Hypertension complicates approximately 10% of all pregnancies and is a leading cause of maternal and foetal mortality and morbidity world-wide. Pre-eclampsia is a major subgroup of these hypertensive disorders. It is defined as a rise in blood pressure to 140/90mm Hg or greater accompanied by proteinuria and usually presents after 20 weeks of pregnancy. Much of the early research into this disorder has concentrated on the determination of vasoactive compounds such as the renin-angiotensin system and prostacyclin. In the 1980's it was discovered that an inorganic free radical molecule, nitric oxide (NO), was released from the endothelium cell lining of the vasculature and was involved in regulating vasodilation of the vasculature walls via smooth muscle.It was also shown to have cytotoxic effects on bacteria, to inhibit platelet aggregation and to act as a neurotransmitter. The aim of this research was to assess the role of nitric oxide in preeclampsia. This was accomplished by the analysis of its oxidation products nitrite and nitrate in plasma from women with pre-eclampsia compared with those from normotensive pregnancies. A simple and robust assay for nitrite and nitrate was developed using ion chromatography. Initial experiments using isocratic elution with conductivity detection on a Dionex QIC system with an AS4A-SC column showed promise but were unsatisfactory due to the interference from chloride ions. Successive improvements to the technique involved changing the elution system to a gradient, initially to one with carbonate and subsequently to chloride, changing the detector system to direct UV detection at 214nm and changing the column to a high capacity, strong exchanger type. The resulting method shows good resolution, does not suffer from chloride overload and was simple to use. Control results for 200 serum samples showed that the male mean nitrite and nitrate levels were 3.34 ± 5.17 μmol L" and 42.1 ± 33.1 μmol L-1 respectively while female levels were 4.74 ± 11.7 μmol L-' and 37.5 ± 27.9 μmol L" respectively. Addition information on the free-radical status of the pregnant study groups was assessed by determination of lipid peroxides and the peroxynitrite product, 3-nitrotyrosine. An improved GC-MS method was developed to quantify total fatty acids and lipid peroxides. A new reversed phase HPLC technique for the analysis of free 3-nitrotyrosine in human plasma/serum was also developed although sample numbers were not as great as expected. Statistical analysis using F-tests, t-tests and the Mann-Whitney analysis did not show any difference in nitric oxide metabolites, lipids, lipid peroxides or peroxynitrite between gestation matched normotensive pregnant women and those with pre-eclampsia or pregnancy-induced hypertension. Published research has shown a vital role for nitric oxide in the maintenance of blood flow in normal pregnancy. This research does not support evidence for diminished or enhanced nitric oxide production in pre-eclampsia compared with normal pregnancy.

Genome-wide association studies have revealed an association between variation at the SLC4A7 locus and blood pressure. SLC4A7 encodes the electroneutral Na+/HCO3 - co-transporter NBCn1 which regulates intracellular pH (pHi) in a range of tissues, including vascular smooth muscle and endothelium. Notably, the SLC4A7 knockout mouse has been shown to have an altered blood pressure phenotype. This thesis presents a functional study of variants at this locus in primary cultures of vascular smooth muscle and endothelial cells. There were genotype-dependent differences in DNA-nuclear protein interactions by formaldehyde-assisted isolation of regulatory elements, electrophoretic mobility shift assays and DNA pulldown assays. Subsequently, there were also genotypedependent differences in SLC4A7 expression level and NBCn1 availability at the plasma membrane. In turn, SLC4A7 genotype is associated with Na+/HCO3 --dependent steady-state pHi and recovery from intracellular acidosis. The genotypic effect on pHi regulation was independent of the calcineurin activity, or the amino acid substitution E326K resulting from a missense polymorphism. However, in the presence of Na+/H+ exchange activity, the SLC4A7 genotypic effect on net base uptake and steady-state pHi was detected only in vascular smooth muscle cells but not endothelial cells. The finding of a genotypic influence on SLC4A7 expression and pHi regulation in vascular smooth muscle cells provide an insight into the molecular mechanism underlying the association of variation at the SLC4A7 locus with blood pressure.

Thesis (M. S.)--University of North Carolina at Greensboro, 2006. / Title from PDF title page screen. Advisor: Paul G. Davis ; submitted to the School of Health and Human Performance. Includes bibliographical references (p. 65-71).

