22484 research outputs found
Sort by
Redlining’s lasting impacts and green infrastructure in Pittsburgh, Pennsylvania
No full textThe process of redlining, or exclusionary lending practices based on where an applicant lives, is connected by research to lasting disadvantage in urban neighborhoods. Residential Security Maps produced by the Home Owner’s Loan Corporation (HOLC) provide a lens of understanding lending bias towards different urban neighborhoods. A body of research finds that the HOLC’s system of security grading in these maps coincides with ongoing socio-economic patterns in cities, as well as access to nature. This research based on the city of Pittsburgh uses spatial analysis to examine the relationship between the HOLC’s ranking of neighborhoods and current tree canopy, parks, street bike lanes, and greenways to find how green infrastructure varies between neighborhoods of different security grades. In addition, census data is used to provide snapshots of Pittsburgh’s demographics at around the time of the HOLC survey and the distribution in 2020. ArcGIS software was used to perform descriptive statistics, buffer analysis, overlay analysis, and geoprocessing. The results of the analysis indicate that the greatest marker of environmental inequality along the lines of HOLC districts in Pittsburgh is the lack of tree coverage in lowly-ranked areas compared to areas deemed more desirable. The research did not find that lower-graded areas suffered in terms of parks or bike lanes, which is probably due to the centrality of these resources and many redlined areas. However, it is concerning that redlined neighborhoods have fewer trees, given that trees have proven benefits to communities with access to them
Glycosaminoglycan binding of strain-specific polymorphisms in the chikungunya virus E2 glycoprotein
No full textChikungunya virus (CHIKV) is an arthritogenic alphavirus that displays broad cell tropism, interacting with a variety of cell-surface molecules to enter cells and mediate infection. Like many pathogenic viruses, CHIKV uses negatively-charged glycosaminoglycans (GAGs) as attachment factors to enhance binding to cells. Multiple lines of evidence suggest that CHIKV depends on GAGs for efficient infection of mammalian cells and displays strain-specific differences in GAG binding. However, the specific polymorphisms that dictate strain-specific differences in GAG binding have not been identified. We engineered SINV-CHIKV chimeric viruses to contain five basic amino acid polymorphisms at residues in the E2 viral attachment protein that either have putative roles in GAG binding or are present in strains displaying phenotypic differences (K140R, K149R, K221R, K234N, and K252Q). ELISA assays were conducted to determine whether the E2 polymorphic mutants were altered in direct binding to GAGs while cell-binding assays were conducted using human muscle cells (RH30) to determine whether these mutants depend on GAGs for efficient cell attachment. In addition, mutant viruses were also tested in infectivity assays to investigate their dependence on GAGs for infection of biologically-relevant cell lines (RH30, pHDF, tel-HFF) and dependence on GAGs for efficient infection in the presence and absence of a CHIKV entry receptor (Mxra8). Collectively, these studies provide a foundational understanding of the functional consequences of CHIKV E2 basic amino acid polymorphisms in binding to GAGs and cell attachment
Optimizing Patient Outcomes in Diabetes through Pharmacy and Population Health Strategies: A Comprehensive Review of Medication Adherence and Patient Education in UPMC Health Plan
No full textDiabetes, an escalating global health challenge, commands public attention constantly due to its multifaceted impact on individuals and communities. The prevalence of diabetes, particularly type 2 diabetes (T2DM), has reached epidemic proportions, affecting approximately 463 million adults globally in 2019, and is projected to rise to 700 million by 2045. The complex nature of these complications not only compromises the quality of life for individuals with diabetes but also places a substantial burden on our healthcare systems.
This essay delves into the intricate interplay between pharmacy and population health strategies within the UPMC Health Plan, offering a comprehensive analysis focused on the various methods for enhancing patient outcomes in diabetes management. Within the UPMC Health Plan, the pharmacy and population health teams aimed to start from multiple angles to optimize patient outcomes in diabetes management. The Pharmacy Team employs various tactics to enhance medication adherence, including IVR/SMS/live calls, analytic capabilities, and CMS STARS diabetes-related strategies. Additionally, interventions focus on planning all-cause readmissions with meticulous post-discharge medication reconciliation and outreach initiatives. Conversely, the Population Health Team's approach incorporates technology-driven interventions like Targeted Automatic eConsults (TACos), CDCES referral services, and Optimization target populations. While pharmacy interventions emphasize metrics aligning with patient compliance and prescribed medication regimens, the Population Health Team adopts a holistic approach to improve clinical outcomes and quality of life through self-management education.
This essay comprehensively explores differences between the pharmacy and population health strategies, providing insights into the dynamic synergy required to address the multifaceted challenges of diabetes within the UPMC Health Plan. In addition, the essay will provide recommendations to refine these integrated strategies further and include more perspectives on population health management to form a comprehensive framework for healthcare executives to consider
Exploring the Additive Effects of Religious Participation on Multivariate, Demographics Based Machine Learning Models
No full textThrough the 21st century, vaccine hesitancy has had a significant effect on the implementation of vaccine development and rollout in the United States. A known and well documented factor that contributes to this kind of structural hesitancy is regular participation in a religious congregation or community whose doctrine or teachings condemn vaccination and/or modern medicine in some form. The public health contribution of this thesis is to support the use of machine learning in the prediction of public health outcomes, as well as promote the contribution of socially anchored metrics within demographics-based models.
