by Yeung Hiu Yan. / Thesis (M.Phil.)--Chinese University of Hong Kong, 1999. / Includes bibliographical references (leaves 158-171). / Abstracts in English and Chinese. / ACKNOWLEDGEMENT --- p.i / ABBREVIATIONS --- p.ii / ABSTRACT (English & Chinese) --- p.iii / TABLE OF CONTENT --- p.viii / INDEX FOR FIGURES --- p.xii / INDEX FOR TABLES --- p.xvi / Chapter 1. --- INTRODUCTION --- p.2 / Chapter 1.1. --- History of Distraction Osteogenesis --- p.3 / Chapter 1.2. --- Clinical Application of Distraction Osteogenesis --- p.5 / Chapter 1.2.1. --- Limb-Lengthening --- p.5 / Chapter 1.2.2. --- Correction of Deformities and Non-Unions --- p.5 / Chapter 1.2.3. --- Bone Transport --- p.6 / Chapter 1.2.4. --- Reconstruction of the mandible --- p.7 / Chapter 1.3. --- Bone-specific Alkaline Phosphatase (BALP) --- p.8 / Chapter 1.4. --- Osteocalcin --- p.9 / Chapter 1.5. --- Bone Growth Factors --- p.11 / Chapter 1.6. --- Fibroblast Growth Factors (FGFs) --- p.12 / Chapter 1.6.1. --- Acidic Fibroblast Growth Factor (aFGF) --- p.13 / Chapter 1.6.2. --- Basic Fibroblast Growth Factor (bFGF) --- p.14 / Chapter 1.7. --- Transforming Growth Factor-pi (TGF-β1) --- p.16 / Chapter 1.8. --- Fracture Healing --- p.18 / Chapter 1.8.1. --- Histology --- p.18 / Chapter 1.8.2. --- Growth Factor Expression --- p.18 / Chapter 1.9. --- Distraction Osteogenesis --- p.19 / Chapter 1.9.1. --- Histology --- p.19 / Chapter 1.9.2. --- Growth Factor Expression --- p.20 / Chapter 1.10. --- Aim of the Study --- p.21 / Chapter 2. --- METHODOLOGY --- p.23 / Chapter 2.1. --- Animal Model --- p.23 / Chapter 2.1.1. --- Source of Animal --- p.23 / Chapter 2.1.2. --- Animal Operation --- p.23 / Chapter 2.1.3. --- Fracture Healing Model --- p.24 / Chapter 2.1.4. --- Distraction Osteogenesis Model --- p.24 / Chapter 2.2. --- Sample Collection --- p.25 / Chapter 2.2.1. --- Tissue Sample Collection and Preparation --- p.25 / Chapter 2.2.1.1. --- Test for the Complete Decalcification of the Calluses --- p.26 / Chapter 2.2.2. --- Blood Sample Collection and Storage --- p.26 / Chapter 2.3. --- Bone Mineral Density Measurement of the Distracted Callus and the Fracture Callus --- p.27 / Chapter 2.3.1. --- Fracture Healing Group --- p.27 / Chapter 2.3.2. --- Distraction Osteogenesis Group --- p.28 / Chapter 2.4. --- Serum Bone Specific Alkaline Phosphatase (BALP) Activity --- p.28 / Chapter 2.4.1. --- Wheat Germ Lectin (WGL) Precipitation of BALP --- p.28 / Chapter 2.4.1.1. --- Reagent --- p.28 / Chapter 2.4.1.2. --- Preparation and Measurement of Samples --- p.29 / Chapter 2.4.1.3. --- Auto-analyzer Setup --- p.30 / Chapter 2.5. --- Quantification of the Osteocalcin in Serum --- p.30 / Chapter 2.5.1. --- Reagent and Sample Preparation --- p.31 / Chapter 2.5.2. --- Detection Procedures --- p.31 / Chapter 2.6. --- Localization of the Growth Factors in Distraction Osteogenesis and Fracture Healing --- p.32 / Chapter 2.6.1. --- Immunohistochemistry of the Growth Factors --- p.33 / Chapter 2.6.1.1. --- Reagents and Solution Preparation --- p.33 / Chapter 2.6.1.2. --- Experimental Procedure --- p.36 / Chapter 2.6.1.3. --- Evaluation