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A Comprehensive Evaluation of Feasibility and Acceptability of a Nurse-Managed Health Clinic for Homeless and Working Poor Populations: A 3-Year Study
No full text<b>Background/Objectives:</b> Homeless populations have higher rates of chronic illness and mortality than more advantaged peers but have low primary care engagement. Nurse-managed clinics emerged as a possible solution to increase healthcare access for marginalized populations. This paper presents a comprehensive evaluation of feasibility (conceptualized as patient recruitment and retention) and acceptability (conceptualized as patient satisfaction) of a nurse-managed primary care clinic tailored to people experiencing homelessness and poverty. <b>Methods:</b> This is a three-year retrospective chart review study of the clinic&rsquo;s services, patient characteristics, and patient satisfaction. All adult patients for the three-year period were included (<i>N</i> = 514). Feasibility was measured by the number of unique patients seen and visits completed, ratio of completed to scheduled visits, and number of returning patients. Acceptability was measured by a 19-item Likert format (1&ndash;5) patient satisfaction survey. Patient characteristics were captured from intake forms. <b>Results:</b> Most patients were male, African American or White, and non-Hispanic. Regarding social determinants of health (SDOH), most patients did not have college education, were unemployed or unable to work, experienced homelessness, had no primary care provider, and no health insurance. Over three years, 1972 visits were scheduled and 1372 (69.6%) completed. A total of 514 patients were seen (37.5% of all visits), with 858 follow-up visits (62.5%). Returning patients (&ge;2 visits) totaled 59.1%. Yearly data shows steady growth in recruitment and retention. Patient satisfaction with facets of care (access, communication, interpersonal relations) was very high (<i>M</i>range = 4.63&ndash;4.69), including with Nurse Practitioner care, as was global satisfaction (<i>M</i> = 4.71; <i>SD</i> = 0.61; 76.3% very satisfied). <b>Conclusions:</b> Results indicate that a homeless-tailored nurse-managed clinic can recruit and retain homeless and working poor patients (feasibility), with high patient satisfaction with its services and staff (acceptability), independently of patient demographics or SDOH. Challenges related to retention deserve further study as well as the impact of services on the continuity of care, health, and well-being
Fatal Recurrent Splenic Artery Pseudoaneurysm Rupture Despite Prior Successful Embolization in Alcohol-Associated Chronic Pancreatitis: A Case Report
No full text<b>Background and Clinical Significance:</b> Splenic artery pseudoaneurysm (SAP) is a rare but life-threatening complication of chronic pancreatitis. Although endovascular embolization achieves high technical success, recurrence and delayed rupture may occur, particularly in patients with ongoing pancreatic inflammation or alcohol use disorder (AUD). <b>Case Presentation:</b> A 47-year-old woman with alcohol-associated chronic pancreatitis presented with hematochezia, melena, and syncope. CT angiography revealed a 3.6 cm SAP adjacent to a 4.2 cm pancreatic head pseudocyst, and she underwent successful coil embolization. Despite initial stability, she relapsed into heavy alcohol use, experienced recurrent pancreatitis flares, and developed progressive multisystem comorbidities. Surveillance imaging up to three months post-embolization showed pseudocyst fluctuations without early recanalization, but long-term follow-up lapsed. Eight months after embolization, she presented in hemorrhagic shock from recurrent SAP rupture and died despite massive transfusion and emergent splenic artery ligation. <b>Conclusions:</b> Fatal SAP rupture may occur months after technically successful embolization. Sentinel bleeding, AUD relapse, and progressive systemic decline are critical warning signs. Structured post-embolization imaging and multidisciplinary management are essential to improve long-term outcomes
Bayesian LASSO with Categorical Predictors: Coding Strategies, Uncertainty Quantification, and Healthcare Applications
No full textThere is a growing interest in applying statistical machine learning methods, such as LASSO regression and its extensions, to analyze healthcare datasets. The existing study has examined LASSO and group LASSO regression with categorical predictors that are widely used in healthcare studies to represent variables with nominal or ordinal categories. Despite the success of these studies, statistical inference procedures and quantifying uncertainty for regression with categorical predictors have largely been overlooked, partly due to the theoretical challenges practitioners face when applying these methods in behavioral research. In this article, we aim to fill this gap by investigating from a Bayesian perspective. Specifically, we conduct Bayesian LASSO analysis with categorical predictors under different coding strategies, and thoroughly investigate the impact of four representative coding strategies on variable selection and prediction. In particular, we have conducted uncertainty quantification in terms of marginal Bayesian credible intervals by leveraging the advantage that fully Bayesian analysis can enable exact statistical inference even on finite samples. In this study, we demonstrate that the variable selection, estimation and prediction of Bayesian LASSO are influenced by the coding strategies with the real-world Medical Expenditure Panel Survey (MEPS) data. The performance of Bayesian LASSO has also been compared with LASSO and linear regression
