Systems and methods for diagnosing the likelihood of a gastrointestinal disorder

Abstract

Provided herein are systems and methods for detecting flatulence and / or diagnosing or predicting bowel disorders. Provided are devices that can be sized for positioning on or near a user's undergarments. The devices can include a processor, a power source, a gas sensor positioned at an opening in the device housing and configured to generate an electrical signal indicative of the presence of flatulence, and a communication module for transmitting data to a remote computing device. The processor can be programmed to read the electrical signal output by the gas sensor, record flatulence data based on the signal, associate the flatulence data with time data, and output the timed flatulence data via the communication module. Also provided are methods for assessing bowel activity using such devices, which can include specific food challenge studies, assessing dietary or pharmaceutical interventions, and / or continuous flatulence monitoring.

Description

Cross-reference to related applications

[0001] This application claims priority to U.S. Provisional Patent Application No. 63 / 601,174, filed November 20, 2023, the entire contents of which are incorporated herein by reference. Government Support Statement

[0002] none. Technical Field

[0003] The various methods, systems, devices, and processes described herein generally relate to the field of gastrointestinal (GI) tract sensing. In specific embodiments, the disclosure herein describes devices and methods for sensing gaseous properties (such as flatulence) from a patient's GI tract to diagnose the likelihood of GI conditions and / or the effectiveness of various GI interventions. Background Technology

[0004] It is estimated that 40% of adults suffer from gastrointestinal disorders. The most common causes of these disorders are the presence of small intestinal bacterial overgrowth (SIBO) and varying degrees of malabsorption of specific carbohydrates, such as lactose or fructose intolerance. A common symptom of these disorders is excessive gas production, which leads to bloating, abdominal pain, and increased flatulence. However, these symptoms are not specific to any particular disorder and cannot be measured clinically for diagnosis.

[0005] SIBO occurs when the microbiome in the small intestine overgrows, leading to excessive gas production (especially hydrogen) due to bacterial fermentation (Dukowicz et al., 2007). It is estimated that approximately 36 million American adults suffer from SIBO (Porter et al., 2017). The main causes of SIBO are multifactorial, but it has been identified that certain types of food—such as malabsorbable carbohydrates known as FODMAPs (fermentable, oligosaccharides, disaccharides, and monosaccharides, as well as polyols)—trigger microbial proliferation because they are poorly absorbed by the gut and thus serve as a food source for microbial fermentation (Spiller, 2017). One of the main consequences of SIBO is excessive hydrogen production, which has been hypothesized as a primary cause of symptoms such as bloating, abdominal pain, constipation, diarrhea, and flatulence. These symptoms can range from mild discomfort to severe pain, significantly impacting quality of life. The main causes of these symptoms are summarized in Figure 1. In short, the fate of sugars and nutrients reaching the small intestine is either intestinal absorption or as nutrients for the microbiome residing there. Essentially, there is competition for the same food between the body and the microbiome. Under normal circumstances, a healthy balance exists between the gut and the gut microbiota, and thus, the amount of fermentation gas does not cause any discomfort to the individual. The gut, characterized by an anaerobic environment, promotes sugar fermentation by bacteria, resulting in the production of gas mainly composed of hydrogen (H2) and methane (CH4), with a lower contribution from other gases such as hydrogen sulfide (H2S) and carbon dioxide (CO2) (Kirk, 1949; Suarez et al., 1998; Tomlin et al., 1991). However, if the bacterial population becomes excessive, the balance is disrupted, and fermentation increases to abnormal levels, producing excess gas. This excess gas increases pressure on the intestinal wall, leading to bloating and pain. This gas is eventually expelled from the body as flatulence (commonly known as farts), but this time, due to excessive carbohydrate fermentation, the quantity, frequency, and volume of flatulence are greater (Figure 1). Similarly, if an individual presents with malabsorption syndrome, the same healthy homeostasis is disrupted because poor nutrient absorption means more unabsorbed carbohydrates are available for bacterial fermentation. In both cases—whether due to bacterial overgrowth or impaired absorption—the end result is increased bacterial fermentation, leading to excessive gas production and flatulence. This explains why people experience significant flatulence after consuming certain foods (such as beans or nuts) or some sugar-free candies containing fiber and carbohydrates that are not easily absorbed by the intestines. Many people experiencing SIBO are unaware of their condition (Rao and Bhagatwala, 2019) and allow the problem to go untreated, leading to a life of persistent discomfort. It has been suggested that SIBO may increase the risk factors for pancreatic and bile duct cancer (Ma et al., 2019).