of Immunohistochmical Staining Results --- p.37 / Chapter 2.6.2. --- Verification of the Primary Antibody Used in the Study --- p.37 / Chapter 2.6.2.1. --- Tissue Preparation --- p.37 / Chapter 2.6.2.2. --- Antibody to Acidic Fibroblast Growth Factor (aFGF) --- p.38 / Chapter 2.6.2.2.1. --- Immunohistochemistry of Goat Brain and Growth Plate --- p.38 / Chapter 2.6.2.2.2. --- Dot Blot --- p.38 / Chapter 2.6.2.2.2.1. --- Materials and Reagents --- p.38 / Chapter 2.6.2.2.2.2. --- Procedures --- p.39 / Chapter 2.6.2.2.3. --- Sodium Dodecylsulphate Polyacrylamide Gel Electrophoresis (SDS-PAGE) --- p.41 / Chapter 2.6.2.2.3.1. --- Materials and Reagents --- p.41 / Chapter 2.6.2.2.3.2. --- Procedures --- p.42 / Chapter 2.6.2.2.4. --- Western Blotting --- p.43 / Chapter 2.6.2.2.4.1. --- Materials and Reagents --- p.43 / Chapter 2.6.2.2.4.2. --- Procedures --- p.44 / Chapter 2.6.2.3. --- Antibody to Basic Fibroblast Growth Factor --- p.45 / Chapter 2.6.2.4. --- Antibody to Transforming Growth Factor-β1 --- p.45 / Chapter 3. --- RESULTS --- p.53 / Chapter 3.1. --- Animal Model --- p.53 / Chapter 3.1.1. --- Fracture Healing Animal Model --- p.53 / Chapter 3.1.1.1. --- Radiography of Fracture Healing --- p.53 / Chapter 3.1.2. --- Distraction Osteogenesis Animal Model --- p.54 / Chapter 3.1.2.1. --- Gross Morphology of Distraction Osteogenesis --- p.54 / Chapter 3.1.2.2. --- Radiography of Distraction Osteogenesis --- p.55 / Chapter 3.2. --- Bone Mineral Density (BMD) Measurement --- p.56 / Chapter 3.2.1. --- In Fracture Healing --- p.56 / Chapter 3.2.2. --- Distraction Osteogenesis --- p.57 / Chapter 3.3. --- Bone-specific Alkaline Phosphatase Activity in Goat Serum --- p.59 / Chapter 3.3.1 --- ", Fracture Healing" --- p.59 / Chapter 3.3.2. --- Distraction Osteogenesis --- p.59 / Chapter 3.4. --- Serum Osteocalcin Measurement --- p.60 / Chapter 3.4.1. --- Fracture Healing --- p.60 / Chapter 3.4.2. --- Distraction Osteogenesis --- p.60 / Chapter 3.5. --- Histology --- p.61 / Chapter 3.5.1. --- Fracture Healing --- p.61 / Chapter 3.5.2. --- Distraction Osteogenesis --- p.64 / Chapter 3.6. --- Verification of Primary Antibody Used in the Study --- p.67 / Chapter 3.6.1. --- Antibody to Acidic Fibroblast Growth Factor --- p.67 / Chapter 3.6.1.1. --- Dot Blot --- p.67 / Chapter 3.6.1.2. --- Western Blotting --- p.68 / Chapter 3.6.1.3. --- Immunohistochemistry of Goat Brain and Growth Plate --- p.68 / Chapter 3.6.2. --- Antibody to Basic Fibroblast Growth Factor --- p.69 / Chapter 3.6.2.1. --- Dot Blot --- p.69 / Chapter 3.6.2.2. --- Immunohistochemistry of Goat Brain and Growth Plate --- p.69 / Chapter 3.6.3. --- Antibody to Transforming Growth Factor-β1 --- p.70 / Chapter 3.6.3.1. --- Western Blotting --- p.70 / Chapter 3.6.3.2. --- Immunohistochemistry of Growth Plate --- p.70 / Chapter 3.7. --- Localization of Growth Factors in Fracture Healing and Distraction Osteogenesis --- p.70 / Chapter 3.7.1. --- Acidic Fibroblast Growth Factor --- p.71 / Chapter 3.7.1.1. --- Fracture Healing --- p.71 / Chapter 3.7.1.2. --- Distraction Osteogenesis --- p.72 / Chapter 3.7.2. --- Basic Fibroblast