 1.60; 95% CI: 1.24, 2.08; p < 0.001) while low social media use was not associated with sadness or hopelessness (AOR = 1.02; 95% CI: 0.74, 1.40; p = 0.911). The interaction term between social media use and sleep duration was not statistically significant (p = 0.193).
Conclusion: High social media use frequency is significantly associated with increased odds of persistent sadness or hopelessness among U.S. adolescents. The results suggest that public health interventions should focus on reducing high social media use and promoting sufficient sleep hygiene. Longitudinal studies with more nuanced measures of digital engagement and mental health measures are needed to establish causal relationships and inform evidence-based interventions.
Keywords: adolescent mental health, social media, sleep duration, YRBS, sadness, hopelessnes
CD90 as a Target for In Vivo Gene and Cell Therapies
No full textThesis (Ph.D.)--University of Washington, 2025Hematopoiesis occurs through a hierarchical process of differentiation beginning with multipotent self-renewing hematopoietic stem cells (HSCs) that persist throughout the human lifespan and ending with transient terminally differentiated unipotent cells. This progression allows for a malleable hematopoietic system capable of responding to pathological and physical assaults to the body. The hierarchical differentiation of the hematopoietic system can be exploited in the clinic for benign and malignant hematology. A patients' pathological hematopoietic system can be cured by replacing their HSCs with new HSCs from a healthy donor (i.e. allogeneic transplantation). Furthermore, the advent of gene modification therapies allows for pathological mutations in a patient's HSCs to be treated by gene therapy and re-transfused into patients (i.e autologous transplantation). The best examples of this therapeutic platform being the treatment of hemoglobinopathies. The reliance of current ex vivo HSC gene therapy approaches on prolonged in vitro culture time, toxic myeloablative conditioning regimens to remove unmodified HSCs, and highly specialized infrastructure severely impact the accessibility of these therapie. Therefore, novel HSC-specific gene therapy platforms with simplified manufacturing protocols for in vivo applications are needed. Previous work refined the target for HSC gene therapy and identified a phenotypically defined subset of cells (CD34+CD90+) that is exclusively responsible for rapid recovery onset, robust long-term multilineage engraftment, and entire reconstitution of the bone marrow (BM) stem cell compartment. My long-term goal is to target this refined HSC subset ex vivo and in vivo for gene and cellular therapies. In this dissertation I develop CD90-recognizing chimeric molecules successfully engineered them onto viral vectors. First proof-of-concept studies in vitro showed that CD90-targeted lentiviral vectors (CD90-LVs) delivered their cargo, with high specificity, into CD90+ cell lines and primary human CD34+CD90+ HSCs. Encouraged by these results, we applied and evaluated the safety of CD90-targeted viral vectors for in vivo applications to transduce and edit human HSCs.
Sufficient conditioning with low-dose chemotherapeutics before transplantation of gene-edited HSC is crucial for efficient ex vivo HSC gene therapy. This pretreatment, while genotoxic, enables the engraftment of gene-therapy cell products at therapeutic chimeric thresholds. In vivo gene therapy does not require pre-conditioning. Therefore, targeted, nongenotoxic selection platforms post-vector delivery can be leveraged to increase gene marked/corrected chimerism. Targeting the CD34+CD90+ HSC pool for these enrichment strategies would significantly improve gene modified chimerism. I hypothesized that using CD90-targeted gene and cellular therapies in vivo will not only efficiently deliver gene therapeutics to human HSCs with high target specificity, but gene-marked/edited HSCs can subsequently repopulate the hematopoietic hierarchy after anti-CD90-targeted selection (i.e., chimeric antigen receptor (CAR) T cells).
In summary, CD90-LVs can be easily implemented into currently existing ex vivo HSC gene therapy approaches to replace the utilization of cytotoxic chemical transduction enhancers and increase the efficiency of HSC delivery. Furthermore, the ability to target LVs to HSCs in vivo will significantly enhance the accessibility of HSC gene therapy in areas with limited biomedical infrastructure. Additionally, protecting HSCs from CD90-targeted immunotherapies and discovering CD90-dependent signaling pathways will minimize off-target toxicities of potential therapies and open a new category of anti-tumor targets in the clinic. This strategy could also enrich gene-edited HSCs to treat sickle cell disease (SCD)
Bayesian Vector Flow Mapping (B-VFM)
Get PDFThesis (Ph.D.)--University of Washington, 2025Cardiovascular disease, the leading cause of death in the United States, underscores the need for improved diagnostic imaging tools. While current clinical assessments of heart function rely primarily on global metrics such as ejection fraction, chamber pressure, and flow rate, regional flow imaging offers complementary insight. Intracardiac flow properties such as vortex formation and blood residence time reveal physiologic patterns that are not captured by global measures alone and can provide predictive information on pathological remodeling and thrombus risk. Echocardiography, a non-invasive, non-ionizing, portable, and relatively inexpensive modality, is widely used in clinical practice. Color-Doppler echocardiography, in particular, provides flow information along the ultrasound beam direction and serves as the foundation for vector flow mapping (VFM), a technique to reconstruct two-dimensional velocity fields in the left ventricle. However, existing VFM methods remain limited: they are highly sensitive to noise, rely on heuristic hyperparameter selection, and treat reconstruction as a deterministic problem without quantifying measurement uncertainty. These limitations hinder the reliability of VFM in challenging clinical conditions where imaging data are imperfect. This thesis introduces Bayesian Vector Flow Mapping (B-VFM), a hierarchical probabilistic framework for reconstructing intracardiac velocity fields from color-Doppler data while explicitly modeling uncertainty. First, we perform a theoretical error analysis of ultrasound acquisition to characterize sources of