MEDICA@MUSC (Medical University of South Carolina)
Not a member yet
1558 research outputs found
Sort by
Identifying Functional Imaging Markers in Psychosis Using fMRI
Get PDFMajor types of psychotic disorders include schizophrenia (SCZ), bipolar disorder (BP) and schizoaffective disorder (SZA). These disorders have profound and overlapping symptoms with marked cognitive deficits, and their diagnosis relies on symptom clusters. The treatments for psychosis are usually focused on positive symptoms such as delusions and hallucinations. Although cognitive impairments underlie both positive and negative symptoms, functional brain imaging biomarkers that can reliably predict a patient\u27s cognitive deficits are still lacking. Therefore, this project used functional MRI to explore the feasibility of using functional connectivity (FC) to predict cognitive performance.
A total of 207 subjects (BP: 79, SZ/SZA: 48, and HC: 80) with high functional MRI image (fMRI) quality (SNR\u3e 100, motion \u3c 0.3) were selected from the McLean MATRICS dataset. Subjects were divided into a discovery cohort (n=104) and an age, gender, and head motion matched validation cohort (n=103). The hypothesis was that FC could predict cognitive performance in the discovery cohort and that the prediction models could be generalized to the validation cohort. The connectomes for each subject were obtained by calculating the whole- brain connectivity using networks from the individualized functional parcellation as region of interests (ROIs). Models were trained to predict the 8 cognitive scores in the discovery cohort, respectively. The generalizability of these models was tested by applying these models to the validation cohort.
The trained models were able to predict 6 out of 8 cognitive scores using a LOOCV procedure. Models for working memory, composite score and attention score could be generalized to the validation cohort. A total of 35 FC features were identified as important for predicting performance in these cognitive domains. Significant differences between patients and controls were found for 13 of these features when considered individually.
In summary, this project has established a framework for biomarker discovery that may have clinical relevance for the diagnosis of psychosis early in the disease process by providing possible FC features that can be detected using fMRI and may help guide therapeutic interventions. The identified biomarkers also provide convergent evidence for network dysfunction in psychosis and suggest personalized treatment targets
HIV Treatment Utilization: An Exploration of One Ryan White Clinic
Get PDFAccording to the findings of recent research, the HIV epidemic is still an active problem all over the world. Some racial and ethnic groups have a significantly higher likelihood of contracting HIV, even though many efforts have been made at all levels of government, from the municipal to the federal, to raise public awareness of the recognized risk. There are two focal points for the HIV epidemic. While prevention is essential, infected patients must also be encouraged to continue treatment to accomplish viral suppression and delay the onset of AIDS. When we look more deeply at a local Ryan White program, we continue to understand better the supplementary services provided and the extent to which patients are utilizing these extension services
Multivariate Longitudinal Prognostic Factors: Improving Prediction and Association Modeling with a Single Binary Outcome Using Smoothing Splines and Composite Variables
No full textDatasets that are used in clinical research settings often contain repeated measures of multiple biomarkers of interest. Longitudinal analysis methods generally are designed for outcome variables that are also repeated measures, and many methods assume that data is balanced, i.e. collected at the same equally spaced timepoints for each individual. In many observational studies, however, this is often not the case. Datasets such as electronic health records are often large and complex, with a variety of lab values collected at often infrequent and unequally spaced times, and outcomes are sometimes a single distal endpoint. The values of some biomarkers might be subject to a high level of variation, due to acute conditions, normal fluctuations, or measurement error. In addition, there is often a correlation between variables that needs to be considered in a statistical analysis. Some existing methods fail to take this correlation into account and can also suffer drawbacks such as long computation times and problems with convergence. The goal of this dissertation is to develop methodology to address these issues that will be applicable in a variety of settings. In Aim 1, we have developed a method to smooth data from multiple continuous variables collected over time using multivariate tensor product smoothing splines, so that the correlation between these variables can be taken into account in the smoothing process. Smoothing creates a balanced dataset with reduced noise that we then examined for patterns using a robust fuzzy clustering algorithm. Our method resulted in better clustering accuracy than the use of individually smoothed variables. In Aim 2, we developed a method for the prediction of a binary distal outcome in a logistic model using fuzzy clusters as predictors, while taking into account the uncertainty in the clustering process. The third aim was the development of an R package to provide an accessible tool so that these methods can be easily applied in future research. In this work, we also examined the use of a composite score calculated from a number of collected biomarkers and compare it to the multivariate analysis using the individual biomarkers. Such a composite variable can be calculated either on observed values or on smoothed values of each biomarker and could offer a simpler implementation and interpretation in some instances. We apply our methods in two settings, where we analyze data collected for up to 7 days from acute liver failure (ALF) patients by the Acute Liver Failure Study Group (ALFSG; NCT005184400), and data collected in liver function tests and other lab work in a primary care setting at the Medical University of South Carolina. The methodology developed in this dissertation adds a valuable tool to the research field for utilizing multivariate longitudinal biomarker data when examining a distal binary outcome that can be applied in a variety of research settings
