Single-step label-free nanowell immunoassay accurately quantifies serum stress hormones within minutes

A computer vision enhanced smart phone platform for microfluidic urine glucometry

Glucose is an important biomarker for diagnosing and prognosing various diseases, including diabetes and hypoglycemia, which can have severe side effects, symptoms, and even lead to death in patients. As a result, there is a need for quick and economical glucose level measurements to help identify those at potential risk. With the increase in smartphone users, portable smartphone glucose sensors are becoming popular. In this paper, we present a disposable microfluidic glucose sensor that accurately and rapidly quantifies glucose levels in human urine using a combination of colorimetric analysis and computer vision. This glucose sensor implements a disposable microfluidic device based on medical-grade tapes and glucose analysis strips on a glass slide integrated with a custom-made polydimethylsiloxane (PDMS) micropump that accelerates capillary flow, making it economical, convenient, rapid, and equipment-free. After absorbing the target solution, the disposable device is slid into the 3D-printed main chassis and illuminated exclusively with Light Emitting Diode (LED) illumination, which is pivotal to color-sensitive experiments. After collecting images, the images are imported into the algorithm to measure the glucose levels using computer vision and average RGB values measurements. This article illustrates the impressive accuracy and consistency of the glucose sensor in quantifying glucose in sucrose water. This is evidenced by the close agreement between the computer vision method used by the sensor and the traditional method of measuring in the biology field, as well as the small variation observed between different sensor performances. The exponential regression curve used in the study further confirms the strong relationship between glucose concentrations and average RGB values, with an R-square value of 0.997 indicating a high degree of correlation between these variables. The article also emphasizes the potential transferability of the solution described to other types of assays and smartphone-based sensors.

Single-Response Duplexing of Electrochemical Label-Free Biosensor from the Same Tag

Multiplexing is a valuable strategy to boost throughput and improve clinical accuracy. Exploiting the vertical, meshed design of reproducible and low-cost ultra-dense electrochemical chips, the unprecedented single-response multiplexing of typical label-free biosensors is reported. Using a cheap, handheld one-channel workstation and a single redox probe, that is, ferro/ferricyanide, the recognition events taking place on two spatially resolved locations of the same working electrode can be tracked along a single voltammetry scan by collecting the electrochemical signatures of the probe in relation to different quasi-reference electrodes, Au (0 V) and Ag/AgCl ink (+0.2 V). This spatial isolation prevents crosstalk between the redox tags and interferences over functionalization and binding steps, representing an advantage over the existing non-spatially resolved single-response multiplex strategies. As proof of concept, peptide-tethered immunosensors are demonstrated to provide the duplex detection of COVID-19 antibodies, thereby doubling the throughput while achieving 100% accuracy in serum samples. The approach is envisioned to enable broad applications in high-throughput and multi-analyte platforms, as it can be tailored to other biosensing devices and formats.

Cell phone microscopy enabled low-cost manufacturable colorimetric urine glucose test

Glucose serves as a pivotal biomarker crucial for the monitoring and diagnosis of a spectrum of medical conditions, encompassing hypoglycemia, hyperglycemia, and diabetes, all of which may precipitate severe clinical manifestations in individuals. As a result, there is a growing demand within the medical domain for the development of rapid, cost-effective, and user-friendly diagnostic tools. In this research article, we introduce an innovative glucose sensor that relies on microfluidic devices meticulously crafted from disposable, medical-grade tapes. These devices incorporate glucose urine analysis strips securely affixed to microscope glass slides. The microfluidic channels are intricately created through laser cutting, representing a departure from traditional cleanroom techniques. This approach streamlines production processes, enhances cost-efficiency, and obviates the need for specialized equipment. Subsequent to the absorption of the target solution, the disposable device is enclosed within a 3D-printed housing. Image capture is seamlessly facilitated through the use of a smartphone camera for subsequent colorimetric analysis. Our study adeptly demonstrates the glucose sensor's capability to accurately quantify glucose concentrations within sucrose solutions. This is achieved by employing an exponential regression model, elucidating the intricate relationship between glucose concentrations and average RGB (Red-Green-Blue) values. Furthermore, our comprehensive analysis reveals minimal variation in sensor performance across different instances. Significantly, this study underscores the potential adaptability and versatility of our solution for a wide array of assay types and smartphone-based sensor systems, making it particularly promising for deployment in resource-constrained settings and undeveloped countries. The robust correlation established between glucose concentrations and average RGB values, substantiated by an impressive R-square value of 0.98709, underscores the effectiveness and reliability of our pioneering approach within the medical field.

