Wearable microfluidic bioaffinity sensor for automated molecular analysis

Abstract

Some implementations of the present invention relate to a wearable biosensor device comprising: an iontophoresis module configured to stimulate generation of a sweat sample from a user's skin, the sweat sample comprising a biomarker; a microfluidic module configured to collect the sweat sample, mix the sweat sample with the labeled detection reagent to obtain a mixture comprising a biomarker bound to the labeled detection reagent, and direct the mixture to a detection reservoir of the microfluidic module; and a sensor assembly comprising a bioaffinity sensor configured to quantify a biomarker of the mixture in the detection reservoir to determine a concentration of the biomarker present in the sweat sample. The biocompatible sensor includes an electrode that is functionalized to bind to the biomarker of the mixture. Bioaffinity sensors can quantify biomarkers to determine their concentration at a nanomole or picomole order of sensitivity.

Description

Technical Field

[0001] The present invention generally relates to systems and methods for biomarker monitoring using wearable biosensor devices. Specific implementations relate to automatic and non-invasive monitoring of protein or hormone biomarkers using wearable microfluidic bioaffinity sensors that collect sweat samples. Background Art

[0002] Recent advances in flexible electronics and digital health have transformed conventional laboratory tests into remote wearable molecular sensing capable of real-time monitoring of physiological biomarkers. Sweat contains a rich variety of biochemical molecules from electrolytes and metabolites to large proteins, and importantly, it is easily obtained by non-invasive techniques. However, currently reported wearable biosensors are mainly limited to detecting limited selection of biomarkers, such as electrolytes and metabolites, at μM or greater concentrations via ion-selective and enzyme sensors or direct oxidation / reduction. For example, most clinically relevant protein biomarkers including C-reactive protein (CRP) exist in blood at nM to pM levels, while the expected protein levels in sweat are expected to be much lower than in blood. Commercial point-of-care biomarker monitors are still bulky in size and cannot reach picomolar levels of sensitivity to assess biomarker levels in non-invasively available alternative biofluids (such as sweat and saliva). Summary of the invention

[0003] The technology described herein relates to wearable bioaffinity sensor systems and methods capable of automatic and real-time monitoring of low levels of biomarkers, such as hormones 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 including a biomarker; a microfluidics 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 microfluidics module; and a sensor assembly including a bioaffinity sensor configured to quantify the biomarker of the mixture in the detection reservoir to determine the concentration of the biomarker present in the sweat sample, the bioaffinity sensor including electrodes functionalized to bind to the biomarker of the mixture.

[0005] In some implementations, the labeled detection reagent includes a first nanoparticle conjugated to a detection antibody that binds to the biomarker; and the surface of the electrode includes a second nanoparticle conjugated to a capture antibody that binds to the biomarker.

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

[0007] In some implementations, the wearable biosensor device is configured to quantify a biomarker of a mixture, thereby determining 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 including a labeled detection reagent, the reagent reservoir being configured to update the sweat sample with the labeled detection reagent; a mixing channel for mixing the sweat sample updated with the labeled detection reagent to form a mixture including the labeled detection reagent bound to the biomarker; a detection reservoir for receiving the mixture from the mixing channel; and an outlet for providing outflow of the sweat sample from the detection reservoir.

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

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

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

[0012] In some implementations, the FPCB is reusable and configured to be removably coupled to the patch; and the FPCB includes a processor configured to perform in-situ signal processing of signals received from the sensor assembly, and a wireless communication module configured to communicate wirelessly in real time with a mobile device.

[0013] In one embodiment, a method includes: receiving a sweat sample collected from the skin via an inlet of a microfluidic module, the sweat sample including a protein or hormone biomarker; reconstituting the sweat sample with a detection reagent configured to bind to the protein or hormone biomarker in a reagent reservoir of the microfluidic module, the detection reagent including an electroactive label molecule; combining the detection reagent with the protein or hormone biomarker in a mixing channel of the microfluidic module to form a mixture including the protein or hormone biomarker bound to the detection reagent; collecting the mixture of protein or hormone biomarkers bound to the detection reagent in a detection reservoir of the microfluidic module to bind the protein or hormone biomarker to an electrode of a sensor assembly; updating 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; and estimating the concentration of the protein or hormone biomarker present in the sweat sample by measuring the amount of the electroactive label present on the electrode surface.

[0014] In some implementations, estimating the concentration of a protein or hormone biomarker present in a sweat sample includes estimating the concentration of a protein 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 implementations, the method further includes: obtaining one or more additional biophysical sensor measurements using one or more additional sensors of the sensor assembly, the one or more additional biophysical sensor measurements comprising the temperature of the skin, the pH level of the sweat sample, or the ionic strength of the sweat sample; and calibrating the estimated concentration of the protein or hormone biomarker based on the one or more additional biophysical sensor measurements.

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

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

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

[0019] In one embodiment, a method includes: adhering 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 within the microfluidic module to obtain a mixture, the mixture including a reagent that binds 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 implementations, the method further includes monitoring the health status of the user in real time based on the concentration of the protein or hormone biomarker estimated using the sensor assembly.

[0021] In some implementations, real-time monitoring of the user's health status includes comparing the concentration of a protein or hormone biomarker estimated using the sensor assembly to a threshold value to determine the user's biological condition. For example, the concentration of CRP or some 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 implementations, the health condition includes: heart disease, chronic obstructive pulmonary disease, inflammatory bowel disease, active infection or past infection.

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

[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 of implementations according to the disclosed technology. This summary is not intended to limit the scope of any invention described herein, which is defined by the claims and equivalents. BRIEF DESCRIPTION OF THE DRAWINGS

[0025] According to one or more implementations, the present invention is described in detail with reference to the following drawings. The drawings are provided for illustrative purposes only and only describe example implementations. In addition, it should be noted that for clarity and ease of description, the elements in the drawings are not necessarily drawn to scale.

[0026] Figure 1A An environment for automatic and non-invasive biomarker monitoring using a wearable biosensor device including a sweat sensor patch according to some implementations of the present invention is shown.

[0027] Figure 1B A schematic diagram showing a device in operation and adhered to the skin according to some implementations of the present invention. Figure 1AA cross-sectional view of a sweat sensor patch.

[0028] Figure 1C Optical images of sensor patches according to some implementations of the invention are shown.

[0029] Figure 1D An optical image of a vertically stacked assembly of a fully integrated biosensor device including a sensor patch and a FPCB according to some implementations of the present invention is shown.

[0030] Figure 1E An exploded view of a wearable biosensor device according to some implementations of the invention is shown.

[0031] Figure 2 is a flow chart illustrating an example method of assembling a sweat sensor patch according to some implementations of the invention.

[0032] Figure 3 Implementations that may be used during assembly of a microfluidics module according to some implementations of the invention are illustrated.

[0033] Figure 4 Components of a microfluidics module and sensor assembly that may be used during automated bioaffinity sensing according to some implementations of the invention are illustrated.

[0034] Figure 5 is an example of the use of some implementations according to the present invention Figure 4 An operational flow chart of example operations performed by components of a biosensor device illustrated in FIG. 1 during automated bioaffinity sensing.

[0035] Fig. 6A The characteristic realization of automated wearable CRP in situ detection using labeled CRP detection antibody (dAb)-conjugated AuNPs is demonstrated.

[0036] Fig. 6A Reconstitution and incubation operations within a microfluidic module of a wearable bioaffinity sensor according to certain implementations of the present invention are illustrated.

[0037] Figure 6B The updating and detection operations within the microfluidics module of a wearable bioaffinity sensor according to certain implementations of the present invention are illustrated.

[0038] Figure 6C Detection operations performed by a wearable bioaffinity sensor according to a specific implementation of the present invention are illustrated.

[0039] Figure 7An enlarged view of a working electrode surface according to some implementations of the present invention is shown, 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 a microfluidics module.

[0040] Figure 8 An enlarged plan view of the electronics of a FPCB of a wearable biosensor device according to some implementations of the present invention is illustrated.

[0041] Fig. 9 is a block diagram illustrating an example electronic system of a biosensor device for protein or hormone biomarker sensing according to some implementations of the invention.

[0042] Fig.10 An example graphical user interface (GUI) according to some implementations of the present invention is illustrated that may be presented to a user by running a mobile application used in conjunction with a wearable biosensor device for non-invasive automatic biomarker monitoring.

[0043] Fig.11 Scanning electron microscope (SEM) images of grating pattern engraved graphene of a LEG electrode for CRP sensing, LEG-AuNPs of a LEG electrode for CRP sensing, a vector pattern engraved LEG electrode for pH sensing, and a vector pattern engraved electrode for temperature sensing according to one characteristic implementation are shown.

[0044] Fig. 12A Schematic diagram illustrating the layers of a functionalized LEG-AuNP working electrode of a bioaffinity sensor according to a particular implementation of the invention.

[0045] Fig. 12B The surface functionalization process of the LEG-AuNP working electrode of the bioaffinity sensor according to a specific implementation of the present invention is illustrated.

[0046] Fig. 12C Shown is a SEM image of a mesoporous LEG electrode according to a specific implementation of the invention.

[0047] Fig.12D A transmission electron microscope (TEM) image of an AuNP-decorated graphene flake according to a particular implementation of the present invention is shown.

[0048] Fig.12E The current response and SEM images of CRP sensors based on LEG modified with poly(pyrrolidonic acid) (PPA) and pyrenebutyric acid (PBA) are illustrated.

[0049] Fig.12FGraphs illustrating the current response of CRP sensors based on AuNP / self-assembled monolayer (SAM) and laser-engraved graphene oxide (LEGO) by electrochemical oxidation, and the comparison of sensor performance for different functionalization methods.

[0050] Figure 12G The batch-to-batch variation in electrochemical performance of LEG electrodes and LEG-AuNP electrodes according to some implementations of the present invention is shown.

