Wearable microfluidic bioaffinity sensors for automated molecular analysis

Abstract

Some embodiments of the present disclosure relate to a wearable biosensor device that includes an iontophoresis module configured to stimulate the production of a sweat sample from a user's skin, the sweat sample containing a biomarker; a microfluidic module configured to collect the sweat sample, mix the sweat sample with a labeled detection reagent to obtain a mixture containing the biomarker bound to the labeled detection reagent, and direct the mixture to a detection reservoir of the microfluidic module; and a sensor assembly including a bioaffinity sensor configured to quantify the biomarker in the mixture in the detection reservoir and determine the concentration of the biomarker present in the sweat sample. The bioaffinity sensor includes an electrode functionalized to bind to the biomarker in the mixture. The bioaffinity sensor can quantify the biomarker and determine the concentration of the biomarker with sensitivity on the order of nanomolar or picomolar.

Description

[Technical Field]

[0001] The present specification relates to systems and methods for monitoring biomarkers using wearable biosensor devices, and is particularly directed to embodiments that automatically and non-invasively monitor protein or hormone biomarkers using wearable microfluidic bioaffinity sensors that collect sweat samples. [Background technology]

[0002] Recent advances in flexible electronics and digital health have transformed traditional laboratory testing into remote, wearable molecular sensors capable of monitoring physiological biomarkers in real time. Sweat contains a wealth of biochemical molecules, ranging from electrolytes and metabolites to large proteins. Importantly, sweat has the advantage of being easily accessible via noninvasive techniques. However, currently reported wearable biosensors are primarily limited to detecting only a limited number of biomarkers, such as electrolytes and metabolites at micromolar or higher concentrations, using ion-selective sensors, enzymatic sensors, or direct redox reactions. For example, while the majority of clinically important protein biomarkers, including C-reactive protein (CRP), are present at nanomolar to picomolar levels in blood, protein concentrations in sweat are expected to be much lower than those in blood. Commercially available point-of-care biomarker monitors are still bulky, unable to reach picomolar levels, and unable to assess biomarker levels in alternative biofluids that can be collected noninvasively, such as sweat and saliva. Summary of the Invention

[0003] The technology described herein relates to wearable bioaffinity sensor systems and methods that enable automated, real-time monitoring of low-level biomarkers, such as hormone and protein biomarkers.

[0004] In one embodiment, a wearable biosensor device includes an iontophoresis module configured to stimulate the production of a sweat sample from a user's skin, the sweat sample containing a biomarker; a microfluidic module configured to collect the sweat sample, mix the sweat sample with a labeled detection reagent to obtain a mixture containing the biomarker bound to the labeled detection reagent, and direct the mixture to a detection reservoir of the microfluidic module; and a sensor assembly including a bioaffinity sensor configured to quantify the biomarker in the mixture in the detection reservoir and determine the concentration of the biomarker present in the sweat sample, the bioaffinity sensor including an electrode functionalized to bind to the biomarker in the mixture.

[0005] In some embodiments, the labeled detection reagent comprises first nanoparticles conjugated to a detection antibody that binds to the biomarker, while the surface of the electrode comprises second nanoparticles conjugated to a capture antibody that binds to the biomarker.

[0006] In some embodiments, the first nanoparticle and the second nanoparticle are gold nanoparticles (AuNPs). In some embodiments, the biomarker comprises a protein biomarker or a hormone biomarker. In certain embodiments, the biomarker comprises CRP.

[0007] In some embodiments, the wearable biosensor device is configured to quantify the biomarkers of the mixture and determine the concentration with a sensitivity of 1 micromolar or less, 100 nanomolar or less, 10 nanomolar or less, 1 nanomolar or less, 100 picomolar or less, or 10 picomolar or less.

[0008] In some embodiments, the microfluidic module includes an inlet for collecting a sweat sample, a reagent reservoir containing a labeled detection reagent, the reagent reservoir configured to refresh the sweat sample with the labeled detection reagent, a mixing channel for mixing the sweat sample refreshed with the labeled detection reagent to form a mixture containing the labeled detection reagent bound to the biomarker, a detection reservoir for receiving the mixture from the mixing channel, and an outlet for discharging the sweat sample from the detection reservoir.

[0009] In some embodiments, the sensor assembly further includes a temperature sensor configured to measure the temperature of the skin, an ionic strength sensor configured to measure the ionic strength of the sweat sample, and / or a pH sensor configured to measure the pH level of the sweat sample. In some embodiments, the wearable biosensor device is configured to calibrate measurements from the bioaffinity sensor based on measurements by the temperature sensor, the ionic strength sensor, and / or the pH sensor.

[0010] In some embodiments, the sensor assembly comprises a multiplexed sensor array fabricated using laser-engraved graphene (LEG), the multiplexed sensor array comprising a bioaffinity sensor, a temperature sensor, an ionic strength sensor, and / or a pH sensor.

[0011] In some embodiments, the wearable biosensor device includes a disposable patch including an iontophoresis module, a microfluidic module, and a sensor assembly, the disposable patch including an adhesive member for directly adhering the disposable patch to the skin, and a flexible printed circuit board (FPCB) coupled to the disposable patch, the FPCB configured to receive signals from the sensor assembly and to provide power to the wearable biosensor device.

[0012] In some embodiments, the FPCB is reusable and configured to removably couple to the disposable patch, the FPCB including a processor configured for in situ signal processing of signals received from the sensor assembly and a wireless communication module configured for real-time wireless communication with a mobile device.

[0013] In one example, the method includes receiving a sweat sample collected from the skin via an inlet of a microfluidic module, the sweat sample containing a protein or hormone biomarker; reconstituting the sweat sample in a reagent reservoir of the microfluidic module with a detection reagent configured to bind to the protein or hormone biomarker, the detection reagent comprising an electroactively labeled molecule; combining the detection reagent with the protein or hormone biomarker in a mixing channel of the microfluidic module to form a mixture containing the protein or hormone biomarker bound to the detection reagent; collecting the mixture of the protein or hormone biomarker bound to the detection reagent in a detection reservoir of the microfluidic module and binding the protein or hormone biomarker to electrodes of a sensor assembly; refreshing the microfluidic module with one or more additional sweat samples without the detection reagent and removing unbound detection reagent via an outlet of the microfluidic module; and estimating the concentration of the protein or hormone biomarker present in the sweat sample by measuring the amount of electroactive label present on the electrode surface.

[0014] In some embodiments, estimating the concentration of a protein biomarker or hormone biomarker present in the sweat sample comprises estimating the concentration of the protein biomarker or hormone biomarker with a sensitivity of 1 micromolar or less, 100 nanomolar or less, 10 nanomolar or less, 1 nanomolar or less, 100 picomolar or less, or 10 picomolar or less.

[0015] In some embodiments, the method further includes obtaining one or more additional biophysical sensor measurements, including skin temperature, pH level of the sweat sample, or ionic strength of the sweat sample, using one or more additional sensors of the sensor assembly, and calibrating the estimated concentration of the protein biomarker or hormone biomarker based on the measurements of the one or more additional biophysical sensors.

[0016] In some embodiments, the method further comprises inducing the sweat sample with an iontophoresis module in contact with the skin prior to receiving the sweat sample via the inlet.

[0017] In some embodiments, the protein biomarker is CRP. In some embodiments, the detection reagent further comprises a first nanoparticle that is conjugated with a detection antibody that binds to CRP, and the surface of the electrode comprises a second nanoparticle that is conjugated with a capture antibody that binds to CRP.

[0018] In some embodiments, the first nanoparticle and the second nanoparticle are gold nanoparticles and the electroactive label molecule is a redox molecule.

[0019] In one example, the method includes applying a patch including a microfluidic module and a sensor assembly to a user's skin; collecting a sweat sample obtained from the skin in the microfluidic module; mixing the sweat sample with a reagent in the microfluidic module to obtain a mixture including the reagent bound to a protein or hormone biomarker contained in the sweat sample; and estimating the concentration of the protein or hormone biomarker in the sweat sample from the mixture using the sensor assembly.

[0020] In some embodiments, the method further comprises monitoring the health status of the user in real time based on the concentration of the protein biomarker or hormone biomarker estimated using the sensor assembly.

[0021] In some embodiments, monitoring the user's health status in real time includes comparing the concentration of a protein biomarker or hormone biomarker estimated using the sensor assembly to a threshold value to determine the user's biological status. For example, the concentration of CRP or other inflammatory biomarker estimated using the sensor assembly can be compared to a threshold value to determine whether the user is currently experiencing an inflammatory response.

[0022] In some embodiments, the condition includes heart disease, chronic obstructive pulmonary disease, inflammatory bowel disease, an active infection, or a past infection.

[0023] In some embodiments, the method further includes presenting to a user in real time the concentration of the protein biomarker or hormone biomarker estimated using the sensor assembly via a mobile device communicatively coupled to the patch via a wireless communication medium.

[0024] Other features and aspects of the disclosed technology will become apparent from the following detailed description, taken in conjunction with the accompanying drawings, which illustrate, by way of example, features according to embodiments of the disclosed technology. This summary is not intended to limit the scope of the invention(s) described herein, which scope is defined by the claims and equivalents. [Brief explanation of the drawings]

[0025] The present disclosure, in accordance with one or more embodiments, will now be described in detail with reference to the following figures. The figures are provided for illustrative purposes only and merely depict exemplary embodiments. Furthermore, it should be noted that for clarity and clarity of illustration, elements in the figures have not necessarily been drawn to scale.

