Microneedle wound monitoring systems, devices and methods

Abstract

A wound monitoring system comprises a microneedle scaffold layer including a microneedle array positioned on a first surface of the microneedle scaffold layer, a microfluidics layer above the microneedle scaffold layer and opposite the microneedle array, and a sensor layer above the microfluidic layer and opposite the microneedle scaffold layer. A wound monitoring method comprises providing and applying the system to a subject to collect interstitial fluid (ISF) from the microneedle array to the assay chamber via the microneedle scaffold layer and the microfluidics layer, collecting physiological data based on dielectric or chemical properties of the ISF as measured by the electrochemical sensor array, and categorizing a wound physiological state. A microneedle array comprises a plurality of microneedles comprising polylactic acid (PLA), wherein the microneedles have a height of 500- 950 pm, base diameter of 150-450 pm, swelling percentage of 25%-175%, and porosity of 30%- 80%.

Description

MICRONEEDLE WOUND MONITORING SYSTEMS, DEVICES AND METHODSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims priority to U.S Provisional Application No. 63 / 487,375 filed February 28, 2023, the contents of which are incorporated by reference herein in its entirety. a BACKGROUND OF THE INVENTION

[0002] The risk of superficial surgical site infection (SSI) in the U.S. ranges from 1-3%, but this may be higher in some patients with risk factors such as obesity, tobacco use, and diabetes, and may be higher in specific types of surgery, such as breast surgery. Post-surgical incision monitoring is typically conducted at a scheduled post-operative visit, but SSIs can occur any time during the post-op period, up to and beyond 30 days, with symptoms most commonly developing within 14 days. Outside of the post-operative visit, the detection of an SSI is largely subjective and contingent on self-reported symptoms by patients that develop at home.Clinicians rely on patients to communicate incisional concerns so that outpatient antibiotic therapy can be initiated in a timely fashion. If infections are detected early, treatment with antibiotics is effective and low cost. However, some infections require more intensive treatment such as intravenous antibiotics or other procedures, often leading to rehospitalization, which costs an average of $20,842 per patient. Subjective symptom reporting is a pain point because it requires accurate recognition by the patient and effective communication of symptoms to the surgical team. This in turn delays the initiation ofoutpatient treatment, which could potentially be more effective if started earlier. Many postoperative SSIs could be prevented from escalating to invasive treatment with a more reliable method of detecting the infection, and improved engagement between clinical teams and patients in an outpatient setting. Thus there is a need in the art for improved wound monitoring systems, devices and methods.SUMMARY OF THE INVENTION

[0003] Some embodiments of the invention disclosed herein are set forth below, and any combination of these embodiments (or portions thereof) may be made to define another embodiment.

[0004] In one aspect, a wound monitoring system comprises a microneedle scaffold layer including a microneedle array positioned on a first surface of the microneedle scaffold layer, a microfluidics layer above the microneedle scaffold layer and opposite the microneedle array, and a sensor layer above the microfluidic layer and opposite the microneedle scaffold layer.

[0005] In one embodiment, the system further comprises a computing system communicatively connected to sensor layer, comprising a processor and a non-transitory computer-readable medium with instructions stored thereon, which when executed by the processor, perform steps comprising receiving collected wound physiology data from an electrochemical sensor array via a transceiver of the sensor layer, calculating changes in the physiology data over time, and categorizing the wound physiological state via a machine learning algorithm based on the change in the physiology data.

[0006] In one embodiment, the system is configured to monitor a general physiological state other than a wound physiological state.

[0007] In one embodiment, the microneedle array comprises a plurality of porous biocompatible polymer microneedles comprising polylactic acid (PLA), wherein the microneedles have a height of 500 pm to 950 pm, a base diameter of 150 pm to 450 pm, a swelling percentage of 25% to 175%, and a porosity of 30% to 80%.

[0008] In one embodiment, the porous microneedles are formed via removal of polyethylene glycol (PEG) from a PEG and PLA mixture comprising 5% to 50% PEG and 5% to 50% PLA, and wherein a degree of porosity of the microneedles is tunable based on the PEG and PLA mixture concentration.

[0009] In one embodiment, the PEG comprises a molecular mass of 6 kDa to 20 kDa, and wherein pore size of the microneedles is tunable based on the PEG molecular mass.

[0010] In one embodiment, the microfluidics layer comprises a plurality of interconnected microchannel inlet ports, an assay chamber in fluid connection with the plurality of microchannel inlet ports, and an open capillary pump in fluid connection with the assay chamber.

[0011] In one embodiment, the plurality of microchannel inlet ports are in fluid connection with the microneedle scaffold layer, and are configured to enable directional transport of fluid from a wound to the assay chamber.

[0012] In one embodiment, the sensor layer comprises a transceiver and an electrochemical sensor array communicatively connected to the transceiver, configured to measure a dielectric value of collected interstitial fluid ( I SF) .

[0013] In one embodiment, the computing system comprises a watchdog circuit communicatively connected to the sensor array, configured to be periodically activated.

[0014] In one embodiment, the system is configured as a waterproof wound dressing or surgical bandage.

[0015] In another aspect, a wound monitoring method comprises providing the system as described above, applying the system to a subject to collect interstitial fluid (ISF) from the microneedle array to the assay chamber via the microneedle scaffold layer and the microfluidics layer, collecting wound physiology data based on dielectric or chemical properties of the ISF as measured by the electrochemical sensor array, and categorizing a wound physiological state.

[0016] In one embodiment, the wound physiological state is categorized as at least one of normal, abnormal, healthy, inflamed, and infected.

[0017] In one embodiment, the step of categorizing a wound physiological state comprises calculating changes in the physiological data over time, and categorizing the wound state via a machine learning algorithm based on the change in the physiological data.

[0018] In one embodiment, the step of collecting wound physiological data comprises checking for a connection with the receiver device and performing the measurement if the connection passes as true.

[0019] In another aspect, a microneedle array comprises a plurality of porous biocompatible polymer microneedles comprising polylactic acid (PLA), wherein the microneedles have a height of 500 pm to 950 pm, a base diameter of 150 pm to 450 pm, a swelling percentage of 25% to 175%, and a porosity of 30% to 80%.

[0020] In one embodiment, the porous microneedles are formed via removal of polyethylene glycol (PEG) from a PEG and PLA mixture comprising 5% to 50% PEG and 5% to 40% PLA, and wherein a degree of porosity of the microneedles is tunable based on the PEG and PLA mixture concentration.

[0021] In one embodiment, the PEG comprises a molecular mass of 6 kDa to 20 kDa, and wherein a pore size of the microneedles is tunable based on the PEG molecular mass.

[0022] In another aspect, a microneedle array manufacturing method comprises laser patterning polydimethylsiloxane (PDMS) with a sacrificial acrylic layer to create a mold, silanizing the mold, forming the microneedle array by applying a polyethylene glycol (PEG) and polylactic acid (PLA) polymer mixture to the mold, removing the microneedle array from the mold, and removing the PEG from the microneedle array to form a porous PLA microneedle array.

[0023] In one embodiment, the method further comprises cleaning the mold prior to the step of silanizing the mold, wherein cleaning the mold comprises, soaking the mold in a surfactant for 6 hours to 24 hours, rinsing the mold with deionized water (DI), soaking the mold in isopropyl alcohol (I PA) for 0.33 hours to 24 hours, rinsing the mold with DI a second time, drying the mold via clean compressed air, and removing dust via tape.

[0024] In one embodiment, the step of silanizing the mold comprises submerging the mold in 30% hydrogen peroxide, sonicating the mold at room temperature for 5 minutes to form hydroxyl groups on the mold surface, blow drying the mold with clean compressed nitrogen gas or clean compressed air, desiccating the mold in a vacuum chamber for 12 or more hours in the presence of 100 pL of silane, rinsing the mold with isopropyl alcohol (I PA), and blow drying the mold with clean compressed nitrogen gas or clean compressed air a second time.

[0025] In one embodiment, the step of forming the microneedle array comprises combining PEG and PLA in a vessel via a magnetic stir bar, placing the vessel into a 60°C to 80°C thermal bath, degassing the PEG and PLA mixture in a filtered Erlenmeyer flask for using a vacuum pump, heating the mold via a hot plate to 80°C for 5 minutes to 10 minutes, pipetting or injecting the PEG and PLA mixture into the mold, pressing the PEG and PLA mixture via a mold press, desiccating the mold in a vacuum desiccator for 2 minutes to 10 minutes , sonicating the mold in a heated sonicator at 50°C for three 10 minute cycles, drying the mold, and curing the mold for 12 or more hours in a vacuum oven at 40°C to 60°C.

[0026] In one embodiment, the steps of removing the microneedle array from the mold and removing the PEG from the microneedle array to form a porous PLA microneedle arraycomprises removing the mold from a vacuum oven, liberating the microneedle array from the mold via lifting the edge with a tweezer, soaking the microneedle array in 1:1 deionized water (DI) and methanol solution for at least 72 hours to remove PEG from the PLA, and removing the microneedle array from the solution.

[0027] In one embodiment, the step of laser patterning PDMS comprises laser patterning a variable photolithographic design with a range of geometric parameters and microchannel flow rates on a single PDMS device.

[0028] In one embodiment, the geometric parameters are optimized and microchannel flow rates are evaluated using small particle tracking methods.BRIEF DESCRIPTION OF THE DRAWINGS

[0029] The foregoing purposes and features, as well as other purposes and features, will become apparent with reference to the description and accompanying figures below, which are included to provide an understanding of the invention and constitute a part of the specification, in which like numerals represent like elements, and in which:

[0030] FIG. 1 depicts an exemplary wound monitoring system in accordance with some embodiments.

