The Role of Psychosocial Factors in Oral Health and Related Major Chronic Conditions

Psychosocial factors may be a common pathway that increases the susceptibility to co-occurring oral health conditions and other non-communicable chronic conditions. This thesis aimed to investigate the role of psychosocial stress in the co-occurrence of oral health conditions and systemic chronic conditions. First, a scoping review was conducted which found psychosocial stress to be positively associated with both oral and other chronic diseases. Next, a cluster analysis of oral health and multimorbidity profiles was conducted which showed middle-aged and older Canadians to have varying health profiles based on their oral health and multimorbidity status. We also found that individuals with inadequate oral health and multimorbidity to be more likely to report experiencing psychological distress or adverse childhood experiences. Further research can be directed to better understand the contribution of factors of psychosocial stress to the co-occurrence of oral health and multimorbidity in Canadians over the life-course

MRI Measures of Neurodegeneration as Biomarkers of Alzheimer's Disease

Indiana University-Purdue University Indianapolis (IUPUI)Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease. Many researchers believe that an effective AD treatment will prevent the development of disease rather than treat the disease after a diagnosis. Therefore, the development of tools to detect AD-related pathology in early stages is an important goal. In this report, MRI-based markers of neurodegeneration are explored as biomarkers of AD. In the first chapter, the sensitivity of cross-sectional MRI biomarkers to neurodegenerative changes is evaluated in AD patients and in patients with a diagnosis of mild cognitive impairment (MCI), a prodromal stage of AD. The results in Chapter 1 suggest that cross-sectional MRI biomarkers effectively measure neurodegeneration in AD and MCI patients and are sensitive to atrophic changes in patients who convert from MCI to AD up to 1 year before clinical conversion. Chapter 2 investigates longitudinal MRI-based measures of neurodegeneration as biomarkers of AD. In Chapter 2a, measures of brain atrophy rate in a cohort of AD and MCI patients are evaluated; whereas in Chapter 2b, these measures are assessed in a pre-MCI stage, namely older adults with cognitive complaints (CC) but no significant deficits. The results from Chapter 2 suggest that dynamic MRI-based measures of neurodegeneration are sensitive biomarkers for measuring progressive atrophy associated with the development of AD. In the final chapter, a novel biomarker for AD, visual contrast sensitivity, was evaluated. The results demonstrated contrast sensitivity impairments in AD and MCI patients, as well as slightly in CC participants. Impaired contrast sensitivity was also shown to be significantly associated with known markers of AD, including cognitive impairments and temporal lobe atrophy on MRI-based measures. The results of Chapter 3 support contrast sensitivity as a potential novel biomarker for AD and suggest that future studies are warranted. Overall, the results of this report support MRI-based measures of neurodegeneration as effective biomarkers for AD, even in early clinical and preclinical disease stages. Future therapeutic trials may consider utilizing these measures to evaluate potential treatment efficacy and mechanism of action, as well as for sample enrichment with patients most likely to rapidly progress towards AD

Role of network topology based methods in discovering novel gene-phenotype associations

The cell is governed by the complex interactions among various types of biomolecules. Coupled with environmental factors, variations in DNA can cause alterations in normal gene function and lead to a disease condition. Often, such disease phenotypes involve coordinated dysregulation of multiple genes that implicate inter-connected pathways. Towards a better understanding and characterization of mechanisms underlying human diseases, here, I present GUILD, a network-based disease-gene prioritization framework. GUILD associates genes with diseases using the global topology of the protein-protein interaction network and an initial set of genes known to be implicated in the disease. Furthermore, I investigate the mechanistic relationships between disease-genes and explain the robustness emerging from these relationships. I also introduce GUILDify, an online and user-friendly tool which prioritizes genes for their association to any user-provided phenotype. Finally, I describe current state-of-the-art systems-biology approaches where network modeling has helped extending our view on diseases such as cancer.La cèl•lula es regeix per interaccions complexes entre diferents tipus de biomolècules. Juntament amb factors ambientals, variacions en el DNA poden causar alteracions en la funció normal dels gens i provocar malalties. Sovint, aquests fenotips de malaltia involucren una desregulació coordinada de múltiples gens implicats en vies interconnectades. Per tal de comprendre i caracteritzar millor els mecanismes subjacents en malalties humanes, en aquesta tesis presento el programa GUILD, una plataforma que prioritza gens relacionats amb una malaltia en concret fent us de la topologia de xarxe. A partir d’un conjunt conegut de gens implicats en una malaltia, GUILD associa altres gens amb la malaltia mitjancant la topologia global de la xarxa d’interaccions de proteïnes. A més a més, analitzo les relacions mecanístiques entre gens associats a malalties i explico la robustesa es desprèn d’aquesta anàlisi. També presento GUILDify, un servidor web de fácil ús per la priorització de gens i la seva associació a un determinat fenotip. Finalment, descric els mètodes més recents en què el model•latge de xarxes ha ajudat extendre el coneixement sobre malalties complexes, com per exemple a càncer

