Finger-powered agglutination lab chip with CMOS image sensing for rapid point-of-care diagnosis applications.

Agglutination is an antigen-antibody reaction with visible expression of aggregation of the antigens and their corresponding antibodies. Applications extend to the identification of acute bacterial infection, hemagglutination, such as blood grouping, and diagnostic immunology. Our finger-powered agglutination lab chip with external CMOS image sensing was developed to support a platform for inexpensive, rapid point-of-care (POC) testing applications related to agglutination effects. In this paper, blood grouping (ABO and Rh grouping) was utilized to demonstrate the function of our finger-powered agglutination lab chip with CMOS image sensing. Blood antibodies were preloaded into the antibody reaction chamber in the lab chip. The blood sample was pushed through the antibody reaction chamber using finger-powered pressure actuation to initiate a hemagglutination reaction to identify the blood type at the on-chip detection area using our homemade CMOS image sensing mini-system. Finger-powered actuation without the need for external electrical pumping is excellent for low-cost POC applications, but the pumping liquid volume per finger push is hard to control. In our finger-powered agglutination lab chip with CMOS image sensing, we minimized the effects of different finger push depths and achieved robust performance for the test results with different push depths. The driving sample volume per finger push is about 0.79 mm3. For different chips and different pushes, the driven sample volume per finger push was observed to vary in the range of 0.64 to 1.18 mm3. The red blood cells were separated from the plasma on-chip after the whole blood sample was finger pumped and before the red blood cells reached the antibody chamber via an embedded plasma-separation membrane. Our homemade CMOS image mini-system robustly read and identified the agglutination results on our agglutination lab chip.

A microfluidic approach towards hybridoma generation for cancer immunotherapy.

Dendritic cells/tumor fusions have shown to elicit anti-cancer immunity in different cancer types. However, the application of these vaccines for human cancer immunotherapy are limited by the instable quality and insufficient quanity of fusion cells. We present a cell electrofusion chip fabricated using soft lithography technique, which combines the rapid and precise cell pairing microstructures and the high yield electrofusion micro-electrodes to improve the cell fusion. The design uses hydrodynamic trapping in combination with positive dielectrophoretic force (pDEP) to achieve cell fusion. The chip consists of total 960 pairs of trapping channels, which are capable of pairing and fusing both homogeneous and heterogeneous types of cells. The fused cells can be easily taken out of the chip that makes this device a distinguishable from other designs. We observe pairing efficiency of 68% with fusion efficiency of 64%.

GTfold: enabling parallel RNA secondary structure prediction on multi-core desktops.

BACKGROUND: Accurate and efficient RNA secondary structure prediction remains an important open problem in computational molecular biology. Historically, advances in computing technology have enabled faster and more accurate RNA secondary structure predictions. Previous parallelized prediction programs achieved significant improvements in runtime, but their implementations were not portable from niche high-performance computers or easily accessible to most RNA researchers. With the increasing prevalence of multi-core desktop machines, a new parallel prediction program is needed to take full advantage of today's computing technology. FINDINGS: We present here the first implementation of RNA secondary structure prediction by thermodynamic optimization for modern multi-core computers. We show that GTfold predicts secondary structure in less time than UNAfold and RNAfold, without sacrificing accuracy, on machines with four or more cores. CONCLUSIONS: GTfold supports advances in RNA structural biology by reducing the timescales for secondary structure prediction. The difference will be particularly valuable to researchers working with lengthy RNA sequences, such as RNA viral genomes.