A paper-based wearable patch for real-time quantitative lactate monitoring

Abstract

Disclosed herein is a printable liquid lactate sensor composition comprising silk fibroin in an amount of 0.1% to 30% by weight, lactate oxidase activated by lactate to produce hydrogen peroxide, peroxidase activated by hydrogen peroxide, and a chromogenic substrate that changes color upon activation of the peroxidase. Disclosed herein is a wearable sensor comprising the printable liquid lactate sensor composition. Disclosed herein is a silk-based colorimetric wearable sensing patch for monitoring lactate concentration and pH in sweat.

Description

[Technical Field]

[0001] Priority claims This application is related to and incorporates by reference for all purposes and claims priority to U.S. Application No. 63 / 483,959, filed February 8, 2023, and U.S. Application No. 63 / 512,534, filed July 7, 2023.

[0002] STATEMENT REGARDING FEDERALLY FUNDED RESEARCH This invention was made with government support under Grant No. N00014-19-1-2399 awarded by the U.S. Navy, Office of Naval Research. The government has certain rights in this invention.

[0003] Sequence Listing Not applicable. [Background technology]

[0004] background Wearable sensors can be used for real-time, continuous health monitoring in healthcare and wellness. In particular, the use of skin-conforming flexible interfaces has attracted considerable interest for continuous, painless sampling and analysis of biofluids to extract meaningful pathophysiological information. In contrast, conventional techniques for biomarker analysis are difficult to adapt to real-time, ambulatory monitoring due to their invasive sampling protocols, biological sample preparation, and reagent stabilization. There is a need for storage-stable, noninvasive, wearable sensors. Summary of the Invention [Means for solving the problem]

[0005] overview Silk-based colorimetric wearable sensing patches for monitoring lactate concentration and pH in sweat are disclosed herein. These sensing patches can be comfortably worn during exercise or daily activities and address the shortcomings of existing wearable sensing technologies by providing a long shelf life, a wide sensing range, high reproducibility, and compactness. The use of silk fibroin as a stabilizer for biologically labile components broadens its applicability in the fabrication of storage-stable sensing patches that can be used after years of storage. These flexible silk-based sensors pave the way for real-time detection of multiple analytes for continuous health and performance monitoring. Finally, combining these wearable sensors with machine learning models for image recognition provides rapid, quantitative readouts characterized by high accuracy, high sensitivity, and sensing range, suitable for applications in both sports medicine and clinical settings.

[0006] In some aspects, the technology described herein relates to a printable liquid lactate sensor composition, the composition comprising silk fibroin in an amount of 0.1% to 30% by weight; lactate oxidase that is activated by lactate to produce hydrogen peroxide; peroxidase that is activated by hydrogen peroxide; and a chromogenic substrate that changes color upon activation of the peroxidase.

[0007] In some aspects, the technology described herein provides a wearable sensor for detecting lactate, the wearable sensor comprising a biopolymer substrate embedded with lactate oxidase, which is activated by lactate to produce hydrogen peroxide, peroxidase, which is activated by hydrogen peroxide, and a chromogenic substrate that changes color upon activation of the peroxidase, the biopolymer substrate comprising silk fibroin in an amount of 1% to 100% by weight, and a quantitative bulk colorimetric change of the biopolymer substrate as the lactate concentration within the biopolymer substrate is varied over a range having a lower limit of 0.1 mM to 100 mM, for example, but not limited to, 1 mM to 90 mM, 0.5 mM to 70 mM, 10 mM to 50 mM, for example, but not limited to, 0.1 mM, 0.5 mM, 1 mM, or 10 mM, and an upper limit of 100 mM, 90 mM, 70 mM, or 50 mM. This article is about wearable sensors that will change the way we use our products.

[0008] In some aspects, the technology described herein relates to a sweat sensor comprising a biopolymer substrate embedded with lactate oxidase, which is activated by lactate to produce hydrogen peroxide, peroxidase, which is activated by hydrogen peroxide, and a chromogenic substrate that changes color upon activation of the peroxidase, wherein the biopolymer substrate comprises silk fibroin in an amount of 1% to 100% by weight, wherein the quantitative bulk colorimetric change of the biopolymer substrate changes as the lactate concentration within the biopolymer substrate changes over a range having a lower limit of 0.1 mM to 100 mM, for example, but not limited to, 1 mM to 90 mM, 0.5 mM to 70 mM, 10 mM to 50 mM, for example, but not limited to, 0.1 mM, 0.5 mM, 1 mM, or 10 mM, and an upper limit of 100 mM, 90 mM, 70 mM, or 50 mM.

[0009] In some embodiments, the technology described herein relates to a colorimetric sensor for detecting target chemicals in a fluid sample, the sensor comprising: one or more detection regions on a substrate, the detection regions comprising silk fibroin; one or more enzymatic reagents configured to detect one or more target chemicals in the fluid sample; and one or more chromogenic substrates configured to indicate the relative amounts of the one or more target chemicals in the fluid sample.

[0010] In some aspects, the technology described herein relates to a method for detecting a target chemical in a fluid sample, the method including: training a target chemical detection model using a plurality of images of a colorimetric sensor, the sensor having a plurality of reference detection regions and a plurality of sample detection regions having predetermined concentrations of the target chemical in the fluid sample; and predicting the concentration of the target chemical on the colorimetric sensor using the trained target chemical detection model.

[0011] In some aspects, the technology described herein relates to a method of manufacturing a colorimetric sensor for detecting a target chemical in a sample fluid, the method including preparing one or more paper substrates with at least one of a silk fibroin solution, one or more enzyme reagents, or one or more chromogenic substrates; and placing the one or more paper substrates on a film substrate.

[0012] These and other systems, methods, objects, features, and advantages of the present disclosure will become apparent to those skilled in the art from the following detailed description of the preferred embodiment and drawings.

[0013] All documents cited herein are hereby incorporated by reference in their entirety. Unless expressly stated otherwise or clear from the context, reference to a singular item should be understood to include a plural item, and vice versa. Unless stated otherwise or clear from the context, grammatical constraints are intended to represent any and all disjunctive and conjunctive combinations of coordinated clauses, sentences, words, etc.

[0014] The present disclosure and the following detailed description of certain embodiments thereof can be understood with reference to the following figures. [Brief explanation of the drawings]

[0015] [Figure 1] Figure 1 depicts: A) Schematic of the fabrication of a paper-based lactate-sensing patch. Lactate oxidase (LOx), horseradish peroxidase (HRP), and dye are added to a silk fibroin solution to form a chromogenic enzyme ink, which is then drop-cast onto paper. A circular lactate-sensing interface is laser cut and applied to a Tegaderm™ film, resulting in a wearable patch whose color shifts from yellow to dark red with increasing concentrations of lactate in sweat. B) Schematic of the LOx / HRP cascade reaction. LOx oxidizes lactate to produce pyruvate and hydrogen peroxide, which are used by HRP to oxidize the chromogenic substrate, resulting in a visible color change. C) Photograph of the lactate-sensing patch applied to the skin. The inset shows the circular lactate-sensing interface before (top) and after (bottom) the colorimetric response.

[0016] [Figure 2-1]Figure 2 shows the following: A) Calibration curves for silk-based chromogenic enzyme inks with and without the deposition of a chitosan base layer on Whatman Grade 1 filter paper. Sensing range with chitosan: 0–90 mM; sensing range without chitosan: 0–50 mM. Colored circles indicate the colorimetric response recorded at different lactate concentrations. B) Sensing range and sensitivity values ​​for silk-based chromogenic enzyme inks with and without the deposition of a chitosan base layer on Whatman Grade 1 filter paper after 8, 24, and 120 hours of storage at 60°C. The inset shows the colorimetric response of silk-based chromogenic enzyme inks with a chitosan base layer on Whatman Grade 1 filter paper after 120 hours of storage at 60°C. C) Calibration curves for silk-based and water-based chromogenic enzyme inks with a chitosan base layer on Whatman Grade 1 filter paper. Sensing range for silk-based ink: 0–90 mM; sensing range for water-based ink: 0–10 mM. Colored circles indicate the colorimetric response recorded at different lactate concentrations. D) Sensing range and sensitivity values ​​for silk-based and water-based chromogenic enzyme inks with a chitosan base layer on Whatman Grade 1 filter paper after 8, 24, and 120 hours of storage at 60°C. The inset shows the colorimetric response of water-based chromogenic enzyme inks with a chitosan base layer on Whatman Grade 1 filter paper after 120 hours of storage at 60°C. E) Retained activity for silk-based and water-based chromogenic enzyme inks with a chitosan base layer on Whatman Grade 1 filter paper after 18, 21, and 24 months of storage at 4°C. The inset shows the colorimetric response of silk-based and water-based chromogenic enzyme inks after 24 months of storage at 4°C. F) Retained activity for silk-based and water-based chromogenic enzyme inks with a chitosan base layer on Whatman Grade 1 filter paper after 8 hours, 24 hours, 5 days, and 2.5 months of storage at 60°C. The inset shows the colorimetric response of silk-based and water-based chromogenic enzyme inks upon 2.5 months of storage at 60°C. [Figure 2-2] Same as above. [Figure 2-3] Same as above.

[0017] [Figure 3]Figure 3 shows: A) Calibration curve for bromocresol green (BG) silk-based chromogenic pH-sensing ink on Whatman Grade 1 filter paper. Sensitivity: -39.8 ± 1.3. Sensing range: pH 3-7. B) Calibration curve for nitrazine yellow (NY) silk-based chromogenic pH-sensing ink on Whatman Grade 1 filter paper. Sensitivity: -76.1 ± 1.4. Sensing range: pH 5.5-7.5. C) Calibration curve for phenol red (PR) silk-based chromogenic pH-sensing ink on Whatman Grade 1 filter paper. Sensitivity: -40.9 ± 1.9. Sensing range: pH 6.5-8.5. Colored circles indicate the colorimetric response recorded at different pHs for each ink. D) Sensing range for bromocresol green, nitrazine yellow, and phenol red silk-based chromogenic pH-sensing ink on Whatman Grade 1 filter paper.

[0018] [Figure 4-1]Figure 4 shows the following: A) Schematic of the SVM model training and lactate prediction process. Sweat with known lactate concentrations is drop-cast onto the sensor. After the colorimetric response, images of the sensor are acquired under different lighting conditions. The images are used to train an SVM model and evaluate its performance. The trained SVM model is capable of predicting lactate concentrations when the sensor images are given as input. B) (Top) Scatter plot of the complete dataset in three-dimensional RGB color space for different lactate concentrations (0-50 mM). (Bottom) Scatter plot of the complete dataset in two-dimensional blue channel vs. red channel plane for different lactate concentrations (0-50 mM). C) The SVM model was trained using 1,316 images of the wearable sensor's colorimetric response when challenged with known lactate concentrations ranging from 0 to 50 mM. D) (Top) The performance of the SVM model was evaluated using 564 images of the wearable sensor's colorimetric response when challenged with known lactate concentrations ranging from 0 to 50 mM. (Bottom) Confusion matrix for the 564 evaluation images showing that the SVM model correctly classified the images into six lactate concentration categories with an overall accuracy of 89.3%. E) Images of the colorimetric response of the wearable sensor before (left), during (center), and after (right) a treadmill exercise session. The predicted lactate concentration at the end of the session was 30 mM. [Figure 4-2] Same as above.

[0019] [Figure 5] Figure 5 shows the colorimetric change of the sensor in the green channel before and after exposure to interfering substances (NaCl, KCl, urea, NH4Cl, CaCl2, MgCl2 at concentrations of 40 mM, 3 mM, 22 mM, 3 mM, 0.4 mM, and 50 μM, respectively) compared to the colorimetric change when exposed to lactate at a concentration of 5 mM.