[0051] Figure 12G Included are graphs showing oxidation peak heights in cyclic voltammograms (CVs) of LEG electrodes and LEG-AuNP electrodes according to some implementations of the present invention.

[0052] Fig.12H Included are graphs showing comparison of the electrochemical performance of redox probe conjugated dAbs and dAb conjugated AuNPs.

[0053] Fig.12I is a TEM image showing dispersed dAb-loaded AuNPs with a protein corona shell.

[0054] Fig.12J A square wave voltammetry (SWV) voltammogram of a CRP sensor according to a particular implementation of the invention is shown.

[0055] Figure 12K Shows Fig.12J Corresponding calibration graph of the CRP sensor.

[0056] Figure 12L is a graph illustrating the selectivity of the CRP sensor toward potential interferents in sweat.

[0057] Figure 12M is another graph illustrating the selectivity of the CRP sensor toward potential interferents in sweat.

[0058] Fig.12N is a graph showing the validation of a CRP sensor in human sweat and saliva samples according to a characteristic implementation of the present invention.

[0059] Fig.13A A high-level schematic diagram of sweat CRP evaluation for non-invasive monitoring of various health conditions that may be associated with elevated CRP in a healthy or patient population is described in accordance with some implementations of the invention.

[0060] Fig. 13B Shown is 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 without COPD, according to some implementations of the invention.

[0061] Fig. 13C Shown is a box and whisker plot of a study of CRP levels in sweat and serum samples from healthy participants, heart failure patients with reduced ejection fraction (HFrEF), and heart failure patients with preserved ejection fraction (HFPEF), according to some implementations of the invention.

[0062] Fig.13D Shown is a box and whisker plot of a study of CRP levels in sweat and serum samples from three patients who developed active infection over the next two days, according to some implementations of the invention.

[0063] Fig.13E is a graph showing the calculated correlation of serum and sweat CRP levels.

[0064] Fig.14A Included are graphs showing in vivo multiplexed physicochemical analysis and CRP analysis of healthy never-smokers using wearable sensors with real-time sensor calibration according to some implementations of the invention.

[0065] Fig. 14B Included are graphs showing in vivo multiplexed physicochemical analysis and CRP analysis of healthy smokers using wearable sensors with real-time sensor calibration according to some implementations of the present invention.

[0066] Fig. 14C Included are graphs showing in vivo multiplexed physicochemical analysis and CRP analysis of patients with COPD using wearable sensors with real-time sensor calibration according to some implementations of the invention.

[0067] Fig.14D Included are diagrams showing in vivo multiplexed physicochemical analysis and CRP analysis of participants previously having COVID-19 using wearable sensors with real-time sensor calibration according to some implementations of the present invention.

[0068] Fig.15A Included are graphs showing the measured admittance response of an impedance ionic strength sensor in NaCl solution.

[0069] Fig. 15B Included with Fig.15A Calibration plot of the associated impedance ion strength sensor.

[0070] Fig. 15C Included are graphs showing simulated CRP-dAb concentration changes over time at the working electrode.

[0071] Fig.15D Simulated CRP-dAb concentrations are shown illustrating the stages of automated sweat sampling and guidance of reagents towards in situ CRP detection.

[0072] Fig.15E Included are graphs showing the change in admittance of a LEG ionic strength sensor as a function of time during four stages of an automated CRP sensing process in a laboratory flow test using artificial sweat.

[0073] Fig.15F Included are graphs showing the admittance response of a LEG ionic strength sensor as a function of time at different flow rates in a laboratory flow test using artificial sweat.

[0074] Figure 15G Included are admittance plots showing the effect of flow rate on microfluidic automated CRP sensing.

[0075] Fig.15H Included are voltammograms showing the effect of flow rate on microfluidic automated CRP sensing.

[0076] Fig.15I Included are admittance plots showing the effect of ionic strength on microfluidic automated CRP sensing.

[0077] Fig.15J Included are voltammograms showing the effect of ionic strength on microfluidic automated CRP sensing.

[0078] The drawings are not exhaustive and do not limit the invention to the precise form disclosed. DETAILED DESCRIPTION

[0079] Despite recent efforts to develop wearable bioaffinity biosensors for trace-level biomarkers (e.g., cortisol), accurate and in situ detection of biomarkers (e.g., sweat proteins or hormone biomarkers) remains a major challenge due to their extremely low concentrations (e.g., nM or pM levels) and large inter- and intra-person variations in sweat composition. For example, detection of protein biomarkers typically requires the integration of bioaffinity receptors, such as antibodies and aptamers. However, these techniques typically require lengthy target incubations, labor-intensive washing steps, and the addition of redox solutions for signal transduction. In addition, the current turnaround time (1 day or more) of high-sensitivity clinical biomarker tests (e.g., high-sensitivity CRP tests (hsCRP)) may not meet the needs of frequent assessments. For example, in addition to hospitalized cases that require close monitoring of inflammatory states, many chronic diseases (e.g., COPD and inflammatory bowel disease) can benefit from home, daily or frequent, fully automated, and non-invasive assessments of CRP for disease management.

[0080] Therefore, there is a need for a wearable biosensing technology that allows for automatic in-situ monitoring of ultra-low levels of circulating biomarkers in home and community environments. To this end, some implementations of the present invention relate to systems and methods for wearable and real-time electrochemical detection of low-concentration protein and hormone biomarkers (e.g., inflammatory biomarkers in sweat). According to some implementations of the present invention, a biosensor device for biomarker sampling may include: an iontophoresis module that stimulates sweat production, a microfluidic module for sweat sampling and for guiding and replacing via 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 contained in sweat. A specific implementation relates to a wearable wireless patch that includes the aforementioned components for real-time electrochemical detection of low-level concentrations of biomarkers in sweat. During in-body operation, the patch can be conformally adhered to the skin by a medical adhesive, wherein in-situ biomarker sensing is performed in the microfluidics without direct sensor-skin contact. In a specific embodiment, the inflammatory biomarker CRP can be monitored in a sweat sample.

[0081] According to some specific implementations, the biosensor device can utilize a bioaffinity sensor (e.g., a CRP sensor) to quantify a biomarker (e.g., CRP) via an electrode functionalized with a capture antibody (e.g., an anti-CRP capture antibody) conjugated with nanoparticles. According to some specific implementations, the bioaffinity sensor can be part of a graphene-based sensor array that also includes sensors for ionic strength, pH, and / or temperature measurements for true calibration of the bioaffinity sensor.

[0082] Various benefits can be achieved by implementing the systems and methods described herein. First, the wearable biosensor device described herein can achieve real-time, non-invasive and wireless biomarker analysis in both healthy and patient populations. This can promote the management and / or detection of chronic diseases by providing real-time sensitive analysis of biomarkers present in user sweat. Second, by combining specific nanomaterials and chemical techniques (such as a combination of Au nanoparticles conjugated with capture receptors (such as antibody-immobilized mesoporous graphene-Au nanoparticles) for efficient target recognition and thionine-labeled detection antibodies for signal transduction and amplification), the technology described herein can achieve sweat CRP or other biomarker analysis with high sensitivity, selectivity and efficiency. For example, compared with previous wearable technologies for monitoring biomarkers (previously reported LEG-based sensors for detecting metabolites at μM or higher levels), the technology described herein can be used to achieve high-sensitivity in-situ detection of ultra-low levels of biomarkers, wherein the sensitivity is improved by 6 orders of magnitude (such as picomolar levels).

[0083] Third, the biosensor device module described herein can enable autonomous sweat induction, sampling, reagent guidance, and fully automated bioaffinity sensing in situ on the user's skin. Further, by utilizing multiple sensor modalities in some implementations, the impact of interpersonal variation on wearable sensing can be mitigated and allow real-time biomarker data calibration. These additional sensor modalities can also be used to provide a more comprehensive assessment of physiological state.

[0084] Still further, experiments involving measuring CRP levels in patients were performed using the techniques described herein, and the presence of CRP was confirmed in human sweat from healthy subjects, and elevated CRP levels were found in sweat from patients with various chronic and acute inflammatory conditions associated with health conditions, including heart failure, COPD, and active and past infections (e.g., COVID-19). Moreover, by performing experiments involving measuring CRP levels in patients using the techniques described herein, a strong correlation between sweat and serum CRP levels was found in both healthy and patient populations, indicating the utility of the techniques described herein in non-invasive disease classification, monitoring, and / or management.

[0085] These and other benefits achieved by implementing the techniques described herein are described further below.

[0086] Figures 1A to 1E An example biosensor device 300 including a sweat sensor patch 100, and an environment for using the biosensor device 300, according to some implementations of the present invention are illustrated. Figure 1A to Figure 1B As described in the example of FIG. 3 , the sweat sensor patch 100 of the biosensor device 300 can be adhered to the skin 10 of a user (e.g., a human patient). As shown in FIG. 3 , a cross-sectional view of the sweat sensor patch 100 in operation and adhered to the skin 10 is shown. Figure 1B As described, the sweat sensor patch 100 may include a backing layer / substrate 110 and one or more layers 115 including a medical adhesive (e.g., medical tape) for attaching the sensor patch 100 directly to the skin 100. The iontophoresis electrodes 129 may engage the skin 100 with a layer of a hydrogel 140 applied therebetween to stimulate the production of sweat 30. The hydrogel 140, which may be a component of the sensor patch 100, may be an agarose gel (carbachol) containing carbachol. An electric current may travel to the electrodes 129, which enables the carbachol to be delivered transdermally to the sweat glands, thereby triggering the influx of a sweat stimulant into the skin 10 and stimulating the production of sweat 30 as needed. Considering that potential users of this technology may include sedentary and immobile patients, an iontophoresis module including an electrode pair may provide the benefit of on-demand delivery of a hydrogel (e.g., a cholinergic agonist carbachol from carbachol) for autonomous sweat stimulation throughout daily activities without strenuous exercise.