[0026] [Figure 1A] 1 illustrates a usage environment for a wearable biosensor device including a sweat sensor patch for automatic and non-invasive biomarker monitoring, according to some embodiments of the present disclosure. [Figure 1B] 1B shows a cross-sectional view of the sweat sensor patch of FIG. 1A in operation and attached to the skin, according to some embodiments of the present disclosure. [Figure 1C] 1 shows an optical image of a sensor patch according to some embodiments of the present disclosure. [Figure 1D] 1 shows an optical image of a vertical stack assembly of a fully integrated biosensor device including a sensor patch and an FPCB according to some embodiments of the present disclosure. [Figure 1E] FIG. 1 is an exploded view of a wearable biosensor device according to some embodiments of the present disclosure. [Figure 2] FIG. 1 is a flow diagram illustrating an example method for assembling a sweat sensor patch according to some embodiments of the present disclosure. [Figure 3] 1 illustrates what may be used during assembly of a microfluidic module according to some embodiments of the present disclosure. [Figure 4]1 illustrates components of a microfluidic module and sensor assembly that can be utilized during automated bioaffinity sensing, according to some embodiments of the present disclosure. [Figure 5] FIG. 5 is an operational flow diagram illustrating exemplary operations performed during automated bioaffinity sensing using components of the biosensor device shown in FIG. 4 according to some embodiments of the present disclosure. [Figure 6A] Specific embodiments for achieving automated and wearable in situ CRP detection using AuNPs conjugated with labeled CRP detection antibodies (dAbs) are shown, as well as the reconstitution and incubation operation within a microfluidic module of a wearable bioaffinity sensor according to specific embodiments of the present disclosure. [Figure 6B] 1 illustrates refresh and detection operations within a microfluidic module of a wearable bioaffinity sensor, according to certain embodiments of the present disclosure. [Figure 6C] 1 illustrates a sensing operation performed by a wearable bioaffinity sensor according to certain embodiments of the present disclosure. [Figure 7] FIG. 1 is a close-up view of the working electrode surface conceptually illustrating the binding process at the working electrode surface between a capture antibody on the electrode surface and a biomarker bound to a detection antibody received via the microfluidic module, according to some embodiments of the present disclosure. [Figure 8] FIG. 10 is an enlarged plan view of the electronics of the FPCB of a wearable biosensor device according to some embodiments of the present disclosure. [Figure 9] FIG. 1 is a block diagram illustrating an example of the electronic system of a biosensor device used to sense protein or hormone biomarkers, according to some embodiments of the present disclosure. [Figure 10]1 illustrates an exemplary graphical user interface (GUI) that may be presented to a user by executing a mobile application used in conjunction with a wearable biosensor device for non-invasive automated biomarker monitoring, according to some embodiments of the present disclosure. [Figure 11] 1A-1C are scanning electron microscope (SEM) images of a LEG electrode that is graphene engraved in raster mode for CRP sensing, a LEG-AuNP LEG electrode for CRP sensing, a LEG electrode engraved in vector mode for pH sensing, and an electrode engraved in vector mode for temperature sensing, according to certain embodiments. [Figure 12A] FIG. 1 is a schematic diagram of the layers of a functionalized LEG-AuNP working electrode of a bioaffinity sensor, according to certain embodiments of the present disclosure. [Figure 12B] 1 illustrates the surface functionalization process of the LEG-AuNP working electrode of a bioaffinity sensor according to certain embodiments of the present disclosure. [Figure 12C] 1 is an SEM image of a mesoporous LEG electrode according to certain embodiments of the present disclosure. [Figure 12D] 1 is a transmission electron microscope (TEM) image of graphene flakes decorated with AuNPs, according to certain embodiments of the present disclosure. [Figure 12E] Amperometric response and SEM images of CRP sensor based on LEG modified with poly(pyrrole propionic acid) (PPA) and pyrene butyric acid (PBA) are shown. [Figure 12F] Amperometric response of CRP sensors based on AuNP / self-assembled monolayers (SAMs) and laser-engraved graphene oxide by electrochemical oxidation (LEGO) and plots showing the sensor performance comparison of different functionalization methods are shown. [Figure 12G]1 shows the batch-to-batch variation in electrochemical performance of LEG and LEG-AuNP electrodes according to some embodiments of the present disclosure, and includes plots showing the oxidation peak heights in cyclic voltammograms (CVs) of LEG and LEG-AuNP electrodes according to some embodiments of the present disclosure. [Figure 12H] Included are plots showing a comparison of the electrochemical performance of a redox probe conjugated to a detection antibody (dAb) and a redox probe conjugated to AuNPs conjugated to a detection antibody (dAb). [Figure 12I] TEM image showing dispersed detection antibody (dAb)-loaded AuNPs with a protein corona shell. [Figure 12J] 1 shows square wave voltammetry (SWV) voltammograms of a CRP sensor according to certain embodiments of the present disclosure. [Figure 12K] FIG. 12J shows the calibration plot of the corresponding CRP sensor. [Figure 12L] 1 is a plot showing the selectivity of a CRP sensor to potential interferents in sweat. [Figure 12M] 10 is another plot showing the selectivity of the CRP sensor to potential interfering factors in sweat. [Figure 12N] 1 is a plot showing validation of a CRP sensor in human sweat and saliva samples, according to certain embodiments of the present disclosure. [Figure 13]Figure 13A shows a schematic diagram illustrating an overview of the assessment of CRP in sweat for noninvasive monitoring of various health conditions potentially associated with elevated CRP in healthy or patient populations, according to some embodiments of the present disclosure. Figure 13B shows a box-and-whisker plot of a study of CRP levels in iontophoretically extracted sweat and serum samples from patients with chronic obstructive pulmonary disease (COPD) and patients without COPD, according to some embodiments of the present disclosure. Figure 13C shows a box-and-whisker plot of a study of CRP levels in sweat and serum samples from healthy participants, patients with heart failure with reduced ejection fraction (HFrEF), and patients with heart failure with preserved ejection fraction (HFpEF), according to some embodiments of the present disclosure. Figure 13D shows a box-and-whisker plot of a study of CRP levels in sweat and serum samples from three patients who had active infections for two consecutive days, according to some embodiments of the present disclosure. Figure 13E shows a plot illustrating the results of a calculation of the correlation between serum CRP levels and sweat CRP levels. [Figure 14A] 1 includes plots showing on-body multiplexed physicochemical and CRP analysis with real-time sensor calibration in healthy non-smokers using wearable sensors, according to some embodiments of the present disclosure. [Figure 14B] 1 includes plots showing on-body multiplexed physicochemical and CRP analysis with real-time sensor calibration in healthy smokers using wearable sensors, according to some embodiments of the present disclosure. [Figure 14C] 1 includes plots showing on-body multiplexed physicochemical and CRP analysis with sensor calibration in real time for COPD patients using wearable sensors, according to some embodiments of the present disclosure. [Figure 14D] 1 includes plots showing on-body multiplexed physicochemical and CRP analysis with real-time sensor calibration of participants who have had COVID-19 using wearable sensors, according to some embodiments of the present disclosure. [Figure 15]FIG. 15 includes plots showing measured admittance responses of an impedance-based ionic strength sensor in a NaCl solution. FIG. 15B includes a calibration plot of the impedance-based ionic strength sensor related to FIG. 15A. FIG. 15C includes plots showing simulated changes in CRP-detecting antibody (dAb) concentration over time on the working electrode. FIG. 15D shows the stages of automated sweat sampling and reagent routing for in situ CRP detection in a simulated CRP-detecting antibody (dAb) concentration simulation. FIG. 15E includes plots showing the admittance change of the LEG ionic strength sensor as a function of time during four stages of the automated CRP sensing process in a laboratory flow test using artificial sweat. FIG. 15F includes plots showing the admittance response of the LEG ionic strength sensor as a function of time at different flow rates in a laboratory flow test using artificial sweat. FIG. 15G includes admittance plots showing the effect of flow rate on microfluidic automated CRP sensing. Figure 15H includes voltammogram plots showing the effect of flow rate on microfluidic automated CRP sensing. Figure 15I includes admittance plots showing the effect of ionic strength on microfluidic automated CRP sensing. Figure 15J includes voltammogram plots showing the effect of ionic strength on microfluidic automated CRP sensing.

[0027] These drawings are not intended to be exhaustive or to limit the present disclosure to the precise forms disclosed. DETAILED DESCRIPTION OF THE INVENTION

[0028] Although recent progress has been made in developing wearable bioaffinity biosensors for trace biomarkers such as cortisol, accurate in situ detection of biomarkers such as sweat proteins and hormone biomarkers remains a significant challenge due to their extremely low concentrations (e.g., nM or pM levels) and the large interindividual and intraindividual variability in sweat composition. For example, detection of protein biomarkers typically requires the integration of bioaffinity receptors such as antibodies or aptamers. However, such technologies typically require lengthy target incubations, laborious washing steps, and the addition of redox solutions for signal transduction. Furthermore, the current turnaround time (more than one day) for highly sensitive clinical biomarker tests, such as high-sensitivity CRP tests (hsCRP), may not meet the needs of frequent testing. For example, in addition to hospitalized cases requiring close monitoring of inflammatory status, many chronic diseases, such as COPD and inflammatory bowel disease, would benefit from home, daily or frequent, fully automated, and noninvasive measurement of CRP for disease management.

[0029] Therefore, there is a need for wearable biosensing technologies that can automatically monitor ultra-low levels of circulating biomarkers in situ in the home or community. To this end, some embodiments of the present disclosure are directed to systems and methods for wearable, real-time electrochemical detection of low-concentration protein and hormonal biomarkers, such as inflammatory biomarkers, in sweat. According to some embodiments of the present disclosure, a biosensor device for biomarker sampling may include an iontophoresis module for sweat stimulation, a microfluidic module for sweat sampling and routing and exchange of labeled reagents, and an electrochemical bioaffinity sensor (including, but not limited to, an immunosensor, a DNA sensor, and / or an aptamer sensor) for quantifying biomarkers in sweat. Certain embodiments are directed to a wearable wireless patch containing the aforementioned components for real-time electrochemical detection of low-level concentrations of biomarkers in sweat. When worn, the patch can be flexibly attached to the skin using a medical adhesive, allowing in situ biomarker detection within the microfluidics without direct sensor contact with the skin. In certain examples, the inflammatory biomarker CRP can be monitored in sweat samples.

[0030] According to some particular embodiments, the biosensor device can utilize a bioaffinity sensor (e.g., a CRP sensor) for quantifying a biomarker (e.g., CRP) via an electrode functionalized with a capture antibody (e.g., an anti-CRP capture antibody) conjugated to a nanoparticle. According to some particular embodiments, the bioaffinity sensor can be part of a graphene-based sensor array that also includes sensors for ionic strength, pH, and / or temperature measurement for real-time calibration of the bioaffinity sensor.

[0031] Various advantages can be realized by implementing the systems and methods described herein. First, the wearable biosensor devices described herein can enable real-time, noninvasive, and wireless biomarker analysis in both healthy and patient populations. This can facilitate the management and / or detection of chronic diseases by providing real-time, highly sensitive analysis of biomarkers present in a user's sweat. Second, the technology described herein can achieve sweat CRP or other biomarker analysis with high sensitivity, selectivity, and efficiency by combining specific nanomaterials and chemical technologies (e.g., mesoporous graphene-Au nanoparticles immobilized with capture receptors such as antibodies for efficient target recognition, combined with Au nanoparticles conjugated with thionine-labeled detection antibodies for signal transduction and amplification). For example, in contrast to previously reported conventional wearable technologies for biomarker monitoring using LEG-based sensors that detect metabolites at micromolar or higher levels, the technology described herein can be used to achieve highly sensitive detection of ultralow levels of biomarkers in situ, with a six-order of magnitude (e.g., picomolar) improvement in sensitivity.

[0032] Third, the biosensor device modules described herein can enable autonomous sweat induction, sampling, reagent routing, and fully automated bioaffinity sensing in situ on the user's skin. Furthermore, in some embodiments, multiple sensor modalities can be utilized to mitigate the effects of inter-individual variability on wearable sensing and enable real-time calibration of biomarker data. These additional sensor modalities can also provide a more comprehensive assessment of physiological status.