[0031] FIG. 2 depicts another exemplary wound monitoring system in accordance with some embodiments.

[0032] FIG. 3A depicts another exemplary wound monitoring system in accordance with some embodiments.

[0033] FIG. 3B depicts an exemplary sensor that may be utilized in the exemplary system of FIG. 3A in accordance with some embodiments.

[0034] FIG. 3C depicts another exemplary wound monitoring system in accordance with some embodiments.

[0035] FIG. 4A depicts a circuit diagram of an exemplary wireless interrogator in accordance with some embodiments.

[0036] FIG. 4B depicts an exemplary computing environment in which aspects of the present invention may be practiced in accordance with some embodiments.

[0037] FIG. 5A is a CAD image depicting exemplary microchannels and capillary pump in accordance with some embodiments. Scale bars are 25 pm.

[0038] FIG. 5B is a CAD image depicting the exemplary microchannels depicted in FIG. 5A showing the divergence of flow channels into wider capillary channels in accordance with some embodiments. Scale bars are 500 pm.

[0039] FIG. 5C is a light microscope image depicting exemplary PMDS test models showing the saw-tooth shaped capillary channels in accordance with some embodiments. Scale bars are400 pm.

[0040] FIG. 6 is a flow chart depicting an exemplary wound monitoring method in accordance with some embodiments.

[0041] FIG. 7 is a flow chart depicting an exemplary microneedle array manufacturing method in accordance with some embodiments.

[0042] FIG. 8A depicts an exemplary machine learning architecture for categorizing a wound physiological state in accordance with some embodiments.

[0043] FIG. 8B depicts an exemplary machine learning schematic for categorizing a wound state in accordance with some embodiments.

[0044] FIG. 8C depicts an exemplary machine learning schematic for receiving biosignals, post-processing the signals, and categorizing a wound state based on the relative fluctuation of the signals in accordance with some embodiments.

[0045] FIG. 9 depicts a scanning electron micrograph (SEM) image of an exemplary fabricated experimental PDMS microneedle mold.

[0046] FIG. 10 depicts an SEM image of an exemplary experimental microneedle fabricated via 6 kDa PEG and a non-silanized mold in accordance with some embodiments.

[0047] FIG. 11 depicts an SEM image of an exemplary experimental microneedle fabricated via 10 kDa PEG and a silanized mold in accordance with some embodiments.

[0048] FIG. 12 depicts an SEM image of an exemplary experimental microneedle fabricated via 6 kDa PEG in accordance with some embodiments.

[0049] FIG. 13 depicts an SEM image of an exemplary experimental microneedle fabricated via 10 kDa PEG in accordance with some embodiments.

[0050] FIG. 14 depicts an SEM image of an exemplary experimental microneedle fabricated via 20 kDa PEG in accordance with some embodiments.

[0051] FIG. 15 depicts an SEM image showing the porous structure of an exemplary experimental microneedle fabricated via 10% 10 kDa PEG and 16% PLA in accordance with some embodiments.

[0052] FIG. 16A is a plot depicting exemplary experimental results for microneedle water extraction for multiple PEG / PLA mixtures in accordance with some embodiments.

[0053] FIG. 16B is a plot depicting exemplary experimental results for microneedle porosity for multiple PEG / PLA mixtures in accordance with some embodiments.

[0054] FIG. 17 is a plot and images depicting exemplary experimental results for microneedle fluid extraction from Hydrogel for multiple PEG / PLA mixtures in accordance with some embodiments.

[0055] FIG. 18 is a plot and images depicting exemplary experimental results for a Parafilm mechanical test for multiple PEG / PLA mixtures in accordance with some embodiments.

[0056] FIG.19 are plots showing further exemplary experimental results for fluid extraction and mechanical tests in accordance with some embodiments.

[0057] FIG. 20 is a plot depicting exemplary simulation results for fluidic extraction in relation to multiple microneedle sizes in accordance with some embodiments.

[0058] FIG. 21A is a plot depicting exemplary experimental results showing the average real part of permittivity for dielectric measurements on multiple artificial interstitial fluids (ISF) in accordance with some embodiments.

[0059] FIG. 21B is a plot depicting exemplary experimental results showing p-values for dielectric measurements on multiple artificial interstitial fluids (ISF) in accordance with some embodiments.

[0060] FIG. 22A depicts an exemplary schematic for a dynamics experiment in accordance with some embodiments.

[0061] FIG. 22B depicts an exemplary schematic for an experimental pH and temperature sensor for the dynamics experiment of FIG. 19A in accordance with some embodiments.

[0062] FIG. 23A depicts an exemplary circuit diagram for the sensor of FIG. 19B in accordance with some embodiments.

[0063] FIG. 23B is a plot depicting an exemplary experimental result of resistance versus temperature for the dynamics experiment of FIG. 19A in accordance with some embodiments.

[0064] FIG. 24 is a plot depicting an exemplary experimental result of Sil curves versus frequency measured from the antenna sensor bonded on a copper base at different temperature increments in accordance with some embodiments.

[0065] FIG. 25 shows exemplary computational fluid dynamics (CFD) simulation results demonstrating filling of a patch with fluid pulled by microneedles at a first simulation time step in accordance with some embodiments.

[0066] FIG. 26 shows exemplary CFD simulation results demonstrating filling of a patch with fluid pulled by microneedles at a second simulation time step in accordance with some embodiments.

[0067] FIG. 27 shows exemplary CFD simulation results demonstrating filling of a patch with fluid pulled by microneedles at a third simulation time step in accordance with some embodiments.

[0068] FIG. 28 depicts an exemplary diagram of the simulation parameters in accordance with some embodiments.

[0069] FIG. 29 depicts another exemplary diagram of the simulation parameters in accordance with some embodiments.

[0070] FIG. 30 depicts an image of an exemplary experimental electrochemical sensor and related experimental results in accordance with some embodiments.

[0071] FIG. 31A depicts an image of an exemplary microneedle array penetrating human skin in accordance with some embodiments.

[0072] FIG. 31B depicts an image of an exemplary ex-vivo human skin model after insertion of a microneedle array in accordance with some embodiments.

[0073] FIG. 31C depicts a micrograph image showing exemplary experimental results of microneedle penetration efficacy testing on a human skin model in accordance with some embodiments.

[0074] FIG. 32A depicts a schematic showing an exemplary microneedle design with variable parameters in accordance with some embodiments.

[0075] FIG. 32B depicts a schematic showing an exemplary microfluidic channel with a variable geometric parameters in accordance with some embodiments.

[0076] Fig. 32C depicts a schematic showing an exemplary microchannel with variable geometric parameters in accordance with some embodiments.

[0077] FIG. 33 depicts experimental results of varying the parameters in different channels in accordance with some embodiments.

[0078] FIG. 34 depicts an overlay of a velocity vector field on real space epifluorescence micrograph of an exemplary microfluidics channel in accordance with some embodiments.

[0079] FIG. 35A is a plot depicting exemplary experimental results showing the x- components of the fluid flow rate across different microfluidic channels in accordance with some embodiments.

[0080] FIG. 35B is a plot depicting exemplary experimental results showing the magnitude of the fluid flow rate across different exemplary microfluidic channels in accordance with some embodiments.

[0081] FIG. 36A is a plot depicting exemplary experimental results of Sil magnitude versus frequency curves for different artificial interstitial fluids in accordance with some embodiments.

[0082] FIG. 36B is a plot depicting exemplary experimental results of the change in resonant frequency of artificial interstitial fluid versus artificial ISF pH as compared to baseline measurements in accordance with some embodiments.

[0083] FIG. 37 depicts an exemplary computing environment in which aspects of the present invention may be practiced in accordance with some embodiments.DETAILED DESCRIPTION OF THE INVENTION

[0084] It is to be understood that the figures and descriptions of the present invention have been simplified to illustrate elements that are relevant for a clearer comprehension of the present invention, while eliminating, for the purpose of clarity, many other elements found in systems and methods of wound monitoring. Those of ordinary skill in the art may recognize that other elements and / or steps are desirable and / or required in implementing the present invention. However, because such elements and steps are well known in the art, and because they do not facilitate a better understanding of the present invention, a discussion of such elements and steps is not provided herein. The disclosure herein is directed to all such variations and modifications to such elements and methods known to those skilled in the art.

[0085] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which thisinvention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, exemplary methods and materials are described.

[0086] As used herein, each of the following terms has the meaning associated with it in this section.

[0087] The articles "a" and "an" are used herein to refer to one or to more than one ( / .e., to at least one) of the grammatical object of the article. By way of example, "an element" means one element or more than one element.

[0088] "About" as used herein when referring to a measurable value such as an amount, a temporal duration, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, ±1%, and ±0.1% from the specified value, as such variations are appropriate.

[0089] Ranges: throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Where appropriate, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.

[0090] Referring now in detail to the drawings, in which like reference numerals indicate like parts or elements throughout the several views, in various embodiments, presented herein are microneedle wound monitoring systems, devices and methods.

[0091] Referring now to FIG. 1, FIG. 2, FIG. 3A, FIG. 3B, and FIG. 3C, exemplary embodiments for a wound monitoring system 100 are shown. In some embodiments, the system 100 includes a microneedle scaffold layer 101 including a microneedle array 102 positioned on a first surface of the microneedle scaffold layer 101, a microfluidics layer 103 above the microneedle scaffold layer 101 and opposite the microneedle array 102, and a sensor layer 107 above the microfluidic layer 103 and opposite the microneedle scaffold layer 101.