[0020] [Figure 6]Figure 6 shows: A) Calibration curves for silk-based chromogenic enzyme inks with and without the deposition of a chitosan base layer on Ahlstrom Grade 55 filter paper. B) Sensing range and sensitivity values ​​after 8, 24 and 120 hours of storage at 60°C for silk-based chromogenic enzyme inks with and without the deposition of a chitosan base layer on Ahlstrom Grade 55 filter paper. C) Calibration curves for silk-based and water-based chromogenic enzyme inks with a chitosan base layer on Ahlstrom Grade 55 filter paper. D) Sensing range and sensitivity values ​​after 8, 24 and 120 hours of storage at 60°C for silk-based and water-based chromogenic enzyme inks with a chitosan base layer on Ahlstrom Grade 55 filter paper.

[0021] [Figure 7] Figure 7 shows: A) Calibration curves for silk-based chromogenic enzymatic ink with and without the deposition of a chitosan base layer on Whatman Grade 4 filter paper. B) Sensing range and sensitivity values ​​for silk-based chromogenic enzymatic ink with and without the deposition of a chitosan base layer on Whatman Grade 4 filter paper after 8, 24 and 120 hours of storage at 60°C. C) Calibration curves for silk-based and water-based chromogenic enzymatic ink with a chitosan base layer on Whatman Grade 4 filter paper. D) Sensing range and sensitivity values ​​for silk-based and water-based chromogenic enzymatic ink with a chitosan base layer on Whatman Grade 4 filter paper after 8, 24 and 120 hours of storage at 60°C. DETAILED DESCRIPTION OF THE INVENTION

[0022] Detailed Description Before describing the present disclosure in further detail, it should be understood that the present disclosure is not limited to the particular embodiments described. It is also understood that the terminology used herein is for the purpose of describing particular embodiments only, and is not intended to be limiting. The scope of the present disclosure is limited only by the claims. As used herein, the singular forms "a," "an," and "the" include plural embodiments unless the context clearly dictates otherwise.

[0023] In this application, unless otherwise clear from the context, (i) the term "a" may be understood to mean "at least one"; (ii) the term "or" may be understood to mean "and / or"; (iii) the terms "comprising" and "including" may be understood to encompass the listed element or step, whether presented by itself or with one or more additional elements or steps; (iv) the terms "about" and "approximately" are used as equivalents and may be understood to allow for standard deviation as would be understood by one of ordinary skill in the art; and (v) when ranges are provided, the endpoints are included.

[0024] Approximately: As used herein, the term "approximately" or "about" as applied to one or more values ​​of interest refers to a value similar to a stated reference value. In certain embodiments, the term "approximately" or "about" refers to a range of values ​​within 25%, 20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, 1%, or less in either direction of (higher or lower than) the stated reference value, unless otherwise stated or otherwise clear from the context (except when such number exceeds 100% of possible values).

[0025] Composition: As used herein, may be used to refer to a separate physical entity containing one or more specified components. Generally, unless otherwise specified, a composition may be in any form, e.g., gas, gel, liquid, solid, etc. In some embodiments, a "composition" may refer to a combination of two or more entities for use in a single embodiment or as part of the same article. It is not necessary in all embodiments for the combination of entities to result in a physical mixture, i.e., for the components of the composition to be capable of combination as separate co-entities of each; however, many practitioners in the art will find it advantageous to prepare a composition that is a mixture of two or more components in a pharmaceutically acceptable carrier, diluent, or excipient, which may allow the components of the combination to be administered at the same time.

[0026] Improve, increase, or reduce: As used herein or its grammatical equivalents, refers to a value compared to a baseline measurement, e.g., a measurement in a similar composition previously made according to known methods.

[0027] Substantially: As used herein, the term "substantially" refers to a qualitative state exhibiting a complete or nearly complete degree or degree of a desired characteristic or property. Those skilled in the art of biology will understand that biological and chemical phenomena rarely, if ever, go to completion and / or progress toward perfection, or achieve or avoid absolute results. The term "substantially" is therefore used herein to capture the potential lack of perfection inherent in many biological and chemical phenomena.

[0028] It should be apparent to those skilled in the art that many additional modifications beyond those already described are possible without departing from the concept of the present invention. In interpreting this disclosure, all terms should be interpreted in the broadest possible manner consistent with the context. Variations on the term "comprising" should be interpreted as referring to elements, components, or steps in a non-exclusive manner, such that the referenced element, component, or step may be combined with other elements, components, or steps not expressly mentioned. Embodiments referred to as "comprising" a particular element are also contemplated as "consisting essentially of" and "consisting of" that element. When two or more ranges are recited for a particular value, the present disclosure contemplates all combinations of upper and lower limits of that range not expressly recited. For example, recitation of values ​​from 1 to 10 or from 2 to 9 also contemplates values ​​from 1 to 9 or from 2 to 10.

[0029] As used herein, "silk fibroin" refers to silk fibroin protein produced or otherwise created by silkworms, spiders, or other insects (Lucas et al., Adv. Protein Chem., 13: 107-242 (1958)). Any type of silk fibroin can be used in the different embodiments described herein. Silk fibroin produced by silkworms, such as Bombyx mori, is the most common and represents an environmentally friendly renewable resource. For example, silk fibroin used in silk films can be obtained by extracting sericin from B. mori cocoons. Organic silkworm cocoons are also commercially available. Many different silks exist, however, that can be used, including spider silk (e.g., obtained from Nephila clavipes), transgenic silk, genetically engineered silk, such as silk from bacteria, yeast, mammalian cells, transgenic animals, or transgenic plants, and variants thereof. See, for example, WO 97 / 08315 and US Pat. No. 5,245,012, each of which is incorporated herein by reference in its entirety.

[0030] Disclosed herein is a shelf-stable, non-invasive, paper-based, colorimetric, wearable lactate sensor. This sensor leverages silk's ability to control the concentration of, print, and functionally preserve labile transduced biomolecules in a shelf-stable digital patch format for optical readout. This novel approach overcomes key challenges associated with commercializing colorimetric wearable sensors (e.g., enzyme thermolability, narrow sensing range, low sensitivity, and qualitative response) by demonstrating an unprecedented combination of stability (i.e., up to two years under refrigerated conditions), wide sensing range, and high sensitivity. Additionally, real-time quantitative signal readout is achieved using machine learning-driven image analysis, enabling assessment of physiological status using a simple smartphone camera.

[0031] Noninvasive, continuous health monitoring allows access to physiological and pathological information for assessing human well-being. In this context, wearable sensors offer great utility by providing painless sampling of biological fluids and real-time analysis of relevant biomarkers, with emerging applications in healthcare and sports medicine. Although both electrochemical and colorimetric enzymatic wearable sensors have been widely investigated for this purpose, challenges still exist in this important application space: electrochemical sensors can be bulky and uncomfortable to wear, while colorimetric sensors exhibit narrow sensing ranges and provide binary or qualitative responses that are difficult to read by eye. The commercialization of both types of sensors faces limitations imposed by the biological transduction mechanism, where the instability of the enzymes used for detection significantly reduces the shelf life of the sensors.

[0032] Disclosed herein is a shelf-stable, skin-attached, non-invasive, paper-based colorimetric wearable lactate sensor that combines high sensitivity and a wide linear sensing range by using a composite ink formed by silk fibroin and a chromogenic enzyme mixture. The composite ink leverages silk fibroin's demonstrated ability to stabilize labile entities (e.g., enzymes, cells, small molecules, proteins, nucleic acids, antioxidants, and fragile products) to create sweat-sensing patches with a long shelf life (i.e., 2 years under refrigerated conditions). These patches, combined with machine learning-assisted image analysis, provide quantitative real-time readouts with high sensitivity (i.e., 80-100%) and specificity (i.e., 95-100%).

[0033] Sweat is of particular interest because it is relatively easy to access and contains several biomarkers that correlate with physical stress, dehydration, infection, and disease. Sweat monitoring can reveal important information about the physiological state of patients and athletes. The distribution of sweat glands across the skin allows for its sampling throughout the body, making it an excellent biological fluid for both localized and distributed sensing. Contrasting its ease of sampling, sweat is a complex biological fluid that is affected by environmental and physiological interferences, making its analysis difficult. As the end product of glycolysis, lactate in sweat is a desirable target for noninvasive monitoring. Lactate levels provide insight into a patient's health status and are used as a diagnostic biomarker for anoxic conditions caused by impaired oxygen transport, as well as a predictor of mortality in trauma patients. In addition, its production increases during high-intensity physical activity, relative to the level of fatigue encountered during exercise and the subject's fitness level. While its accumulation causes soreness that can inhibit further physical activity, its production is thought to be essential for improving endurance. Therefore, lactate monitoring can improve athletic performance while also preventing injuries caused by overtraining. Until now, conventional lactate detection technologies have relied on blood samples and, due to their invasive sampling protocols (e.g., venipuncture or finger prick), are not suitable for real-time ambulatory monitoring. The wearable sensor disclosed herein is lightweight, flexible, and comfortable on the skin, allowing it to be worn on different parts of the body for extended periods of time, providing continuous, distributed sampling of sweat without causing discomfort. The sensors disclosed herein can be obtained by functionalizing filter paper by applying one or more layers (e.g., 1, 2, 3, etc.) of sensing ink, for example, by drop casting or inkjet printing. In embodiments, drop casting does not require a thickener because the ink is a liquid solution with a relatively low viscosity. In embodiments, the sensors disclosed herein can include filter paper. Without wishing to be bound by any particular theory, the smaller pore size of some filter papers may be the reason they exhibit the best results in terms of sensing range.

[0034] Disclosed herein is a printable liquid lactate sensor composition that includes silk fibroin in an amount between 0.1% and 30% by weight, lactate oxidase that is activated by lactate to produce hydrogen peroxide, peroxidase that is activated by hydrogen peroxide, and a chromogenic substrate that changes color upon activation of the peroxidase.

[0035] Also disclosed herein is a solid-state sensor comprising a biopolymer substrate formed from the printable liquid lactate sensor composition. The lactate oxidase, peroxidase, and chromogenic substrate may be embedded in the biopolymer substrate.

[0036] Also disclosed herein is a wearable sensor for detecting lactate, comprising a biopolymer substrate having embedded lactate oxidase that is activated by lactate to produce hydrogen peroxide, a peroxidase that is activated by hydrogen peroxide, and a chromogenic substrate that changes color upon activation of the peroxidase. The biopolymer substrate comprises silk fibroin in an amount of 1% to 100% by weight.

[0037] Also disclosed herein is a sweat sensor comprising a biopolymer substrate embedded with lactate oxidase, which is activated by lactate to produce hydrogen peroxide, peroxidase, which is activated by hydrogen peroxide, and a chromogenic substrate, which changes color upon activation of the peroxidase. The biopolymer substrate comprises silk fibroin in an amount of 1% to 100% by weight.

[0038] The sensing range of the sensors disclosed herein (e.g., solid-state sensors, wearable sensors, sweat sensors, lactate sensors, etc.) significantly exceeds that of other known lactate sensing systems, which in some embodiments can be a greater than three-fold increase in the lactate concentration that can be sensed. The inventors surprisingly achieved quantitative bulk colorimetric changes in varying biopolymer substrates as the lactate concentration within the biopolymer substrate varied from 0.1 mM to 100 mM. In some embodiments, quantitative bulk colorimetric changes were achieved at lactate concentrations ranging from 1 mM to 90 mM. In some embodiments, quantitative bulk colorimetric changes were achieved at lactate concentrations ranging from 0.5 mM to 70 mM. In some embodiments, quantitative bulk colorimetric changes were achieved at lactate concentrations ranging from 10 mM to 50 mM. In some embodiments, quantitative bulk colorimetric changes were achieved over a range of lactate concentrations having lower limits of 0.1 mM, 0.5 mM, 1 mM, or 10 mM. In some embodiments, quantitative bulk colorimetric changes were achieved over a range of lactate concentrations having upper limits of 100 mM, 90 mM, 70 mM, or 50 mM. In embodiments, the bulk quantitative colorimetric change occurs substantially instantly upon contact with solutions having different lactate concentrations.

