Now showing items 1-20 of 1108

    • Maryland Poison Center Annual Report 2020

      University of Maryland, Baltimore. Maryland Poison Center, 2020
    • Tracking COVID-19 Cases, Hospitalizations, and Deaths in U.S Nursing Homes Throughout the Pandemic

      Wallem, Alexandra; Kepczynska, Paulina; Simoni-Wastila, Linda; Qato, Danya; Fleming, Sean; Le, Tham; Yang, Jeanne (2021-04-30)
    • Bile Acids as Biomarkers and Evolutionary Phenotypes

      Shiffka, Stephanie; Swaan, Peter W.; Kane, Maureen A.; 0000-0002-3571-1836 (2021)
      Bile acids (BAs) are the amphipathic end products of cholesterol metabolism and represent a critical means of cholesterol excretion. BAs have a plethora of functions, including digestive roles, homeostatic feedback loops, energy metabolism, regulation of the microbiome, inflammation, and more. These effects implicate BAs in physiological and pathological processes throughout the body, not just within the enterohepatic circuit. To date, BAs have been linked to the pathogenesis of multiple types of cancer, type 2 diabetes mellitus, metabolic syndrome, and neurological disorders, among others. In health, BA homeostasis is precisely regulated by a process termed enterohepatic circulation (EHC). Several transport proteins are instrumental to this process, and disruptions in any of these transport systems lead to dysregulation of BA homeostasis, further leading to complications such as cholestasis and liver disease. BA metabolism and the EHC are conserved throughout vertebrate evolution, but the BA pool of more modern species has been modified to be more hydrophilic while still retaining properties of digestive surfactants. Though EHC is well-characterized, the understanding of eukaryotic transporters in this process is lacking, especially at the molecular level. Despite the recognition of bile acids as signaling molecules involved in disease progression, there remain numerous BAs that are poorly characterized. This is especially important because BAs are an extremely diverse group of molecules that represent the effects of host and microbiome metabolism. Furthermore, the unique physicochemical properties of these variations confer these molecules with differential levels of cytotoxicity and divergent, sometimes opposing, activation of cell signaling pathways. Thus, the scope of this dissertation is two-fold: first, to further characterize the BA pool in health and injury using cell and animal models; secondly, to use this information in order to probe the transporter responsible for the first step of the enterohepatic circulation, ASBT (SLC10A2). Completion of the first objective yielded improved understanding of BA metabolism in cell culture models and non-human primate laboratory models, as well as in radiation injury in the latter model. Accomplishment of the second objective returned insight into ASBT and BA evolution through the use of multiple vertebrate orthologs.
    • Targeting Zinc Finger Proteins with Exogenous Metals and Molecules: Lessons Learned from Tristetraprolin, a CCCH type Zinc Finger