variability in Doppler measurements and segmentation. These uncertainties are then propagated through the B-VFM formulation, which model priors as Gaussian distributions. Unlike traditional approaches, B-VFM optimizes for hyperparameters and outputs both velocity fields using a probabilistic approach, taking into account local measurement uncertainties. Validation on synthetic cardiovascular flows demonstrates reduced reconstruction errors compared to state-of-the-art, 'vanilla', VFM, and a patient case study highlights the potential of using reconstructed methods with their error maps for further patient analysis. Finally, we discuss the extensibility of this general Bayesian framework, including integration of multimodal imaging, incorporation of more complex priors, and future applications to three-dimensional flow reconstruction. Collectively, this work establishes a principled foundation for uncertainty-aware flow imaging, with the potential to enhance the clinical value of echocardiographic diagnostics in cardiovascular disease
Effects of Atolls on the Distribution and Composition of Suspended Particles in the Western Tropical Pacific
No full textAtolls are unique geomorphic structures that influence suspended sediment dynamics and microbial communities through their interaction with wave energy, currents, and limited landmass. These factors create localized patterns of suspended particles, yet their impacts on microbial activity and nutrient cycling remain poorly understood. This study investigates how atolls influence suspended sediments' vertical distribution and composition by examining differences in organic and inorganic particle concentrations at varying depths. I hypothesize that atolls promote the accumulation of fine organic matter and microbial activity near the reef, while larger inorganic sediments dominate farther away due to hydrodynamic forces. Fieldwork was conducted near Nam2 Atoll in the western Pacific, where transmissometer data, Conductivity, Temperature, and Depth (CTD) profiles, and water samples were collected across different locations. The composition of suspended particles was determined through microscopy while the proportion between organic and inorganic materials was quantified by the Loss on Ignition (LOI) method. Results indicate a significant decrease in the organic-to-inorganic ratio with depth, with the highest organic concentrations occurring in surface waters (20–100 m) and inorganic sediments increasing at deeper layers (>500 m). Statistical analysis revealed a T-statistic was
0.4129 while the p-value was 0.6889 however, showed no significant difference in organic-inorganic ratios between shallow and deep samples. The observed organic-to-inorganic
ratio trends help clarify how organic carbon is stored and transported in marine environments which can inform climate models and global carbon budget estimates
Improving the Methodology and Instrumentation for On-Scalp Magnetoencephalography (MEG)
No full textThesis (Ph.D.)--University of Washington, 2025The recent implementation of novel on-scalp magnetoencephalography (MEG) sensors, specifically optically pumped magnetometers (OPM), has brought about exciting prospects for more precise measurements of natural human brain activity. In order to leverage the full potential of on-scalp systems, certain challenges must be overcome, requiring improvements in both the methodology and instrumentation of these MEG systems. First, traditional signal space separation (SSS) methods for isolating the nano-Tesla (nT) magnetic fields generated form neuronal activity fail when the MEG sensors are on the scalp, as opposed to elevated above the head in a liquid helium Dewar as with traditional, cryogenic MEG systems made of Superconducting Quantum Interference Devices (SQUID). Next, due to the increased proximity of sensors to the brain, on-scalp systems can in principle capture higher spatial frequencies of magnetic signal topology, but current inverse methods may fail with the increased noise that comes with higher frequency components. Finally, the OPM sensors themselves are more sensitive to low-frequency and DC fields than SQUID MEG systems, so new hardware and magnetic field compensation techniques are needed to reduce the remnant magnetic field around the sensor systems. In this dissertation, we first present the novel multi-SSS (mSSS) method, a straightforward mathematical adaptation to the SSS method to account for the on-scalp sensor geometry with various OPM systems. Next, we explore the applications of a matrix regularization method, Foster's Inverse, on SSS to reduce the detrimental impacts of sensor noise on the reconstruction of the internal brain activity, specifically when focusing on higher order components of the magnetic field. Finally, we discuss challenges and current solutions for reducing the remnant magnetic field in the presence of OPM sensors low enough for desired operation and present the coil compensation system designed for use at the Institute for Learning and Brain Sciences (I-LABS) MEG Center, University of Washington. All three of these projects culminate to an advancement of the methodology and instrumentation needed for successful studies of human brain activity with on-scalp MEG systems
Structure-Based Prediction and Design of Adaptive Immune Receptors Targeting Peptide–MHC Complexes
No full textThesis (Ph.D.)--University of Washington, 2025Adaptive immune recognition depends on the specific interaction between peptides presented by major histocompatibility complexes (MHCs) and the receptors that survey them. Advances in protein structure prediction and design now allow us to computationally model these interactions with unprecedented fidelity and engineer them with remarkably higher success rates. In this dissertation, we develop two complementary structure-based deep learning approaches: one for predicting peptide–MHC specificity by fine-tuning structure prediction networks directly on binding data, and another for designing de novo T cell receptors and TCR-mimic antibodies that recognize peptide–MHC targets with high accuracy. Together, these methods illustrate how integrating structurally-informed deep learning protein design and structure prediction frameworks enable both robust generalization and precise molecular engineering. These tools lay the foundation for programmable, therapeutic recognition of diseased cells and expand our understanding of adaptive immune specificity