Integrated ultrasonic electrochemical biosensor wristband devices, systems, and methods

Abstract

Disclosed are wearable sensor devices, systems and methods for simultaneous and real-time monitoring of physiological and analyte data. In some aspects, a wearable integrated physiological and electrochemical biosensor device includes: a wristband able to conform and reversible attach to a wrist of a subject; an electrochemical sensor contingent that comprises a substrate, one or more electrodes, and a plurality of microneedles protruding from the substrate and interfaced with the electrode(s) to measure the multiple analytes in interstitial fluid of the skin; and a physiological sensor contingent to measure a plurality of physiological health parameters of the subject that includes a plurality of ultrasound transducers to transmit acoustic signals and receive returned acoustic signals associated with blood vessel changes to measure blood pressure and arterial stiffness of the subject, and a plurality of electrophysiological electrodes to concurrently detect electrocardiogram signals to measure heart rate of the subject.

Description

PCT Application Attorney Docket No.: 009062.8575.WO00INTEGRATED ULTRASONIC ELECTROCHEMICAL BIOSENSOR WRISTBAND DEVICES, SYSTEMS, AND METHODSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This patent document claims priority to and benefits of U.S. Provisional Patent Application No. 63 / 750,228, titled “INTEGRATED ULTRASONIC ELECTROCHEMICAL BIOSENSOR WRISTBAND DEVICES, SYSTEMS, AND METHODS” and filed on January 27, 2025. The entire content of the aforementioned patent application is incorporated by reference as part of the disclosure of this patent document.TECHNICAL FIELD

[0002] This patent document relates to wearable sensors, and in particular to devices, systems, and processes that use acoustic and electrochemical biosensing techniques for detection of a variety of health parameters.BACKGROUND

[0003] Biosensors can provide real-time detection of physiological substances and processes in living things. A biosensor is an analytical tool that can detect a chemical, substance, or organism using a biologically sensitive component coupled with a transducing element to convert a detection event into a signal for processing and / or display. Biosensors can use biological materials as the biologically sensitive component, e.g., such as biomolecules including enzymes, antibodies, nucleic acids, etc., as well as living cells. For example, molecular biosensors can be configured to use specific chemical properties or molecular recognition mechanisms to identify target agents, which can be useful in diagnosis and treatments for various health care applications.SUMMARY

[0004] Disclosed are wearable sensor devices, systems and methods for simultaneous and real-time monitoring of physiological and analyte data, including biomarker level concentrations. In some aspects, the disclosed technology includes an integrated wearable, flexible, epidermal multi-modal sensor device including (1) ultrasound transducers as acoustic sensors for the monitoring of blood pressure, heart rate and ECG waveform and (2) microneedle arrays forPCT Application Attorney Docket No.: 009062.8575.WG00continuous and multiplexed electrochemical sensing of one or more analytes, including but not limited to glucose, lactate and alcohol in interstitial fluid.

[0005] Example implementations of the disclosed technology include one or more of the following features and advantages. For example, in some implementations, the chemicalphysical combination of data monitoring by example acoustic and electrochemical biosensor contingents of the disclosed wearable sensor devices enhances health monitoring and establishes the system as a versatile platform technology, offering a holistic view of the patient’s health. For instance, arterial stiffness can also be measured through the ultrasound sensor contingent of the disclosed wearable sensor technology. The device can thus track dynamic changes of metabolic and physical signals. The disclosed technology provides multiple sensing modalities; and the multiple types of measurements can be measured locally around the same place on the body — the wrist.

[0006] The subject matter described in this patent document can be implemented in specific ways that provide one or more of the following features.BRIEF DESCRIPTION OF THE DRAWINGS

[0007] FIG. 1A shows a schematic of an example embodiment and implementation of a multimodal Biomarkers-Linked Ultrasound Electronic (BLUE) wristband platform for tracking simultaneously chemical biomarkers and physical signals.

[0008] FIG. IB shows a cross-section of the skin with microneedle array and ultrasound sensor array.

[0009] FIG. 1C shows a schematic of vessel diameter changes with different morphology showing stiff (upper) and soft (lower) arteries.

[0010] FIG. ID shows a photograph of an example multimodal monitoring system on a subject's wrist.

[0011] FIG. IE shows a photograph of an example of an individual inverted microneedle sensor array.

[0012] FIG. IF shows a photograph of example ultrasound transducers.

[0013] FIG. 1G shows a photograph of an example multimodal monitoring system wristband.

[0014] FIG. 2 shows photographs of an example multimodal monitoring system wristband onPCT Application Attorney Docket No.: 009062.8575.WO00a subject’s wrist as the subject performs physical activities.

[0015] FIGS. 3 A and 3B show schematics and data plots showing parallel signal recording of heartrate, blood pressure, and three biomarker readings (glucose, alcohol, and lactate.)

[0016] FIG. 4 shows a schematic representation of health monitoring throughout various life stages.

[0017] FIG. 5 shows an example process for microneedle fabrication.

[0018] FIGS. 6A-6F show scanning electron microscopy (SEM) images of example microneedles at different etching times.

[0019] FIGS. 7 A and 7B show data plots showing example thickness measurement data for chromium (Cr) and platinum (Pt) films.

[0020] FIGS. 8A-8D show SEM images of example microneedles before and after skin penetration.

[0021] FIGS. 9A-9E show photographs of example micro needles during bending.

[0022] FIGS. 9F-9O show simulations of flexural strength in an example microneedle array.

[0023] FIGS. 10 A- 10C show photographs of an example microneedle array and holder.

[0024] FIGS. 11A-11D show photographs of an example microneedle array and cover during bending.

[0025] FIGS. 12A-12D show schematics comparing skin contact for a rigid microneedle array with a flexible microneedle array at various bending angles.

[0026] FIG. 13 shows data plots showing example current, blood pressure, and electrocardiogram (ECG) signals measured during different wrist bending angles.

[0027] FIGS. 14A and 14B show data plots showing example microneedle electrochemical measurements performed over 1000 bending cycles.

[0028] FIGS. 14C and 14D show photographs of an example ECG electrode before and during bending.

[0029] FIGS. 14E and 14F show data plots showing the resistance of an example ECG electrode and full wristband measured over 1000 bending cycles.

[0030] FIGS. 15A-15F show SEM images of an example ECG electrode before and after bending.

[0031] FIGS. 16A-16C show photographs of example ultrasound sensors before and during bending.PCT Application Attorney Docket No.: 009062.8575.WG00

[0032] FIGS. 16D-16E show data plots showing example ultrasound pulse-echo measurements performed over 1000 bending cycles.

[0033] FIGS. 17A-17F show data plots showing a simulation of microneedle skin penetration.

[0034] FIGS. 18A-18L show data plots showing a simulation of skin surface forces during microneedle penetration.

[0035] FIGS. 19A-19D show data plots showing a simulation of microneedle displacement during skin penetration.

[0036] FIGS. 20A-20C show photographs of three example sizes of a multi-modal wristband system.

[0037] FIGS. 21A-21F show data plots showing crosstalk of the chronoamperometry, ECG, and ultrasound signals.

[0038] FIG. 22 A shows data plots showing the blood pressure waveforms before and after exercise.

[0039] FIG. 22B shows a data plot showing the calculated augmentation index (AIx) before and after exercise.

[0040] FIG. 23 shows a data plot showing the viability of cells with a control, SU 8 microneedles, and SU 8 + Cr + Pt microneedles.

[0041] FIGS. 24A-24C show data plots showing calibration of glucose oxidase (GOx) (glucose (Glu)), alcohol oxidase (AOx) (alcohol (Ale)), and lactate oxidase (LOx) (lactate (Lac)) modified microneedles in PBS solution.

[0042] FIGS. 24D-24F show data plots showing selectivity studies for GOx, AOx, and LOx modified microneedles in PBS solution.

[0043] FIGS. 24G-24I show data plots showing stability studies for GOx, AOx, and LOx modified microneedles in PBS solution.

[0044] FIGS. 25A-25C show data plots showing calibration of GOx (Glu), AOx (Ale), and LOx (Lac) modified microneedles in artificial interstitial fluid solution.

[0045] FIGS. 25D-25F show data plots showing selectivity studies for GOx, AOx, and LOx modified microneedles in artificial interstitial fluid solution.

[0046] FIGS. 25G-25I show data plots showing stability studies for GOx, AOx, and LOx modified microneedles in artificial interstitial fluid solution.PCT Application Attorney Docket No.: 009062.8575.WG00

[0047] FIGS. 26A-26G shows data plots showing real-time microneedle sensing of glucose, alcohol, and lactate compared to monitoring performed by a blood glucose monitor, breathalyzer, and lactate blood meter.

[0048] FIG. 27 shows photographs of subjects’ skin following microneedle removal.

[0049] FIGS. 28 A and 28B show data plots showing measured glucose, blood pressure, and heartrate as non-fasting and fasting subjects consume a meal.

[0050] FIGS. 28C and 28D show data plots showing 8 second blood pressure signals from non-fasting and fasting subjects before and after meal consumption.

[0051] FIGS. 28E and 28F show data plots showing calculated augmentation indices from non-fasting and fasting subjects before and after meal consumption.

[0052] FIGS. 29A and 29B show data plots showing measured alcohol levels, blood pressure, and heartrate as a non-drinker and drinker subjects consume a glass of wine.

[0053] FIGS. 29C and 29D show data plots showing 8 second blood pressure signals from non-drinker and drinker subjects before and after consuming a glass of wine.

[0054] FIGS. 29E and 29F show data plots showing calculated augmentation indices from non-drinker and drinker subjects before and after consuming a glass of wine.

[0055] FIGS. 30A and 30B show data plots showing measured lactate levels, blood pressure, and heartrate as a non-athlete and athlete exercise.

[0056] FIGS. 30C and 30D show data plots showing 8 second blood pressure signals from a non-athlete and athlete before and after exercise.

[0057] FIGS. 30E and 30F show data plots showing calculated augmentation indices from non-athlete and athlete before and after exercise.

[0058] FIG. 31 A shows a photograph of an example wristband connected to a wireless ultrasound circuit, battery, and wireless potentiostat.

[0059] FIG. 3 IB shows a schematic of an example wristband connected to a wireless potentiostat and a laptop computer.

[0060] FIG. 32A shows data plots showing measured glucose, blood pressure, and heartrate as a subject consumes a meal and iced tea.

[0061] FIG. 32B shows data plots showing 8 second blood pressure signals from a subject before and after meal and iced tea consumption.

[0062] FIG. 32C shows a data plot showing calculated augmentation indices from a subjectPCT Application Attorney Docket No.: 009062.8575.WG00before and after meal and iced tea consumption.

[0063] FIGS. 32D and 32E show the Parkes error grids of microneedle glucose measurements and continuous glucose measurements vs. measurements using a glucose blood meter.

[0064] FIGS. 33 A and 33B show data plots showing real-time glucose monitoring using microneedles, continuous glucose monitoring, and a glucose blood meter.

[0065] FIGS. 34A shows data plots showing simultaneous monitoring of interstitial fluid glucose, alcohol, and lactate levels, along with blood pressure and heart rate for a subject during consumption of a meal and alcohol and during exercise.

[0066] FIG. 34B shows data plots showing 8 second blood pressure signals from a subject before and after consuming a meal and alcohol and performing exercise.

[0067] FIG. 34C shows data plots showing calculated augmentation indices from a subject before and after consuming a meal and alcohol and performing exercise.

[0068] FIGS. 34D-34F show data plots showing alcohol, lactate, and blood pressure measurements using an example wristband plotted against measurements obtained from commercially-available devices.

[0069] FIG. 35A shows data plots showing measured glucose, blood pressure, and heartrate as a subject consumes a coffee.

[0070] FIG. 35B shows data plots showing 8 second blood pressure signals before and after a subject consumes a coffee.

[0071] FIG. 35C shows data plots showing calculated augmentation indices before and after a subject consumes a coffee.

[0072] FIG. 35D shows data plots showing measured glucose, blood pressure, and heartrate as a subject consumes an energy drink.

[0073] FIG. 35E shows data plots showing 8 second blood pressure signals before and after a subject consumes an energy drink.

[0074] FIG. 35F shows data plots showing calculated augmentation indices before and after a subject consumes an energy drink.

[0075] FIG. 35G shows data plots showing measured glucose, blood pressure, and heartrate as a subject consumes an iced coffee beverage.

[0076] FIG. 35H shows data plots showing 8 second blood pressure signals before and afterPCT Application Attorney Docket No.: 009062.8575.WG00a subject consumes an iced coffee beverage.

[0077] FIG. 351 shows data plots showing calculated augmentation indices before and after a subject consumes an iced coffee beverage.

[0078] FIG. 35J shows a data plot showing the caffeine content of a coffee, an energy drink, and an iced coffee beverage.

[0079] FIG. 36A shows data plots showing measured glucose, blood pressure, and heartrate as a subject consumes a meal and a dessert.

[0080] FIG. 36B shows data plots showing 8 second blood pressure signals before and after a subject consumes a meal and a dessert.

[0081] FIG. 36C shows data plots showing calculated augmentation indices before and after a subject consumes a meal and a dessert.

[0082] FIGS. 37 A and 37D show data plots showing measured glucose, alcohol level, lactate level, blood pressure, and heartrate as a subject consumes a meal and alcohol and performs exercise.

[0083] FIGS. 37B and 37E show data plots showing 8 second blood pressure signals before and after a subject consumes a meal and alcohol and performs exercise.

[0084] FIGS. 37C and 37F show data plots showing calculated augmentation indices before and after a subject consumes a meal and alcohol and performs exercise.

[0085] FIG. 38A shows data plots showing measured glucose, alcohol level, lactate level, blood pressure, and heartrate as a subject consumes a meal and performs exercise.

[0086] FIG. 38B shows data plots showing 8 second blood pressure signals before and after a subject consumes a meal and performs exercise.

[0087] FIG. 38C shows data plots showing calculated augmentation indices before and after a subject consumes a meal and performs exercise.

[0088] FIG. 39 shows a chord diagram showing the interrelation of different activities and the response of multiple biomarkers and vital signs.

[0089] FIG. 40 shows a schematic of an example system for measurement of ultrasound sensor power consumption.

[0090] FIG. 41 A shows a schematic of arterial distension and diameter change induced by pulse.

[0091] FIG. 41B shows a schematic of pulse wave propagation and reflection in an arterialPCT Application Attorney Docket No.: 009062.8575.WO00tree.DETAILED DESCRIPTION

[0092] Disclosed are wearable sensor devices, systems and methods for simultaneous and real-time monitoring of physiological and analyte data, including biomarker level concentrations. In some aspects, the disclosed technology includes an integrated wearable multi-modal sensor device including (1) ultrasound transducers as acoustic sensors for the monitoring of blood pressure, heart rate and ECG waveform and (2) microneedle arrays for interstitial fluid continuous and multiplexed electrochemical sensing of one or more analytes, including but not limited to glucose, lactate and alcohol.

[0093] Implementations of the disclosed technology allow the direct, real-time measurement of the interstitial fluid biochemical level without the need for extraction processes, along with its integration with acoustic sensing components for hemodynamic activity monitoring, which allows the comprehensive, minimally invasive access to multiple physiological signals towards the diagnosis, monitoring, and treatment of various diseases and symptoms, which has not been previously demonstrated in a miniaturized, minimally invasive, wearable form factor. The disclosed technology provides a high level of sensor integration between the biochemical sensor’s microneedle (s) based and acoustic vital sign sensors. In contrast, current techniques have not incorporated multiplexed sensors in a single patch / device (e.g., blood pressure and chemical biomarker integration). The chemical-physical combined / integrated sensor contingents of the disclosed technology can enhance health monitoring and establish a versatile platform technology, offering a holistic view of the patient’s health. For instance, arterial stiffness can also be measured through the ultrasound sensor contingent of the disclosed wearable sensor technology. The device can thus track dynamic changes of metabolic and physical signals. The disclosed technology provides multiple sensing modalities; and the multiple types of measurements can be measured locally around the same place on the body — the wrist.

[0094] In some embodiments in accordance with the disclosed technology, a wristband wearable sensor device includes integrated chemical and physical sensors in one soft, flexible band, where the chemical sensor contingent uses miniaturized microneedle sensors to check chemicals in the body, like glucose (sugar levels), and the physical sensor contingent tracks physical health signs like blood pressure, heart rate, and arterial stiffness. The exemplaryPCT Application Attorney Docket No.: 009062.8575.WG00wristband sensor device is able to monitor both chemical and physical changes of the subject wearing the wristband sensor device in real time. In example implementations, the exemplary wristband sensor device can use gentle acoustic waves to collect this data, e.g.. helping to track how the subject’s body responds to eating, exercising, and / or other daily activities. The exemplary wristband sensor device can be especially useful for managing diabetes and understanding how the body functions throughout the day.

[0095] Example embodiments of the disclosed wristband wearable sensor device can include an integrated Biomarkers-Linked Ultrasound Electronic (BLUE) wristband platform, fusing the rich multiplexed chemical sensing capability of microneedle sensor arrays with critical physical sensors, e.g., blood pressure, electrocardiogram, heart rate, and arterial stiffness, into a single flexible platform. The example integrated BLUE wristband platform addresses current technological gaps by combining multiplexed chemical sensing (e.g., including a highly reliable continuous ISF glucose monitoring) with real-time detection of key physiological signals using a soft ultrasonic sensor array on a single soft wristband. The example integrated BLUE wristband platform can thus track dynamic changes of metabolic and physical signals relevant to glucose metabolism and complications of diabetes, capturing effects of transient stimulations, and uncovering correlations between daily activities and physio-metabolic responses.