[0006] Currently, the most common method for diagnosing SIBO and malabsorption is the hydrogen breath test. Unfortunately, this method has poor sensitivity and accuracy, and it is also time-consuming and expensive. Breath-based tests have several drawbacks, including: the concentration of the gas of interest in the breath is relatively low (making sensing difficult); and breath tests are active / disruptive tests that cannot be performed for extended periods. Low sensitivity, specificity, and accuracy are due to the low concentration (0-100 ppm) of gases produced by microorganisms in the breath (Nichols et al., 2021; Sachdev and Pimentel, 2013). Breath tests and symptom assessments are often subjective and may not provide an accurate picture of an ongoing disease. Furthermore, the tests are time-consuming, usually performed in a physician's office, analyze only one type of carbohydrate malabsorption at a time, and do not capture everyday symptoms. Physicians often end up relying on their patients' medical history and symptoms, and diagnose the condition mostly after a lengthy process of ruling out other possible diseases. This can place an additional burden on the healthcare system and on patients. Moreover, even if SIBO is correctly diagnosed, monitoring the condition can be challenging. Although breath tests are technically available for follow-up of interventions, their poor sensitivity, inconvenience, and time-consuming nature mean that they are rarely used in practice to assess treatment efficacy when SIBO treatment requires antibiotics such as metronidazole, levofloxacin, or rifaximin (Pimentel, 2009).

[0007] Therefore, there is a significant need for innovative technologies that can bridge the diagnostic gap in gastrointestinal diseases. Summary of the Invention

[0008] This disclosure provides devices and methods to overcome the above-mentioned shortcomings by providing various systems, devices, techniques and methods (including diagnostic methods, training methods, efficacy determination methods, food effect determination methods, monitoring methods, etc.) for detecting gastrointestinal gases produced during intestinal microbial fermentation, thereby enabling the assessment of intestinal microbial activity.

[0009] According to one aspect of the invention, a device is provided, comprising: a housing having a contour that allows the housing to be secured to underwear when worn by a user in a region close to the user's perineum; a processor located within the housing; a gas sensor located within the housing and including at least one sensing element, wherein a composition of the at least one sensing element interacts with at least one gaseous component in the user's gastrointestinal gas emissions to generate an electrical signal indicating the presence or concentration of the gaseous component; a communication module located within the housing and connected to the processor to transmit data from the processor to a remote computing device; and a memory located within the housing and having an instruction set stored thereon, which, when executed by the processor, causes the processor to: read the electrical signal generated by the gas sensor; record gastrointestinal gas data based on the electrical signal; correlate the gastrointestinal gas data with time data corresponding to the time when the electrical signal was generated to generate timed gastrointestinal gas data; and output the timed gastrointestinal gas data via the communication module.

[0010] In an alternative, such a device may further include a power source located within the housing and connected to provide power to the processor, communication module, and memory throughout a study conducted via the device for a duration of at least 12 hours.

[0011] In another alternative, such power sources may include electrolytic double-layer capacitors (EDLCs) connected to provide rechargeable power to the device.

[0012] In another alternative, such a device may have a gas sensor positioned aligned with an opening in the housing near the user’s perineal area, the opening being configured to allow gas to permeate to at least one sensing element.

[0013] In another alternative, such devices may also include a temperature sensor and a motion sensor, both housed within the housing and connected to provide signals to a processor; and the instruction set may further enable the processor to generate an indication, based on the temperature sensor signals and the motion sensor signals, whether the device is being worn by a user.

[0014] In another alternative, the gas sensor of such a device may be an electrochemical sensor comprising a reference electrode and a counter electrode, wherein at least one sensing element is a working electrode comprising a material that exhibits a change in current due to a redox reaction when exposed to a hydrogen-based gas.