      Ok, Kiwon; Michel, Sarah L. J.; 0000-0002-7724-8860 (2021)
      Zinc (Zn) plays a key role in inflammatory response, including regulating the nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) signaling pathway. Among the signaling proteins involved in the NF-κB pathway, many are known zinc finger proteins (ZFs), including Tristetraprolin (TTP). TTP is a non-classical CCCH-type Zinc Finger protein (ZF), that contains two Cys3His zinc binding domains and is a key regulator of the inflammatory response. TTP is a potential target for exogenous gold (Au) and copper (Cu), as well as hydrogen sulfide, an emerging gasotransmitter. To understand how TTP is targeted by other metals, the interactions of TTP were investigated using a combination of bioinorganic chemistry tools including as optical spectroscopy, native electrospray ionization mass spectrometry (ESI-MS), and X-ray absorption study (XAS). The first metal investigated was Cu(I). I discovered that Cu(I) can bind to the tandem ZF construct of TTP (TTP-2D) and disrupt structure and function. This finding indicates a potential relationship between Cu toxicity and metal-regulation of ZFs. The second metal investigated was Au(III). I discovered that the reactivity of TTP-2D with gold complex leads to Au exchange forming a series of Aux-TTP-2D complexes, with reduction of the gold from Au(III) to Au(I). These protein species are then functionally inactive (no RNA binding). When the same experiments were performed with TTP bound to RNA, the Zn-TTP/RNA complex is not disrupted by the Au-complex suggesting a protective role for RNA. To understand how H2S, a signaling molecule, targets Zn-TTP-2D, its reactivity was determined using a combination of cryo-ESI-MS, fluorescence, and electron paramagnetic resonance (EPR) spectroscopies. We found that the H2S oxidizes the cysteine residues of Zn-TTP via a mechanism that involves atmospheric oxygen, a persulfide intermediate and a radical reaction. The results of these biochemical studies of TTP will be presented in the context of TTP’s biological role. In addition, development of a method to follow Zn speciation in inflammatory cells via liquid chromatography connected to inductively coupled plasma (LC-ICP-MS), will be presented. Here, I use THP-1 cells, which are a human monocyte cell line as a model for inflammation, and demonstrate an approach to separate the zinc-proteome.
    • Use of Machine Learning To Predict COPD Treatments and Exacerbations in Medicare Older Adults: A Comparison of Multiple Approaches

      Le, Tham Thi; Simoni-Wastila, Linda (2021)
      Background: Multiple comorbidities, suboptimal adherence to maintenance medications (MMs), and exacerbations remain clinically important problems among older adults with chronic obstructive pulmonary disease (COPD). To better understand comorbidity profiles and to facilitate risk-based strategies for disease management, this dissertation quantified the prevalence and newly diagnosed rates of comorbidities, and validated predictive models of COPD medication non-adherence and exacerbations in the older Medicare population. Methods: Comorbidities were quantified in COPD beneficiaries and compared with matched non-COPD individuals using multivariable logistic regression. In a cohort of COPD beneficiaries with prevalent and new MM use, logistic and LASSO regressions were used to cross-validate the prediction of one-year non-adherence to MMs using different sets of predictors. A time-varying design was applied to assess improvement in predicting COPD exacerbations of the super learner versus component approaches (logistic regression, elastic net regression, random forest, gradient boosting, and neural network). Results: COPD beneficiaries had significantly increased odds of 40 measured comorbidities relative to matched non-COPD controls. The best-performing models in predicting MM non-adherence were those including initial MM adherence as a predictor, with validated Area Under the ROC Curves (AUC: 0.871-0.881). In predicting COPD exacerbations there were time-varying estimates of predictive accuracy and associations between predictors and the exacerbation outcome. Super learner performed slightly better (AUC: 0.650-0.761) than individual machine learning methods. Conclusions: Comorbidity burden is substantial and increases over time among Medicare older adults with COPD. Generated models achieved good and average discrimination in predicting COPD medication non-adherence and exacerbations, respectively. COPD hospitalization, oxygen supplementation, COPD treatment adherence, and numbers of inpatient visits were the most important predictors of COPD medication non-adherence and exacerbations. Super learner demonstrates a slight improvement compared to component methods, suggesting potential usability in augmenting prediction. Validated models with good discrimination can be adopted using friendly tools to optimizing resources for risk-based management and interventions of COPD.
    • The Creation of Objective Performance Criteria and Generation of Predictive Models among Medical Devices in a Vascular Space