[0096] In some aspects, the disclosed integrated wearable multi-modal sensor devices, sensors, and methods include an integrated multi-modal sensor wristband device with microneedles biosensors, ECG, and ultrasound acoustic transducers for the continuous monitoring of multiple biomarkers and physiological parameters, including glucose, lactate, alcohol, blood pressure, heart rate, and blood vessels stiffness, e.g., thereby expanding continuous diabetes parameter monitoring.

[0097] Comprehensive glycemic control information should account for more than a single (glucose) signal. The development of closed-loop systems towards effective management of diabetes thus requires the inclusion of additional chemical and physical inputs that affect the disease pathophysiology and reflect cardiovascular risks in diabetes patients. In the example implementations of the disclosed technology, a hybrid flexible wristband sensing platform that integrates a microneedle array for multiplexed biomarker sensing and an ultrasonic array for blood pressure, arterial stiffness, and heart-rate monitoring was utilized. As shown by the example embodiments and results below, the integrated system provides a continuousPCT Application Attorney Docket No.: 009062.8575.WG00comprehensive evaluation of the metabolic and cardiovascular status toward advancing personalized diabetes management, improving glycemic control, and alerting for cardiovascular risks. The multimodal platform was tested successfully, offering continuous glucose, lactate, and alcohol monitoring, along with simultaneous ultrasonic measurements of blood pressure, arterial stiffness, and heart-rate to support the understanding of the interplay between interstitial fluid (ISF) biomarkers and physiological parameters during common activities. By expanding the continuous monitoring of diabetes patients to additional biomarkers and key cardiac signals, such integrated multiplexed chemical-physical health-monitoring platform holds considerable promise for addressing the limitations of existing single-modality glucose monitoring systems toward enhanced management of diabetes and related cardiovascular risks.

[0098] Diabetes is a chronic disease, characterized by elevated blood glucose levels, affecting numerous people and leading to major complications. Over the past two decades, continuous glucose monitoring (CGM) systems have become the standard of care in managing diabetic subjects. Existing CGM systems rely on a needle-based subcutaneous enzymatic electrochemical biosensor, which closely tracks the dynamics of glucose concentrations in the interstitial fluid (ISF). Such systems have evolved rapidly over the past two decades, showing impressive technological advances and capabilities toward optimal therapeutic interventions and improved glycemic control. Yet, all current commercial CGM systems are limited to the measurement of a single parameter (blood glucose concentration). Considering the complexity of diabetes, CGM platforms fall short of capturing the full spectrum of diverse symptoms experienced by diabetic patients, and hence do not permit adequate response to glycemic variability due to unanticipated events, such as stress, meals, or physical activities. Multiple studies have revealed the benefit of monitoring additional biomarkers and physical inputs (e.g., blood pressure (BP), heart rate (HR)) that affect insulin sensitivity for mimicking the physiological function of a healthy pancreas towards improving glycemic control and understanding the patient's health. Diabetes has also long been known to be associated with high risks of cardiovascular and kidney diseases. The reduction of cardiac and nephropathy risks represents an important part of evaluating type-one diabetes (T1D) patients. Effective management of diabetes should thus include the continuous monitoring of additional clinically relevant biomarkers (beyond blood glucose) and of multiple cardiovascular vital signals for understanding the pathophysiology of diabetes and patient's health toward reducing TID-relatedPCT Application Attorney Docket No.: 009062.8575.WG00complications and guiding personalized interventions.

[0099] Microneedle-based electrochemical sensors have been developed extensively to enable continuous health monitoring at the molecular level regarding multiple chemical markers. By continuously accessing the dermal interstitial fluid (ISF), whose composition is very close to blood gold standard, such minimally-invasive sensing devices allow real-time collection of important diagnostic information. Microneedle (MN) arrays of multiple individually-addressable sensing electrodes, functionalized with different receptors, can thus capture important dynamic chemical data through the simultaneous detection of multiple ISF biomarkers. While offering rich chemical information, such MN sensor arrays lack important information about key physical parameters relevant to the wearer’s health and well-being. Critical real-time insights about dynamic changes of the wearer’ s cardiovascular status remain unchecked.

[0100] Multimodal wearable sensor systems, monitoring simultaneously multiple physical parameters and biochemical markers, have been introduced recently to address the limitations of current single-modality wearable chemical or physical sensors. However, these multimodal wearable systems have focused solely on sweat-sensing epidermal patches. For example, earlier reports on integrated wearable hybrid sensing platforms have focused on the coupling of heart rate or blood-pressure sensors with single biomarker sweat sensors. Sweat chemical sensors integrated with physical sensors that record skin temperature and galvanic skin response were developed recently to monitor stress levels. The scope and power of multimodal sensing platforms can be greatly enhanced by accessing the rich diagnostic biochemical information offered by the ISF, through simultaneous dynamic recording of multiple ISF biomarkers along with key cardiac physiological signals.

[0101] Here we describe an example integrated Biomarkers-Linked Ultrasound Electronic (BLUE) wristband platform, fusing the rich multiplexed chemical sensing capability of microneedle sensor arrays with critical physical sensors — namely, blood pressure, electrocardiogram, heart rate, and arterial stiffness — into a single flexible platform.

[0102] FIG. 1A shows a schematic illustrating an example hybrid monitoring wristband system 100 that integrates a multiplexed microneedle array 102 that measures simultaneously multiple ISF chemical markers (e.g., glucose (Glu), alcohol (Ale), and lactate (Lac)), via the corresponding oxidase-based recognition reactions and amperometric signal transduction (e.g., glucose oxidase (GOx), alcohol oxidase (AOx), and lactate oxidase (LOx)), respectively. ThePCT Application Attorney Docket No.: 009062.8575.WG00embedded sensors for physiological signals include ultrasound BP transducers 116 and customized silver ink printed biopotential electrodes (ECG) 124, and 126; BP was measured via 10 ultrasound sensors 112 (including transducers 116 and electrodes 114) with a 3 cm x 1 cm area during the movements.

[0103] FIG. IB shows an illustration depicting a cross-section of the skin with microneedle array 102 and ultrasound sensor array 112 (e.g.. showing the microneedles 104 penetrate from the stratum corneum of the epidermis to the dermis for ISF biosensing, providing continuous current signals related to concentrations of blood glucose, alcohol, and lactate; the blood pressure, heart rate, and arterial stiffness can be decoded from the vessel diameter change)

[0104] FIG. 1C shows an illustration depicting vessel diameter changes with different morphology showing stiff (upper) and soft (lower) arteries.

[0105] FIG. ID shows an image of an example multimodal monitoring wristband 100 on the subject’s wrist.

[0106] FIG. IE shows an image of an example individual inverted microneedle sensor array 102.

[0107] FIG. IF shows an image of example ultrasound BP transducers 116.

[0108] FIG. 1G shows an image of an example multimodal monitoring system (including microneedle array 102, ultrasound sensors 112, and ECG sensors 122) along with the entire wristband 100.

[0109] FIG. 2 shows photographs of an example multimodal monitoring system wristband on a subject’s wrist as the subject performs physical activities, such as jumping rope, playing basketball or performing push-ups. The photographs show testing of the mechanical robustness and fitness of the wristband during common outdoor activities. Comparisons of the position of the wristband on the subject’s wrist before and after activities are also shown.

[0110] FIG. 3A shows an illustration depicting a laptop displaying the parallel signals recording of the ultrasound sensors of BP and the MNs of three biomarker readings (e.g., Glu, Ale. and Lac), e.g., where the device processes the multi-source data and displays and summarizes the digital health status with key parameters, and alerts for potential abnormalities, along with the values of the data points of each sensor recorded in real-time.

[0111] FIG. 3B shows a data plot depicting a comparison of the real-time MN glucose monitoring (data points per second; red line) with the response of the glucose blood meter (dataPCT Application Attorney Docket No.: 009062.8575.WG00points per 10 min, black points), and of the CGM (data points per 1 min; green dotted line), over an 80 min period, along with simultaneous recording of HR and BP, which contain diastolic blood pressure (DBP) and Systolic blood pressure (SBP). The yellow box indicates the timepoint when the subject consumed a soft drink after an initial 10 min.

[0112] The present BLUE platform addresses current technological gaps by combining multiplexed chemical sensing (including a highly reliable continuous ISF glucose monitoring) with real-time detection of key physiological signals using a soft ultrasonic sensor array 112 on a single soft wristband 100. The BLUE platform can thus track dynamic changes of metabolic and physical signals relevant to glucose metabolism and complications of diabetes, capturing effects of transient stimulations, and uncovering correlations between daily activities and physio-metabolic responses. This chemical-physical combination enhances health monitoring and establishes the system as a versatile platform technology, offering a holistic view of the patient's health. We emphasized in this work the importance of arterial stiffness, a key indicator of vascular health, measured through an ultrasound sensor 112 — a feature not previously explored in wearable sensors for diabetes.

[0113] The integrated BLUE platform has been carefully engineered to ensure reliable signal acquisition, eliminate cross-talk among its different sensing modalities and avoid signal drifting under mechanical deformations. The microneedle array 102 was designed to allow convenient replacement of the disposable microneedles 104 through the inclusion of a cover 106, holder 108, and adapter 110 system, tailoring the wearing period to minimize allergic reactions and risk of infection. The ability of the wristband system to measure multiple ISF chemical markers and physiological signals simultaneously and continuously was evaluated on multiple healthy and pre-diabetic subjects during diverse daily activities, including food and drink intakes and exercises involving vigorous bodily movements. The presented hybrid system thus addresses the limitations of the existing single-modality CGM devices and presents a unique opportunity for expanding the continuous monitoring of diabetes patients to additional biomarkers and key cardiac signals (e.g., blood pressure, heart rate, and arterial stiffness), associated with diabetes and related cardiovascular risks. This multi-modal approach not only offers a more comprehensive assessment of diabetes patients but also lays the foundation for integrating on a single device the monitoring of key vital signs along with the biosensing of multiple ISF biomarkers. Such ability to generate rich multi-source chemical-physical data is particularlyPCT Application Attorney Docket No.: 009062.8575.WO00attractive for optimal personalized inulin dosing in closed-loop diabetes system.

[0114] Example Microneedle and Ultrasound Sensor Modules

[0115] The rational selection of the specific sensing modules can depend on the target user group and corresponding diagnostic requirements.

[0116] FIG. IB illustrates the microneedle penetration from the epidermis into the dermis, using an example microneedle length of 800 pm that ensures reaching the biomarker-rich dermis layer. The transducer 116 of ultrasound sensors 112 measures the pulse-echo simultaneously from the blood vessel (FIG. 1C). Placing the flexible microneedle 102 and ultrasound sensors 112, on the flexible polyethylene terephthalate (PET) substrate provides the necessary tight skin contact and comfort, without displacing or losing skin contact during diverse daily activities, including rigorous exercise. Additional experiments were performed to demonstrate the robust skin contact of the wristband during physical activities, e.g., shown by FIG. 2. The data illustrates the mechanical integrity of the wristband under different bending angles during common outdoor activities, such as push-ups, basketball games, and jumping rope.

[0117] FIG. ID illustrates wearing the multimodal BLUE wristband 100 on a healthy participant. With the two electrocardiogram (ECG) electrode pads 124 and 126, one pad 124 from the inside is connected to the wearing hand while the opposite side pad 126 is touched with the finger from the other hand to measure the potential difference across the heart (FIG. ID). The multiplexed microneedle sensor array is worn tightly over the wrist to ensure facile and reliable skin penetration.

[0118] FIG. IE displays an inverted view of the band, illustrating the microneedle array 102 with disposable microneedles 104 with a flexible 3D-printed adaptor 110 and holder 108.

[0119] FIG. IF displays a view of the ultrasound sensors 112. The ultrasound sensors 112 are located on the top of the wrist pulses to measure the pulse-echo. For example, an array of 10 such ultrasound sensors 112 (e.g., 3 cm x 1 cm in area) were designed for addressing issues of dislocation and loss of the signals.

[0120] FIG. 1G displays an overview of the wristband 100. The individual sensor modalities have been fabricated on a PET substrate and are spatially separated from each other on the same wristband, for preventing sensor cross talks.

[0121] The smart device displays the continuous response of the individual sensors, with the simultaneous recording of the BP, ECG, and HR and three biomarkers (e.g., glucose (Glu),PCT Application Attorney Docket No.: 009062.8575.WG00alcohol (Ale), and lactate (Lac)) to provide insights into the influence of daily activities on metabolic and physiological processes (FIG. 3A-3B). By capturing and processing large amounts of personal data from the multimodal wristband, a laptop computer, tablet, or mobile device can provide a summary of personal digital health status. We can also implement machine learning (ML) prediction algorithms to alert a user to potential abnormalities.

[0122] The performance of the multimodal wristband was first demonstrated for continuous real-time glucose monitoring using an exemplary microneedle array 102 (e.g., embodied as biocompatible SU8-based MN biosensors) over 70 min and was compared to parallel testing with a blood glucose meter (BGM) and CGM at 10 min and 5 min intervals, respectively. The blood pressure and heart rate signals were collected simultaneously (FIG. 3B)). The corresponding BP and HR values were recorded simultaneously using the integrated ultrasound 112 and ECG sensors 122, respectively. A glucose-rich soft drink was taken after the initial 10 min. Note that the MN sensor 102 and CGM responded identically to the intake of the drink, with their response rising with the glucose level for 20 min and returning to baseline for a total of 50 min. The excellent agreement between these ISF-based MN glucose sensor 102 to the BGM testing is reflected by the mean absolute relative difference (MARD) of 4.68%. The CGM also displays good agreement to the BGM measurements with a MARD of 5.40%. The highly accurate continuous glucose monitoring capability of the MN sensing system is coupled with simultaneous recording of changes in the BP and HR (FIG. 3B). The integrated physical sensors show that both the BP and HR increased with the change of the blood glucose, suggesting dynamic responses to diverse daily activities. In addition, arterial stiffness, which is a critical parameter to evaluate vascular health, is also responsive to daily activities.

[0123] With the rich metabolic ISF profile captured by the multiplexed MN array and simultaneous ultrasound recording of BP and HR, the BLUE system offers the wearers continuous real-time insights into the dynamics of their physiology, toward personalized health, wellness, and nutrition. The wristband platform provides distinct advantages towards continuous monitoring of different age groups, ranging from neonates (type 1) to the elderlies (types 1, 2 or both), including the population with underlying health conditions or at-risk populations, such as cardiac, sepsis or diabetic patients (represented schematically in FIG. 4). For example, simultaneous BP and lactate measurements are important predictors of life-threatening complications from sepsis shock to open-heart surgeries. The hybrid BLUE platform can bePCT Application Attorney Docket No.: 009062.8575.WG00tailored to meet the demands of wearers from diverse populations. By combining rich metabolic ISF biochemical signals (from the multiplexed MN array) with ultrasound-based cardiovascular signals, the wristband platform offers rich dynamic insights into the health and physiological status of individuals toward maintaining their health and managing chronic diseases. Incorporation of advanced data processing clustering and classification techniques may offer timely alerts for abnormalities and prediction of the onset of diseases.

[0124] Example Microneedle Fabrication

[0125] FIG. 5 shows an example process for microneedle fabrication. For example, the flexible microneedle array can be fabricated using a combination of 3D printing, dry etching, and soft lithography. At step 502, a microneedle design was prepared via a 3D printer. At step 504, PDMS was applied on the 3D-printed MNs by 10:1 (w / w) ratio. At step 506, the PDMS was cured at 60°C for 1 hour, followed by peeling off the PDMS to obtain the PDMS I mold via micro-molding. At step 508, the resulting PDMS I mold was employed to transfer the SU8 microneedles onto the glass slide via soft lithography with an ultraviolet curing process, followed by peeling off the PDMS I mold. At step 510, an RIE dry etching process was applied to the SU8 MNs on the glass slide. After 60 min etching time, the tip sharpness of the microneedles reduced to around 10 pm with a length of 1.5mm. At step 512, the second PDMS mold (PDMS II) was applied on the etched microneedles by 10:1 (w / w) and the PDMS was cured at 60° C for 1 hour. After peeling off, the new PDMS II mold is reusable for the next fabrication process. At step 514, the PDMS II mold was employed to fabricate SU8 microneedles on a flexible tape via soft lithography with an ultraviolet curing process. At step 516, after peeling off the PDMS II mold from the flexible tape, the etched microneedles were finally fabricated on the flexible tape substrate. At step 518, the 3D-printed mask mold was added on the top of the microneedles, and the microneedles were sputtered with chromium (Cr) and platinum (Pt) on the separated counter electrode, working electrode, and reference electrode. After removing the mask mold, the microneedles were stored in a petri dish at room temperature for further modification.

[0126] We addressed the challenge of fabricating flexible SU8 microneedles with tip diameters <10pm and lengths >800 pm, as needed for penetrating the skin and reaching the biomarker-rich dermis layer. A 3D printer was employed for fabricating the first mold of the microneedles to address the length challenge and SU8-coating thickness limitation of photolithography. PDMS was applied on the 3D-printed MNs and was cured for 1 hour at 60° C.PCT Application Attorney Docket No.: 009062.8575.WG00The limited resolution of the 3D-printed microneedles resulted in a tip diameter of 215.80 + 29.21 pm. A dry etching process was thus introduced to improve the tip diameter. The second PDMS mold was cast with the etched microneedles for subsequent fabrication of the SU8 microneedles via a soft lithography technique.

