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Biomedical subjects

Homer Pien

Publications and source records attributed to Homer Pien.

2 recordsLinked to original sources

A label-free optical technique for detecting small molecule interactions.

A novel approach for the label-free detection of molecular interactions is presented in which a colorimetric resonant grating is used as a surface binding platform. The grating, when illuminated with white light, is designed to reflect only a single wavelength. When molecules are attached to the surface, the reflected wavelength (color) is shifted due to the change of the optical path of light that is coupled into the grating. By linking receptor molecules to the grating surface, complementary binding molecules can be detected without the use of any kind of fluorescent probe or radioactive label. The detection technique is capable of detecting the addition and removal of small molecules as they interact with receptor molecules on the sensor surface or enzymes in the solution surrounding the sensor. Two assays are presented to exemplify the detection of small molecule interactions with the biosensor. First, an avidin receptor layer is used to detect 244 Da biotin binding. Second, a protease assay is performed in which a 136 Da p-nitroanilide (pNA) moeity is cleaved from an immobilized substrate. Because the sensor structure can be embedded in the plastic surfaces of microtiter plates or the glass surfaces of microarray slides, it is expected that this technology will be most useful in applications where large numbers of biomolecular interactions are measured in parallel, particularly when molecular labels will alter or inhibit the functionality of the molecules under study. Screening of pharmaceutical compound libraries with protein targets, and microarray screening of protein-protein interactions for proteomics are examples of applications that require the sensitivity and throughput afforded by this approach.

Avidin↗

Functional MRI activity characterization using response time shift estimates from curve evolution.

Characterizing the response of the brain to a stimulus based on functional magnetic resonance imaging data is a major challenge due to the fact that the response time delay of the brain may be different from one stimulus phase to the next and from pixel to pixel. To enhance detectability, this work introduces the use of a curve evolution approach that provides separate estimates of the response time shifts at each phase of the stimulus on a pixel-by-pixel basis. The approach relies on a parsimonious but simple model that is nonlinear in the time shifts of the response relative to the stimulus and linear in the gains. To effectively use the response time shift estimates in a subspace detection framework, we implement a robust hypothesis test based on a Laplacian noise model. The algorithm provides a pixel-by-pixel functional characterization of the brain's response. The results based on experimental data show that response time shift estimates, when properly implemented, enhance detectability without sacrificing robustness.

Adult↗