AWARE: A Wearable Awareness with Real-time Exposure, for monitoring alcohol consumption impact through ethyl glucuronide detection

The Evolving Role of Proteins in Wearable Sweat Biosensors

Sweat is an increasingly popular biological medium for fitness monitoring and clinical diagnostics. It contains an abundance of biological information and is available continuously and noninvasively. Sweat-sensing devices often employ proteins in various capacities to create skin-friendly matrices that accurately extract valuable and time-sensitive information from sweat. Proteins were first used in sensors as biorecognition elements in the form of enzymes and antibodies, which are now being tuned to operate at ranges relevant for sweat. In addition, a range of structural proteins, sometimes assembled in conjunction with polymers, can provide flexible and compatible matrices for skin sensors. Other proteins also naturally possess a range of functionalities─as adhesives, charge conductors, fluorescence emitters, and power generators─that can make them useful components in wearable devices. Here, we examine the four main components of wearable sweat sensors─the biorecognition element, the transducer, the scaffold, and the adhesive─and the roles that proteins have played so far, or promise to play in the future, in each component. On a case-by-case basis, we analyze the performance characteristics of existing protein-based devices, their applicable ranges of detection, their transduction mechanism and their mechanical properties. Thereby, we review and compare proteins that can readily be used in sweat sensors and others that will require further efforts to overcome design, stability or scalability challenges. Incorporating proteins in one or multiple components of sweat sensors could lead to the development and deployment of tunable, greener, and safer biosourced devices.

Electrochemical Nanosensors for Sensitization of Sweat Metabolites: From Concept Mapping to Personalized Health Monitoring

Sweat contains a broad range of important biomarkers, which may be beneficial for acquiring non-invasive biochemical information on human health status. Therefore, highly selective and sensitive electrochemical nanosensors for the non-invasive detection of sweat metabolites have turned into a flourishing contender in the frontier of disease diagnosis. A large surface area, excellent electrocatalytic behavior and conductive properties make nanomaterials promising sensor materials for target-specific detection. Carbon-based nanomaterials (e.g., CNT, carbon quantum dots, and graphene), noble metals (e.g., Au and Pt), and metal oxide nanomaterials (e.g., ZnO, MnO2, and NiO) are widely used for modifying the working electrodes of electrochemical sensors, which may then be further functionalized with requisite enzymes for targeted detection. In the present review, recent developments (2018-2022) of electrochemical nanosensors by both enzymatic as well as non-enzymatic sensors for the effectual detection of sweat metabolites (e.g., glucose, ascorbic acid, lactate, urea/uric acid, ethanol and drug metabolites) have been comprehensively reviewed. Along with this, electrochemical sensing principles, including potentiometry, amperometry, CV, DPV, SWV and EIS have been briefly presented in the present review for a conceptual understanding of the sensing mechanisms. The detection thresholds (in the range of mM-nM), sensitivities, linear dynamic ranges and sensing modalities have also been properly addressed for a systematic understanding of the judicious design of more effective sensors. One step ahead, in the present review, current trends of flexible wearable electrochemical sensors in the form of eyeglasses, tattoos, gloves, patches, headbands, wrist bands, etc., have also been briefly summarized, which are beneficial for on-body in situ measurement of the targeted sweat metabolites. On-body monitoring of sweat metabolites via wireless data transmission has also been addressed. Finally, the gaps in the ongoing research endeavors, unmet challenges, outlooks and future prospects have also been discussed for the development of advanced non-invasive self-health-care-monitoring devices in the near future.