[0033] Furthermore, by utilizing the technology described herein to conduct experiments involving measuring CRP levels in patients, the presence of CRP has been confirmed in human sweat from healthy subjects, and elevated CRP levels have been found in the sweat of patients with a variety of chronic and acute inflammations associated with health conditions, including heart failure, COPD, and active and past infections (e.g., COVID-19). Furthermore, by utilizing the technology described herein to conduct experiments involving measuring CRP levels in patients, a strong correlation has been found between sweat and blood serum CRP levels in both healthy and patient populations, demonstrating the utility of the technology described herein in noninvasive disease classification, monitoring, and / or management.

[0034] These and other advantages realized by implementing the techniques described herein are further described below.

[0035] 1A-1E show an example of a biosensor device 300 including a sweat sensor patch 100 and an environment for using the biosensor device 300, according to some embodiments of the present disclosure. As depicted by FIGS. 1A-1B, the sweat sensor patch 100 of the biosensor device 300 can be attached to the skin 10 of a user (e.g., a human patient). As depicted by FIG. 1B, which shows a cross-sectional view of the sweat sensor patch 100 in operation adhered to the skin 10, the sweat sensor patch 100 can include a backing layer / substrate 110 and one or more layers 115 including a medical adhesive (e.g., medical tape) used to directly adhere the sensor patch 100 to the skin 10. Iontophoresis electrodes 129 contact the skin 10 with a layer of hydrogel agent 140 applied therebetween to stimulate sweat 30 production. The hydrogel agent 140, which can be a component of the sensor patch 100, can be an agarose gel containing carbachol (Carbagel). An electric current is passed through the electrodes 129, causing transdermal transport of carbachol to the sweat glands, inducing the flow of the sweat stimulant to the skin 10 and stimulating sweat production as needed. Considering that potential users of the present technology may include sedentary or immobile patients, an iontophoresis module containing a pair of electrodes may provide the advantage of on-demand delivery of a hydrogel agent (e.g., carbachol, a cholinergic agonist derived from carbachol) for autonomous sweat stimulation through daily activities without the need for strenuous exercise.

[0036] In operation, biosensor device 300 is configured to collect biophysical data of a user, including data related to biomarkers collected from the user's sweat 30, and communicate the data to mobile device 50 via wireless communication link 20. Wireless communication link 20 may be a radio frequency link, such as a Bluetooth® or Bluetooth® Low Energy (LE) link, a Wi-Fi® link, a ZigBee link, or any other suitable wireless communication link. In some embodiments, a low energy and / or short-range wireless communication link may be preferably used for data transfer. Mobile device 50 may be a smartphone, a smartwatch, a head-mounted display (HMD), or other suitable mobile device capable of executing an application that displays health information (e.g., inflammatory biomarker data, temperature data, etc.) associated with data received from biosensor device 300. In some embodiments, the application may analyze and / or organize the data collected from biosensor device 300.

[0037] 1C shows an optical image of a sensor patch 100 according to some embodiments of the present disclosure. The imaged sensor patch in this example is a disposable microfluidic graphene sensor patch. FIG. 1D shows an optical image of a vertically stacked assembly of a fully integrated biosensor device 300 including the sensor patch 100 and FPCB 200 shown in FIG. 1C. The scale bar in both optical images is 0.5 cm.

[0038] 1E is an exploded view of a biosensor device 300 according to some embodiments of the present disclosure. The biosensor device 300 includes a sweat sensor patch 100 and an FPCB 200. The sweat sensor patch 100 includes a backing substrate 110, a sensor assembly 120, a microfluidic layer / module 130, and a hydrogel 140. The backing substrate 110 can be made of a polyimide film or other suitable material, particularly a lightweight, flexible, heat-resistant, and / or chemically resistant material. For example, a microfluidic biosensor patch can be fabricated on a polyimide substrate via CO2 laser engraving.

[0039] As shown, the sensor assembly 120 can include bioaffinity sensors 121a-121c and additional sensors 122-124. The bioaffinity sensors 121a-121c can include a working electrode 121a including a coating that selectively binds to a biomarker of interest present in a sweat sample, a reference electrode 121b, and a counter electrode 121c for sweat biomarker capture and electrochemical analysis. In certain embodiments, the bioaffinity sensors 121a-121c are inflammatory biomarker sensors (e.g., CRP sensors) that bind to an inflammatory biomarker of interest (e.g., CRP). In some embodiments, the working electrode 121a can be coated with nanoparticles conjugated to an antibody that binds to the biomarker of interest. In certain embodiments, the working electrode 121a is functionalized with AuNPs conjugated to capture antibodies (cAbs). For example, the capture antibodies (cAbs) can be anti-CRP capture antibodies (cAbs). The AuNPs can be electrodeposited. In certain embodiments, reference electrode 121b is an Ag / AgCl reference electrode. The foregoing design allows for sensitive and efficient electrochemical detection of trace levels of sweat biomarkers, such as hormones or proteins, including CRP, in situ on the skin. For example, in some embodiments, sensor assembly 120, including bioaffinity sensors 121a-121c, is configured to determine biomarker concentrations with sensitivity of sub-1 micromolar, sub-100 nanomolar, sub-10 nanomolar, sub-1 nanomolar, sub-100 picomolar, or even sub-10 picomolar. Other nanoparticles that can be conjugated to antibodies that bind to biomarkers include iron oxide nanoparticles, quantum dots, silver nanoparticles, copper nanoparticles, copper oxide nanoparticles, and the like.

[0040] The additional sensors can include a temperature sensor 122, a pH sensor 123, and an ionic strength sensor 124. In one embodiment, the temperature sensor 122 is a strain-insensitive temperature sensor. In one embodiment, the pH sensor is a potentiometric sweat pH sensor. In one embodiment, the ionic strength sensor is an impedance-type ionic strength sensor. As described further below, the integration of additional pH, temperature, and ionic strength sensors enables real-time, personalized calibration of biomarker data, mitigating detection errors induced by inter-individual sample matrix variability and providing a more comprehensive assessment of physiological status. In some embodiments, the sensor combination, including bioaffinity sensors 121a-121c and sensors 122-124, can be implemented as a multiplexed sensor array. In other embodiments, some of the additional sensors can be omitted, or other additional sensors can be included to enable calibration.

[0041] The sensor assembly 120, including the electrode 129, bioaffinity sensors 121a-121c, and sensors 122-124, can be formed as a LEG sensor assembly. Fabrication as a LEG potentially allows for large-scale production of biosensor systems at relatively low cost via CO2 laser engraving. LEG sensors are advantageous because they can be printed using modified conventional printers. Printable wearable sensor patches can be fabricated at large scale at relatively low cost. This potentially enables disposable sensor patches that can be worn by individuals for extended periods (e.g., 12-24 hours) and replaced daily, collecting health information without invasive testing or requiring human patients to return to a physical laboratory for repeated testing.

[0042] 11 shows SEM images of a raster-mode engraved graphene LEG electrode for CRP sensing (image 1110), a LEG-AuNP LEG electrode for CRP sensing (image 1120), a vector-mode engraved LEG electrode for pH sensing (image 1130), and a vector-mode engraved LEG electrode for temperature sensing (image 1140), according to certain embodiments. Scale bars for images 1110 and 1120 are 10 μm and 1 μm, respectively. Scale bars for images 1130-1140 are 2 μm.

[0043] The FPCB 200 can be configured for iontophoretic sweat induction, sensor data acquisition, and / or wireless communication with a mobile device 50. Upon assembly, the FPCB 200 can be coupled onto the patch 100 to form a fully integrated wearable biosensor device 300. The FPCB 200 can be configured as a reusable electronic system coupled with a disposable point-of-care sensor patch 100. A battery 251 (e.g., a lithium battery) powers the system and enables functionality such as wireless communication. In other embodiments, the biosensor device 300 may be powered by other or additional means, such as human movement, a small solar panel, and / or a biofluid-powered system that uses collected sweat flow to power the device.

[0044] FIG. 2 is a flow diagram illustrating an example of a method for assembling a sweat sensor patch 100 according to some embodiments of the present disclosure. FIG. 2 is described in conjunction with FIG. 3, which illustrates layers 210-230 that can be used during assembly of a microfluidic module 130. The flexible microfluidic module 130 can be assembled by stacking laser-cut layers 210-230. The layers 210-230 can be formed with cutouts for one or more inlets, reagent reservoirs, mixing channels, detection reservoirs, one or more outlets, one or more channels, hydrogel, and / or other components of the sweat sensor patch 100. In this particular example, layer 210 is configured as a reservoir layer 210, layer 220 is configured as an inlet layer 220, and layer 230 is configured as a collection layer 230. The collection layer 230 is patterned with one or more wells for collecting sweat. The inlet layer 220 can include one or more inlets and / or channels through which sweat flows. The reservoir layer 210 can include an inlet and / or a reservoir that receives sweat flowing through the flow path, and an outlet through which the sweat may drain after sampling.

[0045] Reservoir layer 210 and collection layer 230 can each be a patterned medical adhesive member, such as double-sided medical tape. Inlet layer 220 can be formed of a thermoplastic polymer resin, such as polyethylene terephthalate (PET). As depicted, inlet layer 220 can be laminated / adhered onto reservoir layer 210 to form assembly 225. Collection layer 230 can be laminated / adhered onto assembly 225 to form assembly 235, which corresponds to microfluidic module 130.

[0046] 2 also depicts a backing layer 110 (e.g., a polyimide layer) onto which the sensor assembly 120 can be printed or otherwise deposited to form assembly 245. In further assembly of the sensor patch 100, a hydrogel agent 140 can be applied to assembly 235, which can then be laminated / glued onto assembly 245.

[0047] In some embodiments, biosensor device 300 can be designed to have good mechanical flexibility and stability to allow practical use during physical activity. For example, individual sensors can be designed to exhibit minimal fluctuations under a moderate bending radius (e.g., 5 cm). Furthermore, sensors can be designed to be less susceptible to strain, if desired.

[0048] It should also be understood that assembly methods other than that illustrated in FIG. 2 are contemplated, and that other biosensor assemblies besides wearable patches are contemplated according to the technology described herein. For example, components of biosensor device 300, including one or more components of FPCB 200 and sensor patch 100, may instead be integrated into a wearable device such as a smartwatch or HMD. For example, FPCB 200 and sweat sensor patch 100 components may be incorporated into the area of the smartwatch that contacts the user's skin. In this example, the smartwatch itself may run an application that displays health information related to the collected data and / or alternatively, communicate data to another mobile device 50, such as a smartphone or wearable HMD, running such an application.

