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Scoping Out Scopes: A Comparative Study of DriScope Aid Jet Stream Versus Air Drying to Prevent Healthcare-Associated Infections
No full textBackground: Healthcare-associated infections (HAIs) can impact patient safety on a daily basis. Many of these infections can be traced to medical devices used to treat patients. Endoscopes or “scopes,” are medical instruments used to examine, diagnose, and treat patients internally. Because of their use in vulnerable areas of the body, the cleaning, disinfection, and drying process of endoscopes is critical to preventing outbreaks. Proper drying is essential to prevent bacteria from growing inside disinfected endoscopes.
Methods: This study was done at an urban acute care hospital and compared two different drying methods. 10 endoscopes were hung vertically and air dried while another 10 went through automated drying with a DriScope Aid Jet Stream Device in a case-control method. Moisture inside the endoscopes was measured using litmus paper and a borescope examination. Microbial cultures of endoscopes were taken to measure if there was any microbial colony growth in the internal channels. A Fisher’s exact test was performed to analyze the findings.
Results: The litmus paper showed that all 10 air dried endoscopes contained water droplets after drying while none of the DriScoped endoscopes showed water on the litmus paper. Upon borescope examination, the study team observed water droplets inside one of the DriScoped endoscopes. One DriScoped and two air dried endoscopes had colony growth. Statistical analysis showed a significant association (p < 0.0001) between the drying method and moisture seen on the litmus paper.
Conclusion: Automated drying of endoscopes through the DriScope device was significantly associated with less water retention in the endoscopes. Technicians should be encouraged to continue using this device whenever possible in order to ensure dry endoscopes. The public health relevance of this research is that it aims to reduce HAIs, prevent outbreaks associated with endoscopes, and ensure patient safety by utilizing the most appropriate methods to dry medical devices
Chemical Tools to Inhibit and to Modify Proteins
No full textThe field of chemical biology has delivered diverse chemical tools that have been developed to answer fundamental biological questions. These chemical tools include small molecules, peptides, and oligonucleotides, which have been chemically modified for perturbing, profiling, modifying, and visualizing biomacromolecules. Herein, I detail my contributions to the fields of sulfotransferase biology and covalent protein modification through the development of small molecule- and oligonucleotide-based chemical tools to perturb, profile, and modify proteins. First, I describe the design and synthesis of novel isoform-specific allosteric inhibitors of two sulfotransferases, SULT1A3 and SULT2B1b, which regulate neurotransmitter and cholesterol metabolism, respectively. The allosteric inhibitors detailed within represent an entirely innovative approach to the inhibition of an ‘undruggable’ enzyme class that has immense implications in human diseases such as major depression, Alzheimer’s, and cancer.
I then transition from small-molecule-based to nucleic acid-based tools, and present covalent aptamers as versatile tools for endogenous protein modification and inhibition. The aptamers are functionalized with cleavable electrophiles that transfer functional labels to nucleophilic residues on protein targets. My efforts have driven the extension of this protein labeling approach from a test tube to the cell surface, modifying cancer biomarkers for various biological applications
Defining and Tailoring Effective Practices in Teaching Social Determinants of Health for Online, Undergraduate Programs
No full textSocial determinants of health (SDOH) greatly impact an individual’s health, yet health care professionals are undereducated on addressing SDOH when identified. While many factors influence and drive educational practices, there is a lack of guidance in understanding best practices in teaching SDOH in online undergraduate health programs. Through my position as an adjunct instructor at Lamar University, I am interested in understanding my role as an adjunct instructor and tailoring previous literature to continue to advocate for more SDOH integration in course curriculum. The purpose of this quality improvement initiative was to 1) examine students’ perceptions and understanding of SDOH in their communities, 2) how health outcomes associated with their chosen SDOH impact program planning and understand approaches via photo essay assignments and peer-to-peer