LOUIS University of Alabama in Huntsville
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Implementing sickle cell pain treatment guidelines to improve vaso-occlusive pain in adults with sickle cell disease
No full textVaso-occlusive pain (VOP) episodes represent a major complication of sickle cell disease (SCD), associated with increased utilization of opioids and emergency room visits. The approach to managing pain is challenging in adults with SCD due to the complexity and uncertainty of the pathophysiology that drives both acute and chronic pain. Opioids are often effective in managing VOP, even though they do not target specific biomarkers of vaso-occlusion. However, there are no specific guidelines on how to utilize opioids for pain management in SCD. Consequently, pain management is often tailored to the personal preferences of individual healthcare professionals, which can lead to inconsistencies in treatment plans and potential mismanagement. A clinical practice change was implemented to improve VOP management by introducing standardized sickle cell pain treatment guidelines that included individualized pain action plans, optimizing disease management therapies to reduce the frequency of acute VOP episodes when used consistently, and incorporating depression screening. Thirty-four participants (male 11 and female 23), who were Hgb SS (n=26), Hgb SC (n=4), Hgb SB0 (n=3), SB+ (n=1), age 18 years or older, participated in this clinical practice change. The Wilcoxon signed-rank test showed a statistically significant difference in the number of ER visits pre- and post-intervention (p\u3c 0.001). The Friedman test showed a statistically significant difference in PHQ-9 scores pre- and post-intervention (p\u3c 0.001). Patients who participated in this clinical practice change had a reduction in ER visits and PHQ-9 scores
A nurse-driven protocol to improve sleep among patients recovering from stroke in an inpatient rehabilitation facility
No full textThis quality improvement project investigated the impact of using best practices from the literature to implement a novel protocol consisting of the Insomnia Severity Index (ISI) and an electronic medical record (EMR)-integrated automatic order into the workflow of nurses employed on a 41-bed stroke and cardiac unit of an inpatient rehabilitation facility. The project aimed to increase the number of adult patients recovering from stroke who received a minimum average of seven hours of sleep each night over a 10-day inpatient stay. The project used hourly rounding observations from nursing staff combined with ISI scores to assess the impact of nonpharmacological interventions on quantity of hours slept her night and patients’ subjective quality of sleep. In addition to patient outcomes, a pre and posttest on nursing confidence using the new interventions and a set of knowledge questions were implemented immediately prior to launch and at the end of the project. The authors found most patients (n = 30, or 91%) did not self-report a high enough score (≥15) to trigger the protocol even though they were not achieving the healthy minimum standard of seven hours of sleep per night. This suggests either the ISI was unable to accurately capture the status of the patient’s sleep, the patient was inaccurately self-reporting, or the patient’s subjective perception of their sleep did not match the nurses’ observations. The intervention was successful in achieving a high rate of compliance among nurses; over 95% of patients admitted to the unit were captured, and the median compliance rates were over 90%. The two pre and post-education survey questions most reflective of the new interventions had the most statistically significant differences in scores, suggesting nurses felt more competent helping patients with sleep hygiene and the use of the ISI as a result of the education. Given that sleep disturbances are common among patients recovering from stroke and contribute to poorer outcomes in terms of cognitive function, mood, and overall quality of life, nurse-driven nonpharmacological interventions should be considered for all patients regardless of self-report of sleep disturbance as a matter of caution and best practice
Studies toward the synthesis of an antibody–drug conjugate (ADC) as an approach to treat hepatocellular carcinoma by inhibiting cathepsins
No full textThe treatment of hepatocellular carcinoma (HCC) experiences several limitations due to the advanced stage of the disease upon diagnosis. Thus, the focus of HCC treatment in recent years has been on the development and design of chemotherapeutic agents capable of targeting and eliminating HCC that has metastasized from the liver tissue. Antibody-drug conjugates (ADCs) are a novel technology whose development is being pursued due to decreased displays of off-target toxicity that are experienced following current systemic treatment options. This work seeks to develop an ADC that implements the cytotoxic effects of NN9-OH, a vinyl-sulfone covalent inhibitor of lysosomal cathepsins as an option to treat HCC. The synthesis of NN9-OH has been optimized and the compound has been characterized in both structure and inhibitory capabilities. However, the next phase of the ADC synthesis, the conjugation of NN9-OH to the linker, MC-Val-Ala-OH, is still in progress
From Manuscript to Machine: AI-Driven Transcriber
No full textA significant bottleneck in the research process of the pre-digital era is accessing the necessary documents to perform said research. The obvious answer is digital documents; however, the process of digitization and transcribing takes time and manpower. This poster presents an AI-backed process for converting documents from physical pages to a digital text.https://louis.uah.edu/honors-399/1027/thumbnail.jp
Investigation of Leading-edge Active Flow Control method using Fluidic Oscillator on NACA0018 Airfoil
No full texthttps://louis.uah.edu/rceu-hcr/1510/thumbnail.jp
Pushing the limits of Fermi-GBM data
Get PDFThe Fermi Gamma-ray Burst Monitor (GBM) is a wide-field survey instrument in a low-Earth, low-inclination orbit. It contains twelve sodium iodide detectors and two bismuth germanate detectors which together span an energy range from ~8 keV to ~40 MeV and observe the entire unocculted sky (~70%). The highest resolution GBM data product is the time-tagged event data which has ~2 microseconds temporal resolution and 128 pseudo-logarithmically spaced spectral channels. All of these factors make Fermi the perfect instrument for detecting a wide variety of high-energy terrestrial and astrophysical phenomena. In this dissertation, I implement new data analysis techniques to explore the edge cases of GBM data: GRB 221009A in the extremely high count-rate regime and weak terrestrial gamma-ray flashes with lightning associations in the extremely low count-rate regime