[0087] During operation, the biosensor device 300 is configured to collect biophysical data corresponding to the user, including data associated with biomarkers collected from the user's sweat 30, and transmit the data to the mobile device 50 via the wireless communication link 20. The wireless communication link 20 may be a radio frequency link, such as or Low Energy (LE) links, The mobile device 50 may be a smart phone, a smart watch, a head mounted display (HMD), or other suitable mobile device that can run an application that displays health information (e.g., inflammatory biomarker data, temperature data, etc.) associated with the data received from the biosensor device 300. In some implementations, the application can analyze and / or organize the data collected from the biosensor device 300.

[0088] Figure 1C An optical image of a sensor patch 100 according to some implementations of the invention is shown. The imaging sensor patch in this example is a disposable microfluidic graphene sensor patch. Figure 1D Shown include Figure 1C Optical images of a vertically stacked assembly of a fully integrated biosensor device 300 of sensor patch 100 and FPCB 200 are shown. In both optical images, the scale bar is 0.5 cm.

[0089] Figure 1E An exploded view of a biosensor device 300 according to some implementations of the present invention is shown. The biosensor device 300 includes a sweat sensor patch 100 and a 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 chemical-resistant material. For example, the microfluidic biosensor patch can be connected via a CO 2 The laser engraving is produced on a polyimide substrate.

[0090] As shown, the sensor assembly 120 may include bioaffinity sensors 121a-121c and additional sensors 122 and 124. The bioaffinity sensors 121a-121c may 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 a particular embodiment, 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 implementations, the working electrode 121a may be coated with nanoparticles conjugated to an antibody that binds to a biomarker of interest. In a particular implementation, the working electrode 121a is functionalized with AuNPs conjugated to a capture antibody (cAb). For example, the cAb may be an anti-CRP cAb. The AuNPs may be electrodeposited. In a specific implementation, the reference electrode 121b is an Ag / AgCl reference electrode. The above design can enable highly sensitive and efficient electrochemical detection of trace-level sweat biomarkers, such as hormones or proteins, including CRP, in situ on the skin. For example, in some implementations, the sensor assembly 120 including the bioaffinity sensors 121a-121c is configured to determine the concentration of the biomarker with a sensitivity of 1 micromole or less, 100 nanomole or less, 10 nanomole or less, 1 nanomole or less, 100 picomoles or less, or even 10 picomoles or less. Other nanoparticles that can be conjugated to antibodies that bind to biomarkers can include iron oxide nanoparticles, quantum dots, silver nanoparticles, copper nanoparticles, copper oxide nanoparticles, etc.

[0091] Additional sensors may include a temperature sensor 122, a pH sensor 123, and an ion strength sensor 124. In one implementation, the temperature sensor 122 is a strain insensitive temperature sensor. In one implementation, the pH sensor is a potentiometric sweat pH sensor. In one implementation, the ion strength sensor is an impedance ion strength sensor. As further described below, having additional, integrated pH, temperature, and ion strength sensors can enable real-time personalized biomarker data calibration to mitigate sensing errors caused by changes in human sample matrix and provide a more comprehensive assessment of physiological state. In some implementations, a combination of sensors including bioaffinity sensors 121a-121c and sensors 122-124 can be implemented as a multiplexed sensor array. In other implementations, some of the additional sensors may be excluded, or other additional sensors may be included to enable calibration.

[0092] The sensor assembly 120 including the electrode 129, the bioaffinity sensors 121a-121c and the sensors 122-124 can be formed as a LEG sensor assembly. LEG manufacturing can enable large-scale production of biosensor systems at relatively low cost via CO2 laser engraving. LEG sensors can be advantageous because they can be printed using a modified conventional printer. Printable wearable sensor patches can be manufactured on a large scale at relatively low cost. This can allow for disposable sensor patches that can be worn by individuals for extended periods of time (e.g., 12-24 hours), which can be replaced at a daily level, and which can collect health information without the need for invasive testing and human patients to enter a physical laboratory for repeated testing.

[0093] Fig.11 SEM images of a grating pattern engraved graphene (image 1110) of a LEG electrode for CRP sensing, LEG-AuNP (image 1120) of a LEG electrode for CRP sensing, a vector pattern engraved LEG electrode for pH sensing (image 1130), and a vector pattern engraved electrode for temperature sensing (image 1140) are shown according to one characteristic implementation. The scale bars of images 1110-1120 are 10 μm and 1 μm. The scale bars of images 1130-1140 are 2 μm.

[0094] The FPCB 200 can be configured for iontophoretic sweat induction, sensor data acquisition, and / or wireless communication with a mobile device 50. During assembly, the FPCB 200 can be joined on top of the patch 100 to form a fully integrated wearable biosensor device 300. The FPCB 200 can be configured as a reusable electronic system that is joined to a disposable point-of-care sensor patch 100. A battery 251 (e.g., a lithium battery) can power the system to enable functions such as wireless communication. In other implementations, the biosensor device 300 can be powered by other or additional means, such as by human motion, by a small solar panel, and / or by a biofluid powered system that uses a collected sweat stream to power the device.

[0095] Figure 2 is a flow chart illustrating an example method of assembling sweat sensor patch 100 according to some implementations of the invention. Figure 2 will with Figure 31 and 2. The microfluidic module 130 is described together, which illustrates layers 210-230 that can be used during the assembly of the microfluidic module 130. The flexible microfluidic module 130 can be assembled by stacking laser cut layers 210-230. Cuts can be formed in layers 210 and 230 for one or more inlets, reagent reservoirs, mixing channels, detection reservoirs, one or more outlets, one or more channels, hydrogels, 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 can be patterned with one or more wells to collect sweat. The inlet layer 220 may include one or more inlets and / or channels through which sweat flows. The reservoir layer 210 may include a reservoir for receiving sweat flowing through an inlet and / or channel, and an outlet through which sweat flows after sampling.

[0096] Each of the reservoir layer 210 and the collection layer 230 can be a patterned medical adhesive, such as a double-sided medical tape. The inlet layer 220 can be formed of a thermoplastic polymer resin, such as polyethylene terephthalate (PET). As described, the inlet layer 220 can be stacked / adhered above the reservoir layer 210 to form an assembly 225. The collection layer 230 can be stacked / adhered above the assembly 225 to form an assembly 235 corresponding to the microfluidic module 130.

[0097] Figure 2 100, a backing layer 110 (e.g., a polyimide layer) is also described, on which the sensor component 120 can be printed or otherwise deposited to form the component 245. During further assembly of the sensor patch 100, the hydrogel 140 can be applied to the component 235, and the component 235 can be stacked / adhered on top of the component 245.

[0098] In some implementations, the biosensor device 300 can be designed to have good mechanical flexibility and stability for practical use during sports activities. For example, each individual sensor can be designed so that it shows minimal changes under a moderate bending radius of curvature (e.g., 5 cm). In addition, more strain-insensitive sensor designs can be included as needed.

[0099] It should be understood that, in addition to Figure 2In addition to the method illustrated in , other assembly methods are contemplated, and other biosensor components besides wearable patches are also contemplated as being in accordance with the techniques described herein. For example, components of the biosensor device 300 including one or more components of the FPCB 200 and the sensor patch 100 may alternatively be integrated into a wearable device such as a smart watch or HMD. For example, the FPCB 200 and components of the sweat sensor patch 100 may be incorporated into an area of ​​the smart watch that contacts the user's skin. In this example, the smart watch may itself run an application that displays health information associated with the collected data, and / or alternatively transmit the data to another mobile device 50, such as a smartphone or a wearable HMD, which runs an application as described above.

[0100] Figure 4 Components 130 of a microfluidic module and sensor assembly 120 that can be used during automated bioaffinity sensing according to some implementations of the present invention are illustrated. As described, the microfluidic module 130 may include various fluidically coupled components, including an inlet 131 for receiving a sweat sample, a reagent reservoir 132 including a detection reagent, a mixing channel 133, a detection reservoir 134 for capturing and quantifying sweat biomarkers, and an outlet 135 providing a channel for the sweat sample to flow out. As described above, the sensor assembly 120 may include a pH sensor 123, an ionic strength sensor 124, and a biosensor including a working electrode 121a, a reference electrode 121b, and a counter electrode 121c.

[0101] Figure 5 is an example of the use of some implementations according to the present invention Figure 4 An operational flow chart of example operations performed by components of the biosensor device 300 during automated bioaffinity sensing is shown in FIG. FIG. 6A to FIG. 6C To describe Figure 5 , which illustrates a specific implementation for achieving in situ automated wearable CRP detection using labeled CRP dAb-conjugated AuNPs. However, it should be understood that the biosensor device 300 described herein can be configured to achieve automated detection of other biomarkers in sweat besides CRP, especially biomarkers that may be present in low (e.g., picomolar or nanomolar) concentrations, including hormones, proteins, peptides, etc.

[0102] Operation 510 includes receiving a biofluid sample including a biomarker via inlet 131. The biofluid sample may be a sweat sample that is autonomically induced using an iontophoresis module as described above (eg, using electrodes 129 and carbachol 140), and which may flow into microfluidics module 130 via inlet 131.

[0103] Operation 520 includes reconstituting a biological fluid sample with a detection reagent in a reagent reservoir 132, the detection reagent being configured to bind to a biomarker contained in the biological fluid, the detection reagent comprising an electroactive labeling molecule. The detection reagent may be deposited in the reagent reservoir 132 prior to collection of the biological fluid. When the biological fluid enters the reagent reservoir 132, it carries with it the deposited detection reagent. For example, Fig. 6A Reconstitution 610 in a reagent reservoir storing labeled CRP dAb-conjugated AuNPs is illustrated. Electroactive redox molecules such as thionine (TH) can be used to label nanoparticle conjugates to enable direct electrochemical sensing. Nanoparticles conjugated with electroactive redox molecules and dAbs enable efficient electrochemical signal transduction (signal switching) and signal amplification.

