Ultradense Electrochemical Chips with Arrays of Nanostructured Microelectrodes to Enable Sensitive Diffusion-Limited Bioassays

Ultradense Electrochemical Chip and Machine Learning for High-Throughput, Accurate Anticancer Drug Screening

Despite the potentialities of electrochemical sensors, these devices still encounter challenges in devising high-throughput and accurate drug susceptibility testing. The lack of platforms for providing these analyses over the preclinical trials of drug candidates remains a significant barrier to developing medicines. In this way, ultradense electrochemical chips are combined with machine learning (ML) to enable high-throughput, user-friendly, and accurate determination of the viability of 2D tumor cells (breast and colorectal) aiming at drug susceptibility assays. The effect of doxorubicin (anticancer drug model) was assessed through cell detachment electrochemical assays by interrogating Ru(NH3)63+ with square wave voltammetry (SWV). This positive probe is presumed to imply sensitive monitoring of the on-sensor cellular death because of its electrostatic preconcentration in the so-called nanogap zone between the electrode surface and adherent cells. High-throughput assays were obtained by merging fast individual SWV measurements (9 s) with the ability of chips to yield analyses of Ru(NH3)63+ in series. The approach's applicability was demonstrated across two analysis formats, drop-casting and microfluidic assays. One should also mention that fitting a multivariate descriptor from selected input data via ML proved to be essential to providing accurate determinations (98 to 104%) of cell viability and half-maximal lethal concentration of the drug. The achieved results underscore the potential of the method in steering electrochemical sensors toward enabling high-throughput drug screening in practical applications.

Ultradense Array of On-Chip Sensors for High-Throughput Electrochemical Analyses

High-throughput sensors are valuable tools for enabling massive, fast, and accurate diagnostics. To yield this type of electrochemical device in a simple and low-cost way, high-density arrays of vertical gold thin-film microelectrode-based sensors are demonstrated, leading to the rapid and serial interrogation of dozens of samples (10 μL droplets). Based on 16 working ultramicroelectrodes (UMEs) and 3 quasi-reference electrodes (QREs), a total of 48 sensors were engineered in a 3D crossbar arrangement that devised a low number of conductive lines. By exploiting this design, a compact chip (75 × 35 mm) can enable performing 16 sequential analyses without intersensor interferences by dropping one sample per UME finger. In practice, the electrical connection to the sensors was achieved by simply switching the contact among WE adjacent fingers. Importantly, a short analysis time was ensured by interrogating the UMEs with chronoamperometry or square wave voltammetry using a low-cost and hand-held one-channel potentiostat. As a proof of concept, the detection of Staphylococcus aureus in 15 samples was performed within 14 min (20 min incubation and 225 s reading). Additionally, the implementation of peptide-tethered immunosensors in these chips allowed the screening of COVID-19 from patient serum samples with 100% accuracy. Our experiments also revealed that dispensing additional droplets on the array (in certain patterns) results in the overestimation of the faradaic current signals, a phenomenon referred to as crosstalk. To address this interference, a set of analyses was conducted to design a corrective strategy that boosted the testing capacity by allowing using all on-chip sensors to address subsequent analyses (i.e., 48 samples simultaneously dispensed on the chip). This strategy only required grounding the unused rows of QRE and can be broadly adopted to develop high-throughput UME-based sensors. In practice, we could analyze 48 droplets (with [Fe(CN)6]4-) within ∼8 min using amperometry.

Artificial Intelligence in Point-of-Care Biosensing: Challenges and Opportunities

The integration of artificial intelligence (AI) into point-of-care (POC) biosensing has the potential to revolutionize diagnostic methodologies by offering rapid, accurate, and accessible health assessment directly at the patient level. This review paper explores the transformative impact of AI technologies on POC biosensing, emphasizing recent computational advancements, ongoing challenges, and future prospects in the field. We provide an overview of core biosensing technologies and their use at the POC, highlighting ongoing issues and challenges that may be solved with AI. We follow with an overview of AI methodologies that can be applied to biosensing, including machine learning algorithms, neural networks, and data processing frameworks that facilitate real-time analytical decision-making. We explore the applications of AI at each stage of the biosensor development process, highlighting the diverse opportunities beyond simple data analysis procedures. We include a thorough analysis of outstanding challenges in the field of AI-assisted biosensing, focusing on the technical and ethical challenges regarding the widespread adoption of these technologies, such as data security, algorithmic bias, and regulatory compliance. Through this review, we aim to emphasize the role of AI in advancing POC biosensing and inform researchers, clinicians, and policymakers about the potential of these technologies in reshaping global healthcare landscapes.