A simple, low-cost instrument for electrochemiluminescence immunoassays based on a Raspberry Pi and screen-printed electrodes

Deep Learning-Assisted Smartphone-Based Electrochemiluminescence Visual Monitoring Biosensor: A Fully Integrated Portable Platform

A novel, portable deep learning-assisted smartphone-based electrochemiluminescence (ECL) cost-effective (~10$) sensing platform was developed and used for selective detection of lactate. Low-cost, fast prototyping screen printing and wax printing methods with paper-based substrate were used to fabricate miniaturized single-pair electrode ECL platforms. The lab-made 3D-printed portable black box served as a reaction chamber. This portable platform was integrated with a smartphone and a buck-boost converter, eliminating the need for expensive CCD cameras, photomultiplier tubes, and bulky power supplies. This advancement makes this platform ideal for point-of-care testing applications. Foremost, the integration of a deep learning approach served to enhance not just the accuracy of the ECL sensors, but also to expedite the diagnostic procedure. The deep learning models were trained (3600 ECL images) and tested (900 ECL images) using ECL images obtained from experimentation. Herein, for user convenience, an Android application with a graphical user interface was developed. This app performs several tasks, which include capturing real-time images, cropping them, and predicting the concentration of required bioanalytes through deep learning. The device's capability to work in a real environment was tested by performing lactate sensing. The fabricated ECL device shows a good liner range (from 50 µM to 2000 µM) with an acceptable limit of detection value of 5.14 µM. Finally, various rigorous analyses, including stability, reproducibility, and unknown sample analysis, were conducted to check device durability and stability. Therefore, the developed platform becomes versatile and applicable across various domains by harnessing deep learning as a cutting-edge technology and integrating it with a smartphone.

Development of a real-time and multitasking system for long-term monitoring of aqueous metallic elements using plasma spectroscopy

In this work, we integrated a Raspberry Pi (RPi) board, an open-sourced hardware, with a spectrometer, a high voltage DC power source, and a plasma system to develop a multi-tasking monitoring system for metallic elements in solution. In this system, RPi precisely controls voltage pulses, synchronizes them with the spectrometer, and performs real-time analysis using data acquired in real-time. This integration enables continuous monitoring of multiple metallic elements in solutions of varying conductivities. Synchronization of voltage pulses and spectrometer triggering is crucial for reliable measurements and prolongs the lifetime of the electrode. This multitasking capability significantly improves the quality of the overall spectroscopic data and enables operation in a long-term manner. Two operating modes are proposed, namely regular detection mode (RDM) and event-based mode (EBM). RDM is used to identify the existence of metallic elements and EBM is used for quantification upon detection. A 24-h long-term test shown in this work demonstrates the system capability in of utilizing RDM to monitor the presence of Pb and Mg every 30 min. Injection of Pb- and/or Mg-containing solutions is performed to activate EBM for quantification analysis. Instant warning messages can be sent upon metal detection showcasing the system potential for real-time monitoring and efficient quantification. We believe this work can contribute to multiple fields such as environmental monitoring, industrial quality control, or process monitoring.

A novel point-of-care diagnostic prototype system for the simultaneous electrochemiluminescent sensing of multiple traumatic brain injury biomarkers

Traumatic brain injuries (TBI) are typically acquired when a sudden violent event causes damage to the brain tissue. A high percentage (70-85%) of all TBI patients are suffering from mild TBI (mTBI), which is often difficult to detect and diagnose with standard imaging tools (MRI, CT scan) due to the absence of significant lesions and specific symptoms. Recent studies suggest that a screening test based on the measurement of a protein biomarker panel directly from a patient's blood can facilitate mTBI diagnosis. Herein, we report a novel prototype system designed as a precursor of a future hand-held point-of-care (POC) diagnostic device for the simultaneous multi-biomarker sensing, employing a microarray-type spatially resolved electrochemiluminescence immunoassay (SR-ECLIA). The small tabletop prototype consists of a screen-printed electrode compartment to conduct multi-analyte ECL sandwich assays, a potentiostat module and a light collection module, all integrated into a compact 3D-printed housing (18.2 × 16.5 × 5.0 cm), as well as an sCMOS detector. Based on this design concept, further miniaturization, system integration, performance optimization and clinical evaluation shall pave the way towards the development of a portable instrument for use at the site of accident and healthcare. To demonstrate the system's feasibility, current performance and efficiency, the simultaneous detection of three mTBI biomarkers (GFAP, h-FABP, S100β) in 50% serum was achieved in the upper pg mL-1 range. The proposed device is amenable to the detection of other biomarker panels and thus could open new medical diagnostic avenues for sensitive multi-analyte measurements with low-volume biological sample requirements.

An integrated microfluidic electrochemiluminescence device for point-of-care testing of acute myocardial infarction

Heart-type fatty acid binding protein (H-FABP) is an early biomarker for acute myocardial infarction. The concentration of H-FABP in circulation sharply increases during myocardial injury. Therefore, fast and accurate detection of H-FABP is of vital significance. In this study, we developed an electrochemiluminescence device integrated with microfluidic chip (designed as m-ECL device) for on-site detection of H-FABP. The m-ECL device is consisted of a microfluidic chip that enable easy liquid handling as well as an integrated electronic system for voltage supply and photon detection. A sandwich-type ECL immunoassay strategy was employed for H-FABP detection by using Ru (bpy)₃²⁺ loaded mesoporous silica nanoparticles as ECL probes. This device can directly detect H-FABP in human serum without any pre-treatment, with a wide linear range of 1-100 ng/mL and a low limit of detection of 0.72 ng/mL. The clinical usability of this device was evaluated using clinical serum samples from patients. The results obtained from m-ECL device are well matched with those obtained from ELISA assays. We believe this m-ECL device has extensive application prospects for point-of-care testing of acute myocardial infarction.