[0104] Operation 530 includes combining the detection reagent with the biomarker contained in the biological fluid sample within the mixing channel 133 to form a mixture. Fig. 6A The combination 620 of the detection reagent in the mixing channel is illustrated, and the detection reagent includes AuNP and the redox molecule TH conjugated with CRPdAb. In the illustrated example, the mixing channel 133 has a serpentine shape, which can promote the combination and control the amount of binding time. For example, the serpentine shape can promote the dynamic combination between CRP and dAb. In other implementations, the mixing channel 133 can include different shapes.

[0105] Operation 540 includes collecting the mixture from the mixing channel 133 in the detection reservoir 134 to bind the biomarker previously bound to the labeled detection reagent to the working electrode 121a. For example, Figure 6B An incubation process 630 is illustrated, through which the CRP-dAb is allowed to bind to the anti-CRP cAb functionalized LEG-AuNP working electrode. As the mixture enters the detection reservoir 134 from the mixing channel 133, it can slowly fill the chamber before exiting the outlet 135. The size of the detection reservoir 134 can be optimized to allow sufficient time to bind to the working electrode 121a. By way of further example, Figure 7 An enlarged view of the working electrode surface is shown conceptually illustrating the binding process that can occur at the working electrode surface between a capture antibody on the electrode surface and a biomarker bound to a detection antibody received via a microfluidic module. For simplicity, the AuNPs of the working electrode are not shown in this example.

[0106] Operation 550 includes refreshing the microfluidic module 130 with one or more additional biological fluid samples that do not contain detection reagents to remove unbound detection reagents from the detection reservoir 134 via the outlet 135. For example, a fresh stream of sweat may continue to enter and refresh the microfluidics to remove unbound detection reagents and achieve removal of passive markers prior to detection. By way of example, Figure 6B An updating operation 640 is illustrated, by which unbound detection reagent mixture is removed. By performing an updating operation, quantification of biomarkers contained in a sweat sample may be improved.

[0107] Operation 560 includes measuring the amount of electroactive label present at the surface of the working electrode to estimate the concentration of the biomarker. Any of a number of voltammetric techniques that relate current to concentration can be applied to measure the amount of electroactive label bound to the electrode surface. For example, differential pulse voltammetry (DPV), SWV, linear sweep voltammetry (LSV), or some other voltammetric technique can be used to make the measurement. It should be noted that because the electroactive label molecules are directly conjugated to the detection reagent, their amount can be directly related to the amount of biomarker between the cAb and dAb at the electrode surface. By way of example, Figure 6C Detection operation 650 is illustrated, via which SWV is used to measure the amount of TH bound to the working electrode surface. As described in this example, when TH molecules are directly conjugated to CRP dAb-immobilized AuNPs, the amount they bind is directly related to the amount of CRP "sandwiched" between the cAb and dAb-immobilized AuNPs at the electrode surface, and therefore to the initial concentration of CRP in solution.

[0108] Depending on the binding environment, there may be significant inter-person variation in the composition of the biofluid sample, which may affect the rate at which the biomarker binds to the detection reagent and affect the accuracy of the estimated concentration of the biomarker. For example, as discussed further below, it was found during experiments that pH, electrolyte concentration, and temperature can all affect sensor readings of CRP concentration expressed as current measurements. Therefore, in some implementations, in order to further improve the quantification of biomarkers contained in the biofluid sample, the effects of temperature, pH, and / or ionic strength on 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 biofluid sample in the detection reservoir 134.

[0109] In some implementations, to mitigate differences in the binding environment, electrolytes may be introduced into the detection reservoir 134. For example, a high level of buffer salt may be deposited in the reagent reservoir along with the dAb to mitigate potential changes in the binding environment caused by changes in sweat composition.

[0110] Figure 8An enlarged plan view of electronics that may be implemented in FPCB 200 according to certain embodiments is illustrated. As indicated by the dashed lines indicating the 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 instrument module 850. In this example, the scale bar is 5 mm.

[0111] Fig. 9 9 is a block diagram illustrating an example electronic system 900 of a biosensor device 300 for CRP sensing according to a specific embodiment. As described, the components of the electronic system 900 can be powered using a battery 925. The electronic system 900 includes an iontophoresis (IP) electrode 901 for iontophoresis 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, which includes an ion intensity sensor 911, a biomarker sensor 912, a temperature sensor 913, and a pH sensor 914, which, for example, generate signals routed to a multiplexer 915 after signal processing. In this example, multiple sensors are coupled 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. Via the illustrated electronic device, the FPCB can be configured to perform current-controlled iontophoresis, multiplexed electrochemical measurements (including voltammetry, impedance determination, and potentiometry), signal processing, and wireless communication. The system can also accurately obtain the dynamic responses of the integrated LEG-based pH, ionic strength, and skin temperature sensors for real-time CRP sensor calibration.

[0112] Fig.10 An example graphical user interface (GUI) 1000 according to some implementations of the present invention is illustrated, which can be presented to a user (e.g., a patient) by running a mobile application used in conjunction with a wearable biosensor device 300 for non-invasive automatic biomarker monitoring. For example, the application can be run by a mobile device 50 wirelessly coupled to the biosensor device 300. During runtime, the GUI can display real-time data (processed or otherwise) acquired by the biosensor device 300. The GUI can also display acquired historical data. For example, based on a sweat sample collected by the biosensor device 300, data such as CRP concentration (e.g., in ng / mL), pH, and skin temperature can be acquired and presented in real time. The data can be plotted over time to provide an indication of the user's inflammation level or other biological level over time. The GUI can provide an indication (e.g., via text or visual signs) of whether the user's measured health data is within a normal range or within an abnormal range. The GUI can also provide an indication of the status of the biosensor device 300 (e.g., whether it is currently connected to a mobile device).

[0113] In some implementations, the mobile application may itself perform processing of sensor measurements received from the biosensor device 300 prior to user display. For example, in one implementation, the mobile application may be configured to convert biomarker concentrations based on an acquired voltammogram (e.g., a SWV voltammogram) and corresponding real-time acquired values ​​of calibrated sensors (e.g., an ion strength sensor, a pH sensor, and a temperature sensor).

[0114] In some implementations, sweat samples can be collected for a period of time without reapplying the hydrogel. The period of time can be from about two hours to a full twenty-four hours of a day. Updated samples can be periodically or continuously collected in a microfluidic patch, mixed with a labeled reagent, directed to a detection reservoir, analyzed, and then flushed out through an outlet. The entire process illustrated above can be combined and integrated on a single sweat sensor patch. After a full day or other time period, a new sweat sensor patch with a new hydrogel can be applied, and the aforementioned process for biomarker detection can be repeated. The process can be repeated every day over an extended period of days, weeks, or even months. The process can also be restarted after an interruption of a period of days, weeks, or months to evaluate changes in medical conditions.

[0115] In some implementations, the microfluidic sweat collection patch can be optimized to achieve the fastest update time between samples. Several parameters can be selected for optimization. These parameters can include, for example, the placement of one or more inlets relative to each other and the reagent reservoir, the shape and distance of the mixing channel, the number of one or more inlets, the distance between the inlet 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.

[0116] In some implementations, the microfluidic sweat collection patch can be designed to eliminate leakage of sweat samples. For example, electrical stimulation can be applied to several adjacent sweat glands while avoiding the sweat glands directly under the inlet. The patch can be designed to allow sweat samples to be collected only from glands that are not in contact with the hydrogel, and to prevent sweat from leaking from adjacent sweat glands (which mix with the hydrogel). This can be achieved by applying pressure on the gland where the sample is obtained and by applying specialized tape to the adjacent glands and attaching the skin patch using a fixing adhesive. The application of the hydrogel can also be limited to the optimal part of the patch to minimize interference.

[0117] In some implementations, dynamic and automatic wearable biomarker sensing can be achieved by incorporating a capillary rupture valve and a sensor array into a single disposable sensor patch, if desired.

[0118] Experimental and simulation results

[0119] According to a specific embodiment of the present invention, various experiments and simulations are performed using a biosensor device 300 and / or its components for wirelessly, autonomously and non-invasively monitoring CRP levels. The design of this specific biosensor device 300 and its associated experimental and simulation results are described in further detail below. Although these experimental and simulation results illustrate some advantages of using the technology described herein, it should be understood that the present invention is not limited to the discussion of the results and observations of the following description using specific example embodiments. For example, in addition to CRP, the wearable method can be suitable for evaluating other trace-level disease-related protein biomarkers on demand. In addition, the operating principles described herein can be easily adapted to investigate a wide range of biomarkers (e.g., proteins, hormones, cytokines, etc.), including biomarkers indicating the presence of inflammation or some other biological condition.

[0120] Fabrication of multiplexed microfluidic sensor patches

[0121] A specific embodiment of a microfluidic sensor patch was fabricated as follows. A 50 W CO 2 The laser cutter raster engraved the PI film at the focal height (8% power, 15% speed, 1000 dots per inch) to make LEG-based iontophoresis IP electrodes, connecting leads, impedance, CRP working electrode, counter electrode, and reference electrode. The pH electrode and temperature sensor were engraved in vector mode at 1% and 3% power (15% speed, 1000 dots per inch (PPI)), respectively. -1 The LEG electrode was electrochemically cleaned via cyclic voltammetry from −0.2 to 1.2 V in 1 M HCl for 10 cycles followed by 200 μL of cyclic voltammetry at 0.1 V s -1 The working electrode of the pH sensor was prepared by electrodeposition of polyaniline pH sensing film via cyclic voltammetry from -0.2 to 1.2 V for 10 cycles. A shared Ag / AgCl reference electrode was made by electrodeposition of Ag on a LEG electrode in a solution containing silver nitrate, sodium thiosulfate, and sodium bisulfite (250 mM, 750 mM, and 500 mM, respectively) 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), followed by dropping a 10 μL aliquot of 0.1 M iron (III) chloride for 1 min. AuNPs were electrodeposited on the LEG CRP working electrode via pulse deposition (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 for 40 cycles.