[0127] FIGS. 6A-6F show SEM images of microneedles before etching and after 30, 45, and 60 minutes of etching. By increasing the etching time from 30 min to 45 and 60 min, we obtained the desirable microneedles, as shown in Table 1. In particular, the 60 min etching resulted in a tip diameter of around 5-10 pm, and a needle length of >1 mm, which offers smooth skin penetration without breaking the microneedle via biocompatible material.Table 1

[0128] After the fabrication process, SEM images taken by FEI Apreo SEM showed that the obtained microneedles within the size of 1.5 mm in length and 10.81 + 0.35 pm in the tip radius.

[0129] FIG. 8A shows an SEM image of microneedles prior to skin penetration.

[0130] FIGS. 8B-8D show SEM images of microneedles after skin penetration. The SEM images showed that after the on-body testing, the microneedles still maintain their original geometry, and the tips were not broken.

[0131] FIGS. 7A-7B show data plots showing the thickness of sputtered Cr and Pt films on microneedles. The thickness of the first Cr layer was determined by baseline of red (left) shaded area and measured thickness of green (right) shaded area which is 188.40 + 26.87 in FIG. 7A. The thickness of the first Cr layer, with second layer of Pt, was determined by baseline of red (left) shaded area and measured thickness of green (right) area which is 337.76 ± 44.08 in FIG. 7B. The film thickness was determined by Dektak stylus profiler with standard scan, length 600 pm, range 6.5 pm, profile of hill and valleys, resolution of 0.1 pm / pt, and stylus force of 3 mg.

[0132] Mechanical performance of the example sensing platformsPCT Application Attorney Docket No.: 009062.8575.WG00

[0133] FIGS. 9A-9B show photographs of the microneedles bending after sputtering of Cr (200nm) and Pt (300nm) layers, where FIG. 9A shows inward 0.35 rad bending and FIG. 9B shows outward 0.35 rad bending.

[0134] FIGS. 9C-9E show photographs of bending of flexible microneedles on tape before sputtering, where FIG. 9C shows bending of 3.14 rad (scale bar = 6.5 mm), FIG. 9D shows outward bending of 0.79 rad (scale bar = 4 mm), and FIG. 9E shows inward bending of 0.79 rad (scale bar = 7 mm).

[0135] FIG. 10A shows a photograph of a microneedle cover with SU 8 microneedle on the tape after sputtering Cr and Pt layers (scale bar = 3 mm).

[0136] FIG. 10B shows a flexible microneedle array with cover, compared to the size of a penny coin (scale bar = 5 mm).

[0137] FIG. 10C shows a flexible wristband including the ECG, microneedle holder, microneedle and ultrasound sensors, compared to the coin size (scale bar = 12 mm).

[0138] FIG. 11A shows a photo of a flexible microneedle array with cover (scale bar = 4 mm).

[0139] FIG. 11B shows a flexible microneedle with cover bent outward 0.35 rad (scale bar = 4 mm).

[0140] FIG. 11C shows a flexible microneedle on holder without cover inward bent 0.35 rad (scale bar = 4 mm).

[0141] FIG. 11D shows a microneedle holder with microneedle and cover bent 0.79 rad (scale bar = 4 mm).

[0142] FIGS. 12A-12D show schematics of microneedle skin contact at various bending angles. The flexible microneedles (FIGS. 12C-12D) are designed to bend with the skin, maintaining tight contact without compromising signal quality from the dermis layer. In comparison, rigid microneedles (FIGS. 12A-12B) are unable to maintain tight contact.

[0143] FIG. 13 shows data plots showing measured current levels, BP, and ECG waveforms while the participant performed wrist bending (up, down, left, and right) to evaluate wristband reliability. The signal acquisition across all sensing modalities remains unaffected when the wrist is bent in different directions.

[0144] The flexibility of the microneedle with tape substrate was demonstrated by an inward bending of 0.35 rad (FIG. 9A) and outward bending of 0.35 rad (FIG. 9B), along with 0.79 rad ofPCT Application Attorney Docket No.: 009062.8575.WG00outward and inward bending (FIGS. 9C-9E) and the assembly cover with the microneedles (FIGS. 11B-11D). After sputtering of Cr (188.40 ± 26.87 pm) and Pt (337.76 ± 44.08 pm) layers, the microneedles maintained their flexibility with 0.35 rad bending.

[0145] After fully bending the wristband around the subjects’ wrists, we found an average bending angle of 0.31 rad, with a minimum of 0.26 rad and a maximum of 0.35 rad across 20 participants. The adjustable mechanical properties enhanced the sensing signals, while ensuring comfort without the sensation of wearing a rigid foreign object (FIGS. 12A-12D). To evaluate further the effects of bending, we included a new experimental setup, which demonstrates that the signal acquisition across all sensing modalities remains reliable with various wrist bending and motions (FIG. 13).

[0146] Before the hands-on fabrication, the simulation of the surface forces distribution was considered for the microneedle design by COMSOL multiphysics 6.1. A force was applied on the center, with the thumb pushing the microneedles to penetrate the skin. The surface forces distribution showed the highest force on the center and the side, with most of the microneedles were in the blue area with lower forces; the design was to avoid the higher-pressure areas to achieve a good performance of the microneedle sensors.

[0147] FIGS. 9F-9O shows data plots and diagrams depicting the simulation of the microneedle flexural strength with the force of 20 N / m2on the back (FIG. 9F) and top of the microneedles (FIG. 9G). FIGS. 9H-9I show diagrams of the mesh used for the simulation. FIGS. 9J-9L show views of the simulation of the microneedles while applying the force of 20 N / m2on the center of the microneedles from the back. FIGS. 9M-9O show views of the simulation of the microneedles while applying the force of 20 N / m2on the center of the microneedles from the front.

[0148] FIGS. 14A and 14B show data plots depicting electrochemical performance of bending 1000 times, where FIG. 14A shows chronoamperometry signals measured at 0. 400. 800 and 1000 cycle counts, and FIG. 14B shows currents measured at every 200 cycles counts with 0.3 V for 60 s and error bars (n=5).

[0149] FIGS. 14C and 14D show photographs of ECG before bending and after bending 0.35 rad.

[0150] FIG. 14E shows a data plot depicting biopotential electrodes resistance measured on the two edges at every 200 cycles during the 1000 bending cycles with error bars (n=5).PCT Application Attorney Docket No.: 009062.8575.WO00

[0151] FIG. 14F shows a data plot depicting full wristband resistance (silver ink-printed connection wires) measured during 1000 cycles at resistance points of each 200 cycles with error bars (n=5).

[0152] FIGS. 15A-15F show SEM images of the screen-printed ECG before and after 1000 bending cycles. FIG. 15A shows an initial image of screen-printed ECG pad using a magnification of 800x (scale bar = 40 pm). FIG. 15C shows an initial image of screen-printed ECG pad using a magnification of 1500x (scale bar = 20 pm). FIG. 15E shows an initial image of screen- printed ECG pad using a magnification of 3000x (scale bar = 10 pm). FIG. 15B shows an SEM image after bendinglOOO cycles using a magnification of 800x (scale bar = 50 pm). FIG. 15D shows an SEM image after bendinglOOO cycles using a magnification of 1500x (scale bar = 20 pm). FIG. 15F shows an SEM image after bendinglOOO cycles using a magnification of 3000x (scale bar = 10 pm). Bending has no significant effect on the electrode pad surface, demonstrating the robustness of the screen-printed ECG.

[0153] FIGS. 16A-16C show photographs of the ultrasound sensors, before, and after 20° bending.

[0154] FIG. 16D shows data plots depicting pulse-echo measured at 0, 400, 800 and 1000 cycles numbers.

[0155] FIG. 16E shows a data plot depicting 0.35 rad. bending of the ultrasound sensors for up to 1000 times, with pulse-echo measurements every 200 cycles with error bars (n=5).

[0156] FIGS. 17A-17F show data plots of a simulation of microneedle skin penetration. FIGS. 17A-17B show the mesh of the substrate, microneedles, and skin used in the simulation. FIG. 17C shows the microneedles before force was applied to the top of the microneedles. FIGS. 17D-17F show the microneedles after force was applied to the top of the microneedles and as the microneedles penetrate the skin.

[0157] The microneedles’ surface forces distribution during penetration was studied via simulated microneedles penetrating the skin. The simulation shows 4 steps of the microneedles penetrated skin, including before penetration, after the microneedle’s tips penetrated the skin, half of the microneedle’s length penetrated the skin, and fully penetrating the skin. The highest force was on the microneedles’ tips with 2 xlO-3M N / m2with a force of 1200 N / m2. Sharper microneedle tips can result in a broken microneedle tip. The full penetration of microneedles surface forces distribution is displayed using different scale bars. The lower scale barPCT Application Attorney Docket No.: 009062.8575.WG00demonstrates the distribution of the skin surface forces, and with increasing the scale, higher scale adjustments lost the skin surface forces distribution. The simulation displays the red area on the skin, which is equivalent to higher pressure marks on the practical skin penetration.

[0158] FIGS. 18A-18L show data plots of a simulation of the microneedles fully penetrated and of the distribution of skin surface forces. FIGS. 18A-18C show views of the mesh used in the simulation. FIGS. 18D-18L show the distribution of forces at different scales.

[0159] The simulation of microneedle displacement was studied with a selected surface using 4 microneedles, and with the microneedle penetrating the skin by increasing the force. The microneedle displacement showed penetration of a 900 pm microneedle tip, with the skin displacement around the microneedle.

[0160] FIGS. 19A-19D show data plots of results from the simulation of microneedles penetrating the skin with displacement via selected surface analysis. FIG. 19A shows the selected surface (shaded area). FIG. 19B shows the displacement of the 4 microneedles penetrating the skin. FIGS. 19C-19D show further details of the displacement of a selected microneedle.

[0161] The mechanical properties of flexural strength were simulated by applying a pressure of 1000 N / m2in outward and inward directions, along with surface forces distribution during microneedle skin penetration, which demonstrated the surface stress maps, with the highest stress on the edge of the microneedle and the center of the microneedle. Therefore, the microneedles’ design avoided damage by preventing direct pressure to the high-pressure area. The required microneedle penetration pressure is around 0.1-3 N. One can find that after skin penetration, the microneedle maintains the tip integrity (FIGS. 8A-8D): the SU8 tensile strength is 60 M N / m2on the SU8 2000 series.

[0162] The robust performance of microneedles, biopotential electrodes, the full wristband, and ultrasonic array under deformation were validated by 1000 repeated 0.35 rad bending, indicating that the Pt-coated microneedles coated could maintain reliable performance on a flexible substrate (FIGS. 14A-14B). The images of FIGS. 14C-14D illustrate the flexibility of the biopotential ECG electrodes which maintain valid readings with a few resistance fluctuations of 0.5 Q (FIG. 14E), along with the full wristband maintaining a resistance of 31 Q (FIG. 14F). SEM images indicate no significant surface damage (FIGS. 15A-15F). The ultrasonic array was fabricated through transfer printing methods, and this flexible structural design allows thePCT Application Attorney Docket No.: 009062.8575.WO00bending of 0.35 rad, as shown in FIGS. 16A-16C, along with the sensor’s robustness which was assessed by time-of-flight measurements under the bending test of a 1000 repetitive bends (FIGS.16D-16E).

[0163] Crosstalk Study

[0164] FIGS. 20A-20C show photographs of three sizes of example wristbands.

[0165] FIGS. 21A and 21B show data plots showing crosstalk of the electrochemical signals of chronoamperometry (CA) to ECG and ultrasound.

[0166] FIGS. 21C and 21D show data plots showing crosstalk of ECG to the CA and ultrasound.

[0167] FIGS. 21E and 21F show data plots showing crosstalk of ultrasound to ECG and CA signals.

[0168] It is critical to avoid crosstalk effects between the different sensing modalities. To assess such potential crosstalk, we evaluated first the influence of the physiological sensors upon the performance of the electrochemical MN sensor array on the smallest size of the wristband (e.g., 125 mm). The wristbands were manufactured in three sizes: 125mm, 145mm, and 165mm (FIGS. 20A-20C). Continuous MNs current signals were thus recorded while switching the ECG and BP on / off repeatedly every 30s (FIGS. 21A-21B). By separating the ECG, BP, and MN modalities with 3 cm, the ECG and BP displayed minimal current peak noise. The crosstalk resiliency of the ECG measurements was investigated by switching the MNs and BP on / off repeatedly every 3s during the ECG measurements. The example results indicate that the ECG readings are not affected by the electrochemical and acoustic BP operations (FIGS. 21C-21D). Apparently, placing the ECG sensors 5 cm and 3 cm away from the MN and BP sensors, respectively, eliminates such cross talks. Similarly, the ultrasonic sensor is not affected by switching the MNs and ECG sensors on / off repeatedly every 3s during BP measurements (FIGS.21E-21F). By spatially separating and isolating the individual sensor modalities, the integrated wristband system offers a stable multimodal wearable sensor performance, with no apparent crosstalk.

[0169] Arterial Stiffness and Compliance Study

[0170] FIG. 22A shows data plots showing the BP waveforms before and after exercise, which induced changes in the arterial compliance (top and bottom pressure waveforms represent before and after exercise, respectively).PCT Application Attorney Docket No.: 009062.8575.WG00

[0171] FIG. 22B shows the calculation of the augmentation index (AIx) derived from the systolic peak (Pi) and augmented peak (P2) values in the BP waveform. The average augmentation index increased from 7.79% before exercise to 25.02% after exercise, suggesting decreased arterial stiffness.

[0172] The arterial compliance can be extrapolated from the BP waveform by calculating the augmentation index (AIx). The BP waveform profile exhibits variations before and after exercise, reflecting changes in the systemic vascular resistance throughout the whole arterial tree (FIG. 22A). The AIx, defined as the difference between the systolic peak and the augmented peak (P1-P2), normalized by the systolic peak (Pi), can serve as a suitable metric to characterize the arterial compliance (FIG. 22B). The average Aix increased from an initial 7.79% value before exercise to 25.02% after exercise due to vasodilation, reflecting the effect of exercise on the arterial stiffness.

[0173] Reliability and Accuracy of the Biomarker Sensing

[0174] THP-1 cells were cultivated in RPMI medium (Thermo Fisher, Waltham, USA) with a disposable portion of the bare SU 8 microneedle and after sputtering Cr + Pt film in order to determine the biocompatibility of microneedle. The MTS kit (Colorimetric, abl97010) was used to complete this procedure. In general, human macrophages THP-1 (1x106 / ml) were cultivated for 24 hours at 37 °C after being seeded into a 6-well plate with 3 ml media per well. After being UVC-sterilized for 30 minutes, the microneedle samples were submerged for 1 hour and 24 hours, to prepare for the cytotoxicity testing. Next, each well of 500 pL THP-1 cell solution was transferred to a sterile tube, and each tube was added and mixed with 50 pL of MTS kit. Following a 40-minute incubation period at 37 °C, the cytotoxicity results were evaluated at UV 490 nm using the BioTek Synergy Mx microplate reader.

[0175] FIG. 23 shows a data plot indicating the biocompatibility of the microneedles by the viability of the control sample (without microneedle) compared with SU 8 microneedles (before sputtering), and SU 8 + Cr + Pt microneedles (after sputtering). Both before and after sputtering microneedles showed biocompatibility with cell viability values similar to the control. Reliable, accurate, stable biomarker responses and biocompatibility (FIG. 23) are essential to pave the way for on-body measurements, considering potential complications of surface biofouling and enzyme stability during extended sensing operations.

[0176] FIGS. 24A-24C show data plots showing calibration graphs for GOx (Glu.), AOxPCT Application Attorney Docket No.: 009062.8575.WO00(Ale.), and LOx (Lac.) modified microneedles respectively for sensitivity test conducted from 0 mM to 10 mM (with ImM increments; a-k) in-vitro in PBS solution. The calibration graphs showed a linear response with R-squared of 0.99 at applied potential of +0.3V for 60s.

[0177] FIGS. 24D-24F show data plots showing selectivity studies for GOx, AOx, and LOx modified microneedle conducted in PBS solution in the presence of possible interference species such as uric acid (200 pM), acetaminophen (100 pM), ascorbic acid (200 pM), ethanol (1 mM), glucose (ImM), and lactate (ImM) at applied potential +0.3V for 60 s.

[0178] FIGS. 24G-24I show data plots showing the stability performance of GOx, AOx, and LOx modified microneedles measured with intervals of 5-min using glucose (ImM), ethanol (ImM), and lactate (ImM) respectively in PBS solution, at applied potential +0.3V for 60 s for 10 hours.

[0179] FIGS. 25A-25C show data plots showing calibration graphs for GOx (Glu.), AOx (Ale.), and LOx (Lac.) modified microneedles respectively for sensitivity test conducted from 0 mM to 10 mM (with ImM increments; a-k) in-vitro in artificial IFS. The calibration graphs showed a linear response with R-squared of 0.99 at applied potential of +0.3V for 60s.

[0180] FIGS. 25D-25F show data plots showing selectivity studies for GOx, AOx, and LOx modified microneedle conducted in artificial IFS in the presence of possible interference species such as uric acid (200 pM), acetaminophen (100 pM), ascorbic acid (200 pM), ethanol (1 mM), glucose (ImM), and lactate (ImM) at applied potential +0.3V for 60 s.