[0049] 4 illustrates components of a microfluidic module 130 and sensor assembly 120 that can be utilized during automated bioaffinity sensing, according to some embodiments of the present disclosure. As depicted, the microfluidic module 130 can include various fluidly coupled components, including an inlet 131 for receiving a sweat sample, a reagent reservoir 132 containing detection reagents, a mixing channel 133, a detection reservoir 134 for capture and quantification of sweat biomarkers, and an outlet 135 that provides a flow path for the sweat sample to exit. As described above, the sensor assembly 120 can include a pH sensor 123, an ionic strength sensor 124, and a biosensor including a working electrode 121 a, a reference electrode 121 b, and a counter electrode 121 c.

[0050] Figure 5 is an operational flow diagram illustrating exemplary operations performed during automated bioaffinity sensing using components of the biosensor device 300 shown in Figure 4, according to some embodiments of the present disclosure. Figure 5 will be described with reference to Figures 6A-6C, which illustrate specific embodiments for achieving automated, wearable in situ CRP detection using AuNPs conjugated with a labeled CRP-detecting antibody (dAb). However, it should be understood that the biosensor device 300 described herein can be configured to achieve automated detection of other biomarkers in sweat in addition to CRP, particularly biomarkers that may be present at low concentrations (e.g., picomolar or nanomolar), including hormones, proteins, peptides, and the like.

[0051] Operation 510 includes receiving a biological fluid sample containing biomarkers via inlet 131. The biological fluid sample may be a sweat sample that is autonomously induced using the iontophoresis module (e.g., using electrodes 129 and carbagel 140) as described above, and may flow into microfluidic module 130 via inlet 131.

[0052] Operation 520 involves reconstituting the biological fluid sample in a reagent reservoir 132 with a detection reagent configured to bind to a biomarker contained in the biological fluid, the detection reagent including an electroactive labeling molecule. The detection reagent can be placed in the reagent reservoir 132 before collection of the biological fluid. When the biological fluid enters the reagent reservoir 132, the biological fluid carries away the detection reagent. For example, FIG. 6A shows reconstitution 610 in a reagent reservoir storing AuNPs conjugated with a labeled CRP detection antibody (dAb). The nanoparticle conjugates can be labeled with an electroactive redox molecule, such as thionine (TH), to achieve direct electrochemical sensing. Nanoparticles conjugated to an electroactive redox molecule and a detection antibody (dAb) enable efficient electrochemical signaling (signal ON) and signal amplification.

[0053] Operation 530 includes combining the detection reagent and the biomarker contained in the biological fluid sample in the mixing channel 133 to form a mixture. FIG. 6A shows the combination 620 of the detection reagent, including a CRP detection antibody (dAb) and AuNPs conjugated with a redox molecule TH, in the mixing channel. In the illustrated example, the mixing channel 133 has a serpentine shape, which can promote binding and control the amount of binding time. For example, the serpentine shape can promote dynamic binding between the CRP and the detection antibody (dAb). In other embodiments, the mixing channel 133 can be of a different shape.

[0054] Operation 540 involves collecting the mixture from the mixing channel 133 into the detection reservoir 134, allowing the biomarker pre-bound to the labeled detection reagent to bind to the working electrode 121a. For example, FIG. 6B shows an incubation process 630 in which a CRP detection antibody (dAb) is allowed to bind to a LEG-AuNP working electrode functionalized with an anti-CRP capture antibody. As the mixture enters the detection reservoir 134 from the mixing channel 133, it is allowed to slowly fill the chamber before exiting through the outlet 135. The size of the detection reservoir 134 can be optimized to allow sufficient time for binding to the working electrode 121a. As a further example, FIG. 7 is a close-up view of the working electrode surface conceptually illustrating the binding process that can occur at the working electrode surface between the capture antibody on the electrode surface and the biomarker bound to the detection antibody received via the microfluidic module. For simplicity, the AuNPs on the working electrode are not shown in this example.

[0055] Operation 550 includes refreshing the microfluidic module 130 with one or more additional biological fluid samples without detection reagent to remove unbound detection reagent from the detection reservoir 134 via the outlet 135. For example, a freshly collected sweat stream can continue to enter the microfluidic module 130 to refresh the microfluidic module 130, remove unbound detection reagent, and remove passive labels prior to detection. By way of example, FIG. 6B illustrates a refreshment operation 640 in which unbound detection reagent mixture is removed. Performing a refreshment operation can improve quantification of biomarkers contained in the sweat sample.

[0056] Operation 560 involves measuring the amount of electroactive label present on the working electrode surface to estimate the concentration of the biomarker. To measure the amount of electroactive label bound to the electrode surface, any one of a number of voltammetric techniques that correlate current to concentration can be used. For example, differential pulse voltammetry (DPV), SWV, linear sweep voltammetry (LSV), or other voltammetric techniques can be used for the measurement. It should be noted that because the electroactive label molecule is directly conjugated to the detection reagent, its amount can be directly correlated to the amount of biomarker between the capture antibody (cAb) and the detection antibody (dAb) on the electrode surface. As an example, Figure 6C shows detection operation 650 in which SWV is used to measure the amount of TH bound to the working electrode surface. As depicted in this example, the TH molecules are directly conjugated to the CRP capture antibody (dAb)-immobilized AuNPs, so that the amount of binding is directly correlated to the amount of CRP “sandwiched” between the capture antibody (cAb) and the detection antibody (dAb)-immobilized AuNPs on the electrode surface, and thus to the initial concentration of CRP in the solution.

[0057] Depending on the binding environment, the composition of a biological fluid sample may vary significantly from one individual to another, which may affect the rate at which a biomarker binds to a detection reagent and thus the accuracy of the estimated biomarker concentration. For example, as described below, experiments have shown that pH, electrolyte concentration, and temperature can all affect the sensor readout of CRP concentration, expressed as a current measurement. Thus, in some embodiments, to further improve the quantification of biomarkers in a biological fluid sample, the effects of temperature, pH, and / or ionic strength on the biomarker sensor readings can be calibrated in real time based on readings from the temperature sensor 122, pH sensor 123, and / or ionic strength sensor 124 of the biological fluid sample in the detection reservoir 134.

[0058] In some embodiments, electrolytes may be introduced into the detection reservoir 134 to mitigate differences in the binding environment. For example, a high concentration of buffer salts can be placed alongside the detection antibody (dAb) in the reagent reservoir to mitigate potential changes in the binding environment due to variations in sweat composition.

[0059] 8 is an expanded plan view of electronics that may be implemented in FPCB 200, according to certain embodiments. As depicted by dashed lines indicating different modules, FPCB 200 may include a signal processing and wireless communication module 810, an iontophoresis module 820, a power management module 830, a battery 840, and an electrochemical sensor measurement module 850. In this example, the scale bar is 5 mm.

[0060] FIG. 9 is a block diagram illustrating an example of an electronic system 900 of a biosensor device 300 used for CRP sensing, according to certain embodiments. As depicted therein, components of the electronic system 900 can be powered using a battery 925. The electronic system 900 includes an iontophoresis (IP) electrode 901 for iontophoretic sweat collection. The IP electrode 901 can be electrically coupled to a current mirror 902 and a boost converter 903. The electronic system also includes a multiplexed sensor array including an ionic strength sensor 911, a biomarker sensor 912, a temperature sensor 913, and a pH sensor 914, which generate signals that, after signal processing, for example, are routed to a multiplexer 915. In this example, these multiple sensors are connected to an analog-to-digital converter 916 using an analog front end 910. In this example, wireless communication is implemented using a programmable system-on-chip (PSoC) BLE module 920. Through the illustrated electronic circuitry, the FPCB can be configured to perform current-controlled iontophoresis, multiplexed electrochemical measurements (including voltammetry, impedance measurements, and potentiometry), signal processing, and wireless communication. The system can also accurately obtain the dynamic responses of integrated LEG-based pH, ionic strength, and skin temperature sensors for real-time CRP sensor calibration.

[0061] FIG. 10 illustrates an exemplary graphical user interface (GUI) 1000 that may be presented to a user (e.g., a patient) by executing a mobile application used in combination with a wearable biosensor device 300 for noninvasive, automated biomarker monitoring, according to some embodiments of the present disclosure. For example, the application may be executed by a mobile device 50 wirelessly coupled to the biosensor device 300. During execution, the GUI may display real-time data (processed or otherwise) acquired by the biosensor device 300. The GUI may also display acquired historical data. For example, data such as CRP concentration (e.g., in ng / mL), pH, and skin temperature may be acquired and presented in real time based on sweat samples collected by the biosensor device 300. The data may be plotted over time to provide a representation of the user's inflammation level or other biological level over time. The GUI may provide an indication (e.g., via text or visual markers) of whether the user's measured health data is within a normal or abnormal range. The GUI may also provide an indication of the status of the biosensor device 300 (e.g., whether it is currently connected to a mobile device).

[0062] In some embodiments, the mobile application can itself process the sensor measurements received from the biosensor device 300 before displaying them to the user. For example, in one embodiment, the mobile application can be configured to convert biomarker concentrations based on the obtained voltammogram (e.g., SWV voltammogram) and corresponding real-time values obtained from calibration sensors, such as ionic strength sensors, pH sensors, and temperature sensors.

[0063] In some embodiments, sweat samples can be collected without reapplication of the hydrogel for a period of time. This period can range from about 2 hours to 24 hours per day. Refreshed samples can be periodically or continuously collected in the microfluidic patch, mixed with a labeled reagent, flowed into the detection reservoir, analyzed, and then released through the outlet. The entire process described above can be integrated into a single sweat sensor patch. After a full day or other period of time, a new sweat sensor patch with a new hydrogel can be applied, and the aforementioned process for biomarker detection can be repeated. This process can be repeated daily for extended periods of days, weeks, or months. This process can also be restarted after intervals of days, weeks, or months to assess changes in the disease state.

[0064] In some embodiments, the microfluidic sweat collection patch may be optimized to achieve the fastest refresh time between samples. Several parameters may be selected for optimization. These parameters may include, for example, the location of the inlets relative to one another and their placement relative to the reagent reservoir, the shape and distance of the mixing channel, the number of inlets, the distance between the inlets and the reagent reservoir, the shape and distance of the mixing channel, the shape and size of the detection reservoir, the placement and distance of the outlet relative to the detection reservoir, and other factors.

[0065] In some embodiments, the microfluidic sweat collection patch may be designed to eliminate leakage of the sweat sample. For example, electrical stimulation may be applied to several adjacent sweat glands while avoiding the glands directly beneath the inlet. The patch may be designed to collect sweat samples only from glands not in contact with the hydrogel, preventing leakage of sweat from adjacent glands (which may be mixed with the hydrogel). This can be achieved by applying pressure to the gland from which the sample is collected, applying special adhesive tape to adjacent glands, and using adhesive materials that can securely attach and fix the skin patch. The use of hydrogel may also be limited to optimal portions of the patch to minimize interference.

[0066] In one embodiment, the capillary bursting valve and sensor array can be integrated into a single disposable sensor patch, if desired, to enable dynamic, automatic, and wearable biomarker sensing.

