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Rheumatoid Arthritis (RA) is a chronic, systemic autoimmune disease, primarily affecting the small joints of the hands, wrists and feet. Disease onset is insidious with classical symptoms including pain, morning stiffness, and persistent synovitis; accompanied by fatigue, malaise and weight loss. RA patients also suffer from a number of extra-articular manifestations and additional comorbidities, including rheumatoid nodules and scleritis; as well as an increased risk of cardiovascular disease (CVD), lymphoma and infection. Ultimately, RA is a devastatingly progressive disease, if a diagnosis and an effective therapeutic regimen is not established quickly. Identifying when a therapy is proving ineffective can be difficult, however, and is reliant upon accurate formal joint counts, CRP measurements and patient global assessments. These are potentially insensitive means of assessing remission and may result in delayed changes to therapeutic regimens. The identification of new biomarkers to improve the predictive value of these evaluation models would therefore greatly benefit patient care. Based upon current evidence, it has been hypothesized that RA patients who fail to make a clinical improvement following the initiation of csDMARD and bDMARD therapeutic regimens are identifiable by their profile of the following CD4+ T-cell populations: follicular helper, memory helper, antigen-specific and naturally-occurring regulatory CD4+ T-cells. As such, flow cytometric assays have developed for the identification and enumeration of these respective CD4+ T-cell populations; the relative and absolute counts of which were investigated within a cohort of healthy controls, with normal distributions established. Subsequently, we have identified distinct alterations to the CD4+ T-cell compartments of newly-diagnosed, treatment-naïve and longstanding RA patients, with clear associations with patient clinical outcomes observed. Of notable significance, the relative distribution of CCR4- Th2-like follicular helper CD4+ T-cells within newly-diagnosed, treatment-naïve RA patients at the time of diagnosis, has proven highly predictive of therapeutic efficacy; with changes in the relative distribution of Th17-like follicular helper and Th17 memory helper CD4+ T-cells also associated with changes to disease activity over time. Furthermore, consistent with previous studies, the CMV status of newly-diagnosed, treatment-naïve and longstanding RA patients, is associated with an aggravated clinical course; with changes in CMV-specific CCR4+ Th17 cell responses related to changes in disease activity 12-months after the initiation of a csDMARD therapeutic regimen. No such relationships could, however, be discerned within the naturally-occurring regulatory CD4+ T-cell compartment, limiting the utility of employing naturally-occurring regulatory CD4+ T-cell measurements in therapeutic efficacy monitoring. Nevertheless, these preliminary results indicate that the addition of follicular helper, memory helper and CMV-specific CD4+ T-cell responses to current remission assessment practices, could improve the identification of RA therapy non-responders, enabling the swift adjustment of RA patient therapeutic regimens and, consequently, improving disease management.

I present two new methods for the calculation of surface properties. Firstly, a method of thermodynamic integration to calculate surface free energies. A strain is applied to a unit cell of the bulk material, that opens up a vacuum gap and creates two surfaces. A parameter s describes this process, from s = 0 (the bulk material) to s = si (large vacuum gap). The difference in free energy between these two systems is then calculated by the integration of the stress on the unit cell over s. I use this general theory to find the surface free energy of the titanium dioxide (110) surface using density functional theory. The second part of the thesis gives a general transition state theory method for the calculation of the desorption rate of a molecule from a surface, at any coverage and temperature. This approach depends on the density of molecules as a function of the distance from the surface, and I show that this can be found from the potential of mean force. This is especially useful at low temperatures, where experiments are conducted but brute force simulation is computationally unfeasible. I use this theory to calculate the desorption rate of water from the (001) surface of magnesium oxide at 100 1200K and 0 2/3 coverage, with classical potentials. An important outcome of these calculations is that the frequency prefactor (from the Polanyi-Wigner equation) is dependent on temperature.