Blood pressure is one of the most important indicators of human health. The symptoms of many cardiovascular diseases like stroke, atrial fibrillation, and acute myocardial infarction are usually indicated by the abnormal variation of blood pressure. Severe symptoms of diseases like coronary syndrome, rheumatic heart disease, arterial aneurysm, and endocarditis also usually appear along with the variation of blood pressure. Most of the current blood pressure measurements rely on the Korotkoff sounds method that focuses on one-time blood pressure measuring but cannot supervise blood pressure continuously, which cannot effectively detect diseases or alert patients. Previous researches indicating the relationship between photoplethysmogram (PPG) signal and blood pressure brought up the new research direction of blood pressure measurement method. Ideally, with the continuous supervision of the PPG signal, the blood pressure of the subject could be measured longitudinally, which matches the current requirements of blood pressure measurement better as an indicator of human health. However, the relationship between blood pressure and PPG signal is very comprehensive that is related to personal and environmental status, which leads to the research challenge for many previous works that tried to find the mapping from PPG signal to blood pressure without considering other factors. In this thesis, we propose two statistical methods modeling the comprehensive relationships among blood pressure, PPG signals, and other factors for blood pressure prediction. We also express the modeling and predicting process for the real data set and provide accurate prediction results that achieve the international blood pressure measurement standard. In the first part, we propose the Independent Variance Components Mixed- model (IVCM) that introduces the variance components to describe the relationship among observations. The relationship indicators are collected as information to divide observations into different groups. The latent impacts from the properties of groups are estimated and used for predicting the multiple responses. The Stochastic Approximation Minorization-maximization (SAM) algorithm is used for IVCM model parameter estimation. As the expansion of Minorization-maximization (MM) algorithm, the SAM algorithm could provide comparable-level estimations as MM algorithm but with faster computing speed and less computational cost. We also provide the subsampling prediction method for IVCM model prediction that could predict multiple responses variables with the conditional expectation of the model random effects. The prediction speed of the subsampling method is as fast as the SAM algorithm for parameter estimation with very small accuracy loss. Because the SAM algorithm and subsampling prediction method requires assigning tuning parameters, a great amount of simulation results are provided for the tuning parameter selection. In the second part, we propose the Groupwise Reweighted Mixed-model (GRM) to describe the variation of random effects as well as the potential components of mixture distributions. In the model, we combine the properties of mixed-model and mixture model for modeling the comprehensive relationship among observations as well as between the predictive variables and the response variables. We bring up the Groupwise Expectation Minorization-maximization (GEM) algorithm for the model parameter estimation. Developed from MM algorithm and Expectation Maximization (EM) algorithm, the algorithm estimates parameters fast and accurate with adopting the properties of the diagonal blocked matrix. The corresponding prediction method for GRM model is provided as well as the simulations for the number of components selection. In the third part, we apply the IVCM model and the GRM model in modeling real data and predicting blood pressure. We establish the database for modeling blood pressure with PPG signals and personal characteristics, extract PPG features from PPG signal waves, and analyze the comprehensive relationship between PPG signal and blood pressure with the IVCM model and the GRM model. The blood pressure prediction results from different models are provided and compared. The best prediction results not only achieve the international blood pressure measurement standard but also show great performance in high blood pressure prediction

Blood pressure is one of the most important indicators of human health. The symptoms of many cardiovascular diseases like stroke, atrial fibrillation, and acute myocardial infarction are usually indicated by the abnormal variation of blood pressure. Severe symptoms of diseases like coronary syndrome, rheumatic heart disease, arterial aneurysm, and endocarditis also usually appear along with the variation of blood pressure. Most of the current blood pressure measurements rely on the Korotkoff sounds method that focuses on one-time blood pressure measuring but cannot supervise blood pressure continuously, which cannot effectively detect diseases or alert patients. Previous researches indicating the relationship between photoplethysmogram (PPG) signal and blood pressure brought up the new research direction of blood pressure measurement method. Ideally, with the continuous supervision of the PPG signal, the blood pressure of the subject could be measured longitudinally, which matches the current requirements of blood pressure measurement better as an indicator of human health. However, the relationship between blood pressure and PPG signal is very comprehensive that is related to personal and environmental status, which leads to the research challenge for many previous works that tried to find the mapping from PPG signal to blood pressure without considering other factors. In this thesis, we propose two statistical methods modeling the comprehensive relationships among blood pressure, PPG signals, and other factors for blood pressure prediction. We also express the modeling and predicting process for the real data set and provide accurate prediction results that achieve the international blood pressure measurement standard. In the first part, we propose the Independent Variance Components Mixed- model (IVCM) that introduces the variance components to describe the relationship among observations. The relationship indicators are collected as information to divide observations into different groups. The latent impacts from the properties of groups are estimated and used for predicting the multiple responses. The Stochastic Approximation Minorization-maximization (SAM) algorithm is used for IVCM model parameter estimation. As the expansion of Minorization-maximization (MM) algorithm, the SAM algorithm could provide comparable-level estimations as MM algorithm but with faster computing speed and less computational cost. We also provide the subsampling prediction method for IVCM model prediction that could predict multiple responses variables with the conditional expectation of the model random effects. The prediction speed of the subsampling method is as fast as the SAM algorithm for parameter estimation with very small accuracy loss. Because the SAM algorithm and subsampling prediction method requires assigning tuning parameters, a great amount of simulation results are provided for the tuning parameter selection. In the second part, we propose the Groupwise Reweighted Mixed-model (GRM) to describe the variation of random effects as well as the potential components of mixture distributions. In the model, we combine the properties of mixed-model and mixture model for modeling the comprehensive relationship among observations as well as between the predictive variables and the response variables. We bring up the Groupwise Expectation Minorization-maximization (GEM) algorithm for the model parameter estimation. Developed from MM algorithm and Expectation Maximization (EM) algorithm, the algorithm estimates parameters fast and accurate with adopting the properties of the diagonal blocked matrix. The corresponding prediction method for GRM model is provided as well as the simulations for the number of components selection. In the third part, we apply the IVCM model and the GRM model in modeling real data and predicting blood pressure. We establish the database for modeling blood pressure with PPG signals and personal characteristics, extract PPG features from PPG signal waves, and analyze the comprehensive relationship between PPG signal and blood pressure with the IVCM model and the GRM model. The blood pressure prediction results from different models are provided and compared. The best prediction results not only achieve the international blood pressure measurement standard but also show great performance in high blood pressure prediction

Thesis (Ph. D.)--Indiana University, 2006. / Includes bibliographical references. Also available online (PDF file) by a subscription to the set or by purchasing the individual file.

Thesis (Ph. D.)--Indiana University, 2006. / Includes bibliographical references.

Diss. (sammanfattning) Göteborg : Univ. , 2009. / Härtill 3 uppsatser.

Thesis (M.S.)--University of Connecticut, 2004. / Includes bibliographical references (leaves 42-47). Also available online (PDF file) by a subscription to the set or by purchasing the individual file.

Thesis (M.S.)--University of Connecticut, 2004. / Includes bibliographical references (leaves 42-47).