[0092] In some embodiments, system 100 can further include computing systems 112 and 113 (such as computer 600 of FIG. 37) communicatively connected to sensor layer 107. In some embodiments, the computing systems 112 and 113 include a watchdog circuit, a processor and a non-transitory computer-readable medium with instructions stored thereon, which when executed by the processor, perform a wound monitoring method including steps comprising receiving collected wound physiology data from an electrochemical sensor array of the sensor layer 107 via a transceiver of the sensor layer 107, calculating changes in the physiological data over time, and categorizing the wound physiological state via a machine learning algorithm based on the change in the physiological data. In some embodiments, the computing system categorizes a wound physiological state as normal, abnormal, healthy, inflamed, infected, or any other suitable state or combination thereof. In some embodiments, the computing system112 comprises a smart phone, a desktop computer, a thin client, a cloud based computingsystem, a smart watch, a laptop computer, or other suitable computing device or combination thereof. In some embodiments, the system 100 is configured to monitor a general physiological state other than a wound physiological state. In some embodiments, the system 100 can be applied to any wound type, including but not limited to, elective incisions, surgical incisions, combat wound, scrape, cut, or sores, or can be place on any suitable portion of the body to perform monitoring of physiological data. In some embodiments, the system 100 can be applied to any elective incision to help detect surgical site infections early, objectively, and at home. The appeal is that it can be used on any wound on the body. Examples of other uses include monitoring of any internal or external anatomic location and any post-operative operations including, but not limited to, plastic surgery, orthopedic surgery, neurosurgery, vascular surgery, gastro-intestinal surgery, or similar.

[0093] The computing systems 112 and 113 and sensor layer 107 can be communicatively connected via any suitable wireless means, including by not limited to, any 802.11 standards, cellular WAN infrastructures such as 3G, 4G / LTE, or 5G networks, Bluetooth®, Bluetooth® Low Energy (BLE), Zigbee®, infrared, near-field communication (NFC), and / or any suitable wired means including but not limited to, Ethernet, fiberoptic, coaxial, and copper wire, or any combination thereof.

[0094] In some embodiments, the microneedle scaffold layer 101 comprises a polymer, biocompatible polymer, or any other suitable substrate or combination thereof. In some embodiments, the microneedle scaffold layer 101 has a length of 15 mm to 100 mm, a width of15 mm to 100 mm, and / or a thickness of 1 mm to 10 mm. In some embodiments, themicroneedle scaffold layer 101 is shaped as a square, rectangle, triangle, pentagon, hexagon, polygon, circle, oval, or other suitable shape, or shaped specific to a body part in which the system 100 may be placed to monitor a wound. In some embodiments the microneedles 102 configured in a hexagonal array including three needle regions, packed with microneedles of 1- 2mm interspacing.

[0095] In some embodiments, the microneedle array 102 comprises a plurality of porous biocompatible polymer microneedles. In some embodiments, the microneedles are made of polylactic acid (PLA), polylactic-co-glycolic acid (PLGA), or other suitable biocompatible polymer or combination thereof. In some embodiments, the microneedles are coated with an additional biomaterial coating, for example hyaluronic acid or other suitable biocompatible coating or combination thereof. In some embodiments, the microneedles have a height of 500 pm to 950 pm, a base diameter of 150 pm to 450 pm, a height to base diameter aspect ratio of 19:3 to 10:9, a swelling percentage of 25% to 175%, and / or a porosity of 30% to 80%. In some embodiments, the microneedles 102 are shaped generally as cones, pyramids, tetrahedrons, cylindrical, or other suitable shape or combination thereof. In some embodiments, the microneedles 102 are generally oriented perpendicular to scaffold layer 101. In some embodiments, the microneedles 102 are generally oriented angled (i.e. not perpendicular) in relation to scaffold layer 101.

[0096] In some embodiments, the porous microneedles are formed via removal of polyethylene glycol (PEG) from a PEG and PLA mixture comprising 5% to 50% PEG and 5% to50% PLA, about 10% PEG and about 24% PLA, about 10% PEG and about 16% PLA, or any othersuitable combination. In some embodiments, the degree of porosity of the microneedles is tunable based on the PEG and PLA mixture concentration. In some embodiments, the degree of porosity is about 50%. In some embodiments, the PEG comprises a molecular mass of 6 kDa to 20 kDa, about lOkDa, or other suitable molecular mass. In some embodiments, the pore size of the microneedles is tunable based on the PEG molecular mass. In some embodiments, the pore size of the microneedles is from 1 nm to 5000 nm. In some embodiments, multiple combinations of different PEG molecular mass can be used to generate a distribution of porosity across the microneedles.

[0097] In some embodiments, the microfluidics layer 103 comprises a plurality of interconnected microchannel inlet ports 104, an assay chamber 105 in fluid connection with the plurality of microchannel inlet ports 104, and an open capillary pump 106 in fluid connection with the assay chamber 105. In some embodiments, the plurality of microchannel inlet ports 104 are in fluid connection with the microneedle array 102 via the microneedle scaffold layer 101, and are configured to enable directional transport of fluid such as ISF from a wound to the assay chamber 105. In some embodiments, the inlet microchannel inlet ports 104 that connect with porous microneedles 102 to move fluid into the microchannels are patterned with an asymmetrically changing shape and channel interconnections to facilitate directional fluid flow into assay chamber 105. In some embodiments, the microfluidics layer 103 has a thickness of 1 mm to 10 mm, and / or a volume capacity of 1 pL to 1 mL. In some embodiments, the microfluidics layer 103 comprises polylactic acid (PLA) or other suitable biocompatible polymer or combination thereof. In some embodiments, the microchannel inlet ports 104, capillary channels, assay chamber 105, and / or open capillary pump 106 of the microfluidics layer 103 arepatterned into the top side of the microneedle scaffold layer 101 in order to direct interstitial fluid from the microchannel inlet ports 104 into the assay chamber 105. In some embodiments, a negative pressure gradient is created with the open capillary pump 106 to passively pump the ISF. In some embodiments, the system 100 is configured to extract about 2 pL of ISF per hour.

[0098] In some embodiments, system 100 further includes a wound cover 111 above the sensor layer 107 opposite the microfluidics layer 103. In some embodiments, the wound cover 111 comprises a surgical bandage, a waterproof bandage, adhesive skin closure, or other suitable cover or combination thereof.

[0099] In some embodiments, the sensor layer 107 comprises a transceiver 110 and an electrochemical sensor array 108 communicatively connected to the transceiver 110. In some embodiments, the electrochemical sensor array 108 is configured to measure a dielectric value of collected interstitial fluid (ISF). In some embodiments, the sensor layer further includes a battery, rechargeable battery, wireless energy collecting antenna, or other suitable energy storage device or energy transfer device, or combination thereof to power the components (108, 109, 110) of the sensor layer 107. In some embodiments, the electrochemical sensor array 108 is positioned above, on, proximate to, or within either partially or fully the assay chamber 105. In some embodiments, the electrochemical sensor array 108 directly or indirectly measures dielectric values of the ISF. In some embodiments, various physiological data can be calculated based on the measured dielectric value of the ISF, including but not limited to pH, temperature, cortisol, glucose, osmolality, immune cell factors, glucocorticoids, alcohol, or any other suitable physiological analyte or combination thereof. In someembodiments, the electrochemical sensor array 108 can be configured to measure frequency responses of ISF at different temperature and pH states with a 95% confidence and a signa l-to- noise ratio of at least 5 dB. In some embodiments, the sensor layer 107 comprises a computing system 113.

[0100] In some embodiments, the sensor layer 107 can further include a dielectric sensor, a pH sensor, a temperature sensor, or other suitable sensor or combination thereof, either as a primary sensor in place of the electrochemical sensor array 108, or as a secondary sensor 109 in addition to the electrochemical sensor array 108. In one embodiment, the electrochemical sensor array 108 comprises a multi-sensor as shown in FIG. 2 or a SansEC sensor as shown in FIG. 3B. In some embodiments the sensor layer 107 is re-usable.

[0101] In some embodiments, sensor platform is used to measure the electrical signatures of chemical and physiological analytes of interstitial fluid in the assay chamber. An example approach is the SansEC (LAR-TOPS-193), a novel sensor platform initially developed by NASA, implemented into the system 100 to measure the dielectric properties of interstitial fluid in the assay chamber. When the dermal interstitial fluid at the wound undergoes changes related to the onset of infection (e.g., pH and temperature), the spectral properties of the sensor will deviate with detectable significance from the baseline spectrum, thus enabling detection of the onset of infection. In some embodiments, the system 100 is configured to work with other types of broad-spectrum electrochemical sensors that utilize an array of electrodes to detect the electrical signals generated by analytes (e.g., pH, temperature, enzymatic activity) in the interstitial fluid to detect the onset of infection.

[0102] In some embodiments, the radiofrequency (RF) electromagnetic field is transmitted in real-time to a smartphone application which can include a novel signal processing machine learning algorithm to process measurement data, and assign dielectric thresholds to categories representing baseline conditions, the wound healing response, and development of an infection, and allows clinicians to access the application, enabling them to monitor the state of the wound and generate a more accurate diagnosis. In some embodiments the wound state data may be reported in a visual cue that is simple and easy for clinical teams and patients to interpret, with color-coded signals used to represent baseline (green), inflamed (yellow), and high infection risk (red) states of the wound. In some embodiments, the data is transmitted via a vector network analyzer.