[0067] Experimental and simulation results

[0068] Various experiments and simulations were conducted using a biosensor device 300 and / or its components used to wirelessly, autonomously, and noninvasively monitor CRP levels according to a particular embodiment of the present disclosure. The design of this particular biosensor device 300 and the associated experimental and simulation results are described in further detail below. While these experimental and simulation results illustrate some of the benefits of utilizing the techniques described herein, it should be understood that the present disclosure is not limited by the following discussion, which describes results and observations utilizing a particular illustrative embodiment. For example, beyond CRP, this wearable approach could be adapted for on-demand assessment of trace levels of other disease-related protein biomarkers. Furthermore, the operating principles described herein can be readily adapted to investigate a wide range of biomarkers (e.g., proteins, hormones, cytokines, etc.), including biomarkers indicating the presence of inflammation or other biological conditions.

[0069] Fabrication of multiplexed microfluidic sensor patches

[0070] A specific embodiment of a microfluidic sensor patch was fabricated as follows. To fabricate the LEG-based iontophoresis IP electrode, connecting lead wire, impedance, CRP working electrode, counter electrode, and reference electrode, a PI film was raster-engraved at the focal height using a 50W CO2 laser cutter (8% power, 15% speed, 1000 points per inch). The pH electrode and temperature sensor were engraved using vector mode at 1% and 3% power, respectively (15% speed, 1000 points per inch (PPI)). The pH sensor working electrode was prepared by electrochemically cleaning the LEG electrode with 1 M HCl and subjecting it to 10 cycles of cyclic voltammetry from -0.2 to 1.2 V at 0.1 V / s. A polyaniline pH sensing film was then electrodeposited onto the LEG electrode and subjected to 10 cycles of cyclic voltammetry from -0.2 to 1.2 V at 0.1 V / s. An Ag / AgCl shared reference electrode was fabricated by electrodepositing silver onto the LEG electrode using multiple current steps (30 s at -1 μA, 30 s at -5 μA, 30 s at -10 μA, 30 s at -50 μA, 30 s at -0.1 mA, and 30 s at -0.2 mA) in a solution containing silver nitrate, sodium thiosulfate, and sodium bisulfite (250 mM, 750 mM, and 500 mM, respectively), followed by the instillation of a 10 μL aliquot of 0.1 M iron(III) chloride for 1 min. AuNPs were electrodeposited onto the LEG CRP working electrode by 40 cycles of pulse electrodeposition (two 0.5 s pulses at -0.2 V, separated by a 0.5 s pulse at 0 V) in the presence of 0.1 mM gold(III) chloride trihydrate and 10 mM sulfuric acid.

[0071] Agarose (3% w / w) was dissolved in deionized water and microwaved to prepare hydrogels containing the cholinergic agonist carbachol (placed on IP electrodes). After the agarose was completely dissolved, the mixture was cooled to 165°C, and 1% carbachol for the anode (or 1% KCl for the cathode) was added to the mixture and stirred until homogeneous. The cooled mixture was poured into a cylindrical mold or an assembled microfluidic patch and allowed to solidify at room temperature. The hydrogels were stored at 4°C until use.

[0072] A microfluidic module was fabricated by assembling a thin PET film (50 μm) sandwiched between double-sided medical adhesives (180 μm top layer, 260 μm bottom layer, 50 μm PET backing). This was then attached to a substrate and cut using a laser cutter at 2.7% power, 1.8% speed, and 1000 PPI vector mode to create channels and reagent reservoirs. Next, only the top layer (180 μm) of the medical adhesive was cut into a circle using 4% power, 10% speed, and 1000 PPI vector mode. The circular top layer was peeled off to create a detection reservoir. A 130 μm adhesive was cut to create a sweat accumulation layer. Labeled detection antibody (dAb)-AuNPs were dropped and dried in the reagent reservoir. The sample was then stored dry at 4 °C before being attached to a sensor patch.

[0073] Functionalization of LEG-AuNP CRP working electrode

[0074] In one specific example, a LEG-AuNP CRP working electrode was functionalized as follows: The LEG-AuNP working electrode was immersed overnight in (200-proof) ethanol containing 0.5 mM mercaptoundecanoic acid (MUA) and 1 mM mercaptohexanol (MCH) for SAM formation. After rinsing with ethanol and then deionized water and drying under airflow, the electrode was incubated in a humid chamber for 35 min at room temperature with 10 μL of a mixture of 0.4 M N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide (EDC) and 0.1 M N-hydroxysulfosuccinimide sodium salt (sulfo-NHS) in 2-(N-morpholino)ethanesulfonic acid monohydrate (MES) buffer, pH 5.0. Covalent immobilization of CRP capture antibody (cAb) was performed by adding 10 μL of anti-CRP solution (250 μg / mL in phosphate-buffered saline (PBS), pH 7.4) to the electrode and incubating for 2.5 hours at room temperature. The electrode was then blocked with 1.0% bovine serum albumin (BSA) in PBS for 1 hour. The electrode was stored in 1% BSA in PBS until use.

[0075] CRP detection antibody conjugation

[0076] In one specific example, conjugation of CRP detection antibodies was achieved as follows: 20 nm carboxylic acid-functionalized PEGylated AuNPs were activated with a mixed solution of EDC and Sulfo-NHS (30 mg / mL and 36 mg / mL, respectively) in 10 mM MES buffer (pH 5.5) for 30 minutes. The conjugates were washed with 1X PBS containing 0.1% Tween® 20 (PBST) and centrifuged at 6500 rcf (relative centrifugal force) for 30 minutes. After removing the supernatant, 50 μg / mL polystreptavidin R (PS-R) was added and crosslinked at room temperature for 1 hour. After centrifugation at 3500 rcf for 30 minutes and removal of the supernatant, 5 μg / mL biotinylated anti-CRP detection antibodies (dAbs) in 1% BSA prepared in 1X PBS (pH 7.4) were incubated for 1 hour at room temperature. After another wash (centrifugation at 2000 rcf), the carboxyl groups of the PS-R and detection antibody (dAb) on the AuNPs were activated with a mixed solution of EDC and Sulfo-NHS (30 mg / mL and 36 mg / mL, respectively) in 10 mM MES buffer (pH 5.5) for 30 minutes. After washing by centrifugation at 1500 rcf, the conjugates were incubated with 100 μM thionin for 1 hour. The final conjugate was washed with PBST, centrifuged at 1250 rcf, reconstituted with 1% BSA, and filtered through a 0.2 μm syringe filter to remove all aggregates.

[0077] For direct conjugation of the redox probe to the antibody, 100 μg / mL of detection antibody (dAb) was concentrated and buffer-exchanged using a 100 kDa molecular weight cutoff protein concentrator and reconstituted in 10 mM MES buffer (pH 5.5). The carboxyl groups of the detection antibody (dAb) were activated in the column with a mixture of EDC and Sulfo-NHS (30 mg / mL and 36 mg / mL, respectively) in 10 mM MES buffer (pH 5.5) for 30 minutes. After buffer exchange with 1X PBS (pH 7.4), the column was incubated with 100 μM thionin for 1 hour. The final conjugate was buffer-exchanged with PBS, reconstituted in 1% BSA, and filtered through a 0.2 μm syringe filter to remove any aggregates.

[0078] Electronic Systems Design and Integration

[0079] In one specific example, the electronic system was designed as follows. A two-layer flexible printed circuit board (FPCB) was designed. The FPCB had a rounded rectangular shape (31.7 mm × 25.5 mm), the same size as the microfluidic sensor patch, and a cutout (10 mm × 3.8 mm) was designed to allow the patch to be directly inserted under the FPCB. The electronic system consisted of magnetic leads and a voltage regulator for power management, a boost converter for iontophoresis induction, a BJT array, analog switches, an electrochemical front end for interfacing with the sensor array, an operational amplifier, a voltage divider, and a Bluetooth Low Energy (BLE) module for system control and Bluetooth wireless communication. A BLE connection was established with the wearable device to wirelessly acquire sensor data for calibration and voltammogram analysis. The electronic system was powered by a rechargeable 3.8 V lithium button battery with a capacity of 8 mAh. Filtering and smoothing techniques were used to reduce existing noise due to motion artifacts. On the hardware side, the electrochemical AFE filtered noise from the ADC through a digital filter. On the software side, smoothing algorithms (moving average filter / median filter) were automatically applied in real time.

[0080] Electrochemical characterization of LEG-AuNP immunosensor

[0081] Figures 12A-12B show the surface functionalization process of the LEG-AuNP working electrode of a CRP sensor, according to a specific embodiment. As depicted, AuNPs can be electrodeposited on the LEG surface, followed by the formation of a thiol monolayer with mercaptoundecanoic acid and mercaptohexanol. Because the formation of the SAM layer may rely on specific gold-sulfur bonds, it was observed that immersion of the sensor patch in an alkanethiol solution had negligible effects on other graphene-based electrodes. As shown in Figure 12C (SEM image of the mesoporous LEG electrode, scale bar 100 μm), Figure 12D (transmission electron microscope image of AuNP-modified graphene flakes, scale bar 50 nm), and image 1120 in Figure 11, the pulsed-potential-deposited AuNPs were uniformly distributed throughout the mesoporous graphene structure, demonstrating excellent electrocatalytic performance and providing numerous binding sites for biomolecule immobilization on the particle surface.

[0082] As shown in Figures 12E-12F, the LEG CRP sensor prepared by the functionalization method using thiol SAM-modified LEG-AuNP complexes (AuNP / SAM) achieved superior electrochemical performance compared to other functionalization methods. It was also observed that there was little nonspecific adsorption, significantly improving the sensitivity of the CRP sensor. Figure 12E shows the amperometric response and SEM images of the CRP sensor based on LEG modified with PPA (1210, 1220) and PBA (1230, 1240). Figure 12F shows the amperometric response of the CRP sensor based on AuNP / SAM (1250) and laser-engraved graphene oxide (LEGO) (1260) by electrochemical oxidation. Figure 12F also includes plot 1270, which shows the sensor performance comparison of different functionalization methods. The error bars represent the standard deviation of the mean from three sensors, and S / B is the signal-to-background ratio.

