IoT DEVELOPMENT FOR HEALTHY INDEPENDENT LIVING

The rise of internet connected devices has enabled the home with a vast amount of enhancements to make life more convenient. These internet connected devices can be used to form a community of devices known as the internet of things (IoT). There is great value in IoT devices to promote healthy independent living for older adults. Fall-related injuries has been one of the leading causes of death in older adults. For example, every year more than a third of people over 65 in the U.S. experience a fall, of which up to 30 percent result in moderate to severe injury. Therefore, this thesis proposes an IoT-based fall detection system for smart home environments that not only to send out alerts, but also launches interaction models, such as voice assistance and camera monitoring. Such connectivity could allow older adults to interact with the system without concern of a learning curve. The proposed IoT-based fall detection system will enable family and caregivers to be immediately notified of the event and remotely monitor the individual. Integrated within a smart home environment, the proposed IoT-based fall detection system can improve the quality of life among older adults. Along with the physical concerns of health, psychological stress is also a great concern among older adults. Stress has been linked to emotional and physical conditions such as depression, anxiety, heart attacks, stroke, etc. Increased susceptibility to stress may accelerate cognitive decline resulting in conversion of cognitively normal older adults to MCI (Mild Cognitive Impairment), and MCI to dementia. Thus, if stress can be measured, there can be countermeasures put in place to reduce stress and its negative effects on the psychological and physical health of older adults. This thesis presents a framework that can be used to collect and pre-process physiological data for the purpose of validating galvanic skin response (GSR), heart rate (HR), and emotional valence (EV) measurements against the cortisol and self-reporting benchmarks for stress detection. The results of this framework can be used for feature extraction to feed into a regression model for validating each combination of physiological measurement. Also, the potential of this framework to automate stress protocols like the Trier Social Stress Test (TSST) could pave the way for an IoT-based platform for automated stress detection and management

Biochemical Links between Hormonal Mediators of Psychological Stress and the Expression of MUC1 in Prostate Cancer Cells

A growing body of scientific evidence has demonstrated that chronic psychological stress can not only increase the growth and metastasis of tumors through a number of mechanisms, such as by an increase of VEGF and Bcl-2, but also can decrease the survival of cancer patients. However, no studies have reported the effect of psychological stress with respect to the tumor marker MUC1. Therefore, building upon previous research, the purpose of the present study was to investigate the effect of the stress hormones cortisol and norepinephrine on the tumor marker MUC1, which is highly associated with tumor cell metastasis and is aberrantly glycosylated in most human epithelial carcinomas. Thus, it has been widely used in clinics as an important prognostic marker of disease progression and response to treatment. Overexpression of MUC1 in prostate cancer has been associated with more aggressive disease and an increased risk of recurrence. Using the DU-145 prostate cancer cell line as an experimental model, we sought to determine whether the glucocorticoid cortisol and the catecholamine norepinephrine enhanced the expression of MUC1 at the transcriptional and protein levels, and whether increased MUC1 altered the invasive potential of DU-145 cells. The levels of MUC1 protein expression were assayed by ELISA, flow cytometry, and the colorimetric bradford assay. The mRNA levels of MUC1 were measured by RT-PCR. In addition, cell invasiveness and migration were assayed by the matrigel migration assay. The results indicate that physiologically relevant concentrations of cortisol found in tumor microenvironment (10[superscript -7] M) enhanced the expression of MUC1 by approximately 2-fold after 6 or 10 days of treatment as assayed by ELISA. In addition, flow cytometric analyses revealed that DU-145 cells treated for 3 or 6 days with cortisol up-regulated the cell-surface expression of MUC1 by approximately 2-fold, whereas a 10 day exposure up-regulated the expression by 7-fold. Norepinephrine alone did not alter the expression of MUC1 at any time point in any of the experiments. In addition to these elevated levels of MUC1 protein, the mRNA levels of MUC1 were increased by 6-fold when cells were treated with cortisol for 6 days and by 4-fold when cells were treated for 10 days, while norepinephrine had no effect on mRNA levels. In addition, the matrigel migration assay indicated that cells treated with cortisol for 6-10 days migrated faster through the membrane as compared to untreated cells. Together, data generated from this thesis provide novel evidence of a biochemical link between the glucocorticoid cortisol, a hormonal mediator of psychological stress, and increased levels of the tumor marker MUC 1. Findings arising from this thesis raise the possibility that in prostate cancer the interaction of MUC1 with stress hormones, such as cortisol may increase the expression of MUC1 resulting in the observed increase in disease in psychologically stressed individuals. These novel findings highlight the necessity for future studies designed to investigate further the relationship between hormonal mediators of psychological stress and increased levels of MUC1