      Gressler, Laura; Shaya, Fadia T.; 0000-0003-2042-2174 (2021)
      Background: Objective Performance Criteria (OPC) have been explored as a tool to address the growing pressures to expedite device approval and enhance active surveillance. Existing data infrastructures can be employed to develop OPC to evaluate the use of devices, and can be further leveraged to develop predictive models. The objective of this dissertation was to: (1) Develop a framework for the creation of OPC, (2) Compare the use of stent, atherectomy, and combination of stent and atherectomy, and (3) Formulate a predictive model used to predict the probability of undergoing a major adverse limb event (MALE) or experiencing death following the aforementioned treatments. Methods: The framework was developed in 3 phases through (1) Review of the literature, (2) Engagement of key stakeholders, and (3) Feedback from an advisory committee. Retrospective cohort studies were conducted using the Vascular Quality Initiative (2010-2018). Logistic regression and the Fine-Gray subdistribution hazard model were used to compare short- and long-term MALE, respectively. A generalized linear model (GLM), a Least Absolute Shrinkage and Selection Operator (LASSO) regularized GLM, a gradient boosted decision tree, and random forest model were compared when used to predict MALE and mortality. Results: The developed framework consisted of 5 elements: (1) Identification of Medical Devices, (2) Engagement of Key Stakeholders, (3) Selection of Data Source, (4) Performance of Appropriate Statistical Analyses, (5) Reporting of Findings. The odds of short-term MALE (0.94;95%CI:0.77-1.14) and hazards of long-term MALE (0.92;95%CI:0.82-1.04) were not significantly different in the combination stent and atherectomy group when compared to stent alone. The most effective predictive model was the gradient boosted decision tree (Area Under the Curve (AUC)= 0.7539) for MALE and the LASSO regularized GLM (AUC=0.7930) for mortality. Conclusions: The developed framework provides a guide and needed foundation for the continued generation of OPC. Applying the identified statistical steps in the framework to an existing data infrastructure showed that patients receiving combination stent and atherectomy do not experience significantly different rates of MALE compared to stent alone. Predictive models generated using the infrastructure demonstrated the ability of machine learning techniques to generate robust predictive models within the vascular space.
    • Excipient Screening and Spray Drying Process Optimization of Cell-based and Protein-based Biologics with Feasibility Demonstration of Oral Delivery

      Lu, Yuwei; Hoag, Stephen W.; 0000-0001-7081-3611 (2021)
      Biologics-based therapeutics, such as proteins and cells, have gained increasing popularity over the past few years. Formulation and process strategies have been applied to achieve quality biologics products, prioritizing desired efficacy and safety over shelf –life. In this thesis research, spray drying formulation development strategies were developed for a novel biotherapeutics ABAB antibody producing Sb-ABAB cells for the treatment of Clostridium difficile infection (CDI) and a recombinant human serum albumin (rHSA) using carbohydrate, protein-based, or other excipients and excipient combinations. Excipient functionality was explored using spectroscopy-based chemometrics investigation. In addition, novel mass spectroscopy based in cell-fast photochemical oxidation of proteins (ICFPOP-MS) was used in combination with homology labeling to probe the excipient - protein interactions. In addition, excipients and water activity effects on the storage stability of the Sb-ABAB spray dried product were explored to optimize shelf life. Subsequently, multivariate data analysis and design of experiments (DOE) were applied to explore the effects of spray dry process parameters on critical quality attributes of the protein- based and cell-based biological products. The spray dried protein/cell powders were further developed into oral dosage forms acceptable for patient use, such as tablets and capsules. The feasibility of developing oral protein tablets using IgG as model protein and enteric-coated Sb-ABAB capsules were explored. For example, compression force, particle size and storage relative humidity effects on the stability of the IgG tablets were investigated via analytical and biophysical analysis. In addition, colon targeted delivery of the Sb-ABAB minicapsules was developed and in vitro release assay was conducted to evaluate the enteric coating efficiency. In conclusion, cell-based and protein-based therapeutics were successfully spray dried while achieving desirable stability during the drying process. Furthermore, protein tablets and controlled release Sb-ABAB capsules were successfully developed, offering a novel alternative delivery approach to biologics products.
    • Optimizing Pain Management in Medically Complex Long-Term Care Residents

