Optimized condition for etching fused-silica phase gratings with inductively coupled plasma technology

Polymer deposition is a serious problem associated with the etching of fused silica by use of inductively coupled plasma (ICP) technology, and it usually prevents further etching. We report an optimized etching condition under which no polymer deposition will occur for etching fused silica with ICP technology. Under the optimized etching condition, surfaces of the fabricated fused silica gratings are smooth and clean. Etch rate of fused silica is relatively high, and it demonstrates a linear relation between etched depth and working time. Results of the diffraction of gratings fabricated under the optimized etching condition match theoretical results well. (c) 2005 Optical Society of America.

Fabrication of fused silica phase gratings with inductively coupled plasma technology

Inductively coupled plasma (ICP) technology is a new advanced version of dry-etching technology compared with the widely used method of reactive ion etching (RIE). Plasma processing of the ICP technology is complicated due to the mixed reactions among discharge physics, chemistry and surface chemistry. Extensive experiments have been done and microoptical elements have been fabricated successfully, which proved that the ICP technology is very effective in dry etching of microoptical elements. In this paper, we present the detailed fabrication of microoptical fused silica phase gratings with ICP technology. Optimized condition has been found to control the etching process of ICP technology and to improve the etching quality of microoptical elements greatly. With the optimized condition, we have fabricated lots of good gratings with different periods, depths, and duty cycles. The fabricated gratings are very useful in fields such as spectrometer, high-efficient filter in wavelength-division-multiplexing system, etc..

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.