[0103] In some embodiments, and referring to FIG. 4A and FIG. 4B, the sensor layer 107 comprises a computing system 113. In some embodiments, computing system 113 comprises a first and second watchdog circuit, a microcontroller, a vector network analyzer, and a Bluetooth module. Computing system 113 enables wireless data telemetry between the wound monitoring device 100 and receiver mobile device. In some embodiments, the first watchdog circuit activates the transmitter module, which uses any suitable wired or wireless communication link including ethernet, fiber optic connections, Bluetooth, cellular networks, and the like, to test the connection between wound monitoring system and receiver device. If the connection passes as true, the transmitter module activates a second watchdog circuit, which in turn activates the microcontroller and initiates a frequency sweep measurement of wound fluid physiological parameters, then relays this data back to the receiver device via the communication link. In some embodiments, the device is periodically activated 1 -2 times perhour which minimizes power consumption. In some embodiments, in the event of a failure to connect to wound monitoring device, the receiver device will receive a notification of failed measurement with instructions to keep device in range for the next connection attempt, notifying both the patient and clinical team of the failed measurement attempt. In some embodiments, and after establishing connection between the wound monitoring device and the mobile receiver device, real-time physiological data is transmitted to a secure and compliant cloud storage server, at which point the data is analyzed using a machine learning algorithm described below. In some embodiments, the classifier output predicts wound infection risk, and this result is relayed to a clinical electronic health / medical record (EHR / EMR) software platform in a simple, color-coded format, and accessed by clinical teams.

[0104] In some embodiments, system 100 is configured to collect data in real-time or near- real time and transmit it to a smartphone app or similar that is accessed by medical care providers, enabling real-time or near real-time remote monitoring of a patients wound. In some embodiments the system 100 is configured to alert a patient and / or medical care provider upon determination of an infection being likely based on a threshold value being exceeded, based on statistically significant changes in physiological measurements over time. In one example embodiment, a healthy baseline can comprise a temperature of 37.0°C and a pH of 6.5, an inflamed threshold can comprise a temperature of 37.5°C and a pH of 5.0, and an infection threshold can comprise a temperature of 39.0°C and a pH of 7.5. In some embodiments, relative fluctuations of dielectric properties, such as permittivity and impedance, are tracked over time to establish a baseline, and changes in physiological data are compared to the baseline. In some embodiments, once a change persists over a set time and / or becomesstatistically significant relative to the initial healthy baseline, the wound can be classified as inflamed and / or infected. In some embodiments, clinical tracking of wound state data over time will identify real-time trends in the wound-healing process that may generate benefits through predictive analytics, patient-specific medical interventions, and highly accurate preinfection warning.

[0105] Referring now to FIG. 6, a wound monitoring method 400 is shown. The method starts at Operation 401 where a wound monitoring 100 is provided.

[0106] At Operation 402, the system 100 is applied to a subject or patient to collect interstitial fluid (ISF) from the microneedle array 102 to the assay chamber 105 via the microneedle scaffold layer 101 and the microfluidics layer 103. In some embodiments, natural capillary action is used to collect the ISF, while in other embodiments a pump such as open capillary pump 106 is utilized to collect ISF.

[0107] At Operation 403, wound physiology data is collected. In some embodiments, the wound physiology data is collected based on dielectric properties of the ISF as measured by the electrochemical sensor array 108. In some embodiments, wound physiology data is collected via a dielectric sensor, a pH sensor, a temperature sensor, or other suitable sensor or combination thereof. In some embodiments, the physiology data comprises pH, temperature, cortisol, glucose, osmolality, immune cell factors, glucocorticoids, alcohol, or any other suitable physiological analyte or combination thereof.

[0108] The method 400 ends at Operation 404, where a wound physiological state is categorized. In some embodiments, the wound physiological state is categorized as normal,abnormal, healthy, inflamed, infected, or other suitable category or combination thereof. In some embodiments, categorization of the wound physiological state comprises calculating changes in the physiology data over time, and categorizing the wound state via a machine learning algorithm, artificial intelligence, or similar based on the change in the physiology data. Further details of an exemplary machine learning algorithm are shown in FIG. 8A, FIG. 8B, and FIG. 8C and described below.

[0109] Referring now to FIG. 7, a microneedle array manufacturing method 500 is shown. The method 500 starts at Operation 501, where polydimethylsiloxane (PDMS) or similar is laser patterned with a sacrificial layer comprising acrylic or another suitable material to create a mold.

[0110] In some embodiments, Operation 501 further comprises laser patterning a variable photolithographic design with a range of geometric parameters and microchannel flow rates on a single PDMS device. In some embodiments, the geometric parameters are optimized and the microchannel flow rates are evaluated using small particle tracking methods.

[0111] At Operation 502, the mold is silanized. In some embodiments the step of silanizing the mold comprises, submerging the mold in 30% hydrogen peroxide, sonicating the mold at room temperature for 5 minutes to form hydroxyl groups on the mold surface, blow drying the mold with clean compressed nitrogen gas or clean compressed air, desiccating the mold in a vacuum chamber for 12 or more hours in the presence of 100 pL of silane, rinsing the mold with isopropyl alcohol (IPA), and / or blow drying the mold with clean compressed nitrogen gas or clean compressed air a second time.

[0112] At Operation 503, a microneedle array is formed by applying a polyethylene glycol(PEG) and polylactic acid (PLA) polymer mixture to the mold. In some embodiments, the porous microneedles are formed via removal of polyethylene glycol (PEG) from a PEG and PLA mixture comprising 5% to 50% PEG and 5% to 50% PLA, about 10% PEG and about 24% PLA, about 10% PEG and about 16% PLA, or any other suitable combination. In some embodiments, the degree of porosity of the microneedles is tunable based on the PEG and PLA mixture concentration. In some embodiments, the degree of porosity is about 50%. In some embodiments, the PEG comprises a molecular mass of 6 kDa to 20 kDa, about lOkDa, or other suitable molecular mass. In some embodiments, the pore size of the microneedles is tunable based on the PEG molecular mass. In some embodiments, the pore size of the microneedles is from 1 nm to 5000 nm. In some embodiments, multiple combinations of different PEG molecular mass can be used to generate a distribution of porosity across the microneedles.

[0113] In some embodiments, the step of forming the microneedle array comprises combining PEG and PLA of a set mixture and molecular weights to achieve a desired porosity and pore size in a vessel via a magnetic stir bar, placing the vessel into a 60°C to 80°C thermal bath, degassing the PEG and PLA mixture in a filtered Erlenmeyer flask for using a vacuum pump, heating the mold via a hot plate to 80°C for 5 minutes to 10 minutes, pipetting or injecting the PEG and PLA mixture into the mold, pressing the PEG and PLA mixture via a mold press, desiccating the mold in a vacuum desiccator for 2 minutes to 10 minutes, sonicating the mold in a heated sonicator at 50°C for three 10 minute cycles, drying the mold, and / or curing the mold for 12 or more hours in a vacuum oven at 40°C to 60°C.

[0114] At Operation 504, the microneedle array is removed from the mold. The method 500 ends at Operation 505, where PEG is removed or leeched from the microneedle array to form a porous PLA microneedle array.

[0115] In some embodiments, the steps of removing the microneedle array from the mold and removing the PEG from the microneedle array to form a porous PLA microneedle array comprises removing the mold from a vacuum oven, liberating the microneedle array from the mold via lifting the edge with a tweezer or similar, soaking the microneedle array in 1:1 deionized water (DI) and methanol solution for at least 72 hours to remove PEG from the PLA and / or removing the microneedle array from the solution.

[0116] In some embodiments, the method 500 further includes cleaning the mold prior to the step of silanizing the mold. In some embodiments cleaning the mold comprises soaking the mold in a surfactant for 6 hours to 24 hours, rinsing the mold with deionized water (DI), soaking the mold in isopropyl alcohol (IPA) for 0.33 hours to 24 hours, rinsing the mold with DI a second time, drying the mold via clean compressed air; and / or removing dust via tape.

[0117] Referring now to FIG. 8A, FIG. 8B, and FIG. 8C, details of an example machine learning (ML) classification algorithm are shown. In some embodiments, the ML algorithm comprises a classification decision tree algorithm to correctly categorize a wound state based on the collected dielectric data of the ISF, and changes of the collected dielectric data over time, or compared to a baseline or threshold value. In some embodiments, the ML classification algorithm comprises a decision tree, a nearest-neighbor classifier, a k-nearest-Z1neighbor (kNN) classifier, a logistic regression, random forest, a binary classifier, a stochastic gradient classifier, a reinforcement learning classifier, or any other suitable classifier.

[0118] In some embodiments, the ML algorithm comprises a multi-layers neural network artificial intelligence (Al) or ML architecture that receives electrochemical signals from the sensor 108 and performs steps comprising, post-processing and denoising of signal, extraction of unique features from the signal, self-learning and adaptation to variations and noise within the signal, the application of classification algorithms to distinguish "normal" from "nonnormal" samples, and outputting predicted physiological state of wound sample in a human- readable, color-coded format. For example, in a wound classification setting, the samples are classified as "healthy", "inflamed", or "infected" based on the relative fluctuations of their electrochemical signal over time.

[0119] In some embodiments, an input frequency from 20 GHz to 30 GHz is chosen. In some embodiments, a decision tree is utilized to split, identify and tag a sample with accuracy of at least 95% or at or near 100%. In some embodiments, the input comprises a dielectric value of the ISF such as permittivity. In some embodiments, the output comprises a wound state tag such as normal, abnormal, healthy, inflamed, infected, or any other suitable state tag or combination thereof.