      Kuzucan, Aida; Simoni-Wastila, Linda; 0000-0003-0893-7028 (2021)
      Problem statement: While much needed clinical research has emphasized appropriate opioid stewardship in the general population, the needs of long-term nursing home care (LTC) residents remain largely ignored. Methods: This dissertation identified emerging trends in opioid therapy and initial opioid dosing patterns among LTC Medicare beneficiaries using Medicare Parts A, B and D claims, the Minimum Data Set 3.0 (MDS) and LTCFocus datasets. Aim 1 is a repeat cross-section study using resident and facility adjusted generalized estimating equations (GEE) to examine patterns of opioid use alone and in conjunction with pain-adjuvant medications among general, hospice, cancer, non-cancer chronic pain and dementia-related LTC stays from 2011 to 2015. Aim 2 identifies common patterns of average morphine equivalent daily dosing (MEDD) across six 30-day intervals starting with the first opioid prescription using latent class growth modeling (LCGM). Multivariate multinomial regression quantifies associations between different opioid use patterns over time and resident characteristics. Aim 3 accesses the odds of falls among residents with the highest probability of belonging to each of the commonly identified opioid dosing patterns with a facility clustered GEE model. Results: From 2011 to 2015, adjusted analyses found no constant significant changes in dose, duration, or frequency of opioid use. Increased use of anticonvulsant and skeletal muscle relaxants in opioid-related stays, particularly among residents with dementia, were found. LCGM identified four common opioid dosing patterns; extended high, short-term, intermittent and restart. Almost half of LTC residents received extended high opioid dosing. Multinomial regression found significant associations between sex, race, U.S. geographical region, pain diagnosis and receipt of other pain treatments with receipt of extended high dose therapy. Fall odds were found to be similar in the extended high and short-term groups. Models did find increased odds of falls in groups with less opportunity to develop tolerance (i.e., the restart and intermittent groups). Findings were not consistently significant in stratified analyses. Conclusions: Opioid use varies by resident characteristics. Opioid dosing varies over the course of therapy. More research on what factors lead to decisions regarding pain treatment and the impact of opioid dosing strategies on health-related outcomes are warranted.
    • Targeting the Activator Protein-1 Complex to Inhibit Airway Smooth Muscle Cell Hyperproliferation in Asthma

      Defnet, Amy Elizabeth; Shapiro, Paul, Ph.D.; Kane, Maureen A. (2021)
      Hyperproliferation of airway smooth muscle (ASM) cells leads to increased ASM mass causing airway obstruction in inflammatory diseases such as asthma. Currently, there are no effective therapies to modulate ASM cell proliferation that contributes to debilitating bronchoconstriction in severe asthmatics. Previous studies suggest that activator protein-1 (AP-1) transcription factor expression is upregulated in airway cells in asthma and inhibition of AP-1 could mitigate the hyperproliferation of ASM cells. AP-1 activity has been shown to be enhanced by upstream extracellular signal-regulated kinase (ERK1/2) signaling or antagonized by retinoic acid receptor (RAR)-mediated signaling. The overall goal of the current study was to evaluate the therapeutic potential of a combination therapy of an ERK1/2 inhibitor and RAR agonist to modulate AP-1 complex formation and activation. Aim 1 studies tested the hypothesis that a novel function-selective ERK1/2 inhibitor, referred to as SF-3-030, would mitigate off-target toxicity while regulating platelet-derived growth factor (PDGF) induced AP-1 activity and ASM cell proliferation. In Aim 2 studies we evaluated the role of retinoids in controlling AP-1 complex formation and identified a RARγ isoform-specific agonist, CD1530, as a potential therapeutic option for inhibition of AP-1 activity and ASM cell hyperproliferation. Aim 3 studies determined whether a polypharmacological approach of combining ERK1/2 inhibition and RAR agonism to target two different aspects of the AP-1 complex activation and formation would have an additive effect in preventing ASM hyperproliferation. Overall, these studies help further our understanding of how AP-1 signaling causes the hyperproliferation of ASM cells while elucidating possible therapeutic treatment options through ERK1/2 inhibition and RAR agonism.
    • Data Snapshot 2020

      University of Maryland, Baltimore. School of Pharmacy. Maryland Poison Center, 2020