[0122] Iontophoresis hydrogels containing the cholinergic agent carbachol (placed on the IP electrode) were prepared by dissolving agarose (3% w / w) in deionized water using a microwave oven. After the agarose was completely dissolved, the mixture was cooled to 165°C, and 1% carbachol was added to the above mixture for the anode (or 1% KC1 for the cathode) and stirred until homogeneous. The cooled mixture was cast into cylindrical molds or assembled microfluidic patches and cured at room temperature. The hydrogels were stored at 4°C until use.

[0123] The microfluidic module was prepared with an assembly of a thin PET film (50 μm) sandwiched between a double-sided medical adhesive (180 μm top layer, 260 μm bottom layer, 50 μm PET backing), which was attached to a substrate and cut through using a laser cutter at 2.7% power, 1.8% speed, 1000 PPI vector mode to make channels and reagent reservoirs. Next, a circular outline was cut through only the top layer (180 μm) of the medical adhesive using 4% power, 10% speed, 1000 PPI vector mode. The circular top layer was peeled off to make a detection reservoir. A sweat accumulation layer was prepared by cutting through a 130 μm adhesive. Labeled dAb-AuNPs were dripped and dried in the reagent reservoir and stored in a dry state at 4°C before assembly with the sensor patch.

[0124] Functionalization of LEG-AuNP CRP working electrode

[0125] In a specific embodiment, the LEG-AuNP CRP working electrode is functionalized as follows. The LEG-AuNP working electrode is immersed in standard 200-proof ethanol containing 0.5 mM mercaptoalkanoic acid (MUA) and 1 mM mercaptohexanol (MCH) overnight to form a SAM. After rinsing with ethanol and then deionized (DI) water and drying under air flow, the electrode is incubated for 35 minutes at room temperature in a wet chamber with 10 μL of a mixture solution containing 25 mM 2-(N-morpholino)ethanesulfonic acid hydrate (MES) buffer (pH 5.0) containing 0.4 M N-(3-dimethylaminopropyl)-N'-ethylcarbodiimide (EDC) and 0.1 M N-hydroxysuccinimide sulfonic acid sodium salt (sulfo-NHS). By dripping 10 μL of anti-CRP solution (250 μg mL -1 Covalent attachment of CRP cAb was performed in phosphate buffered saline (PBS), pH 7.4) and incubated for 2.5 hours at room temperature, followed by a 1 hour blocking step with 1.0% bovine serum albumin (BSA) prepared in PBS. The electrodes were stored in PBS containing 1% BSA until use.

[0126] CRP detection antibody conjugate

[0127] In a specific embodiment, the CRP detection antibody conjugation is achieved as follows: -1 and 36 mg mL -1 20 nm carboxylic acid functionalized PEGylated AuNPs were activated for 30 min in 10 mM MES buffer (pH 5.5) containing 0.1% The conjugate was washed with 20% 1X PBS (PBST) and centrifuged at 6500 relative centrifugal force (rcf) for 30 min. After removing the supernatant, 50 μg mL -1 Polystreptavidin R (PS-R) was added and allowed to crosslink for 1 hour at room temperature. After centrifugation at 3500 rcf for 30 minutes and removal of the supernatant, 5 μg mL -1 Biotinylated anti-CRP dAb was incubated in 1% BSA for 1 hour. After another round of washing (centrifugation at 2000 rcf), the cells were washed with a mixture of EDC / sulfo-NHS (30 mg mL -1 and 36 mg mL -1 The carboxyl groups of PS-R and dAb on AuNP were activated for 30 minutes with 10 mM MES buffer (pH 5.5) containing 5% PBS. After a washing step using centrifugation at 1500 rcf, 100 μM thionine was incubated for 1 hour. The final conjugate was washed with PBST, centrifuged at 1250 rcf, reconstituted in 1% BSA, and filtered through a 0.2 μm syringe filter to remove all aggregates.

[0128] For direct redox probe conjugation to antibodies, 100 μg mL -1 The dAb was buffer exchanged and reconstituted in 10 mM MES buffer (pH 5.5). -1 and 36 mg mL -1 The carboxyl groups of the dAb were activated in the column for 30 minutes with 10 mM MES buffer (pH 5.5) containing 1X PBS (pH 7.4). After buffer exchange with 1X PBS (pH 7.4), 100 μM thionine was incubated 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 all aggregates.

[0129] Electronic systems design and integration

[0130] In a specific embodiment, the electronic system is designed as follows. A 2-layer flexible printed circuit board FPCB is designed. The FPCB outline is designed as a rounded rectangle (31.7mm×25.5mm), the same size as the microfluidic sensor patch, so that the patch can be directly inserted under the FPCB via a cutout (10mm×3.8mm). The electronic system includes: a magnetic reed and a voltage regulator for power management; a boost converter, a BJT array, and an analog switch for iontophoresis induction; an electrochemical front end, an operational amplifier, and a voltage divider for the sensor array interface; and a BLE module for system control and Bluetooth wireless communication. A BLE connection is established with the wearable device, and sensor data is acquired wirelessly for calibration and voltammogram analysis. A rechargeable 3.8V lithium button battery with a capacity of 8mAh is used to power the electronic system. In order to reduce the existing noise caused by motion artifacts, filtering and smoothing techniques are used. On the hardware side, the electrochemical AFE filters out noise from the ADC via a digital filter. On the software side, a smoothing algorithm (moving average filter / median filter) is automatically applied in real time.

[0131] Electrochemical characterization of LEG-AuNP immunosensor

[0132] FIG. 12A to FIG. 12B The surface functionalization process of the LEG-AuNP working electrode of the CRP sensor according to a specific embodiment is illustrated. As described, AuNPs can be electrodeposited on the LEG surface, followed by thiol monolayer assembly using mercaptoalkanoic acid and mercaptohexanol. Since the formation of the SAM layer can rely on specific gold-sulfur bonding, it is observed that immersing the sensor patch in an alkanethiol solution has a negligible effect on other graphene-based electrodes. Fig. 12C (Shows the SEM image of the mesoporous LEG electrode, the scale bar is 100 μm), Fig.12D (showing a transmission electron microscopy image of AuNP-modified graphene flakes, scale bar is 50 nm) and Fig.11 As shown in image 1120, it is also observed that the pulse potential deposited AuNPs are uniformly distributed throughout the mesoporous graphene structure and have excellent electrocatalytic ability, and a large number of binding sites are formed on the particle surface for biomolecule immobilization.

[0133] like FIG. 12E to FIG. 12F As described, it was also observed that the LEG CRP sensor prepared by the functionalization method relying on LEG-AuNPs modified with thiol SAM composite (AuNP / SAM) achieved excellent electrochemical performance relative to other functionalization methods, significantly improving the sensitivity of the CRP sensor with almost no nonspecific adsorption. Fig.12E The current responses and SEM images of CRP sensors based on LEG modified with PPA (1210, 1220) and PBA (1230, 1240) are illustrated. Fig.12F The current response of CRP sensors based on AuNP / SAM (1250) and laser-engraved graphene oxide (LEGO) (1260) by electrochemical oxidation is illustrated. Fig.12F Also included is a graph 1270 illustrating a comparison of sensor performance for different functionalization methods, where the error bars represent the standard deviation of the mean of 3 sensors and S / B is the ratio of signal to background.

[0134] The formation of LEG-AuNP complexes was observed by the ratio of the increase in the intensity of the D and G bands in the Raman spectrum due to the presence of AuNPs. The various sensor modification steps on the LEG electrode were characterized by X-ray photoelectron spectroscopy. It was observed that the intensity of Au4f increased significantly after AuNP deposition, while N1 increased only after the cAb fixation step, indicating successful electrode preparation. After each modification step, DPV and electrochemical impedance spectroscopy (EIS) were used to electrochemically further characterize the LEG surface. It was observed that after the immobilization of SAM and cAb proteins, the peak current height in the DPV voltammogram decreased and the resistance in the Nyquist diagram increased, indicating that SAM and cAb hindered the electron transfer at the interface. This is due to the increase in surface coverage caused by non-conductive substances. Moreover, it was found that the negatively charged carboxylate functional groups in the SAM layer caused the repulsion of the negatively charged redox indicator ferrocyanide and further reduced the electron transfer rate. Subsequent modification of the SAM layer with EDC / NHS chemicals replaced the negatively charged carboxylate groups with neutral NHS-ester groups. This was empirically observed as an increase in the peak current height. Figure 12G As described above, since the main processes including laser engraving, electrochemical deposition and solution processing are all mass-producible, this electrode manufacturing process shows high batch-to-batch reproducibility. In particular, Figure 12G The LEG electrode (Figure 1281) and the LEG-AuNP electrode (Figure 1282) are shown, along with 0.1M KCl and 5mM [Fe(CN) 6 ] 3- , scan rate 50mV s -1 Figure 5. Plot of oxidation peak height in CV, where the bars represent the standard deviation of the mean from 3 sensors.

