Penalized for Precarious Support: A Luck Egalitarian Critique of the Social Support Criterion Used in Determining Organ Transplant Eligibility

The social support criterion is a significant factor used by U.S. transplant centers to determine whether someone is eligible to be placed on the transplant list. Although intended to fairly allocate scarce donor organs, it exacerbates inequities to transplant accessibility, unfairly excluding nearly one-fifth of individuals seeking transplant listing. Ethical concerns regarding this criterion have already been widely discussed, particularly due to bias in assessing the adequacy of support and inconclusive evidence that a strong social network improves transplant outcomes. However, luck egalitarianism— the view that individuals should not be penalized for circumstances beyond their control—has largely been overlooked in this context. Inadequate social support is a circumstance largely shaped by mental health conditions, complicated familial dynamics, geographic isolation, and socioeconomic status. Excluding candidates on this basis reinforces structural inequities. This paper begins by demonstrating how luck egalitarianism is already implicitly operating in the transplant system, particularly in the case of Alcohol-Related End Stage Liver Disease, and should be extended to evaluate the social support criterion. After establishing its relevance, the concept of luck egalitarianism is then applied to critique the use of the social support criterion, drawing on official OPTN guidelines and ethical principles. Finally, this paper addresses the objection that removing this criterion would necessitate the elimination of all other non-voluntary criteria, such as poor medical status and financial instability. Ultimately, the social support criterion is unjust as it penalizes individuals for circumstances outside of their control without sufficient utilitarian justification for doing so. Transplant centers should instead expand social support programs to help candidates meet necessary standards for listing

Characterization of Recombinant SARS-CoV-2 BA.5 Reporter Gene Viruses

The emergence of SARS-CoV-2 caused a global pandemic, and its continued impact highlight the need to study viral replication dynamics and immune escape mechanisms. Bacterial artificial chromosomes (BAC) are a well-established platform to generate SARS-CoV-2 infectious cDNA clones, providing a powerful tool to investigate functional mutations that trigger adaptive phenotypic changes, determine virus replication kinetics, and screen compounds for antiviral activity. In this study, I developed and characterized an infectious cDNA clone to rescue recombinant SARS-CoV-2 Omicron BA.5 viruses engineered to express either a fluorescent (mCherry) or chemiluminescent (Nluc) reporter gene. Using a BAC-based approach, I inserted the reporter genes as an independent transcriptional unit upstream of the N gene to preserve viral integrity and ensure stable expression during infection. The rescued reporter viruses replicated efficiently in VeroE6/TMPRSS2 cells and produced strong, quantifiable reporter gene signals that correlated well with viral spread and cytopathic effects. Interestingly, we observed temperature-dependent replication differences between the two constructs, with rBA.5 Nluc performing better at 37°C and rBA.5 mCherry showing greater efficiency at 33°C. Both reporter viruses exhibited slightly faster replication kinetics compared to the wild-type (WT) isolate, likely due to adaptation during the cloning or rescuing process. Importantly, neutralization assays using human sera demonstrated the utility of these constructs for antiviral screening. I also generated a Nluc encoding recombinant SARS-CoV-2 in the Mu variant background which will be used to investigate features of long COVID in a mouse model system. This recombinant infectious clone system could provide a flexible and powerful tool for studying SARS-CoV-2 replication and pathogenesis, allowing rapid testing of antiviral agents and vaccines in a high-throughput format

Characterizing the Temperature-Dependent Infection of Respiratory Syncytial Virus

Respiratory syncytial virus (RSV) is a leading cause of seasonal respiratory illness in the United States and a major contributor to lower respiratory tract infections (LRTIs) in infants and older adults. A defining characteristic of RSV infection is the formation of syncytia (giant multinucleated cells) mediated by the virus fusion (F) protein. While upper respiratory tract infections are typically mild, progression to the lower airways is often associated with more severe disease. However, the mechanisms underlying this disparity remain incompletely understood. We hypothesized that the physiological temperature gradient along the respiratory tract (33 °C in the upper airways and 37 °C in the lower airways) modulates RSV replication dynamics and cytopathic effects. To test this, we infected Vero cells with laboratory-adapted RSV A2 and B1 strains and compared basic aspects of virus replication at 33 °C and 37 °C, analyzing viral growth kinetics, plaque morphology, and syncytia formation using growth curve assays, plaque assays, and immunofluorescence microscopy. Our findings indicate that the higher respiratory tract temperature (37 °C) enhances in vitro RSV infection compared to 33 °C. This was evidenced by more rapid syncytium formation, increased cytopathic effects, larger plaques and syncytia, and broader viral antigen distribution, particularly for the A2 strain. These results suggest that temperature is a key modulator of RSV replication