[0120] In some embodiments, the ML algorithm is trained on known wound state tagged data, in which features are extracted from the training data and then analyzed. A feature output / database is then built and a deep learning module is then used to output a wound state classification. In some embodiments, the output is fed back to the database. Real time datacan then be input into the ML algorithm to have features extracted and analyzed, and then have a wound state classification output.

[0121] In some embodiments, the ML algorithm will track wound state data trends over the incisional healing process, generating benefits through predictive analytics and accurate preinfection warning provided to clinical teams. These improved analytics may allow the tailoring of perioperative care to specific patient wound conditions, to evaluate hospital-based or geographic wound healing trends, and to objectively track successful tissue healing. The use of this software will also enhance engagement between patients and their clinical teams to relay wound state data from the outpatient (at home) setting.EXPERIMENTAL EXAMPLES

[0122] The invention is now described with reference to the following Examples. These Examples are provided for the purpose of illustration only and the invention should in no way be construed as being limited to these Examples, but rather should be construed to encompass any and all variations which become evident as a result of the teaching provided herein.

[0123] Without further description, it is believed that one of ordinary skill in the art can, using the preceding description and the following illustrative examples, make and utilize the present invention and practice the claimed methods. The following working examples therefore, specifically point out exemplary embodiments of the present invention, and are not to be construed as limiting in any way the remainder of the disclosure.

[0124] Experimental molds were fabricated via the methods described herein. An exemplary PDMS microneedle mold is shown in FIG. 9. Further tests were run comparing the quality of microneedle fabrication using non-silanized and silanized molds. FIG. 10 depicts an SEM image of a microneedle fabricated via 6 kDa PEG and a non-silanized mold while FIG. 11 depicts an SEM image of a microneedle fabricated via 10 kDa PEG and mold silanization.Adding a silanization step directly after the standard cleaning step in the protocol gave an improvement in helping to match the needle size to the mold dimension. The 10 kDa needle shows marked improvement in size when compared to the 6 kDa needle. Additional steps to improve the needle fabrication process include incorporating a mold press that is used to physically apply pressure to the heated PEG / PLA mixture after being pipetted onto the mold, adding a vacuum degassing step in between mold press steps to help liberate air that is trapped in the PEG / PLA mixture due to the infill of the needle mold, and adding a pre-gassing step with a filtered Erlenmeyer flask under vacuum for the PEG / PLA to remove bubbles that are incorporated during the mixing steps. Together, these steps have led to a robust patch fabrication protocol where patch to patch variance is more strongly associated with experimental parameters of interest (e.g. PEG concentration, PLA concentration, PEG Molecular weight, microneedle geometry).

[0125] A combined approach of computational fluid dynamics (CFD) modeling and benchtop testing was employed to establish feasibility of the microneedle (MN) design and to provide a distinct set of design criteria to explore in prototyping efforts. CFD simulations in Ansys Fluent were performed to investigate the relationship between microneedle geometry and fluid extraction capabilities. A schematic of the MN-skin interface was prepared usingSolidworks (see FIG. 28 and FIG. 29). The schematic includes a 1.5mm thick dermal layer with40% ISF concentration, a 100 urn thick epidermis with negligible ISF concentration, and the porous MN patch and needles. The diffusive transport of ISF through the dermis was modeled to calculate the theoretical ISF flow rate at the dermis-MN interface for a set of needles with varying height, diameter, and surface area (see FIG. 20). Flow into the MNs was calculated using transient simulations, determining that that maximizing needle height resulted in increased ISF extraction from the dermis into the MN scaffold.

[0126] Details of the fabrication of the PDMS molds follows using methods derived from Nejad et al. 2018. Sylgard 184 elastomer and crosslinker were added to a weigh boat in a 10:1 ratio. The elastomer and crosslinker were stirred vigorously to mix. The mixture was placed under vacuum in a desiccation chamber for 30 minutes until bubbles were removed from the mixture. The mixture was poured into custom acrylic chambers with 22x22x4 mm inner dimension. To ensure the needle molds had a controlled thickness, inverse acrylic lids were etched with a laser cutter to generate an indent with dimensions 16xl6xlmm. The inverse acrylic lids were placed, indent side down, onto the filled acrylic chambers. The assembled acrylic chambers with PDMS infill were placed into an over at 80°C to bake overnight. The molds were retrieved from the oven. The PDMS molds were retrieved from the assembled acrylic chambers using a razor blade to gently remove the inverse acrylic lid, followed by gently freeing the vertical edges of the PDMS mold from the sides of the acrylic chamber. The molds were loaded into a laser cutter with a 1 / 16" thick acrylic sheet (x,y dimension 16x16mm) placed into the indent present in the mold. The laser cut a pattern comprising of an array of asteriskstyle, overlapping lines that cut into the PDMS at the centers of each asterisk. The molds were removed from the laser cutter, and the 1 / 16" thick acrylic sheet was removed from the mold.

[0127] Cleaning of the molds included the soaking the molds in Conflikt soap (1%) for 6 hours, rinsing the molds with DI, soaking the molds for 20min in IPA, rinsing the molds in DI, drying the molds with clean compressed air, and using scotch tape to remove dust.

[0128] The molds were silanized by placing clean molds into a petri dish with 30% hydrogen peroxide solution such that the molds were submerged. The petri dishes containing the molds were placed into a bath sonicator and sonicated at room temperature for 5 minutes to form hydroxyl groups on the mold surface. Molds were then removed and blown dry with clean compressed nitrogen gas or air. Following the drying step, the molds were placed into the bottom half of a clean petri dish and set into a vacuum desiccator. A microscope coverslip (22x22mm) was placed into the desiccator, and subsequently about a 100 pL droplet of silane was deposited onto the coverslip. The lid was then placed onto the desiccator, and the vacuum pump was turned on to evacuate the chamber overnight. In the morning, molds were removed, rinsed with IPA, and blown dry with clean compressed nitrogen or air. After blow drying, the molds were placed into a clean petri dish.

[0129] Microneedles were fabricated by combining PLA and PEG in a 50 mL falcon tube with a magnetic stir bar. Note, various concentrations of PLA / PEG were tested. The falcon tube was placed into a thermal bath (RO water) at 80°C with another stir bar to stir the bath and the mixtures stir bar. The PLA / PEG mixture was then degassed in a filtered Erlenmeyer flask for about 5 minutes using a vacuum pump. The molds were then heated on a hot plate at 80°C for about 5-10 minutes. The PLA / PEG was then pipetted into the mold. A "mold press" pressedthe PLA / PEG into the mold to help with the infill. The mold was then placed into a vacuum desiccator and run for 2 minutes and then removed. The step of pipetting the PEG / PLA to the step of removing the mold from the desiccator was repeated 3 times, except on the 3rd cycle the molds were not pressed with the mold press. Then the molds were sonicated in a heated sonicator at 50°C for three 10 min cycles with 1 min off in between consecutive cycles. The molds were then removed from the sonicator, dried outside of petri dishes, and placed into new petri dish. The molds then were transferred to a ENT Cleanroom vacuum oven to cure overnight with temperature set to tick 49°C. Samples were then removed from the vacuum oven the following morning and the microneedle patch was liberated from the mold using tweezers by gently lifting along the edges followed by peeling gently from one of the sides. The microneedle patch was then placed into 1:1 deionized watenmethanol to soak for 72 hours to removes the PEG from the hardened PLA network. After 72 hours the patch was removed and place into a fresh petri dish.

[0130] PEG as a porogen for the PLA at various PEG molecular weights were explored as shown in FIG. 12, FIG. 13, and FIG. 14. 6 kDa PEG made good full needles but lacked pores. 10 kDa PEG gave full needle size and had some porous structure. 20 kDa PEG produced nubby needles, but a more defined pore structure. PEG may have blocked some PLA polymerization.

[0131] Needles were cross-sectioned using focused ion beam milling to expose cut faces that demonstrate porous polymer microstructure as shown in FIG. 15. FIG. 15 shows an SEM image showing the porous structure of an exemplary experimental microneedle fabricated via10% 10 kDa PEG and 16% PLA.

[0132] Fluid extraction and porosity of the microneedles was then explored, and results are shown in FIG. 16A, FIG. 16B, FIG. 17, FIG. 18, and FIG. 19.. Referring to FIG. 16A and FIG.16B, the microneedles were weighed, and their dry mass was recorded. The microneedles were placed in a dish of deionized water and left for 10 minutes to allow fluid uptake. After 10 minutes, the microneedle patches were dried with a Kimwipe to remove water on the outer surface. The microneedles were weighed again, and a wet mass recorded. Percent swelling as defined by Equation 1: 100 (1)was calculated based on the increase of mass due to water uptake and percent porosity was calculated based on Equation 2:where pPrefers to the density of the solid polymer (Poly, D, Lactic Acid = 1.24 g / cm3) while p0is the density of water and is assumed to be 1.0 g cm-3. Methods were derived from Lui et al. 2020.

[0133] These results demonstrate that the microneedles have porous structure and can rapidly uptake fluid, then swell to retain the fluid. Results from the 0% PEG control demonstrated that only a minimal amount of fluid is taken up without the inclusion of PEG, likely due to open space in the polymer network that remains after purging of the solvent. Data from the PEG groups suggests that 10% PEG and 16% PLA maximizes fluid uptake. The 24% PLAgroup showed slightly less fluid uptake, likely because more of the polymer network is occupied by solid PLA polymer compared to the 16% group.

