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INVESTIGATING THE PLASMODIUM DOSE-DEPENDENT INHIBITORY ACTION OF LUMEFANTRINE AND TAFENOQUINE
No full textMalaria remains a major global health challenge, with 597,000 annual deaths across 83 countries according to the WHO. The parasite’s ability to detoxify free heme from hemoglobin catabolism into inert hemozoin is essential for its survival, making hemozoin crystallization a critical drug target. This thesis investigates the lumefantrine and tafenoquine inhibitory mechanisms on hemozoin formation, using both biochemical assays and pulse drug assays in NF54 Plasmodium falciparum cell culture to gain insight into the stage-specific windows of action. Hemozoin crystal formation occurs in two steps - crystal nucleation which occurs in late ring stages and crystal extension that occurs in trophozoite stages. Lumefantrine potently inhibits hemozoin crystal extension, IC50 of 8.2μM, but has no measurable effect on nucleation, even at drug concentrations as high as 200μM. Despite this, lumefantrine showed robust stage-independent parasite inhibition, with IC50 values ranging from 1–3.5μM across all intraerythrocytic stages, including early rings where hemoglobin digestion and hemozoin formation are minimal suggesting non-canonical effects beyond hemozoin interference. Tafenoquine inhibited hemozoin nucleation at high in vitro 44.6μM concentrations with no appreciable impact on crystal extension. Tafenoquine also inhibited P. falciparum growth across all blood stages in pulse assays, but its potency was stage-dependent, with IC50 values increasing from 1–5μM in ring stages to 10–20μM in late trophozoites. Typically, a liver stage drug, tafenoquine, requires cytochrome P450 activation in hepatocytes, so its blood stage action may be attributed to a Plasmodium non-mitochondrial target. By exploring hemozoin-targeting mechanisms and the stage-specific effects of tafenoquine and lumefantrine, we can further understand the window of action of these antimalarials and explore potential non-canonical pathways of disruption
Competing risks analysis for a COVID-19 clinical trial
Full text linkIn COVID-19 clinical trials, time to death and time to recovery are both important components to evaluating effectiveness of potential treatments. However, in the presence of competing risks, traditional survival analysis which only considers one event as the outcome of interest may not provide a complete picture of the effect of treatment on patient outcomes. This study applied competing risks analysis to re-examine data from the Adaptive COVID-19 Treatment Trial, evaluating the effect of remdesivir treatment on time to death and time to discharge. We estimated the cumulative incidence functions of time to death and time to discharge, stratified by treatment arm and additionally by ventilation history to understand treatment effect on patients of varying clinical severities. Treatment effect was evaluated using cause-specific proportional hazards and Fine-Gray regression.
Time on ventilation is also an important intermediate outcome, though estimation of cumulative incidence is not available using traditional competing risks methods. By using quality of life-weighted survival, we estimated cumulative incidence functions for time on ventilation.
Our findings show that remdesivir significantly increased cause-specific hazard of recovery (HR: 1.29; 95% CI: 1.12 – 1.49) but did not significantly reduce cause-specific hazard of death (HR: 1.13; 95% CI: 0.76 – 1.38). Under the Fine-Gray model, treatment significantly increased cumulative incidence of recovery (sHR: 1.32; 95% CI: 1.15 – 1.52) but did not significantly impact cumulative incidence of death (sHR: 1.27; 95% CI: 0.98 – 1.48). Cumulative incidence function estimates suggest that treatment was most effective at reducing hospitalization time for patients who recovered without ventilation but did not impact recovery for patients who required ventilation. Remdesivir may have slightly reduced progression to ventilation for those who were eventually discharged but did not impact ventilation duration. Future directions include regression model development for intermediate events and hierarchical analysis to account for clustering within multi-site trials
QUANTIFYING THE EPIDEMIOLOGICAL IMPACT OF INVESTIGATING TUBERCULOSIS OUTBREAKS AMONG PERSONS EXPERIENCING HOMELESSNESS IN THE UNITED STATES
Full text linkThe World Health Organization designates the United States as a country with a low overall burden of tuberculosis, however people experiencing homelessness in the United States still face a disproportionate burden of disease compared to the general population. People experiencing homelessness have a higher prevalence of tuberculosis risk factors such as HIV, diabetes, smoking, and substance use disorders. Residence in homeless shelters and other congregate living environments can also increase tuberculosis transmission risk. This study employed an outbreak response modeling framework to estimate tuberculosis care cascade participation among people experiencing homelessness during outbreaks during 2025-2035. Our results indicate that outbreak investigations could prevent 4,560 tuberculosis cases among people experiencing homelessness over the ten-year prediction period. Raising the cluster size threshold for initiating outbreak investigations decreases the number of cases averted. The most influential factors for tuberculosis prevention in or model were the reproduction number, the percentage of case contacts evaluated, and the number of contacts per case. These findings support interventions that provide individual or small-group housing for people experiencing homelessness, implement universal masking or infectious disease screening protocols, and enhance outbreak response procedures to maximize contact evaluation coverage