[0083] The formation of the LEG-AuNP complex was observed by an increase in the intensity ratio of the D band to the G band in the Raman spectrum due to the presence of AuNPs. Individual sensor modification steps on the LEG electrode were evaluated by X-ray photoelectron spectroscopy. Results showed that the intensity of Au4f significantly increased after AuNP deposition, while the N1s band increased only after the capture antibody (cAb) immobilization step, indicating successful electrode preparation. The electrochemical properties of the LEG surface after each modification step were further investigated using DPV and electrochemical impedance spectroscopy (EIS). After immobilization of the SAM and capture antibody (cAb) proteins, the peak current height decreased in the DPV voltammogram and the resistance increased in the Nyquist plot, indicating that the SAM and capture antibody (cAb) inhibited electron transfer at the interface. This was due to increased surface coverage by non-conductive species. Furthermore, we found that the negatively charged carboxylate functional groups in the SAM layer repelled the negatively charged redox indicator ferricyanide, resulting in a further decrease in the electron transfer rate. Subsequent modification of the SAM layer by EDC / NHS reaction replaces the negatively charged carboxylate groups with neutral NHS-ester groups, which was experimentally observed as an increase in the peak current height. As shown in Figure 12G, which depicts the batch-to-batch variability of the electrochemical performance of the LEG and LEG-AuNP electrodes, this electrode fabrication process exhibited high batch-to-batch reproducibility, due to the fact that all key steps, including laser engraving, electrochemical deposition, and solution processing, were mass-producible. In particular, Figure 12G shows the results of the electrochemical performance of the LEG and LEG-AuNP electrodes fabricated using 0.1 M KCl and 5 mM [Fe(CN)6] 3- , includes plots showing the oxidation peak heights of the cyclic voltammograms (CVs) at the LEG electrode (plot 1281) and the LEG-AuNP electrode (plot 1282) at a scan rate of 50 mV / s, with the bars representing the standard deviation of the average from three sensors.

[0084] In this particular example, to achieve trace-level sweat CRP analysis, PEGylated AuNPs with a large surface area-to-volume ratio were functionalized with PS-R to increase the loading of biotinylated detection antibodies (dAbs), subsequently improving sensitivity. For example, Figure 12H includes plots 1285-1286, which show a comparison of the electrochemical performance of a redox probe conjugated to a detection antibody (dAb) and a redox probe conjugated to AuNPs conjugated to a detection antibody (dAb). Plot 1285 shows the SWV voltammograms of a CRP sensor modified with a redox probe conjugated to a detection antibody (dAb) and a redox probe conjugated to AuNPs conjugated to a detection antibody (dAb). Plot 1286 shows the corresponding peak currents for a CRP sensor modified with a redox probe conjugated to a detection antibody (dAb) and a CRP sensor modified with a redox probe conjugated to AuNPs conjugated to a detection antibody (dAb). In the plot, the solid and dotted lines represent the sensor response at 0 and 10 ng / mL of CRP, respectively. Error bars represent the standard deviation of the mean for three sensors.

[0085] In this particular example, crosslinking of redox-labeled TH to carboxylate residues on a detection antibody (dAb)-loaded AuNP enabled one-step direct electrochemical detection. The TH-labeled detection antibody (dAb)-loaded AuNPs bound to a mesoporous graphene electrode upon CRP recognition, allowing TH located at the exosite site of the protein to be in close proximity to the mesoporous graphene surface for electron transfer. Successful immobilization of the detection antibody (dAb) was confirmed based on various observations. For example, dynamic light scattering confirmed the increase in the hydrodynamic size of the PEGylated AuNPs after each conjugation step: immobilization of PS-R, binding of a biotinylated detection antibody (dAb), and inactivation of BSA following conjugation of the redox molecule TH. Successful immobilization of the detection antibody (dAb) was confirmed by the observed shift in ultraviolet-visible (UV-Vis) absorbance of the AuNP conjugate after each modification step and also by TEM images showing dispersion of the detection antibody (dAb)-loaded AuNPs with a protein corona shell ( Figure 12 I).

[0086] The performance of CRP in this particular example was evaluated by SWV in a PBS solution spiked with CRP (Figure 12J). It was observed that the increase in peak current height for TH reduction showed a linear relationship with increasing target concentration (Figure 12K). In particular, Figure 12J shows the SWV voltammogram, and Figure 12K shows the corresponding calibration plot of the CRP sensor in 1X PBS (pH 7.4) containing 0–20 ng / mL CRP and 1% BSA. The error bars represent the standard deviation of the mean from three sensors. In this particular example, the sensor was observed to be capable of detecting picomolar levels of CRP with an ultralow limit of detection of approximately 8 pM. Detection was performed on 10 batches of 1X PBS (pH 7.4) in the presence of 0 and 5 ng / mL CRP, and the sensor was also observed to exhibit good batch-to-batch reproducibility. The detection accuracy of this particular example is expected to be further improved by automating the sensor preparation and modification process (e.g., automated fluid dispensing or inkjet printing).

[0087] We also observed that the LEG-AuNP CRP immunosensor exhibited high selectivity against other potentially interfering proteins and hormones due to the sandwich assay format. For example, Figures 12L and 12M are plots showing the selectivity of the CRP sensor against potential interferences in sweat, with error bars representing the standard deviation of the mean from three sensors. Considering interpersonal variability during human testing, we investigated the effects of sweat pH, ionic strength, temperature, and sample volume on the antibody-antigen binding kinetics and redox probe electron transfer rate, which are involved in CRP sensing accuracy, and further mitigated these effects by introducing an appropriate calibration mechanism, which will be described later. Cl changes within a physiologically relevant range. - Potential fluctuations using an Ag / AgCl pseudo-reference electrode in the presence of HCl resulted in a slight shift in peak potential, but the impact on overall peak current density (and therefore CRP quantification) was found to be negligible. The accuracy of the CRP sensor for biofluid analysis was verified by the laboratory gold standard enzyme-linked immunosorbent assay (ELISA) using human sweat and saliva samples, as shown in Figure 12N, a plot showing validation of the CRP sensor in human sweat samples (n = 13 biological replicates) and saliva samples (n = 6 biological replicates). Furthermore, the disposable CRP sensor maintained stable sensor performance for 10 days when stored in PBS in a refrigerator at 4 °C.

[0088] Assessment of sweat CRP for noninvasive monitoring of systemic inflammation

[0089] Inflammatory processes and immune responses are associated with a wide range of physical and mental disorders that contribute significantly to modern morbidity and mortality worldwide. The three leading causes of death worldwide—ischemic heart disease, stroke, and COPD—are each characterized by chronic inflammation. While acute inflammatory responses are important survival mechanisms, chronic inflammation contributes to long-term silent disease progression through irreversible tissue damage. Delays in the diagnosis and treatment of chronic diseases impose a significant economic burden on patients and healthcare systems.

[0090] Although there are no standard biomarkers for measuring and predicting systemic chronic inflammation, CRP, an acute-phase protein synthesized by hepatocytes in response to various acute and chronic stimuli, is closely associated with chronic inflammation and associated mortality risk in several disease states. Plasma CRP is stable, lacks circadian fluctuations, and is insensitive to common medications such as corticosteroids, making it highly attractive to clinicians as a convenient means of assessing a patient's physiological inflammatory state. There is also growing interest in exploring the efficacy of serial CRP monitoring for therapeutic decision-making.

[0091] Currently, circulating CRP levels are assessed clinically in specific laboratories via invasive blood sampling from patients. Commercially available point-of-care CRP monitors are still bulky and lack the picomolar sensitivity required to assess CRP levels in noninvasively accessible alternative biofluids, such as sweat and saliva. A convenient at-home method for monitoring inflammatory biomarkers such as CRP could potentially improve patient outcomes and reduce costs by monitoring disease progression and initiating earlier treatment and intervention.

[0092] Thus, the use of the LEG-AuNP CRP sensor to assess sweat CRP was evaluated as a versatile, cost-effective, noninvasive approach to monitor systemic inflammation in various disease states. For example, Figure 13A depicts a schematic diagram representing the overall perspective of assessing sweat CRP for noninvasive monitoring of various health conditions that may be associated with elevated CRP in healthy or patient populations, including infectious diseases, pulmonary diseases, cardiovascular diseases, and inflammatory bowel diseases.

[0093] Prior to these evaluations, we performed proteomic characterization of different types of sweat samples using bottom-up proteomic analysis to confirm the presence of CRP in sweat produced by iontophoresis and strenuous exercise. Using a recombinant CRP protein standard as a reference, CRP was identified in both exercise and iontophoretic sweat samples collected from human subjects.

[0094] In one study, we used the LEG AuNP CRP sensor to assess CRP levels in healthy subjects grouped by smoking status (current smokers, ex-smokers, and never-smokers). The results of this study are shown in Figure 13B, which contains box-and-whisker plots of CRP levels in sweat and serum samples extracted by iontophoresis from COPD patients (n = 10 biological replicates) and patients without COPD (n = 24 biological replicates). Participants were classified into five subgroups: current smokers with COPD (n = 6 biological replicates) or current smokers without COPD (n = 10 biological replicates), ex-smokers with COPD (n = 4 biological replicates) and ex-smokers without COPD (n = 9 biological replicates), and never-smokers without COPD (n = 5 biological replicates). We observed that serum and sweat CRP levels were higher in current smokers compared with ex-smokers and never-smokers, consistent with previous reports on the effect of current smoking on serum CRP. In COPD patients, serum and sweat CRP levels were observed to be higher in former smokers than in current smokers, suggesting that irreversible tissue damage and chronic inflammation occur in COPD patients even after smoking cessation. These results suggest that monitoring sweat CRP in COPD patients may be useful for tracking disease progression and predicting exacerbations in this patient population.

[0095] In another preliminary study, the LEG AuNP CRP sensor was used to assess CRP levels in patients with heart failure (HF). Chronic systemic inflammation may be associated with an increased risk of cardiovascular events. The results of this study are shown in Figure 13C. This figure includes box-and-whisker plots of CRP levels in sweat and serum samples from healthy participants (n = 7 biological replicates), HF patients with reduced ejection fraction (HFrEF, n = 7 biological replicates), and HF patients with preserved ejection fraction (HFpEF, n = 9 biological replicates). Results obtained using the sensor showed that serum and sweat CRP levels were significantly elevated in HFpEF patients but not in HFrEF patients, consistent with previous studies. These results indicate that investigating sweat CRP dynamics using the technology described herein may be of great value in predicting disease progression and clinical outcomes in HFpEF.

[0096] In addition to chronic infections such as COPD and HF, acute infections (such as COVID-19) can trigger severe inflammatory responses. Furthermore, in a pilot study using the LEG AuNP CRP sensor, CRP levels were assessed over two consecutive days in hospitalized patients with active infection. The results of this study are shown in Figure 13D. This figure includes box-and-whisker plots of CRP levels in sweat and serum samples (n = 3 biological replicates) from three patients with active infection over two consecutive days. The dotted line represents the mean sweat and serum CRP levels of healthy participants. Both serum and sweat CRP levels were significantly increased (more than 10-fold on average) in patients with active infection compared to healthy subjects, indicating a significant elevation of sweat CRP in acute inflammation. In the plots in Figures 13B–13D, the lower whiskers represent the minimum value, the upper whiskers represent the maximum value, and the box within the frame represents the mean value.