[0181] FIGS. 25G-25I show data plots showing the stability performance of GOx, AOx, and LOx modified microneedles measured with intervals of 5-min using glucose (ImM), ethanol (ImM), and lactate (ImM) respectively in artificial IFS, at applied potential +0.3V for 60 s for 10 hours.

[0182] Individual MN sensors - for glucose, alcohol, and lactate- were characterized in vitro for their sensitivity, selectivity, and stability in PBS (FIGS. 24A-24I) and in artificial interstitial fluid (AISF) (FIGS. 25A-25I). The chronoamperometric measurements for the in vitro characterization of the two-electrode system MNs patch (Pt vs Pt MNs) were recorded via Bluetooth using a potentiostat (Sensit BT, PalmSens; PSTrace software version 5.9). All measurements were performed in PBS and AISF solutions. The sensitivity of each of the MN sensors (glucose, lactate, and alcohol) was evaluated using chronoamperometric measurements from 0 mM to 10 mM at an applied potential +0.3 V (n=3) for 60s; the resulting calibration plotsPCT Application Attorney Docket No.: 009062.8575.WG00(insets) showed a great linearity response. The stability of each patch of glucose, lactate, and alcohol was examined using each analyte concentration of 1 mM every 5 mins for 10 hours. Last, the selectivity study was performed by adding potential interferences, common in the ISF, such as ascorbic acid (200 pM), uric acid (200 pM), and acetaminophen (100 pM), along with 1 mM of glucose, alcohol, and lactate in both PBS and AISF environments at applied potential +0.3 V (n=3) for 60s. The current signal response from the different interferences species is negligible compared to the different target analytes signal.

[0183] On-body Monitoring with a Single Daily Activity Stimulation

[0184] FIG. 26A shows real-time microneedle sensing with an initial warm-up period of 40 min. In the top panel, blood glucose levels were measured every 10 min by glucose blood meter, and a soft drink was taken after 70 min blood glucose measurement; a potential of +0.3 V was applied during monitoring and data points were collected every 0.5 second over total 150 min monitoring time. In the middle panel alcohol levels were measured every 5 min by breathalyzer, and an alcohol drink was taken after 55 min alcohol breathalyzer measurement: a potential of +0.3 V was applied during monitoring and data points were collected every 0.5 second over a total 150 min monitoring time. In the bottom panel, blood lactate levels were measured every 10 min by a lactate blood meter; a 20 min exercise was started after 60 min using a potential of +0.3 V; data points were collected every 0.5 second over a total 150 min monitoring time.

[0185] The body normally maintains specific metabolite concentration ranges, such as 3.9-5.5 mmol / L glucose and 0.5-2.2 mmol / L lactate. The continuous individual measurement of glucose, alcohol, and lactate was investigated using drinking a soft drink, drinking wine, and exercising with vigorous-intensity exercise intensity, respectively. Excellent agreement was observed between the trends of the on-body wristband multiplexed MN data and the readings of the commercial metabolite blood meters (FIG. 26A). The results suggest that the individual biomarker MN sensors offer highly accurate and reliable long-term monitoring.

[0186] The example hybrid BLUE platform was tested in multiple on-body trials on healthy subjects who performed various daily activities. The wristband was thus used to acquire multiple chemical signals, including glucose, alcohol, or lactate, and physiological signals, including BP, HR, and arterial stiffness, to examine the complex response of subjects to different stimulations, e.g., taking food, alcohol, or exercises, and skin recovery rate.

[0187] The on-body human testing of the flexible micro needle’s patches (e.g., for glucose,PCT Application Attorney Docket No.: 009062.8575.WO00alcohol, and lactate monitoring) was performed on eleven healthy subjects. Once the wristband sensor was placed on the subject, the readout stabilization of the device took around 30 min to 60 min (the time varies from one subject to another). Afterwards, the on-body testing was started by recording the baseline of the analyte under study and, following with this, the subject began the necessary activity for each analyte. All measurements were performed by using chronoamperometry (SensitBT, PalmSens; PSTrace software version 5.9) at an applied potential +0.3 V and transferring the digital data via Bluetooth to computer. The total time to complete each individual analyte study was 3 hours and 8 hours. For simultaneous detection of two and three multiple analytes, the subject followed a similar activity, and the test took place over 7 hours.

[0188] The activities performed to cause variation of the different analytes’ levels are detailed below:

[0189] Glucose monitoring: In the case of one-experiment conditions, the human subjects drank a soft drink (Coca-Cola original, 500 mL). For the two-experiment conditions, the first increase of glucose was pursued by providing the subjects a full meal that comprised a hamburger, French fries, and Coca-Cola original (500 mL). After the glucose levels decreased, the subjects had a high-sugar dessert (vanilla ice-cream) to induce a second glucose peak. The experiments based on one or two conditions ended when the glucose levels returned back to normal levels.

[0190] Lactate monitoring: Under one-experiment conditions, the subjects performed 20 minutes of stationary biking, while in the case of two-experiment conditions, the subjects repeated the physical activity when the lactate levels decreased close to the normal levels. The experiments based on one or two conditions ended when the lactate levels returned back to normal levels.

[0191] Alcohol monitoring: All human subjects consumed a glass of wine (The Collection Red Blend Wine, 13.5% vol. alcohol, 150 mL). In some conditions, a second increase of alcohol levels was pursued by providing the subjects with a glass of alcohol containing glucose (Kahlua, 16% vol. alcohol, 150 mL, 16.67 mg of caffeine, 405.41 kcal and 60.81 grams of carbohydrates.). Experiments involved monitoring alcohol alone, or both glucose with alcohol simultaneously, by monitoring changes in the corresponding current signals with intake of a glass of wine.PCT Application Attorney Docket No.: 009062.8575.WO00

[0192] To identify the effects of each biomarker on key physical vital signs, such as BP, HR, and arterial stiffness, we carried out different single daily-activity stimulations on each metabolite (glucose, alcohol, and lactate) while monitoring physical vital signs using healthy subjects. In glucose studies, fasting is one of the methods to identify type II diabetes and its benefits of reducing cardiovascular risk. Notably, glucose always reaches the highest level after a meal, along with the different decline rates of the glucose level, BP, and arterial stiffness, which vary based on individual health conditions. Thus, the continuous hybrid-sensor recording can reflect their health condition and provide more personal information for clinical diagnosis. The coupled alcohol-physical monitoring was performed on subjects who have distinct alcohol consumption habits with no significant effects, but the recordings of the non-drinker subject displayed increased BP and HR with reduced AIx. While it is difficult to distinguish the difference between drinking habits and damage caused to healthy subject in a few hours of monitoring, the data indicate the importance of the personally customized recording of the history because the damage from alcohol is progressively worse and may reflect symptoms relevant to the BP and arterial stiffness. For the coupled lactate-physical monitoring on a nonactive subject and an athlete, the investigation presented post-exercise hypotension by stiffness reduction with increasing vessel diameter in non-active subject. Exercise-induced hypertension and arterial stiffness are common diseases for athletes who ought to maintain attention to their physical vital signs while training. The glucose, lactate, and alcohol data, collected by the wristband sensors, were validated at 10 min intervals using a commercial blood glucose meter (ACCU-CHEK), blood lactate meter (NOVA Biomedical), and breathalyzer (BACtrack S80 Pro), respectively.

[0193] FIGS. 26B and 26C show data plots showing glucose monitoring with subject 1 and subject 2, a soft drink was taken after 20 min of baseline; the blood glucose levels were measured every 10 min by glucose blood meter; MNs used a potential of +0.3 V during the 90 min monitoring time.

[0194] FIGS. 26D and 26E show data plots showing alcohol monitoring with subject 3 and subject 4, an alcohol drink was taken after 15 min of baseline recording; the alcohol levels were measured every 5 min by breathalyzer; MNs used a potential of +0.3 V with total 90 min monitoring time.

[0195] FIGS. 26F and 26G show data plots showing lactate monitoring with subject 5 andPCT Application Attorney Docket No.: 009062.8575.WO00subject 6, a 20 min exercise started after a recording the baseline for 20 min; blood lactate levels were measured every 10 min by lactate blood meter; MNs used an applied a potential of +0.3 V over the 90 min monitoring time.

[0196] FIG. 27 shows microneedle skin penetration and recovery during 15 min on different subjects. Each subject wears the microneedle for up to 3 hours; the recovery rate is different for each subject and normally the microneedle penetration marks disappear after 15 min.

[0197] The measurement of fasting and postprandial glucose levels is important for diabetic subjects toward adjusting their medication, diet, and lifestyle. Hypoglycemia can often be associated with symptoms of hunger, dizziness, rising HR, confusion, diaphoresis, and syncope, where the intake of sugar after fasting may induce postprandial hyperemia that leads to an increase of arterial stiffness. The ability of the BLUE platform to record a person’s response to the same food intake was examined by monitoring the ISF glucose level along with the physiological signals, using a healthy subject who consumed the same food in two separate on-body trials after fasting for >8 h overnight versus taking a meal within 4 h before the trial.

[0198] FIGS. 28A and 28B show data plots of ISF glucose concentrations from a non-fasting subject and fasting subject, respectively, with BP, and HR responses before and after the intake of a complete meal.

[0199] FIGS. 28C and 28D show data plots showing 8 second blood pressure signals from non-fasting and fasting subjects before and after meal consumption.

[0200] FIGS. 28E and 28F show data plots showing AIx for non-fasting and fasting subjects calculated at 0 mins and 30 mins with a mean value and error bars (n=7).

[0201] While showing similar preprandial glucose levels (87 mg / dl and 86 mg / dl), the glucose levels rose to 166 mg / dl with over 8 h of fasting and to 133 mg / dl without fasting, after taking the same food with a different meal frequency. Simultaneously, the pre-prandial BP level at fasting (e.g., 107 / 59 mmHg) was lower than that without fasting (e.g., 113 / 62 mmHg), while the artery is less stiff after fasting (e.g., AIx of 0.37 + 0.04) compared to the non-fasting case (e.g., AIx of 0.26 ± 0.06). The lower stiffness with fasting may be caused by the dilation of the vascular system to accommodate the blood volume. The latter may be caused by the vascular systems being dilated to accommodate the blood volume depletion, reflecting that those fasting benefits the elasticity of the vascular system, which can potentially mitigate delay vascular aging and reduce cardiovascular risks. After the food intake, both trials showed an increase in BP,PCT Application Attorney Docket No.: 009062.8575.WG00with the fasting group displaying a larger BP increase (e.g., 132 / 80 mmHg) than the non-fasting group (e.g., 127 / 71 mmHg). This investigation demonstrates the interplay of blood glucose, HR, and BP under non-fasting and fasting conditions. In both trials, the MARD of the MN glucose sensor versus BGM was 2.16% (e.g., 26 paired data points), reflecting the high accuracy of the MN sensing platform.

[0202] Ingestion of alcohol is associated with a decrease in BP due to its vasodilation effect. Yet, extended, excessive alcohol ingestion is shown to significantly increase BP and arterial stiffness. Furthermore, alcohol ingestion often has complex interplay with different diseases. The diabetic population has a higher risk of hypertension even from moderate ingestion of alcohol. Alcohol tolerance also varies between individuals. For non-drinkers, alcohol consumption can cause more narrowing of the blood vessels which leads to increased BP and HR. Thus, the integrated wristband can be used to track the individual’s alcohol intake along with the corresponding cardiovascular responses. Here, two on-body trials were conducted comparing the response of a healthy habitual drinker and a non-drinker’s response to an identical alcohol-ingestion stimulation.

[0203] FIGS. 29A and 29B show data plots showing ISF alcohol levels from a non-drinker and drinker subject, respectively, with BP, and HR signal responses collected after the consumption of a glass of wine (150ml 13.5% alcohol) for 140 min.

[0204] FIGS. 29C and 29D show data plots showing 8 second blood pressure signals from non-drinker and drinker subjects before and after consuming a glass of wine.

[0205] FIGS. 29E and 29F show data plots showing calculated AIx values from non-drinker and drinker subjects at 0 mins and 30 min with a mean value and error bars (n=7).

[0206] The wristband results were compared with a breathalyzer and a commercial BP monitor at 10 min. The BP waveform before and after the alcohol intake indicates elevated BP level in both subjects, while the AIx indicates the non-drinker has significant arterial stiffness increase compared to the drinker subject.

[0207] FIGS. 29A and 29B display the ISF alcohol profiles for two subjects over two hours after drinking an equal amount of alcohol. The drinker and non-drinker demonstrated the blood alcohol concentration dropping at 3.29 mmol / hour and 3.93 mmol / hour, with the highest blood alcohol level of 0.043 % and 0.053 %, respectively. Such MN alcohol sensing leads to a MARD of 8.84 % compared with a commercial breathalyzer (26 paired data points). Note that inPCT Application Attorney Docket No.: 009062.8575.WG00response to the drinking, the non-drinker has shown a rising BP from 78 mmHg to 109 mmHg, and of HR from 62 bpm to 70 bpm, along with reduced AIx by vasoconstriction from 0.20 ± 0.02 at 0 min to 0.13 ± 0.04 at 30 min. In contrast, drinking has a negligible effect on the BP, HR, and arterial stiffness of the drinker. The non-drinker has a lower arterial stiffness than the drinker (0.17 ± 0.02), consistent with the effect of alcohol consumption upon increasing arterial stiffness. Overall, in this trial, the wristband device provided real-time quantitative alcohol. BP, HR, and arterial stiffness data for comprehensive assessment of the individual’s response to alcohol consumption.

[0208] Vigorous-intensity daily exercise has shown benefits in reducing the onset of various cardiovascular diseases. Exercise induces rapid metabolic and cardiac changes, with the responses highly dependent on one’s fitness and activity levels. In addition to HR and BP, lactate is one of the most commonly tracked indicators that reflects one’s training intensity or related diseases of cardiogenic shock, liver failure, and sepsis. High-intensity training improves the athletic performance by increasing the indicators of lactate threshold, which relates to the lactate clean rate. To demonstrate the ability to track individuals with different fitness levels, the athlete and non-athlete conducted a 20 min of intense exercise, while comparing their continuous MN lactate response to that of a commercial blood lactate monitor. The non-athlete subjects exercised less than 3 days per week, 30-60 min per day with a total of less than 6-12 hours per month. In contrast, the athlete subjects have 4 hours of training per day, 20 hours per week with a total of 80 hours per month.

[0209] FIGS. 30A and 30B show data plots showing ISF lactate levels from a non-athlete subject and athlete subject, respectively, with BP, and HR signal responses collected after within 20 min of vigorous-intensity exercise.

[0210] FIGS. 30C and 30D show data plots showing 8 second blood pressure signals from a non-athlete and athlete before and after exercise.

[0211] FIGS. 30E and 30F show data plots showing calculated AIx values at 0 min and 30 min with mean value and error bars (n=7).

[0212] The wristband results were compared with a blood lactate meter and an arm BP monitor every 10 min. The BP waveform, before and after the exercise, indicates elevated BP level in both subjects, while the AIx indicates that the non-athlete has significant larger increase of arterial stiffness than the athletic.PCT Application Attorney Docket No.: 009062.8575.WG00

[0213] The non-athlete exhibits a greater increase in the lactate level from 1.78mM to 8.67mM after 10 min of exercise, and to 10.22 mM after 20 min of exercise (FIGS. 30A-30B). In comparison, the lactate level of the athlete rose from 2.89mM to 6.33mM after 10 min exercise and to 8.56 mM after 20 min exercise. After stopping the exercise, the lactate level decreased for both subjects, with that of the non-athlete dropping to 2 mM lactate within 10 min and to the rest baseline of 2.89 mM after 30 min rest. In contrast, the athletic subject displayed a faster lactate clearance rate, dropping to 4.89 mM within 10 min and to the rest baseline (e.g., 2.78 mM) within 20 min. Along with those real-time lactate measurements, the BLUE platform offers convenient continuous monitoring of key physiological signals, thus shading simultaneous insights into the cardiac and metabolic effects of the exercise. For example, after 20 min exercise, the athlete demonstrated a higher BP of 179 / 96 mmHg than the non-active one (e.g., 131 / 49 mmHg), along with a lower HR of 115 bpm compared to that (e.g., 128 bpm) of the non-athlete. The non-athlete displayed a reduced BP (e.g., 102 / 62 mmHg at 60 min to 98 / 47 mmHg at 140 min) and arterial stiffness (e.g., AIx of 0.19 ± 0.04 to 0.32 ± 0.04) after 10 min of exercise. The wristband device can track the BP dynamics during exercise (e.g., as high as 179 / 96 mmHg for the athletic subject and post exercise hypotension 131 / 49 mmHg for the non-athlete), indicting its promise to serve as an effective tool for alerting exercise-related hypertension and post exercise hypotension. The lactate MN sensing provided a MARD of 7.97% compared to the blood lactate meter (e.g., 26 paired data points).

[0214] FIG. 31 A shows a photograph of the on-body experimental setup. The wristband is connected to the compact wireless potentiostat (Sensit BT) and to the wireless ultrasound circuit via a micro-coax cable.

[0215] FIG. 3 IB shows a schematic of an example experimental setup. Micro-coax wires are employed to connect the microneedles from the wristband to the potentiostat and digital data is transferred via Bluetooth to the computer.

