Deep Reinforcement Learning-Based Approach for Efficient and Reliable Droplet Routing on MEDA Biochips

The micro-electrode-dot-array (MEDA) architecture provides precise droplet control and real-time sensing in digital microfluidic biochips. Previous work has shown that trapped charge under microelectrodes (MCs) leads to droplets being stuck and failures in fluidic operations. A recent approach utilizes real-time sensing of MC health status, and attempts to avoid degraded electrodes during droplet routing. However, the problem with this solution is that the computational complexity is unacceptable for MEDA biochips of realistic size. Consequently, in this work, we introduce a deep reinforcement learning (DRL)-based approach to bypass degraded electrodes and enhance the reliability of routing. The DRL model utilizes the information of health sensing in real time to proactively reduce the likelihood of charge trapping and avoid using degraded MCs. Simulation results show that our approach provides effective routing strategies for COVID-19 testing protocols. We also validate our DRL-based approach using fabricated prototype biochips. Experimental results show that the developed DRL model completed the routing tasks using a fewer number of clock cycles and shorter total execution time, compared with a baseline routing method. Moreover, our DRL-based approach provides reliable routing strategies even in the presence of degraded electrodes. Our experimental results show that the proposed DRL-based routing is robust to occurrences of electrode faults, as well as increases the lifetime and usability of microfluidic biochips compared to existing strategies.

Manipulation, Sampling and Inactivation of the SARS-CoV-2 Virus Using Nonuniform Electric Fields on Micro-Fabricated Platforms: A Review.

Micro-devices that use electric fields to trap, analyze and inactivate micro-organisms vary in concept, design and application. The application of electric fields to manipulate and inactivate bacteria and single-celled organisms has been described extensively in the literature. By contrast, the effect of such fields on viruses is not well understood. This review explores the possibility of using existing methods for manipulating and inactivating larger viruses and bacteria, for smaller viruses, such as SARS-CoV-2. It also provides an overview of the theoretical background. The findings may be used to implement new ideas and frame experimental parameters that optimize the manipulation, sampling and inactivation of SARS-CoV-2 electrically.

Multiplexed Point-of-Care Electrical Detection of COVID-19 Biomarkers Using Enzymatically Amplified Metallization on Nanostructured Surfaces

We present a multiplexed, electronic enzyme-linked immunosorbent assay (E2LISA) microchip for direct electrical detection and quantitation of multiple biomarkers from a single microliter-scale drop of sample. Spatially distinct spots on the microchip, each containing an interdigitated microelectrode array, are coated with specific capture agents and used to bind different analytes. Enzyme-labeled probes are then used to convert this analyte binding to an electrical impedance signal via the amplified, localized deposition of silver on the nanostructured, catalytic surface of the chip prepared using gold nanoparticles. We use this microchip with a custom handheld, cellphone interfaced reader to detect COVID-19 biomarkers including antigen-specific antibodies and viral antigens. Further, we demonstrate the multiplexed measurement of distinct antibody responses in serum samples from convalescent COVID-19 patients versus uninfected vaccine recipients. © 2021 MicroTAS 2021 - 25th International Conference on Miniaturized Systems for Chemistry and Life Sciences. All rights reserved.

Adaptive Droplet Routing for MEDA Biochips via Deep Reinforcement Learning

Digital microfluidic biochips (DMFBs) based on a micro-electrode-dot-array (MEDA) architecture provide fine-grained control and sensing of droplets in real-time. However, excessive actuation of microelectrodes in MEDA biochips can lead to charge trapping during bioassay execution, causing the failure of microelectrodes and erroneous bioassay outcomes. A recently proposed enhancement to MEDA allows run-time measurement of microelectrode health information, thereby enabling synthesis of adaptive routing strategies for droplets. However, existing synthesis solutions are computationally infeasible for large MEDA biochips that have been commercialized. In this paper, we propose a synthesis framework for adaptive droplet routing in MEDA biochips via deep reinforcement learning (DRL). The framework utilizes the real-time microelectrode health feedback to synthesize droplet routes that proactively minimize the likelihood of charge trapping. We show how the adaptive routing strategies can be synthesized using DRL. We implement the DRL agent, the MEDA simulation environment, and the bioassay scheduler using the OpenAI Gym environment. Our framework obtains adaptive routing policies efficiently for COVID-19 testing protocols on large arrays that reflect the sizes of commercial MEDA biochips available in the marketplace, significantly increasing probabilities of successful bioassay completion compared to existing methods. © 2022 EDAA.

Minimization of MEDA Biochip-Size in Droplet Routing

With the increasing demand for fast, accurate, and reliable biological sensor systems, miniaturized systems have been aimed at droplet-based sensor systems and have been promising. A micro-electrode dot array (MEDA) biochip, which is one kind of the miniaturized systems for biochemical protocols such as dispensing, dilutions, mixing, and so on, has become widespread due to enabling dynamical control of the droplets in microfluidic manipulations. In MEDA biochips, the electrowetting-on-dielectric (EWOD) technique stands out since it can actuate droplets with nano/picoliter volumes. Microelectrode cells on MEDA actuate multiple droplets simultaneously to route locations for the purpose of the biochemical operations. Taking advantage of the feature, droplets are often routed in parallel to achieve high-throughput outcomes. Regarding parallel manipulation of multiple droplets, however, the droplets are known to be initially placed at a distant position to avoid undesirable mixing. The droplets thus result in traveling a long way for a manipulation, and the required biochip size for routing is also enlarged. This paper proposes a routing method for droplets to reduce the biochip size on a MEDA biochip with the allowance of splitting during routing operations. We mathematically derive the routing problem, and the experiments demonstrate that our proposal can significantly reduce the biochip size by 70.8% on average, compared to the state-of-the-art method.

Portable and Label-Free Quantitative Loop-Mediated Isothermal Amplification (LF-qLamp) for Reliable COVID-19 Diagnostics in Three Minutes of Reaction Time: Arduino-Based Detection System Assisted by a pH Microelectrode.

Loop-mediated isothermal amplification (LAMP) has been recently studied as an alternative method for cost-effective diagnostics in the context of the current COVID-19 pandemic. Recent reports document that LAMP-based diagnostic methods have a comparable sensitivity and specificity to that of RT-qPCR. We report the use of a portable Arduino-based LAMP-based amplification system assisted by pH microelectrodes for the accurate and reliable diagnosis of SARS-CoV-2 during the first 3 min of the amplification reaction. We show that this simple system enables a straightforward discrimination between samples containing or not containing artificial SARS-CoV-2 genetic material in the range of 10 to 10,000 copies per 50 µL of reaction mix. We also spiked saliva samples with SARS-CoV-2 synthetic material and corroborated that the LAMP reaction can be successfully monitored in real time using microelectrodes in saliva samples as well. These results may have profound implications for the design of real-time and portable quantitative systems for the reliable detection of viral pathogens including SARS-CoV-2.