[0135] 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 dAb and subsequently enhance sensitivity. Fig.12HFigures 1285-1286 showing the comparison of electrochemical performance of redox probe conjugated dAb and dAb conjugated AuNPs are included. Figure 1285 shows the SWV voltammogram of the CRP sensor modified with redox probe conjugated dAb and dAb conjugated AuNPs. Figure 1286 shows the corresponding peak current of the CRP sensor modified with redox probe conjugated dAb and dAb conjugated AuNPs. In the figure, the solid line and the dashed line represent 0 and 10 ng mL, respectively. -1 Sensor response of CRP. Error bars represent standard deviation from the mean of 3 sensors.

[0136] In this particular example, one-step direct electrochemical detection was enabled by cross-linking the redox-tagged TH to carboxylate residues on dAb-loaded AuNPs. Since the TH-tagged dAb-loaded AuNPs are bound to the mesoporous graphene electrode upon CRP recognition, the TH located at the protein exosite is in close proximity to the graphene surface in each mesopore for electron transfer. The successful immobilization of the dAb was confirmed based on a variety of observations. For example, the successful immobilization was confirmed by the following from the increase in the hydrodynamic size of the PEGylated AuNPs observed after each conjugation step: dynamic light scattering: PS-R immobilization, biotinylated dAb binding and redox molecule TH conjugation, followed by BSA inactivation. The successful immobilization of the dAb was also confirmed from the shift in the ultraviolet-visible (UV-Vis) absorbance of the AuNP conjugates observed after each modification step and from TEM images showing dispersed dAb-loaded AuNPs with a protein corona ( Fig.12I ).

[0137] In this particular example, the performance of CRP was evaluated by SWV in PBS solutions spiked with CRP ( Fig.12J ). It was observed that the increase in the peak current height of TH reduction showed a linear relationship with increasing target concentration ( Figure 12K ). In particular, Fig.12J The SWV voltammogram is shown. Figure 12K The CRP sensor was shown to have a range of 0-20 ng ml -1 Corresponding calibration graphs of CRP and 1% BSA in 1x PBS (pH 7.4), where the error bars represent the standard deviation from the mean of three sensors. In this particular example, it was observed that the sensor could detect picomolar levels of CRP with an ultra-low limit of detection of the order of 8 pM. -1The sensor was also observed to exhibit good batch-to-batch reproducibility when the detection was performed in 10 batches of 1X PBS (pH 7.4) in the presence of CRP. It is expected that the sensing accuracy of this particular embodiment can be further improved by automating the sensor preparation and modification process (e.g., via automated fluid dispensing or inkjet printing).

[0138] It was also observed that the LEG-AuNP CRP immunosensor exhibited high selectivity over other potential interfering proteins and hormones attributed to the sandwich assay format. Figure 12L and Figure 12M is a graph illustrating the selectivity of the CRP sensor to potential interferences in sweat, where the error bars represent the standard deviation from the mean of three sensors. Taking into account the inter-personal variation during human studies, the effects of sweat pH, ionic strength, temperature, and sample volume on antibody-antigen binding kinetics and redox probe electron transfer rates on CRP sensing accuracy were investigated and mitigated by introducing a suitable calibration mechanism, which will be further described below. It was found that Cl varying within a physiologically relevant range - The potential change using the Ag / AgCl pseudo-reference electrode in the presence of 200 Å concentrations resulted in a small shift in the peak potential, but its effect on the overall peak current density (and hence CRP quantification) was found to be negligible. Validation of the CRP sensor in human sweat samples (n=13 biological replicates) and saliva samples (n=6 biological replicates) is shown in Figure 1. Fig.12N As shown, the accuracy of the CRP sensor for biofluid analysis was validated by the laboratory gold standard enzyme-linked immunosorbent assay (ELISA) using human sweat and saliva samples. It was also observed that the disposable CRP sensor maintained stable sensor performance over a period of 10 days when stored in PBS in a refrigerator at 4°C.

[0139] Evaluation of sweat CRP for noninvasive monitoring of systemic inflammation

[0140] Inflammatory processes and immune responses are associated with a wide range of physical and psychiatric conditions that contribute significantly to modern morbidity and mortality worldwide. The three leading causes of death worldwide, ischemic heart disease, stroke, and COPD, are characterized by chronic inflammation. While the acute inflammatory response is a key survival mechanism, chronic inflammation contributes to the long-term silent progression of disease through irreversible tissue damage. Delayed diagnosis and treatment of chronic diseases imposes a heavy economic burden on patients and healthcare systems.

[0141] Although there is no standardized biomarker for the measurement and prediction of systemic chronic inflammation, CRP (an acute phase protein synthesized by hepatocytes in response to a wide range of acute and chronic stimuli) is closely related to chronic inflammation and the risk of death in several disease states. The stable nature of CRP in plasma, the absence of circadian rhythm changes and its insensitivity to commonly used drugs (such as corticosteroids) make it very attractive to clinicians as a convenient means to assess the physiological inflammatory state of patients. There is also increasing interest in exploring the effectiveness of serial CRP measurements for treatment decisions.

[0142] Currently, circulating CRP levels are clinically assessed in specific laboratories that rely on invasive blood draws from patients. Commercial point-of-care CRP monitors are still bulky in size and cannot reach picomolar levels of sensitivity to assess CRP levels in non-invasively accessible alternative biofluids such as sweat and saliva. An easily accessible means of monitoring inflammatory biomarkers such as CRP at home could improve patient outcomes and reduce cost factors by monitoring disease progression and initiating early treatment and intervention.

[0143] Therefore, the use of LEG-AuNP CRP sensors was evaluated as a versatile, cost-effective, and noninvasive method for assessing sweat CRP to monitor systemic inflammation in various disease states. Fig.13A A high-level schematic of sweat CRP evaluation for noninvasive monitoring of various health conditions that may be associated with elevated CRP in healthy or patient populations, including infection, lung disease, cardiovascular disease, and inflammatory bowel disease, is described.

[0144] Prior to performing these evaluations, proteomic characterization of different types of sweat samples using bottom-up proteomic analysis was performed to confirm the presence of CRP in iontophoretic and strenuous exercise-generated sweat. CRP was identified in exercise and iontophoretic sweat samples from human subjects using a recombinant CRP protein standard as a reference.

[0145] In one study, the LEG AuNP CRP sensor was used to evaluate CRP levels in healthy subjects grouped according to smoking status (current, former, and never smokers). Fig. 13BIllustrated, it includes box and whisker plots of CRP levels in iontophoretically extracted sweat and serum samples from patients with COPD (n=10 biological replicates) and without COPD (n=24 biological replicates). Participants were classified into five subgroups: current smokers with COPD (n=6 biological replicates) or without COPD (n=10 biological replicates), former smokers with COPD (n=4 biological replicates) and without COPD (n=9 biological replicates), and never smokers without COPD (n=5 biological replicates). It was observed that current smokers had higher levels of CRP in serum and sweat compared to former smokers and never smokers, which is consistent with previous reports on the effect of current smoking on serum CRP. In COPD patients, serum and sweat CRP values ​​were observed to be greater in former smokers than in current smokers, which is consistent with irreversible tissue damage and chronic inflammation in COPD patients, even after smoking cessation. The foregoing experimental results suggest that monitoring sweat CRP in COPD patients may therefore be useful in tracking disease progression and / or predicting exacerbations in this patient population.

[0146] In another pilot study, LEG AuNP CRP sensors were used to evaluate CRP levels in patients with heart failure (HF). Chronic systemic inflammation may be associated with an increased risk of cardiovascular events. Fig. 13C Illustrated, it includes box and whisker plots of CRP levels in sweat and serum samples from healthy participants (n=1 biological replicate), HF patients with reduced ejection fraction (HFrEF; n=1 biological replicate), and HF patients with preserved ejection fraction (HFpEF; n=9 biological replicates). The sensor results show that serum and sweat CRP values ​​are significantly elevated in patients with HFpEF, but not in patients with HFrEF, which is consistent with past studies. The aforementioned experimental results suggest that studying the dynamics of sweat CRP using the techniques described herein may potentially be of high value in predicting HFpEF disease progression and clinical outcomes.

[0147] In addition to chronic infections in COPD and HF, acute infections such as COVID-19 can lead to severe inflammatory responses. In a further preliminary study, the LEG AuNP CRP sensor was used to evaluate CRP levels in hospitalized patients with active infections over two consecutive days. Fig.13DIllustrated, it includes box and whisker plots of CRP levels in sweat and serum samples from three patients with active infection (n=3 biological replicates) on two consecutive days. The dashed line represents the mean of sweat and serum CRP levels in healthy participants. A significant increase in serum and sweat CRP was identified in patients with active infection compared to healthy subjects (average of more than 10-fold), indicating the presence of highly elevated sweat CRP in acute inflammation. FIG. 13B to FIG. 13D In the graph, the bottom whisker represents the minimum value, the top whisker represents the maximum value, and the square in the box represents the mean value.

[0148] 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. Fig.13E As an example, it is a graph showing the correlation of serum and sweat CRP levels, where 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 between sweat CRP concentration and serum CRP concentration was obtained. In one study, this correlation with serum CRP concentration appeared to be higher than those obtained from saliva and urine samples, suggesting the great potential of using sweat CRP for non-invasive monitoring of systemic inflammation to manage various chronic and acute health conditions.