[0216] On-body monitoring with multiple daily-activity stimulations

[0217] FIG. 32A shows data plots showing glucose monitoring with before a meal of 30 min and after a meal of 3 hours via two stimulations, first a meal taken at 60 min and second a cup of iced tea consumed at 170 min. The data plots show a comparison of ISF MN glucose monitoring (every 1 s, red line) against glucose measurements with CGM (every 5 min, green dotted line) and BGM (every 10 min, black points), along with simultaneous monitoring of BP and HR.PCT Application Attorney Docket No.: 009062.8575.WG00

[0218] FIG. 32B shows the subject’s 8-second BP data recorded before a meal at 60 min, after a meal at 70 min, before a cup of tea at 170 min, and after a cup of tea at 180 min.

[0219] FIG. 32C shows the subject’s AIx calculated at 60 min, 70 min, 170 min, and 180 min with mean value and error bars (n=7).

[0220] FIG. 32D shows the Parkes error grid of MN versus BGM (106 points).

[0221] FIG. 32E shows the Parkes error grid of CGM versus BGM (72 points) with all glucose monitoring data.

[0222] FIG. 33 A shows data plots of continuous (70 min) glucose monitoring of subject 1 and subject 2. For subject 1 (top panel), a soft drink was taken after 10 min of baseline; the blood glucose levels (black points) were measured every 10 min by a glucose blood meter, CGM (green dotted line) was measured every 5 min, and MNs glucose data points (red solid line) were collected every 0.5 seconds by applying a potential of +0.3 V. The MARD of MNs to blood glucose meter is 4.68% and MNs to CGM is 6.03%. For subject 2 (bottom panel), a soft drink was taken after 20 min of baseline; the blood glucose levels (black points) were measured every 10 min by a glucose blood meter, CGM (green dotted line) was measured every 5 min, and MNs glucose data points (red solid line) were collected every 0.5 seconds by applying a potential of +0.3 V. The MARD of MNs to blood glucose meter is 1.98% and MNs to CGM is 7.22%.

[0223] FIG. 33B top panel shows data plots showing continuous (280 min) glucose monitoring of subject 3 with 90 min of warm-up period and 30 min of baseline, a meal was taken after 120 min of monitoring time and an ice cream was taken after 220 min of monitoring time; the blood glucose levels (black points) were measured every 10 min by a glucose blood meter from 90 min to 280 min, CGM (green dotted line) was measured every 5 min from 85 min to 280 min, and MNs glucose data points (red solid line) were collected every 0.5 seconds by applying a potential of +0.3 V. The MARD of MNs to blood glucose meter is 4.43% and MNs to CGM is 7.27%. For subject 4 (middle panel), continuous (250 min) glucose monitoring was performed with 30 min of warm-up period and 30 min of baseline recording; a meal was taken after 60 min of monitoring time and an ice tea was taken after a 170 min monitoring time; the blood glucose levels were measured every 10 min by a glucose blood meter from 30 min to 250 min (black points), the CGM response was measured every 5 min from 30 min to 250 min (green dotted line), while the MNs glucose data points were collected every 0.5 seconds by applying a potential of +0.3 V (red solid line). The MARD of MNs to blood glucose meter is 4.67% andPCT Application Attorney Docket No.: 009062.8575.WG00MNs to CGM is 5.90%. For subject 5 (bottom panel), continuous (300 min) glucose monitoring was performed with 55 min of warm-up period and 30 min of baseline recording; a meal was taken after 85 min of monitoring time and an ice cream was taken after 185 min of monitoring time; the blood glucose levels (black points) were measured every 10 min by a glucose blood meter from 55 min to 290 min, CGM (green dotted line) was measured every 5 min from 0 min to 300 min; the MNs glucose data points (red solid line) were collected every 0.5 seconds by applying a potential of +0.3 V. The MARD of MNs to blood glucose meter is 2.40% and MNs to CGM is 6.49%.

[0224] The example BLUE system was tested on healthy subjects performing diverse activities for 220 min and 400 min to assess and understand the body response during such different daily stimulations. The performance and accuracy of the wristband MN for continuous glucose monitoring was compared with that of commercial CGM and BGM via two glucose-rich stimulations, along with simultaneous measurements of BP, HR, and arterial stiffness (FIG. 32A) Following the initial stabilization and a 30-minute baseline recording, the glucose levels started to increase with the food intake at 60 min, while the BP and HR increased from 110 / 62 mmHg and 58 bpm (e.g., at 60 min) to 129 / 78 mmHg and 71 bpm (e.g., at 70 min), respectively, and with the averaged AIx decreasing from 0.211+ 0.05 (at 60 min) to 0.175 + 0.06 (at 70 min). The glucose level reached the highest level of 156 mg / dL (8.67mM) at 100 min, along with BP and HR of 113 / 59 mmHg and 76 bpm. Subsequently, the glucose readings of the three systems decreased with time, reaching 106 mg / dL at 170 min, along with BP and HR readings of 116 / 64 mmHg and 70 bpm, respectively, and an averaged AIx increasing to 0.223 + 0.03. Upon intake of iced tea at 170 min the glucose started to increase again, while the BP and HR increased to 127 / 69 mmHg and 71 bpm with an averaged AIx decreased to 0.202 + 0.04 (180 min). Overall, the MNs displayed similar glucose trends compared to the corresponding blood glucose levels and CGM profiles, with good accuracy, as indicated from the Parkes error grids. The first grid demonstrates comparison of the MNs glucose measurements with those of BGM, with all 158 points (100%) in zone A (FIG. 32D). The second grid compares the CGM measurements with the BGM readings, with all (100%) of the points in zones A+B (71 points), including 98.59% in zone A and 1.41% in zone B (FIG. 32E). The MARD of the MNs glucose sensor against the CGM was 6.58% (71 paired data points with blood meter) while that against blood was 3.70% (158 paired data points with blood meter). The increasing BP and HR after the meal can bePCT Application Attorney Docket No.: 009062.8575.WO00attributed to an increased blood supply, as the gastrointestinal tract increasingly splanchnic oxygen is consumed for digestive activities, which increases blood flow rate to support the oxygen requirements. Similarly, intake of the caffeine-containing iced tea stimulates the central nervous system and leads to higher heart contractility, HR and BP.

[0225] FIGS. 34A-34C show simultaneous monitoring of ISF glucose, alcohol, lactate levels, along with BP, HR, and arterial stiffness, for 400 min with the intake of a meal at 20 min, alcohol drink at 80 min, and 20 min of vigorous-intensity exercise at 200 min on a healthy subject.

[0226] FIG. 34A shows data plots showing the subject acquired ISF glucose levels, alcohol levels, and lactate levels along with BP and HR over 400 min. Meanwhile, blood glucose and lactate levels were measured via a blood meter every 20 min, while alcohol levels via breathalyzer every 20 min. The additional points were measured every 10 min, from 80 min to 120 min for alcohol and every 10 min from 200 min to 240 min for lactate and glucose. The subject acquired ISF glucose levels, alcohol levels and lactate levels along with BP and HR over 400 min. Meanwhile, blood glucose and lactate levels were measured via a blood meter every 20 min, while alcohol levels via breathalyzer every 20 min. The additional points were measured every 10 min, from 80 min to 120 min for alcohol and every 10 min from 200 min to 240 min for lactate and glucose.

[0227] FIG. 34B shows data plots showing the 8-second BP data recorded at 20 min, 30 min, 80 min, 90 mins, 200 min, 220 min, and 240 min during the different intakes and stimulations.

[0228] FIG. 34C shows a data plot showing AIx calculated at 20 min, 30 min, 80 min, 90 mins, 200 min, 220 min, and 240 min, which is before and after each stimulation with mean value and error bars (n=7).

[0229] FIG. 34D shows a data plot showing correlation of the total 90 paired MN alcohol measurements versus a commercial breathalyzer.

[0230] FIG. 34E shows a data plot showing correlation of the total 123 paired NM lactate measurements versus blood lactate measurements on the commercial meter.

[0231] FIG. 34F shows a data plot showing correlation of the total 175 paired SBP and DBP measurements based on the US sensor versus a commercial BP device.

[0232] FIGS. 35A-35I show data plots showing two-hour monitoring conducted to assess the impact of various caffeine drinks on glucose levels and BP.PCT Application Attorney Docket No.: 009062.8575.WO00

[0233] FIGS. 35A-35C shows data plots for subject 1, who took a French vanilla coffee after 20 min of baseline with 200 mg caffeine, 49g carbohydrate, and 260 calories. Blood glucose levels (glucose meter), blood pressure, and heart rate were measured every 10 min for total 140 min with arterial stiffness at 0 min and 30 min. MNs relied on an applied potential of +0.3 V and a monitoring time of 140 min.

[0234] FIGS. 35D-35F show data plots for subject 2, who consumed an energy drink after 20 min of baseline with 114 mg caffeine, 40g carbohydrate, and 160 calories. Blood glucose levels (glucose meter), blood pressure, and heart rate were measured every 10 min for total 140 min with arterial stiffness at 0 min and 30 min. MNs used a potential of +0.3 V and a total of 140 min monitoring time.

[0235] FIGS. 35G-35I show data plots for subject 3, who consumed an iced vanilla coffee after 20 min of baseline with 75 mg caffeine, 53g carbohydrate, and 290 calories. Blood glucose levels (glucose meter), blood pressure, and heart rate were measured every 10 min for total 140 min with arterial stiffness at 0 min and 30 min. MNs used an applied potential of +0.3 V and a total of 140 min monitoring time.

[0236] FIG. 35J shows a data plot comparing caffeine content of various drinks. The drinks represent different levels of caffeine content: Coffee I is French vanilla coffee, boasting 200 mg of caffeine, while an energy drink contains 114 mg of caffeine, and the iced coffee (a Frappuccino vanilla coffee), offers a milder 75 mg of caffeine.

[0237] FIGS. 36A-36C show data plots showing extended simultaneous in vivo monitoring of blood pressure, along with glucose. During a 400-minute period, ISF glucose, blood pressure, and heart rate parameters were recorded (FIG. 36A). Consumption of food occurred at 40 min, followed by ice cream at 240 min. The wristband results were compared with commercial BGM at 20 min and arm blood pressure monitor at 10 min. Blood pressure data signals, lasting 8 seconds each, were recorded at 30, 40, 240, and 250 minutes (FIG. 36B). Arterial stiffness, as represented by AIx values, was calculated at 30 min, 40 min, 240 min, and 250 min (FIG. 36C).

[0238] The BLUE platform provides continuous simultaneous multi-channel sensing based on MNs for glucose, lactate, and alcohol and ultrasound transducers and electrodes for BP, HR, and arterial stiffness (FIGS. 34A-34C). This allows us to assess the interacting chemicalphysical response to different stimulations. We studied the correlations between the dynamically-changing levels of the three different biomarkers and the physiological signalsPCT Application Attorney Docket No.: 009062.8575.WO00during diverse daily activities over 6.5 hours. The results show that the intake of the meal (at 30 min) increased the glucose and BP levels while reducing the AIx (e.g., increased arterial stiffness). Consuming a glass of sugar-rich alcohol (at 85 min) led to increasing glucose and alcohol levels, as well as to higher BP. Food in the stomach accounts for the decreasing alcohol adsorption rate. A 20 min of vigorous-intensity exercise (2 hours after the alcohol intake) led to a significant increase of the lactate level, BP and HR, while reducing the glucose level to 66 mg / dL and increasing the AIx. Additionally, consumption of different caffeine drinks (containing 200 mg, 114 mg, and 75 mg caffeine, FIG. 351) and of an ice cream (FIGS. 36A-36C) also stimulated the BP and HR. These example results illustrate that the caffeine intake results in different effects on different individuals. FIGS. 34D and 34E display the accuracy of the individual biomarker MN signals versus the commercially measured data obtained with BLM and breathalyzer, while FIG. 34F shows the accuracy of the ultrasound BP measurements versus a commercial arm BP monitor. The corresponding r2values for the BP, alcohol, and lactate data (0.98. 0.98, and 0.98, respectively) reflect the high accuracy and linearity of the BLUE platform compared to established gold standards.

[0239] FIGS. 37A-37F and 38A-C show data plots depicting example results of extended simultaneous in vivo monitoring of BP along with multi biomarkers in high-risk pre-diabetes during different daily activities.

[0240] FIGS. 37A-37C show data plots directed to a high-risk pre-diabetes subject 1.

[0241] In FIG. 37A, during a 370-minute period, ISF glucose, blood pressure, and heart rate parameters were recorded. Consumption of food occurred after 30 min of baseline, followed by drink at 150 min and exercise at 250 min. The wristband results were compared with commercial BGM at 20 min and arm blood pressure monitor at 10 min.

[0242] FIG. 37B shows data plots showing blood pressure data signals, lasting 8 seconds each, recorded at 30, 40, 150. 160, 250, 270 and 360 minutes.

[0243] FIG. 37C shows a data plot showing arterial stiffness, as represented by AIx values, calculated at 30 min, 40 min, 150 min, 160 min, 250min, 270 min, and 360 min.

[0244] FIGS. 37D-37F show data plots directed to a high-risk pre-diabetes subject 2.

[0245] FIG. 37D shows ISF glucose, blood pressure, and heart rate parameters recorded over a 370-min period. Consumption of food occurred after 30 min of baseline, followed by drink at 140 min and exercise at 230 min. The wristband results were compared with commercial BGMPCT Application Attorney Docket No.: 009062.8575.WO00at 20 min and arm blood pressure monitor at 10 min (upper panel).

[0246] FIG. 37E shows blood pressure data signals, lasting 8 seconds each, recorded at 30, 40, 140, 150. 230, 250, and 360 minutes.

[0247] FIG. 37F shows arterial stiffness, as represented by AIx values, calculated at 30 min, 40 min, 140 min, 150 min, 230 min, 250 min, and 360 min.

[0248] FIGS. 38A-38C show data plots directed to a high-risk pre-diabetes subject 3.

[0249] FIG. 38A shows ISF glucose, blood pressure, and heart rate parameters recorded over a 300-minute period. Consumption of food occurred after 30 min of baseline, followed by exercise at 210 min. The wristband results were compared with commercial BGM at 20 min and arm blood pressure monitor at 10 min (upper panel).

[0250] FIG. 38B shows blood pressure data signals, lasting 8 seconds each, recorded at 30, 40, 170, 210, 220, 230, and 300 minutes.

[0251] FIG. 38C shows arterial stiffness, as represented by AIx values, calculated at 30 min, 40 min, 170 min, 210 min, 220 min, 230 min, and 300 min.

[0252] Table 2 shows a comparison of different subjects’ BMI, body energy consumption, blood lactate level related to lactate threshold and lactate clearance rate. The table provides quantitative data for each subject, such as body mass index (BMI), and body energy compositions (J / s) with blood lactate levels after 20 min exercise and after 20 min rest. These metrics were obtained using the following equations, which provide an objective basis for classification:Body mass indexJBody energy consumption (BEC): Calories

[0253] To ensure proper comparison across subjects, we monitored and controlled the exercise intensities using the same level of resistance of the stationary bike. Vigorous intensity was defined as the target heart rate range during the activity, which was around 77% to 93% of the subject’s maximum heart rate (119 to 144 bpm), and the 20-minute bike consumed around 200- 300 calories, which was calculated and provided by the stationary bike. In addition, muscle and fat mass are generally related to the personal lactate threshold value, with the personal lactate threshold value is inversely proportional to the body mass. Therefore, higher BMI resultsPCT Application Attorney Docket No.: 009062.8575.WO00in lower lactate threshold values and increasing blood lactate levels after 20 min of exercise (high-risk pre-diabetic subject in FIG. 37 A compared to FIG. 34A). In FIGS. 30A-30F, the athlete and non-athlete have similar body energy consumption at the same normal BMI range, but the non-athlete has lower lactate threshold with higher blood lactate level after 20 min exercise and a lower lactate clearance rate with higher lactate level after 20 min rest (see Table 2). Comparing the healthy and high-risk pre-diabetic subjects in FIG. 34A and FIG. 37D, the high-risk pre-diabetic subject has lower lactate clearance rates, from 7.89mM to 6.44mM after 20 min rest. All three high-risk pre-diabetic subjects have lower lactate clearance rates.Table 2

[0254] The healthy subjects, e.g., college students, who may be at high-risk for pre-diabetes stage, did not feel sick or have any symptoms before the on-body experiments, which further highlight the importance of this multimodal sensor. Additionally, pre-diabetes population have a 50% risk of developing diabetes within five years and face increased risks of related chronic conditions similar to diabetes stages. The high-risk pre-diabetes subjects were diagnosed through one of this two condition (1) impaired glucose tolerance (IGT) with glucose level of 2 hours at 140-199 mg / dL or (2) impaired fasting glucose (IFG) with fasting glucose level at 100—PCT Application Attorney Docket No.: 009062.8575.WO00125 mg / dL.

[0255] Comparing the high-risk pre-diabetes subjects (FIGS. 37A-F and 38A-38C) to the healthy subject (FIG. 34A), the healthy individual maintained stable lactate levels during the intake of meals and alcohol, with lactate levels returning to baseline within 30 minutes of postexercise. The high-risk subjects had low lactate clearance, with elevated lactate during the intake of meals and alcohol, taking over 60 minutes to normalize after 20 min of exercise. Additionally, the BP of high-risk subjects was significantly increased during meals and alcohol and maintained at 10 mmHg higher.