Establishing Biological Plausibility for Cognitive Frailty

Cognitive frailty is considered a potentially reversible age-related condition characterized by the simultaneous presence of both physical frailty and cognitive decline. The concept of cognitive frailty existing in older adults is indisputable, although the mechanisms and the directional relationship behind the dynamic association remain unexplained. Mechanisms have been suggested, often linking cognitive frailty to cognitive impairment or as a component of frailty but without an understanding of the biological bases for these associations we cannot not move forward with intervention trials. This dissertation examines the biological mechanisms for cognitive frailty. The study is the first to use a large number of protein and genetic markers identified by a systematic review to define the underlying pathology for cognitive frailty. We use an innovative Boosted trees machine learning technique for developing a population based predictive model. Xgboost is based in boosted trees and provides more efficient and accurate predictive modeling with large datasets and a rapid / robust framework for feature selection. Statistical modeling is used to design, test, and validate an accurate method for and identifying and classifying the features that predict individuals with cognitive frailty. The tree boosting model is used for the evaluation of multiple variables simultaneously and provides a high predictive value with low bias. The results presented within this dissertation create a foundation of understanding for a new aging condition and encourage translational research focused on the detection and prevention of cognitive frailty

Allostatic load as a predictor of all-cause and cause-specific mortality in the general population: Evidence from the Scottish Health Survey

Allostatic load is a multiple biomarker measure of physiological ‘wear and tear’ that has shown some promise as marker of overall physiological health, but its power as a risk predictor for mortality and morbidity is less well known. This study has used data from the 2003 Scottish Health Survey (SHeS) (nationally representative sample of Scottish population) linked to mortality records to assess how well allostatic load predicts all-cause and cause-specific mortality. From the sample, data from 4,488 men and women were available with mortality status at 5 and 9.5 (rounded to 10) years after sampling in 2003. Cox proportional hazard models estimated the risk of death (all-cause and the five major causes of death in the population) according to allostatic load score. Multiple imputation was used to address missing values in the dataset. Analyses were also adjusted for potential confounders (sex, age and deprivation). There were 258 and 618 deaths over the 5-year and 10-year follow-up period, respectively. In the fully-adjusted model, higher allostatic load (poorer physiological ‘health’) was not associated with an increased risk of all-cause mortality after 5 years (HR = 1.07, 95% CI 0.94 to 1.22; p = 0.269), but it was after 10 years (HR = 1.08, 95% CI 1.01 to 1.16; p = 0.026). Allostatic load was not associated with specific causes of death over the same follow-up period. In conclusions, greater physiological wear and tear across multiple physiological systems, as measured by allostatic load, is associated with an increased risk of death, but may not be as useful as a predictor for specific causes of death.REF Compliant by Deposit in Stirling's Repositor

Correspondence between hair cortisol concentrations and 30-day integrated daily salivary and weekly urinary cortisol measures