Growth Factor --- p.73 / Chapter 3.7.2.1. --- Fracture Healing --- p.73 / Chapter 3.7.2.2. --- Distraction Osteogenesis --- p.74 / Chapter 3.7.3. --- Transforming Growth Factor-β1 --- p.75 / Chapter 3.7.3.1. --- Fracture Healing --- p.75 / Chapter 3.7.3.2. --- Distraction Osteogenesis --- p.76 / Chapter 4. --- DISCUSSION --- p.142 / Chapter 4.1. --- The Biochemical Events in Fracture Healing --- p.142 / Chapter 4.2. --- The Biochemical Events in Distraction Osteogenesis --- p.147 / Chapter 4.3. --- Limitations of the present study --- p.153 / Chapter 4.4. --- Future Study --- p.154 / Chapter 5. --- CONCLUSION --- p.156 / BIBLIOGRAPHY --- p.158

Cheung Siu-king. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2000. / Includes bibliographical references (leaves [103-113]). / Abstracts in English and Chinese. / ACKNOWLEDGMENT --- p.i / TABLE OF CONTENTS --- p.ii / LIST OF ABBREVIATIONS --- p.vi / LIST OF TABLES --- p.vi / LIST OF FIGURES --- p.ix / ABSTRACT (ENGLISH VERSION) --- p.x / ABSTRACT (CHINESE VERSION) --- p.xii / Chapter 1. --- INTRODUCTION --- p.1 / Chapter 1.1. --- ADOLESCENT IDIOPATHIC SCOLIOSIS --- p.1 / Chapter 1.1.1. --- prevalence and geographic patterns of ais --- p.1 / Chapter 1.1.2. --- CLINICAL ASPECTS OF AIS --- p.3 / Chapter 1.1.3. --- ETIOLOGY OF AIS --- p.8 / Chapter 1.2. --- OBJECTIVES OF THIS STUDY --- p.24 / Chapter 2. --- SUBJECTS AND METHODS --- p.25 / Chapter 2.1. --- STUDY DESIGN --- p.25 / Chapter 2.2. --- SUBJECTS RECRUITMENT --- p.25 / Chapter 2.2.1. --- ais subjects --- p.25 / Chapter 2.2.2. --- control subjects --- p.25 / Chapter 2.2.3. --- GROUPING ACCORDING TO THE CHRONOLOGICAL AGE --- p.26 / Chapter 2.2.4. --- informed Consent --- p.26 / Chapter 2.2.5. --- EVALUATION OF COBB'S ANGLE --- p.26 / Chapter 2.3. --- ANTHROPOMETRIC ASSESSMENTS --- p.26 / Chapter 2.4. --- BMD MEASUREMENTS --- p.28 / Chapter 2.4.1. --- measured by dexa --- p.28 / Chapter 2.4.2. --- measured by pqct --- p.30 / Chapter 2.5. --- BONE FORMATION MARKER ： BALP --- p.32 / Chapter 2.5.1. --- SERUM COLLECTION --- p.32 / Chapter 2.5.2. --- ABBOTT METHODS FOR SERUM ALP ACTIVITY --- p.32 / Chapter 2.6. --- BONE RESORPTION MARKER ： DPD --- p.34 / Chapter 2.6.1. --- PYRILINK-D KITS REAGENT --- p.34 / Chapter 2.6.2. --- CREATININE ASSAY --- p.34 / Chapter 2.7. --- GENETIC MARKER - POLYMORPHISM OF ESTROGEN RECEPTOR GENE --- p.38 / Chapter 2.7.1. --- DIGESTION OF PERIPHERAL BLOOD CELLS --- p.38 / Chapter 2.7.2. --- QUANTITATION OF DNA --- p.39 / Chapter 2.7.3. --- CONFIRMATION OF INTEGRITY OF DNA --- p.39 / Chapter 2.7.4. --- POLYMERASE CHAIN REACTION (PCR) --- p.39 / Chapter 2.7.5. --- REACTION BUFFER --- p.39 / Chapter 2.8. --- STATISTICS --- p.45 / Chapter 3. --- RESULTS --- p.46 / Chapter 3 .1 --- SUBJECT DISTRIBUTION OF AIS AND NORMAL CONTROL --- p.46 / Chapter 3.1.1. --- "mean ages of menarche, breast development and pubic hair development" --- p.47 / Chapter 3.1.2. --- "PUBERTAL STATUES OF DIFFERENT AGE GROUPS EVALUATED BY MENARCHE, BREAST DEVELOPMENT AND PUBIC HAIR DEVELOPMENT" --- p.48 / Chapter 3.2. --- ANTHROPOMETRIC ASSESSMENTS --- p.49 / Chapter 3.2.1. --- OVERALL