2012 GREAT Day Program

SUNY Geneseo’s Sixth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1006/thumbnail.jp

Is There A Place for Race As a Legal Concept

What does race mean? The word race is omnipresent in American social, political, and legal discourse. The concept of race is central to contemporary debate about affirmative action, racial profiling, hate crimes, health inequities, and many other issues. Nevertheless, the best research in genetics, medicine, and the social sciences reveals that the concept of race is elusive and has no reliable definition. This article argues that race is an unnecessary and potentially pernicious concept. As evidenced by the history of slavery, segregation, the Holocaust, and other human tragedies, the idea of race can perpetuate prejudices and misconceptions and serve as justification for systematic persecution. Race suggests that human beings can be divided into subspecies, some of which are morally and intellectually inferior to others. The law has important symbolic and expressive value and is often efficacious as a force that shapes public ideology. Consequently, it must undermine the notion that race is a legitimate mechanism by which to categorize human beings. Furthermore, the focus on rigid racial classifications obfuscates political discussion concerning affirmative action, scientific research, and social inequities. When we speak of racial diversity, discrimination, or inequality, it is unclear whether we are referring to color, socio-economic status, continent of origin, or some other factor. Because the term race subsumes so many different ideas in people\u27s minds, it is not a useful platform for social discourse. The article proposes that race be replaced in future statutory and jurisprudential texts by other, more precise terminology, including color, continent of origin, national origin, and descent from ancestors of a particular color, national origin, or religion. Thus, legislators would engage in more careful statutory drafting and determine their legislative goals more precisely. In addition, the law would teach that, at most, the attributes we have called race refer only to superficial characteristics such as skin color or birthplace of one\u27s ancestors, a lesson that could make a valuable contribution to social progress

Identification of novel microRNAs as potential biomarkers for the early diagnosis of ovarian cancer using an in-silico approach

Philosophiae Doctor - PhDOvarian cancer (OC) is the most fatal gynaecologic malignancy that is generally diagnosed in the advanced stages, resulting in a low survival rate of about 40%. This emphasizes the need to identify a biomarker that can allow for accurate diagnosis at stage I. MicroRNAs (miRNAs) are appealing as biomarkers due to their stability, non-invasiveness, and differential expression in tumour tissue compared to healthy tissue. Since they are non-coding, their biological functions can be uncovered by examining their target genes and thus identifying their regulatory pathways and processes. This study aimed to identify miRNAs and genes as candidate biomarkers for early stage OC diagnosis, through two distinct in silico approaches. The first pipeline was based on sequence similarity between miRNAs with a proven mechanism in OC and miRNAs with no known role. This resulted in 9 candidate miRNAs, that have not been previously implicated in OC, that showed 90-99% similarity to a miRNA involved in OC. Following a series of in silico experimentations, it was uncovered that these miRNAs share 12 gene targets that are expressed in the ovary and also have proven implications in the disease. Since the miRNAs target genes contribute to OC onset and progression, it strengthens the notion that the miRNAs may be dysregulated as well. Using TCGA, the second pipeline involved analysing patient clinical data along with implementing statistical measures to isolate miRNAs and genes with high expression in OC. This resulted in 26 miRNAs and 25 genes being shortlisted as the potential candidates for OC management. It was also noted that targeting interactions occur between 15 miRNAs and 16 genes identified through this pipeline. In total, 35 miRNAs and 37 genes were identified from both pipelines

2016 IMSAloquium, Student Investigation Showcase

Welcome to the twenty-eighth year of the Student Inquiry and Research Program (SIR)! This is a program that is as old as IMSA. The SIR program represents our unending dedication to enabling our students to learn what it is to be an innovator and to make contributions to what is known on Earth.https://digitalcommons.imsa.edu/archives_sir/1026/thumbnail.jp

Computational functional prediction of novel long noncoding RNA in TCGA Glioblastoma multiforme sample