MyoD Functions as an Oncogene in Rhabdomyosarcoma by Promoting Survival Through Differentiation and CYLD
Get PDFRhabdomyosarcoma (RMS) is the most common soft tissue cancer among
children, characterized by a skeletal muscle lineage that is impaired from
undergoing terminal differentiation. NF-κB is constitutively active in cancer cells
and plays a critical role in cell survival. Although NF-κB is also activated in RMS,
surprisingly we find that these tumors are less dependent on NF-κB to overcome
stress-induced cell death. Instead, RMS cells survive by being partially
differentiated under the control of the myogenic transcription factor MyoD. Loss of
MyoD or cellular reprogramming dedifferentiates RMS tumor cells and promotes
cell death when exposed to stress. Further, use of a CRISPR screen identified the
tumor suppressor gene, CYLD, controlled by MyoD mediated DNA
methyltransferase activity to regulate RMS survival. Together, results reveal
oncogenic functions of MyoD that enhance RMS survival through pro-differentiation
and anti-cell death activities; findings that challenge the long existing
paradigm of MyoD in RMS pathogenesis
Crosstalk Between the Extracellular Matrix and the Cell- Cell Junction - Associated RNAi Machinery Regulates Colon Cancer Cell Behavior
Get PDFColon cancer is the third most common and second deadliest type of cancer. Colon cancer is broadly characterized by compromised epithelial integrity and by aberrant extracellular matrix (ECM) remodeling. However, a potential mechanistic connection between epithelial integrity and ECM remodeling that could be contributing to the disease progression, has not been explored yet. The Adherens Junction (AJ) is a cell-cell adhesion complex composed of cadherin and catenin family proteins and essential for establishing and maintaining epithelial tissue integrity. Our previous work revealed that PLEKHA7, an E-cadherin-p120 catenin partner, recruits the microprocessor and the RNA-induced silencing complex (RISC), key components of the RNAi machinery, as well as distinct sets of miRNAs and mRNAs, specifically to mature apical AJs. Through this recruitment, PLEKHA7 regulates processing and silencing activity of a set of miRNAs to suppress expression of several of their oncogenic mRNA targets. Here, to comprehensively interrogate the cellular mechanisms that are regulated by the AJ-associated RNAi machinery, we depleted PLEKHA7 from colon epithelial Caco2 cells and performed whole cell RNA sequencing, followed by pathway analysis. This analysis revealed that the top group of mRNAs that are upregulated upon PLEKHA7 depletion are those of ECM remodeling components. In particular, the master ECM remodeling regulators MEP1A, MMP1, and LOX, were among the top upregulated mRNAs upon PLEKHA7 depletion. We identified two PLEKHA7- regulated miRNAs, namely miR-24 and miR-30c, to be mediating suppression of MMP1, and LOX by PLEKHA7. PLEKHA7 knockout in Caco2 cells results in elevated MMP1 and LOX enzymatic activities, as well as in increased migration and invasion rates that depend on MMP1 and LOX activities. Corroborating the in vitro findings, Plekha7 knockout in mice results in aberrant collagen deposition in the colonic lamina propria and increased colon muscle layer thickness, both indicators of ECM remodeling and fibrosis. In turn, we also found that collagen and other ECM components differently impact junctional localization and complex formation of the AJ-associated RNAi machinery, impying for a potential negative feedback loop. Along these lines, increased ECM mechanical tension, which is typically observed under fibrotic and tumor conditions in the colon, also disrupted junctional localization of PLEKHA7 and of RNAi components. In summary, our data reveal a novel mechanism, whereby a bi-directional crosstalk between the ECM and epithelial AJs, mediated by the RNAi machinery, regulates pro-tumorigenic cell behavior in the colon
Machine Learning Approaches to Understanding and Predicting Cancer Screening Follow Through with Population and Health System Data
Get PDFIntroduction
Cancer is the second leading cause of death in the United States and cancer screening is a primary tool to reduce mortality. However, not all who are recommended to be screened actually follow through. This study investigates whether electronic medical record and geographic data is suitable to predict which patients are at risk of missing recommended screenings. The goal of this investigation is to design a data informed system that can automate the prediction of those at risk for missing screenings and provide insights into underlying reasons. This will enable resources to be focused to increase cancer screening adherence, with the overall goal of reducing mortality from cancer. Methods Data for this study was sourced from de-identified electronic medical records from the Medical University of South Carolina’s patient population and publicly available geographic datasets. This data was used to train a series of machine learning models to predict which patients would follow through with cancer screening tests, and describe underlying associations to diagnoses data, cancer histories and social determinants of health. Results This study found that it was possible to systematically identify small groups of female patients that are unlikely to follow through with mammogram screening. However, similar results were not found predicting lung cancer screening follow-though. Additionally, patterns associating social determinants at the county level cannot be used to make accurate predictions about individual patient follow through. It was also demonstrated that the core relationship between screening and mortality does not hold in high proportion minority areas. Conclusion
This study successfully shows that an automated system for identifying small groups of patients unlikely to complete mammogram screening is achievable and sets forth a methodology to development. It also provides valuable insights into the nature of social determinants associated with patients and their limits when geographically attributed