REVIEW OF ANTHROPOMETRIC ASSESSMENTS --- p.49 / Chapter 3.2.2. --- ANTHROPOMETRIC ASSESSMENTS ACCORDING TO THE CHRONOLOGICAL AGE --- p.50 / Chapter 3.3. --- BMD PROFILE OF AIS PATIENTS --- p.51 / Chapter 3.3.1. --- ABMD MEASURED BY DEXA (OVERALL REVIEW) --- p.51 / Chapter 3.3.2. --- ABMD IN DIFFERENT AGE GROUPS --- p.52 / Chapter 3.3.3. --- VBMD MEASURED BY PQCT (OVERALL REVIEW) --- p.52 / Chapter 3.3.4. --- VBMD IN DIFFERENT AGE GROUPS --- p.53 / Chapter 3.3.5. --- PREVALENCE OF OSTEOPENIA IN AIS PATIENTS --- p.53 / Chapter 3.3.6. --- SYMMETRY OF BILATERAL PROXIMAL FEMUR AND DISTAL TIBIA … --- p.54 / Chapter 3.3.7. --- CORRELATION OF ABMD AND VBMD WITH ANTHROPOMETRIC PARAMETERS AND SPINAL DEFORMITY --- p.54 / Chapter 3.4. --- BONE FORMATION MARKER- BALP --- p.55 / Chapter 3.5. --- BONE RESORPTION MARKER -DPD --- p.56 / Chapter 3.6. --- GENETIC MARKER -ESTROGEN RECEPTOR GENE --- p.57 / Chapter 4 --- DISCUSSION…… --- p.84 / Chapter 4.1 --- BONE MINERAL DENSITY OF AIS PATIENTS --- p.84 / Chapter 4.2 --- ANTHROPOMETRIC MEASUREMENTS --- p.89 / Chapter 4.3 --- BONE BIOCHEMICAL TURNOVER MARKER --- p.91 / Chapter 4.4 --- GENETIC MARKER - ER GENE --- p.97 / Chapter 4.4.1 --- OSTEOPORTIC CANDIDATE GENE- ER GENE --- p.98 / Chapter 4.4.2 --- NO CORRELATION BETWEEN ER GENE AND AIS --- p.99 / Chapter 4.5 --- SUMMARY --- p.100 / Chapter 5. --- CONCLUSION --- p.101 / BIBLIOGRAPHY --- p.XIV / APPENDIX --- p.XXV

Soft tissue infection in a diabetic foot with an ulcer is often clinically obvious but the diagnosis of osteomyelitis underlying a diabetic foot ulcer is challenging. It has been calculated that there are over 1 million amputations worldwide for diabetes related complications every year, many preceded by an ulcer complicated by osteomyelitis. <br /> This research encompasses two studies attempting to add to the ways in which osteomyelitis is diagnosed. <br /> The first was examining the role of inflammatory blood markers in recognising and separating ulcers with cutaneous infection from both suspected and proven osteomyelitis. The response of the body to produce these markers when an injury occurs is well known but arguments exist as to the capacity of the individual with diabetes to do so. Despite the recognition and allowance for common confounding factors no trend was found. This study may have been more difficult than originally thought due to the many interactions of the diseased state of diabetes, the drugs used to control it and the many other confounders that would have influenced the inflammatory process and as such the level of the markers. <br /> The second study was comparing a new form of scanning technique (SPECT/CT) to the technique most commonly used as a ‘gold standard’ – MRI. The results of each type of scan were compared to the clinical diagnosis and each other. The SPECT/CT scan appears to show some good results and may be a more suitable scan for individuals who are unable to have a MRI for example due to the need to introduce a renally excreted drug to help make the images clearer but it does mean introducing a small amount of radiation into the individual.