[0039] In embodiments of the compositions or sensors disclosed herein, the chromogenic substrate of the composition and / or biopolymer substrate includes a baseline colorant, e.g., a yellow dye (e.g., an acid yellow dye such as Acid Yellow 34). The chromogenic substrate may also include sodium 3,5-dichloro-2-hydroxygenzenesulfonate and / or 4-aminoantipyrine. In some cases, the chromogenic substrate includes a yellow dye, e.g., an acid yellow dye, e.g., Acid Yellow 34, sodium 3,5-dichloro-2-hydroxybenzenesulfonate, and 4-aminoantipyrine. In embodiments of the compositions or sensors disclosed herein, the composition and / or biopolymer substrate includes a baseline buffer and / or electrolyte mixture, where the baseline buffer and / or electrolyte mixture is optionally tailored to mimic human sweat. In embodiments of the sensors disclosed herein, the biopolymer substrate may include a base layer of chitosan.

[0040] Disclosed herein is an article of clothing or a wearable patch comprising a plurality of the sensors disclosed herein. The article of clothing or the wearable patch may include a reference color spot having a predetermined known color for colorimetric analysis of an image of the sensor or plurality of sensors.

[0041] Disclosed herein is a colorimetric sensor for detecting target chemicals in a fluid sample, the sensor comprising one or more detection regions on a substrate, one or more enzyme reagents configured to detect one or more target chemicals in the fluid sample, and one or more chromogenic substrates configured to indicate the relative amounts of the one or more target chemicals in the fluid sample. In embodiments, the detection regions comprise silk fibroin. In embodiments, the detection regions comprise chitosan. In embodiments, the colorimetric sensor may include an imaging device (e.g., a multispectral camera) for detecting colorimetric changes in the one or more detection regions after contact with a fluid sample containing one or more target chemicals for the one or more enzyme reagents. A processor may be connected to the imaging device and configured to detect the target enzymes and quantify the amount of the target enzymes in the fluid. The processor may include a machine learning model configured to train the sensor to detect and quantify chemicals in the fluid sample. The machine learning model may be trained using multiple images of the colorimetric sensor responding to known concentrations of one or more target chemicals. In embodiments, the one or more enzyme reagents include lactate oxidase (LOx) and may further include horseradish peroxidase (HRP). The target chemical may include lactate. In embodiments, the fluid sample may include a biological fluid (e.g., sweat). In embodiments, the substrate includes a flexible material configured to cover and conform to the sensing surface. In embodiments, the colorimetric sensor further includes one or more pH-sensing regions on the substrate configured to detect the pH level of the fluid sample. The pH-sensing regions may include one or more chromogenic pH-sensing indicators that may define a pH range. In embodiments, the multiple detection regions may be arranged in a predetermined pattern. The processor may generate a spatial distribution map of the one or more target chemicals based on the predetermined pattern of the detection regions.

[0042] Disclosed herein is a method for detecting a target chemical in a fluid sample, the method including: training a target chemical detection model using multiple images of a colorimetric sensor, the sensor having multiple reference detection regions and multiple sample detection regions with predetermined concentrations of the target chemical in the fluid sample; and predicting the concentration of the target chemical on the colorimetric sensor using the trained target chemical detection model. In embodiments, the multiple images may be acquired under multiple light conditions or may be acquired using a multispectral camera. In embodiments, the multiple images are acquired using an imaging device for detecting colorimetric changes in the multiple sample detection regions after contact with a fluid sample containing the target chemical. In embodiments, the multiple images may be divided into multiple categories of target chemicals with predetermined concentrations. In embodiments, the multiple images are labeled with their individual concentrations of the target chemical and combined into a dataset.

[0043] Disclosed herein is a method for manufacturing a colorimetric sensor for detecting a target chemical in a sample fluid, the method comprising preparing one or more paper substrates with at least one of a silk fibroin solution, one or more enzyme reagents (e.g., lactate oxidase (LOx)), or one or more chromogenic substrates, and disposing the one or more paper substrates on a film substrate. The method may further comprise preconditioning the one or more paper substrates with a chitosan solution. The one or more enzyme reagents may further comprise horseradish peroxidase (HRP). In embodiments, the method may further comprise disposing one or more pH-sensing regions on the film substrate configured to detect the pH level of the sample fluid. The pH-sensing regions may comprise one or more chromogenic pH-sensing indicators that can define a pH range. In embodiments, the one or more paper substrates may be arranged in a predetermined pattern on the film substrate.

[0044] According to various embodiments, a variety of functionalizing agents may be used with the sensors and other embodiments described herein.

[0045] According to various embodiments, an appropriate amount of one or more functionalizing agents may be used for any application. In some embodiments, the amount of each functionalizing agent may be about 1 μg / ml to 1,000 μg / ml (e.g., about 2-1,000, 5-1,000, 10-1,000, 10-500, or 10-100 μg / ml). In some embodiments, the amount of each functionalizing agent may be at least 1 μg / ml (e.g., at least 5, 10, 15, 20, 25, 50, 100, 200, 300, 400, 500, 600, 700, 800, or 900 μg / ml). In some embodiments, the amount of an individual functionalizing agent is up to 1,000 μg / ml (e.g., 900, 800, 700, 600, 500, 400, 300, 200, 100, 90, 80, 70, 60, 50, 40, 30, 20, 10, or 5 μg / ml).

[0046] In some embodiments, the functionalizing agent may include one or more sensing agents, e.g., sensing dyes. The sensing agents / sensing dyes are environmentally sensitive and generate a measurable response to one or more environmental factors. In some embodiments, the environmentally sensitive agent or dye may be present in the composition in an amount effective to change the composition from a first chemical-physical state to a second chemical-physical state in response to an environmental parameter (e.g., a change in pH, light intensity or exposure, temperature, pressure or strain, voltage, a physiological parameter of the subject, and / or the concentration of a chemical species in the surrounding environment) or an externally applied stimulus (e.g., optical interrogation, acoustic interrogation, and / or applied heat). In some cases, the sensing dye is present to provide one optical appearance under one given set of environmental conditions and a second, different optical appearance under a different, given set of environmental conditions. Suitable concentrations for the sensing agents described herein may be those for the colorants and additives described elsewhere herein. Those skilled in the chemical sensing art will be able to determine the concentrations of the inks described herein that are appropriate for use in sensing applications.

[0047] In some embodiments, the first chemical-physical state and the second chemical-physical state may be physical properties of the composition, such as mechanical, chemical, acoustic, electrical, magnetic, optical, thermal, radiological, or organoleptic properties. Exemplary sensing dyes or agents include, but are not limited to, pH-sensitive agents, heat-sensitive agents, pressure- or strain-sensitive agents, photosensitive agents, or potentiometric agents.

[0048] Exemplary pH-sensitive dyes or agents include, but are not limited to, cresol red, methyl violet, crystal violet, ethyl violet, malachite green, methyl green, 2-(p-dimethylaminophenylazo)pyridine, paramethyl red, metanil yellow, 4-phenylazodiphenylamine, thymol blue, meta-cresol purple, orange IV, 4-o-tolylazo-o-toluindine, quinaldine red, 2,4-dinitrophenol, eri Sulosin disodium salt, benzopurpurine 4B, N,N-dimethyl-p-(m-tolylazo)aniline, p-dimethylaminoazobenzene, 4,4'-bis(2-amino-1-naphthylazo)-2,2'-stilbenedisulfonic acid, tetrabromophenolphthalein ethyl ester, bromophenol blue, Congo red, methyl orange, ethyl orange, 4-(4-dimethylamino-1-naphthylazo)-3-methoxybenzenesulfonic acid, Bromocresol Green, Resazurin, 4-phenylazo-1-napthylamine, Ethyl Red 2-([-dimethylaminophenyazo)pyridine, 4-(p-ethoxyphenylazo)-m-phenylene-diamine monohydrochloride, Resorcinol Blue, Alizarin Red S, Methyl Red, Propyl Red, Bromocresol Purple, Chlorophenol Red, p-Nitrophenol, Alizarin 2-(2,4- Dinitrophenylazo)1-naphthol(napthol)-3,6-disulfonic acid, bromothymol blue, 6,8-dinitro-2,4-(1H)quinazolinedione, brilliant yellow, phenol red, neutral red, m-nitrophenol, cresol red, turmeric, meta-cresol purple, 4,4'-bis(3-amino-1-naphthylazo)-2,2'-stilbenedisulfonic acid, thymol blue, p-naphtholbenzein, phenolphthalein, o-cresolphthalein, ethylbis(2,4-dimethylphenyl)ethanoate, thymolphthalein, nitrazine yellow, alizarin yellow R, alizarin, p-(2,4-dihydroxyphenylazo)benzenesulfonic acid, 5,5'-indigodisulfonic acid, 2,4,6-trinitrotoluene, 1,3,5-trinitrobenzene, and Clayton Yellow.

[0049] Exemplary photoresponsive dyes or agents include, but are not limited to, photochromic compounds or agents such as triarylmethanes, stilbenes, azasilbenes, nitrones, fulgides, spiropyrans, naphthopyrans, spiro-oxydines, quinones, derivatives and combinations thereof.

[0050] Exemplary potentiometric dyes include, but are not limited to, substituted aminonaphthylehenylpridinium (ANEP) dyes, such as di-4-ANEPPS, di-8-ANEPPS, and N-(4-sulfobutyl)-4-(6-(4-(dibutylamino)phenyl)hexatrienyl)pyridinium (RH237).

[0051] Exemplary temperature-sensitive dyes or agents include, but are not limited to, thermochromic compounds or agents, such as thermochromic liquid crystals, leuco dyes, fluoran dyes, and octadecylphosphonic acid.

[0052] Exemplary pressure or strain sensitive dyes or agents include, but are not limited to, spiropyran compounds and agents.

[0053] Exemplary chemosensitive dyes or agents include, but are not limited to, antibodies such as immunoglobulin G (IgG), which can change color from blue to red in response to bacterial contamination.

[0054] In some embodiments, the functionalizing agent comprises one or more additives, dopants, or biologically active agents suitable for the desired intended purpose. In some embodiments, the additives or dopants may be present in an amount effective to impart optical or organoleptic properties to the composition. Exemplary additives or dopants that impart optical or organoleptic properties include, but are not limited to, dyes / pigments, flavorants, fragrance compounds, and particulate or fibrous fillers.

[0055] Additionally or alternatively, additives, dopants, or biologically active agents may be present in an amount effective to "functionalize" the composition to impart desired mechanical properties or add functionality to the composition. Exemplary additives, dopants, or biologically active agents that impart desired mechanical properties or add functionality include, but are not limited to, environmentally sensitive / sensing dyes; active biomolecules; conductive or metal particles; micro- and nanofibers (e.g., reinforcing silk nanofibers, carbon nanofibers); nanotubes; inorganic particles (e.g., hydroxyapatite, tricalcium phosphate, bioglass); drugs (e.g., antibiotics, small molecules, or low-molecular-weight organic compounds); proteins and their fragments or complexes (e.g., enzymes, antigens, antibodies, and their antigen-binding fragments); DNA / RNA (e.g., siRNA, miRNA, mRNA); cells and their fractions (viruses and virus particles; prokaryotic cells such as bacteria; eukaryotic cells such as mammalian cells and plant cells; fungi).

[0056]

[0057] In some embodiments, the additive or dopant comprises a fragrance compound. Exemplary fragrance compounds include ester fragrance compounds, terpene fragrance compounds, cyclic terpenes, and aromatic fragrance compounds, such as, but not limited to, geranyl acetate, methyl formate, methyl acetate, methyl propionate, methyl butyrate, ethyl acetate, ethyl butyrate, isoamyl acetate, pentyl butyrate, pentyl pentanoate, octyl acetate, benzyl acetate, methyl anthranilate, myrecene, geraniol, nerol, citral, citronellal, citronellol, linalool, nerolidol, limonene, camphor, menthol, calone, terpineol, alpha-ionone, thujone, eucalyptol, benzaldehyde, eugenol, cinnamaldehyde, ethyl maltol, vanillin, anisole, anethole, estragole, and thymol.

