Passive sweat wearable: A new paradigm in the wearable landscape toward enabling "detect to treat" opportunities.

Growing interest over recent years in personalized health monitoring coupled with the skyrocketing popularity of wearable smart devices has led to the increased relevance of wearable sweat-based sensors for biomarker detection. From optimizing workouts to risk management of cardiovascular diseases and monitoring prediabetes, the ability of sweat sensors to continuously and noninvasively measure biomarkers in real-time has a wide range of applications. Conventional sweat sensors utilize external stimulation of sweat glands to obtain samples, however; this stimulation influences the expression profile of the biomarkers and reduces the accuracy of the detection method. To address this limitation, our laboratory pioneered the development of the passive sweat sensor subfield, which allowed for our progress in developing a sweat chemistry panel. Passive sweat sensors utilize nanoporous structures to confine and detect biomarkers in ultra-low sweat volumes. The ability of passive sweat sensors to use smaller samples than conventional sensors enable users with sedentary lifestyles who perspire less to benefit from sweat sensor technology not previously afforded to them. Herein, the mechanisms and strategies of current sweat sensors are summarized with an emphasis on the emerging subfield of passive sweat-based diagnostics. Prospects for this technology include discovering new biomarkers expressed in sweat and expanding the list of relevant detectable biomarkers. Moreover, the accuracy of biomarker detection can be enhanced with machine learning using prediction algorithms trained on clinical data. Applying this machine learning in conjunction with multiplex biomarker detection will allow for a more holistic approach to trend predictions. This article is categorized under: Diagnostic Tools > Diagnostic Nanodevices Nanotechnology Approaches to Biology > Nanoscale Systems in Biology Diagnostic Tools > Biosensing.

A machine learning-based on-demand sweat glucose reporting platform.

Diabetes is a chronic endocrine disease that occurs due to an imbalance in glucose levels and altering carbohydrate metabolism. It is a leading cause of morbidity, resulting in a reduced quality of life even in developed societies, primarily affected by a sedentary lifestyle and often leading to mortality. Keeping track of blood glucose levels noninvasively has been made possible due to diverse breakthroughs in wearable sensor technology coupled with holistic digital healthcare. Efficient glucose management has been revolutionized by the development of continuous glucose monitoring sensors and wearable, non/minimally invasive devices that measure glucose concentration by exploiting different physical principles, e.g., glucose oxidase, fluorescence, or skin dielectric properties, and provide real-time measurements every 1-5 min. This paper presents a highly novel and completely non-invasive sweat sensor platform technology that can measure and report glucose concentrations from passively expressed human eccrine sweat using electrochemical impedance spectroscopy and affinity capture probe functionalized sensor surfaces. The sensor samples 1-5 µL of sweat from the wearer every 1-5 min and reports sweat glucose from a machine learning algorithm that samples the analytical reference values from the electrochemical sweat sensor. These values are then converted to continuous time-varying signals using the interpolation methodology. Supervised machine learning, the decision tree regression algorithm, shows the goodness of fit R2 of 0.94 was achieved with an RMSE value of 0.1 mg/dL. The output of the model was tested on three human subject datasets. The results were able to capture the glucose progression trend correctly. Sweet sensor platform technology demonstrates a dynamic response over the physiological sweat glucose range of 1-4 mg/dL measured from 3 human subjects. The technology described in the manuscript shows promise for real-time biomarkers such as glucose reporting from passively expressed human eccrine sweat.

An approach to rapidly assess sepsis through multi-biomarker host response using machine learning algorithm.

Sepsis is a life-threatening condition and understanding the disease pathophysiology through the use of host immune response biomarkers is critical for patient stratification. Lack of accurate sepsis endotyping impedes clinicians from making timely decisions alongside insufficiencies in appropriate sepsis management. This work aims to demonstrate the potential feasibility of a data-driven validation model for supporting clinical decisions to predict sepsis host-immune response. Herein, we used a machine learning approach to determine the predictive potential of identifying sepsis host immune response for patient stratification by combining multiple biomarker measurements from a single plasma sample. Results were obtained using the following cytokines and chemokines IL-6, IL-8, IL-10, IP-10 and TRAIL where the test dataset was 70%. Supervised machine learning algorithm naïve Bayes and decision tree algorithm showed good accuracy of 96.64% and 94.64%. These promising findings indicate the proposed AI approach could be a valuable testing resource for promoting clinical decision making.

