Biomedical Applications | ECE | Virginia Tech


Biomedical Applications

Magnified photograph of sensor fiber inserted in a drop of water

The fiber is inserted into the small drop (nanoliter) of solution generated by the needle tip of a 26-gauge syringe

A research team from ECE and biochemistry has developed a fiber-optic sensor for DNA sequence detection that costs 20 times less than currently available technology. The new sensor method could be used in fields such as genetics, pathology, pharmacognetics, food safety, criminology, and civil defense, including applications such as detecting disease or biological weapons, according to Kristie Cooper, an ECE research scientist.

The new method involves immobilizing capture DNA on the tip of an optical fiber. When the rpobe is exposed to the target DNA, the capture and target materials bind together as a hybrid and the tip of the probe thickens and is measured by an interferometer.

The capture DNA is immobilized on the tip by a nanoscale self-assembly technique in which single monolayers of film are deposited by alternating the charge of each assembled monolayer.

Traditional DNA detection methods involve some form of labeling, such as fluorescence, to signal a binding event, according to Cooper. "Fluorescent labeling is very expensive and cumbersome," she said. "Fluorescent dyes bleach out in light and reading the arrays involves highly precise and expensive instrumentation."

Advantages of the sensor method include speed of detection and ease of use. "Current methods of ferifying TB, such as the TB Rapid Cultivation Detection Technique, require about 30 days to obtain definitive results," she explained. "Using a fiber sensor, based on our method, direct detection of DNA could be completed in minutes."

The team's tests show the new sensor can detect DNA quantities as small as 1.7 ng with contact times of about five minutes.

A research team from ECE, physics, and chemistry is working to blend MEMS and nanotechnology to create inexpensive, portable gas sensors that can be used in a variety of applications, including breath analysis, biomedical diagnostics, pharmaceuticals, food processing, and environmental monitoring.

There are currently two basic methods of gas sensing: measuring in place, or first separating the gas into its constituents and analyzing them instantly, according to ECE's Masoud Agah, who is faculty lead on the effort. "Electronic noses tend to be in the first category, however, they have not been able to identify vapors in the gas mixture composed of more than five components," he said.

The second method, 50-year-old gas chromatography (GC) has become the most common approach for analyzing gas mixtures. Although GC equipment tends to be large, fragile, and relatively expensive, with high power consumption, about $1 billion worth, or 30,000 GC instruments, are sold annually, he noted.

Agah is working with Randy Heflin of physics and Larry Taylor of chemistry to develop micro-GC instruments that are less expensive and consume less power while providing faster analysis (less than five seconds) and greatly increased portability for in-field use. The team is employing MEMS technology for the first time to develop special miniaturized multicapillary columns having on-chip temperature programming capability and to use nanotechnology to coat the column walls with nano-structured materials such as ionically self-assembled films.

Diagram of cell division controlThe "wiring diagram" (left) represents the basic components that control the cell-division cycle in a yeast cell. In collaboration with John Tyson, University Distinguished Professor of Biology, William Baumann is building models of the cell cycle that accurately capture the noise in the process due to the random nature of the chemical reactions.

The goal is to understand why cells with certain mutations divide successfully with a certain probability something that cannot be understood with noiseless models. Being able to predict the effect of mutations is a key way to validate cell cycle models and so this work may lead to improving the knowledge of cell cycle control. Ultimately, comprehending cell-cycle control is important for understanding and treating diseases where this system has broken down, such as in cancer.

Amy Bell is applying signal and image processing techniques to microscope images in an effort to understand how viral and host cells interact. The goal is to develop a procedure that will rapidly identify viruses and the illness and mortality risks that they present.

Bell is collaborating with Karen Duca, a biophysicist with the Virginia Bioinformatics Institute (VBI). The team recently received a $400,000 grant from the National Institutes of Health (NIH) to support their work in visualizing viruses spreading through cells.

When Duca introduces a virus into cells in a laboratory dish, she infects only the cell's center. As the virus moves out from the center in its attempt to infect other healthy cells, Duca indentifies and stains relevant markers from the virus and the host. Under ultraviolet lighting, the chemical stains become fluorescent, allowing Bell and Duca to capture images of the laboratory dish at regular time invervals.

Bell then works to remove the signal noise from the microscope and the fluorescent markers and other sources. The microscope cannot capture the entire well at once, so multiple subimages are taken and assembled in matrix fashion. A "montage" artifact arises from the microscope's uneven illumination, which is brighter in the center and dissipates nearer the edges of the dish for each sub-image.

Bell's team has developed a method to remove the montage artifact, based on a model that reflects the physics of fluorescent microscopy. They have also designed a novel algorithm for estimating and removing the "spectral overlap" noise that arises from using multiple fluorescent markers in the experiment. Bell's image denoising methods are critical for performing an accurate quantitative analysis of the host-virus interaction dynamics.

Ultimately, the interdisciplinary work should contribute to knowledge of what starts an immune response and even help to quickly determine the danger of a virus without waiting to identify it.

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