Avaliou-se a influência da exposição ao flúor (F) através da água de beber, da velocidade de crescimento das unhas, da idade e do gênero na concentração deste elemento nas unhas das mãos e dos pés. Em adição, verificou-se a correlação entre as concentrações de F na saliva total, saliva do ducto da parótida e unhas das mãos e dos pés. Participaram do estudo 300 indivíduos, das faixas etárias de 3-7,14-20, 30-40 e 50-60 anos, residentes de cinco comunidades brasileiras, sendo duas no Estado de São Paulo (Pirajuí e Bauru, com água não fluoretada e 0,7 mgF/L na água de abastecimento, respectivamente) e três no Estado da Paraíba (Cajazeirinhas, Brejo dos Santos e Brejo das Freiras, com 0,2, 0,7 e 1,7 mgF/L na água de consumo, respectivamente). Foram coletadas duas ou três (apenas em Bauru) amostras de água de beber, além de duas amostras de: unhas dos pés, unhas das mãos, saliva total e saliva do ducto da parótida de cada indivíduo. O F nas amostras foi analisado com eletrodo íon-específico. Os dados obtidos foram analisados estatisticamente através de análise de variância e regressão linear (p<0,05). A exposição ao F através da água de beber, a velocidade de crescimento das unhas, a idade e o gênero influenciaram a concentração de F nas unhas das mãos e dos pés, sendo que o fator de exposição ao F através da água foi o que exerceu maior influência pelo modelo de regressão linear adotado. As unhas dos pés (R2=0,46) se mostraram melhores indicadoras de exposição ao F do que as das mãos (R2=0,24). Foi encontrada uma correlação negativa significativa entre velocidade de crescimento das unhas e concentração de F nas mesmas. Houve correlação positiva significativa entre concentração de F nas unhas das mãos e dos pés com: saliva total (r=0,36 e r=0,41) e saliva do ducto da parótida (r=0,25 e r=0,53), respectivamente. Também se observou correlação positiva entre saliva total e do ducto da parótida (r=0,24), bem como entre concentração de F na água e saliva total (r=0,41) e saliva do ducto (r=0,65). Todos os fatores testados influenciaram os níveis de F nas unhas e, portanto, devem ser levados em consideração quando se utiliza este marcador biológico. / The influence of fluoride (F) concentration in the drinking water, nails growth rate, age and gender upon the F content in fingernail and toenail were evaluated. In addition, the correlations among the F concentrations in whole saliva, parotid ductal saliva and finger/toenails were verified. Three hundred volunteers of 3-7, 14-20, 30-40, 50-60 years participated. They were residents of five Brazilian communities, two in Sao Paulo State (Pirajuí and Bauru, non-fluoridated and 0.7 mgF/L artificially fluoridated drinking water, respectively) and three in Paraiba State (Cajazeirinhas, Brejo dos Santos and Brejo das Freiras, 0.2, 0.7 and 1.73 mgF/L naturally fluoridated drinking water, respectively). Two or three samples of drinking water, and two samples of fingernails, toenails, whole saliva and ductal saliva were collected from each volunteer, with one-week interval period between the collections. F in water, whole saliva, ductal saliva and nails was determined using the ion-sensitive electrode. Data were analyzed by ANOVA and linear regression (p<0.05). The F exposure from the drinking water, nails growth rate, age and gender influenced the levels of F in fingernails and toenails. Considering the model of multivariate linear regression adopted, F exposure from the water influenced the most. Toenails (R2=0.46) seemed to be better indicators of F than fingernails (R2=0.24). It was found a significant negative correlation between nails growth rate and their content of F. Positive correlations were found between F concentration in fingernails and toenails and: F concentration in whole saliva (r=0.36 and r=0.41) and in parotid ductal saliva (r=0.25 and r=0.53), respectively. Moreover, it was observed a positive correlation between whole and parotid saliva (r=0.24), as well as between F concentration in the drinking water and whole (r=0.41) and parotid saliva (r=0.65). All factors that influenced nails F concentration must be taken into account when using them as biological markers.

