The promise of affordable diagnostics Researchers have invented a new type of diagnostic test system that combines a cheap mechanism for paper-based diagnosis with an ultrasensitive state-of-the-art transistor. With other, the same discovery could be a vital step towards creating inexpensive and easy home tests for different diseases.
Bridging the Gap
At-home diagnostic tests have traditionally been limited in functionality, typically supplying only whether a target molecule is present or absent (resulting qualitative information). In contrast, highly sensitive biosensors eg field-effect transistors (FETs) exhibit high promise although difficulty to convert into commercial products is due to the complex testing conditions.
For a while now, University of Chicago and UCLA research team been trying to hit on the perfect combination. Combining a high-performance FET alongside an inexpensive paper diagnostic cartridge, the researchers have developed a new biosensor which is capable of giving quantitative results with accuracy. Combined with machine learning algorithms, the convergence method applied to wearable sensor technologies could be pivotal in changing how we conduct at-home testing and diagnostics.
The key to attaining nigh-omniscient accuracy
How the FET Is Used With the Team The secret to making this work with the team is in how the FET and cartridge were designed to work together. The high sensitivity of the FET allows it to detect small concentration of biological molecules, in combination with a paper-based platform, which forms a low-cost and user-friendly proof-of-concept of our system.
The researchers have developed a novel testing platform that can overcome the limitations of the individual components unique to old FET-based biosensors. The testing environment can be simplified with the help of a paper based cartridge which is having porous sensing membrane, thereby reducing the necessity of control conditions as dictated by normal FETs.
The accuracy, as well as precision of test results also gets improved when machine learning is used. The team developed deep-learning kinetic analysis algorithms to optimise FET performance, achieving cholesterol level measurements with >97% accuracy compared to clinical laboratory results.
Conclusion
It is an innovative marriage of technologies, one that could point the way to what at-home diagnostics will be like in the future. By marrying the high sensitivity of FETs with a low-cost, easy-to-use paper-based platform and making this combination exponentially more powerful with machine learning capabilities, the new type biosensor developed by the researchers could offer an entirely new way to monitor our health and diagnose disease. This serves as a proof of concept that at-home testing is well-liked and, in future, this innovative method could establish an avenue to economical, obtainable and more accurate diagnostic resources having the condition of them powerfully reaching individuals for being their own healthcare direction!
