Effect of pressure on mechanical behavior of filled elastomers

The Young's modulus, stress-strain curves, and failure properties of glass bead-filled EPDM vulcanizates were studied under superposed hydrostatic pressure. The glass bead-filled EPDM was employed as a representation of composite systems, and the hydrostatic pressure controls the filler-elastomer separation under deformation. This separation shows up as a volume change of the system, and its infuence is reflected in the mechanical behavior as a reinforcing effect of variable degree.

The strain energy stored in the composite system in simple tension was calculated by introducing a model which is described as a cylindrical block of elastomer with two half spheres of filler on each end with their centers on the axis of the cylinder. In the derivation of the strain energy, assumptions were made to obtain the strain distribution in the model, and strain energy-strain relation for the elastomer was also assumed. The derivation was carried out for the case of no filler-elastomer separation and was modified to include the case of filler-elastomer separation.

The resulting strain energy, as a function of stretch ratio and volume of the system, was used to obtain stress-strain curves and volume change-strain curves of composite systems under superposed hydrostatic pressure.

Changes in the force and the lateral dimension of a ring specimen were measured as it was stretched axially under a superposed hydrostatic pressure in order to calculate the mechanical properties mentioned above. A tensile tester was used which is capable of sealing the whole system to carry out a measurement under pressure. A thickness measuring device, based on the Hall effect, was built for the measurement of changes in the lateral dimension of a specimen.

The theoretical and experimental results of Young's modulus and stress-strain curves were compared and showed fairly good agreement.

The failure data were discussed in terms of failure surfaces, and it was concluded that a failure surface of the glass-bead-filled EPDM consists of two cones.

Technology for Single Cell Protein Analysis in Immunology and Cancer Prognostics

The first chapter of this thesis deals with automating data gathering for single cell microfluidic tests. The programs developed saved significant amounts of time with no loss in accuracy. The technology from this chapter was applied to experiments in both Chapters 4 and 5.

The second chapter describes the use of statistical learning to prognose if an anti-angiogenic drug (Bevacizumab) would successfully treat a glioblastoma multiforme tumor. This was conducted by first measuring protein levels from 92 blood samples using the DNA-encoded antibody library platform. This allowed the measure of 35 different proteins per sample, with comparable sensitivity to ELISA. Two statistical learning models were developed in order to predict whether the treatment would succeed. The first, logistic regression, predicted with 85% accuracy and an AUC of 0.901 using a five protein panel. These five proteins were statistically significant predictors and gave insight into the mechanism behind anti-angiogenic success/failure. The second model, an ensemble model of logistic regression, kNN, and random forest, predicted with a slightly higher accuracy of 87%.

The third chapter details the development of a photocleavable conjugate that multiplexed cell surface detection in microfluidic devices. The method successfully detected streptavidin on coated beads with 92% positive predictive rate. Furthermore, chambers with 0, 1, 2, and 3+ beads were statistically distinguishable. The method was then used to detect CD3 on Jurkat T cells, yielding a positive predictive rate of 49% and false positive rate of 0%.

The fourth chapter talks about the use of measuring T cell polyfunctionality in order to predict whether a patient will succeed an adoptive T cells transfer therapy. In 15 patients, we measured 10 proteins from individual T cells (~300 cells per patient). The polyfunctional strength index was calculated, which was then correlated with the patient's progress free survival (PFS) time. 52 other parameters measured in the single cell test were correlated with the PFS. No statistical correlator has been determined, however, and more data is necessary to reach a conclusion.

Finally, the fifth chapter talks about the interactions between T cells and how that affects their protein secretion. It was observed that T cells in direct contact selectively enhance their protein secretion, in some cases by over 5 fold. This occurred for Granzyme B, Perforin, CCL4, TNFa, and IFNg. IL- 10 was shown to decrease slightly upon contact. This phenomenon held true for T cells from all patients tested (n=8). Using single cell data, the theoretical protein secretion frequency was calculated for two cells and then compared to the observed rate of secretion for both two cells not in contact, and two cells in contact. In over 90% of cases, the theoretical protein secretion rate matched that of two cells not in contact.