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Chemical Sensors to Sniff out Diseases in Human Breath
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Chemical Sensors to Sniff out Diseases in Human Breath

Chemical Sensors to Sniff out Diseases in Human Breath
News

Chemical Sensors to Sniff out Diseases in Human Breath

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Researchers at University at Buffalo have announced that they are developing a rugged Breathalyzer-type device that, just like the nose of a human - or other mammal - will contain thousands of chemical sensors "trained" to recognize complex chemical patterns, some of which are known biomarkers for certain diseases.

"These volatile biomarkers are free for the asking and taking," said Frank V. Bright, Ph.D., UB Distinguished Professor in the Department of Chemistry in the College of Arts and Sciences, A. Conger Goodyear Professor of Chemistry and principal investigator.

"They emanate from us all of the time. They are large in volume, much safer to handle than biofluids and available through totally non-invasive means."

Called gaseous metabolites, these are the same odors that some animals use to identify their offspring, owners, mates, prey or competitors.

So far, multiple volatile chemicals have been detected by other scientists as biomarkers, correlating their presence and concentration with human diseases ranging from diabetes and AIDS to lung cancer and various mental illnesses.

While there are other electronic "noses" already on the market, they cannot correlate reliably their read-outs to a particular disease state, Bright said.

"The UB device will be unique because it will be designed to exploit, and in some ways mimic, the concepts of olfaction," he continued.

"Despite the fact that we might encounter numerous really smelly things in our lifetime, it is not as if there are billions of discrete sensors within our noses that nature designed a priori to respond selectively to every possible smelly odor."

"Rather, there are suites of receptors in our nasal passages and the collective response from all of these receptors to an odor or set of odors can be discriminated," he said.

In the same way, the UB device will contain individual chemical sensors, perhaps as many as a million, which collectively will produce a pattern revealing the chemical signature of a patient's breath, which may be related to a particular disease state.

That pattern will then be used to "train" neural networks, groups of connected artificial neurons capable of learning new information, to discriminate potentially between patients with specific diseases.

"The power of neural networks in this research is that they will pull out the important features and save them so that when they are exposed to a chemical pattern they have 'seen' before, the device will elicit the right response," said Albert H. Titus, Ph.D., assistant professor of electrical engineering in the UB School of Engineering and Applied Sciences and a co-investigator on the project.

He added that with neural network processing, the size of the sensor elements can stay very small, each measuring about 10 micrometers in size.

Titus is building complementary metal oxide semiconductor (CMOS) arrays that simultaneously will read the signals produced by each of the sensor elements.

"The issue with this application is can you come up with a unique ensemble of sensor elements that exhibit enough diversity to respond to a large variety of small, chemically similar species to give you a chance of realizing the chemical fidelity that you need?" asked Bright.

To achieve that fidelity, he said the chemical sensors will be made out of xerogels, porous glass-like materials that consist of nanoscopic pores, which can be tuned to recognize specific chemicals or classes of chemicals.

Bright explained that the envisioned device will work as follows: As the breath sample flows through the breath-testing device, the individual sensing elements will change their color or intensity; those changes will be detected by the CMOS array, producing electrical signals that then can be processed by the neural network.

"The Oishei Foundation's generosity will enable our team to have a prototype ready for clinical testing within a year," said Bright.

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