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Amendment: Literary and Art Journal (2025)
Get PDFAmendment is a VCU student-produced progressive literature and art journal that provides a platform for students to promote equality, tolerance, and social progression through artistic expression.https://scholarscompass.vcu.edu/amendment/1023/thumbnail.jp
From Student to Teacher: LGBTQ+ Pre-service Teachers’ Physical Education Experiences
Get PDFThe first phase of an explanatory sequential design explored Lesbian, Gay, Bisexual, Transgender, Queer or Questioning (LGBTQ+) pre-service teachers’ retrospective experiences from primary and secondary school (K-12) physical education, as well as their physical education experiences in one post-secondary institution. Ahmed’s (2006; 2010) interpretation of orientation was invoked as a conceptual consideration. Quantitative results from the cross-sectional online survey indicated that K-12 physical education remains an unsafe and uncomfortable learning space for many LGBTQ+ students for a myriad of reasons. Conventional qualitative content analysis generated four categories about K-12 class pedagogy and structure, gender and sexuality, change rooms, and the role of teachers and peers. Fortunately, experiences in one post-secondary physical education program offers glimmers of hope
MODELING VACCINATION STRATEGIES AND INTERVENTION IMPACTS ON INFECTIOUS DISEASE DYNAMICS
No full textThis dissertation explores how mathematical and statistical modeling can inform vaccination strategies and evaluate the impact of interventions during infectious disease outbreaks. Motivated by the challenges observed during the COVID-19 pandemic, the research integrates compartmental modeling, Bayesian inference, and agent-based simulation to assess policy effectiveness and disease control across varying contexts. The first study develops a SEIRDV model that incorporates government interventions and vaccination rollout to examine COVID-19 dynamics in Qatar. A Bayesian approach is used to estimate parameters and compute time-varying transmission rates. The model captures shifts in transmission following policy changes and quantifies the reduction in deaths attributable to vaccination. Building on this, the second study introduces two compartmental models to account for reinfection dynamics and varying vaccine efficacy. Using a Bayesian framework with the Metropolis-Hastings algorithm, the study compares models through scenario analysis and formal metrics such as Bayes factors and Hellinger distance. Results demonstrate the critical importance of early vaccination in minimizing reinfections and hospital burden. The final study employs an agent-based modeling framework, SAFE-ABM, to simulate structured interactions across families, workplaces, and schools. The model evaluates targeted interventions for essential workers, including mobility restrictions, school closures, and rotational workforce strategies. To support robust uncertainty assessment, we introduce a novel Bayesian uncertainty quantification framework specifically designed for agent-based simulations. This framework systematically captures variation in transmission, recovery, and mortality rates. Findings suggest that rotational workforce policies combined with quarantine measures are most effective in curbing workplace outbreaks and household transmission. These studies provide a comprehensive and methodologically innovative view of how vaccination strategies and targeted interventions can be modeled to support public health decision-making during pandemics
ASSESSING DISABILITY AND HOUSING AFFORDABILITY
No full textThis dissertation investigates the relationship between disability and housing affordability in the United States, using data from the 2023 American Housing Survey (AHS). Grounded in critical disability theory and utilizing the biopsychosocial model, the study examines how disability and disability type interact regarding housing cost burden (measured via housing cost-to-income ratio, or HCIR), while accounting for a range of socioeconomic and demographic variables.
The research questions are (a) Controlling for relevant socioeconomic and demographic characteristics, how and to what extent does disability status shape housing cost burdens for U.S. household members? and (b) To what extent do additional variables, such as the number of disabled individuals in a household, type of disability, number of children, income sources, poverty level, housing tenure, and household demographic factors (e.g., age, race, cohabitation, marital status), mediate, moderate, or otherwise contribute to the relationship between disability status and housing affordability? Specifically, how do these factors interact to explain variations in housing cost burdens for disabled individuals across U.S. households?
The research employs quantitative analyses in SPSS, including multiple linear regression, ANOVA, and ANCOVA to assess the correlation between disability and housing affordability. Findings are mixed as to whether households with disabled members experience significantly higher housing cost burdens than those without, especially when controlling for confounding factors. Notably, households with physical and self-care disabilities faced the highest housing cost burdens. The burden was exacerbated for households with multiple disabled members, those led by disabled women, and those relying on government assistance. These disparities reflect systemic inequalities and reinforce the framing of disability as an important factor to consider in housing policy.