ZEUS (ZIF-based electrochemical ultrasensitive screening) device for isopentane analytics with focus on lung cancer diagnosis.

Breath analytics is currently being explored for the development of point-of-care devices in non-invasive disease detection. It is based on the measurement of volatile organic compounds (VOCs) and gases that are produced by the body because of the metabolic pathways. The levels of these metabolites vary due to alteration in the endogenous oxidative stress-related metabolic pathways and can be correlated to understand the underlying disease condition. The levels of exhaled hydrocarbons in human breath can be used to design a rapid, easy to use method for lung cancer detection. This work outlines the development of an electrochemical sensing platform that can be used for the non-invasive diagnosis of lung cancer by monitoring isopentane levels in breath. This electrochemical sensor platform involves the use of [BMIM]BF4@ZIF-8 for sensing the target analyte. This synthesized nanocomposite offers advantages for gas sensing applications as it possesses unique properties such as an electrochemically active Room Temperature Ionic Liquid (RTIL) and a crosslinking Metal Organic Framework (MOF) that provides increased surface area for gas absorption. This is the first report of a hydrocarbon-based sensor platform developed for lung cancer diagnosis. The developed sensor platform displays sensitivity and specificity for the detection of isopentane up to 600 parts-per-billion. We performed structural and morphological characterization of the synthesized nanocomposite using various analytical techniques such as PXRD, FESEM, FTIR, and DLS. We further analyzed the electrochemical activity of the synthesized nanocomposite using a standard glassy carbon electrode. The application of the nanocomposite for isopentane sensing was done using a commercially available carbon screen printed electrode. The results so obtained helped in strengthening our hypothesis and serve as a proof-of-concept for the development of a breathomics-enabled electrochemical strategy. We illustrated the specificity of the developed nanocomposite by cross-reactivity studies. We envision that the detection platform will allow sensitive and specific sensing of isopentane levels such that it can used for point of care applications in noninvasive and early diagnosis of lung cancer, thereby leading to its early treatment and decrease in mortality rate.

Design and Electrochemical Characterization of Spiral Electrochemical Notification Coupled Electrode (SENCE) Platform for Biosensing Application.

C-reactive protein (CRP) is considered to be an important biomarker associated with many diseases. During any physiological inflammation, the level of CRP reaches its peak at 48 h, whereas its half-life is around 19 h. Hence, the detection of low-level CRP is an important task for the prognostic management of diseases like cancer, stress, metabolic disorders, cardiovascular diseases, and so on. There are various techniques available in the market to detect low-level CRP like ELISA, Western blot, etc. An electrochemical biosensor is one of the important miniaturized platforms which provides sensitivity along with ease of operation. The most important element of an electrochemical biosensor platform is the electrode which, upon functionalization with a probe, captures the selective antibody-antigen interaction and produces a digital signal in the form of potential/current. Optimization of the electrode design can increase the sensitivity of the sensor by 5-10-fold. Herein, we come up with a new sensor design called the spiral electrochemical notification coupled electrode (SENCE) where the working electrode (WE) is concentric in nature, which shows better response than the market-available standard screen-printed electrode. The sensor is thoroughly characterized using a standard Ferro/Ferri couple. The sensing performance of the fabricated platform is also characterized by the detection of standard H2O2 using a diffusion-driven technique, and a low detection limit of 15 µM was achieved. Furthermore, we utilized the platform to detect a low level (100 ng/mL) of CRP in synthetic sweat. The manuscript provides emphasis on the design of a sensor that can offer good sensitivity in electrochemical biosensing applications.