Characterization of cortisol production, regulation and function is of considerable interest and relevance given its ubiquitous role in virtually all aspects of physiology, health and disease risk. The quantification of cortisol concentration in hair has been proposed as a promising approach for the retrospective assessment of integrated, long-term cortisol production. However, human research is still needed to directly test and validate current assumptions about which aspects of cortisol production and regulation are reflected in hair cortisol concentrations (HCC). Here, we report findings from a validation study in a sample of 17 healthy adults (mean ± SD age: 34 ± 8.6 yrs). To determine the extent to which HCC captures cumulative cortisol production, we examined the correspondence of HCC, obtained from the first 1cm scalp-near hair segment, assumed to retrospectively reflect 1-month integrated cortisol secretion, with 30-day average salivary cortisol area-under-the curve (AUC) based on 3 samples collected per day (on awakening, +30 min, at bedtime) and the average of 4 weekly 24-hr urinary free cortisol (UFC) assessments. To further address which aspects of cortisol production and regulation are best reflected in the HCC measure, we also examined components of the salivary measures that represent: 1) production in response to the challenge of awakening (using the cortisol awakening response [CAR]), and 2) chronobiological regulation of cortisol production (using diurnal slope). Finally, we evaluated the test-retest stability of each cortisol measure. Results indicate that HCC was most strongly associated with the prior 30-day integrated cortisol production measure (average salivary cortisol AUC) (r = 0.61, p = 0.01). There were no significant associations between HCC and the 30-day summary measures using CAR or diurnal slope. The relationship between 1-month integrated 24-hr UFC and HCC did not reach statistical significance (r = 0.30, p = 0.28). Lastly, of all cortisol measures, test-retest correlations of serial measures were highest for HCC (month-to-month: r = 0.84, p < 0.001), followed by 24-hr UFC (week-to-week: r’s between 0.59 and 0.68, ps < 0.05) and then integrated salivary cortisol concentrations (week-to-week: r’s between 0.38 and 0.61, p’s between 0.13 and 0.01). These findings support the contention that HCC provides a reliable estimate of long-term integrated free cortisol production that is aligned with integrated salivary cortisol production measured over a corresponding one-month period

Novel Machine Learning and Wearable Sensor Based Solutions for Smart Healthcare Monitoring

The advent of IoT has enabled the design of connected and integrated smart health monitoring systems. These health monitoring systems can be utilized for monitoring the mental and physical wellbeing of a person. Stress, anxiety, and hypertension are the major elements responsible for the plethora of physical and mental illnesses. In this context, the older population demands special attention because of the several age-related complications that exacerbate the effects of stress, anxiety, and hypertension. Monitoring stress, anxiety, and blood pressure regularly can prevent long-term damage by initiating necessary intervention or clinical treatment beforehand. This will improve the quality of life and reduce the burden on caregivers and the cost of healthcare. Therefore, this thesis explores novel technological solutions for real-time monitoring of stress, anxiety, and blood pressure using unobtrusive wearable sensors and machine learning techniques. The first contribution of this thesis is the experimental data collection of 50 healthy older adults, based on which, the works on stress detection and anxiety detection have been developed. The data collection procedure lasted for more than a year. We have collected physiological signals, salivary cortisol, and self-reported questionnaire feedback during the study. Salivary cortisol is an established clinical biomarker for physiological stress. Hence, a stress detection model that is trained to distinguish between the stressed and not-stressed states as indicated by the increase in cortisol level has the potential to facilitate clinical level diagnosis of stress from the comfort of their own home. The second contribution of the thesis is the development of a stress detection model based on fingertip sensors. We have extracted features from Electrodermal Activity (EDA) and Blood Volume Pulse (BVP) signals obtained from fingertip EDA and Photoplethysmogram (PPG) sensors to train machine learning algorithms for distinguishing between stressed and not-stressed states. We have evaluated the performance of four traditional machine learning algorithms and one deep-learning-based Long Short-Term Memory (LSTM) classifier. Results and analysis showed that the proposed LSTM classifier performed equally well as the traditional machine learning models. The third contribution of the thesis is to evaluate an integrated system of wrist-worn sensors for stress detection. We have evaluated four signal streams, EDA, BVP, Inter-Beat Interval (IBI), and Skin Temperature (ST) signals from EDA, PPG, and ST sensors. A random forest classifier was used for distinguishing between the stressed and not-stressed states. Results and analysis showed that incorporating features from different signals was able to reduce the misclassification rate of the classifier. Further, we have also prototyped the integration of the proposed wristband-based stress detection system in a consumer end device with voice capabilities. The fourth contribution of the thesis is the design of an anxiety detection model that uses features from a single wearable sensor and a context feature to improve the performance of the classification model. Using a context feature instead of integrating other physiological features for improving the performance of the model can reduce the complexity and cost of the anxiety detection model. In our proposed work, we have used a simple experimental context feature to highlight the importance of context in the accurate detection of anxious states. Our results and analysis have shown that with the addition of the context-based feature, the classifier was able to reduce misclassification by increasing the confidence of the decision. The final and the fifth contribution of the thesis is the validation of a proposed computational framework for the blood pressure estimation model. The proposed framework uses features from the PPG signal to estimate the systolic and diastolic blood pressure values using advanced regression techniques