[0134] Referring to FIG. 17 microneedles fluid extraction was then tested on agarose gel. Agarose hydrogel (1.5% wt%) in PBS (98.5%) with 0.1% rhodamine 6G dye was prepared as described in Yadav et al. 2022 and using methods derived from Sumontha et al. 2022. The dish was left at room temperature overnight to cure into agarose gel. Microneedle samples were weighed, then inserted into the agarose gel using a 100g weight applied for 10s. Microneedles were left in the gel for 30 minutes to allow fluid extraction. Then, needles were weighed and imaged. These results demonstrate that the microneedles have porous structure and can uptake fluid from a hydrogel. The 0% PEG control demonstrated the least amount of fluid uptake but did show some uptake after 30 minutes likely because there is open space in the porous polymer network after purging of the solvent. Once again, the 10% PEG 16% PLA group appears to show the greatest fluid uptake. Lower PEG concentration causes less pores to be formed. Higher PLA concentration leaves less space in the polymer network for uptake of fluid. The dye was included in the agarose gel so that the uptake of fluid could be visualized. Images show that the needle-side of the microneedle scaffold is filled with red dye. Intact needles can be seen in the zoomed in image.

[0135] Referring to FIG. 18, mechanical strength of the microneedles was tested. Parafilm M was folded into 8 equal layers totaling 1mm thickness (0.125 mm per layer) and taped down to a Styrofoam lid. Microneedle arrays were inserted into the parafilm using a 1kg weight (~1N Force) for 30 seconds, were carefully removed and parafilm was examined under a light microscope. The number of holes successfully formed in each layer were counted and the ratioof total needles to holes created in each parafilm layer was calculated. Methods were derived from Larraneta et al. 2O16.The 10% PEG 24% PLA group performed the best of the needles tested, and 2 / 3 of the needles were able to penetrate 3 layers of parafilm, while the 16% PLA could only partially penetrate 2 layers of parafilm. This suggests that the 24% PLA group demonstrates improved mechanical strength over 16% PLA.

[0136] FIG. 19 shows further exemplary experimental results for fluid extraction (left) where the percent swelling due to fluid extraction of MNs with varying PLA and PEG concentrations after immersion into an agarose gel. * p < 0.05 vs. 0% PEG control group. FIG.19 shows further exemplary experimental results for mechanical tests (right) where the percent of holes created by the MNs after insertion into multi-layered Parafilm. * p < 0.05 vs. 0% PEG group. # p < 0.05 vs. 10% PEG 16% PLA group. The results demonstrate that the inclusion of PEG as a porogen significantly improves the porosity and volumetric swelling capacity of the microneedles (FIG. 19). Furthermore, early-stage mechanical tests have demonstrated that increased PLA concentration and the inclusion of PEG significantly improves microneedle mechanical strength and penetration (FIG. 19).

[0137] Referring to FIG. 20, an Ansys CFD simulation was performed to find microneedle properties to maximize fluid extraction. For needle height, a maximum height of 950 pm was set by pain threshold as detailed in Gill et al. 2008, and a minimum height of 500 pm was set by needing to penetrate stratum to reach fluid-rich dermis. For needle diameter, a minimum diameter of 250 pm was set by needle fabrication and mechanical testing as many needles below this size do not fully form and lack structural integrity. The simulation was initialized witha boundary condition of water in patch equal to 0, and the simulation used arbitrary time steps shown as integers to represent each iteration of fluid transport. This is why there is a gap shown between steps 1-2. Early results demonstrate that taller microneedles can more quickly extract fluid into patch.

[0138] Referring to FIG. 21A and FIG. 21B a static ISF dielectric experiment was performed with the goal of determining frequency range(s) for best detecting wound state changes, which can then be used to define trace width and length for various electrochemical sensors like the SansEC. Dielectric measurements were performed on an artificial interstitial fluid with set pH and temperature values that corresponded to {baseline = pH 6.5, Temp 37°C; inflammation = pH 5.0, Temp 37.5°C; infected = pH 7.5, Temp 39°C}. The measurements were made using a Keysight E5080B Vector Network Analyzer with an open-ended coaxial probe (Keysight slim- form). Measurements were taken across a range of frequencies (200MHz - 20Ghz) where the permittivity of each sample was measured at 3 spatially separated regions within the fluid. The pH of the artificial fluid samples was tuned prior to the experiment and validated on the day of experiment. The temperature was set in real-time using a VWR hotplate. The results of the experiment show that the system is able to detect a statistically significant difference between the baseline and infected states, as well as between the inflamed and infected states. The region of the frequency sweep that gives the lowest (not significant per 95% confidence) p- values for the third comparison of baseline & inflamed is around 3.0 GHz and 6.8 GHz. The artificial ISF was made as described in Bollella et al., 2019, and comprised 2.5 mM CaCI2, 5.5 mM glucose, lOmM Hepes, 3.5 mM KCI, 0.7 mM MgSo4, 123 mM NaCI, 1.5 mM NaH2PO4, and7.4 mM saccharose.

[0139] Dielectric properties of ISF were also explored. Referring to FIG. 22A, FIG. 22B, FIG.23A, and FIG. 23B, details of a dynamics experiment are shown. Dynamic tests were performed where the frequency response was measured semi-continuously as the fluid was driven from a baseline state to an inflamed state and then to an infected state. This was done using a microfluidic setup with gravity driven flow from an upper reservoir where the pH and temperature of the fluid were set into the assay reservoir. Fluid in the assay chamber was measured using a custom-built validation sensor (FIG. 22B) and the VNA and nanoVNA to measure the dielectric response through time.

[0140] Referring to FIG. 24, example experimental results of Sil curves versus frequency are shown. The experiment aimed to understand the dielectric properties of the ISF by measuring frequencies of the fluid using a spectrum analyzer to map fluid resonances. By varying the fluid properties of pH and temperature, the hypothesis that the resonant frequencies shift due to a given property perturbation was tested. Further the degree to which shifts correlate to the physiological state as measured by a validation assay was characterized. The outcome of the experiment helped to find the appropriate communication frequency range to inform future sensor design. Additionally, data from this study was used to generate an early-stage proof of concept design for the machine learning analysis software.

[0141] Artificial ISF was prepared as follows. First, artificial ISF was formulated comprising 2.5 mM CaCI2, 5.5 mM glucose, lOmM Hepes, 3.5 mM KCI, 0.7 mM MgSo4, 123 mM NaCI, 1.5 mM NaH2PO4, 7.4 mM saccharose, in Milli-Q water (see Bollella et al., 2019). The following were added to an empty glass beaker: CaCI2(2.5mM:367.5 mg); Glucose(5.5 mM:991 mg);Hepes(10 mM:2.383 g); KCI(3.5 mM:261 mg); MgSo4(0.7 mM:84 mg); NaCI(123 mM:7.188 g);Nah2PO4(1.5 mM:207 mg); Saccharose(7.4 mM:2.533 g). Then beaker was with Milli-Q water until IL total volume. Next, pH of solution was measured with pH strip, and if needed adjusted to 7.0 pH with titration of NaOH (basic) or HCL (acidic). This was set aside as stock ISF solution. Next, 30 mL of stock ISF was aliquoted into a 50 mL falcon tube, the temperature of fluid was adjusted to 37.0°C, and pH of fluid was adjusted to 6.5. Finally, a baseline reading was performed.

[0142] Next an "inflamed" artificial ISF fluid was produced and measured by adjusting the temperature to 37.5°C and the pH to about 5. Next an "infected" artificial ISF fluid was produced and measured by adjusting the temperature to 39°C and the pH to 7.5 or more.

[0143] Specifics of the reagents utilized are detailed in the table below:

[0144] The equipment was setup as follows. The validation temperature sensor setup included connecting the Celsius Fast-Response ±0.1°C Temperature Sensor with I2C Level Converter through JST GH connector, connecting the I2C Level Converter with Arduino on the 5v side: (a) +5v - 5v; (b) SCL - A5; (c) SDA - A4; (d) GND - GND, and connecting the Arduino with the PC which included compiling and uploading the program and reading temperature on the Serial Output Monitor window. The validation pH sensor setup included connecting the pH probe with the Analog pH meter through BNC converter, connecting the analog pH meter with the Analog Isolator, connecting the Analog Isolator with Arduino: (a) + - 5v; (b) - - GND; (c) A - AO, and connecting the Arduino with PC which included checking the status light to make sure that every component is active, running the program, and calibrating the probe with solutions (a) pH = 7; (b) pH = 4; (c) pH = 10. The command for calibration is "CAL, 7", "CAL, 4", "CAL, 10" and "CAL, CLEAR".

[0145] The experimental equipment utilized is detailed in the table below:

[0146] The dielectric measurement experiment was performed as follows. First the pH and temperature sensors were set up and calibrated. Then the interstitial fluid sample was placed on a hot plate and heated to the temperature corresponding to its state. Then the sensors were used to validate the pH and temperature for healthy, inflamed, and infected interstitial fluids samples and average values were calculated and recorded where measurements were made using a Keysight E5080B Vector Network Analyzer with an open- ended coaxial probe (Keysight slim-form). Measurements were taken across a range of frequencies 200MHz - 20Ghz where the permittivity of each sample was measured at 3 spatially separated regions within the fluid. Then the Sil (reflection coefficient) vs frequency plots was recorded. This was repeated three times for each type of sample. Finally, the plots were analyzed, and the shifting was observed and recorded. Each group of experiments produced 9 plots similar to that of FIG. 24.