This dissertation proposes a family of algorithms for deriving signatures of mutually associated features, to which we refer as attractor metafeatures, or simply attractors. Specifically, we present multi-cancer attractor derivation algorithms, identifying correlated features in signatures from multiple biological data sets in one analysis, as well as the groups of samples or cells that exclusively express these signatures. Our results demonstrate that these signatures can be used, in proper combinations, as biomarkers that predict a patient’s survival rate, based on the transcriptome of the tumor sample. They can also be used as features to analyze the composition of the tumor. Through analyzing large data sets of 18 cancer types and three high-throughput platforms from The Cancer Genome Atlas (TCGA) PanCanAtlas Project and multiple single-cell RNA-seq data sets, we identified novel cancer attractor signatures and elucidated the identity of the cells that express these signatures. Using these signatures, we developed a prognostic biomarker for breast cancer called the Breast Cancer Attractor Metagenes (BCAM) biomarker as well as a software platform to analyze the tumor sample, called Analysis of the Single-Cell Omics for Tumor (ASCOT).

Endosomal dysfunction has been mechanistically linked to Alzheimer’s Disease (AD). To date, no in vivo biomarkers for this cellular deficit exist. Yet such biomarkers are required for determining its prevalence in AD and tracking its time course—both in disease progression and potential clinical trials. With this goal in mind, we made use of an assortment of mouse models bearing AD-related endosomal trafficking defects through selective deletion of retomer core proteins. We collected CSF and brain exosomes from these retromer-deficient models and performed a battery of molecular inquiries which included lipidomic and proteomic screens, as well as hypothesis-driven biochemistry. The results of this comprehensive investigation include the first characterization of the murine CSF lipidome and the deepest characterization to date of the murine CSF proteome. Herein, we report that VPS26a haploinsufficiency in the brain imparts no detectable protein changes in the CSF as measured by labeled LC-MS/MS at three months of age. This deficit does, however, cause a reliable reduction of CSF sphingomyelin d18:1/18:1, which is exacerbated by age, extending to other sphingomyelins and other lipid classes including dihydrosphingomyelins and monohexosylceramides. Complete knockout of its paralog VPS26b promotes an enrichment of BACE1-cleaved APP CTFs (Beta-CTFs) in brain-derived exosomes and may alter exosomal biogenic pathways. Similar trends were seen in a neuronal-specific knockout (via Camk2-Cre recombinase) of retromer’s linchpin, VPS35. Most importantly, an unbiased proteomic screen of CSF collected from mice with a selective knock out of VPS35 in forebrain neurons (engineered using the Camk2 system) uncovered a total of 71 hits (52 parametric and 19 nonparametric) from the 1505 proteins detected. Pathway analysis and follow-up studies identified two distinct molecular categories with previously established relevance to AD: BACE1 substrates and MAPT (more commonly referred to as tau). We report that, both in vivo and in vitro, neuronal-selective knockout of VPS35 causes increased secretion of the N-terminal fragments (NTFs) of BACE1 substrates APLP1 and CHL1 as well as total tau, and importantly, that these events occur independent of cell death. Further, we find evidence of convergence of these pathways in both mouse and human CSF. However, as these BACE1 substrates likely accumulate in plaques, we propose CSF total tau as a biomarker of endosomal dysfunction with utility over the entire course of AD progression. We have identified and validated a series of in vivo biomarkers that are reflective of AD-associated endosomal dysfunction. While clearly sensitive to this cellular pathology, future work is required to determine their specificity. Additionally, follow-up studies are required to show that interventions which rescue endosomal dysfunction affect this molecular profile. The identified biomarkers hold great promise for early detection of endosomal dysfunction in AD and for tracking its course, during the disease progression and for clinical trials. Furthermore, the unexpected but validated finding, showing that increased CSF tau is reflective of AD-associated endosomal dysfunction, suggests that endosomal dysfunction is a universal deficit shared among AD patients in its earliest stages of disease.