Analysis of Borrelia burgdorferi derived extracellular vesicles from bacterial cultures and plasma

Lyme Disease is the most common tickborne disease in the United States with over 63,000 cases reported in 2022. However, the actual number of cases of Borrelia burgdorferi (Bb) is estimated to be ten-fold higher. Post-Treatment Lyme Disease Syndrome (PTLDS) is a condition where a patient experiences chronic symptoms despite being treated with antibiotics. There is controversy around whether PTLDS is caused by a persistent bacterial infection or post-infection auto-immune reactivity. Some evidence suggests the failure of symptoms to resolve with more aggressive antibiotic therapy could be due to antibiotic tolerance of persistent bacteria. This persistence phenotype is induced and maintained by the bacterial stringent response pathway. We hypothesize that mRNA transcripts encoding stringent response proteins can be isolated from bacteria-derived microvesicles from the serum of mice infected with Bb. Bb extracellular vesicle (BbEV) isolation from bacterial cultures and mouse plasma was conducted using size- exclusion chromatography or ultracentrifugation. It was followed by a density gradient to separate the BbEV from mouse-derived EVs. The BbEVs were tested by performing a RNA extraction, RT-PCR, and analyzing fractions on the Zetaview particle analyzer. We have been able to confirm the ability to extract extracellular vesicles from bacterial cultures and are continuing to optimize the assays to detect BbEVs in Bb-infected mouse plasma. In conclusion, while the controversy of the mechanism of PTLDS is still debated, this study is laying the groundwork for utilizing BbEVs in plasma for detection of genes representative of bacterial infection and potentially of pathways promoting persistence that could lead to PTLDS

Risk-Adjusted Time-to-Event Modeling: Integrating Absolute Risk and Biomarker-Driven Splitting

Alzheimer’s Disease (AD) progression exhibits substantial heterogeneity, complicating risk stratification using biomarkers and risk factors during the critical preclinical phase. Standard models often struggle with interactions and confounding. We developed and evaluated the Personalized Risk-Adjusted Survival Tree (PRAST), using risk-adjusted recursive partitioning to identify interpretable, biomarker-defined prognostic subgroups in longitudinal AD data. PRAST adjusts for risk factors via node-specific Cox models, generating a transformed outcome Y ∗ to isolate biomarker effects. Tree splits minimize Y ∗ ariance based on biomarkers, with complexity controlled by pre-stopping rules. Simulations versus benchmarks (CART, CTree, Cox) confirmed PRAST’s effectiveness. It excelled at subgroup recovery (high Adjusted Rand Index) and showed good noise resistance versus CART, validating its risk-adjustment mechanism for capturing biomarker-driven heterogeneity. Standard metrics like C-index require suitable risk scores derived from the PRAST structure. Application to the PAC dataset (n = 1136) yielded four distinct prognostic subgroups via interpretable splits (hippocampus, SPARE-AD, memory) after risk adjustment. These biomarker-defined subgroups showed significantly different cognitive decline trajectories (p < 0.0001), demonstrating PRAST’s utility for revealing meaningful heterogeneity. This stratification offers insights into diverse AD pathways and suggests potential for clinical applications like targeted monitoring or trial enrichment in early AD