Development and Performance Evaluation of an IoT-Integrated Breath Analyzer

Although alcohol consumption may produce effects that can be beneficial or harmful, alcohol consumption prevails among communities around the globe. Additionally, alcohol consumption patterns may be associated with several factors among communities and individuals. Numerous technologies and methods are implemented to enhance the detection and tracking of alcohol consumption, such as vehicle-integrated and wearable devices. In this paper, we present a cellular-based Internet of Things (IoT) implementation in a breath analyzer to enable data collection from multiple users via a single device. Cellular technology using hypertext transfer protocol (HTTP) was implemented as an IoT gateway. IoT integration enabled the direct retrieval of information from a database relative to the device and direct upload of data from the device onto the database. A manually developed threshold algorithm was implemented to quantify alcohol concentrations within a range from 0 to 200 mcg/100 mL breath alcohol content using electrochemical reactions in a fuel-cell sensor. Two data collections were performed: one was used for the development of the model and was split into two sets for model development and on-machine validation, and another was used as an experimental verification test. An overall accuracy of 98.16% was achieved, and relative standard deviations within the range from 1.41% to 2.69% were achieved, indicating the reliable repeatability of the results. The implication of this paper is that the developed device (an IoT-integrated breath analyzer) may provide practical assistance for healthcare representatives and researchers when conducting studies involving the detection and data collection of alcohol consumption patterns.

A Proof-of-Concept Electrochemical Skin Sensor for Simultaneous Measurement of Glial Fibrillary Acidic Protein (GFAP) and Interleukin-6 (IL-6) for Management of Traumatic Brain Injuries

This work demonstrates the use of a noninvasive, sweat-based dual biomarker electrochemical sensor for continuous, prognostic monitoring of a Traumatic Brain Injury (TBI) with the aim of enhancing patient outcomes and reducing the time to treatment after injury. A multiplexed SWEATSENSER was used for noninvasive continuous monitoring of glial fibrillary acidic protein (GFAP) and Interleukin-6 (IL-6) in a human sweat analog and in human sweat. Electrochemical impedance spectroscopy (EIS) and chronoamperometry (CA) were used to measure the sensor response. The assay chemistry was characterized using Fourier Transform Infrared Spectroscopy (FTIR). The SWEATSENSER was able to detect GFAP and IL-6 in sweat over a dynamic range of 3 log orders for GFAP and 2 log orders for IL-6. The limit of detection (LOD) for GFAP detection in the sweat analog was estimated to be 14 pg/mL using EIS and the LOD for IL-6 was estimated to be 10 pg/mL using EIS. An interference study was performed where the specific signal was significantly higher than the non-specific signal. Finally, the SWEATSENSER was able to distinguish between GFAP and IL-6 in simulated conditions of a TBI in human sweat. This work demonstrates the first proof-of-feasibility of a multiplexed TBI marker combined with cytokine and inflammatory marker detection in passively expressed sweat in a wearable form-factor that can be utilized toward better management of TBIs. This is the first step toward demonstrating a noninvasive enabling technology that can enable baseline tracking of an inflammatory response.

A new generation of transdermal alcohol biosensing technology: practical applications, machine -learning analytics and questions for future research

The use of transdermal alcohol monitors has burgeoned in recent years, now encompassing hundreds of thousands of individuals globally. A new generation of sensors promises to expand the range of applications for transdermal technology exponentially, and advances in machine-learning modeling approaches offer new methods for translating the data produced by transdermal devices. This article provides (1) a review of transdermal sensor research conducted to date, including an analysis of methodological features of past studies potentially key in driving reported sensor performance; (2) updates on methodological developments likely to be transformative for the field of transdermal sensing, including the development of new-generation sensors featuring smartphone integration and rapid sampling capabilities as well as developments in machine-learning analytics suited to data produced by these novel sensors and; (3) an analysis of the expanded range of applications for this new generation of sensor, together with corresponding requirements for sensor accuracy and temporal specificity. We also note questions as yet unanswered and key directions for future research.