[0256] Initial fasting glucose levels were 102.33 mg / dL (subject 1 (FIG. 37A), BPs of 98 / 62 mmHg and AIx of 0.16 + 0.02), 112.75 mg / dL (subject 2 (FIG. 37D), BPs of 117 / 63 mmHg and AIx of 0.23 ± 0.05), and 97.5 mg / dL (subject 3 (FIG. 38A), BP of 135 / 99 mmHg and AIx of 0.16 ± 0.01). To compared with healthy subject 87.00 mg / dL (FIG. 28A), BPs of 112 / 59 mmHg and AIx of 0.37 ± 0.04), all three high-risk pre-diabetes subjects have higher arterial stiffness during fasting. Based on the two conditions, subjects 1 and 2 were diagnosed with the condition IFG, while subject 3 had high-risk with the condition IFG and had the highest BP (high-risk prehypertension). Post-meal, the first subject indicated the highest glucose level and glucose peaked at 240 mg / dL (subject 1, BPs of 127 / 66 mmHg and AIx of 0.23 ± 0.02), 161 mg / dL (subject 2, BPs of 138 / 79 mmHg and AIx of 0.13 ± 0.01), and 168 mg / dL (subject 3, BPs of 160 / 104 mmHg and AIx of 0.2 ± 0.01). Subject 1 had glucose above 140 mg / dL for 140 minutes was diagnosed with the condition IGT, while the others were elevated for 70 minutes with high-risk with the condition IGT. Additionally, subject 3 still had a high BP of 149 / 103 mmHg after 30 min of meal.

[0257] In the alcohol-after-meal experiment, only two subjects were allowed to drink alcohol. After drinking 200 ml of alcohol, subjects 1 and 2 had glucose levels dropped to around 110 mg / dL with increased BP to 137 / 100 mmHg (subject 1 AIx of 0.15 ± 0.02) and 147 / 96 (subject 2 AIx of 0.10 ± 0.05) and lactate levels of 38 mg / dL (subject 1). After 20 min exercise, glucose fell to 60-90 mg / dL, while lactate rose to 70-80 mg / dL, and subject 3 had the highest BP peak at 180 / 98 mmHg. After 50 minutes, glucose returned to 80-100 mg / dL for all three subjects (subject 1 AIx of 0.37 ± 0.04, subject 2 AIx of 0.37 ± 0.07, and subject 3 AIx of 0.38 ± 0.03), along with subject 3 had decreasing BP to 129 / 77 mm Hg. Compared to the initial BP of 137 / 99 mmHg in Subject 3, exercise significantly contributed to the reduction of BP and benefit toPCT Application Attorney Docket No.: 009062.8575.WG00reduce the risk of hypertension.

[0258] These example findings suggest that lifestyle changes, particularly increased exercise, can improve glucose levels and BP in high-risk pre-diabetes subjects. Moreover, alcohol may lower glucose levels but is not a recommended method of managing glucose. Without such lifestyle changes for the high-risk pre-diabetes subjects, there is a significant risk of progressing to diabetes or hypertension, especially for subject 1 who is required to track glucose levels more frequently and suggested to consult a doctor.

[0259] The pre-diabetic subjects, discussed in FIGS. 37A-37F and 38A-38C, experience health conditions and issues similar to diabetic ones, including high blood pressure, lower lactate clearance, and increased arterial stiffness. Lactate levels, blood pressure, and arterial stiffness are significantly influenced by meals, drinks, and exercise. Elevated lactate levels can harm the body by decreasing blood pH and cause serious damage to the organs. Additionally, high blood pressure and arterial stiffness are diabetes-related indicators of cardiovascular risks. The BLUE system addresses several critical gaps in the current management of pre-diabetes and diabetes. First, it can provide rapid feedback on the effect of lifestyle changes, helping to maintain motivation. Second, its ability to quantify improvements in metabolic and physiological signals can offer users an instant gratification. Third, it can provide timely alerts towards early intervention for preventing the progression from pre-diabetes to diabetes. Finally, it can collect personalized multi-source rich data to generate customized disease management plans.

[0260] The real-time multimodal platform offers individuals with high-risk diabetes the opportunity to modify their lifestyles through feedback that reflects changes in their health status. The system offers tracking capabilities for patients with pre-diabetes and diabetes, enabling them to manage their insulin dosing, along with timely dietary and life-style interventions, based on the rich real-time multi-source data. It provides tailored personalized recommendations for diet, exercise, and daily activities, along with real-time feedback to prevent significant fluctuations in blood glucose, lactate levels, blood pressure, and arterial stiffness. Unlike healthy individuals, fluctuations in glucose, lactate, blood pressure, and arterial stiffness can lead to severe complications for diabetes patients, such as stroke, kidney disease, and amputations. However, there is no one-size-fits-all treatment for diabetes. Each patient has unique health issues. For example, the 3 high-risk pre-diabetes participants experienced different levels of progressive conditions (FIGS. 37A-37F and 38A-38C). Generating and processing their dynamic multi-PCT Application Attorney Docket No.: 009062.8575.WG00source data offers the opportunity for predicting abnormalities and personalizing their disease management.

[0261] FIG. 39 shows a chord diagram demonstrating the interrelation of different activities and the response of multiple biomarkers and vital signs. It shows the correlation between sensors and activities across three distinct activities: meal, exercise, and wine consumption. The sensors employed in this analysis included glucose, lactate, alcohol, blood pressure (BP), heart rate (HR), and arterial stiffness. The scale employed in the diagram denoted the major and minor effect levels of each activity on each sensor.

[0262] Continuous multi-parameter monitoring can facilitate understanding of the influence of diverse daily activities on an individual’s biosignals and health (FIGS. 34A-34C). The correlation between the different sensor outputs and the subject’s activities can be visualized in a chord diagram (FIG. 39). Such a diagram supports understanding of the interplay between ISF biomarkers with physical signals during different activities and potentially enable personalized interventions that enhance one’s health and wellness.

[0263] The hybrid wristband bridges the existing technological gap between multi-signal transduction of electrochemical and physical vital signs and lays the foundation for nextgeneration wearable ‘lab-on-a wristband’ hybrid systems capable of continuous chemical-electrophysiological-physical hybrid monitoring towards improved remote, telemetric, and personalized healthcare medical outcomes. Computational techniques, processing (fusing and mining) the large multidimensional temporal data, will be critical for reliable real-time assessment of the health status, advanced disease prediction and early warning of abnormalities. The big data of daily activities could facilitate the early prediction of abnormal physiological changes prior to the onset of symptoms. With further innovations, this multimodal sensing technology would push the limits to reshape healthcare and wellness by providing comprehensive and personalized health analyses. By adding more sensing parameters, we envision a fully integrated multiplexed wearable health-monitoring device that offers insights into the comprehensive physiological status of individuals towards the prevention and effective management of chronic diseases. Multimodal wristband platform, fusing temporal profiles of key physical and chemical parameters, would thus offer a multitude of distinct advantages, including early disease detection, timely alerts during unanticipated events, and guiding personalized treatment. The pairing and choice of the specific chemical and physical sensorsPCT Application Attorney Docket No.: 009062.8575.WG00into a single wristband device, and hence the rational selection of the sensing modules, would rely on the specific user case (e.g., target disease or population).

[0264] The example study aims to showcase the integration of multiple chemical and physical sensors in a single wristband address several critical gaps in managing pre-diabetes and diabetes beyond the traditional CGM-based diabetes management. By providing continuous comprehensive metabolic and cardiovascular sensing, the new multimodal BLUE platform holds considerable promise for advancing personalized diabetes management and maintaining glycemic control while alerting for cardiac risks. The wristband hybrid system thus offers highly accurate continuous monitoring of multiple metabolites (beyond glucose) and cardiac physiological signals affecting the pathophysiology of diabetes and associated with related cardiovascular risks. Such simultaneous real-time ultrasound and electrochemical sensing, combined with advanced data processing, would facilitate more comprehensive disease diagnostics and early prediction and timely alerts of abnormal physiological changes. The individual electrochemical and ultrasound sensing modalities of the hybrid platform offer excellent sensitivity, selectivity, reproducibility, and accuracy (without cross-talk) to support understanding of the correlation and interplay between ISF biomarkers and physical signals during diverse daily activities. Such ‘all-in-one’ multiplexed chem-phys wearable healthmonitoring device thus provides useful rich multi-source insights into the comprehensive physiological status and glycemic variability of individuals towards optimal closed-loop insulindelivery systems and enhanced management of diabetes and other chronic diseases, greatly beyond the limited chemical data of single modality CGM devices. Exemplary fabricated SU-8 biocompatible microneedles have advanced the progress and performance of on-body signals. Such integration of flexible microneedles and ultrasound sensors offers rich transient physio-metabolic profiles, relevant to hypertension during hyperglycemia, post-exercise hypoglycemia, and elevated lactate from diabetic lactic acidosis, during diverse activities along with real-time feedback. The exemplary BLUE sensing platform is currently driven by external circuits, equipment (e.g., potentiostat, pulse-receiver, and Bioradio), and powered separately (FIGS. 31A-31B).

[0265] FIG. 40 shows a schematic illustration of a testing setup for ultrasound sensor power consumption. The power consumption of the ultrasound transducers is calculated through measurements of the voltage drop and current.PCT Application Attorney Docket No.: 009062.8575.WG00

[0266] The BLUE sensing platform is driven by external circuitries (i.e., potentiostat, ultrasound pulser-receiver, and Bioradio). The platform collects signals separately via Bluetooth and wire connection per second and records all the signals by computer. The MNs are driven by potentiostat with rechargeable batteries, the ECG electrodes are driven by Bioradio, with a built-in battery, and ultrasound sensors power via a bench-top commercial pulser-receiver. The MNs and ECG transfer the digital data via Bluetooth while the ultrasound sensors transfer the data via wire.

[0267] The power being consumed by the multi-sensor platform is minimal, while the data analysis and transmission are too power-hungry to be powered by conventional coin cells for extended use. The power consumption is 3.92 mW with a sampling rate of 1 Hz. Lowering the frequency of the data transfer of the individual sensing modalities can lead to a significant reduction in the power consumption to 0.94 mW (e.g., commercial monitoring glucose every 5 min instead of every 1 sec) while sacrificing the real-time monitoring. The ultrasound power consumption is -614 mW7. The total power consumption is 617.92 mW.

[0268] This technology could also be powered via a fully integrated circuit with rechargeable batteries, wireless data transfer via Bluetooth, and Wi-Fi to electronic devices. Such an integrated device may be implemented through the use of flexible printed circuit boards, as well as rechargeable and flexible batteries, and optimization of the system miniaturization, Bluetooth power consumption, crosstalk limitation, and comfortable wearing.

[0269] The multimodal BLUE platform provides continuous comprehensive metabolic and cardiovascular sensing towards advancing personalized diabetes management, improving glycemic control and alerting for cardiac risks. While MN arrays offer tremendous promise for parallel measurements of multiple ISF biomarkers, their chemical diagnostic power is greatly enhanced by simultaneously measuring key physical signals, such as BP, heart rate, and arterial stiffness. The multimodal wristband can be used to provide the following: (1) large-scale clinical trials and extensive validation using T1D patients with changing meal plans and diverse physical activities to ensure the effectiveness and reliability of the device in real- world settings; (2) wearing the hybrid wristband over extended periods and incorporating it with data science to provide early prediction of disorders and diseases; (3) full system integration of the multi-source device, including electronic interface for controlling the different sensing modalities, along with the batteries and data transfer system (4) expansion to additional diabetes-related biomarkers,PCT Application Attorney Docket No.: 009062.8575.WG00such as B-hydroxybutyrate, cortisol, insulin, and potassium; (5) expansion to obese individuals who have a high prevalence of diabetes and cardiovascular disease; (6) monitoring additional physical vital signs, such as temperature and SpCh; (7) combining with advanced ML clustering algorithms to predict abnormalities, health trends and future events and support-closed-loop operations; (8) pairing with additional chemical and physical sensors, depending on the specific user case (e.g., target disease or population). By adding more chem-phys sensing parameters, and coupling with advanced data processing, the new multiplexed health-monitoring platform would offer rich multi- source insights into the comprehensive health status of individuals towards guiding personalized interventions and managing chronic diseases. Achieving full system integration is the ultimate objective for this powerful multi-source device. We believe that this work has illustrated the tremendous promise and significance of a wearable platform — with multiple metabolite and vital sign sensing modalities — beyond the current glucose-sensing based diabetes monitoring.

[0270] Example Embodiments of Methods in Accordance with the Disclosed Technology

[0271] Example Matericds and Chemicals

[0272] Acetaminophen (AP), alcohol oxidase (AOx) solution from Pichia Pastoris (e.g., 40 U mg1), L-ascorbic acid (AA), bovine serum albumin (BSA) from lyophilized powder, calcium chloride anhydrous (CaCh), chitosan (medium molecular weight), disodium hydrogen phosphate (NaiHPCh), pure ethanol (200 proof), glacial acetic acid (HOAc), ^-globulins from bovine blood, D-(+)-glucose anhydrous, glucose oxidase (GOx) from aspergillus (e.g., 142,838 U g1), glutaraldehyde, L-lactic acid, magnesium sulfate anhydrous (MgSO4), phosphate buffer solution (PBS. 1.0 M, pH 7.4), polyvinyl chloride (PVC). potassium chloride (KC1). sodium bicarbonate (NaHCO3), sodium chloride (NaCl), sodium dihydrogen phosphate dihydrate (NaH2PO4-2H2O), silver flakes, tetrahydrofuran (THF), Titron X-100 solution, toluene, and uric acid (UA) were purchased from Sigma-Aldrich. Lactate oxidase (LOx) (e.g., Ill U mg1) was purchased from Toyobo Corp. All aqueous solutions were prepared using doubly deionized water. Polydimethylsiloxane (PDMS) Sylgard® 184 silicone elastomer (Ellsworth Adhesives) and SU-8 2100 were provided by WPI and Kayakli, respectively. Biocompatible BioMed photocurable resin (clear resin) was purchased from Formlabs, while the flexible photocurable resin (white UV tough resin) was purchased from Anycubic. SEBS (styrene-ethylene-butylene-styrene) G1645 and MD1648 tri-block copolymers were obtained from Kraton. CustomPCT Application Attorney Docket No.: 009062.8575.WG00stainless-steel stencils were ordered from Metal Etch Services. Ecoflex (part A and part B) were purchased from Smooth-On, Inc. All reagents and solvents were used without further modification and purification.

[0273] Fabrication of flexible microneedle patch

[0274] The example MNs were designed in SolidWorks and were printed in a Formlabs 3D printer (Model: Form 3. resolution 25 pm). After the printing procedure, the 3D printed microneedles were washed in an isopropanol bath, followed by a post curing step under UV-lamp (Form Cure from Formlabs) for one hour.

[0275] The silicone elastomer kit was mixed with the curing agent to a ratio of 10 to 1. The mixed PDMS was cast into the 3D-printed microneedles attached to a glass petri dish. Then this was degassed in a vacuum pump for 30 minutes at 30 in Hg. PDMS mold was cured for 60 minutes at 60 °C.

[0276] The SU-8 solution was poured into the PDMS mold and degassed in a vacuum pump for 10 minutes. After the SU-8 was cured in a UV-lamp (Black Ray Eamp, Model UVE - 21, wavelength 365nm), the PDMS molds were detached from the SU-8 microneedles and SU-8 microneedles were attached to glass slides by soft lithography for further processing. The prepared SU-8 microneedles were etched by plasma dry etch for 60 min to increase the tip sharpness, then repeat the soft lithography step to attach the etched microneedle on tape.

[0277] The 3D printed masks were attached to the microneedle with a design of 4 separated electrodes. Before sputtering, the microneedles were cleaned by nitrogen gun and attached to the sample stage. The microneedles were sputtered first with a Cr film of 188.40 ± 26.87 pm followed by a Pt film of 337.76 ± 44.08 pm using a Denton Discovery 635 sputtering system.

[0278] Preparation of the silver inks

[0279] A homemade stretchable silver ink was used as the circuit connections to various components of the wristband. The formulation of the ink included silver flakes, SEBS resin (G1645, 4 g in 10 mL toluene), and toluene, using a weight ratio of4:2:l. Homogeneous composition was obtained by mixing with a planetary mixer (FlackTek, Inc SpeedMixer DAC 150.1 FVZ) at 1,800 rpm for 10 minutes.

[0280] Fabrication of the wristband

[0281] The SEBS substrate was prepared by dissolving MD1648 tri-block copolymers in toluene (40 wt.%) and mixing on a linear shaker (Scilogex, SK-L 180-E). The SEBS blend wasPCT Application Attorney Docket No.: 009062.8575.WO00then cast onto the polyethylene terephthalate (PET) sheet with a doctor blade to achieve 300 pm thickness, followed by curing at ambient temperature for 1 hour.

[0282] The wristband circuit was designed on AutoCAD 2023 (Autodesk) and patterned onto a custom stencil (Metal Etch Services). The circuit connection was fabricated by screen-printing formulated silver ink onto the SEBS substrate with the stencil. A chloride / lactate treatment, adapted from the previous study, enhanced conductivity and stability. Briefly, the treatment proceeded by spraying the aqueous solution (containing 100 mM NaCl and 50 mM lactic acid) evenly onto the silver pattern and drying it in the oven at 80 °C for 5 minutes total of three rounds. Afterward, a washing step with DI water was conducted, followed by curing in the oven at the same temperature until thoroughly dried.