[0149] Clinical in vivo evaluation

[0150] A clinical in vivo evaluation of a wearable biosensor system including a multiplexed LEG sensor array was performed on healthy subjects (including never smokers and current smokers) as well as patients with COPD and after COVID-19 infection. FIG. 14A to FIG. 14D Some results of an in vivo evaluation of a multiplexed sensor patch for noninvasive automated inflammation monitoring are exemplified in Fig.14A ), healthy smokers( Fig. 14B ), patients with COPD ( Fig. 14C ) and participants with prior COVID-19 ( Fig.14D) with real-time sensor calibration of wearable sensors for in vivo multiplexing physicochemical analysis and CRP analysis. During the in vivo test, it was observed that the wearable system was conformally laminated on the subject's arm, chemically induced and analyzed sweat, and non-invasively and wirelessly acquired inflammatory biomarker information. In the above test, after the ionic strength sensor indicated that the detection reservoir was fully updated, the in-situ pH, temperature and CRP sensor readings were obtained. Based on the obtained SWV voltammogram and the corresponding real-time obtained ionic strength, pH and temperature values, the CRP concentration was converted in the mobile application. As expected, compared with healthy subjects who had never smoked, increased CRP levels were observed from current smokers. The CRP levels of COPD patients and post-COVID subjects were significantly higher than those of non-smoking healthy subjects, suggesting the prospect of using the biosensor device 300 in practical non-invasive systemic inflammation monitoring and disease management applications. In vitro analysis of sweat and serum from post-COVID subjects confirmed the in vivo observation that patients who experienced moderate symptoms during COVID may still present low-grade inflammation after the onset of COVID, as indicated by slightly increased CRP levels. Similar to serum, sweat CRP levels were observed to remain generally stable during the 30-minute test period, and no significant changes were observed for chemically induced sweat samples at different body locations, including forearms, legs, upper arm locations, thighs, and back.

[0151] Characterization of a multiplexed microfluidic patch for automated immune sensing

[0152] As the microfluidic module passively channels sweat over the skin, the impedance ionic strength sensor can automatically capture the status of the detection reservoir (reagent flow and renewal). FIG. 15A to FIG. 15B shows the measured admittance response of the impedance ionic strength sensor in NaCl solution ( Fig.15A ) and the corresponding calibration graph ( Fig. 15B ), where the error bars represent the standard deviation of the mean from 3 sensors. As described, the measured admittance signal of the impedance ionic strength sensor shows a log-linear response with electrolyte concentration. Due to the large changes in electrolyte and pH levels observed between people in both exercise and chemically induced sweat samples, high levels of buffer salts are deposited with the dAb in the reagent reservoir to mitigate potential changes in the binding environment caused by changes in sweat composition. This addition introduces an electrolyte gradient between the sweat (mixture) reconstituted with the detection reagent and the fresh sweat that subsequently enters the detection reservoir. According to the numerical simulations described further below, the guidance of sweat and detection reagent can be summarized in four steps: reconstruction (I), incubation (II), renewal (III) and detection (IV).

[0153] When a sweat sample containing CRP molecules enters the microfluidic patch, it is expected that the detection antibody deposited in the solid state will dissolve and diffuse along the concentration gradient in the detection chamber. The collision between the CRP molecules and the antibody will lead to antigen-antibody binding events along the microfluidic channel before they finally reach the detection chamber. It is also expected that the introduction of serpentine microfluidic channels will promote the mixing and binding of antigen-antibody complexes.

[0154] To visualize and estimate the time scale of binding events at various locations of the microfluidic module, simulations of the CRP-antibody reversible binding reaction and mass transport processes of reactants and products were performed by finite element analysis (FEA). Using FEA, tetrahedral elements with a fine mesh allow modeling of source diffusion in 3D space with verified accuracy. Chemical reaction rates can be described by the law of mass action.

[0155] r = k f c CRP ·c antibody -k r c complex

[0156] Among them, r, k f , k r 、c CRP , and c complex represent the reaction rate, forward reaction coefficient, reverse reaction coefficient, CRP concentration, antibody concentration, and CRP-antibody complex concentration, respectively. The forward and reverse reaction coefficients are assumed to be 5.96×10 4 M - 1 s -1 and 2.48×10 -3 s -1 Assuming the concentration of CRP in sweat is 1 ng mL -1 The fluid behavior can be described by the Navier-Stokes equations for incompressible flow.

[0157]

[0158] Where p, v, p and μ represent liquid density, flow rate, pressure and viscosity respectively. Sweat flow rate is 1.5 μg mL -1 . Convection diffusion is described by the following equation:

[0159]

[0160] Where c and D represent the concentration and diffusion coefficient. The diffusion coefficient of CRP is 5×10 -11 m -2 s -1The diffusion coefficients of antibodies and CRP-antibody complexes were set to be 1×10 -12 m -2 s -1 same.

[0161] FIG. 15C to FIG. 15D The results of performing FEA are illustrated. Fig. 15C The simulated CRP-dAb concentration change over time at the working electrode is shown, with the center point on the working electrode of the inset indicating the location of the concentration change plot. Fig.15D Simulated CRP-dAb concentrations are shown illustrating the stages of automated sweat sampling and reagent guidance towards in situ CRP detection: reconstitution (I), incubation (II), renewal (III) and detection (IV). Scale bar 200 μm.

[0162] Based on the observed results, the binding and trafficking of CRP to the detection antibody can be divided into four stages. Fig.15D The graph shows the concentration of the CRP-detection antibody complex formed. During the reconstitution phase, the detection antibody diffuses along the concentration gradient. The binding of CRP begins to occur in the center of the reagent reservoir. As more sweat containing CRP molecules enters the reagent reservoir, more antigen-antibody complexes are formed, such as Fig.15D Example. The antigen-antibody complex travels along the flow direction to enter the detection chamber. After the serpentine mixing channel, the antigen-antibody complex slowly and evenly distributes in the detection chamber, allowing binding to the capture antibody immobilized at the bottom of the detection chamber to occur (incubation phase).

[0163] Based on the observed FEA results, after all pre-deposited detection antibodies in the reagent reservoir are reconstructed, form antigen-antibody complexes with sweat CRP or are flushed into the detection reservoir, the concentration of the detection antibody in the reagent reservoir is gradually exhausted. The continuous flow of sweat into the microfluidic module will no longer lead to the formation of more antibody-antigen complexes, as indicated by the concentration in the reagent reservoir during the renewal phase. Therefore, the fresh sweat flow that depletes the antigen-antibody complex continues to enter the detection chamber and flushes the unbound antibody-antigen complex in the chamber toward the outlet. Finally, all unbound antibody-antigen complexes and detection antibodies (which are labeled with electroactive molecules) will be updated from the detection chamber, as shown in the detection phase. At this stage, detection is performed, and since the concentration of the complex in the detection chamber converges to zero (indicated by the concentration), the electrochemical signal obtained is specific and related to the antigen-antibody complex bound to the working electrode surface.

[0164] Based on the average physiological sweat rate (1.5 μL min -1) under microfluidic flow testing with artificial sweat (0.2X PBS), it was observed that the admittance signal was initially close to zero when no fluid entered the chamber during the reconstitution phase; upon reconstitution, the high-salt-loaded detection reagent flowed into the detection chamber, the admittance reached its peak, and gradually decreased as the high-salt-loaded reagent was flushed out of the detection chamber by the newly secreted sweat. Fig.15E As an example, it shows the use of artificial sweat (0.2X PBS) at 1.5 μL min in a laboratory flow test. -1 Figure 2 Flow rate and admittance change of the LEG ion intensity sensor as a function of time during the above four stages of the automated CRP sensing process. In this example flow test, a yellow fluorescein isothiocyanate (FITC)-albumin fluorescent marker was used to simulate the flow of sweat CRP, and a red peridinin chlorophyll protein complex (PerCP) was used instead of the dAb-loaded AuNPs. The scale bar is 200 μm. Because the electrolyte content in iontophoretic sweat can remain relatively stable for the same individual, the admittance response is observed to reach a plateau after all reagents have been renewed by natural sweat, indicating that the working electrode is ready for electrochemical CRP detection. Further experimental flow tests using fluorescent proteins (fluorescein isothiocyanate-albumin as a CRP surrogate and peridinin chlorophyll protein as a detection reagent) showed similar incubation and renewal process trends to the simulation and electrolyte flow tests. Based on sweat rate information collected from 24 current and former smokers with and without COPD, flow rates ranged from 0.5 to 3.5 μL min -1 The flow tests with changes between and show similar admittance patterns with stabilization periods after various renewal processes. Fig.15F , which shows the ionic strength sensor in artificial sweat (0.2X PBS) at 0.5 to 3.5 μL min -1 The admittance response at different flow rates is calculated. As the preloaded salt and dye are refreshed from the detection reservoir, the admittance gradients at different flow rates converge to zero. The average sweat volume induced during this process before taking the sensor reading is based on Fig.15F The flow rate shown and the admittance measured were estimated to be 21 μL.

[0165] The performance of the CRP sensor based on this automated electrolyte monitoring mechanism was evaluated in multiple microfluidic flow tests. FIG. 15G to FIG. 15H Figures 1 to 2 are graphs illustrating the effect of flow rate on microfluidic automated CRP sensing. Figures I to J are graphs illustrating the effect of ionic strength on microfluidic automated CRP sensing. The solid and dashed lines represent the flow rate at 1 and 5 ng mL -1 The tests were performed in CRP. The SWV electrochemical measurements were started during the admittance stabilization period. An increase in concentration (from 1 to 5 ng mL -1) resulted in a significant increase in SWV peak current, which was not observed at physiologically relevant flow rates (1, 1.5, 2.5, and 3.5 μL min -1 ) No significant differences in CRP sensor response were observed for the same concentration at 20 °C and 40 °C. Although higher flow rates can also lead to faster turnover of the detection chamber and thus shorter incubation times for the detection antibody and CRP, relatively small increases in CRP signal were observed at different incubation times corresponding to physiologically relevant sweat rates (between 5 and 20 minutes).