Nanostructured biosensors with DNA-based receptors for real-time detection of small analytes

In zahlreichen lebenswichtigen Bereichen haben sich Biosensoren als unverzichtbare Messgeräte erwiesen. Der Nachweis von spezifischen Molekülen im Körper für eine frühzeitige Krankheitserkennung erfordert empfindliche und zugleich zuverlässige Messmethoden. Ein rasantes Fortschreiten im Bereich der Nanotechnologie führt dabei zur Entwicklung von Materialien mit neuen Eigenschaften, und damit verbunden, auch zu innovativen Anwendungsmöglichkeiten im Bereich der Biosensorik. Das Zusammenspiel von Nanotechnologie und Sensortechnik gewährleistet die Konstruktion von Sensoren mit empfindlicheren Nachweisgrenzen und kürzeren Reaktionszeiten. Die Option zur Integration und Miniaturisierung stellen daher einen erfolgreichen Einsatz in direkter Patientennähe in Aussicht, sodass Nanobiosensoren die Brücke zwischen Laborddiagnostik und Standardanwendungen schließen können. Die folgende Arbeit widmet sich der Anwendung von nanostrukturierten Biosensoren für einen empfindlichen und markierungsfreien Nachweis von Zielmolekülen. Ein Hauptaugenmerk liegt dabei auf der kontinuierlichen Messung von Biomarkern mit kompakten Auslesesystemen, die eine direkte Signalmeldung und somit eine Detektion in Echtzeit ermöglichen. Dies erfordert zunächst die sorgfältige Funktionalisierung von Sensoroberflächen mit geeigneten DNA-basierten Rezeptoren. Infolgedessen werden beispielhaft verschiedene Sensorsysteme, Analyten und Charakterisierungsmethoden vorgestellt sowie universelle Strategien für die erfolgreiche Konfiguration von Nanobiosensorplattformen präsentiert. Das erste Anwendungsbeispiel widmet sich einem plasmonischen Biosensor, bei dem vertikal ausgerichtete Gold-Nanoantennen Signale mittels sog. lokalisierter Oberflächenplasmonenresonanz (LSPR) erzeugen. Mit dem Sensor konnte erfolgreich die Immobilisierung, das nachträgliche Blocken sowie die anschließende Hybridisierung von DNA nachgewiesen werden. Mithilfe des LSPR-Sensors wurden gleichzeitig grundlegende Hybridisierungsmechanismen auf nanostrukturierten und planaren Oberflächen verglichen und damit verbunden die einzigartigen optischen Eigenschaften metallischer Nanostrukturen betont. In einem zweiten Anwendungsbeispiel misst ein elektrischer Biosensor kontinuierlich die Konzentration des Stressmarkers Cortisol im menschlichen Speichel. Der direkte, markierungsfreie Nachweis von Cortisol mit Silizium-Nanodraht basierten Feldeffekttransistoren (SiNW FET) wurde anhand zugrunde liegender Ladungsverteilungen innerhalb des entstandenen Rezeptor-Analyte-Komplexes bewertet, sodass ein Nachweis des Analyten innerhalb der sog. Debye-Länge ermöglicht wird. Die erfolgreiche Strategie zur Oberflächenfunktionalisierung im Zusammenspiel mit dem Einsatz von SiNW FETs auf einem tragbaren Messgerät wurde anhand des Cortisolnachweises im Speichel belegt. Ein übereinstimmender Vergleich der gemessenen Corisolkonzentrationen mit Werten, die mit einer kommerziellen Alternative ermittelt wurden, verdeutlichen das Potential der entwickelten Plattform. Zusammenfassend veranschaulichen beide vorgestellten Nanobiosensor-Plattformen die vielseitige und vorteilhafte Leistungsfähigkeit der Systeme für einen kontinuierlichen Nachweis von Biomarkern in Echtzeit und vorzugsweise in Patientennähe.:Kurzfassung I Abstract III Abbreviations and symbols V Content VII 1 Introduction 1 1.1 Scope of the thesis 4 1.2 References 6 2 Fundamentals 9 2.1 Biosensors 9 2.2 Influence of nanotechnology on sensor development 10 2.3 Biorecognition elements 12 2.3.1 Biorecognition element: DNA 13 2.3.2 Aptamers 14 2.3.3 Immobilization of receptors 15 2.4 Transducer systems 17 2.4.1 Optical biosensors - surface plasmon resonance 17 2.4.2 Electric Biosensors – Field-effect transistors (FETs) 21 2.5 Metal oxide semiconductor field-effect transistor - MOSFET 21 2.6 Summary 26 2.7 References 27 3 Materials and methods 33 3.1 Plasmonic biosensors based on vertically aligned gold nanoantennas 33 3.1.1 Materials 33 3.1.2 Manufacturing of nanoantenna arrays 34 3.1.3 Surface modification and