[0097] In further studies, the LEG AuNP CRP sensor was used to analyze CRP levels in samples from healthy subjects and patient populations with various inflammatory conditions. The results are shown in Figure 13E, which shows the correlation between serum and sweat CRP levels. The correlation coefficient was obtained by Pearson correlation analysis (n = 80, P < 0.00001). Using the CRP sensor, a high correlation coefficient (r) of 0.844 was obtained between sweat and serum CRP concentrations. This correlation with serum CRP concentrations appears to be higher than that obtained from saliva or urine samples in one study, suggesting great potential for using sweat CRP for noninvasive monitoring of systemic inflammation for the management of various chronic and acute health conditions.

[0098] Clinical Wear Evaluation

[0099] Clinical wearable evaluation of a wearable biosensor system containing a multiplexed LEG sensor array was conducted on patients with COPD and COVID-19 infection, as well as healthy subjects (including both never-smokers and current smokers). Part of the results of the wearable evaluation of the multiplexed sensor patch for noninvasive automated inflammation monitoring are shown in Figures 14A–14D. Figures 14A–14D show wearable multiplexed physicochemical and CRP analysis with real-time sensor calibration using wearable sensors from a healthy non-smoker (Figure 14A), a healthy smoker (Figure 14B), a COPD patient (Figure 14C), and a participant previously infected with COVID-19 (Figure 14D). In the wearable test, the wearable system was conformally laminated to the subject's arm and chemically induced and analyzed sweat to obtain inflammatory biomarker information noninvasively and wirelessly. In the aforementioned test, pH, temperature, and CRP sensor measurements were obtained in situ after the ionic strength sensor indicated complete refresh of the detection reservoir. CRP concentrations were converted using the mobile application based on the obtained SWV voltammograms and the corresponding real-time ionic strength, pH, and temperature values. As expected, elevated CRP levels were observed in current smokers compared with nonsmokers in healthy subjects. CRP levels in COPD patients and post-COVID subjects were significantly higher than those in non-smoking healthy subjects, suggesting the promise of practical noninvasive systemic inflammation monitoring and disease management applications using the biosensor device 300. In vitro analysis of sweat and serum samples from post-COVID subjects confirmed the wearable observation that patients who experienced moderate symptoms during COVID may still exhibit low-grade inflammation after COVID infection, as indicated by a slight increase in CRP levels. Similar to serum, sweat CRP levels remained fairly stable throughout the 30-minute test period, with no substantial differences observed between chemically induced sweat samples from various body sites, including the forearm, leg, upper arm, thigh, and back.

[0100] Characterization of multiplexed microfluidic patches for automated immune sensing

[0101] By passively conducting sweat on the skin through the microfluidic module, the impedance-based ion strength sensor can automatically monitor the state of the detection reservoir (reagent flow and refreshment). Figures 15A-15B show the admittance response measurements (Figure 15A) and corresponding calibration plot (Figure 15B) of the impedance-based ion strength sensor in NaCl solution. The error bars represent the standard deviation of the mean from three sensors. As depicted, the admittance signal measurements of the impedance-based ion strength sensor showed a log-linear response to electrolyte concentration. Because large interindividual variations in electrolyte and pH levels were observed in both exercise-induced and chemically-induced sweat samples, a high concentration of buffer salt was added along with the detection antibody (dAb) in the reagent reservoir to mitigate potential changes in the binding environment due to variations in sweat composition. This addition created an electrolyte gradient between the sweat (mixture) reconstituted with the detection reagent and the new sweat that subsequently entered the detection reservoir. According to the numerical simulations described below, the routing of sweat and detection reagents can be summarized into four stages: reconstitution (I), incubation (II), refreshment (III), and detection (IV).

[0102] When a sweat sample containing CRP molecules entered the microfluidic patch, the detection antibodies, which had been in a solid state, were expected to dissolve and diffuse into the detection chamber along the concentration gradient. Collisions between the CRP molecules and the antibodies would trigger antigen-antibody binding events along the microfluidic channel before finally reaching the detection chamber. The introduction of a serpentine microchannel was also expected to facilitate the mixing and binding of the antigen-antibody complexes.

[0103] To visualize and estimate the time scale of binding events at various locations in the microfluidic module, simulations of the CRP-antibody reversible binding reaction and the mass transport processes of reactants and products were performed using finite element analysis (FEA). Using FEA, tetrahedral elements with a fine mesh allowed us to model source diffusion in three-dimensional space with verified accuracy. The chemical reaction rate can be described by the law of mass action.

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[0104] Figures 15C-15D show the results of running the FEA. Figure 15C shows the simulated change in CRP-detection antibody (dAb) concentration on the working electrode over time. The center point of the working electrode in the inset image indicates the location of the concentration change plot. Figure 15D shows the simulated CRP-detection antibody (dAb) concentration, illustrating the various stages of automated sweat sampling and reagent routing for in situ CRP detection: reconstitution (I), incubation (II), refreshment (III), and detection (IV). The scale bar is 200 μm.

[0105] Based on the observed results, the binding and transport of CRP and the detection antibody can be divided into four stages. The map in Figure 15D shows the concentration of the formed CRP-detection antibody complex. During the reconstitution stage, the detection antibody diffuses along the concentration gradient. CRP binding begins in the center of the reagent reservoir. As more sweat containing CRP molecules enters the reagent reservoir, more antigen-antibody complexes are formed, as shown in Figure 15D. The antigen-antibody complexes move along the flow direction and enter the detection chamber. After the meandering mixing channel, the antigen-antibody complexes are slowly and evenly distributed throughout the detection chamber, allowing them to bind to the capture antibody immobilized at the bottom of the detection chamber (incubation stage).

[0106] According to the observed FEA results, after all the detection antibodies pre-loaded in the reagent reservoir are reconstituted and form antigen-antibody complexes with sweat CRP, or are flowed into the detection reservoir, the detection antibody concentration in the reagent reservoir gradually decreases. The continuous inflow of sweat into the microfluidic module no longer leads to the formation of additional antibody-antigen complexes, as indicated by the concentration in the reagent reservoir during the refresh phase. Therefore, the new sweat flow, with its reduced antigen-antibody complexes, continues to enter the detection chamber, flushing the unbound antibody-antigen complexes in the chamber toward the outlet. Eventually, the unbound antibody-antigen complexes and the detection antibody (labeled with an electroactive molecule) are refreshed from the detection chamber, as indicated by the detection phase. During this phase, detection occurs, and the complex concentration in the detection chamber converges to zero (indicated by the concentration), so the resulting electrochemical signal is specific and correlates to the antigen-antibody complex bound to the working electrode surface.

[0107] Based on microfluidic flow tests using artificial sweat (0.2X PBS) at an average physiological sweat rate (1.5 μL / min), we observed that the admittance signal was initially near zero when no liquid entered the chamber during the reconstitution phase. When the reconstituted, high-salt detection reagent entered the detection chamber, the admittance peaked and then gradually decreased as the high-salt reagent was flushed out of the detection chamber by newly secreted sweat. This is shown in Figure 15E. Figure 15E shows the admittance change of the LEG ion strength sensor as a function of time during the four-step automated CRP detection process described above in a laboratory flow test using artificial sweat (0.2X PBS) at a flow rate of 1.5 μL / min. In this example flow test, a yellow fluorescein isothiocyanate (FITC)-albumin fluorescent label was used to mimic the flow of sweat CRP, and red peridinin chlorophyll protein complexes (PerCP) were used in place of the detection antibody (dAb)-loaded AuNPs. The scale bar is 200 µm. Because the electrolyte content in iontophoretic sweat is relatively stable within the same individual, the admittance response was observed to reach a plateau after all reagents were refreshed by natural sweat, indicating that the working electrode was ready for electrochemical CRP detection. Further experimental flow tests using fluorescent proteins (fluorescein isothiocyanate-albumin as a CRP surrogate and peridinin chlorophyll protein as the detection reagent) showed similar trends in the incubation and refreshing process as the simulation and electrolyte flow tests. Based on sweat rate information collected from 24 current and former smokers with and without COPD, flow tests with varying flow rates from 0.5 to 3.5 µL / min showed similar admittance patterns that reached a plateau after various refreshing treatments. This is shown in Figure 15F, which shows the admittance response of the ionic strength sensor in artificial sweat (0.2X PBS) at different flow rates from 0.5 to 3.5 µL / min. The slope of the admittance at different flow rates converges to zero as the pre-added salt and dye are refreshed from the detection reservoir.As shown in Figure 15F, the average sweat volume induced during this process before sensor readings were taken was estimated to be 21 μL based on flow rate and admittance measurements.

[0108] The performance of the CRP sensor based on this automatic electrolyte monitoring mechanism was evaluated in multiple microfluidic flow experiments. Figures 15G–H are plots showing the effect of flow rate on microfluidic automated CRP sensing. Figures 15I–J are plots showing the effect of ionic strength on microfluidic automated CRP sensing. The solid and dotted lines represent experiments conducted with 1 ng / mL and 5 ng / mL CRP, respectively. SWV electrochemical measurements were initiated during the period when the admittance reached a plateau. While increasing concentrations (1 ng / mL–5 ng / mL) increased the height of the SWV peak current, physiologically relevant flow rates (1, 1.5, 2.5, and 3.5 μL / min) did not substantially affect the CRP sensor response at the same concentrations. While a higher flow rate may result in faster refreshing of the detection chamber and a shorter incubation time between the detection antibody and CRP, a relatively small increase in CRP signal was observed even with incubation times corresponding to physiologically relevant sweat rates (5–20 min).

[0109] Although the binding conditions were preconditioned with the addition of salt, in the initial flow tests with varying electrolyte concentrations (we selected 0.1X and 0.2X PBS as artificial sweat to simulate individual differences in sweat electrolyte concentrations), the SWV signal slightly decreased with lower electrolyte concentrations, due to the effect of electrolyte levels on the reduction rate of TH. Similar to the in vitro selectivity results, no significant interference with the CRP detection signal was observed in the flow tests. Furthermore, flow tests using artificial sweat with different pH levels resulted in changes in the SWV signal. These results suggest that although sweat rate calibration may not be necessary, additional in situ signal calibration based on sweat pH and electrolyte levels may be necessary to mitigate individual differences in CRP detection accuracy. Compared to previously reported passive wearable microfluidic sensors that require strenuous exercise to induce sweating and cannot reach submillimeter sensitivity, the technology described herein offers an attractive solution for fully automated microfluidic sweat induction, collection, and highly accurate quantitative analysis suitable for home monitoring of clinically important trace-level biomarkers.

[0110] Real-time CRP sensor calibration during wear testing

[0111] The effects of pH, electrolytes, and temperature were investigated and all were found to be factors that could affect the sensor readout of CRP. To account for the effects from the binding environment, in a specific example, a multivariate model consisting of four independent variables, namely, temperature, pH, electrolytes, CRP concentration ([CRP]), and a dependent variable, namely, peak current expressed in potential (mV), was constructed based on the following equation:

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[0112] As used herein, a "processing device" may be implemented as a single processor that performs processing operations, or a combination of special purpose and / or general purpose processors that perform processing operations. A processing device may include a CPU, GPU, APU, DSP, FPGA, ASIC, SOC, and / or other processing circuitry.