[0058] In some embodiments, the additive or dopant comprises a colorant, such as a dye or pigment. In some embodiments, the dye or pigment imparts color or grayscale to the composition. The colorant can be different from the sensing agent and / or sensing dye described below. Any organic and / or inorganic pigments and dyes can be included in the ink. Exemplary pigments suitable for use in the present disclosure include International Color Index or CI Pigment Black Nos. 1, 7, 11, and 31; CI Pigment Blue Nos. 15, 15:1, 15:2, 15:3, 15:4, 15:6, 16, 27, 29, 61, and 62; CI Pigment Green Nos. 7, 17, 18, and 36; CI Pigment Orange Nos. 5, 13, 16, 34, and 36; CI Pigment Violet Nos. 3, 19, 23, and 27; and CI Pigment Red Nos. 3, 17, 22, 23, 48:1, 48:2, and 57:1. , 81:1, 81:2, 81:3, 81:5, 101, 114, 122, 144, 146, 170, 176, 179, 181, 185, 188, 202, 206, 207, 210 and 249, CI Pigment Yellow Nos. 1, 2, 3, 12, 13, 14, 17, 42, 65, 73, 74, 75, 83, 30, 93, 109, 110, 128, 138, 139, 147, 142, 151, 154 and 180, D&C Red No. 7, D&C Red No. 6 and D&C Red No. 34, carbon black pigments (e.g., Regal 330, Cabot Corporation), quinacridone pigments (Quinacridone Magenta (228-0122), available from Sun Chemical Corporation, Fort Lee, NJ), diarylide yellow pigments (e.g., AAOT Yellow (274-1788), available from Sun Chemical Corporation); and phthalocyanine blue pigments (e.g., Blue 15:3 (294-1298), available from Sun Chemical Corporation). Classes of dyes suitable for use in the present invention can be selected from acid dyes, natural dyes, direct dyes (either cationic or anionic), basic dyes, and reactive dyes.Acid dyes, also considered anionic dyes, are soluble in water and mostly insoluble in organic solvents, and are selected from yellow acid dyes, orange acid dyes, red acid dyes, purple acid dyes, blue acid dyes, green acid dyes, and black acid dyes. EP 0745651, incorporated herein by reference, describes many acid dyes suitable for use in the present disclosure. Exemplary yellow acid pigments include Acid Yellow 1 (International Color Index or CI 10316); Acid Yellow 7 (CI 56295); Acid Yellow 17 (CI 18965); Acid Yellow 23 (CI 19140); Acid Yellow 29 (CI 18900); Acid Yellow 36 (CI 13065); Acid Yellow 42 (CI 22910); Acid Yellow 73 (CI 45350); Acid Yellow 99 (CI 13908); Acid Yellow 194; and Food Yellow 3 (CI 15985). Exemplary orange acid pigments include Acid Orange 1 (CI 13090 / 1); Acid Orange 10 (CI 16230); Acid Orange 20 (CI 14603); Acid Orange 76 (CI 18870); Acid Orange 142; Food Orange 2 (CI 15980); and Orange B.

[0059] Exemplary red acid dyes include Acid Red 1 (CI 18050); Acid Red 4 (CI 14710); Acid Red 18 (CI 16255); Acid Red 26 (CI 16150); Acid Red 2.7 (CI 45430, BASF) Acid Red 52 (CI 45100); Acid Red 73 (CI 27290); Acid Red 87 (CI 45380); Acid Red 94 (CI 45440), Acid Red 194; and Food Red 1 (CI 14700). Exemplary purple acid dyes include Acid Violet 7 (CI 18055); and Acid Violet 49 (CI 42640). Exemplary blue acid dyes include Acid Blue 1 (CI 42045); Acid Blue 9 (CI 42090); Acid Blue Acid Blue 22 (CI 42755); Acid Blue 74 (CI 73015); Acid Blue 93 (CI 42780); and Acid Blue 158A (CI 15050). Exemplary green acid dyes include Acid Green 1 (CI 10028); Acid Green 3 (CI 42085); Acid Green 5 (CI 42095); Acid Green 26 (CI 44025); and Food Green 3 (CI 42053). Exemplary black acid dyes include Acid Black 1 (CI 20470); Acid Black 194 (Basantol® X80, available from BASF Corporation, an azo / 1:2CR-complex).

[0060] Exemplary direct dyes for use in the present disclosure include Direct Blue 86 (CI 74180); Direct Blue 199; Direct Black 168; Direct Red 253; and Direct Yellow 107 / 132 (CI not assigned).

[0061] Exemplary natural dyes for use in the present disclosure include alkanet (CI 75520, 75530); annatto (CI 75120); carotene (CI 75130); chestnut; cochineal (CI 75470); cutch (CI 75250, 75260); dividivi; oak (CI 75240); Hypernic (CI 75280); logwood (CI 75200); osage orange (CI 75660); paprika; quercitron (CI 75720); sanrou (CI 75100); sandalwood (CI 75510, 75540, 75550, 75560); sumac; and turmeric (CI 75300). Exemplary reactive dyes for use in the present disclosure include Reactive Yellow 37 (a monoazo dye); Reactive Black 31 (a disazo dye); Reactive Blue 77 (a phthalocyanine dye), and Reactive Red 180 and Reactive Red 108 dyes. Also suitable are colorants described in The Printing Ink Manual (5th ed., Leach et al. eds. (2007), pages 289-299. Other organic and inorganic pigments and dyes, as well as combinations thereof, can be used to achieve the desired color.

[0062] In addition to, or instead of, a visible colorant, the compositions provided herein can contain an ETV fluorophore that is excited in the ETV range and emits light at higher wavelengths (typically 400 nm and above). Examples of ETV fluorophores include, but are not limited to, materials from the coumarin, benzoxazole, rhodamine, naphthalimide, perylene, benzanthrone, benzoxanthone, or benzothia-xanthone families. The addition of a UV fluorophore (e.g., an optical brightener) can help maintain maximum visible light transmittance. The amount of colorant, if present, is generally 0.05% to 5% or 0.1% to 1% by weight of the composition.

[0063] For non-white compositions, the amount of pigment / pigment is generally present in an amount of at or about 0.1 wt% to at or about 20 wt% based on the weight of the composition. In some applications, the non-white ink can include 15 wt% or less pigment / pigment, or 10 wt% or less pigment / pigment, or 5 wt% pigment / pigment, or 1 wt% pigment / pigment based on the weight of the composition. In some applications, the non-white ink can include 1 wt% to 10 wt%, or 5 wt% to 15 wt%, or 10 wt% to 20 wt% pigment / pigment based on the weight of the composition. In some applications, the non-white ink can contain dye / pigment in an amount that is 1 wt%, 2 wt%, 3 wt%, 4 wt%, 5%, 6 wt%, 7 wt%, 8 wt%, 9 wt%, 10 wt%, 11 wt%, 12 wt%, 13 wt%, 14 wt%, 15%, 16 wt%, 17 wt%, 18 wt%, 19 wt%, or 20 wt%, based on the weight of the composition.

[0064] For white compositions, the amount of white pigment is generally present in an amount of at or about 1 wt % to at or about 60 wt % based on the weight of the composition. In some applications, more than 60 wt % of the white pigment may be present. Preferred white pigments include titanium dioxide (anatase and rutile), zinc oxide, lithopone (a calcined coprecipitate of barium sulfate and zinc sulfide), zinc sulfide, precipitated barium sulfate and alumina hydrate, and combinations thereof, any of which may be combined with calcium carbonate. For some applications, the white ink can comprise 60 wt% or less white pigment, or 55 wt% or less white pigment, or 50 wt% white pigment, or 45 wt% white pigment, or 40 wt% white pigment, or 35 wt% white pigment, or 30 wt% white pigment, or 25 wt% white pigment, or 20 wt% white pigment, or 15 wt% white pigment, or 10 wt% white pigment, based on the weight of the composition. For some applications, the white ink can comprise 5 wt% to 60 wt%, or 5 wt% to 55 wt%, or 10 wt% to 50 wt%, or 10 wt% to 25 wt%, or 25 wt% to 50 wt%, or 5 wt% to 15 wt%, or 40 wt% to 60 wt% white pigment, based on the weight of the composition. In some applications, the non-white ink may be 5%, 6%, 7%, 8%, 9%, 10%, 11%, 12%, 13%, 14%, 15%, 16%, 17%, 18%, 19%, 20%, 21%, 22%, 23%, 24%, 25%, 26%, 27%, 28%, 29%, 30%, 31%, 32%, 33%, 34%, 35%, 36%, 37%, 38%, 39%, 40%, 41%, 42%, 43%, 44%, 45%, 46%, 47%, 48%, 49%, 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, 100%, 101%, 102%, 103%, 104%, 105%, 106%, 107%, 108%, 109%, 110% 110%, 111% 112% 113% 114% 115% 116% 117% 118% 119% 120% 121% 122% 123% %, 33wt%, 34wt%, 35%, 36wt%, 37wt%, 38wt%, 39wt%, 40wt%, 41wt%, 42wt%, 43wt%, 44wt%, 45%, 46wt%, 47wt%, 48wt%, 49wt%, 50wt%, 51wt%, 52wt%, 53wt%, 54wt%, 55%, 56wt%, 57wt%, 58wt%, 59wt%, or 60wt% of the dye / pigment.

[0065] In some embodiments, the additive or dopant comprises a conductive additive. Exemplary conductive additives include, but are not limited to, graphite, graphite powder, carbon nanotubes, and metal particles or nanoparticles, such as gold nanoparticles. In some embodiments, the conductive additive is biocompatible and non-toxic.

[0066] In some aspects, the functionalizing agent is a wound healing agent. As used herein, a "wound healing agent" is a compound or composition that actively promotes the wound healing process.

[0067] Exemplary wound healing agents include, but are not limited to, dexpanthenol; growth factors; enzymes, hormones; povidone-iodine; fatty acids; anti-inflammatory agents; antibiotics; antimicrobial agents; antiseptics; cytokines; thrombin; analgesics; opioids; aminoxyl; furoxan; nitrosothiols; nitrates and anthocyanins; nucleosides such as adenosine; and nucleotides such as adenosine diphosphate (ADP) and adenosine triphosphate (ATP); neurotransmitters / neuromodulators such as acetylcholine and 5-hydroxytryptamine (serotonin / 5-HT); histamine and catecholamines such as adrenaline and noradrenaline; lipid molecules such as 5-sphingosine-1-phosphate and lysophosphatidic acid; amino acids such as arginine and lysine; peptides such as bradykinin, substance P and calcium gene-related peptide (CGRP); nitric oxide; and any combination thereof.

[0068] The methods and systems described herein may be deployed, in part or in whole, through machines having computers, computing devices, processors, circuits, and / or servers that include computer-readable instructions, program code, hardware configured to execute instructions and / or functionally perform one or more operations of the methods and systems disclosed herein. The terms computer, computing device, processor, circuit, and / or server as used herein should be understood broadly.

[0069] Any one or more of the terms computer, computing device, processor, circuit, and / or server include any type of computer that can access instructions stored on, for example, a non-transitory computer-readable medium in communication therewith, where the computer performs the operations of the systems or methods described herein when executing the instructions. In certain embodiments, such instructions themselves comprise a computer, computing device, processor, circuit, and / or server. Additionally or alternatively, a computer, computing device, processor, circuit, and / or server may be separate hardware devices, one or more computing resources distributed across hardware devices, and / or may include such aspects as logic circuits, embedded circuits, sensors, actuators, input and / or output devices, network and / or communication resources, memory resources of any type, processing resources of any type, and / or hardware devices configured to be responsive to determined conditions to functionally perform one or more operations of the systems and methods herein.