[0283] Fabrication of the ECG Sensor

[0284] The ultrasound rods were assembled onto the wristband in a solvent-welding process, which enables firm bonding between the parts with different elastic moduli. 2 pL droplet of toluene / ethanol mixture (3:7 v / v) was applied to the designated position to partially dissolve the silver connections and SEBS allowing the mounting of the ultrasound rods onto the substrate. The solvent was then allowed to evaporate at room temperature, facilitating the re-crosslinking of SEBS to secure the welding between the substrate and the silver inks to the ultrasound rods. Subsequently, a top layer of silver interconnects was adhered to the ultrasound rods and sealed with Ecoflex rubber. This was followed by curing in the oven at 60 °C for 30 minutes. Finally, the external circuit connector was solvent- welded with a 10 pL toluene / ethanol mixture onto the SEBS substrate in a similar fashion.

[0285] Microneedles modification

[0286] The modification of the three different microneedle working electrodes (glucose, lactate and alcohol) relied on a drop cast layer-by-layer technique. The enzymes solutions, GOx (5 U / pL) for glucose and LOx (12 U / pL) for lactate, were prepared in BSA (10 mg / mL in 0.1 M PBS). For alcohol, the enzyme (AOx, final concentration 4 U / pL) was mixed with BSA (10 mg / mL in 0.1 M PBS) and chitosan (1 wt.% in 0.1 M HO Ac) in a ratio 2:1:1. Five layers (4 pL) of each enzyme solution were drop-casted successively on top of the microneedles. For glucose and lactate MN sensors, 2 extra layers were added (4 pL): a solution of 2 v / v% GA in DI water followed by a 1 % chitosan solution (in HO Ac 0.1 M). Finally, in all the working microneedles, a protective layer consisting in a solution of 2 wt.% of PVC (in THF with 1 mM Triton X-100)PCT Application Attorney Docket No.: 009062.8575.WG00was cast to avoid leaching and biofouling problems. After modification, the patches were stored overnight in the fridge (4 °C) for further in vitro and on-body testing.

[0287] Calibration of the arterial blood pressure waveform

[0288] FIG. 41 A shows schematics of an artery at peak diastole and peak systole.

[0289] The blood pressure waveform is calibrated by Equation (1). With pdrepresents the diastolic pressure, Addenotes the diastolic arterial cross-sectional area, and a signifies the rigidity coefficient, the arterial blood pressure (BP) waveform, denoted as p(t), is described as follows:

[0290] Given the assumption of rotational symmetry in the arterial structure, the arterial cross-sectional area denoted as A(t), with d(t) representing the diameter waveform of the specific target artery, can be computed as follows:

[0291] It can be assumed that the human blood vessels exhibit an elastic behavior with minor viscoelastic effects. This implies that the relationship between pressure and vessel diameter demonstrates a relatively modest hysteresis effect, which is below 0.2%. Equation (1) can be employed effectively to reconstruct precise arterial blood pressure (BP) waveforms based on vessel diameter waveforms.

[0292] Equation (3) is:a_ Agintps-pg')As-Ad

[0293] Equation (3) can be used to calculate a, where Asis the systolic arterial cross-section and ps, representing the systolic pressure, can be measured by commercialized BP cuff. An accurate value of p(t) can be obtained by utilizing the equation mentioned above with briefly calibrated a and pdand carefully measured d(t) (vessel diameter). The accuracy of the measured d(t) can be evaluated by utilizing a clinical ultrasonic machine to capture the systolic diameter at the same vascular location.

[0294] Blood pressure calibration coefficient

[0295] In the examples presented in this patent document, every waveform presented in the study has been adjusted using a commercial pressure cuff for calibration. As long as there are no significant physiological changes or vascular reconstructions in the subject, calibration of thePCT Application Attorney Docket No.: 009062.8575.WO00device only has to be done once, because even though the pulse pressure may vary from one heartbeat to another, the diastolic pressure remains relatively consistent under stable physiological conditions.

[0296] According to equations (1) and (2), the connection between the measured signal, arterial diameter, and the value of blood pressure (BP), can be described as follows:

[0297] In this context, dDrepresents the minimum diameter observed within a single cardiac cycle (FIG. 41A), which corresponds to the diastolic blood pressure (BP). In this case, the actualvalue, represented as Znp(t), is directly related to the signal — - - 1. The calibration d Dcoefficient, denoted as f, can be defined as the ratio between these two values:" <

[0298] The calibration coefficient f is influenced by two parameters: the diastolic pressure pdand the rigidity coefficient a. These two parameters can vary between different subjects and need to be recalibrated each time the physiological state changes, such as idling or exercising. The rigidity coefficient can be calibrated as Equation (3). As the blood travels through the arterial tree, it is observed that the rigidity coefficient (cz) and diastolic pressure (pd) do not undergo substantial changes. Consequently, the calibration of the brachial artery allows for the acquisition of BP waveforms at various positions, including the wrist, neck, and foot within the same subject. In different scenarios, as long as a and pdremain relatively stable, the need for frequent recalibration of the device is unnecessary.

[0299] Arterial stiffness measurement

[0300] FIG. 4 IB shows a schematic of pulse wave propagation and reflection in an arterial tree.

[0301] During various daily activities, the blood pressure waveform contour exhibit fluctuations due to variations in systolic peak to reflected peak values. These differences are the result of varying arterial stiffness levels. The variation of arterial stiffness is represented by distinct reflected pulse levels from the distal ends of the arterial tree (FIG. 41B).

[0302] To assess arterial stiffness, the pulse wave decomposition analysis method is employed. This method allows us to decompose pulse waveforms into forward waves, generatedPCT Application Attorney Docket No.: 009062.8575.WG00by the heartbeat, and reflected waves, which can be considered as backpropagations from the distal ends of the arterial tree. A compliant or ‘soft’ artery tends to dilate more easily, leading to weaker reflections and slower backpropagation due to the lower impedance of the arterial tree. Consequently, this manifests as a weaker or delayed secondary peak in the pulse waveform. In contrast, a strong and rapidly occurring secondary peak indicates stiffer arterial networks with higher impedance. Here, we employ AIx to discern the differences between the two peaks in the waveform.

[0303] Equation (6) is:

[0304] In Equation (6), Pi represents the systolic peak and P2 represents the secondary / reflected peak. In the exemplary study, we conducted a beat-to-beat analysis of the blood pressure waveform. This analysis allows us to calculate beat-to-beat augmentation index, which is then averaged to reduce the potential error rate caused by waveform distortion.

[0305] Quantitation: Current conversion to concentration

[0306] Converting the MN current response to the blood concentration was achieved by using equations correlating the lb and IM signals related to the metabolite concentrations measured with the commercial blood meters, where lb is the current reading on the baseline and IM is the reading on the max value.

[0307] Monitoring accuracy

[0308] The mean absolute relative difference (MARD) for continuously glucose monitoring data accuracy was calculated by the equations of (7) absolute relative difference (ARD) and (8) mean absolute relative difference (MARD):ARDt=|MNt~Rtl100% (7)Rt

[0309] Where t is the monitoring time point (every 5 min for CGM and 10 min for blood glucose meter), MNtis the microneedle glucose measured value at the time point, and Rtis the reference glucose measured value at the time point (CGM or blood glucose meter value), N is the total number of data measurement sets of MN to R.

[0310] Data collection and transfer

[0311] Micro-coax wires were employed to connect the microneedles from the wristband toPCT Application Attorney Docket No.: 009062.8575.WG00the potentiostat (SensitBT) and digital data was transferred via Bluetooth to the computer (PSTrace 5.9 software). The ECG pad was adapted to a micro-coax wire and connected to the electric transducer (BioRadio) and digital data was transferred via Bluetooth to the computer (Biocapture software). The ultrasound sensor was adapted to a micro-coax wire and connected to a pulser-receiver (Olympus 5072PR) wire connected to the computer for data collection (Lab View 2018 software).

[0312] Statistics and reproducibility

[0313] On-body measurements were conducted in eleven healthy subjects. Each individual calibration was followed by the current conversion to concentration discussed in the methods section. After collecting all data values, r2indicated the high accuracy of the sensor separately for each target analyte, for alcohol with 90 points, lactate with 123 points, glucose with 158 points, and Blood pressure with 175 points; all pair with measurements using commercial devices.

[0314] MARD and Parkes errors grid were used to assess the clinical accuracy of the on-body measurements (vs gold standard assays). MARD of alcohol is 8.84 % compared with a commercial breathalyzer (26 paired data points), MARD of lactate is 7.97% compared with a commercial lactate blood meter (26 paired data points), MARD of CGM is 6.58% compared with a commercial glucose blood meter (71 paired data points), and MARD of MNs glucose is 3.7% compared with a commercial glucose blood meter (158 paired data points).

[0315] For mechanical bending tests (FIGS.14A-14B, 14E-14F, 16D-16E), the error bar was used to evaluate the flexible structural stability with a sample size of n=5. For arterial stiffness, the error bar was used to evaluate the AIx values plotted with mean value with a sample size of n=7. No data was excluded from the analysis.

[0316] For in-vitro experiments (FIGS. 24A-24I, 25A-25I), the error bar was used to evaluate the reproducibility of alcohol, lactate, and glucose with a sample size of n=3. No data was excluded from the analysis.Examples

[0317] In some embodiments in accordance with the present technology (example Al), a wearable integrated physiological and electrochemical biosensor device includes a wristband configured to conform and reversible attach to a wrist of a subject; an electrochemical sensor contingent configured to simultaneously measure multiple analytes of the subject, thePCT Application Attorney Docket No.: 009062.8575.WG00electrochemical sensor contingent comprising an insulative substrate, one or more electrodes, and a plurality of microneedles protruding from the substrate and interfaced with the one or more electrodes to measure an electrical signal corresponding to an analyte in interstitial fluid (ISF) of the skin; and a physiological sensor contingent configured to measure a plurality of physiological health parameters of the subject concurrently with the measured multiple analytes, the physiological sensor contingent comprising (i) a plurality of ultrasound transducers to transmit acoustic signals and receive returned acoustic signals associated with blood vessel changes to measure blood pressure (BP) and arterial stiffness (AS) of the subject, and (ii) a plurality of electrophysiological electrodes to concurrently detect electrocardiogram (ECG) signals to measure heart rate (HR) of the subject.

[0318] Example A2 includes the device of example Al or any of examples A1-A17, wherein the device further includes a holder, comprising a base side that is coupled to an interior side of the wristband; a top side with an opening to allow the microneedles to project outward through the opening to contact and penetrate within skin of the wrist of the subject; and a hinge coupled between the base side and the top side and configured to allow the top side to rotate so as to enable the electrochemical sensor contingent to be removed from the holder when the wristband is not worn by the subject.

[0319] Example A3 includes the device of example Al or example A2 or any of examples A1-A17, wherein the electrochemical sensor contingent further comprises a cover to couple to the substrate, wherein the cover includes a plurality of apertures that align with the plurality of microneedles so as to allow the microneedles to project outward through the apertures to contact and penetrate within skin of the wrist of the subject.

[0320] Example A4 includes the device of example Al or any of examples Al -A 17, wherein the plurality of microneedles is configured with a conductive layer over at least a portion of a nonconductive microneedle base.

[0321] Example A5 includes the device of example A4 or any of examples Al -A 17, wherein the nonconductive microneedle base includes SU-8 and the conductive layer includes a metallic layer.

[0322] Example A6 includes the device of example Al or any of examples Al -A 17, wherein the metallic layer includes chromium (Cr) and platinum (Pt).

[0323] Example A7 includes the device of example Al or any of examples Al -A 17, whereinPCT Application Attorney Docket No.: 009062.8575.WG00the one or more electrodes includes a plurality of electrodes configured to have at least one working electrode (WE) and at least one reference electrode (RE) and / or at least one counter electrode (CE), and wherein at least one of the plurality of microneedles is configured on an electrode of the plurality of electrodes.

[0324] Example A8 includes the device of example A7 or any of examples A1-A17, wherein the plurality of electrodes includes four electrodes.

[0325] Example A9 includes the device of example A7 or example A8 or any of examples A1-A17, wherein the plurality of electrodes is disposed on the substrate or is disposed inside of the substrate.

[0326] Example A10 includes the device of example Al or any of examples A1-A17, wherein at least some of the plurality of microneedles include a microneedle length of at least 800 pm and a microneedle tip diameter of 10 pm or less.

[0327] Example All includes the device of example Al or any of examples A1-A17, wherein the physiological sensor contingent is configured to measure blood pressure, heart rate, and arterial stiffness decodable from blood vessel diameter change characterized by the acoustic signals.

[0328] Example A12 includes the device of example Al or any of examples Al -A 17, wherein the plurality of electrophysiological electrodes include silver ink printed biopotential electrodes.

[0329] Example A13 includes the device of example Al or any of examples Al -A 17, wherein the plurality of ultrasound transducers is configured to measure the acoustic signals associated with the subject’s BP via 10 ultrasound sensors with a 3 cm x 1 cm area.

[0330] Example A 14 includes the device of example Al or any of examples Al -A 17, wherein the wearable integrated physiological and electrochemical biosensor device integrates a multiplexed microneedle array operable to simultaneously measure the multiple analytes in interstitial fluid (ISF) via corresponding oxidase-based recognition reactions and amperometric signal transduction.

[0331] Example A15 includes the device of example Al or any of examples Al -A 17, wherein the microneedles are configured to penetrate from a stratum comeum region of the epidermis to the dermis of the subject’s skin for sensing the multiple analytes in interstitial fluid (ISF) to provide continuous current signals related to concentrations of the multiple analytes.PCT Application Attorney Docket No.: 009062.8575.WO00

[0332] Example Al 6 includes the device of example Al or any of examples Al -A 17, wherein the multiple analytes include two or more of glucose, alcohol, and / or lactate.

[0333] Example A17 includes the device of example Al or any of examples A1-A16, wherein the device is operable to track dynamic changes of metabolic and physical signals relevant to glucose metabolism and complications of diabetes, capturing effects of transient stimulations, and uncovering correlations between daily activities and physio-metabolic responses.

[0334] In some embodiments in accordance with the present technology (example Bl), a wearable integrated physiological and electrochemical biosensor device includes a wristband configured to conform and reversibly attach to a wrist of a subject; an electrochemical sensor contingent configured to measure multiple analytes of the subject, the electrochemical sensor contingent comprising an insulative substrate, two or more electrodes, and a plurality of microneedles protruding from the insulative substrate and interfaced with the two or more electrodes to measure an electrical signal corresponding to at least one of the multiple analytes in interstitial fluid (ISF) of skin; and a physiological sensor contingent configured to measure a plurality of physiological health parameters of the subject concurrently with the measured multiple analytes, the physiological sensor contingent comprising (i) a plurality of ultrasound transducers configured to transmit acoustic signals and receive returned acoustic signals associated with blood vessel changes to measure blood pressure (BP) and arterial stiffness (AS) of the subject, and (ii) a plurality of electrophysiological electrodes to concurrently detect electrocardiogram (ECG) signals to measure heart rate (HR) of the subject.

[0335] Example B2 includes the device of example Bl or any of examples B1-B15. further comprising a holder, comprising: a base side that is coupled to an interior side of the wristband; a top side with an opening to allow the microneedles to project outward through the opening to contact and penetrate within skin of the wrist of the subject; and a hinge coupled between the base side and the top side and configured to allow the top side to rotate so as to enable the electrochemical sensor contingent to be removed from the holder when the wristband is not worn by the subject.

[0336] Example B3 includes the device of example Bl or any of examples B1-B15, wherein the electrochemical sensor contingent further comprises a cover to couple to the insulative substrate, wherein the cover includes a plurality of apertures that align with the plurality ofPCT Application Attorney Docket No.: 009062.8575.WG00microneedles so as to allow the microneedles to project outward through the apertures to contact and penetrate within skin of the wrist of the subject.

[0337] Example B4 includes the device of example Bl or any of examples B1-B15, wherein the plurality of microneedles is configured with a conductive layer over at least a portion of a nonconductive microneedle base.

[0338] Example B5 includes the device of example B4 or any of examples B1-B15, wherein the nonconductive microneedle base includes SU-8 and the conductive layer includes a metallic layer.

[0339] Example B6 includes the device of example B5 or any of examples B1-B15, wherein the metallic layer includes chromium (Cr) and platinum (Pt).

[0340] Example B7 includes the device of example Bl or any of examples B1-B15, wherein the two or more electrodes include at least one working electrode (WE) and at least one counter electrode (CE), and wherein at least one of the two or more electrodes comprises a conductive layer which covers at least a portion of at least one of the plurality of microneedles.

[0341] Example B8 includes the device of example B7 or any of examples B1-B15, wherein the two or more electrodes include four electrodes.

[0342] Example B9 includes the device of example B7 or example B8 or any of examples B1-B15, wherein at least a portion of the two or more electrodes is disposed on the insulative substrate or is disposed inside of the insulative substrate.

[0343] Example B10 includes the device of example Bl or any of examples B1-B15, wherein the microneedles are configured to penetrate from a stratum comeum region of epidermis to dermis of the skin for sensing the multiple analytes in interstitial fluid (ISF).

[0344] Example Bll includes the device of example B10 or any of examples B1-B15, wherein at least some of the plurality of microneedles include a microneedle length of at least 800 pm and a microneedle tip diameter of 10 pm or less.

[0345] Example B12 includes the device of example Bl or any of examples B1-B15, wherein the plurality of microneedles is configured with one or more functionalization layers, each of which is configured to detect at least one of the multiple analytes via corresponding oxidase-based recognition reactions and amperometric signal transduction.