Electrochemical sensors for cortisol detection: Principles, designs, fabrication, and characterisation

Psychological stress is a major factor contributing to health discrepancies among individuals. Sustained exposure to stress triggers signalling pathways in the brain, which leading to the release of stress hormones in the body. Cortisol, a steroid hormone, is a significant biomarker for stress management due to its responsibility in the body's reply to stress. The release of cortisol in bloodstream prepares the body for a "fight or flight" response by increasing heart rate, blood pressure, metabolism, and suppressing the immune system. Detecting cortisol in biological samples is crucial for understanding its role in stress and personalized healthcare. Traditional techniques for cortisol detection have limitations, prompting researchers to explore alternative strategies. Electrochemical sensing has emerged as a reliable method for point-of-care (POC) cortisol detection. This review focuses on the progress made in electrochemical sensors for cortisol detection, covering their design, principle, and electroanalytical methodologies. The analytical performance of these sensors is also analysed and summarized. Despite significant advancements, the development of electrochemical cortisol sensors faces challenges such as biofouling, sample preparation, sensitivity, flexibility, stability, and recognition layer performance. Therefore, the need to develop more sensitive electrodes and materials is emphasized. Finally, we discussed the potential strategies for electrode design and provides examples of sensing approaches. Moreover, the encounters of translating research into real world applications are addressed.

Electrokinetic Enrichment and Label-Free Electrochemical Detection of Nucleic Acids by Conduction of Ions along the Surface of Bioconjugated Beads

In this paper, we report a method to integrate the electrokinetic pre-enrichment of nucleic acids within a bed of probe-modified microbeads with their label-free electrochemical detection. In this detection scheme, hybridization of locally enriched target nucleic acids to the beads modulates the conduction of ions along the bead surfaces. This is a fundamental advancement in that this mechanism is similar to that observed in nanopore sensors, yet occurs in a bed of microbeads with microscale interstices. In application, this approach has several distinct advantages. First, electrokinetic enrichment requires only a simple DC power supply, and in combination with nonoptical detection, it makes this method amenable to point-of-care applications. Second, the sensor is easy to fabricate and comprises a packed bed of commercially available microbeads, which can be readily modified with a wide range of probe types, thereby making this a versatile platform. Finally, the sensor is highly sensitive (picomolar) despite the modest 100-fold pre-enrichment we employ here by faradaic ion concentration polarization (fICP). Further gains are anticipated under conditions for fICP focusing that are known to yield higher enrichment factors (up to 100,000-fold enrichment). Here, we demonstrate the detection of 3.7 pM single-stranded DNA complementary to the bead-bound oligoprobe, following a 30 min single step of enrichment and hybridization. Our results indicate that a shift in the slope of a current-voltage curve occurs upon hybridization and that this shift is proportional to the logarithm of the concentration of target DNA. Finally, we investigate the proposed mechanism of sensing by developing a numerical simulation that shows an increase in ion flux through the bed of insulating beads, given the changes in surface charge and zeta potential, consistent with our experimental conditions.