[0070] Network and / or communication resources include, but are not limited to, local area networks, wide area networks, wireless, the Internet, or any other known communication resources and protocols. Examples and non-limiting examples of hardware, computers, computing devices, processors, circuits, and / or servers include, but are not limited to, general-purpose computers, servers, embedded computers, mobile devices, virtual machines, and / or emulated versions of one or more of these. Examples and non-limiting examples of hardware, computers, computing devices, processors, circuits, and / or servers may be physical, logical, or virtual. A computer, computing device, processor, circuit, and / or server may be a distributed resource included as an aspect of several devices and / or may be included as an interoperable set of resources that perform the described functions of the computer, computing device, processor, circuit, and / or server, functioning together to perform the operations of the computer, computing device, processor, circuit, and / or server. In certain embodiments, each computer, computing device, processor, circuit, and / or server may be on separate hardware, and / or one or more hardware devices may include aspects of two or more computers, computing devices, processors, circuits, and / or servers, for example, as separate executable instructions stored on the hardware device and / or as logically separated aspects of a set of executable instructions, with some aspects of the hardware device including part of a first computer, computing device, processor, circuit, and / or server and some aspects of the hardware device including part of a second computer, computing device, processor, circuit, and / or server.

[0071] The computer, computing device, processor, circuit, and / or server may be part of a server, client, network infrastructure, mobile computing platform, stationary computing platform, or other computing platform. A processor may be any type of computational or processing device capable of executing program instructions, code, binary instructions, etc. A processor may be or include a single processor, a digital processor, an embedded processor, a microprocessor, or any variant, such as a coprocessor (mathematics coprocessor, graphics coprocessor, communication coprocessor, etc.), that can directly or indirectly facilitate the execution of program code or program instructions stored therein. Additionally, a processor may enable the execution of multiple programs, threads, and codes. Threads may be executed simultaneously to enhance processor performance and facilitate simultaneous operation of applications. In implementations, the methods, program codes, program instructions, etc. described herein may be implemented in one or more threads. A thread may spawn other threads that may be assigned priorities associated with them, and the processor may execute these threads based on priorities or any other order based on instructions provided in the program code. The processor may include memory that stores the methods, codes, instructions, and programs described herein and elsewhere. The processor may access a storage medium through an interface that may store the methods, codes, and instructions described herein and elsewhere. Storage media associated with the processor for storing methods, programs, codes, program instructions, or other types of instructions that may be executed by a computing or processing device may include, but are not limited to, one or more of a CD-ROM, a DVD, memory, a hard disk, a flash drive, RAM, ROM, cache, etc.

[0072] A processor may include one or more cores, which may increase the speed and performance of a multiprocessor. In embodiments, a processor may combine two or more independent cores (called dies), such as a dual-core processor, a quad-core processor, or other chip-level multiprocessor.

[0073] The methods and systems described herein may be deployed, in part or in whole, through machines executing computer-readable instructions on servers, clients, firewalls, gateways, hubs, routers, or other such computers and / or networking hardware. The computer-readable instructions may be associated with a server, which may include a file server, a print server, a domain server, an Internet server, an intranet server, and other variants, such as a secondary server, a host server, a distributed server, etc. A server may include one or more of memory, a processor, computer-readable transient and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other servers, clients, machines, and devices through wired or wireless media, etc. The methods, programs, or codes described herein and elsewhere may be executed by the server. Additionally, other devices required for the execution of the methods described herein may be considered part of the infrastructure associated with the server.

[0074] A server may provide an interface to other devices, including, but not limited to, clients, other servers, printers, database servers, print servers, file servers, communication servers, distributed servers, etc. Additionally, this coupling and / or connection may facilitate remote execution of instructions across a network. The networking of some or all of these devices may facilitate parallel processing of program code, instructions, and / or programs at one or more locations without departing from the scope of this disclosure. Additionally, all devices attached to a server through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions that are executed on different devices. In this implementation, the remote repository may act as a storage medium for the methods, program code, instructions, and / or programs.

[0075] The methods, program codes, instructions, and / or programs may be associated with a client, which may include a file client, a print client, a domain client, an Internet client, an intranet client, and other variants, e.g., a secondary client, a host client, a distributed client, etc. A client may include one or more of memory, a processor, computer-readable transient and / or non-transitory media, storage media, ports (physical and virtual), communication devices, and interfaces capable of accessing other clients, servers, machines, and devices through wired or wireless media, etc. The methods, program codes, instructions, and / or programs described herein and elsewhere may be executed by a client. Additionally, other devices utilized for execution of the methods described in this application may be considered part of the infrastructure associated with the client.

[0076] A client may provide an interface to other devices, including, but not limited to, a server, other clients, printers, database servers, print servers, file servers, communication servers, distributed servers, etc. Additionally, this coupling and / or connection may facilitate remote execution of methods, program code, instructions, and / or programs across a network. The networking of some or all of these devices may facilitate parallel processing of methods, program code, instructions, and / or programs at one or more locations without departing from the scope of this disclosure. Additionally, all devices attached to a client through an interface may include at least one storage medium capable of storing methods, program code, instructions, and / or programs. A central repository may provide program instructions that are executed on different devices. In this implementation, the remote repository may act as a storage medium for the methods, program code, instructions, and / or programs.

[0077] The methods and systems described herein may be deployed, in part or in whole, over a network infrastructure. The network infrastructure may include elements such as computing devices, servers, routers, hubs, firewalls, clients, personal computers, communication devices, routing devices, and other active and passive devices, modules, and / or components known in the art. Computing and / or non-computing devices associated with the network infrastructure may include storage media, e.g., flash memory, buffers, stacks, RAM, ROM, etc., separate from other components. The methods, program codes, instructions, and / or programs described herein and elsewhere may be executed by one or more of the network infrastructure elements.

[0078] The methods, program codes, instructions, and / or programs described herein and elsewhere may be implemented in a cellular network having multiple cells. The cellular network may be either a Frequency Division Multiple Access (FDMA) network or a Code Division Multiple Access (CDMA) network. The cellular network may include mobile devices, cell sites, base stations, repeaters, antennas, towers, etc.

[0079] The methods, program codes, instructions, and / or programs described herein and elsewhere may be implemented in or through a mobile device. Mobile devices may include navigation devices, cellular phones, mobile phones, mobile personal digital assistants, laptops, palmtops, netbooks, pagers, e-readers, music players, etc. These mobile devices may include storage media, such as flash memory, buffers, RAM, ROM, and one or more computing devices, apart from other components. The computing devices associated with the mobile devices may be capable of executing the methods, program codes, instructions, and / or programs stored thereon. Alternatively, the mobile devices may be configured to execute instructions in cooperation with other devices. The mobile devices may communicate with a base station that interfaces with a server and is configured to execute the methods, program codes, instructions, and / or programs. The mobile devices may communicate in a peer-to-peer network, a mesh network, or other communication network. The methods, program codes, instructions, and / or programs may be stored on a storage medium associated with the server and executed by a computing device embedded within the server. The base station may include a computing device and a storage medium. The storage device may store methods, program codes, instructions, and / or programs executed by computing devices associated with the base station.

[0080] The methods, program code, instructions, and / or programs may be stored and / or accessed on machine-readable transient and / or non-transitory media, which may include computer components, devices, and recording media that hold digital data used for computing for some interval of time; semiconductor storage known as random access memory (RAM); mass storage, typically for more permanent storage, e.g., in the form of magnetic storage such as optical disks, hard disks, tapes, drums, cards, and other types; processor registers, cache memory, volatile memory, non-volatile memory; optical storage, e.g., CDs, DVDs; removable media, e.g., flash memory (e.g., USB sticks or keys), floppy disks, magnetic tape, paper tape, punch cards, standalone RAM disks, Zip drives, removable mass storage, offline, etc.; other computer memory, e.g., dynamic memory, static memory, read / write storage, mutable storage, read-only, random access, sequential access, position addressable, file addressable, content addressable, network attached storage, storage area networks, barcodes, magnetic ink, etc.

[0081] Certain operations described herein include interpreting, receiving, and / or determining one or more values, parameters, inputs, data, or other information. Operations that involve interpreting, receiving, and / or determining any value, parameter, input, data, and / or other information include, but are not limited to, receiving data via user input; receiving data over any type of network; reading a data value from a storage location in communication with the receiving device; utilizing a default value as the received data value; estimating, calculating, or deriving a data value based on other information available to the receiving device; and / or updating any of these in response to a later received data value. In certain embodiments, a data value may be received by a first operation as part of receiving the data value and then updated by a second operation. For example, a first operation to interpret, receive, and / or determine a data value may be performed when communication goes down, pauses, or is interrupted, and an updated operation to interpret, receive, and / or determine a data value may be performed when communication resumes.

[0082] Certain logical groupings of operations herein, e.g., of methods or procedures of the present disclosure, are provided to explain aspects of the present disclosure. The operations described herein are generally described and / or represented, and operations may be combined, divided, rearranged, added, or removed in a manner consistent with the disclosure herein. While the context of an operational description may require ordering of one or more operations and / or an order for one or more operations may be explicitly disclosed, it is understood that the order of operations should be construed broadly, where any equivalent grouping of operations to provide equivalent results of the operations is specifically contemplated herein. For example, if a value is used in an operational step, determining the value may be required before that operational step in certain contexts (e.g., if the time delay of data for the operation to achieve a particular effect is important), but may not be required before that operational step in other contexts (e.g., if utilization of a value from a previous execution cycle of the operation is sufficient for that purpose). Thus, in certain embodiments, the order of operations and groupings of operations described are expressly contemplated herein, and in certain embodiments, rearrangements, subdivisions, and / or different groupings of operations are expressly contemplated herein.

[0083] The methods and systems described herein may transform physical and / or intangible items from one state to another. The methods and systems described herein may also transform data representing physical and / or intangible items from one state to another.

[0084] The elements described and represented herein, including flowcharts, block diagrams, and / or operational descriptions, represent and / or describe specific examples of arrangements of elements for illustrative purposes. However, the represented and / or described elements, their functions, and / or these arrangements may be implemented in a machine, for example, through a computer-executable transient and / or non-transitory medium having a processor capable of executing program instructions stored thereon, and / or as a logic circuit or hardware arrangement. Examples of arrangements of programming instructions include, at least, as a monolithic structure of instructions; a stand-alone module of instructions for an element or portion thereof; and / or as a module of instructions with external routines, code, services, etc.; and / or any combination thereof, and all such implementations are contemplated as being within the scope of embodiments of the present disclosure. Examples of such machines include, but are not limited to, personal digital assistants, laptops, personal computers, mobile phones, other handheld computing devices, medical equipment, wired or wireless communication devices, transducers, chips, calculators, satellites, tablet PCs, e-books, gadgets, electronic devices, devices with artificial intelligence, computing devices, networking equipment, servers, routers, etc. Furthermore, the elements and / or any other logical components described and / or depicted herein may be implemented in machines capable of executing program instructions. Thus, while the foregoing flowcharts, block diagrams, and / or operational descriptions illustrate functional aspects of the disclosed systems, any arrangement of program instructions that implements these functional aspects is contemplated herein. Similarly, it will be recognized that the various steps identified and described above may be modified, and that the order of steps may be adapted to particular applications of the technology disclosed herein. Additionally, any steps or operations may be divided and / or combined in any manner that provides functionality similar to the described operations.All such variations and modifications are contemplated in this disclosure. The methods and / or processes described above, and their steps, may be implemented in hardware, program code, instructions, and / or programs, or any combination of hardware and methods, program code, instructions, and / or programs suitable for a particular application. Examples of hardware include dedicated or specific computing devices, specific aspects or components of specific computing devices, and / or arrangements of hardware components and / or logic circuitry for performing one or more of the operations of the methods and / or systems. The processes may be implemented in one or more microprocessors, microcontrollers, embedded microcontrollers, programmable digital signal processors, or other programmable devices, together with internal and / or external memory. The processes may also, or instead, be embodied in application-specific integrated circuits, programmable gate arrays, programmable array logic, or any other device or combination of devices that can be configured to process electrical signals. It will further be appreciated that one or more of the processes may be embodied as computer-executable code that can be executed on a machine-readable medium.