discussions. Discussion board posts and reflections were analyze in a qualitative review. Overall, students expressed positive and negative perceptions of certain SDOH domains, discussed how culture plays a role in SDOH context and program planning, and highlighted the gaps of usage or knowledge on existing assets in the community. Students expressed positive feedback regarding peer-to-peer learning, particularly through peer validation, learning from first-person experience, and increasing individual awareness of community assets. Tailoring previous activities outlined in the literature, focusing on student’s community assets and peer-to-peer learning is one effective SDOH teaching strategy to build knowledge. Other drivers to effectively increase SDOH learning include additional instructor knowledge of effective strategies and the individual knowledge, capacity, and permission to incorporate SDOH into course curriculum. After increasing my own confidence in incorporating one aspect of SDOH teaching, future implementation strategies to further SDOH teaching in online undergraduate courses include continued relationship building with department leadership and engaging professional peers’ current initiatives for system change
There’s No Sugar Coating It: Aspartame Does Not Pose A Public Health Risk
No full textAspartame is an artificial sweetener found in thousands of food products with a few notable ones being diet sodas, chewing gum, and frozen meals. Because Aspartame contains significantly less calories than traditional sugar, many have turned to it as a healthier alternative to sugar in their diet. Speculation over negative health effects associated with Aspartame have persisted since its inception in the late 1960’s with the idea of a 0 calorie sweetener being “too good to be true” to many. To this day, Aspartame has been subject to hundreds of studies and public criticism attempting to justify calls for increased regulation or an all out ban. In 2023, the International Agency for Research on Cancer (IARC) classified Aspartame as a Group 2B carcinogen once again sparking the latest bout of controversy and discussion. Despite all of the efforts over the years, no sound evidence is yet to be brought forward proving that Aspartame consumption causes negative health effects.
This essay will explore the biology of Aspartame, its regulatory and approval process, media portrayal, supporting and opposing studies, and ultimately, provide a stance of whether or the public should be concerned about Aspartame consumption in their day to day lives. Aspartame serves as a cautionary tale showing how leniency with the scientific method and external biases can create a long-lasting narrative not based in fact. The public health significance of this topic is that the public remains unsure of the safety of Aspartame, indicative of a larger issue of how scientific information is disseminated and communicated
Advancing Machine Learning for Small Molecule Property Prediction
No full textRecently, machine learning (ML) models have rapidly become the state of the art at various molecular property prediction tasks. The speed of ML models, without sacrificing accuracy, makes them especially attractive in screening contexts, where a large number of potential molecules need to reduced to a number feasible for experimental testing. However, the black box nature and rapid advancement of ML models has resulted in a proliferation of input representations and model architectures. This makes selection of the ``best'' model architecture and input representation for a given task difficult. Additionally, while ML models thrive on having large datasets for training, the amount of labeled structures for properties like receptor-ligand binding affinity is small.
This work sets out to help address these two problems with ML models for molecular property prediction. First, a wide variety of molecular input representations and ML model architectures were trained to predict calculated molecular properties. The characterization of both the performance of these models, and how well they utilize the training data, yields suggestions on how to best select a ML approach for more realistic property prediction tasks, given the amount of compute resources and training data available. Next, in order to address the lack of labeled structural data, a new dataset, CrossDocked2020, was created to expand the PDBbind dataset to expand the available binding pose classification data. By docking ligands into non-cognate, but similar, receptors we were able to expand the ~200,000 poses available from the PDBbind General set into ~22.5 million poses in CrossDocked2020. Various data imputation techniques were then explored to see if they could improve the binding affinity regression of a convolutional neural network (CNN) on CrossDocked2020. The utilization of an ensemble of CNN models to impute the missing binding affinity labels of complexes in CrossDocked2020 had a small, but significant improvement on model performance. Lastly, in order to give further support that the knowledge from this work is applicable in the real world, the CNN developed in this work was utilized to identify a small molecule to disrupt the actin-profilin1 protein-protein binding complex