characterization 35 3.1.4 Measurement setup for detection of analytes 38 3.2 SiNW FET-based real-time monitoring of cortisol 40 3.2.1 Materials 40 3.2.2 Manufacturing of silicon nanowire field effect transistors (SiNW FETs) 42 3.2.3 Integration of SiNW FETs into a portable platform 42 3.2.4 Biomodification and characterization of electronic biosensors SiNW FETs 42 3.2.5 Electric characterization of FETs 47 3.3 References 50 4 Plasmonic DNA biosensor based on vertical arrays of gold nanoantennas 51 4.1 Introduction - Optical biosensors operating by means of LSPR 53 4.2 Biosensing with vertically aligned gold nanoantennas 56 4.2.1 Sensor fabrication, characterization, and integration 56 4.2.2 Integration of microfluidics 58 4.2.3 Immobilization of probe DNA and backfilling 58 4.2.4 Hybridization of complementary DNA strands 62 4.2.5 Surface coverage and hybridization efficiency of DNA 69 4.2.6 Refractive index sensing 72 4.2.7 Backfilling and blocking 73 4.3 Summary 75 4.4 References 77 5 Label-free detection of salivary cortisol with SiNW FETs 83 5.1 Introduction 85 5.2 Design, integration, and performance of SiNW FETs into a portable platform 89 5.2.1 Structure and electrical characteristics of honeycomb SiNW FETs 89 5.2.2 Integration of SiNW FET into a portable measuring unit 91 5.2.3 Performance of SiNW FET arrays 93 5.3 Detection of biomolecules with SiNW FETs 102 5.3.1 General considerations for biodetection with FETs 102 5.3.2 Sensing aptamers with FETs 103 5.3.3 Biodetection of the analyte cortisol with SiNW FETs 104 5.3.4 Detection of cortisol with SiNW FETs 112 5.4 Summary 119 5.5 References 121 6 Summary and outlook 131 6.1 Summary 131 6.2 Perspectives – toward multiplexed biosensing applications 134 6.3 References 137 Appendix i A.1 Protocols i A.1.1 Functionalization of gold antennas with thiolated DNA i A.1.2 Functionalization of SiO2 with TESPSA and amino-modified receptors i A.1.3 Functionalization with APTES and carboxyl-modified receptors ii A.1.4 Preparation of microfluidic channels via soft lithography ii A.2 Predicted secondary structures iv A.2.1 Secondary structures of 100base pair target without probe-strands iv A.2.2 Secondary structures of 100base pair target with 25 base pair probe-strand x Versicherung xvii Acknowledgments xix List of publications xxi Peer-reviewed publications xxi Publications in preparation xxi Selected international conferences xxii Curriculum Vitae xxiiiBiosensors have proven to be indispensable in numerous vital areas. For example, detecting the presence and concentration of specific biomarkers requires sensitive and reliable measurement methods. Rapid developments in the field of nanotechnology lead to nanomaterials with new properties and associated innovative applications. Thus, nanotechnology has a far-reaching impact on biosensors' development, e.g., delivery of biosensing devices with greater sensitivity, shorter response times, and precise but cost-effective sensor platforms. In addition, nanobiosensors hold high potential for integration and miniaturization and can operate directly at the point of care - serving as a bridge between diagnostics and routine tests. This work focuses on applying nanostructured biosensors for the sensitive and label-free detection of analytes. A distinct aim is the continuous monitoring of biomarkers with compact read-out systems to provide direct, valuable feedback in real-time. The first step in achieving this goal is the adequate functionalization of nanostructured sensor surfaces with suitable receptors to detect analytes of interest. Due to their thermal and chemical stability with the possibility for customizable functionalization, DNA-based receptors are selected. Thereupon, universal strategies for confining nanobiosensor platforms are presented using different sensor systems, analytes, and characterization methods. As a first application, a plasmonic biosensor based on vertically aligned gold nanoantennas tracked the immobilization, blocking, and subsequent hybridization of DNA by means of localized surface plasmon resonance (LSPR). At the same time, the LSPR sensor was used