[0015] In another alternative, such devices may further include a quasi-solid electrolyte configured to contact the working electrode, the reference electrode, and the counter electrode.

[0016] In another alternative, the quasi-solid electrolyte may include an absorbent substrate impregnated with a hygroscopic material.

[0017] In another alternative, the working electrode may be a platinum-based electrode, and the reference and counter electrodes may comprise conductive carbon.

[0018] In another alternative, the gas sensor of such a device may be a metal oxide semiconductor sensor, and at least one sensing element may include a metal oxide sensing layer of the metal oxide semiconductor sensor, wherein the metal oxide semiconductor sensor exhibits a change in resistance in response to the adsorption of hydrogen-based gas at the metal oxide sensing layer.

[0019] On the other hand, a system for determining the likelihood of a gut condition is provided. The system may include: a processor; a communication module; and a memory in communication with the processor. The memory may store software instructions that, when executed by the processor, cause the processor to: receive gas sensing data from a gas sensor, the gas sensing data including a time series of measurements indicating the concentration of gas in a subject's gastrointestinal gas emissions during a given time period; receive compliance data indicating whether the subject was wearing the gas sensor when the time series measurements were acquired; receive additional user data indicating the time of consumption of a food of interest relative to the time series of measurements; perform at least one of the following: determining, based on the gas sensing data, the time of the first gastrointestinal gas emission after the consumption of the food of interest; determining a microbiome activity index; or determining the total number of gastrointestinal gas emissions during a given time period after the consumption of the food of interest; and outputting an indicator to a user of the impact of the food consumption on the subject.

[0020] In an alternative, the software instructions of such a system may further enable the processor to: receive, in connection with receiving additional user data, the time when the subject consumed food including the target carbohydrates or amino acids after fasting.

[0021] In an alternative, the software instructions of such a system could further enable the processor to: output to the user an indicator of the likelihood that the subject has a gut condition including carbohydrate malabsorption.

[0022] In terms of alternatives, a given time period for such a system can be at least three hours.

[0023] In alternatives, such systems can provide a given time period of at least one week; the time series of measurements can span the user's daily activities to provide gastrointestinal gas monitoring; and the microbiome activity index can be calculated relative to all the food the user eats within a given time period.

[0024] In an alternative, such systems may include additional user data indicating all the food a user eats during a given time period, and software instructions may further enable the processor to identify trigger foods that, when eaten by the user, cause the microbiome activity index to increase by a threshold amount compared to the user's baseline microbiome activity index.

[0025] In alternatives, additional user data for such systems is determined via a user interface that allows users to upload photos of the food they eat for processing by a food recognition AI model, or allows users to input nutritional information about the food they eat; and in which software instructions further enable the processor to determine the components in the food that may cause increased gastrointestinal activity.

[0026] In alternatives, compliance data for such systems is derived from at least one of the following: the output of a temperature sensor disposed within a housing containing a gas sensor; or the output of a motion sensor disposed within a housing containing a gas sensor.

[0027] In alternatives, such systems may include additional user data and mobile device displays, and the software instructions further enable the processor to: prompt the user via the display to ensure that the gas sensor is being worn near the perineal area; prompt the user to begin the diet plan; and prompt the user to consume the food of interest at a given time after the diet plan has begun.

[0028] In an alternative, the software instructions of such a system further enable the processor to: monitor the peak value of the signal value in the gas sensing data that indicates the gastrointestinal gas emission; and to determine that a confirmed gastrointestinal gas emission has occurred when the signal value increases from the baseline level to a peak value exceeding a threshold for no more than a given time period and then falls back to the baseline level.

[0029] In an alternative, the microbiome activity index indicates the number of events in which gas sensing data exceeds a threshold, and is calculated using the absolute value of the first derivative of the time series of the measurement.

[0030] In alternatives, the gas sensor of such a system can be any of the aforementioned alternative devices, and such a system can be used to perform any of the following methods.

[0031] In alternatives, the processor, communication module, and memory are part of the mobile device, the gas sensor can be any of the aforementioned alternatives, and the system allows subjects to assess the effects of food outside of a medical clinic without the involvement of the medical clinic.