[0147] The development of the machine learning (ML) algorithm for categorizing a wound state based on the measured analytes (pH, temperature, etc.) was based on a decision tree analysis. The analysis of the ML algorithm was based on the Sci kit-Lea rn implementation of the decision tree classifier, and included the three spatially varied samples per each temperature and pH combination which are averaged to give e' and e" frequency response curves in regions identified as being most responsive. These mean curves were used as the training set for the classifier and generates the figure of the decision tree. A subset of the raw data from each spatial location was then tested against the classifier and returned the correct classification.

[0148] Referring to FIG 25, FIG. 26, and FIG. 27, exemplary CFD simulation results demonstrating filling of a patch with fluid pulled by microneedles at a first, second, and thirdsimulation time steps, respectively, are shown. Referring to FIG. 28 and FIG. 29, Ansys CFD simulation parameters are shown. The simulation modeled transport of ISF in dermis and at the skin / microneedle interface with mass transport equations as well as modeled transport through microneedles with a capillary action-driven pressure gradient.

[0149] Referring to FIG. 30, one exemplary electrochemical sensor 108 for use in the system 100 that was explored is the SansEC sensor. The sensor is a conductive wire spiral that requires no electrical connections. FIG. 30 shows an image of the SansEC sensor (top left), a schematic of the sensor being interrogated by an antenna connected to a VNA (top right), and representative Sil reflection coefficient measurements (bottom) for the sensor on a dielectric substrate (blue line) and the sensor in free space (green line).

[0150] The SansEC is a novel sensor platform initially developed by NASA, implemented into embodiments of the system 100 to measure the dielectric properties of interstitial fluid in the assay chamber 105. The SansEC is a wire loop that when inductively coupled to a magnetic field, modulates characteristic spectral frequency components of the dielectric (interstitial fluid) onto the field. When the dermal interstitial fluid at the wound undergoes changes related to the onset of infection (e.g., pH and temperature), the spectral properties of the SansEC sensor will deviate with detectable significance from the baseline spectrum, thus enabling detection of the onset of infection. In some embodiments, the modulated radiofrequency (RF) electromagnetic field is transmitted in real-time to a smartphone application which can include a novel signal processing machine learning algorithm to process measurement data, and assign dielectric thresholds to categories representing baseline conditions, the wound healing response, and development of an infection, and can allow clinicians access to the application,enabling them to monitor the state of the wound and generate a more accurate diagnosis. The sensor's responding reflection coefficient's (Sil) frequency, amplitude, phase shift, and derived impedance parameters are correlated to the magnitude of change of ISF temperature and pH.

[0151] Referring to FIG. 31A, FIG. 31B, and FIG. 31C, poly-lactic acid (PLA) microneedle array was tested for the penetration efficiency on human skin models. FIG. 31B depicts an ex- vivo human skin model after insertion of PLA microneedle array coated with Trypan Blue due. The indentations show where the microneedle array penetrated the skin. FIG. 31C depicts a sample micrograph showing H&E staining of human skin model after microneedle penetration testing. The region of the skin after microneedle perforation is shown with corresponding dimensions, demonstrating microneedle penetration efficacy.

[0152] Referring to FIG. 32A, FIG. 32B, and FIG. 32C, a schematic showing the microfluidic design with variable parameters is depicted. The Wafer diameter was 75 mm, and the varied parameters are: the horizontal jitter distance between the triangles, d. The tested range for d was 0 - 332 pm (see FIG. 33). This was varied over channels 7 through 12. The microchannel width ratio a is defined as W2 / W1. This was varied over channels 1 through 6 with W1 = 901 pm for all microchannels. The tested range for W2 was 424 - 616 pm (see FIG. 33). The width ratio a for channels 7 to 12 was constant and equal to 0.68 (=616 / 902). The parameters a and d for the microchannels with format "channel it [a : d]", where d is given in pm, are as follows: 1 [0.68:244], 2 [0.64:244], 3 [0.60:244], 4 [0.68:244], 5 [0.51:244], 6 [0.47:244], 7 [0.68:0], 8 [0.68:52], 9 [0.68:102], 10 [0.68:212], 11 [0.68:272], 12 [0.68:332], Referring to FIG. 33, a table is depicted showing a results summary of the varying parameters across the microchannels.

[0153] Referring to FIG. 34, an overlay of velocity vector field extracted from particle image velocimetry (PIV) analysis on real-space epifluorescence of microfl uidics channel #6 is depicted. Channels obstructions are indicated by the white-outlined triangles. Velocity vectors are shown in red. The affine scaling of a given vector is proportional to its magnitude. Scale bar: 250 pm. Referring to FIG 35A and FIG. 35B, bar charts of PIV results are depicted. FIG. 35A depicts the x-component of the fluid flow rate (from right to left as viewed in FIG. 35A). FIG. 35B depicts the magnitude of the fluid flow rate.

[0154] Referring to FIG. 36A and FIG. 36B, exemplary sensor measurements of artificial interstitial fluid (ISF) are depicted. FIG. 36A depicts Sil reflection coefficient magnitudes measured with resonant antennae (SansEC) from artificial ISF at pH and temperatures representing "baseline", "inflamed", and "infected" wounds. Resonant frequency response was identified at 380 - 440 MHz. p < 0.05 for infected vs. baseline samples, p < 0.05 for infected vs. inflamed samples, p < 0.05 for inflamed vs baseline samples. FIG. 36B depicts the change in resonant frequency ( Afr) of artificial ISF at physiologically varying pH vs ambient background measurements with resonant antennae (SansEC). Mean + SEM, n = 4 different resonant antennae (SansEC) sensors for each pH condition. * p < 0.05 for baseline (6 - 6.5 pH) vs inflamed (4.5 - 5.5 pH). # p < 0.05 for infected (7 - 7.5 pH) vs inflamed (4.5 - 5.5 pH). Linear regression (red line) shows strong correlation between ISF pH and Afr, with R2= 0.96.COMPUTING ENVIRONMENT

[0155] In some aspects of the present invention, software executing the instructions provided herein may be stored on a non-transitory computer-readable medium, wherein thesoftware performs some or all of the steps of the present invention when executed on a processor.

[0156] Aspects of the invention relate to algorithms executed in computer software. Though certain embodiments may be described as written in particular programming languages, or executed on particular operating systems or computing platforms, it is understood that the system and method of the present invention is not limited to any particular computing language, platform, or combination thereof. Software executing the algorithms described herein may be written in any programming language known in the art, compiled or interpreted, including but not limited to C, C++, C#, Objective-C, Java, JavaScript, MATLAB, Python, PHP, Perl, Ruby, or Visual Basic. It is further understood that elements of the present invention may be executed on any acceptable computing platform, including but not limited to a server, a cloud instance, a workstation, a thin client, a mobile device, an embedded microcontroller, a television, or any other suitable computing device known in the art.

[0157] Parts of this invention are described as software running on a computing device. Though software described herein may be disclosed as operating on one particular computing device (e.g. a dedicated server or a workstation), it is understood in the art that software is intrinsically portable and that most software running on a dedicated server may also be run, for the purposes of the present invention, on any of a wide range of devices including desktop or mobile devices, laptops, tablets, smartphones, watches, wearable electronics or other wireless digita l / cel lula r phones, televisions, cloud instances, embedded microcontrollers, thin client devices, or any other suitable computing device known in the art.

[0158] Similarly, parts of this invention are described as communicating over a variety of wireless or wired computer networks. For the purposes of this invention, the words "network", "networked", and "networking" are understood to encompass wired Ethernet, fiber optic connections, wireless connections including any of the various 802.11 standards, cellular WAN infrastructures such as 3G, 4G / LTE, or 5G networks, Bluetooth®, Bluetooth® Low Energy (BLE) or Zigbee® communication links, or any other method by which one electronic device is capable of communicating with another. In some embodiments, elements of the networked portion of the invention may be implemented over a Virtual Private Network (VPN).

[0159] FIG. 37 and the following discussion are intended to provide a brief, general description of a suitable computing environment in which the invention may be implemented. While the invention is described above in the general context of program modules that execute in conjunction with an application program that runs on an operating system on a computer, those skilled in the art will recognize that the invention may also be implemented in combination with other program modules.

[0160] Generally, program modules include routines, programs, components, data structures, and other types of structures that perform particular tasks or implement particular abstract data types. Moreover, those skilled in the art will appreciate that the invention may be practiced with other computer system configurations, including hand-held devices, multiprocessor systems, microprocessor-based or programmable consumer electronics, minicomputers, mainframe computers, and the like. The invention may also be practiced in distributed computing environments where tasks are performed by remote processing devicesthat are linked through a communications network. In a distributed computing environment, program modules may be located in both local and remote memory storage devices.

[0161] FIG. 37 depicts an illustrative computer architecture for a computer 600 for practicing the various embodiments of the invention. The computer architecture shown in FIG. 37 illustrates a conventional personal computer, including a central processing unit 650 ("CPU"), a system memory 605, including a random-access memory 610 ("RAM") and a readonly memory ("ROM") 615, and a system bus 635 that couples the system memory 605 to the CPU 650. A basic input / output system containing the basic routines that help to transfer information between elements within the computer, such as during startup, is stored in the ROM 615. The computer 600 further includes a storage device 620 for storing an operating system 625, application / program 630, and data.