[0166] Although the binding conditions were pre-adjusted with deposited salts, flow tests performed with different initial electrolyte concentrations (0.1X and 0.2X PBS were selected as artificial sweat to simulate inter-individual variations in sweat electrolyte concentrations) showed a slight decrease in the SWV signal at lower electrolyte concentrations, due to the effect of electrolyte levels on the rate of TH reduction. Similar to the in vitro selectivity results, no major interference with the CRP detection signal was observed in the flow test. In addition, flow tests using artificial sweat with different pH levels resulted in different SWV signals. These results indicate that sweat rate calibration may not be required, but additional in-situ signal calibration using sweat pH and electrolyte levels may be required to mitigate inter-individual variations in CRP detection accuracy. Compared to previously reported passive wearable microfluidic sensors that rely on strenuous exercise to induce sweat and cannot achieve sensitivity below mM levels, the technology described herein can be an attractive fully automated microfluidic sweat induction, harvesting, and high-accuracy quantitative analysis solution for home monitoring of clinically relevant trace-level biomarkers.

[0167] Real-time CRP sensor calibration during in vivo studies

[0168] The effects of pH, electrolytes, and temperature were investigated, all of which were found to be factors that can affect the sensor readout of CRP. In order to account for effects from the binding environment, in a particular embodiment, a multivariate model was constructed from the following: four independent variables: temperature, pH, electrolytes, CRP concentration ([CRP]); and dependent variable: peak current expressed in potential (mV) based on the following equation:

[0169] Peak current = A × [CRP] × pH m ×[Electrolyte] n × Temperature j

[0170] In a particular embodiment, the coefficients were solved using a nonlinear least squares fit and found to be: A = -0.5117; m = 0.6862; n = 0.1068; J = -0.6135. This model exhibits good accuracy in predicting the signal measured by the sensor (r 2=0.94). During in vivo operation, readings from the pH, temperature, electrolyte and CRP sensors can therefore be used to back-calculate the actual concentration of CRP in real time based on the fitted model.

[0171] In this document, a "processing device" may be implemented as a single processor that performs processing operations or a combination of dedicated 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 circuits.

[0172] The terms "substantially" and "approximately" used throughout the present invention (including the claims) are used to describe and account for small fluctuations, such as due to variations in processing. For example, they can mean less than or equal to ±5%, such as less than or equal to ±2%, such as less than or equal to ±1%, such as less than or equal to ±0.5%, such as less than or equal to ±0.2%, such as less than or equal to ±0.1%, such as less than or equal to ±0.05%.

[0173] To the extent applicable, unless explicitly stated otherwise herein, the terms "first", "second", and "third", etc. herein are used merely to illustrate the corresponding objects described by these terms as separate entities and are not intended to indicate a temporal sequence.

[0174] Unless otherwise expressly stated, the terms and phrases used in this document and their variations should be interpreted as open-ended and not restrictive. As examples of the above: the term "including" should be interpreted to mean "including, but not limited to", etc.; the term "example" is used to provide an illustrative example of the item in question, rather than an exhaustive or limiting list thereof; the term "one" or "an" should be interpreted to mean "at least one", "one or more", etc.; and adjectives (such as "conventional", "traditional", "ordinary", "standard", "known", and terms of similar meaning) should not be interpreted as limiting the item to a given time period or items available at a given time, but should be interpreted to cover conventional, traditional, ordinary or standard technologies available or known at any time now or in the future. Similarly, where this document refers to technology that will be understood or known by a person of ordinary skill in the art, such technology covers technology that will be understood or known by a person of ordinary skill in the art at any time now or in the future.

[0175] In some cases, the presence of expansion words and phrases such as "one or more," "at least," "but not limited to," or other similar phrases should not be understood to mean that a narrower case is expected or required when such expansion phrases may not be present.

[0176] In addition, the various embodiments set forth herein are described in terms of exemplary block diagrams, flow charts, and other illustrations. It will be apparent to one of ordinary skill in the art after reading this document that the illustrated embodiments and their various alternatives may be implemented without limitation to the illustrated examples. For example, the block diagrams and their accompanying descriptions should not be interpreted as requiring a specific architecture or configuration.

[0177] The terms "substantially" and "approximately" used throughout the present invention (including the claims) are used to describe and account for small fluctuations, such as due to variations in processing. For example, they can mean less than or equal to ±5%, such as less than or equal to ±2%, such as less than or equal to ±1%, such as less than or equal to ±0.5%, such as less than or equal to ±0.2%, such as less than or equal to ±0.1%, such as less than or equal to ±0.05%.

[0178] To the extent applicable, unless explicitly stated otherwise herein, the terms "first", "second", and "third", etc. herein are used merely to illustrate the corresponding objects described by these terms as separate entities and are not intended to indicate a temporal sequence.

[0179] Although various embodiments of the present invention have been described above, it should be understood that they are presented only in an exemplary manner and are not restrictive. Similarly, the various drawings may describe the example architecture or other configurations of the present invention, which is done to help understand the features and functions that may be included in the present invention. The present invention is not limited to the illustrated example architecture or configuration, but a variety of alternative architectures and configurations can be used to achieve the desired features. In fact, it is obvious to those skilled in the art how to implement alternative functions, logical or physical divisions and configurations to achieve the desired features of the present invention. Moreover, a large number of different constituent module names other than those described herein can be applied to various divisions. In addition, with respect to flow charts, operational descriptions, and method claims, unless otherwise specified in the context, the order in which the steps are presented herein should not require the implementation of various embodiments to perform the functions described in the same order.

[0180] Although the present invention is described above according to various exemplary embodiments and implementations, it should be understood that the various features, aspects, and functions described in one or more individual embodiments are not limited to their applicability to the specific embodiments in which they are described, but can be applied to one or more other embodiments of the present invention, either alone or in various combinations, whether or not such embodiments are described, and whether or not such features are presented as part of the described embodiments. Therefore, the breadth and scope of the present invention should not be limited by any of the above exemplary embodiments.

[0181] It should be understood that all combinations of the aforementioned concepts (provided that these concepts are not mutually inconsistent) are considered to be part of the inventive subject matter disclosed herein. In particular, all combinations of the claimed subject matter appearing in the present invention are considered to be part of the inventive subject matter disclosed herein.

Claims

1. A wearable biosensor device comprising: an iontophoresis module configured to stimulate production of a sweat sample from the user's skin, the sweat sample including the biomarker; a microfluidics 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 microfluidics module; as well as A sensor assembly, including a bioaffinity sensor, is configured to quantify the biomarker of the mixture in the detection reservoir to determine the concentration of the biomarker present in the sweat sample, the bioaffinity sensor including an electrode functionalized to bind to the biomarker of the mixture.

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

3. The wearable biosensor device according to claim 2, wherein: The first nanoparticle and the second nanoparticle are gold nanoparticles; and The biomarkers include protein biomarkers or hormone biomarkers.

4. The wearable biosensor device according to claim 1, wherein: The bioaffinity sensor is configured to quantify the biomarker of the mixture to determine 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.

5. The wearable biosensor device according to claim 1, wherein: The microfluidic module comprises: an inlet for collecting the sweat sample; a reagent reservoir, including the labeled detection reagent, the reagent reservoir being 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 the mixture including the labeled detection reagent bound to the biomarker; The detection reservoir is used to receive the mixture from the mixing channel; and An outlet is used to provide the sweat sample to flow out of the detection reservoir.

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

7. The wearable biosensor device according to claim 6, wherein: The sensor assembly includes a multiplexed sensor array fabricated using laser engraved graphene (LEG), the multiplexed sensor array including the bioaffinity sensor, the temperature sensor, the ionic strength sensor, and the pH sensor.

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

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

10. A method comprising: receiving a sweat sample collected from the skin via an inlet of the microfluidic module, the sweat sample comprising a protein or hormone biomarker; reconstitute the sweat sample within a reagent reservoir of the microfluidics module with a detection reagent configured to bind to the protein or hormone biomarker, the detection reagent comprising an electroactive labeling molecule; combining the detection reagent with the protein or hormone biomarker in a mixing channel of the microfluidic module to form a mixture including 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 to bind the protein or hormone biomarker to an electrode of a sensor assembly; refreshing the microfluidic module with one or more additional sweat samples that do not contain detection reagent to remove unbound detection reagent via an outlet of the microfluidic module; as well as The concentration of the protein or hormone biomarker present in the sweat sample is estimated by measuring the amount of the electroactive label present at the surface of the electrode.

11. The method according to claim 10, wherein: Estimating the concentration of the protein or hormone biomarker present in the sweat sample includes estimating the concentration of the protein 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.

12. The method according to claim 10, further comprising: using one or more additional sensors of the sensor assembly to obtain one or more additional biophysical sensor measurements, the one or more additional biophysical sensor measurements comprising a temperature of the skin, a pH level of the sweat sample, or an ionic strength of the sweat sample; as well as The estimated concentration of the protein or hormone biomarker is calibrated based on the one or more additional biophysical sensor measurements.

13. The method according to claim 10, further comprising: Prior to receiving the sweat sample via the inlet, the sweat sample is induced using an iontophoresis module in contact with the skin.

14. The method according to claim 10, wherein: The protein marker is C-reactive protein (CRP); The detection reagent also includes a first nanoparticle conjugated to a detection antibody that binds to the CRP; and The surface of the electrode includes second nanoparticles conjugated to a capture antibody that binds to the CRP.

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

16. A method comprising: adhering a patch including the microfluidic module and the sensor assembly to the user's skin; collecting a sweat sample obtained from the skin in the microfluidic module; mixing the sweat sample with a reagent within the microfluidic module to obtain a mixture, the mixture comprising the reagent that binds to a protein or hormone biomarker contained in the sweat sample; as well as The concentration of the protein or hormone biomarker in the sweat sample is estimated from the mixture using the sensor assembly.

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

18. The method according to claim 17, wherein: Real-time monitoring of the health condition of the user includes comparing the concentration of the protein or hormone biomarker estimated using the sensor assembly with a threshold value to determine the biological condition of the user.

19. The method according to claim 17, wherein: The health conditions included: heart disease, chronic obstructive pulmonary disease, inflammatory bowel disease, active infection or past infection.

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

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