Towards More Efficient Chemical Processes: Enhancing Propylene Selective Oxidation through Forced Dynamic Operation and Ethylene-Ethane Separation with Metal-organic Framework Membranes
No full textThis dissertation examines two ways to increase efficiency in petrochemical processes: enhancing raw material utilization in catalytic selective oxidation of hydrocarbons and reducing energy consumption in alkanes and olefins separation. Selective oxidation of hydrocarbons constitutes a major class of catalytic processes, accounting for about 25% of industrial organic molecules synthesized with heterogeneous catalysts. The oxidation of propylene to acrolein over bismuth-molybdate-based mixed metal oxide (MMO) catalysts is a key example, with annual production exceeding 500,000 metric tons. This process is typically conducted in fixed-bed reactors under steady-state conditions. However, motivated by the oxygen storage capacity and diverse oxygen species in oxide catalysts, forced dynamic operation (FDO) has attracted interest in this reaction and selective oxidation processes in general. This study presents investigation of the selective oxidation of propylene to acrolein using MMO catalysts, emphasizing FDO for enhanced reactor performance. Our investigation demonstrates a significant increase in acrolein yield with FDO over steady-state operation, as high as 40%. Analyses reveal that in a lower temperature regime where catalyst re-oxidation is rate-limiting, FDO augments lattice oxygen availability, directing the conversion towards acrolein and acrylic acid. Complimentary kinetic studies elucidate the underlying mechanism that benefits FDO. Spatiotemporal analysis reveals evolution of activity, selectivity, and temperature along the bed, offering insights for the construction of a reaction network and reactor optimization. The second part of this dissertation extends to the separations of light alkane and olefin. Ethylene-ethane separation via cryogenic distillation ranks among the most energy-demanding operations, particularly since ethylene production exceeds 200 million metric tons annually. Membrane-based separation is a compelling, more energy-efficient alternative. We present ethylene-ethane separation by a zeolitic imidazolate framework (ZIF-8) membrane with experiments, showing the potential of ZIF-8. A complementary membrane model is developed that is used to estimate transport parameters and to interpret the experimental findings. Despite ethane exhibits stronger adsorption, overall permeation favors ethylene due to its higher diffusivity. The main strength of this study is its analytical approach, which integrates experimental validation and transport modeling, adaptable to other gas mixtures or nanoporous membrane materials
The Data Remediation That Never Ends: Reconciling UH Digital Collections Data Across Multiple Systems
No full textThe authors will discuss an ongoing “pet project” to reconcile digital collections data across multiple systems, including enriching WorldCat records, remediating post-Alma implementation local eCollections and batchloading local ePortfolios. Historically, University of Houston Libraries simultaneously described its archival finding aids and digital collections in EAD, Dublin Core, and MARC. As systems evolved, various platforms were migrated, resulting in MARC description of finding aids and digital collections becoming partially automated through OAI-PHM harvesting. However, the original MARC records for these various finding aids and digital collections persist in WorldCat. Digital Collections WorldCat records currently create false-positive searches due to link-rot, local changes in collection titles and extent, diacritical mark errors, and conflicts with a separately maintained local authority file. From 2019 to today, various formal and informal/ad-hoc projects have attempted to reconcile descriptive metadata across various systems. The presentation title is a reference to the eponymous children's song, “The song that never ends,” which is self-referential and infinitely iterative work. The authors hope to enhance the discovery and access of various UH Libraries digital collections, at least until the next “refrain.”Librarie
Non-integrated Defect Relations for General Divisors
No full textIn my dissertation, I establish two non-integrated defect relations. The first applies to meromorphic maps from a Kahler manifold, whose universal covering is a ball, into a projective variety that intersects general divisors properly. This is achieved by incorporating the beta constants into the defect relation. The second pertains to holomorphic maps from a complete open Riemann surface into a smooth projective variety, intersecting a generic smooth hypersurface of sufficiently high degree
