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Advancing Diagnostics with More Powerful Biosensors

Researchers at the National Institute of Standards and Technology (NIST) in the United States (U.S.) have developed biosensors to detect the presence of or predisposition to various illnesses, including cancer. A novel biosensor chip with an accurate and low-cost architecture may enhance access to high-quality examinations.

The capacity to detect these signs, called biomarkers, enables medical practitioners to make vital early diagnoses and give individualised therapies. Because traditional screening procedures might be time-consuming, costly, or limited in what they can reveal, they also combined the biosensors with extremely low-power FET Internet of Things (IoT) devices to boost the sensors’ responsiveness. The FET was created at CEA-LETI to amplify signals in smartwatches, personal assistants, and other gadgets.

“This is a scalable technique. In principle, we can integrate hundreds, if not thousands, of sensors in an area of one square millimetre into a console the size of a smartphone, which is far less burdensome than some of the latest equipment used in the clinic,” said NIST researcher Arvind Balijepalli, a co-author of the new study developed by researchers at NIST Brown University and the French government-funded research institute CEA-Leti.

The researchers reported the results of a study that proves the device’s excellent sensitivity and precision despite its modularity, which is commonly associated with decreased performance, in a paper recently uploaded online from the 2018 IEEE International Electron Devices Meeting.

DNA sensor

The biosensor recognises biomarkers by detecting how DNA threads bond to the device. Its modular architecture distinguishes it from related sensors, lowering costs by making mass production more accessible and allowing the most expensive parts to be reused.

Like other DNA biosensors, the device makes use of the fact that a single DNA strand is ready for chemical bonding when it is not coupled with another within the recognisable double helix. Instead, a portion of the device has single strands of DNA coated on it. When these “probes” come into contact with DNA biomarkers with a matched or complementary genetic sequence, the two strands join, sending a signal that the gadget detects.

When a strand of target DNA binds to a probe, it causes a voltage shift that may be measured using a semiconductor device called a field-effect transistor (FET). As the molecules pop on and off the sensor, these voltage shifts can occur hundreds of times per second. This method tells you whether a DNA strand is attached to a probe and how long it takes to connect and disengage.

Improving signal detection

FET-based methods have yet to hit the mainstream, however. A significant stumbling block is their single-use nature, which has now seemed necessary but has increased its cost. The signal gets harder to measure because of the electrical signal’s noise when they must travel longer within electronics.

DNA probes in FET-based sensors usually are attached to the transistor directly, which converts the signal into readable data and limits noise. But the probes and whole device signal are weaker after exposure to a sample. Then they utilise the Internet of Things (IoT) FET to accommodate the losses. The NIST authors paired their circuitry with a specific type of low-power FET developed at CEA-LETI that is used in smartwatches, personal assistants, and other devices to amplify signals and compensate for the lost sensitivity.

The researchers found that the binding kinetics were sensitive enough to make accurate measurements even at low concentrations. Overall, the modular design performed similarly to integrated, nonmodular FET-based biosensors. The modular design performed similarly to integrated, nonmodular FET-based biosensors. The next step in their research is determining if their sensor can perform similarly with varying DNA sequences caused by mutations. Because many diseases are caused or exacerbated by altered DNA, this skill is critical for clinical diagnosis.


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