to evaluate fundamental hybridization mechanisms on nanostructured and planar surfaces, emphasizing the unique optical properties of metallic nanostructures. In a second application, an electric sensor based on silicon nanowire field-effect transistors (SiNW FET) monitored the level of the stress marker cortisol in human saliva. Based on evaluating the underlying charge distributions within the resulting receptor-analyte complex of molecules, the detection of cortisol within the Debye length is facilitated. Thus, direct, label-free detection of cortisol in human saliva using SiNW FET was successfully applied to the developed platform and compared to cortisol levels obtained using a commercial alternative. In summary, both presented platforms indicate a highly versatile and beneficial performance of nanobiosensors for continuous detection of biomarkers in real-time and preferably point-of-care (POC).:Kurzfassung I Abstract III Abbreviations and symbols V Content VII 1 Introduction 1 1.1 Scope of the thesis 4 1.2 References 6 2 Fundamentals 9 2.1 Biosensors 9 2.2 Influence of nanotechnology on sensor development 10 2.3 Biorecognition elements 12 2.3.1 Biorecognition element: DNA 13 2.3.2 Aptamers 14 2.3.3 Immobilization of receptors 15 2.4 Transducer systems 17 2.4.1 Optical biosensors - surface plasmon resonance 17 2.4.2 Electric Biosensors – Field-effect transistors (FETs) 21 2.5 Metal oxide semiconductor field-effect transistor - MOSFET 21 2.6 Summary 26 2.7 References 27 3 Materials and methods 33 3.1 Plasmonic biosensors based on vertically aligned gold nanoantennas 33 3.1.1 Materials 33 3.1.2 Manufacturing of nanoantenna arrays 34 3.1.3 Surface modification and characterization 35 3.1.4 Measurement setup for detection of analytes 38 3.2 SiNW FET-based real-time monitoring of cortisol 40 3.2.1 Materials 40 3.2.2 Manufacturing of silicon nanowire field effect transistors (SiNW FETs) 42 3.2.3 Integration of SiNW FETs into a portable platform 42 3.2.4 Biomodification and characterization of electronic biosensors SiNW FETs 42 3.2.5 Electric characterization of FETs 47 3.3 References 50 4 Plasmonic DNA biosensor based on vertical arrays of gold nanoantennas 51 4.1 Introduction - Optical biosensors operating by means of LSPR 53 4.2 Biosensing with vertically aligned gold nanoantennas 56 4.2.1 Sensor fabrication, characterization, and integration 56 4.2.2 Integration of microfluidics 58 4.2.3 Immobilization of probe DNA and backfilling 58 4.2.4 Hybridization of complementary DNA strands 62 4.2.5 Surface coverage and hybridization efficiency of DNA 69 4.2.6 Refractive index sensing 72 4.2.7 Backfilling and blocking 73 4.3 Summary 75 4.4 References 77 5 Label-free detection of salivary cortisol with SiNW FETs 83 5.1 Introduction 85 5.2 Design, integration, and performance of SiNW FETs into a portable platform 89 5.2.1 Structure and electrical characteristics of honeycomb SiNW FETs 89 5.2.2 Integration of SiNW FET into a portable measuring unit 91 5.2.3 Performance of SiNW FET arrays 93 5.3 Detection of biomolecules with SiNW FETs 102 5.3.1 General considerations for biodetection with FETs 102 5.3.2 Sensing aptamers with FETs 103 5.3.3 Biodetection of the analyte cortisol with SiNW FETs 104 5.3.4 Detection of cortisol with SiNW FETs 112 5.4 Summary 119 5.5 References 121 6 Summary and outlook 131 6.1 Summary 131 6.2 Perspectives – toward multiplexed biosensing applications 134 6.3 References 137 Appendix i A.1 Protocols i A.1.1 Functionalization of gold antennas with thiolated DNA i A.1.2 Functionalization of SiO2 with TESPSA and amino-modified receptors i A.1.3 Functionalization with APTES and carboxyl-modified receptors ii A.1.4 Preparation of microfluidic channels via soft lithography ii A.2 Predicted secondary structures iv A.2.1 Secondary structures of 100base pair target without probe-strands iv A.2.2 Secondary structures of 100base pair target with 25 base pair probe-strand x Versicherung xvii Acknowledgments xix List of publications xxi Peer-reviewed publications xxi Publications in preparation xxi Selected international conferences xxii Curriculum Vitae xxii