[0085] Computer executable code may be created using a structured programming language, e.g., C, an object-oriented programming language, e.g., C++, or any other high-level or low-level programming language (including assembly language, hardware description languages, and database programming languages ​​and techniques), which may be stored, compiled, or interpreted for execution on one of the above devices, as well as heterogeneous combinations of processors, processor architectures, or combinations of different hardware and computer-readable instructions, or any other machine capable of executing program instructions.

[0086] Thus, in one aspect, each method and combination thereof described above may be embodied in computer-executable code that performs its steps when executed on one or more computing devices. In another aspect, the method may be embodied in a system that performs its steps, may be distributed across devices in many ways, or all functionality may be incorporated into a dedicated stand-alone device or other hardware. In another aspect, the means for performing the steps associated with the processes described above may include any of the hardware and / or computer-readable instructions described above. All such permutations and combinations are contemplated in embodiments of the present disclosure.

[0087] As described herein, machine learning models may be trained using supervised learning or unsupervised learning. In supervised learning, a model is created using a set of labeled examples, where each example has a corresponding target label. In unsupervised learning, a model is created using unlabeled examples. A collection of examples builds a dataset, usually referred to as a training dataset. During training, a model is created using this training data to learn relationships between examples in the dataset. The training process may include various stages, such as data collection, preprocessing, feature extraction, model training, model evaluation, and model fine-tuning. The data collection stage may include collecting a representative dataset, typically from multiple users, that covers a range of possible scenarios and positions. The preprocessing stage may include cleaning and preparing the examples in the dataset and may include filtering, normalization, and segmentation. The feature extraction stage may include extracting relevant features from the examples to capture relevant information for the task. The model training stage may include training a machine learning model on the preprocessed, feature-extracted data. The model may include a support vector machine (SVM), an artificial neural network (ANN), a decision tree, etc. for supervised learning, or an autoencoder, Hopfield, a restricted Boltzmann machine (RBM), a deep belief, a generative adversarial network (GAN), or other networks, or clustering for unsupervised learning. The model evaluation stage may include evaluating the performance of the trained model on a separate validation data set to ensure it generalizes well to new, off-the-shelf examples. Based on the results of the evaluation, fine-tuning the model may include refining the model by adjusting its parameters, changing the features used, or using a different machine learning algorithm. The process may be repeated until the model's performance on the validation data set is satisfactory, and the trained model can then be used to make predictions.

[0088] In embodiments, trained models may be periodically fine-tuned for specific user groups, applications, and / or tasks. Fine-tuning an existing model may improve the performance of the model for an application while avoiding completely retraining the model for the application.

[0089] In embodiments, fine-tuning a machine learning model may involve adjusting its hyperparameters or architecture to improve its performance for a particular user population or application. The fine-tuning process may occur after initial training and evaluation of the model, and may involve adjusting one or more hyperparameters and architecture methods.

[0090] Hyperparameter tuning involves adjusting the values ​​of a model's hyperparameters, such as the learning rate, regularization strength, or number of hidden units. This can be done using methods such as grid search, random search, or Bayesian optimization. Architectural modification may involve modifying the structure of the model, for example, adding or removing layers, changing activation functions, or altering connections between neurons, to improve its performance.

[0091] Online training of machine learning models involves the process of updating the model as new examples become available, allowing it to adapt to changes in data distribution over time. With online training, the model is trained incrementally as new data becomes available, allowing it to adapt to changes in data distribution over time. Online training can also be useful for user populations whose usage habits of stimulation devices are changing, allowing the model to be updated in near real time.

[0092] In embodiments, online training may include adaptive filtering, in which a machine learning model is trained online to learn the underlying structure of new examples and to remove noise or artifacts from the examples.

[0093] While the present disclosure has been disclosed in connection with the preferred embodiments shown and described in detail, various modifications and improvements thereon will become readily apparent to those skilled in the art. Accordingly, the spirit and scope of the present disclosure is not limited by the foregoing examples, but is to be understood in the broadest sense permitted by law.

[0094] Other features and advantages of the present invention will be apparent from the description of its preferred embodiments and from the claims. Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which this invention belongs. Although methods and materials similar or equivalent to those described herein can be used in the practice and testing of the present invention, suitable methods and materials are described below. In addition, the materials, methods, and examples are merely illustrative and not intended to be limiting. [Example]

[0095]

[0096] Materials: Sodium carbonate, lithium bromide, horseradish peroxidase type I (HRP) (P8125), sodium 3,5-dichloro-2-hydroxybenzenesulfonate (B), 4-aminoantipyrine (A), sodium lactate, chitosan, acetic acid, phenol red sodium salt, nitrazine yellow, bromocresol green sodium salt, Whatman™ Grade 1 filter paper, and Whatman™ Grade 4 filter paper were purchased from Sigma-Aldrich (USA). Acid Yellow 34 (Y) was purchased from Chem Cruz (USA). Lactate oxidase Grade III (LOx) was purchased from Toyobo (USA). Tegaderm™ film (size: 4.4 × 4.4 cm) was purchased from 3M (USA). All chemicals were used as received, and where possible, they complied with trace metal standards. The chromogenic substrates (i.e., A, B, and Y) were selected considering their toxicity levels and avoiding the use of harmful or carcinogenic compounds. Bombyx mori silk cocoons were purchased from Tajima Shoji (Japan). Deionized (DI) water with a resistivity of 18.2 MΩ cm was obtained using a Milli-Q reagent-grade water system and used to prepare the aqueous solutions.

[0097] Preparation of Silk Fibroin Solution: Silk fibroin was extracted according to a previously reported protocol. Briefly, finely chopped Bombyx mori silk cocoons were boiled in a 0.02 M sodium carbonate solution for 120 min to remove the sericin layer. The fibers were washed three times for 20 min in DI water, dried overnight, and dissolved in a 9.3 M lithium bromide solution at 60 °C for 4 h. The resulting solution was dialyzed against deionized water for two days, with six changes of deionized water at regular intervals. The final solution was centrifuged twice at 9000 rpm for 20 min at 4 °C, followed by filtration to obtain a 7–8 wt% silk fibroin solution.

[0098] Preparation of chromogenic enzyme ink: Silk-based chromogenic enzyme ink was made from a 4 wt% silk fibroin solution containing 0.1 M PBS as an ionic background to maintain constant pH when reacting with strongly acidic or strongly basic sweat. The final concentrations of the enzyme reagents were 339 U / mL and 150 U / mL for HRP and LOx, respectively. The chromogenic substrate was then dissolved in the silk-based enzyme mixture to give final concentrations of A, 0.86 mg / mL; B, 1.82 mg / mL; and Y, 0.3 mg / mL.

[0099] Water-based chromogenic enzyme inks were made from DI water containing 0.1 M PBS as an ionic background. The final concentrations of the enzyme reagents were 339 U mL for HRP and LOx, respectively. -1 and 150UmL -1 The chromogenic substrate was then dissolved in a water-based enzyme mixture to give A, 0.86 mg mL -1 ;B, 1.82 mg mL -1 ;Y, 0.3 mg mL -1 to obtain a final concentration of .

[0100] Preparation of chromogenic pH-sensitive ink: Silk-based chromogenic pH-sensitive ink was prepared at 2.5 mg mL -1 The silk fibroin solution was prepared from a 4 wt% silk fibroin solution containing pH indicators (i.e., phenol red sodium salt, nitrazine yellow, bromocresol green sodium salt) at a final concentration of 0.05 wt.

[0101] Fabrication of wearable sensing patches: Three different paper substrates were cut to obtain squares (side length: 3 cm). 150 μL of chromogenic enzyme ink was drop-cast into the center of each square and left to dry at room temperature for 1 hour. This step was repeated three times to obtain three layers of ink. The functionalized paper was laser cut to obtain circles (diameter: 3 mm) using a Trotec Speedy 300 laser cutter with a 75 W CO2 laser. The functionalized paper circles were then placed on Tegaderm™ film to obtain wearable patches.

[0102] Chitosan functionalization: 0.5 w / v% chitosan was dissolved in 2 v / v% acetic acid. To obtain a base layer of chitosan, 150 μL of the solution was drop-cast onto the center of each square and left to dry for 1.5 h before deposition of three layers of chromogenic enzyme ink.

[0103] Preparation of simulated sweat solution: NaCl, KCl, urea, NH4Cl, CaCl2, MgCl2 were dissolved in DI water to obtain final concentrations of 40 mM, 3 mM, 22 mM, 3 mM, 0.4 mM, and 50 μM, respectively.

[0104] Colorimetric response analysis: To induce a colorimetric response, 1 μL of simulated sweat solution at a specific concentration (i.e., 1, 5, 10, 30, 50, 60, or 90 mM) of lactate was drop-cast onto each sensing circle. The fluid was spread over the surface of the sensing circle and mixed with the reagent to generate a colorimetric response. Images of the colorimetric response were collected and analyzed using a Laser Jet Pro MFP M127fn scanner manufactured by HP (USA) with 24-bit color depth and 600 dpi resolution. Responses were quantified using ImageJ as the variation in intensity of the red, green, or blue channel. Specificity was assessed by measuring the variation in the colorimetric response of the sensor in the green channel before and after exposure to interfering substances (NaCl, KCl, urea, NH4Cl, CaCl2, and MgCl2, with concentrations of 40 mM, 3 mM, 22 mM, 3 mM, 0.4 mM, and 50 μM, respectively).

number

[0105] Accelerated degradation studies were performed on lactate-sensing patches fabricated using silk-based or water-based chromogenic enzyme inks, both with and without a chitosan base layer. The patches were stored at 60°C, above the enzyme degradation temperature, for 8, 24, 120 hours, and 2.5 months. After storage, the colorimetric response was analyzed to assess the ability of the silk-based ink to maintain enzyme activity when exposed to lactate fluctuations ranging from 0 to 90 mM. To assess long-term stability, the patches were stored at 4°C for 18, 21, and 24 months. After storage, the colorimetric response was analyzed to assess the ability of the silk-based ink to maintain enzyme activity when exposed to lactate fluctuations ranging from 0 to 90 mM. Retained activity was assessed by measuring the change in the sensor's colorimetric response at 90 mM (green channel) immediately after fabrication and after storage.

number

[0106] Neural network training: A wearable sensing patch for neural network training was fabricated as follows. Whatman™ Grade 1 filter paper was functionalized with a base layer of chitosan solution and three layers of silk-based chromogenic enzyme ink. The paper was laser cut to obtain circles (diameter: 3 mm) using a Trotec Speedy 300 laser cutter with a 75W CO2 laser. Four sensing paper circles were applied to Tegaderm™ film. To enable easier machine learning-driven lactate concentration prediction, four reference colors (RGB values: red (255, 0, 0), green (0, 255, 0), blue (0, 0, 255), and light yellow (253, 252, 188)) were included in the form of non-sensing colored paper circles. The reference colors were printed on Whatman™ Grade 1 filter paper using an HP (USA) Laser Jet Pro MFP M127fn printer with 24-bit color depth and 600 dpi resolution. Circles of non-sensing colored paper (diameter: 3 mm) were laser cut with a 75 W CO2 laser using a Trotec Speedy 300 Laser Cutter and applied onto the Tegaderm™ film on which the sensing circle had previously been applied.

[0107] Image acquisition: 1 μL of simulated sweat solution containing a precise amount of lactate at a specific concentration (i.e., 1, 5, 10, 30, 50 mM) was drop-cast onto each sensing circle. After the colorimetric response, images of the sensor were captured under different lighting conditions using a smartphone camera (Apple, iPhone® SE 2020). The images were used to train a neural network and evaluate its performance. The trained neural network was able to predict lactate concentration when given the sensor image as input.