Understanding the behaviors of internal short circuit and thermal runaway of Li-ion batteries through in situ diagnosis and modeling
No full textInternal short circuit (ISC) caused thermal runaway is a critical challenge for Li-ion batteries that are widely used in various applications. However, currently, there is still a lack of understanding on how exactly an ISC triggers thermal runaway. ISC is a highly localized and transient phenomenon, hence, in situ diagnosis is important in understanding the development of ISC. In this work, experimental methods were developed for simultaneous in situ measurement of ISC temperature, ISC current, and ISC resistance during slow nail penetration of small single-layer Li-ion cells and large-format multiple-layer Li-ion cells. Furthermore, the experimental results of multiple-layer Li-ion cells were used for the development and validation of a thermal-electrochemical coupled model. Then the model was used to enhance the understanding of the experimental results. The investigation with single-layer cells revealed that the dynamic change of the ISC temperature during nail penetration was due to the ISC current change, which was further attributed to the ISC resistance change. In particular, the change of the contact resistance between the nail and the Al foil current collector associated with mechanical rupture or melting of the Al foil plays a critical role in the dynamic behaviors of ISC during nail penetration. The investigation with segmented multi-layer cells not only confirmed the relationship between the ISC temperature, the ISC current, and the ISC resistance, but also revealed an interesting phenomenon. Thermal runaway can be confined to a small segment that is nail penetrated and does not propagate to the rest of the cell. This phenomenon was further investigated using the thermal-electrochemical coupled model. It was found that conductive heat transfer between the segments played an important role in determining if thermal runaway propagates from the small segment to the entire cell. These results demonstrate that integration of in situ diagnosis and numerical modeling can enhance the understanding of how an ISC evolves to trigger thermal runaway. In addition, the observations of the dramatic change in ISC resistance and the confinement of thermal runaway imply potential strategies to mitigate the risk of ISC-caused thermal runaway
Determination of material deformation at truly constant strain rates utilizing full-field stress and strain measurements
Get PDFThe rate sensitivity of materials is an important aspect to consider when designing components; such as in manufacturing processes, aerospace vehicle impacts/propulsion systems, and automobile collisions. Characterizing a material’s rate sensitivity can be expensive and time consuming due to multiple tests being necessary at each given strain-rate to fully characterize the response. This project proposes a methodology to determine the material response at multiple truly constant true strain rates from a single test using non-uniform geometry and the strain localization phenomenon. Finite element simulations using Ansys LS-DYNA are used to design potential new test specimen geometries to induce constant and numerous strain-rates across the specimen. The four most promising geometries are fabricated and tested. Digital Image Correlation (DIC) is utilized to obtain full-field data so that the area specifically around the necked region can be analyzed. Then, using an array of virtual extensometers, the instantaneous cross-sectional area is calculated for the determination of the true stress at a given point. Strain-rates across the specimen are determined with time differentiation and true stress-strain curves are generated. The data is then analyzed to determine the feasibility of inducing multiple constant strain-rates and determining the corresponding material behavior from a single test. The Virtual Fields Method (VFM) is also introduced and used in a simple case to demonstrate its capacity to identify material parameters utilizing the full-field DIC displacement and strain data
Nonlinear dynamics and data driven modeling
Get PDFThe research areas of dynamics and data-driven modeling overlap in existing literature. Some research uses dynamical systems as a test bed while developing new modeling methods. Other papers attempt to use data-driven modeling as a tool to better understand, or potentially predict the behavior of a dynamical system. The primary goal of this thesis is to further the symbiotic relationship between these two research areas. Firstly, this paper provides a concrete example of how a basis function decomposition of the Lorenz system can provide a quantitative performance metric for data-driven modeling methods. Secondly, a novel application of an existing modeling method will yield an unexpected Lorenz-Like equation. Finally, this paper discusses preliminary work toward a novel application of data-driven modeling in the field of optics, a research area that remains at the edge of human knowledge. By exploring these three topics, which exist in the intersection of dynamics and data- driven modeling, this thesis aims to provide tools, insights, and intuition that will aid collaborative research of dynamics and data-driven modeling
Application of machine learning to predict ultrasound wave propagation in biphasic fluid–solid media
Get PDFThe objective of this thesis is to investigate the application of Machine Learn ing (ML) algorithms to predict ultrasound wave propagation in the cartilage tissue of the knee joint. The learning data necessary for the ML algorithm has been generated through finite-element method (FEM)-based simulations for solving the Biot theory equations governing the propagation of continuous ultrasound through the cartilage. Specifically, we computed the ultrasound-induced dilatations and displacements in the microscale cartilage that is represented as consisting of four zones, namely the chondrocyte cell and its nucleus, the pericellular matrix (PCM) that forms a layer around the chondrocyte, and the extracellular matrix (ECM). The chondrocyte–PCM complex, referred to as the chondron, is embedded in the ECM. The top surface of the ECM layer is subjected to specified amplitude and frequency of continuous ul trasound. The learning data for the ML algorithm was generated at the ultrasound frequencies of 0.5, 0.75, 2, 3, 4, and 5 MHz. This data was used to train the ML algorithm and then used to predict ultrasound propagation at a test frequency of 1 MHz. It was found that the ML algorithm predictions showed excellent agreement with the FEM simulation data at the test frequency. The errors between the FEM and ML results at 1 MHz were than less than 1