Collaborative Research: Improving the Performance and Design of Potentiometric Biosensors for the Detection of Extracellular Histones in Blood with Deep Learning

Point of care (POC) sensors aim to provide patients and medical practitioners with diagnostic information when and where it is needed. Potentiometric biosensors, which output a voltage as a function of target biomolecule concentration, are ideally suited for POC use in which sensitivity, speed of detection, portability and compatibility with low-power read-out circuitry are all paramount. Unfortunately, most demonstrations of such sensors stall during the translation from testing well-controlled laboratory solutions to operating in serum or whole blood. The objective of this research is to overcome this hurdle by using a class of advanced artificial intelligence techniques known as Deep Learning to recognize patterns and relationships in the complex data that is collected from blood-based tests to improve sensitivity and drive the design optimization of these sensors. This approach will be applied to the detection of circulating histones in blood, which contribute to the development of Multiple Organ Dysfunction Syndrome (MODS) in critically ill patients. It is estimated that 15% of all intensive care unit (ICU) admissions in the United States result in MODS, costing the healthcare system billions of dollars. Currently, there is no biomarker to identify those patients at increased risk of MODS. The successful development of the proposed Deep Learning-enhanced histone sensor will allow for the early identification of patients that will benefit from more aggressive and targeted therapies to prevent MODS and related complications. These concepts will be integrated with wearable device challenges for high school students, and data will also be included in undergraduate and graduate curricula.

The proposed research consists of answering the following scientific and engineering questions: (1) What is the conventional limit of detection and speed of response of RNA aptamer-functionalized potentiometers to circulating histones? RNA aptamers specific to histones will be used to functionalize gold sensing electrodes to establish, for the first time, the limit of detection and speed of response of extended gate potentiometers capable of early identification of MODS. These devices will be evaluated in buffer, serum and whole blood as benchmarks for POC deployment. (2) How can Deep Learning improve the performance of potentiometric biosensors beyond their conventional limits when assessing whole blood? Potentiometric biosensor performance relies on several factors (e.g., electrode choice, surface functionalization, sample type, etc.), which make their translation to blood analysis a major challenge. We will leverage deep learning techniques to reveal intricate relationships and trends to compensate for the conventional losses in sensitivity observed in blood-based tests. These findings will also drive the optimal design of the potentiometric sensors, thus establishing design rules that can accelerate the development of these sensors across the community. (3) What is the optimal method to develop training data for deep learning? A major obstacle to the application of Machine/Deep Learning techniques to biosensing is the generation of adequate training data. A multiplexed potentiometric biosensing platform, made possible by the use of the extended gate approach, will be developed in order to identify time- and resource-efficient approaches to algorithm training. This effort will establish a standardized protocol that other researchers in the field can leverage in order to accelerate the adoption of potentiometric biosensors in new applications.

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