[0032] In other respects, certain methods are also provided. For example, a method for diagnosing intestinal disorders is provided, comprising: having a subject fast for an initial fasting period; providing a wearable gastrointestinal gas sensor for the subject to wear; administering a dietary intervention for the subject to consume, and recording the time the dietary intervention is consumed; continuously recording gastrointestinal gas data from the wearable gastrointestinal gas sensor throughout a measurement period including at least one hour; determining gastrointestinal gas activity based on the gastrointestinal gas data, the gastrointestinal gas activity comprising at least one of the following: the time elapsed from the consumption of the dietary intervention to the first gastrointestinal gas emission, the total number of gastrointestinal gas emissions, the frequency of gastrointestinal gas emissions, the intensity of gastrointestinal gas emissions, the temporal distribution of gastrointestinal gas emissions, the volume of gastrointestinal gas emissions, and the total volume of all gastrointestinal gas emissions; and providing a diagnosis of intestinal disorder based on gastrointestinal gas activity and the duration of the dietary intervention.

[0033] Among alternative approaches, dietary intervention involves carbohydrates, and intestinal disorders are caused by the malabsorption of carbohydrates.

[0034] In alternative approaches, the intestinal condition is at least one of small intestinal bacterial overgrowth (SIBO) or diarrhea-dominant irritable bowel syndrome (IBS-D), and the diagnosis is made by providing information on gastrointestinal gas activity and dietary interventions to a trained machine learning algorithm configured to determine the likelihood of SIBO or IBS-D based on training data from patients with a history of SIBO or IBS-D and patients without a history of SIBO or IBS-D.

[0035] In alternative approaches, dietary interventions include carbohydrate challenges using at least one of glucose or fructose, and intestinal disorders are secondary malabsorption in individuals suffering from at least one of the following: recent chemotherapy, celiac disease, or inflammatory bowel disease.

[0036] In alternative methods, the wearable gastrointestinal gas sensor includes a device according to any of the alternatives mentioned herein, and may further be implemented by any (in whole or in part) of the alternative systems provided herein.

[0037] On the other hand, a method for assessing intestinal disorders is provided, comprising: providing a wearable gastrointestinal gas sensor to be worn by a subject during a study period; recording gastrointestinal gas data from the wearable gastrointestinal gas sensor during the study period; receiving an indication of the subject's reported stress level during the study period; establishing a baseline gut microbiome activity index by analyzing gastrointestinal gas data during periods of low or no reported stress levels; measuring stress-induced changes in gut microbiome activity by comparing gastrointestinal gas data during periods of elevated reported stress levels to the baseline gut microbiome activity index; quantifying the magnitude of the deviation from the baseline during periods of elevated reported stress levels; determining the temporal relationship between the occurrence of elevated stress levels and changes in gut microbiome activity, including the time delay between stress occurrence and gut microbiome response, and the duration of elevated gut microbiome activity following stress occurrence; and generating a gut-brain reactivity curve based on: the magnitude of changes in gut microbiome activity during periods of elevated reported stress levels; the time delay between stress occurrence and gut microbiome response; and the duration of elevated gut microbiome activity following a stress event.

[0038] On the other hand, wearable gastrointestinal gas sensors of this type of method may include any of the alternative aspects of the devices mentioned herein.

[0039] According to another aspect of this disclosure, a method is provided for diagnosing intestinal disorders (and / or for providing the likelihood of such intestinal disorders or indicators for detecting such intestinal disorders), comprising: fasting a subject for an initial fasting period (which may be of variable duration and / or optional); providing a wearable gastrointestinal gas sensor for the subject to wear; administering a dietary intervention for the subject to consume, and recording the time the dietary intervention is consumed; continuously recording gastrointestinal gas data from the sensor throughout a measurement period including at least one hour; determining gastrointestinal gas activity based on the gastrointestinal gas data, the gastrointestinal gas activity including at least one of the following: the time elapsed from the consumption of the dietary intervention to the first gastrointestinal gas emission, the total number of gastrointestinal gas emissions, the frequency of gastrointestinal gas emissions, the intensity of gastrointestinal gas emissions, the temporal distribution of gastrointestinal gas emissions, the volume of gastrointestinal gas emissions, and the total volume of all gastrointestinal gas emissions; and providing a diagnosis of intestinal disorder based on gastrointestinal gas activity and the duration of the dietary intervention. According to another aspect of this disclosure, a method for long-term monitoring of gut health is provided, comprising: providing a wearable gastrointestinal gas sensor to be worn continuously by a subject or user during the subject's or user's daily activities for a period of at least one week; recording gastrointestinal gas data and food consumption data, wherein the food consumption data can be input via a user interface that accepts uploaded food photos or manually entered nutritional information for processing by a food recognition AI model; calculating a microbiome activity index relative to the consumed food; and identifying foods that, when consumed, cause the microbiome activity index to increase above a threshold compared to the subject's baseline.