[0162] The storage device 620 is connected to the CPU 650 through a storage controller (not shown) connected to the bus 635. The storage device 620 and its associated computer- readable media, provide non-volatile storage for the computer 600. Although the description of computer-readable media contained herein refers to a storage device, such as a hard disk or CD-ROM drive, it should be appreciated by those skilled in the art that computer-readable media can be any available media that can be accessed by the computer 600.

[0163] By way of example, and not to be limiting, computer-readable media may comprise computer storage media. Computer storage media includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules orother data. Computer storage media includes, but is not limited to, RAM, ROM, EPROM,EEPROM, flash memory or other solid state memory technology, CD-ROM, DVD, or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by the computer.

[0164] According to various embodiments of the invention, the computer 600 may operate in a networked environment using logical connections to remote computers through a network 640, such as TCP / IP network such as the Internet or an intranet. The computer 600 may connect to the network 640 through a network interface unit 645 connected to the bus 635. It should be appreciated that the network interface unit 645 may also be utilized to connect to other types of networks and remote computer systems.

[0165] The computer 600 may also include an input / output controller 655 for receiving and processing input from a number of input / output devices 660, including a keyboard, a mouse, a touchscreen, a camera, a microphone, a controller, a joystick, or other type of input device. Similarly, the input / output controller 655 may provide output to a display screen, a printer, a speaker, or other type of output device. The computer 600 can connect to the input / output device 660 via a wired connection including, but not limited to, fiber optic, ethernet, or copper wire or wireless means including, but not limited to, Bluetooth, Nea r-Field Communication (NFC), infrared, or other suitable wired or wireless connections.

[0166] As mentioned briefly above, a number of program modules and data files may be stored in the storage device 620 and RAM 610 of the computer 600, including an operatingsystem 625 suitable for controlling the operation of a networked computer. The storage device620 and RAM 610 may also store one or more applications / programs 630. In particular, the storage device 620 and RAM 610 may store an application / program 630 for providing a variety of functionalities to a user. For instance, the application / program 630 may comprise many types of programs such as a word processing application, a spreadsheet application, a desktop publishing application, a database application, a gaming application, internet browsing application, electronic mail application, messaging application, and the like. According to an embodiment of the present invention, the application / program 630 comprises a multiple functionality software application for providing word processing functionality, slide presentation functionality, spreadsheet functionality, database functionality and the like.

[0167] The computer 600 in some embodiments can include a variety of sensors 665 for monitoring the environment surrounding and the environment internal to the computer 600. These sensors 665 can include a Global Positioning System (GPS) sensor, a photosensitive sensor, a gyroscope, a magnetometer, thermometer, a proximity sensor, an accelerometer, a microphone, biometric sensor, barometer, humidity sensor, radiation sensor, or any other suitable sensor.

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[0297] The disclosures of each and every patent, patent application, and publication cited herein are hereby incorporated herein by reference in their entirety. While this invention has been disclosed with reference to specific embodiments, it is apparent that other embodiments and variations of this invention may be devised by others skilled in the art without departing from the true spirit and scope of the invention.

Claims

CLAIMSWhat is claimed is:

1. A wound monitoring system, comprising: a microneedle scaffold layer including a microneedle array positioned on a first surface of the microneedle scaffold layer; a microfluidics layer above the microneedle scaffold layer and opposite the microneedle array; and a sensor layer above the microfluidic layer and opposite the microneedle scaffold layer.

2. The system of claim 1, further comprising a computing system communicatively connected to the sensor layer, comprising a processor and a non-transitory computer- readable medium with instructions stored thereon, which when executed by the processor, perform steps comprising: receiving collected wound physiological data from an electrochemical sensor array via a transceiver of the sensor layer; calculating changes in the physiological data over time; and categorizing the wound physiological state via a machine learning algorithm based on the change in the physiological data.

3. The system of claim 1, wherein the microneedle array comprises a plurality of porous biocompatible polymer microneedles comprising polylactic acid (PLA), wherein themicroneedles have a height of 500 urn to 950 urn, a base diameter of 150 urn to 450 pm, a swelling percentage of 25% to 175%, and a porosity of 30% to 80%.

4. The system of claim 3, wherein the porous microneedles are formed via removal of polyethylene glycol (PEG) from a PEG and PLA mixture comprising 5% to 50% PEG and 5% to 50% PLA, and wherein a degree of porosity of the microneedles is tunable based on the PEG and PLA mixture concentration.

5. The system of claim 4, wherein the PEG comprises a molecular mass of 6 kDa to 20 kDa, and wherein pore size of the microneedles is tunable based on the PEG molecular mass.

6. The system of claim 1, wherein the microfluidics layer comprises a plurality of interconnected microchannel inlet ports, an assay chamber in fluid connection with the plurality of microchannel inlet ports, and an open capillary pump in fluid connection with the assay chamber.

7. The system of claim 6, wherein the plurality of microchannel inlet ports are in fluid connection with the microneedle scaffold layer, and are configured to enable directional transport of fluid from a wound to the assay chamber.

8. The system of claim 1, wherein the sensor layer comprises a transceiver and an electrochemical sensor array communicatively connected to the transceiver, configured to measure a dielectric or chemical value of collected interstitial fluid (ISF).

9. The system of claim 1, wherein the computing system comprises a watchdog circuit communicatively connected to the sensor array, configured to be periodically activated.

10. The system of claim 1, wherein the system is configured as a waterproof wound dressing or surgical bandage.

11. A wound monitoring method, comprising: providing the system of claim 1; applying the system to a subject to collect interstitial fluid (ISF) from the microneedle array to the assay chamber via the microneedle scaffold layer and the microfluidics layer; collecting wound physiology data based on dielectric or chemical properties of the ISF as measured by the electrochemical sensor array; and categorizing a wound physiological state.

12. The method of claim 11, wherein the wound physiological state is categorized as at least one of normal, abnormal, healthy, inflamed, and infected.

13. The method of claim 11, wherein the step of categorizing a wound physiological state comprises:calculating changes in the physiological data over time; and categorizing the wound state via a machine learning algorithm based on the change in the physiological data.

14. The method of claim 11, wherein the step of collecting wound physiological data comprises: checking for a connection with the receiver device; and performing the measurement if the connection passes as true.

15. A microneedle array, comprising a plurality of porous biocompatible polymer microneedles comprising polylactic acid (PLA), wherein the microneedles have a height of 500 pm to 950 pm, a base diameter of 150 pm to 450 pm, a swelling percentage of 25% to 175%, and a porosity of 30% to 80%.

16. The microneedle array of claim 15, wherein the porous microneedles are formed via removal of polyethylene glycol (PEG) from a PEG and PLA mixture comprising 5% to 50% PEG and 5% to 40% PLA, and wherein a degree of porosity of the microneedles is tunable based on the PEG and PLA mixture concentration.

17. The microneedle array of claim 16, wherein the PEG comprises a molecular mass of 6 kDa to 20 kDa, and wherein a pore size of the microneedles is tunable based on the PEG molecular mass.

18. A microneedle array manufacturing method, comprising: laser patterning polydimethylsiloxane (PDMS) with a sacrificial acrylic layer to create a mold; silanizing the mold; forming the microneedle array by applying a polyethylene glycol (PEG) and polylactic acid (PLA) polymer mixture to the mold; removing the microneedle array from the mold; and removing the PEG from the microneedle array to form a porous PLA microneedle array.

19. The method of claim 18, further comprising cleaning the mold prior to the step of silanizing the mold, wherein cleaning the mold comprises: soaking the mold in a surfactant for 6 hours to 24 hours; rinsing the mold with deionized water (DI); soaking the mold in isopropyl alcohol (IPA) for 0.33 hours to 24 hours; rinsing the mold with DI a second time; drying the mold via clean compressed air; and removing dust via tape.

20. The method of claim 18, wherein the step of silanizing the mold comprises: submerging the mold in 30% hydrogen peroxide;sonicating the mold at room temperature for 5 minutes to form hydroxyl groups on the mold surface; blow drying the mold with clean compressed nitrogen gas or clean compressed air; desiccating the mold in a vacuum chamber for 12 or more hours in the presence of 100 pL of silane; rinsing the mold with isopropyl alcohol (I PA); and blow drying the mold with clean compressed nitrogen gas or clean compressed air a second time.

21. The method of claim 18, wherein the step of forming the microneedle array comprises: combining PEG and PLA in a vessel via a magnetic stir bar; placing the vessel into a 60°C to 80°C thermal bath; degassing the PEG and PLA mixture in a filtered Erlenmeyer flask for using a vacuum pump; heating the mold via a hot plate to 80°C for 5 minutes to 10 minutes; pipetting or injecting the PEG and PLA mixture into the mold; pressing the PEG and PLA mixture via a mold press; desiccating the mold in a vacuum desiccator for 2 minutes to 10 minutes; sonicating the mold in a heated sonicator at 50°C for three 10 minute cycles; drying the mold; and curing the mold for 12 or more hours in a vacuum oven at 40°C to 60°C.

22. The method of claim 18, wherein the step of removing the microneedle array from the mold comprises: removing the mold from a vacuum oven; liberating the microneedle array from the mold via lifting the edge with a tweezer; soaking the microneedle array in 1:1 deionized water (DI) and methanol solution for at least 72 hours to remove PEG from the PLA; and removing the microneedle array from the solution.

23. The method of claim 18, wherein the step of laser patterning PDMS comprises laser patterning a variable photolithographic design with a range of geometric parameters and microchannel flow rates on a single PDMS device.

24. The method of claim 23, wherein the geometric parameters are optimized and microchannel flow rates are evaluated using small particle tracking methods.

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