DEVELOPMENT OF METHODS FOR THE EVALUATION OF CELL-FREE DNA IN INDIVIDUALS WITH CANCER, AUTOIMMUNE, OR VASCULAR DISEASES

Artificial Intelligence (AI) is now a cornerstone of modern dataset analysis. However, quantification of the confidence of AI-based predictions is required in many real-world applications, such as biomedical assay development. My thesis focused on the development of a strategy called MIGHT, which I prove is guaranteed to quantify uncertainty and confidence given sufficient data. The key insight was that it is possible to integrate canonical cross-validation and parametric calibration procedures within a non-parametric ensemble method. Simulations demonstrate that while typical AI based-approaches cannot be trusted to obtain the truth, MIGHT can be. I applied MIGHT to answer an open question in liquid biopsies using cell-free DNA in individuals with or without cancer: which biomarkers, or combinations thereof, can we trust? Surprisingly, we find that combinations of variable sets often decrease rather than increase sensitivity over the optimal single variable set - because some variable sets add more noise than signal. Our work demonstrates the importance of quantifying uncertainty and confidence — with theoretical guarantees — for the interpretation of real-world data. We next apply MIGHT to the analysis of cell-free DNA and circulating proteins in individuals that are of high-risk for being diagnosed with cancer. We performed shallow whole-genome sequencing (~1x) on the cell-free DNA of 1,110 plasma samples from 1,051 individuals with that were diagnosed with no disease, autoimmune disease, vascular disease, or cancer. We developed a comprehensive metric, called fragmentation signatures, that integrated the distributions of fragment positioning, fragment length, and fragment end-motifs. Using this metric, we found that individuals with venous thromboembolism, systemic lupus erythematosus, dermatomyositis, or scleroderma had cfDNA fragmentation signatures that closely mimicked those found in individuals with advanced cancers. Furthermore, these signatures were highly correlated with increases in inflammatory markers in the blood. Though these data put substantial limitations on the specificity of fragmentomics-based tests for cancer diagnostics, they also offer ways to improve the interpretability of such tests. Moreover, they should lead to a better understanding of the cells – most likely inflammatory cells - from which plasma cfDNA is derived. Here, I will describe findings that initiated serendipitously demonstrating these patterns are not specific to cancer patients and can arise in the absence of any neoplastic cells

REGENERATIVE BIONICS: INCORPORATING REINNERVATED MUSCLE INTO BIONIC SYSTEMS FOR PROSTHESIS CONTROL

Within the field of neuroprosthetics, there is a growing use of reinnervated muscle as a component in bionic systems for prosthesis control. Its popularity is due mainly to the pain relief benefits of muscle reinnervation, but it also offers the bio-amplification of nerve signals if the reinnervated muscle can be recorded from long-term. There are many varieties of bionic interfaces available for normal muscle tissue, but there does not yet exist an interface designed based on the unique physiology of muscle tissue that has had to regenerate and recover from major surgical intervention. Or from a different perspective, is it possible to optimize the surgical construction of a denervated muscle target (DMT) such that it is guaranteed to generate the most effective control signals? After performing a pilot study of how differently sized DMTs might function after reinnervation, I developed and validated a versatile rat model of reinnervation using the soleus muscle and tibial nerve. This model is improved compared to the most commonly used model in the DMT field (which transects the peroneal nerve) because it is not debilitating to the animal. Using my model, I studied how completely devascularized DMTs regenerate their vasculature over time. This was motivated by the apparent gap in the scientific literature describing the 3D vascular regeneration patterns of muscle autografts. Finally, I studied how the same DMTs revascularize when there is a surface electrode present, which surprisingly showed little difference from DMTs with no electrode substrate. In summary, Part 1 of this thesis focuses on the electrophysiology of DMTs and how they could be constructed to maximize signal potential, and Part 2 focuses on the vasophysiology of DMTs healing immediately post surgery and how certain bionic hardware might influence regeneration. Future research that builds off of my work should focus on the marriage of 3D tissue imaging data with chronic electromyography data. Further understanding the relationship between regenerating tissue structure and its long-term function will allow us to build new models that could predict how different DMTs will behave over time based on their unique anatomy

Investigation of Tissue Remodeling in Chronic Obstructive Pulmonary Disease

Chronic obstructive pulmonary disease (COPD) is a debilitating chronic disease of the lungs that impacts millions of people worldwide. Cigarette smoking is one of the leading risk factors for developing COPD, but chronic exposure to other ambient air pollutants is also associated with disease development. The lungs of patients with COPD undergo considerable tissue remodeling as the disease progresses leading to airway obstruction and difficulty breathing. Although COPD is a pressing and well described health risk, there are currently no disease modifying treatments that can reverse COPD associated tissue remodeling to restore optimal lung function. Innovative research is necessary to aid in the understanding of the processes driving tissue remodeling in COPD. In this thesis we explore two distinct angles of lung biology and disease to help enhance our understanding of COPD. First, we used precision-cut lung slices (PCLS) exposed to cigarette smoke ex vivo to effectively model COPD. Our findings revealed that repeated, physiologically relevant doses of cigarette smoke mimic the tissue remodeling seen in COPD, offering a scalable and more time-efficient alternative to traditional in vivo models while maintaining the complex 3D architecture and cell-cell relationships absent in in vitro models. Second, we investigate the role of planar cell polarity in the process of epithelial wound closure using primary bronchial epithelial cells derived from COPD patients. Understanding the mechanisms of planar cell polarity can provide valuable insights into epithelial integrity and repair in the context of lung disease. Using primary bronchial epithelial cells from COPD patients, we identified mislocalization of the core planar cell polarity protein, Vangl1, and a significant reduction in the protein ARFRP1, which is known to target Vangl1 to the cell membrane. We propose that loss of ARFRP1 is a key driver in COPD associated epithelial disruption by limiting epithelial cell’s ability to sense their surroundings and migrate towards a wound