[0040] These aspects are not limiting. Other aspects and features of the systems and methods described herein will be provided below. Attached Figure Description

[0041] The patent or application documents contain at least one drawing drawn in color. Upon request and after payment of the necessary fees, the Patent Office will provide a copy of the patent or patent application publication with one or more color drawings.

[0042] The foregoing features of the embodiments will be more readily understood through the following detailed description with reference to the accompanying drawings, wherein:

[0043] Figure 1A This is an exploded view of the components of an electrochemical sensor according to various aspects of this disclosure.

[0044] Figure 1B This is a set of top and side views of components of an electrochemical sensor according to various aspects of this disclosure.

[0045] Figure 2 This is a conceptual block diagram of a sensing device based on various aspects of this disclosure.

[0046] Figure 3 This is a block diagram illustrating the data flow within an example system according to various aspects of this disclosure.

[0047] Figure 4 This is a flowchart illustrating an example process 400 for determining the intestinal condition according to various aspects of this disclosure.

[0048] Figure 5 This is a flowchart illustrating an example protocol method 500 according to various aspects of this disclosure.

[0049] Figures 6A-6C The figures illustrate various prototype designs based on various aspects of this disclosure.

[0050] Figure 7 shows a conceptual diagram of a battery holder and separator according to various aspects of this disclosure.

[0051] Figure 8 The figure illustrates a venogram showing the frequency and intensity of gastrointestinal flatulence as depicted and observed according to various aspects of this disclosure.

[0052] Figure 9 illustrates a comparison of sensor gain levels according to various aspects of this disclosure.

[0053] Figure 10 illustrates the sensor analysis according to various aspects of this disclosure.

[0054] Figure 11 illustrates the calibration curves, response times, interference data, and other experimental results associated with the sensor according to various aspects of this disclosure.

[0055] Figure 12 illustrates the results corresponding to the temperature and accelerometer outputs and the wearable algorithm according to various aspects of this disclosure.

[0056] Figure 13 illustrates the analysis of microbiome activity indices according to various aspects of this disclosure. Detailed Implementation

[0057] As used in this specification and claims, the singular forms “a” (“a”, “an”) and “the” (“the”) include the plural forms, unless the context clearly specifies otherwise.

[0058] As used herein, “approximately,” “roughly,” “substantially,” and “significantly” will be understood by those skilled in the art and will vary to some extent depending on the context in which they are used. If, given the context in which the use of a term is not apparent to those skilled in the art, “approximately” and “roughly” will mean at most plus or minus 10% of that particular term, and “substantially” and “significantly” will mean more than plus or minus 10% of that particular term.

[0059] As used herein, the terms “include” and “including” have the same meaning as the terms “comprise” and “comprising”. The terms “comprise” and “comprising” should be interpreted as “open-ended” transitional terms, which permit the inclusion of additional components beyond those listed in the claims. The terms “compose” and “consisting of” should be interpreted as “closed-ended” transitional terms, which do not permit the inclusion of additional components beyond those listed in the claims. The term “consisting primarily of” should be interpreted as partially closed and permits the inclusion of only additional components that do not fundamentally alter the nature of the claimed subject matter.

[0060] The phrase “such as” should be interpreted as “for example, including”. Furthermore, the use of any and all exemplary language (including, but not limited to, “such as”) is intended only to better illustrate the invention and, unless otherwise required, does not constitute a limitation on the scope of the invention.