ANGLER’S WAY A COLLECTION OF NOVEL CHAPTERS
No full textAngler’s Way is a collection of four novel chapters that follows the lasting friendship of two men, Adam Richards and Colin McCormick, bonded by their experiences in Vietnam and the four decades that followed. Set in post-Vietnam America, the story follows the return of Adam to his hometown of Davisville, Pennsylvania and Colin’s efforts to build a stable family in Prescott, New Jersey. As they near the end of their lives, both men are compelled to reflect upon the choices that shaped their lives and their consequences for themselves and those they love. This work of literary historical fiction explores the themes of personal growth through grief, guilt, resilience and forgiveness
THE RIGHT OF EQUAL ACCESS: HOW PUBLIC ACTION DOCTRINE CONSTITUTIONALIZES THE U.S. CIVIL RIGHTS FRAMEWORK
Full text linkThe way we draw the line between public and private action is vital to the advancement of our collective equality and the survival of the American experiment with democracy, contract and property rights. However, the Court’s nineteenth century “state action” interpretation of the Fourteenth Amendment, which says discrimination is constitutional, has dominated how we draw this line for the past century and a half. This thesis analyzes the history of state action doctrine in U.S. constitutional law and demonstrates that the injustice and instability resulting from its flawed premise and inconsistent application in cases concerning equality precipitates the need for a new approach. To address this problem, this thesis introduces public action theory revealing a path to consistent legislative and judicial interpretation of Equal Protection under the law with a new framework defining what is public and private in our society.
Public action theory is a constitutionally sound reading of the Fourteenth Amendment that combines five legal and legislative precedents into a cohesive, operational whole. The strategy to implement public action doctrine in U.S. congressional and judicial policy includes (1) statutory codification of the English common law right of equal access to public accommodations, (2) grounding of this congressional legislation in the Fourteenth Amendment, (3) coordination of this new legislation with the landmark Americans Disabilities Act, (4) statutory codification of the Supreme Court super precedent set in Shelley v. Kraemer that revised state action doctrine and, (5) a definition of the “state” as a political community of citizens and their government within a geographic boundary.
The public action theory also responds to the Supreme Court’s history of degrading congressional Commerce Clause power in Equal Protection cases and its current aggressive pattern of renovating the architecture of twentieth century U.S. judicial policy by upending precedent on central issues, putting critical civil rights precedents at risk for reversal. This paper demonstrates how public action secures the individual right to access public accommodations and other parts of political, social and economic life free from arbitrary discrimination, holding the potential to stabilize the U.S. civil rights framework
Process Analytical Technology Guided Scale Up of a Quench Crystallization Process with Focus on Impurity Minimization
Full text linkThe scale up of small molecule manufacturing processes presents many challenges due to the need for precise control over unit operations responsible for key properties that impact drug bioavailability, efficacy, and safety. Crystallization is critical since it directly affects purity, particle size, and form of the final product. Filtration and drying further influence physical crystal properties, batch processing times, yield, purity, and quality. To address process knowledge needs for scale up, process engineers have implemented process analytical technology (PAT) for in-line data analysis. This essay establishes the use of PAT in the development of a small molecule API for HIV treatment. Experiment goals included gathering information on impurity formation, nucleation, crystal growth, yield loss, and solvent removal through nitrogen blowdown and heated drying. Over several experiments the entire process was tracked by PAT. Results for a 40-50g scale run in a 500mL reaction vessel showed plate shaped crystals that nucleate after approximately 15% dosing of the reaction mixture to the quench solution, easy filtrations with low yield losses (0.4 - 1.11%), efficient blowdown in 4-6 hours, and final methanol and water contents of 0.03 weight percent (wt%) and 0.01wt% respectively. Bulk density and cake compressibility data gained during these experiments indicate that the large-scale pilot plant will require three filter dryer drops for one batch.