Data for this project was sourced from The Department of Health and Human Services Office of the Assistant Secretary for Planning and Evaluation, The U.S. Department of Agriculture’s Economic Research Survey, and The Association of Statisticians of American Religious Bodies’ U.S. Religion Census. These data were cleaned at the U.S. county level and the remaining variables were categorized into six major demographic categories: education, population, poverty, unemployment, vaccine hesitancy, and religious participation. This cleaning process resulted in 54 usable demographic variables and one outcome variable.
After data cleaning was performed, four machine learning techniques were implemented on the variable set to compare their prediction ability: elastic net, multivariate adaptive regression splines, random forest, and gradient boosted trees. Using the root mean square error and R-squared of each of these models, it was determined that the gradient boosted trees method had the greatest prediction ability with this particular dataset.
Variable selection was performed, and it was determined through importance testing that 26 of the 54 variables had a significant contribution to the model and provided the most substantial prediction ability. Of those 26 variables, two originated from the religion category. Results from the gradient boosted tree analysis indicated a decrease in prediction ability when the selected religion variables were removed from the model, which supports a data-based linkage between vaccine hesitancy and religious participation. Post-hoc hierarchical clustering was performed at a county level to give a visual representation of the demographically constructed clusters and to provide a geographically based comparison between the selected demographics and vaccine hesitancy
The Impact of a Therapeutic HIV-1 Vaccine on HIV-1 Proviral DNA and RNA Transcription in a Phase I Clinical Trial
No full textHuman immunodeficiency virus (HIV) is an ongoing public health issue. Currently, antiretroviral therapy (ART) is used to treat people living with HIV (PLWH), but the virus persists in latent memory T cells despite viral suppression on ART. Recent therapeutic advances are aimed at finding functional cures of HIV. Alongside therapeutic research, there is a need for the development of quantitative assays to accurately measure the viral reservoir within individuals and evaluate the impact of interventions on reducing the size of the reservoir. In this thesis, I implement an automated nucleic acid extraction method followed by quantitative molecular assays to quantify longitudinal changes in Cell-Associated HIV RNA and DNA (CARD) using samples from a Phase I clinical trial. The results will help determine if study participants responded to vaccination with autologous dendritic cells (DC) that were loaded with or without HIV peptides, directly or through natural processing, compared to unloaded DC. As this is the first usage of the automated CARD system on clinical trial samples, we are also further evaluating its performance characteristics compared to the existing manual CARD assay
Predicting the Impact and Trends of SARS-CoV-2 on the Respiratory Viral Season in Pittsburgh Using Interpretable Machine Learning Forecast Models: A Quality Improvement (QI) Retrospective Study
No full textThis Quality Improvement (QI) project utilizes predictive modeling to understand the dynamics of the COVID-19 pandemic, particularly examining the interaction with the respiratory virus season (RVS) encompassing Respiratory Syncytial Virus (RSV), Influenza, and SARS-CoV-2. This project seeks to determine whether COVID-19 will remain an additional burden on laboratories or diminish, making it another respiratory virus in the RVS. The analysis is from October 2015 to December 2023, examining incidence and ICD-10 cases from UPMC Shadyside and Presbyterian hospitals in Pittsburgh. This analysis compared pre- and post-COVID-19 periods, revealing evolving burdens on laboratories and hospitals. Our exploratory data analysis (EDA) visualizes the seasonal trends of the respiratory viruses, highlighting a shift in typical RVS patterns coinciding with the onset of SARS-CoV-2. Simple and Seasonal Naïve forecasting models provide baseline insights, while ARIMA and SARIMA models offer more advanced prediction techniques, acknowledging data complexities post-COVID-19. Despite SARIMA's superior performance, challenges arise due to limited post-pandemic data, emphasizing the need for continued data collection. The public health implications for our research are for proactive healthcare planning and understanding COVID-19's trajectory as a potentially endemic virus. Future endeavors will focus on continued data collection to refine the predictive models, create effective resource allocation strategies, and relieve the healthcare burden for potential future pandemics
Age-associated changes in human circadian rhythms
No full textCellular circadian clocks support homeostasis by synchronizing essential biological processes to the external day-night cycle. Emerging evidence has demonstrated that the functionality of these clocks changes with age. We leverage data from the Genotype-Tissue Expression project (GTEx) to examine age-dependent changes in rhythmic gene expression programs across 4 human tissues: lung, heart, skeletal muscle, and adrenal gland. Our analysis reveals a shift in the age-related timing of gene expression peaks, transitioning from tissue-specific clustering to a broad categorization at dawn and dusk. We observe a decline in the rhythmicity of genes associated with cell growth and differentiation, paralleled by an increase in the rhythmic expression of genes linked to mitochondrial respiration. We find that the inferred circadian clock outputs are highly dependent on the methodological approach—ordering donors by time-of-death or utilizing circadian phase estimation algorithms leads to different interpretations of the data. Our findings offer insights into the aging transcriptional landscape in humans and highlight the influence of methodology in human circadian rhythm research, providing direction for future studies.