Investigating and Improving Student Understanding of Quantum Mechanics Using Research-Validated Clicker Question Sequences and Tutorials
No full textQuantum mechanics is notoriously challenging, and research has found that students struggle with many common difficulties when learning it. It is also proving to be a critical piece of many exciting fields that are all but assured to see great development and expansion in the coming years; the Second Quantum Revolution is upon us. Quantum information science and engineering is a rapidly unfolding field, requiring talent from many disciplines, that aims to leverage the potential of quantum systems for many practical applications. To prepare students for the opportunities afforded by these advances, a strong foundation in quantum mechanics is essential. The work that I present in this dissertation is focused on helping students achieve this understanding. By investigating the common difficulties that students have in key concepts related to quantum mechanics and quantum computing, a guiding framework can be established and followed to develop research-validated, active-engagement instructional tools. These tools include Clicker Question Sequences (CQSs) on (1) the basics of two-state quantum systems, and changing basis in two-state systems; (2) time-development of two-state systems; (3) quantum measurement of two-state systems, and (4) measurement uncertainty in two-state systems. In addition to these, I have developed and validated Quantum Interactive Learning Tutorials (QuILTs) consisting of guided-inquiry teaching-learning sequences for (1) the Bloch sphere and (2) the basics of quantum computing. In each case, cognitive task analysis from both expert and student perspectives was either carried out directly or built upon from the results of prior investigations. I discuss the results of implementations of these learning tools in authentic classroom environments, which involves both online and in-person administrations and multiple instructors. In each case, student performance after engaging with the learning tools increased noticeably, and dramatically for some difficult concepts
Designing Smart Tech Solutions for Enhanced Aging in Place: A Caregiver Evaluation
No full textPurpose: The emerging challenges in caregiving, due to the aging global population and the increasing trend of older adults preferring to age in their home environments, emphasize the need for effective technological solutions. Particularly, smart home technologies and remote monitoring systems are essential to support caregivers and care recipients. The research aims to develop and evaluate a prototype remote monitoring system, CARE360, to enhance the caregiving process and facilitate aging in place.
Methods: The study was conducted in four phases: Conceptualization, System Development, Small-Group Interviews, and Data Analysis. The CARE360 system, a low-fidelity prototype, focused on seven care areas: Overall Activity, General Health, Medication Compliance, Climate Control, Bathroom Use, Bedroom Use, and Wandering Behavior. It integrated various smart devices for comprehensive monitoring. Small-group interviews were conducted with seven informal caregivers to understand their needs in providing care for older adults, highlighting the importance of caregiver and care recipient perspectives, evaluating the CARE360 system, and gathering feedback for improvements. The study employed both quantitative and qualitative methods, including demographic questionnaires, Likert scale ratings, and thematic analysis.
Results: The participants provided insights into the usability and effectiveness of the CARE360 system. The findings demonstrated an acknowledgment of the system's potential to enhance caregiving capabilities and effectively manage the needs of care recipients. Caregivers evaluated various aspects of the system, including its importance, usefulness, and ease of use. The results revealed diverse levels of perceived usefulness and ease of use across the seven monitoring areas. Overall, the feedback was positive, highlighting the system's ability to offer peace of mind and improve the quality of care provided to older adults.