Concerns noticeable in these experiments included substantial amounts of encrustation in the crystallization vessel, low internal cake temperatures during vacuum drying, foaming during filtration, and an impurity present throughout the process that grows during drying. Several experiments focused on understanding this impurity, including determining that methanol is the source of impurity growth. Spiking studies indicated that higher drying temperatures and lower concentrations both correlate positively with impurity formation. This poses a potential issue at the industrial plant, which has lower efficiency in blowdown leading to more optimal conditions for impurity growth. To better understand this risk, along with encrustation and mixing risks, the process will be scaled up to 200g in a 2L vessel. Findings from this 200g scale up will be used to further inform a scheduled pilot plant run at the 250kg scale
Characterization of 2023-2024 H3N2 influenza viruses
Full text linkDuring the 2023-2024 flu season in the Northern Hemisphere, the influenza A virus (IAV) H3N2 dominated from Feb 2024 to May 2024. Seasonal IAV can accumulate mutations on antigenic sites of the hemagglutinin (HA) protein, leading to escape from preexisting immunity and launching annual epidemics. Therefore, surveillance of emerging IAV clades is important for
identifying and understanding the viral adaptations that allow for escape from vaccine induced immunity. The H3N2 subclades found during the 2023-2024 flu season are J.2, J.1, J, and G.2
with J.2 being dominant. A J.2 subclade H3N2 virus
(A/Baltimore/JH23641/2024) was isolated from an infected person and its amino acid sequence was compared with the H3 vaccine strain
(A/Darwin/9/2021-subclade G.2). I used RT-PCR to extract both HA sequences and align them together. There are fifteen mutations between the H3 vaccine and seasonal strains, with three located at antigenic sites. Mutations include I156K, M184I, and N202D, which could be important factors facilitating the H3N2 subclade J.2 dominance in circulation.
To assess the escape from vaccine induced immunity, neutralization assays were performed on sera acquired from healthcare workers pre- and post-vaccination during the 2023-
2024 season. Post-vaccination sera have a higher neutralization titer against both vaccine and circulating H3N2 strains, indicating three mutations on HA antigenic sites do not adversely affect neutralizing antibody titers. We also investigated H3 seasonal virus replication kinetics using low multiplicity of infection (MOI) growth curves in human Nasal Epithelial Cells (hNECs) cultures
under different physiological temperatures. The physiological temperatures did not affect total
infectious virus production but did change the kinetics, with less virus being produced early after
infection and more virus produced late after infection at 33C compared to 37C. The 2023-24 H3N2 J.2 subclade doesn’t have antigenic drift compared with the vaccine strain and no specific
viral replication changes under different physiological temperatures
AUTONOMOUS ROBOTIC SOLUTIONS FOR DIAGNOSTIC AND INTERVENTIONAL TRAUMA APPLICATIONS
No full textUnintentional trauma remains a leading cause of death in the United States, with up to 29% of pre-hospital trauma deaths deemed potentially preventable through timely hemorrhage control. Significant advancements have been made in trauma diagnostics and interventions, including the Focused Assessment with Sonography for Trauma (FAST) examination, which has become an effective point-of-care imaging method for rapidly detecting life-threatening injuries and intra-abdominal free fluid without the use of radiation. Similarly, the Resuscitative Endovascular Balloon Occlusion of the Aorta (REBOA) offers a minimally invasive approach to controlling non-compressible abdominal hemorrhage via femoral artery access, helping maintain vital organ perfusion and improving patient survival chances. However, despite the proven efficacy of these and similar trauma care methods, they all face two critical challenges: they rely on the physical presence of skilled physicians, and are heavily dependent on operator expertise for optimal outcomes. Consequently, neither FAST nor other minimally invasive interventional stabilizing procedures have been incorporated into pre-hospital protocols, underscoring a critical gap in early trauma care delivery. This thesis seeks to address these limitations by introducing autonomous robotic solutions for both trauma diagnostics and intervention, aiming to provide consistent, high-quality care during the critical early phases of trauma treatment.
The key contributions of this dissertation include: (1) the creation of the first non-commercial patient-specific FAST examination phantom featuring perihepatic, perisplenic, and perivesical hemorrhages, offering a durable and realistic platform for validating robotic trauma diagnostic systems; (2) the development and public release of a new abdominal skin dataset that establishes a benchmark for validating skin segmentation algorithms in trauma applications, and improves existing skin segmentation techniques, enabling more reliable autonomous robotic scanning; (3) the development of an autonomous robotic system with a force-based US scanning protocol for thoracoabdominal imaging, validated through simulation and physical experiments that address previously unexplored anatomical complexities; (4) the development of an autonomous US-guided robotic system for femoral artery access, establishing the first end-to-end trauma-specific solution, with validation on 5 complex patient-specific phantoms demonstrating 100% first-attempt success in arterial access; and lastly, extending the work for vascular needle placement to improve on the targeting accuracy of flexible needles, (5) the development of a mechanics-based framework for flexible bevel-tip needle insertion, integrating a non-linear tissue-interaction model and robust controller that demonstrate superior needle deflection prediction in phantoms with more than 3 tissue layers, and improved targeting accuracy under parameter uncertainty in simulation. Collectively, these contributions pave the way towards autonomous robotic care in trauma.