[0346] Example B13 includes the device of example Bl or any of examples B1-B15, wherein the multiple analytes include two or more of glucose, alcohol, or lactate.PCT Application Attorney Docket No.: 009062.8575.WG00

[0347] Example B14 includes the device of example Bl or any of examples Bl -Bl 5, wherein the physiological sensor contingent is configured to measure blood pressure, heart rate, and arterial stiffness decodable from blood vessel diameter change characterized by the acoustic signals.

[0348] Example B15 includes the device of example Bl or any of examples B1-B14, wherein the plurality of electrophysiological electrodes include silver ink printed electrodes.

[0349] In some embodiments in accordance with the present technology (example B16), a method for simultaneously measuring multiple analytes and physiological health parameters in a subject includes providing a wearable integrated physiological and electrochemical biosensor wristband device that measures multiple analytes of the subject by an electrochemical sensor contingent and measures a plurality of physiological health parameters of the subject by a physiological sensor contingent.

[0350] Example B17 includes the method of example B16 or any of examples B16-B20, wherein the plurality of physiological health parameters is measured concurrently with the multiple analytes.

[0351] Example B18 includes the method of example B16 or any of examples B16-B20, wherein the multiple analytes include two or more of glucose, lactate, or alcohol.

[0352] Example B19 includes the method of example B16 or any of examples B16-B20, wherein the plurality of physiological health parameters includes two or more of blood pressure, heart rate, or arterial stiffness.

[0353] Example B20 includes the method of any of examples B16-B19, wherein the wearable integrated physiological and electrochemical biosensor wristband device includes the wearable integrated physiological and electrochemical biosensor device recited in example Bl or any of examples B2-B15.

[0354] In some embodiments in accordance with the present technology (example B21), a wearable integrated physiological and electrochemical biosensor device includes a flexible wristband substrate configured to conform to an appendage of a subject and interface with skin of the subject; an electrochemical sensing module comprising a microneedle array having multiple individually- addressable sensing electrodes functionalized with different receptors for multiplexed detection of biomarkers in interstitial fluid (ISF); and an acoustic sensing module comprising a plurality of ultrasound transducers configured for hemodynamic monitoringPCT Application Attorney Docket No.: 009062.8575.WO00including blood pressure (BP) and arterial stiffness (AS).

[0355] Example B22 includes the device of example B21 or any of examples B21-B38, wherein the microneedle array is disposable, and the electrochemical sensing module further comprises a replaceable microneedle sensor component configured with a holder, a cover, and adaptor assembly to enable convenient replacement of the microneedle array.

[0356] Example B23 includes the device of example B22 or any of examples B21-B38, wherein the cover includes a plurality of apertures aligned with the microneedle array to allow microneedles to project outward through the apertures to contact and penetrate the skin of the subject.

[0357] Example B24 includes the device of example B22 or any of examples B21-B38, wherein the holder includes a hinge mechanism configured to allow rotation of a top side of the holder to enable removal and replacement of the replaceable microneedle component.

[0358] Example B25 includes the device of example B21 or any of examples B21-B38, further comprising: an electrophysiological sensing module comprising a plurality of electrophysiological electrodes to detect electrocardiogram (ECG) signals to measure heart rate (HR) of the subject concurrently with at least one of the hemodynamic monitoring by the acoustic sensing module or the multiplexed detection of biomarkers by the electrochemical sensing module.

[0359] Example B26 includes the device of example B25 or any of examples B21-B38, wherein the electrochemical sensing module, the acoustic sensing module, and the electrophysiological sensing module are spatially separated on the flexible wristband substrate to prevent crosstalk between sensing modalities.

[0360] Example B27 includes the device of example B26 or any of examples B21-B38, wherein electrophysiological electrodes are positioned at least 5 cm from the microneedle array and at least 3 cm from the ultrasound transducers.

[0361] Example B28 includes the device of example B25 or any of examples B21-B38, further comprising: a data processing unit including a processor configured to calculate an augmentation index (AIx) from a blood pressure waveform measured by the acoustic sensing module.

[0362] Example B29 includes the device of example B28 or any of examples B21-B38, wherein the augmentation index is calculated from a systolic peak (Pl) and an augmented peakPCT Application Attorney Docket No.: 009062.8575.WG00(P2) in the blood pressure waveform.

[0363] Example B30 includes the device of example B28 or any of examples B21-B38, wherein the data processing device is disposed on the wearable integrated physiological and electrochemical biosensor device and in data communication with at least one of the electrochemical sensing module, the acoustic sensing module, or the electrophysiological sensing module via an electrical circuit disposed at least partially on the flexible wristband substrate.

[0364] Example B31 includes the device of example B28 or any of examples B21-B38, wherein the data processing device is embodied in an external device in wireless communication with an electrical circuit in electrical communication with at least one of the electrochemical sensing module, the acoustic sensing module, or the electrophysiological sensing module and disposed at least partially on the flexible wristband substrate, the electrical circuit comprising a wireless communication module.

[0365] Example B32 includes the device of example B28 or any of examples B21-B38, further comprising: a display or communication interface, mounted on the flexible wristband substrate or in communication with the data processing device, configured to present continuous response of individual sensors with simultaneous recording of blood pressure, electrocardiogram, heart rate, and biomarker readings.

[0366] Example B33 includes the device of example B21 or any of examples B21-B38, wherein the wearable integrated physiological and electrochemical biosensor device is configured to measure arterial compliance from blood pressure waveform profile variations before and after a physiological event.

[0367] Example B34 includes the device of example B21 or any of examples B21-B38, wherein microneedles of the microneedle array have a tip diameter of 10 pm or less and a length of at least 800 pm.

[0368] Example B35 includes the device of example B21 or any of examples B21-B38, wherein the microneedle array is configured to maintain flexibility with bending of at least 0.35 rad.

[0369] Example B36 includes the device of example B21 or any of examples B21-B38, wherein the plurality of ultrasound transducers is arranged in an array configuration comprising at least 10 transducers.

[0370] Example B37 includes the device of example B21 or any of examples B21-B38,PCT Application Attorney Docket No.: 009062.8575.WG00wherein the flexible wristband substrate comprises polyethylene terephthalate (PET).

[0371] Example B38 includes the device of example B21 or any of examples B21-B37, wherein the biomarkers include one or more of glucose, lactate, or alcohol, and wherein the different receptors comprise oxidase-based enzymatic recognition elements for each biomarker.Conclusion

[0372] Implementations of the subject matter and the functional operations described in this patent document can be implemented in various systems, digital electronic circuitry, or in computer software, firmware, or hardware, including the structures disclosed in this specification and their structural equivalents, or in combinations of one or more of them. Implementations of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-transitory computer readable medium for execution by, or to control the operation of, data processing apparatus. The computer readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a composition of matter effecting a machine-readable propagated signal, or a combination of one or more of them. The term “data processing unit” or “data processing apparatus” encompasses all apparatus, devices, and machines for processing data, including by way of example a programmable processor, a computer, or multiple processors or computers. The apparatus can include, in addition to hardware, code that creates an execution environment for the computer program in question, e.g., code that constitutes processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0373] A computer program (also known as a program, software, software application, script, or code) can be written in any form of programming language, including compiled or interpreted languages, and it can be deployed in any form, including as a stand-alone program or as a module, component, subroutine, or other unit suitable for use in a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored in a portion of a file that holds other programs or data (e.g., one or more scripts stored in a markup language document), in a single file dedicated to the program in question, or in multiple coordinated files (e.g., files that store one or more modules, sub programs, or portions of code). A computer program can be deployed to be executed on one computer or on multiple computers that are located at one site or distributed across multiple sites and interconnected by aPCT Application Attorney Docket No.: 009062.8575.WG00communication network.

[0374] The processes and logic flows described in this specification can be performed by one or more programmable processors executing one or more computer programs to perform functions by operating on input data and generating output. The processes and logic flows can also be performed by, and apparatus can also be implemented as, special purpose logic circuitry, e.g.. an FPGA (field programmable gate array) or an ASIC (application specific integrated circuit).

[0375] Processors suitable for the execution of a computer program include, by way of example, both general and special purpose microprocessors, and any one or more processors of any kind of digital computer. Generally, a processor will receive instructions and data from a read only memory or a random access memory or both. The essential elements of a computer are a processor for performing instructions and one or more memory devices for storing instructions and data. Generally, a computer will also include, or be operatively coupled to receive data from or transfer data to, or both, one or more mass storage devices for storing data, e.g., magnetic, magneto optical disks, or optical disks. However, a computer need not have such devices. Computer readable media suitable for storing computer program instructions and data include all forms of nonvolatile memory, media and memory devices, including by way of example semiconductor memory devices, e.g., EPROM, EEPROM, and flash memory devices. The processor and the memory can be supplemented by, or incorporated in, special purpose logic circuitry.

[0376] While this patent document contains many specifics, these should not be construed as limitations on the scope of any invention or of what may be claimed, but rather as descriptions of features that may be specific to particular embodiments of particular inventions. Certain features that are described in this patent document in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination can in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

[0377] Similarly, while operations are depicted in the drawings in a particular order, thisPCT Application Attorney Docket No.: 009062.8575.WO00should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Moreover, the separation of various system components in the embodiments described in this patent document should not be understood as requiring such separation in all embodiments.

[0378] Only a few implementations and examples are described and other implementations, enhancements and variations can be made based on what is described and illustrated in this patent document.

Claims

1. PCT Application Attorney Docket No.: 009062.8575.WG00CLAIMSWhat is claimed are is:

1. A wearable integrated physiological and electrochemical biosensor device, comprising:a wristband configured to conform and reversibly attach to a wrist of a subject;an electrochemical sensor contingent configured to measure multiple analytes of the subject, the electrochemical sensor contingent comprising an insulative substrate, two or more electrodes, and a plurality of microneedles protruding from the insulative substrate and interfaced with the two or more electrodes to measure an electrical signal corresponding to at least one of the multiple analytes in interstitial fluid (ISF) of skin; anda physiological sensor contingent configured to measure a plurality of physiological health parameters of the subject concurrently with the measured multiple analytes, the physiological sensor contingent comprising (i) a plurality of ultrasound transducers configured to transmit acoustic signals and receive returned acoustic signals associated with blood vessel changes to measure blood pressure (BP) and arterial stiffness (AS) of the subject, and (ii) a plurality of electrophysiological electrodes to concurrently detect electrocardiogram (ECG) signals to measure heart rate (HR) of the subject.

2. The device of claim 1 , further comprising:a holder, comprising:a base side that is coupled to an interior side of the wristband;a top side with an opening to allow the microneedles to project outward through the opening to contact and penetrate within skin of the wrist of the subject; anda hinge coupled between the base side and the top side and configured to allow the top side to rotate so as to enable the electrochemical sensor contingent to be removed from the holder when the wristband is not worn by the subject.

3. The device of claim 1, wherein the electrochemical sensor contingent further comprises a cover to couple to the insulative substrate, wherein the cover includes a plurality of apertures that align with the plurality of microneedles so as to allow the microneedles to project outward through the apertures to contact and penetrate within skin of the wrist of the subject.PCT Application Attorney Docket No.: 009062.8575.WG004. The device of claim 1 , wherein the plurality of microneedles is configured with a conductive layer over at least a portion of a nonconductive microneedle base.

5. The device of claim 4, wherein the nonconductive microneedle base includes SU-8 and the conductive layer includes a metallic layer.

6. The device of claim 5, wherein the metallic layer includes chromium (Cr) and platinum (Pt).

7. The device of claim 1, wherein the two or more electrodes include at least one working electrode (WE) and at least one counter electrode (CE), and wherein at least one of the two or more electrodes comprises a conductive layer which covers at least a portion of at least one of the plurality of microneedles.

8. The device of claim 7, wherein the two or more electrodes includes four electrodes.

9. The device of claim 7 or claim 8, wherein at least a portion of the two or more electrodes is disposed on the insulative substrate or is disposed inside of the insulative substrate.

10. The device of claim 1, wherein the microneedles are configured to penetrate from a stratum corneum region of epidermis to dermis of the skin for sensing the multiple analytes in interstitial fluid (ISF).

11. The device of claim 10, wherein at least some of the plurality of microneedles include a microneedle length of at least 800 pm and a microneedle tip diameter of 10 pm or less.

12. The device of claim 1, wherein the plurality of microneedles is configured with one or more functionalization layers, each of which is configured to detect at least one of the multiple analytes via corresponding oxidase-based recognition reactions and amperometric signal transduction.

13. The device of claim 1, wherein the multiple analytes include two or more of glucose, alcohol, or lactate.PCT Application Attorney Docket No.: 009062.8575.WG0014. The device of claim 1 , wherein the physiological sensor contingent is configured to measure blood pressure, heart rate, and arterial stiffness decodable from blood vessel diameter change characterized by the acoustic signals.

15. The device of claim 1, wherein the plurality of electrophysiological electrodes includes silver ink printed electrodes.

16. A method for simultaneously measuring multiple analytes and physiological health parameters in a subject, comprising: |providing a wearable integrated physiological and electrochemical biosensor wristband device that measures multiple analytes of the subject by an electrochemical sensor contingent and measures a plurality of physiological health parameters of the subject by a physiological sensor contingent.

17. The method of claim 16, wherein the plurality of physiological health parameters is measured concurrently with the multiple analytes.

18. The method of claim 16, wherein the multiple analytes include two or more of glucose, lactate, or alcohol.

19. The method of claim 16, wherein the plurality of physiological health parameters includes two or more of blood pressure, heart rate, or arterial stiffness.

20. The method of any of claims 16-19, wherein the wearable integrated physiological and electrochemical biosensor wristband device includes the wearable integrated physiological and electrochemical biosensor device recited in claim 1 or any of claims 2-15.PCT Application Attorney Docket No.: 009062.8575.WG0021. A wearable integrated physiological and electrochemical biosensor device, comprising:a flexible wristband substrate configured to conform to an appendage of a subject and interface with skin of the subject:an electrochemical sensing module comprising a microneedle array having multiple individually-addressable sensing electrodes functionalized with different receptors for multiplexed detection of biomarkers in interstitial fluid (ISF); andan acoustic sensing module comprising a plurality of ultrasound transducers configured for hemodynamic monitoring including blood pressure (BP) and arterial stiffness (AS).

22. The device of claim 21, wherein the microneedle array is disposable, and the electrochemical sensing module further comprises a replaceable microneedle sensor component configured with a holder, a cover, and adaptor assembly to enable convenient replacement of the microneedle array.

23. The device of claim 22, wherein the cover includes a plurality of apertures aligned with the microneedle array to allow microneedles to project outward through the apertures to contact and penetrate the skin of the subject.

24. The device of claim 22, wherein the holder includes a hinge mechanism configured to allow rotation of a top side of the holder to enable removal and replacement of the replaceable microneedle component.

25. The device of claim 21, further comprising:an electrophysiological sensing module comprising a plurality of electrophysiological electrodes to detect electrocardiogram (ECG) signals to measure heart rate (HR) of the subject concurrently with at least one of the hemodynamic monitoring by the acoustic sensing module or the multiplexed detection of biomarkers by the electrochemical sensing module.

26. The device of claim 25, wherein the electrochemical sensing module, the acoustic sensing module, and the electrophysiological sensing module are spatially separated on the flexible wristband substrate to prevent crosstalk between sensing modalities.

27. The device of claim 26. wherein electrophysiological electrodes are positioned at least 5 cm from the microneedle array and at least 3 cm from the ultrasound transducers.PCT Application Attorney Docket No.: 009062.8575.WG0028. The device of claim 25, further comprising:a data processing unit including a processor configured to calculate an augmentation index (AIx) from a blood pressure waveform measured by the acoustic sensing module.

29. The device of claim 28, wherein the augmentation index is calculated from a systolic peak (Pl) and an augmented peak (P2) in the blood pressure waveform.

30. The device of claim 28, wherein the data processing device is disposed on the wearable integrated physiological and electrochemical biosensor device and in data communication with at least one of the electrochemical sensing module, the acoustic sensing module, or the electrophysiological sensing module via an electrical circuit disposed at least partially on the flexible wristband substrate.

31. The device of claim 28, wherein the data processing device is embodied in an external device in wireless communication with an electrical circuit in electrical communication with at least one of the electrochemical sensing module, the acoustic sensing module, or the electrophysiological sensing module and disposed at least partially on the flexible wristband substrate, the electrical circuit comprising a wireless communication module.

32. The device of claim 28, further comprising:a display or communication interface, mounted on the flexible wristband substrate or in communication with the data processing device, configured to present continuous response of individual sensors with simultaneous recording of blood pressure, electrocardiogram, heart rate, and biomarker readings.

33. The device of claim 21, wherein the wearable integrated physiological and electrochemical biosensor device is configured to measure arterial compliance from blood pressure waveform profile variations before and after a physiological event.

34. The device of claim 21, wherein microneedles of the microneedle array have a tip diameter of 10 pm or less and a length of at least 800 pm.

35. The device of claim 21, wherein the microneedle array is configured to maintain flexibility with bending of at least 0.35 rad.PCT Application Attorney Docket No.: 009062.8575.WO0036. The device of claim 21 , wherein the plurality of ultrasound transducers is arranged in an array configuration comprising at least 10 transducers.

37. The device of claim 21, wherein the flexible wristband substrate comprises polyethylene terephthalate (PET).

38. The device of claim 21, wherein the biomarkers include one or more of glucose, lactate, or alcohol, and wherein the different receptors comprise oxidase-based enzymatic recognition elements for each biomarker.

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