Conclusions: This study lays the foundation for the development of remote monitoring systems, focusing on the CARE360 system and its coverage of seven key care areas. The user-centric design of the CARE360 system, combined with its capability to integrate a variety of smart devices, presents a promising solution for addressing the practical challenges faced by caregivers. However, further research and development are necessary to refine and tailor the CARE360 system to effectively meet the diverse and specific needs of both caregivers and care recipients
Institutionalized Authoritarianism: Political Incentives, Land Resources, and Development Outcomes in China
No full textThis dissertation investigates the political and economic impacts of institutionalized political selection within authoritarian regimes. Contrary to prevailing theories that suggest meritocratic promotion within party systems contributes to regime stability and economic performance, this dissertation indicates that such promotion rules can reduce regime stability and hinder long-term economic growth. I focus on the performance-based promotion rules in the Chinese political system and argue that the career incentives for politicians lead to violations of non-elite property rights, intensifying conflicts between governments and citizens, and undermining regime stability. Additionally, the state's monopoly on economic resources leads to collusion among ruling elites, encouraging rent-seeking behaviors. Moreover, institutionalized promotions prompt lower-ranking officials to misallocate resources in ways that favor their career advancement, thereby impeding the potential for economic growth. By analyzing 600,000 residential land transactions and the career records of local officials from China, I demonstrate that local party secretaries with strong career incentives often manipulate land prices. Using causal mediation analysis with survey and protest data, I illustrate how career-motivated politicians drive increased collective actions, with interventions in land pricing acting as a mechanism. Furthermore, by examining politically connected firms' land transactions, I reveal rent-seeking engagements between government officials and firms, and demonstrate how local officials strategically select auction methods to benefit connected firms. Finally, I develop a formal model within a principal-agent framework to demonstrate an ``amplifying effect," wherein the political incentives of principals exacerbate resource misallocation among subordinates, hindering long-term economic growth potential. Overall, this dissertation challenges the notion that meritocratic promotion of party cadres in authoritarian regimes enhances regime stability and economic performance
Community-based Participatory Research and Praxis at the Nexus of Food, Water, and Energy Justice in Puerto Rico
No full textThis project consists of community-based participatory research and practice in the community of Corcovada, Puerto Rico, where recurrent climatic and non-climatic hazards, including hurricanes, earthquakes, and pandemics, create complex risks across interconnected food, energy, and water (FEW) systems.
Our research-community partnership maps community social capital, co-develops and validates a survey on community health risks and vulnerabilities, and engages in a participatory budgeting process where the community has direct involvement and decision-making power over project funding allocation.
The project continues to sustain and deepen relationships with the community of Corcovada by identifying resources that community members can draw from to exercise agency and make decisions on a program best suited to improve their community’s public health needs. Ultimately, by utilizing this bottom-up, community-centered approach, we seek to support community cohesion, reduce FEW-related public health risks following disasters, and strengthen community resilience
Vibration-Based Nondestructive Estimation of Neutral Temperature in Continuous Welded Rails
No full textThe longitudinal stress in continuous welded rails (CWRs) is a key parameter to guarantee safe operations and avoid rail buckles (sun kink) and pull-aparts occurring at extreme warm and cold temperatures, respectively. To mitigate the effect of longitudinal stress due to temperature variation, any CWR is typically pretensioned to a certain value of the rail neutral temperature (RNT) that is the temperature at which the net longitudinal stress in the rail is zero. However, over the years the RNT decreases to unknown values due to multiple factors, increasing the risk of thermal buckling. Therefore, rail owners and transportation agencies require inspection methods to evaluate the RNT in CWR.
In this study, a novel nondestructive testing (NDT) technique based on finite element analysis, rail vibrations, and machine learning is investigated to infer the RNT in rails. The overarching approach consists of triggering and measuring rail vibrations. The lateral and torsional frequency components of the few lowest modes (< 1 kHz) of vibrations are extracted and fed to a machine learning algorithm (MLA) previously trained with finite element data or benchmark experimental data. This method is expected to predict the longitudinal stress and the RNT with very few experimental data to be collected anytime anywhere. In the long-term, the key advantages of the proposed technique are the: (1) simplicity of the setup to be carried in the field; (2) low-cost of the instrumentation; (3) short duration of the needed measurements.
This dissertation presents the principal results of the study including the implementation of a finite element model of CWR, and the setup and results of one laboratory experiment and two field tests conducted at the Transportation Technology Center in Pueblo (CO) on rails on concrete and wood ties. The results of the experiments demonstrate that the success of the technique is dependent on the accuracy of the numerical model and the ability to properly identify the dynamic characteristics of the rail. The results also show that this methodology is able to predict successfully the neutral temperature of the tested rail, specifically when the MLA is trained on benchmark experimental data