Shaping surface acoustic waves for cardiac tissue engineering

The heart is a non-regenerating organ that gradually suffers a loss of cardiac cells and functionality. Given the scarcity of organ donors and complications in existing medical implantation solutions, it is desired to engineer a three-dimensional architecture to successfully control the cardiac cells in vitro and yield true myocardial structures similar to native heart. This thesis investigates the synthesis of a biocompatible gelatin methacrylate hydrogel to promote growth of cardiac cells using biotechnology methodology: surface acoustic waves, to create cell sheets. Firstly, the synthesis of a photo-crosslinkable gelatin methacrylate (GelMA) hydrogel was investigated with different degree of methacrylation concentration. The porous matrix of the hydrogel should be biocompatible, allow cell-cell interaction and promote cell adhesion for growth through the porous network of matrix. The rheological properties, such as polymer concentration, ultraviolet exposure time, viscosity, elasticity and swelling characteristics of the hydrogel were investigated. In tissue engineering hydrogels have been used for embedding cells to mimic native microenvironments while controlling the mechanical properties. Gelatin methacrylate hydrogels have the advantage of allowing such control of mechanical properties in addition to easy compatibility with Lab-on-a-chip methodologies. Secondly in this thesis, standing surface acoustic waves were used to control the degree of movement of cells in the hydrogel and produce three-dimensional engineered scaffolds to investigate in-vitro studies of cardiac muscle electrophysiology and cardiac tissue engineering therapies for myocardial infarction. The acoustic waves were characterized on a piezoelectric substrate, lithium niobate that was micro-fabricated with slanted-finger interdigitated transducers for to generate waves at multiple wavelengths. This characterization successfully created three-dimensional micro-patterning of cells in the constructs through means of one- and two-dimensional non-invasive forces. The micro-patterning was controlled by tuning different input frequencies that allowed manipulation of the cells spatially without any pre- treatment of cells, hydrogel or substrate. This resulted in a synchronous heartbeat being produced in the hydrogel construct. To complement these mechanical forces, work in dielectrophoresis was conducted centred on a method to pattern micro-particles. Although manipulation of particles were shown, difficulties were encountered concerning the close proximity of particles and hydrogel to the microfabricated electrode arrays, dependence on conductivity of hydrogel and difficult manoeuvrability of scaffold from the surface of electrodes precluded measurements on cardiac cells. In addition, COMSOL Multiphysics software was used to investigate the mechanical and electrical forces theoretically acting on the cells. Thirdly, in this thesis the cardiac electrophysiology was investigated using immunostaining techniques to visualize the growth of sarcomeres and gap junctions that promote cell-cell interaction and excitation-contraction of heart muscles. The physiological response of beating of co-cultured cardiomyocytes and cardiac fibroblasts was observed in a synchronous and simultaneous manner closely mimicking the native cardiac impulses. Further investigations were carried out by mechanically stimulating the cells in the three-dimensional hydrogel using standing surface acoustic waves and comparing with traditional two-dimensional flat surface coated with fibronectin. The electrophysiological responses of the cells under the effect of the mechanical stimulations yielded a higher magnitude of contractility, action potential and calcium transient.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.