According to international human genome sequencing consortium 2004[43], it was known that only less than 2% of the total human genome code for proteins. This ignited quite a surprise in the scientific community. Since then, a lot of researchers are attracted towards the noncoding part of the genome. There are explosion of researches addressing the role of the 98% of the human untranslated regions of the genome. This shows that the transcription is not only limited to the protein coding regions of the genome rather more than 90% of the genome are likely to be transcribed. [43] This will result in the transcription of tens and thousands of the long noncoding RNAs (lncRNAs) with little or no coding potential. However, the molecular mechanism and function of long noncoding RNAs are still an open research topic. Although the functions of limited lncRNAs are identified, there is still a gap in identifying the function of novel lncRNAs. This project implements different computational methods to predict the function of novel lncRNAs identified from TCGA glioblastoma multiforme samples. The methods used in this functional prediction include both expression and sequence-based analysis approach. In expression-based analysis, the co-expressing genes with lncRNAs are used to predict the possible functional relation. In sequence based analysis, the gene-protein and lncRNA-protein interactions together with miRNA-lncRNA interactions are considered towards the possible functional predictions. The result from the integrated functional prediction on the novel lncRNAs show that TCGA_gbm3-153501 novel lncRNA which is co-expressed together with the THBS1 gene with correlation coefficient of more that 0.5 is predicted to function in cell-cell and cell-to-matrix interactions, platelet aggregation, angiogenesis, and tumorigenesis. [202] MSI1, RBM3 and RBM8A are RNA binding proteins (RBPs) that have binding site on both the first top five differentially expressed lncRNAs which are TCGA_gbm-2-104096501, TCGA_gbm-3-153501, TCGA_gbm-5-63687001 and TCGA_gbm-17-10671251 and IGF2 which is among the top 10 differentially expressed genes. Therefore, these lncRNAs are predicted to have functional role in cell proliferation and maintenance of stem cells in the central nervous system

Computational functional prediction of novel long noncoding RNA in TCGA Glioblastoma multiforme sample

According to international human genome sequencing consortium 2004[43], it was known that only less than 2% of the total human genome code for proteins. This ignited quite a surprise in the scientific community. Since then, a lot of researchers are attracted towards the noncoding part of the genome. There are explosion of researches addressing the role of the 98% of the human untranslated regions of the genome. This shows that the transcription is not only limited to the protein coding regions of the genome rather more than 90% of the genome are likely to be transcribed. [43] This will result in the transcription of tens and thousands of the long noncoding RNAs (lncRNAs) with little or no coding potential. However, the molecular mechanism and function of long noncoding RNAs are still an open research topic. Although the functions of limited lncRNAs are identified, there is still a gap in identifying the function of novel lncRNAs. This project implements different computational methods to predict the function of novel lncRNAs identified from TCGA glioblastoma multiforme samples. The methods used in this functional prediction include both expression and sequence-based analysis approach. In expression-based analysis, the co-expressing genes with lncRNAs are used to predict the possible functional relation. In sequence based analysis, the gene-protein and lncRNA-protein interactions together with miRNA-lncRNA interactions are considered towards the possible functional predictions. The result from the integrated functional prediction on the novel lncRNAs show that TCGA_gbm3-153501 novel lncRNA which is co-expressed together with the THBS1 gene with correlation coefficient of more that 0.5 is predicted to function in cell-cell and cell-to-matrix interactions, platelet aggregation, angiogenesis, and tumorigenesis. [202] MSI1, RBM3 and RBM8A are RNA binding proteins (RBPs) that have binding site on both the first top five differentially expressed lncRNAs which are TCGA_gbm-2-104096501, TCGA_gbm-3-153501, TCGA_gbm-5-63687001 and TCGA_gbm-17-10671251 and IGF2 which is among the top 10 differentially expressed genes. Therefore, these lncRNAs are predicted to have functional role in cell proliferation and maintenance of stem cells in the central nervous system

Finding the pathology of major depression through effects on gene interaction networks

The disease signature of major depressive disorder is distributed across multiple physical scales and investigative specialties, including genes, cells and brain regions. No single mechanism or pathway currently implicated in depression can reproduce its diverse clinical presentation, which compounds the difficulty in finding consistently disrupted molecular functions. We confront these key roadblocks to depression research - multi-scale and multi-factor pathology - by conducting parallel investigations at the levels of genes, neurons and brain regions, using transcriptome networks to identify collective patterns of dysfunction. Our findings highlight how the collusion of multi-system deficits can form a broad-based, yet variable pathology behind the depressed phenotype. For instance, in a variant of the classic lethality-centrality relationship, we show that in neuropsychiatric disorders including major depression, differentially expressed genes are pushed out to the periphery of gene networks. At the level of cellular function, we develop a molecular signature of depression based on cross-species analysis of human and mouse microarrays from depression-affected areas, and show that these genes form a tight module related to oligodendrocyte function and neuronal growth/structure. At the level of brain-region communication, we find a set of genes and hormones associated with the loss of feedback between the amygdala and anterior cingulate cortex, based on a novel assay of interregional expression synchronization termed "gene coordination". These results indicate that in the absence of a single pathology, depression may be created by dysynergistic effects among genes, cell-types and brain regions, in what we term the "floodgate" model of depression. Beyond our specific biological findings, these studies indicate that gene interaction networks are a coherent framework in which to understand the faint expression changes found in depression and complex neuropsychiatric disorders