Comparison of comprehensive health score in North American housed giraffe and free-ranging giraffe from South Africa

Stress is evident in many animal species and has been correlated to disease prevalence. During stressful events, allostasis is initiated by physiologic systems to maintain or reestablish homeostasis to protect an organism’s viability. Over time, the acclimation to frequent stress causes systematic dysregulation, leading to the phenomena of increased allostatic load. Allostatic load has been assessed in animal species via serum through selection of representative, multi-system biomarker indices. Perception and number of stress events may impact dysregulation severity, yielding allostatic load as a predictive tool. However, the allostatic load methodology poses application limitations to individuals without historical data and those lacking a conscious recognition of stress. Comprehensive health score may be more encompassing of populations, as it targets biomarkers dysregulated by life events associated with pathology, despite an unknown level of cognition or history. Serum samples were obtained from zoo-housed (n=18) and free-ranging (n=11) giraffe to predict subclinical risk of morbidity and mortality caused by chronic stress. Free-ranging giraffe were younger (m = 7.9 years) on average and had lower cholesterol (p = 0.016) and fructosamine (p = 0.039) levels when compared to captive giraffe (m = 12.8 years). Additionally, free-ranging giraffe had higher cortisol (p = 0.007) levels and NEFA (p = 0.004) status, while DHEA-S (p = 0.548) was found at relatively similar concentrations between the populations. Although suitable composites rely heavily on specific species and environmental factors, comprehensive health score provides a foundation for a more applicable tool in conservation research through comparison of biomarkers across populations. Advisor: Lisa K. Kar

Allostatic load as a predictor of all-cause and cause-specific mortality in the general population: Evidence from the Scottish Health Survey

Allostatic load is a multiple biomarker measure of physiological ‘wear and tear’ that has shown some promise as marker of overall physiological health, but its power as a risk predictor for mortality and morbidity is less well known. This study has used data from the 2003 Scottish Health Survey (SHeS) (nationally representative sample of Scottish population) linked to mortality records to assess how well allostatic load predicts all-cause and cause-specific mortality. From the sample, data from 4,488 men and women were available with mortality status at 5 and 9.5 (rounded to 10) years after sampling in 2003. Cox proportional hazard models estimated the risk of death (all-cause and the five major causes of death in the population) according to allostatic load score. Multiple imputation was used to address missing values in the dataset. Analyses were also adjusted for potential confounders (sex, age and deprivation). There were 258 and 618 deaths over the 5-year and 10-year follow-up period, respectively. In the fully-adjusted model, higher allostatic load (poorer physiological ‘health’) was not associated with an increased risk of all-cause mortality after 5 years (HR = 1.07, 95% CI 0.94 to 1.22; p = 0.269), but it was after 10 years (HR = 1.08, 95% CI 1.01 to 1.16; p = 0.026). Allostatic load was not associated with specific causes of death over the same follow-up period. In conclusions, greater physiological wear and tear across multiple physiological systems, as measured by allostatic load, is associated with an increased risk of death, but may not be as useful as a predictor for specific causes of death