The relationship between the use of virtual communication and the mental health of caregivers of older adults

Use of virtual communication technologies, such as phone calls, video calls, and text messaging between caregivers, older adult care recipients, and healthcare providers increased during the COVID-19 pandemic. However, it remains unclear how use of virtual communication relates to caregiver mental health. Guided by the Source, Message, Channel, Outcome Model of Communication, we conducted a systematic review and cross-sectional analysis of a nationally representative dataset to explore how virtual communication patterns between caregivers, care recipients, and healthcare providers are associated with caregiver mental health outcomes. First, we systematically reviewed 21 studies on virtual communication and mental health among family members of ICU patients. Phone and video calls were the most common forms of communication, primarily between family members and ICU clinicians. Mental health outcomes, including anxiety, depression, stress, and PTSD, either improved or remained stable following virtual communication. Qualitative meta-synthesis revealed two key themes: (1) anxiety related to communication uncertainty and (2) mixed emotional responses to video calls. Next, we analyzed national cross-sectional data from the National Study of Caregiving and National Health and Aging Trends Study. First, we examined communication frequency and modality among non-cohabitating caregivers and care recipients with and without dementia. Care recipients with dementia used internet-based communication (e.g., Facebook, Zoom) less frequently than care recipients without dementia. Among dyads, phone calls were the most common modality, emailing, text messaging and video calls were less commonly used. Caregiver/care recipient dyads with dementia were more likely to use video calls. Communication modality was not associated with caregiver anxiety or depression, but high communication frequency was associated to twice the odds of anxiety. Additionally, we analyzed communication frequency and perceived helpfulness between caregivers and healthcare providers. High communication frequency and high perceived helpfulness were both associated with twice the odds of anxiety among caregivers, but not depression. Overall, this research underscores the complex relationship between virtual communication and caregiver mental health. Frequent, clear, and preferred-modality communication may help support caregiver well-being. These findings are especially relevant as virtual communication remains a vital tool for caregivers who cannot always be physically present for their care recipient

Development of Neuronal Connectivity in the Murine Neocortex

In this thesis, I explore a fundamental question in developmental neurobiology: how is ordered neuronal connectivity established in the brain? I use the murine somatosensory cortex as a model to first explore the coordinated emergence of neuronal subtype-diversity and spatial organization. Then I address the molecular and cellular mechanisms underlying the development of cortical neuron-subtype specific axonal projection patterns. In the cerebral cortex, glutamatergic projection neurons are organized into layers based on their time of birth, as well as into areal domains in the plane perpendicular to the radial axis. In Chapter 2, I describe how the transcription factor Mef2c controls the acquisition of laminar and areal identities in post-mitotic cortical neurons during embryonic development. Chapter 3 focuses on postnatal functions of Mef2c, identifying it as one of the first regulators of long-range intracortical axonal projection targeting. I then describe functional manipulations that demonstrate a role for EphA-EphrinA signaling downstream of Mef2c in mediating homotopic targeting of callosal projections. These observations offer a first glimpse into the molecular logic of interhemispheric projection targeting in the mammalian neocortex. Chapters 4 and 5 focus on a critical aspect of specific intracortical connectivity: the formation of neuron-type specific patterns of intracortical axon collateral arbors. In Chapter 4, I introduce inducible sparse-labeling and genetic manipulation strategies developed in the Kolodkin laboratory to target developing neurons in cortical Layer 2/3, permitting the quantitative analysis of axonal collateral arbors at single neuron resolution. I then discuss their utility in uncovering novel cytoskeletal and cell-surface determinants of laminar specific Layer 2/3 cortical neuron axon branching. In Chapter 5, I detail how sparse-labeling of Layer 6 corticothalamic neurons, combined with brain clearing and lightsheet microscopy, reveals the developmental dynamics of intracortical arbor elaboration by this understudied neuron subtype. This work sets the stage for future studies of molecular mechanisms that dictate divergent patterns of axonal elaboration, within the same target region, by distinct classes of cortical neurons. My thesis work highlights the invariably pleiotropic nature of key regulators of animal development. This work also underscores the importance of temporally controlled, cell-type specific genetic access towards a comprehensive understanding of gene-function in development