This thesis presents the first autonomous robotic systems for US-guided diagnostics and minimally invasive interventions in trauma, validated using patient-specific phantoms that advance current experimental standards. The scientific contributions of this work establish a foundation for advancing robotic integration in emergency medicine, with applications extending beyond trauma care
COMPARISON OF ENERGY STORAGE MARKET RETURN VALUE THROUGH RULE-BASED SYSTEM AND MACHINE LEARNING BASED METHODS IN CALIFORNIA NP-15 HUB
Full text linkHypothesis: Optimizing charge schedules using machine learning enhances wholesale market returns for energy storage over traditional methods.
This research aims to provide guidance for shareholders and researchers in targeting the market value of energy storage and benchmarking various methods for planning storage operation schedules in the wholesale electricity market. Methods are critical to improving the economic performance of energy storage, as the study found that energy storage optimized using the specific methods can boost market revenue by nearly 50% compared to industry common practices. The study examined different methods, including ordinary (rule-based) and machine learning-based methods, to produce the price guidance, which was either transformed from the demand, past price, or predicted price, and then optimized energy storage charge schedules in the wholesale electricity market. For the predicted price methods, machine learning base methods have a mean absolute error (MAE) range from 10.63/MWh while the Common Statistics methods have a MAE range from 26.47/MWh. It reveals the advantage of Machine learning based methods in predicting the wholesale electricity market price change. However, analysis has shown that more accurate market price prediction does not inherently correlate to higher market returns for energy storage. The results show that Quantile Regression-Based methods can capture over 95% of the theoretical maximum market return, which is slightly lower than the best machine learning based approach - Lightgbm. So, some common statistics maintain competitiveness in the energy storage charging schedule optimization, even though it lacks accuracy in predicting the market price change. In conclusion, the research results confirm the hypothesis that machine learning-based energy storage charge optimization enhances wholesale market returns over traditional methods
Algorithms for Quantitative Imaging with Advanced Cone-Beam Computed Tomography Configurations
Full text linkCone-Beam Computed Tomography (CBCT) is an emerging technology that offers several potential advantages over conventional multi-detector CT, including superior spatial resolution, point-of-care accessibility, and flexible mechanical designs enabling application-specific scanner configurations. When combined with the Dual-Energy (DE) acquisition technique, CBCT has the potential to quantify tissue compositions in the imaging target, such as bone mineral density (BMD) and oedema. However, quantitative accuracy of DE CBCT has generally been inferior to that of DE CT due to significant non-idealities in the imaging chain – e.g., elevated fraction of x-ray scatter reaching the detector – limiting its application in clinical settings. In this work, we bridge this gap through the development of advanced algorithms and experimental feasibility studies.
This work begins by a systematic investigation into the impact of x-ray scatter on the quantitative accuracy of DE cone-beam imaging. Simulations and physical experiments were performed for a representative DE task involving areal BMD quantification in the projection domain. Results show that material decomposition accuracy is highly sensitive to small scatter-induced biases, especially for DE protocols with poor spectral separation.
This finding motivated the development of a high-fidelity model-based framework for addressing these projection-domain non-idealities to obtain accurate volumetric DE decompositions from CBCT acquisitions. The proposed framework utilizes the inherent material discrimination ability of the DE data to facilitate the Monte Carlo-based scatter correction, with the addition of detector lag and glare pre-corrections. We demonstrated that this method improved the quantitative accuracy of wide-beam DE CBCT to levels comparable with DE CT in a challenging fat-bone-water decomposition scenario.
Following projection artifact corrections, we developed two practical DE material decomposition algorithms aimed at improving the quantitative accuracy in the presence of metal implants (which typically cause significant artifacts in reconstructed images). These algorithms – which incorporate different types of prior information into the model-based decomposition framework – were validated in an advanced multi-source DE CBCT system configuration and achieved improved BMD quantifications in fractures with orthopedic fixators compared to the existing approaches.
Lastly, we employed deep learning (DL) techniques to address residual artifacts in the reconstructed CBCT and DE CBCT images. Although many existing DL models have been developed to address image artifacts, there is a lack of efficient methods to quantify the reliability of each DL inference on-the-fly. In this work, we proposed an analytical pipeline to quantify voxel-wise uncertainties, and adapted it to an image fusion approach to correct residual CBCT artifacts in real-time. We showed that this method achieved an effective reduction of metal-induced artifacts in intraoperative CBCT imaging scenarios with minimal runtime, potentially improving the efficiency of the surgical workflow. Translation of this approach to DE applications of CBCT is ongoing.
We envision that this work can provide significant advancement toward translating point-of-care CBCT systems to clinical imaging applications requiring accurate biomarker quantifications, enhancing both the diagnostic and therapeutic workflows