[0108] Image preprocessing: Computer vision libraries and machine learning algorithms were used to enable quantitative readout. The brightness and warmth levels of the acquired images were normalized using a reference image (i.e., an image acquired under controlled lighting conditions to avoid differences in shadows and warmth levels that may affect the analysis of the colorimetric response of the sensing circle). The brightness of the acquired images was adjusted to match the reference image in the HSV (hue, saturation, and value) color space to obtain uniform lighting conditions in the dataset. Then, the brightness was adjusted to match the CIELAB (International Commission on Illumination) color space. * a * b * , L * - Brightness, a * - redness, b * -yellowish) color space to calculate the average background color of the reference image b * The values ​​were extracted and set as the standard values. * The values ​​were shifted to match these standard values, but L * The value was set to 130 to avoid brightness differences that may affect colorimetric analysis. Finally, a color-based image filter was used to select only pixels corresponding to the sensitive circle of the patch. OpenCV and Pillow libraries were used to convert the image through different color spaces and create a mask for color extraction.

[0109] Machine Learning: Data was analyzed to determine the best model category for colorimetric analysis of lactate-sensing patches. After initial data exploration, different support vector machine (SVM) models were trained for multi-class classification. Model building was developed using RStudio and the package "e1071" for SVM models.

[0110] The SVM model was trained using three different datasets (DS1, DS2, and DS3) containing photographs of lactate-sensing patches applied to the skin of three different individuals (DS1, n = 955; DS2, n = 594; DS3, n = 665 images). The datasets were studied by analyzing six predicted lactate concentrations (i.e., 0 mM, 1 mM, 5 mM, 10 mM, 30 mM, and 50 mM) or four predicted classes of lactate concentrations (i.e., no lactate = 0 mM, low concentration = 1 mM, intermediate concentration = 5-10 mM, and high concentration = 30-50 mM). Each dataset was divided into six categories corresponding to different lactate concentrations, resulting in three distinct datasets. To obtain a larger, more heterogeneous dataset, Dataset 1, Dataset 2, and Dataset 3 were merged to obtain the complete dataset (n = 1880, Dataset 4). The sensitivity (true positive rate), specificity (true negative rate), positive predictive value (PPV), and negative predictive value (NPV) values ​​were similar across all datasets at each lactate concentration or lactate concentration class evaluated. Generally, values ​​were highest at the lowest lactate concentrations evaluated (e.g., 0 mM, 1 mM). Additional data are provided in U.S. Application Nos. 63 / 483,959 and 63 / 512,534; Matzeu, G., et al. (2020). Large-Scale Patterning of Reactive Surfaces for Wearable and Environmentally Deployable Sensors. Advanced Materials, 32(28), 2001258; and Ruggeri, E., et al. (2023). Paper-Based Wearable Patches for Real-Time, Quantitative Lactate Monitoring. Advanced Sensor Research, all of which are incorporated herein by reference in their entirety for all purposes.

[0111] Colorimetric wearable lactate-sensing patch: A silk-based chromogenic enzyme ink was formulated by incorporating lactate oxidase (LOx) and horseradish peroxidase (HRP) together with a chromogenic substrate in an aqueous solution of regenerated silk fibroin, which allows for stabilization of unstable molecules. The biosensing composite ink was then infiltrated into small (i.e., 3 mm diameter) filter paper discs (Figure 1a), which were then placed in a pre-specified shape on Tegaderm™ wound-dressing film. An additional non-reactive ink containing a reference dye was added to the construct to produce the final wearable patch. These are flexible and comfortable, and can be worn for several hours without causing discomfort. The use of a semipermeable wound-dressing film allows the skin to breathe naturally—permeable to water vapor, oxygen, and carbon dioxide—while ensuring contact with the absorbent bioresponsive disc for sweat collection (Figure 1c).

[0112] Upon contact with sweat, the lactate-responsive disk changes color from yellow to dark red as a function of lactate concentration, while the non-reactive reference circle provides a fiducial marker to correct for lighting artifacts and boundary conditions for the machine learning-driven image processing stage. The colorimetric response of the composite ink follows the LOx / HRP cascade reaction (Figure 1b), where LOx oxidizes lactate to produce pyruvate and hydrogen peroxide, which are then used by HRP to oxidize the chromogenic substrate, resulting in an immediate, visible color change.

[0113] Colorimetric Response Evaluation: Despite the effectiveness of colorimetric sensing techniques for rapid analyte detection, the reproducibility of these systems is compromised by the potential presence of color gradients in the sensing area. Lack of color uniformity compromises the reliability of the readout, especially when coupled with image recognition models to obtain quantitative results. Color gradients in microfluidic paper-based sensors, i.e., the coffee ring effect, are caused by the sample solution transporting the chromogenic substrate and enzyme while diffusing from the center to the edge of the sensing area, resulting in uneven coloration. To circumvent this issue, the paper substrate was modified with a chitosan solution, which binds to the paper through electrostatic interactions, forming a thin film on the porous paper structure and resulting in immobilization and adhesion of the ink components. The effect of depositing a base layer of chitosan on the performance of silk-based inks was investigated using Whatman Grade 1 filter paper (Figure 2a). First, the sensing interface was calibrated using a solution mimicking the sweat composition with lactate concentrations ranging from 0 to 90 mM to evaluate the effect of chitosan on the colorimetric response. The sensitivity and sensing range were calculated by evaluating the slope of the calibration curve (i.e., the logarithm of the lactate concentration (Log C The green channel (G) as a function of the logarithm of the chitosan base layer (with chitosan, −84.11 ± 3.31 G Log C -1 ; without chitosan, -87.29±3.77G Log C -1, 0-60 mM range; avg ± SE, n = 5), an increased sensing range (with chitosan, 0-90 mM; without chitosan, 0-60 mM), and improved reproducibility of the colorimetric response at high lactate concentrations (coefficient of variation in the 50-90 mM range: with chitosan, 4.62 ± 0.68%; without chitosan, 11.34 ± 1.97%; avg ± SE), demonstrating its effectiveness in improving the colorimetric response. The combination of such high sensitivity and a wide linear sensing range is highly useful for the colorimetric detection of lactate, which is typically reported to have an upper detection limit of 25 mM (i.e., lower than the concentration required for the analysis of undiluted sweat). The colorimetric sensor presented in this study has a wide linear sensing range (e.g., 0-90 mM) suitable for both fitness monitoring and disease diagnosis. Specifically, for sports medicine applications, lactate concentrations in undiluted sweat can reach 60 mM and may further increase during training, while for clinical applications, a detection limit of 50 mM may be required. The specificity of the sensor was demonstrated by assessing the absence of colorimetric response after exposure to interfering substances (i.e., urea, sodium, potassium, ammonium, calcium, and magnesium ions) (Figure 5). The sensing performance of Ahlstrom Grade 55 and Whatman Grade 4 filter papers was also evaluated (Figures 6 and 7). Whatman Grade 1 filter paper demonstrated the best performance among the three types of paper. The difference in colorimetric response is due to the pore size of the paper: Whatman Grade 1 has the smallest pore size (i.e., 11 μm) compared to Ahlstrom Grade 55 and Whatman Grade 4 (i.e., 15 and 20–25 μm, respectively). This reduces the transport of chromogenic substrates and enzymes to the edges of the sensing area, increasing both color uniformity and reproducibility.

[0114] Shelf-life evaluation: The commercialization of enzyme sensors has been hindered by the gradual decrease in the stability of enzymes and dried proteins during storage at room temperature. Despite representing their major drawback, this issue has not been addressed in previous studies, and stability testing is often lacking. Here, the demonstrated ability of regenerated silk fibroin to stabilize unstable biomolecules is exploited to improve the thermal stability of sensors. Indeed, encapsulation of enzymes in the silk fibroin matrix retards the degradation of their native structure by reducing their molecular mobility and providing protection against environmental factors such as temperature and pH changes through buffering. To demonstrate the extended shelf-life of sensing interfaces both with and without a chitosan base layer, sensors were subjected to accelerated degradation tests by storing them at 60°C (i.e., above the enzyme's decomposition temperature) for 8, 24, and 120 hours. The ink's ability to preserve enzyme activity was evaluated when exposed to varying lactate concentrations (i.e., 0–90 mM). Storage at 60°C for 8 hours did not affect the sensing range of the sensing interface with chitosan, which showed a linear response in the range of 0 to 90 mM. The sensing range without chitosan was reduced to 0 to 30 mM. Chitosan did not affect the sensitivity in the range of 0 to 30 mM (with chitosan, -67.73 ± 5.86 G Log C -1 ; without chitosan, -72.21±3.75G Log C -1; avg ± SE, n = 3). With increasing storage time at 60 °C to 24 and 120 h, the presence of chitosan caused a slower decrease in the sensing range (with chitosan: 24 h, 0–60 mM; 120 h, 0–30 mM; without chitosan: 24 h, 0–10 mM, 120 h, 0–10 mM), further confirming the combined ability of silk and chitosan to improve the colorimetric response. The important role of silk fibroin in stabilizing the enzyme was demonstrated by comparing the sensing performance of silk-based and water-based inks (both in the presence of a chitosan base layer) when subjected to accelerated degradation tests (i.e., storing them at 60 °C above the enzyme's decomposition temperature for 8, 24, and 120 h). The colorimetric response of the sensing interface was evaluated after 8 h of storage (Figure 2c). In contrast to the silk-based interface, the water-based interface had a narrower sensing range (water-based: 0–10 mM; silk-based: 0–90 mM) and a lower sensitivity in the 0–10 mM range (water-based: −44.24 ± 5.18 G Log C -1 Silk base -70.96±2.32G Log C -1; avg ± SE, n = 3) (Figure 2d). In addition, the colorimetric response of the silk-based interface was stronger (i.e., dark red) than that of the water-based one (i.e., light pink) (Figure 2c, inset). The sensing performance of Ahlstrom Grade 55 and Whatman Grade 4 filter papers was also evaluated after accelerated degradation tests (Figures 6 and 7). In this case, Whatman Grade 1 filter paper again showed the best performance among the three studied papers. The stability of these silk-based sensors is remarkable, as they reach higher upper detection limits (90 mM, 60 mM, and 30 mM, respectively) compared to previously reported colorimetric sensors for lactate detection, even after 8, 24, and 120 h at 60 °C. The long-term stability of the wearable sensors was evaluated after 18, 21, and 24 months of storage at 4 °C (Figure 2e) and after 2.5 months of storage at 60 °C (Figure 2f). The silk-based chromogenic enzyme mixture was able to preserve 66% of its activity after 2.5 months at 60°C, in contrast to the water-based control, whose activity was reduced to 2%. Additionally, when stored at 4°C, the silk-based chromogenic enzyme mixture fully retained its activity for up to 24 months at 4°C, whereas the water-based control's activity was reduced to 17%. These results further demonstrated the ability of silk fibroin to stabilize the chromogenic enzyme mixture, enabling the fabrication of a storage-stable sensor whose colorimetric response remains intact after several years of storage.

[0115] Colorimetric wearable pH-sensing patch: The versatility of this approach and its potential for developing multi-sensing patches were demonstrated by developing silk-based chromogenic inks incorporating pH-responsive molecules (i.e., nitrazine yellow (NY), bromocresol green (BG), and phenol red (PR)). The inks presented in this study exhibit high sensitivity (i.e., BG, -39.8 ± 1.3; NY, -76.1 ± 1.4; PR, -40.9 ± 1.9; avg ± se, n = 3) and reversibility suitable for detecting real-time pH fluctuations. The intensity of the colorimetric response in the RGB color space for each ink was evaluated (Figure 3). The three different inks have three complementary sensing ranges (i.e., BG, pH range 3-7; NY, pH range 5.5-7.5; PR, pH range 6.5-8.5), and by combining them in the same sensing patch, it is possible to read pH values ​​in the physiologically relevant sweat pH range (i.e., pH 3-8.5). Along with lactate, sweat pH is an important variable for health monitoring due to its correlation with sodium concentration, making it an indicator of dehydration. Application of a silk-based sensing interface for both lactate and pH in the same patch provides a storage-stable multi-analyte sensor for comprehensive health monitoring.