This study contributes to the housing and disability studies literatures by updating prior findings with nationally representative, current data, and by emphasizing the importance of disaggregating disability types in housing policy research. The results underscore the urgent need for targeted and inclusive housing policies that consider the dimensions of disability as aligned with the biopsychosocial model. The study shows that opportunities exist to further research disability and housing affordability, and better target policy. Doing so could serve as an important step toward improving the housing affordability experiences of households containing disabled people
Formulation and Pre-clinical Evaluation of Ocular Drug Delivery Systems
No full textThe eye has multiple protective barriers (tear film, cornea, blood-aqueous barrier, blood-retinal barrier) that limit drug penetration and retention, making targeted delivery to intraocular tissues very difficult. The overarching aim of this dissertation is to develop biodegradable nanoparticle and microparticle-based formulations for sustained drug delivery the anterior and posterior segments of the eye, to overcome the limitations of conventional ocular therapies, such as poor bioavailability, rapid drug clearance, and the need for frequent intraocular injection/administration.
For the first aim, fenofibrate-loaded PLGA microparticles (Feno-MP) were developed with high drug loading (25 wt%) and six-month sustained release suitable for intravitreal injection (Chapter 2). Fenofibrate (FDA-approved low-cost oral drug) is a peroxisome proliferator-activated receptor-α (PPARα) agonist. The lead formulation Feno-MP-F6 maintained therapeutic retinal drug levels for six months in rats and rabbits without toxicity. A 6-month-long therapeutic effects of a single dose of fenofibrate-loaded microparticles (Feno-MP) in both DR and AMD models via a non-VEGF PPARα–dependent mechanism was investigated. Therapeutic efficacy was demonstrated across three disease models: STZ-induced diabetic retinopathy, Vldlr-/- wet-AMD, and Abca4-/-/Rdh8-/- dry-AMD mice. Single injection of Feno-MP restored electroretinogram responses, reduced leukostasis, enhanced blood-retinal barrier function, decreased neovascularization and vascular leakage, and preserved photoreceptor survival with improved mitochondrial function.
For the second aim, A190, a novel non-fibrate PPARα agonist superior to fenofibrate in potency, selectivity and safety, was similarly formulated in biodegradable microparticles (A190-MP) providing six-month retinal drug detection. A190-MP demonstrated therapeutic benefits in both wet- and dry-AMD models, improving electroretinography, preserving cone photoreceptor density and outer nuclear layer thickness, reducing vascular pathology, and enhancing mitochondrial function through PPARα-dependent mechanisms (Chapter 3). In vitro studies confirmed A190\u27s protective effects against oxidative stress in photoreceptor cells, with improved cell viability and mitochondrial function via PPARα activation.
Finally, for the third aim, dexamethasone sodium phosphate was encapsulated in PLGA nanoparticles (PLGA-DSP-NP) using zinc chelation bridging, resulting in 250 nm particles with 6.5 wt% drug loading and two-week sustained release, aimed for drug delivery to the front of the eye. In a nitrogen mustard-induced corneal injury model, single subconjunctival injection of PLGA-DSP-NP significantly outperformed topical drops, reducing corneal neovascularization, ulceration, and opacity by inhibiting inflammatory cytokines, angiogenic factors, and endothelial proliferation over the two-week study period (Chapter 4).
Together, these studies establish the pharmacokinetics, safety and efficacy of sustained-release polymeric microparticles and nanoparticles as viable ocular drug delivery systems for both the front and back of the eye. These formulations offer promising translational potential by minimizing dosing frequency, improving therapeutic outcomes, and enhancing patient adherence in the treatment of sight-threatening ocular disorders
Automated Treatment Planning Framework for Cervical Cancer in Brachytherapy
No full textBrachytherapy (BT) is a critical treatment modality for cervical cancer that delivers ionizing radiation directly to the tumor using intracavitary (IC) and/or interstitial (IS) applicators. In high dose-rate (HDR) BT, a remote afterloader guides the radioactive source to specific dwell positions for defined durations, known as dwell times, allowing for a conformal dose to the tumor while minimizing exposure to surrounding organs at risk (OAR). However, due to the inherent nature of ionizing radiation, some dose to OARs is unavoidable, creating a fundamentally conflicting objective in treatment planning: maximizing tumor coverage while minimizing OAR dose. This characterizes the problem as a multi-criteria optimization (MCO) task, where multiple Pareto-optimal solutions are required to represent clinically relevant trade-offs. The nonconvex nature of optimization based on the dose-volume histogram (DVH) further complicates the process, making it computationally intensive and impractical for routine clinical implementation. In addition, time efficiency is critical, as