Nucleic Acid Quantification by Multi-Frequency Impedance Cytometry and Machine Learning

Determining nucleic acid concentrations in a sample is an important step prior to proceeding with downstream analysis in molecular diagnostics. Given the need for testing DNA amounts and its purity in many samples, including in samples with very small input DNA, there is utility of novel machine learning approaches for accurate and high-throughput DNA quantification. Here, we demonstrated the ability of a neural network to predict DNA amounts coupled to paramagnetic beads. To this end, a custom-made microfluidic chip is applied to detect DNA molecules bound to beads by measuring the impedance peak response (IPR) at multiple frequencies. We leveraged electrical measurements including the frequency and imaginary and real parts of the peak intensity within a microfluidic channel as the input of deep learning models to predict DNA concentration. Specifically, 10 different deep learning architectures are examined. The results of the proposed regression model indicate that an R_Squared of 97% with a slope of 0.68 is achievable. Consequently, machine learning models can be a suitable, fast, and accurate method to measure nucleic acid concentration in a sample. The results presented in this study demonstrate the ability of the proposed neural network to use the information embedded in raw impedance data to predict the amount of DNA concentration.

Hemolysis-Inspired, Highly Sensitive, Label-Free IgM Detection Using Erythrocyte Membrane-Functionalized Nanomechanical Resonators

Immunoglobulin detection is important for immunoassays, such as diagnosing infectious diseases, evaluating immune status, and determining neutralizing antibody concentrations. However, since most immunoassays rely on labeling methods, there are limitations on determining the limit of detection (LOD) of biosensors. In addition, although the antigen must be immobilized via complex chemical treatment, it is difficult to precisely control the immobilization concentration. This reduces the reproducibility of the biosensor. In this study, we propose a label-free method for antibody detection using microcantilever-based nanomechanical resonators functionalized with erythrocyte membrane (EM). This label-free method focuses on the phenomenon of antibody binding to oligosaccharides (blood type antigen) on the surface of the erythrocyte. We established a method for extracting the EM from erythrocytes and fabricated an EM-functionalized microcantilever (MC), termed EMMC, by surface-coating EM layers on the MC. When the EMMC was treated with immunoglobulin M (IgM), the bioassay was successfully performed in the linear range from 2.2 pM to 22 nM, and the LOD was 2.0 pM. The EMMC also exhibited excellent selectivity compared to other biomolecules such as serum albumin, γ-globulin, and IgM with different paratopes. These results demonstrate that EMMC-based nanotechnology may be utilized in criminal investigations to identify blood types with minimal amounts of blood or to evaluate individual immunity through virus-neutralizing antibody detection.

Shape Memory Alloy-Based Wearables: A Review, and Conceptual Frameworks on HCI and HRI in Industry 4.0

Ever since its discovery, the applications of Shape Memory Alloys (SMA) can be found across a range of application domains, from structural design to medical technology. This is based upon the unique and inherent characteristics such as thermal Shape Memory Effect (SME) and Superelasticity (or Pseudoelasticity). While thermal SME is used for shape morphing applications wherein temperature change can govern the shape and dimension of the SMA, Superelasticity allows the alloy to withstand a comparatively very high magnitude of loads without undergoing plastic deformation at higher temperatures. These unique properties in wearables have revolutionized the field, and from fabrics to exoskeletons, SMA has found its place in robotics and cobotics. This review article focuses on the most recent research work in the field of SMA-based smart wearables paired with robotic applications for human-robot interaction. The literature is categorized based on SMA property incorporated and on actuator or sensor-based concept. Further, use-cases or conceptual frameworks for SMA fiber in fabric for 'Smart Jacket' and SMA springs in the shoe soles for 'Smart Shoes' are proposed. The conceptual frameworks are built upon existing technologies; however, their utility in a smart factory concept is emphasized, and algorithms to achieve the same are proposed. The integration of the two concepts with the Industrial Internet of Things (IIoT) is discussed, specifically regarding minimizing hazards for the worker/user in Industry 5.0. The article aims to propel a discussion regarding the multi-faceted applications of SMAs in human-robot interaction and Industry 5.0. Furthermore, the challenges and the limitations of the smart alloy and the technological barriers restricting the growth of SMA applications in the field of smart wearables are observed and elaborated.