[0113] The terms "substantially" and "about" as used throughout this disclosure, including the claims, are used to describe and take into account slight variations resulting from processing variations, etc. For example, they may refer to ±5% or less, such as ±2% or less, such as ±1% or less, such as ±0.5% or less, such as ±0.2% or less, such as ±0.1% or less, such as ±0.05% or less.

[0114] As used herein, terms such as "first," "second," "third," etc., are used merely to indicate the respective objects described by these terms as separate entities, and do not imply a chronological order, unless expressly stated otherwise in this specification.

[0115] Terms and phrases used herein, and variations thereof, unless expressly stated otherwise, should be construed as open-ended and not limiting. In the examples above, the term "including" should be read to mean "including, without limitation," etc. The term "example" is used to provide illustrative examples of the items under discussion, not an exhaustive or limiting list thereof. The terms "a" or "an" should be read to mean "at least one," "one or more," etc. Additionally, adjectives such as "conventional," "traditional," "usual," "standard," "known," and similar terms should not be construed as limiting the described items to a given time period or to items available at a given time, but instead should be read to encompass conventional, traditional, ordinary, or standard technology that may be available or known at any time now or in the future. Similarly, when technology that would be apparent or known to those skilled in the art is referred to herein, such technology encompasses technology that would be apparent or known to those skilled in the art, now or at any time in the future.

[0116] The presence of broader language, such as "one or more," "at least," "but not limited to," or other similar terms, should not be understood to imply that a narrower case is intended or required in the absence of such broader language.

[0117] Furthermore, various embodiments defined herein are described in terms of exemplary block diagrams, flow charts, and other illustrations. As will become apparent to those skilled in the art upon reading this specification, the illustrated embodiments and various alternatives thereof can be practiced without being limited to the illustrated examples. For example, block diagrams and their accompanying description should not be construed as mandating a particular architecture or configuration.

[0118] The terms "substantially" and "about" as used throughout this disclosure, including the claims, are used to describe and take into account slight variations resulting from processing variations, etc. For example, they may refer to ±5% or less, such as ±2% or less, such as ±1% or less, such as ±0.5% or less, such as ±0.2% or less, such as ±0.1% or less, such as ±0.05% or less.

[0119] As used herein, terms such as "first," "second," "third," etc., are used merely to indicate the respective objects described by these terms as separate entities, and do not imply a chronological order, unless expressly stated otherwise in this specification.

[0120] While various embodiments of the present disclosure have been described above, it should be understood that they are presented by way of example and not limitation. Similarly, while various diagrams may depict example architectures or other configurations for the present disclosure, this is done to facilitate an understanding of the features and functionality that may be included in the present disclosure. The present disclosure is not limited to the example architectures or configurations shown, and the desired features may be implemented using a variety of alternative architectures and configurations. Indeed, it will be apparent to those skilled in the art how alternative functional, logical, or physical divisions and configurations can be implemented to implement the desired features of the present disclosure. Furthermore, many different component module names other than those described herein may be applied to the various divisions. Furthermore, with respect to flow diagrams, operational descriptions, and method claims, the order in which steps are presented herein does not require that various embodiments be implemented to perform the specified functions in the same order, unless the context dictates otherwise.

[0121] While the present disclosure has been described above in terms of various exemplary examples and embodiments, it should be understood that various features, aspects, and functions described in one or more of the individual embodiments are not limited in applicability to the particular embodiment for which they are described, but instead may be applicable, alone or in various combinations, to one or more other embodiments of the present disclosure, regardless of whether such embodiment is described and whether such features are presented as being part of the described embodiment. Accordingly, the breadth and scope of the present disclosure should not be limited by any of the exemplary examples described above.

[0122] It is to be understood that all combinations of the foregoing concepts (provided that such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing in this disclosure are contemplated as being part of the inventive subject matter disclosed herein.

Claims

1. an iontophoresis module configured to stimulate the production of a sweat sample from the user's skin, the sweat sample including biomarkers; a microfluidic module configured to collect the sweat sample, mix the sweat sample with a labeled detection reagent to obtain a mixture including the biomarker bound to the labeled detection reagent, and direct the mixture to a detection reservoir of the microfluidic module; a sensor assembly including a bioaffinity sensor configured to quantify the biomarkers of the mixture in the detection reservoir and determine the concentration of the biomarkers present in the sweat sample, the bioaffinity sensor including an electrode functionalized to bind to the biomarkers of the mixture; and A wearable biosensor device comprising:

2. the labeled detection reagent comprises a first nanoparticle conjugated to a detection antibody that binds to the biomarker; the surface of the electrode comprises second nanoparticles conjugated to a capture antibody that binds to the biomarker; The wearable biosensor device of claim 1 .

3. the first nanoparticles and the second nanoparticles are gold nanoparticles; The biomarker comprises a protein biomarker or a hormone biomarker. The wearable biosensor device of claim 2 .

4. the bioaffinity sensor is configured to quantify the biomarker in the mixture and determine the concentration with a sensitivity of 1 micromolar or less, 100 nanomolar or less, 10 nanomolar or less, 1 nanomolar or less, 100 picomolar or less, or 10 picomolar or less; The wearable biosensor device of claim 1 .

5. The microfluidic module comprises: an inlet for collecting the sweat sample; a reagent reservoir containing the labeled detection reagent, the reagent reservoir configured to refresh the sweat sample with the labeled detection reagent; a mixing channel for mixing the refreshed sweat sample with the labeled detection reagent to form the mixture containing the labeled detection reagent bound to the biomarker; the detection reservoir for receiving the mixture from the mixing channel; an outlet for allowing the sweat sample to flow from the detection reservoir; The wearable biosensor device of claim 1 , comprising:

6. The sensor assembly includes: a temperature sensor configured to measure the temperature of the skin; an ionic strength sensor configured to measure the ionic strength of the sweat sample; a pH sensor configured to measure a pH level of the sweat sample; 10. The wearable biosensor device of claim 1, wherein the wearable biosensor device is configured to calibrate measurements from the bioaffinity sensor based on measurements from the temperature sensor, the ionic strength sensor, and the pH sensor.

7. 7. The wearable biosensor device of claim 6, wherein the sensor assembly comprises a multiplexed sensor array fabricated with laser-engraved graphene (LEG), the multiplexed sensor array comprising the bioaffinity sensor, the temperature sensor, the ionic strength sensor, and the pH sensor.

8. 10. The wearable biosensor device of claim 1, wherein the wearable biosensor device comprises: a disposable patch comprising the iontophoresis module, the microfluidic module, and the sensor assembly, the disposable patch including an adhesive member for directly adhering the disposable patch to the skin; a flexible printed circuit board (FPCB) coupled to the disposable patch, the FPCB configured to receive signals from the sensor assembly and to provide power to the wearable biosensor device; A wearable biosensor device comprising:

9. 9. The wearable biosensor device of claim 8, wherein the FPCB is reusable and configured to be removably coupled to the disposable patch, and the FPCB includes a processor configured to perform in situ signal processing on signals received from the sensor assembly, and a wireless communication module configured to wirelessly communicate with a mobile device in real time.

10. receiving a sweat sample collected from the skin via an inlet of a microfluidic module, the sweat sample containing protein or hormone biomarkers; reconstituting the sweat sample in a reagent reservoir of a microfluidic module with a detection reagent configured to bind to the protein biomarker or the hormone biomarker, the detection reagent comprising an electroactive label molecule; combining the detection reagent with the protein biomarker or the hormone biomarker in a mixing channel of the microfluidic module to form a mixture comprising the protein biomarker or the hormone biomarker combined with the detection reagent; collecting a mixture of the protein biomarkers or the hormone biomarkers bound to the detection reagents in a detection reservoir of the microfluidic module and binding the protein biomarkers or the hormone biomarkers to electrodes of a sensor assembly; refreshing the microfluidic module with one or more additional sweat samples that do not contain the detection reagent to remove unbound detection reagent via an outlet of the microfluidic module; estimating the concentration of said protein biomarker or said hormone biomarker present in the sweat sample by measuring the amount of electroactive label present on the surface of said electrode; A method comprising:

11. 11. The method of claim 10, wherein estimating the concentration of the protein biomarker or the hormone biomarker present in the sweat sample comprises estimating the concentration of the protein biomarker or the hormone biomarker with a sensitivity of 1 micromolar or less, 100 nanomolar or less, 10 nanomolar or less, 1 nanomolar or less, 100 picomolar or less, or 10 picomolar or less.

12. 11. The method of claim 10, obtaining one or more additional biophysical sensor measurements using one or more additional sensors of the sensor assembly, including the temperature of the skin, the pH level of the sweat sample, or the ionic strength of the sweat sample; calibrating the estimated concentrations of the protein biomarkers or the hormone biomarkers based on measurements of the one or more additional biophysical sensors; A method comprising:

13. 11. The method of claim 10, further comprising inducing the sweat sample with an iontophoresis module in contact with the skin prior to receiving the sweat sample through the inlet.

14. the protein biomarker is C-reactive protein (CRP); the detection reagent further comprises a first nanoparticle conjugated to a detection antibody that binds to CRP; The method of claim 10 , wherein the surface of the electrode comprises second nanoparticles conjugated to a capture antibody that binds to CRP.

15. the first nanoparticles and the second nanoparticles are gold nanoparticles; The method of claim 14, wherein the electroactive labeling molecule is a redox molecule.

16. applying a patch containing the microfluidic module and the sensor assembly to the skin of a user; collecting a sweat sample collected from the skin in the microfluidic module; mixing the sweat sample and a reagent in the microfluidic module to obtain a mixture containing the reagent bound to a protein or hormone biomarker contained in the sweat sample; using the sensor assembly to estimate the concentration of the protein biomarker or the hormone biomarker in the sweat sample from the mixture; A method comprising:

17. 17. The method of claim 16, further comprising monitoring a health status of the user in real time based on the concentration of the protein biomarker or the hormone biomarker estimated using the sensor assembly.

18. 18. The method of claim 17, wherein monitoring the user's health status in real time comprises comparing the concentration of the protein biomarker or the hormone biomarker estimated using the sensor assembly to a threshold value to determine the user's biological status.

19. 18. The method of claim 17, wherein the health condition comprises heart disease, chronic obstructive pulmonary disease, inflammatory bowel disease, an active infection, or a past infection.

20. 17. The method of claim 16, further comprising presenting the concentration of the protein biomarker or the hormone biomarker estimated using the sensor assembly to a user in real time via a mobile device communicatively coupled to the patch via a wireless communication medium.

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