[0116] Machine learning-driven readout: To enable easy quantitative readout of the colorimetric response using a smartphone camera, 1961 images of the sensor at six different concentrations of lactate (i.e., 0, 1, 5, 10, 30, and 50 mM) were acquired and used to develop a machine learning model. Images were acquired after drop-casting of a simulated sweat solution with known lactate concentrations onto the sensor's sensing interface (Figure 4a). To obtain a generic model capable of recognizing images under non-identical light exposure, images were acquired under different light conditions and then randomly divided into two groups: 1373 labeled images were used for model training (Figure 4c), and 588 were used for evaluating its performance (Figure 4d (top)). Images of the sensor were acquired over three different days corresponding to three different datasets, and the combination of the three was analyzed as a fourth dataset (i.e., the complete dataset). A support vector machine (SVM) model was constructed to readout and predict the sensor's colorimetric response as a function of lactate concentration. A plot of the RGB values ​​extracted from each dataset clearly identifies six data clusters (i.e., one for each lactate concentration). The clusters have low standard deviations (i.e., 0.92–4.13), and their organization in three-dimensional space allows for the identification of a suitable separating hyperplane for building an SVM predictive model (Figure 4b). Given the above, an SVM model was found to be ideal for this system, based on early data exploration during which the colorimetric variation of the sensor at different lactate concentrations was analyzed for each dataset. Increasing lactate concentration produces a color change from yellow to red, which corresponds to a decrease in the value of the green channel in the RGB color space during the image analysis stage. The analysis reveals narrow data variance for each lactate concentration, demonstrating the model's ability to account for and correct for different lighting conditions that, if not taken into account, would result in low prediction accuracy for the colorimetric sensor. Predictive models were built for each dataset using 70% of the images in each dataset for model training.The remaining 30% of the images in the dataset were used as input to evaluate how accurately the SVM model classified the sensor images. To calculate the accuracy of the model, a confusion matrix, whose diagonal indicates the percentage of correctly classified images, was evaluated for the full dataset (Figure 4d (bottom)). The model was able to reach an overall accuracy of 93%, 96%, and 92% for datasets 1, 2, and 3, respectively, demonstrating the excellent predictive ability of the model. To demonstrate the classification accuracy of the model for a large, heterogeneous dataset, a model was built and trained on the full dataset (i.e., dataset 4) and reached an overall accuracy of 89.3%.

[0117] After obtaining high predictive accuracy using quantitative classification of images, an additional model was constructed using quantitative classification by dividing each dataset into four categories (i.e., no lactate, low lactate, intermediate lactate, and high lactate). The suitability of the SVM model for this type of classification was confirmed, and evaluation of its classification accuracy revealed high predictive accuracy. Similarly, the model constructed for the full dataset exhibited high performance with an overall accuracy of 98.8%, demonstrating its ability to obtain accurate readouts. Finally, the feasibility of applying machine learning-driven quantitative readouts in a real-world scenario was demonstrated by placing a wearable sensor on the skin of a volunteer during a treadmill exercise session. Images of the wearable sensor after the session were acquired and processed by the SVM model, which predicted a lactate concentration of 30 mM (Figure 4e).

Claims

1. 1. A printable liquid lactate sensor composition, said composition comprising: Silk fibroin in an amount of 0.1% to 30% by weight; lactate oxidase, which is activated by lactate to produce hydrogen peroxide; a peroxidase activated by said hydrogen peroxide; and a chromogenic substrate that changes color upon activation of the peroxidase A composition comprising:

2. 10. A solid-state sensor comprising a biopolymer substrate formed from the printable liquid lactate sensor composition of claim 1, wherein the biopolymer substrate has embedded therein lactate oxidase, peroxidase, and a chromogenic substrate; A sensor wherein the quantitative bulk colorimetric change of the biopolymer substrate changes as the lactate concentration within the biopolymer substrate changes over a range from 0.1 mM to 100 mM, for example, but not limited to, 1 mM to 90 mM, 0.5 mM to 70 mM, 10 mM to 50 mM, for example, but not limited to, having a lower limit of 0.1 mM, 0.5 mM, 1 mM, or 10 mM, and an upper limit of 100 mM, 90 mM, 70 mM, or 50 mM.

3. 1. A wearable sensor for detecting lactate, comprising: A biopolymer substrate embedded with lactate oxidase, which is activated by the lactate to produce hydrogen peroxide, a peroxidase, which is activated by the hydrogen peroxide, and a chromogenic substrate, which changes color upon activation of the peroxidase, wherein the biopolymer substrate comprises silk fibroin in an amount of 1% to 100% by weight. Including, the quantitative bulk colorimetric change of the biopolymer substrate varies as the lactate concentration within the biopolymer substrate varies from 0.1 mM to 100 mM, for example, but not limited to, 1 mM to 90 mM, 0.5 mM to 70 mM, 10 mM to 50 mM, for example, but not limited to, a range having a lower limit of 0.1 mM, 0.5 mM, 1 mM, or 10 mM, and an upper limit of 100 mM, 90 mM, 70 mM, or 50 mM; Wearable sensors.

4. A biopolymer substrate having embedded therein lactate oxidase that is activated by lactate to produce hydrogen peroxide, peroxidase that is activated by said hydrogen peroxide, and a chromogenic substrate that changes color upon activation of said peroxidase, said biopolymer substrate comprising silk fibroin in an amount of 1% to 100% by weight. A sweat sensor comprising: the quantitative bulk colorimetric change of the biopolymer substrate varies as the lactate concentration within the biopolymer substrate varies from 0.1 mM to 100 mM, for example, but not limited to, 1 mM to 90 mM, 0.5 mM to 70 mM, 10 mM to 50 mM, for example, but not limited to, a range having a lower limit of 0.1 mM, 0.5 mM, 1 mM, or 10 mM, and an upper limit of 100 mM, 90 mM, 70 mM, or 50 mM; Sweat sensor.

5. 10. The composition or sensor of any one of the preceding claims, wherein the chromogenic substrate comprises a baseline colorant.

6. 10. A composition or sensor according to claim 1, wherein the baseline colorant is a yellow dye.

7. 10. A composition or sensor according to claim 1, wherein the yellow dye is an acid yellow dye.

8. 10. A composition or sensor according to claim 1, wherein the acid yellow dye is Acid Yellow 34.

9. 10. A composition or sensor according to any one of the preceding claims, wherein the chromogenic substrate comprises sodium 3,5-dichloro-2-hydroxybenzenesulfonate and / or 4-aminoantipyrine.

10. 10. A composition or sensor according to any one of the preceding claims, wherein the composition and / or the biopolymer substrate comprises a baseline buffer and / or electrolyte mixture, optionally tailored to mimic human sweat.

11. 10. The sensor of claim 2, wherein the biopolymer substrate comprises a base layer of chitosan.

12. 10. The sensor of claim 2, wherein the quantitative bulk colorimetric change occurs substantially instantaneously upon contact with solutions having different lactate concentrations.

13. An article of clothing comprising a plurality of sensors according to any one of claims 2 to 12.

14. A wearable patch comprising a plurality of sensors according to any one of claims 2 to 12.

15. 10. An article of clothing or wearable patch according to any of the two preceding claims, further comprising a reference color spot having a predetermined known color for colorimetric analysis of an image of the sensor or sensors.

16. 1. A colorimetric sensor for detecting a target chemical in a fluid sample, said sensor comprising: one or more detection regions on a substrate, said detection regions comprising silk fibroin; one or more enzymatic reagents configured to detect one or more target chemicals in the fluid sample; and one or more chromogenic substrates configured to indicate the relative amount of one or more target chemicals in the fluid sample; a colorimetric sensor comprising:

17. 17. The colorimetric sensor of claim 16, further comprising: an imaging device for detecting a colorimetric change in the one or more detection areas after contact with the fluid sample containing the one or more target chemicals for the one or more enzyme reagents; and a processor connected to the imaging device configured to detect target enzymes and quantify the amount of the target enzymes in the liquid.

18. The colorimetric sensor of claim 17 , wherein the imaging device comprises a multispectral camera.

19. 19. The colorimetric sensor of claim 17 or 18, wherein the processor comprises a machine learning model configured to train the sensor to detect and quantify chemicals in a fluid sample.

20. 20. The colorimetric sensor of claim 19, wherein the machine learning model is trained using multiple images of the colorimetric sensor responding to known concentrations of the one or more target chemicals.

21. 17. The colorimetric sensor of claim 16, further comprising chitosan in the one or more detection regions.

22. 17. The colorimetric sensor of claim 16, wherein the one or more enzymatic reagents include lactate oxidase (LOx).

23. 23. The colorimetric sensor of claim 22, wherein the one or more enzymatic reagents further comprise horseradish peroxidase (HRP).

24. 24. The colorimetric sensor of claim 22 or 23, wherein the target chemical comprises lactate.

25. 17. The colorimetric sensor of claim 16, wherein the fluid sample comprises a biological fluid.

26. 26. The colorimetric sensor of claim 25, wherein the biological fluid comprises sweat.

27. 17. The colorimetric sensor of any one of claims 16 to the immediately preceding claim, wherein the substrate comprises a flexible material configured to cover and conform to the sensing surface.

28. 17. The colorimetric sensor of any one of claims 16 to the immediately preceding claim, further comprising one or more pH sensitive areas on the substrate configured to detect a pH level of the fluid sample.

29. 30. The colorimetric sensor of claim 28, wherein the pH-sensing region comprises one or more chromogenic pH-sensing indicators.

30. 30. The colorimetric sensor of claim 29, wherein the one or more chromogenic pH-sensitive indicators define a pH range.

31. 17. The colorimetric sensor of claim 16, wherein a plurality of the detection regions are arranged in a predetermined pattern.

32. 32. The colorimetric sensor of claim 31, wherein the processor further generates a spatial distribution map of the one or more target chemicals based on the predetermined pattern of the detection regions.

33. 1. A method for detecting a target chemical in a fluid sample, said method comprising: training a target chemical detection model using a plurality of images of a colorimetric sensor, the sensor having a plurality of reference detection regions and a plurality of sample detection regions having predetermined concentrations of the target chemical in the fluid sample; and predicting the concentration of the target chemical on a colorimetric sensor using the trained target chemical detection model. A method comprising:

34. 34. The method of claim 33, wherein the multiple images are acquired in multiple lighting conditions.

35. 35. The method of claim 33 or 34, wherein the plurality of images are acquired using an imaging device for detecting colorimetric changes in the plurality of sample detection regions after contact with the fluid sample containing the target chemical.

36. 34. A method according to any one of claims 33 to the immediately preceding claim, wherein the plurality of images are acquired using a multispectral camera.

37. 34. A method according to any one of claims 33 to the immediately preceding claim, wherein the plurality of images is divided into a plurality of categories of the target chemical at the predetermined concentration.

38. 38. The method of claim 37, wherein the multiple images are labeled with their respective concentrations of the target chemical and combined into a dataset.

39. 1. A method of manufacturing a colorimetric sensor for detecting a target chemical in a sample fluid, said method comprising: Preparing one or more paper substrates with at least one of a silk fibroin solution, one or more enzyme reagents, or one or more chromogenic substrates; and placing the one or more paper substrates onto a film substrate; A method comprising:

40. 40. The method of claim 39, further comprising preconditioning the one or more paper substrates with a chitosan solution.

41. 41. The method of claim 39 or 40, wherein the one or more enzymatic reagents comprise lactate oxidase (LOx).

42. 42. The method of claim 41, wherein the one or more enzyme reagents further comprise horseradish peroxidase (HRP).

43. 40. The method of any one of claims 39 to the immediately preceding claim, further comprising providing one or more pH sensitive areas on the film substrate configured to detect the pH level of the sample fluid.

44. 44. The method of claim 43, wherein the pH-sensing region comprises one or more color-forming pH-sensing indicators.

45. 45. The method of claim 44, wherein the one or more colorimetric pH-sensitive indicators define a pH range.

46. 40. A method according to any one of claims 39 to the immediately preceding claim, wherein the one or more paper substrates are arranged in a predetermined pattern on the film substrate.

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