Neural Mapping of Pain and Addiction: Visualizing Dopaminergic Modulation in the Mesocorticolimbic System
No full textSynaptic transmission facilitates perceived stimuli, dictating integral faculties responsible for addiction and nociception, which are heavily intertwined on a fundamental level, primarily due to the dichotomic role of dopamine (DA). The mesocorticolimbic pathway serves as a crucial network for the dissemination of DA, with the dopaminergic hub of the system — the ventral tegmental area (VTA) — located deep within the midbrain. Connected to the VTA through dopaminergic axons, the nucleus accumbens (NAc) and the prefrontal cortex (PFC) regulate high-level functions regarding learning, reward, and motivation through neurotransmitter interaction, of which DA is an integral part. To dissect these complexities, we leverage novel deep-brain imaging in freely moving models to elucidate how dopaminergic modulation evolves under addictive and nociceptive influences. Understanding the relationship between perinatal nicotine addiction and the mesocorticolimbic pathway is crucial for implementing new treatments for addiction. We apply our novel microstimulation experimental system in rat pups perinatally exposed to nicotine. By using our self-fabricated photo-stimulation (PS) device, we can stimulate the VTA and collect dialysate, which is then used to estimate DA released into the NAc. To further understand the role of DA in the NAc, we apply our latest complementary metal-oxide semiconductor (CMOS) imaging platform to investigate the effects of morphine and cocaine on adult mice. Fluorescence imaging of the NAc using dLight1.2 AAV allows for the visualization of DA molecules in real-time. Our results suggest that changes in extracellular DA can be observed with this adapted system, showing potential for new applications for approaching addiction studies and identifying the unique characteristic trends of DA release for morphine and cocaine. To visualize the nuances of chronic pain treatment and VTA activity, we incorporate our CMOS imaging platform to investigate the effect of morphine on the VTA response during acute nociceptive stimulation in chronic neuropathic pain adult mouse models. Our findings suggest a depression in the fluorescence activity of the VTA is indicative of a pain response. Additionally, morphine significantly reduces the neuronal depression caused by mechanical stimuli and is observable using the CMOS imaging platform, demonstrating a novel way to potentially assess and treat neuropathic pain
Suppression of Shear Flow in Microgravity Strengthens Non-Specific Protein Contacts to Enhance Aggregation
No full textProtein condensation, essential to biological systems and industrial applications, begins with nucleation-formation of a new thermodynamic phase where protein-rich precursors play a crucial role. We investigate how the absence of solution flow influences mesoscopic protein-rich clusters in three protein solutions: p53, hnRNPA2-LC, and lysozyme. The analysis is based on data from the Light Microscopy Module Biophysics (LMMBIO 2,5) experiment conducted aboard the International Space Station (ISS), aimed at understanding why protein crystallization in microgravity often results in unexpectedly low or high crystal numbers. Time series image data were collected using Differential Dynamic Microscopy (DDM) with intervals ranging from 30 minutes to two hours over seven days. To analyze the evolution of cluster populations, we compute image differences between the image pairs at various lag times, followed by Fourier transforms and power spectrum calculations. The power spectra were averaged for each lag time and further over the azimuthal angle to derive the structure-function, which correlates lag time with the wavevector. By assessing the relationship between the structure function and lag time, we obtain the relaxation time for each wavevector, enabling the diffusion coefficient calculation. Finally, the Stokes-Einstein equation estimates cluster size by finding the particle radius. Comparing these findings with ground-based data, where buoyancy-driven convection prevails, will clarify how solution shear flow influences nucleation precursor properties and their role in protein condensation. The results will guide the optimization of protocols in the chemical and pharmaceutical industries. They may also offer new insights into diseases associated with protein aggregation, such as cancer and ALS (Lou Gehrig's disease). In the absence of weak shear flows, such as those driven by temperature gradients on Earth, we find that protein solutions develop loose transient networks. These networks provoke two consequences. First, they increase the effective viscosity of the solution and thereby increase the apparent size of the mesoscopic protein-rich clusters. Second, suppressing internal shear flow within the clusters strengthens the intermolecular contacts responsible for structural integrity. This leads to numerous, larger, and more viscous clusters. These three effects may explain the irreproducible results of protein crystal nucleation in space, where carefully optimized conditions on Earth lead to no crystals, abundant crystal nucleation, or a desired level of crystallization. Transferring the results to fundamental physical understanding of processes occurring on Earth or in any environment reveals the huge role that slow shear flows play in the nucleation of protein solid phase. This role is underlined by the weak van der Waals bonds that enable protein molecular networks, which are susceptible to destruction by the weak shear flows. It is feasible that these effects will be limited to protein systems only