Real-Time In Vivo Sensing of Nitric Oxide Using Photonic Microring Resonators

Real-time in vivo detection of biomarkers, particularly nitric oxide (NO), is of utmost importance for critical healthcare monitoring, therapeutic dosing, and fundamental understanding of NO's role in regulating many physiological processes. However, detection of NO in a biological medium is challenging due to its short lifetime and low concentration. Here, we demonstrate for the first time that photonic microring resonators (MRRs) can provide real-time, direct, and in vivo detection of NO in a mouse wound model. The MRR encodes the NO concentration information into its transfer function in the form of a resonance wavelength shift. We show that these functionalized MRRs, fabricated using complementary metal oxide semiconductor (CMOS) compatible processes, can achieve sensitive detection of NO (sub-μM) with excellent specificity and no apparent performance degradation for more than 24 h of operation in biological medium. With alternative functionalizations, this compact lab-on-chip optical sensing platform could support real-time in vivo detection of myriad of biochemical species.

Real-Time and In Situ Monitoring of the Synthesis of Silica Nanoparticles

The real-time and in situ monitoring of the synthesis of nanomaterials (NMs) remains a challenging task, which is of pivotal importance by assisting fundamental studies (e.g., synthesis kinetics and colloidal phenomena) and providing optimized quality control. In fact, the lack of reproducibility in the synthesis of NMs is a bottleneck against the translation of nanotechnologies into the market toward daily practice. Here, we address an impedimetric millifluidic sensor with data processing by machine learning (ML) as a sensing platform to monitor silica nanoparticles (SiO₂NPs) over a 24 h synthesis from a single measurement. The SiO₂NPs were selected as a model NM because of their extensive applications. Impressively, simple ML-fitted descriptors were capable of overcoming interferences derived from SiO₂NP adsorption over the signals of polarizable Au interdigitate electrodes to assure the determination of the size and concentration of nanoparticles over synthesis while meeting the trade-off between accuracy and speed/simplicity of computation. The root-mean-square errors were calculated as ∼2.0 nm (size) and 2.6 × 10¹⁰ nanoparticles mL^-1 (concentration). Further, the robustness of the ML size descriptor was successfully challenged in data obtained along independent syntheses using different devices, with the global average accuracy being 103.7 ± 1.9%. Our work advances the developments required to transform a closed flow system basically encompassing the reactional flask and an impedimetric sensor into a scalable and user-friendly platform to assess the in situ synthesis of SiO₂NPs. Since the sensor presents a universal response principle, the method is expected to enable the monitoring of other NMs. Such a platform may help to pave the way for translating "sense-act" systems into practice use in nanotechnology.

CMOS-Based Electrokinetic Microfluidics With Multi-Modal Cellular and Bio-Molecular Sensing for End-to-End Point-of-Care System

The importance of point-of-care (POC) bio-molecular diagnostics capable of rapid analysis has become abundantly evident after the outbreak of the Covid-19 pandemic. While sensing interfaces for both protein and nucleic-acid based assays have been demonstrated with chip-scale systems, sample preparation in compact form factor has often been a major bottleneck in enabling end-to-end POC diagnostics. Miniaturization of an end-to-end system requires addressing the front-end sample processing, without which, the goal for low-cost POC diagnostics remain elusive. In this paper, we address bulk fluid processing with AC-osmotic based electrokinetic fluid flows that can be fully controlled, processed and automated by CMOS ICs, fabricated in TSMC 65 nm LP process. Here, we combine bulk fluid flow control with bio-molecular sensing, cell manipulation, cytometry, and separation-all of which are controlled with silicon chips for an all-in-one bio-sensing device. We show CMOS controlled pneumatic-free bulk fluid flow with fluid velocities reaching up to 160 μm/s within a microfluidic channel of 100 × 50 μm ² of cross-sectional area. We incorporate electrode arrays to allow precise control and focused cell flows ( ±2 μm precision) for robust cytometry, and for subsequent separation. We also incorporate a 16-element impedance spectroscopy receiver array for cell and label-free protein sensing. The massive scalability of CMOS-driven microfluidics, manipulation, and sensing can lead to a new design space and a new class of miniaturized sensing technologies.