Public Health Significance: Understanding how circadian function changes with age can contribute to strategies aimed at promoting healthy aging, potentially extending the health span and improving quality of life for older adults
Landscape Analysis of Affinity Groups, Mentoring and Pipeline Programs for Underrepresented Student Identities in Allied Healthcare Professions
No full textDespite numerous calls to increase representation in the healthcare in the United States, there is still a persistent lack of diversity across all health professions (Jackson & Gracia, 2014; Miller & Vaughn, 2023). Individuals that are underrepresented minorities (URMs) often encounter obstacles in both accessing quality education necessary to enter the healthcare field and navigating the workforce during their training and career. This underscores a significant public health issue as it hinders efforts to deliver equitable and culturally competent care, which perpetuates health disparities among minority populations. Examples of such identities are racial/ethnic minorities (REMs), sexual and gender minorities (SGMs), or low socioeconomic status (SES). There have been several diversity, equity, inclusion (DEI) strategies to help URMs enter careers in the health professions. Pipeline programs aim to enhance diversity by recruiting and supporting URMs at the educational level, while affinity groups foster a sense of belonging and support networks within professional settings (Miller & Vaughn, 2023; Patterson & Carline, 2006). Mentorship is another strategy to provide guidance, support, and professional development opportunities for URMs (Atwal et al., 2023). Although there has been widespread recognition of the importance of representation and diversity in healthcare, the existing literature lacks comprehensive evidence-based models to increase the number of URMs into healthcare professional programs and their retention in the workforce. To address this gap, we performed a critical landscape analysis of existing affinity groups, mentoring and pipeline programs. We identified studies using a comprehensive search from the databases Medline, APA PsycInfo, CINAHL, Embase, and Web of Science (1992-2025). A total of sixteen articles were included. Our results provided a description of study aims/objectives, characteristics of mentoring and pipeline programs (training level of students, healthcare training program, population targeted, host program, source of funding and program length), program activities, the experiences of URM students after participation, and the program outcomes (study evaluation method and metrics). By identifying successful strategies and best practices, our research aims to inform current efforts aimed at increasing minority representation and promoting inclusivity in academic environments in the health professions
Lamin B1 Gene Expression in the Peripheral Nervous System of Mice with a Targeted Deletion of an Upstream Silencer
No full textAutosomal Dominant Leukodystrophy (ADLD) is an ultra-rare neurological disorder that affects the white matter of the brain. Although publications have reported 33 families with over 70 ADLD-affected individuals, the exact prevalence of ADLD is unknown due to its rarity.1 The white matter of the brain and spinal cord is made up of bundles of axons, which are encased in an insulating coating called the myelin sheath. White matter functions as an insulating barrier to safeguard axons and enhance electrical signal transmission in the Central and Peripheral Nervous Systems (CNS and PNS). Complications involving cognitive function, balance, and mobility can result due to damage to white matter injury of the CNS. ADLD is inherited in an autosomal dominant manner and is caused by duplications of the LMNB1 gene. In rare cases, ADLD can result due to upstream deletions of the LMNB1 gene. In ADLD patients, normal cellular processes are disrupted in oligodendrocytes, a type of glial cell in the CNS that produces myelin. Because myelin is lost in ADLD patients, neurological symptoms such as impaired cognitive function, muscle stiffness, and motor dysfunction arise.
Nmezi et al. (2023) demonstrated that the LMNB1 upstream regulatory region contains a novel silencer element that regulates lamin B1 expression in oligodendrocytes. The mouse strain Lmnb1-del-19 was generated that deleted the putative silencer element upstream of the LMNB1 gene to recapitulate the nature of the disease. Lmnb1 was overexpressed in oligodendrocytes obtained from these mice, but not in other types of CNS cells. The question remained whether the deletion of the silencer element affected the expression of lamin B1 in Schwann cells, which oversee the myelination of axons in the PNS. This project aimed to determine whether lamin B1 expression in the sciatic nerve, a peripheral nerve, was altered in Lmnb1-del-19 mice when compared to wild-type (WT) controls. Approximately four and seven-month-old Lmnb1-del-19 and WT mice control mice were used. Our results demonstrated that lamin B1 expression between WT and Lmnb1-del-19 mice did not differ
Racial Equity Consciousness Institute Alumni Cohort Certification
No full textThis project was a seven week course that offered an exclusive and immersive learning alumni cohort that took a critical systems thinking approach to deconstructing the complexity and pervasiveness of racism and catalyzing personal and collective strategies to cultivate racial equity. Sessions were held from 6 – 8 pm on Tuesday nights on the 20th floor of the Cathedral of Learning from February to March