Modulating 3D Tumor Microenvironments to Investigate the Impact of Experimental Therapeutics on Glioblastoma Signaling Pathways and Drug Resistance
No full textGlioblastoma multiforme (GBM) is the most aggressive and treatment-resistant primary brain tumor, characterized by high recurrence rates and poor patient outcomes. Standard of care involves surgical resection followed by radiation and chemotherapy, with Temozolomide (TMZ) as the gold standard. However, GBM often evades therapy through dysregulation of key signaling pathways, including downregulation of apoptosis (Bax) and upregulation of survival mechanisms (NF-κB). To confront these challenges, we explored novel therapeutic strategies using three complementary approaches: improving therapeutic efficacy by targeting drug resistance pathways, biomechanical analysis of cell-cell interactions, and increasing tumor microenvironment complexity. In our first study, we investigated the efficacy of apoptosis pathway activation and suppression of survival mechanisms using AAAPT, a targeted approach using therapeutics activated in the presence of tumor-related substrates to enhance chemotherapy efficacy at lower doses. GBM spheroids treated within 3D PEGDA microwells demonstrated increased cell death, upregulation of apoptosis pathway expression, and downregulation of NF-κB expression compared to TMZ alone, suggesting a promising strategy to reduce off-target toxicity. Second, we assessed the biomechanical response of GBM spheroids to TMZ using nanobomb optical coherence elastography (nb-OCE) and Brillouin microscopy. TMZ-treated monocultured GBM spheroids showed significant reductions in stiffness, indicating increased treatment sensitivity. However, co-culturing GBM with human astrocytes (HA) conferred resistance, where treated co-culture spheroids exhibited minimal changes in stiffness, highlighting the stabilizing role of the tumor microenvironment. Lastly, we explored the therapeutic potential of miRNA, to suppress GBM spheroid formation and migration. When combined with TMZ, both miRs enhanced tumor suppression through inhibition of NF-κB and SRC/FAK signaling pathways, suggesting that miR-based therapy could synergize with and enhance TMZ treatment. Together, these findings emphasize the importance of integrating targeted sensitization and molecular therapy, biomechanical assessment, and in vitro microenvironment complexity to develop more effective GBM treatments. By leveraging 3D tumor models and advanced biomechanical tools, we provide new insights into GBM treatment resistance and potential strategies to improve therapeutic outcomes
Digital Twin of Glucose Metabolism in People With Type 1 Diabetes
No full textDigital twins (DTs) present an exciting potential in personalized therapy through the provision of in silico simulations of a patient’s responses to treatments. This study introduces a novel blood glucose prediction and simulation framework that has been tailored to simulate the dynamics of individual glucose-insulin responses in people with Type 1 Diabetes (T1D) which can be exploited for the design, testing and regulatory approval of robust, precise, safe and effective novel insulin therapeutic regimes without the need of any interventions on the individuals. Our framework hinges on a model of glucose dynamics and a method for physiological parameter estimation which utilizes a Liquid Time-Constant Neural Network (LTCNN). The developed DT was trained and validated on synthetic data obtained from an established metabolic model of T1D dynamics, which provided a controlled environment consisting of 10 virtual subjects for evaluating the DT’s prediction performance. We compared the LTCNN with Markov Chain Monte Carlo (MCMC) for the estimation of unknown parameters in the model, exploiting a method recently proposed in the literature. The MCMC-based DT prioritizes interpretable, physiology-driven parameter identification, whereas the LTCNN-based variation emphasizes data-driven dynamics modeling with real-time responsive- ness. Both techniques were tested on four predefined in silico scenarios, and accuracy was measured using metrics such as MARD and RMSE. According to the results, our DT is capable of accurately capturing the glucose behavior of an individual in the presence of variable insulin and meal inputs. The MCMC variation gives mechanical interpretability, whereas the LTCNN model provides fast inference and adaptable learning. This study contributes to the larger field of DT technology in diabetes management by highlighting the trade-offs between statistical inference and neuronal modeling in physiological systems