patients typically remain under anesthesia or experience discomfort during applicator placement and while awaiting finalized treatment plans. To address these challenges, this thesis proposes an automated treatment planning framework that integrates deep learning-based dose prediction with MCO to enable faster and more robust optimization, thus improving clinical workflow, improving plan quality, and supporting high-quality patient-centered care. Current commercial planning algorithms in HDR BT often rely on manual fine-tuning of objective functions and/or dwell times. Inverse planning algorithms typically generate only one treatment plan per optimization run, without guaranteeing the fulfillment of clinical goals in the first attempt. Consequently, planning becomes an iterative and time-consuming process, and the quality of the plan is highly dependent on the experience and skill of the planner. To overcome this limitation, we introduce a knowledge-based planning model (KBP) that predicts D2cc values, detects suboptimal plans, and improves plan quality - particularly relevant for direction-modulated brachytherapy (DMBT), a new applicator technology with limited clinical experience. The KBP-based plan serves as the foundation for a deep learning-based dose distribution prediction model, which is incorporated into the automated framework. This approach facilitates the refinement of suboptimal plans and allows personalized quality control, offering a reliable and accurate tool for independent plan evaluation. Although deep learning-based dose prediction provides a strong starting point, the generated distributions may not be fully optimal. Furthermore, the iterative one plan- per-optimization strategy used in current clinical workflows restricts the ability to explore trade-offs between target coverage and OAR sparing. A significant gap also exists in optimization techniques capable of efficiently handling complex applicators such as DMBT, where determining optimal dwell positions and depths is especially challenging due to the large number of degrees of freedom and variability in patient anatomy. To address these issues, this work integrates a novel parallelized CPU-based MCO algorithm into the planning pipeline. This algorithm enables rapid and automated exploration of the Pareto surface, improving decision-making efficiency and overall treatment quality. The implemented CPU-based MCO algorithm is benchmarked against the standard inverse planning algorithms currently used in the clinic. It features a novel parallel optimization scheme capable of generating thousands of Pareto-optimal plans within seconds. Plan quality is evaluated in comparison to plans produced by the clinical benchmark (BVTPS), and the time required for MCO plan generation is recorded. By integrating the KBP-guided base plan, deep learning-based dose prediction, and the MCO engine, a cohesive automated treatment planning workflow is established. The eight chapters of this thesis present the methods, findings, and scientific contributions across both conventional and advanced DMBT applicators, culminating in a recommended framework for clinical automation of BT planning
CDH3 MEDIATES COLLECTIVE CELL MIGRATION MECHANISMS IN EPITHELIAL MORPHOGENESIS
No full textDuring epithelial morphogenesis, in vivo, epithelial cells form cysts enclosing a single, hollow lumen and extend protrusions as a precursor for tubulogenesis. Cell-cell adhesions (e.g., cadherins) contribute to successful execution of these processes; while there are many different cadherins, one less studied cadherin in epithelial morphogenesis is P-cadherin (CDH3). Here, we investigated the role of CDH3 on successful lumen formation and cell protrusions, using three-dimensional cultures of Madin-Darby canine kidney (MDCK) cells and modifying CDH3 expression. Additionally, we show depletion of CDH3 leads to perturbations of hollow lumen formation, associated with defects in cell protrusions and tubulogenesis. CDH3 knockout cells also exhibited a decrease in migration velocity compared to wild-type cells, suggesting CDH3 acts as a mechanosensor for stable cell protrusion establishment. Finally, we investigate potential migration pathways that may regulate collective cell migration dynamics driving cellular protrusions. Together, the findings in this dissertation suggest CDH3 and YAP signaling have an essential function during epithelial morphogenesis by contributing to successful epithelial sheet migration in 2D; lumen formation, and cell protrusions in 3D
S20, E08: Supreme Court Eras In the Beginning (Aired 10/10/2025)
Get PDFIn a new series, Aughie and Nia are exploring the United States Supreme Court Eras, usually defined by the name of the Chief Justice of the era. In this first episode the first eleven years of the Court (Chiefs Jay, Rutledge, and Ellsworth) are covered as a group.https://scholarscompass.vcu.edu/civil_discourse/1288/thumbnail.jp
S20, E06: Can He Do That? Firing an Independent Regulator (Aired 10/06/2025)
Get PDFAughie and Nia discuss the cases that support the SCOTUS decision to allow the firing of Lisa Slaughter to stand. Slaughter is a member of an independent regulatory agency.https://scholarscompass.vcu.edu/civil_discourse/1287/thumbnail.jp