Due to a lack of a gold standard objective marker, the current practice for diagnosing neuropsychiatric disorders is mostly based on clinical symptoms, which may occur in the late stage of the disease. Clinical diagnosis is also subject to high variance due to between- and within-subject variability of patient symptomatology and between-clinician variability. Effectively modeling disease course and making early predictions using biomarkers and subtle clinical signs are critical and challenging both for improving diagnostic accuracy and designing preventive clinical trials for neurological disorders. Leveraging the domain knowledge that certain biological characteristics (i.e., causal genetic mutation, cognitive reserve) are part of the disease mechanism, we first propose a nonlinear model with random inflection points depending on subject-specific characteristics to jointly estimate the trajectories of the biomarkers. The model scales different biomarkers into comparable progression curves with a temporal order based on the mean inflection point. Meanwhile, it assesses how subject-specific characteristics affect the dynamic trajectory of different markers, which offers information on designing preventive therapeutics and personalized disease management strategy. We use EM algorithm for the estimation. Extensive simulation studies are conducted. The method is applied to biomarkers in neuroimaging, cognitive, and motor domains of Huntington’s disease. Under the same nonlinear random effects model framework, we propose the second model inspired by the neural mass models. Biomarkers are modeled as the average manifestation of the functioning status of neuronal ensembles. A latent liability score is shared across biomarkers to pool information. We use EM algorithm for maximum likelihood estimation, and a normal approximation is used to facilitate numerical integration. The results show that some neuroimaging biomarkers are early signs of the onset of Huntington’s disease. Finally, we develop an online tool that provides the personalized prediction of biomarker trajectory given the medical history and baseline measurements. The third model uses a dynamical system based on differential equations to model the evolution of biomarkers. The dynamical system is not only useful to characterize the temporal patterns of the biomarkers, but also informative of the interaction among the biomarkers. We propose a semiparametric dynamical system based on multi-index models. For estimation and inference, we consider a two-step procedure based on the integral equations from the proposed model. The algorithm iterates between the estimation of the link function through splines and the estimation of the index parameters, allowing for regularization to achieve sparsity. We prove the model identifiability and derive the asymptotic properties of the model parameters. A benefit of the model and the estimation approach is to pool information from multiple subjects to construct the network of biomarkers and provide inference. We demonstrate the empirical improvement over competing approaches with the simulated gene expression data from the third DREAM challenge. It is applied to the electroencephalogram (EEG) data and it reveals different effective connectivity of brain networks for patients with alcohol dependence under different cognitive tasks.

Translocator Protein 18 kDa (TSPO) is a protein that is expressed at low levels in the brain, but upon brain injury or inflammation, increases its expression in the areas of the brain specific to injury. In this way, TSPO can be used as a biomarker of brain inflammation and injury. TSPO is primarily expressed in two cell types, microglia and astrocytes, and is used as a marker of reactive gliosis in various brain pathologies. Currently, there is a paucity of knowledge on the function(s) of TSPO in glial cells. Recent studies using conditional and global TSPO knockout mice have questioned the role of TSPO in translocating cholesterol across the outer mitochondrial membrane as the first step in steroidogenesis. In the brain, microglia and astrocytes exhibit distinct spatial and temporal patterns of TSPO upregulation. These differential patterns are not well characterized across disease models and in particular, are poorly characterized in the early stages of disease, prior to behavioral and clinical disease manifestations. Importantly, these distinct patterns of TSPO upregulation may indicate different functions of TSPO in microglia and astrocytes. We examined TSPO levels in a neurodegenerative transgenic mouse model of Sandhoff disease (SD) and longitudinally compared TSPO levels to behavioral manifestations of disease and other neuropathological endpoints (neurodegeneration, reactive gliosis, ganglioside accumulation). This study confirmed TSPO upregulation prior to neurodegeneration in a brain region-dependent and disease course-dependent way. In brain regions with increased TSPO levels, there was a differential pattern of glial cell activation with astrocytes being activated earlier than microglia during the progression of disease. Immunofluorescent confocal imaging confirmed that TSPO colocalizes with both microglia and astrocyte markers, but the glial source of the TSPO response differs by brain region and age in SD mice. We next wanted to gain insight into the function of TSPO in microglia. We previously demonstrated that TSPO ligands (TSPO-L) (1-100 nM) induced intracellular ROS production which was abrogated by NADPH oxidase (NOX2) inhibitors, thereby indicating an association between TSPO and NOX2. To further elucidate the relationship between TSPO and NOX, we determined the source of ROS production resulting from microglia exposure to TSPO-L. Intracellular and extracellular ROS production was inhibited by NOX inhibitors, but not by a mitochondria permeability transition pore inhibitor, indicating that the source of ROS production is from NOX and not from mitochondria. These findings were confirmed using the mitochondria specific ROS probe MitoSOX. To further explore the TSPO-NOX2 association, we used 3 molecular approaches to examine protein-protein interactions under unstimulated or stimulated conditions (100 ng/mL lipopolysaccharide (LPS) for 18 hours) in primary microglia. 