Next-Generation Continuous Metabolite Sensing toward Emerging Sensor Needs

This article discusses the emergent biosensor technology focused on continuous biosensing of metabolites by non-invasive sampling of body fluids emphasized on physiological monitoring in mobility-constrained populations, resource-challenged settings, and harsh environments. The boom of innovative ideas and endless opportunities in healthcare technologies has transformed traditional medicine into a sustainable link between medical practitioners and patients to provide solutions for faster disease diagnosis. The future of healthcare is focused on empowering users to manage their own health. The confluence of big data and predictive analysis and the internet of things (IoT) technology have shown the potential of converting the abundant health profile data amassed from medical diagnosis of patients into useable information, whilst allowing caregivers to provide suitable treatment plans. The implementation of the IoT technology has opened up advanced approaches in real-time, continuous, remote monitoring of patients. Wearable, point-of-care biosensors are the future roadmap to providing direct, real-time information of health status to the user and medical professionals in this digitized era.

Autonomous, Real-Time Monitoring Electrochemical Aptasensor for Circadian Tracking of Cortisol Hormone in Sub-microliter Volumes of Passively Eluted Human Sweat

The proposed work involves the development of an autonomous, label-free electrochemical sensor for real-time monitoring of cortisol levels expressed naturally in sub-microliter sweat volumes, for prolonged sensing periods of ∼8 h. Highly specific single-stranded DNA (ssDNA) aptamer is used for affinity capture of cortisol hormone eluted in sweat dynamically. The cortisol present in sweat binds to the aptamer capture probe that changes conformation and modulates electrochemical properties at the electrode-buffer interface, which was studied using dynamic light scattering studies for the entire physiological sweat pH. Attenuated total reflectance-Fourier transform infrared spectroscopy and UV-vis spectroscopy were used to optimize the binding chemistry of the elements of the sensor stack. Nonfaradaic electrochemical impedance spectroscopy was used to calibrate the sensor for a dynamic range of 1-256 ng/mL. An R² of 0.97 with an output signal range of 20-50% was obtained. Dynamic cortisol level variation tracking was studied using continuous dosing experiments to calibrate the sensor for temporal variation. The sensor did not show significant susceptibility to noise due to cross-reactive interferents and nonspecific buffer constituents. The performance of the developed aptasensor was compared with the previously established cortisol immunosensor in terms of surface charge behavior and nonfaradaic biosensing. The aptamer sensor shows a higher signal-to-noise ratio, better resolution, and has a larger output range for the same input range as the cortisol immunosensor. The feasibility of deploying the developed aptasensing scheme as continuous lifestyle and performance monitors was validated through human subject studies.

A Rapid Response Electrochemical Biosensor for Detecting Thc In Saliva

Marijuana is listed as a Schedule I substance under the American Controlled Substances Act of 1970. As more U.S. states and countries beyond the U.S. seek legalization, demands grow for identifying individuals driving under the influence (DUI) of marijuana. Currently no roadside DUI test exists for determining marijuana impairment, thus the merit lies in detecting the primary and the most sought psychoactive compound tetrahydrocannabinol (THC) in marijuana. Salivary THC levels are correlated to blood THC levels making it a non-invasive medium for rapid THC testing. Affinity biosensing is leveraged for THC biomarker detection through the chemical reaction between target THC and THC specific antibody to a measure signal output related to the concentration of the targeted biomarker. Here, we propose a novel, rapid, electrochemical biosensor for the detection of THC in saliva as a marijuana roadside DUI test with a lower detection limit of 100 pg/ml and a dynamic range of 100 pg/ml – 100 ng/ml in human saliva. The developed biosensor is the first of its kind to utilize affinity-based detection through impedimetric measurements with a rapid detection time of less than a minute. Fourier transform infrared spectroscopy analysis confirmed the successful immobilization of the THC immobilization assay on the biosensing platform. Zeta potential studies provided information regarding the stability and the electrochemical behavior of THC immunoassay in varying salivary pH buffers. We have demonstrated stable, dose dependent biosensing in varying salivary pH’s. A binary classification system demonstrating a high general performance (AUC = 0.95) was employed to predict the presence of THC in human saliva. The biosensor on integration with low-power electronics and a portable saliva swab serves as a roadside DUI hand-held platform for rapid identification of THC in saliva samples obtained from human subjects.