1) Co-immunoprecipitation (co-IP) revealed that the NOX2 subunits, gp91phox (gp91) and p22phox (p22), co-IP with TSPO supporting a protein-protein interaction. TSPO’s association with gp91 and p22 decreased with activation, but TSPO’s association with VDAC, a mitochondrial protein, remained constant. These findings suggest that microglia activation changes the dynamics of the TSPO-NOX2 interaction. 2) Confocal imaging and colocalization analysis of TSPO/gp91 or TSPO/p22 immunofluorescence confirmed that TSPO colocalizes with both NOX subunits. Under stimulated conditions, TSPO associated with gp91 and TSPO associated with p22, exhibit significantly decreased colocalization with VDAC suggesting a movement from the mitochondria to other cellular compartments. 3) Duolink Proximity Ligation Assay confirmed that TSPO interacts with p22, gp91 and VDAC. Our results suggest a novel TSPO-gp91-p22 interaction with VDAC in primary microglia that is disrupted by microglia activation and may be involved with redox homeostasis with significant implications for a new understanding of TSPO glial cell biology. In summary, the present studies have strengthened the use of TSPO as a preclinical biomarker, confirmed its specific spatiotemporal upregulation in two cell types and have provided a new potential function of TSPO in microglia that has the possibility to revolutionize the TSPO field and to inform neurotoxicity assessments and neurological disease treatments.

Precision medicine represents a shift in medicine where large datasets are gathered for massive patient groups to draw correlations between disease cohorts. An individual patient can then be compared to these large datasets to determine the best treatment strategy. While electronic health records and next generation sequencing techniques have enabled much of the early applications for precision medicine, the human genome only represents a fraction of the information present and important to a person’s health. A person’s proteome (peptides and proteins) and glycome (glycans and glycosylation patterns) contain biomarkers that indicate health and disease; however, tools to detect and analyze such biomarkers remain scarce. Thus, precision medicine databases are lacking a major source of phenotypic data due to the absence of available methods to explore these domains, despite the potential of such data to allow further stratification of patients and individualized therapeutic strategies. Available methods to detect non-nucleic acid biomarkers are currently not well suited to address the needs of precision medicine. Mass spectrometry techniques, while capable of generating high throughput data, lack standardization, require extensive preparative steps, and have many sources of errors. Immunoassays rely on antibodies which are time consuming and expensive to produce for newly discovered biomarkers. Aptamers, analogous to antibodies but composed of nucleotides and isolated through in vitro methods, have potential to identify non-nucleic acid biomarkers but methods to isolate aptamers remain labor and resource intensive and time consuming. Recently, microfluidic technology has been applied to the aptamer discovery process to reduce the aptamer development time, while consuming smaller amounts of reagents. Methods have been demonstrated that employ capillary electrophoresis, magnetic mixers, and integrated functional chambers to select aptamers. However, these methods are not yet able to fully integrate the entire aptamer discovery process on a single chip and must rely on off-chip processes to identify aptamers. In this thesis, new approaches for aptamer selection are developed that aim to integrate the entire process for aptamer discovery on a single chip. These approaches are capable of performing efficient aptamer selection and polymerase chain reaction based amplification while utilizing highly efficient bead-based reactions. The approaches use pressure driven flow, electrokinetic flow or a combination of both to transfer aptamer candidates through multiple rounds of affinity selection and PCR amplification within a single microfluidic device. As such, the approaches are capable of isolating aptamer candidates within a day while consuming <500 µg of a target molecule. The utility of the aptamer discovery approach is then demonstrated with examples in precision medicine over a broad spectrum (small molecule to protein) of molecular targets. Seeking to demonstrate the potential of the device to generate probes capable of accessing the human glycome (an emerging source of precision medicine biomarkers), aptamers are isolated against gangliosides GM1, GM3, and GD3, and a glycosylated peptide. Finally, personalized, patient specific aptamers are isolated against a multiple myeloma patient serum sample. The aptamers have high affinity only for the patient derived antibody.

No description available.