[0061] Furthermore, in cases where conventions such as "at least one of A, B, and C" are used, such constructions are generally intended to convey the meaning of the convention in a manner that would be understood by one of ordinary skill in the art (e.g., "a system having at least one of A, B, and C" would include, but is not limited to, systems having a single A, a single B, a single C, A and B together, A and C together, B and C together, and / or A, B, and C together). Those skilled in the art will further understand that, whether in the specification or the accompanying drawings, virtually any extractive word and / or phrase presenting two or more alternative terms should be understood to account for the possibility of including one term, any one of the terms, or both terms. For example, the phrase "A or B" would be understood to include the possibility of including "A" or "B" or "A and B".

[0062] All languages ​​using terms such as "up to," "at least," "greater than," and "less than" include the number being referenced and refer to a range that can subsequently be subdivided into ranges and subranges. A range includes each individual member. Thus, for example, a group with 1-3 members refers to a group with 1, 2, or 3 members. Similarly, a group with 6 members refers to a group with 1, 2, 3, 4, or 6 members, and so on.

[0063] The modal verb "may" refers to the preferred use or selection of one or more options or choices among several described embodiments or features included in the same embodiment. If no options or choices regarding a particular embodiment or feature included in the same embodiment are disclosed, the modal verb "may" refers to an affirmative action regarding how to make or use the described embodiment or feature included in the same embodiment, or a clear decision regarding a particular skill in using the described embodiment or feature included in the same embodiment. In the latter context, the modal verb "may" has the same meaning and connotation as the auxiliary verb "can".

[0064] This document discloses various embodiments, configurations, materials, devices, systems, methods, and techniques for determining the likelihood of a given individual exhibiting various intestinal-related conditions by measuring gut microbial gas emissions (e.g., gastrointestinal gas). Regarding the devices and systems described below, certain alternative components and materials are described, but these components and materials are not intended to be limiting or necessary. The description of components in such devices and systems is intended to be illustrative only and is neither a minimum nor a limitation on the types of components that may be used in the various embodiments herein. Similarly, the methods described herein are explained with reference to optional steps and modifications, which are not intended to be limiting or necessary. The methods described herein can be performed using hardware such as (or including) the devices and systems described herein, but are not required to be implemented with such hardware, except in specific examples identifying the use of such hardware.

[0065] The embodiments disclosed herein provide for the measurement of the characteristics of intestinal microbial gas emissions (e.g., gastrointestinal gases) and for the determination of data regarding a patient’s gas emissions that could enable a healthcare provider to assess certain conditions involving the patient’s gut.

[0066] The inventors have determined that utilizing gastrointestinal gases as a source of information about a patient's intestinal activity offers certain advantages not present in existing methods for assessing intestinal activity. The correlation between the composition, intensity, quantity, and frequency of gastrointestinal gases reflects the activity of the gut microbiome. The sensing device of this disclosure uses electrochemical sensors to capture and analyze information about gastrointestinal gas emissions; in some embodiments, only a single, specially designed electrochemical sensor as described below.

[0067] In some embodiments, a sensing device including such an electrochemical sensor is positioned within or on a patient's underwear for a period of time during which measurements of such gas emissions are taken and data is acquired. Consequently, certain design considerations can facilitate better data acquisition. For example, the device should be contained within a housing having a size and shape factor that is acceptable and comfortable for the patient to wear in or on their underwear in a suitable location (such as in the gluteal cleft or near the patient's anus) (however, this is contingent upon alternative locations on or within the patient's underwear or clothing, or specific fastening devices (such as straps, tapes, or adhesives) being equally utilized, provided they are sufficiently close to the gastrointestinal gas emissions to allow for proper detection). Similarly, in some embodiments, a design goal may be to give the device a power source and power consumption profile that avoids the need for bulky or external battery compartments or cables. Likewise, in some embodiments, avoiding physical connections (such as USB cables or associated power cords) for data acquisition may be desirable. Furthermore, considering that the device may be worn for extended periods of time, some embodiments avoid the use of consumables that need to be replenished over a period of time, which may interfere with testing, and should be robust to the environment in which they operate (physical forces, temperature, humidity, moisture, foreign objects, etc.).

[0068] In some embodiments, it may be advantageous to reduce the number of components necessary to perform gas emission measurements and / or simplify the types or number of measurements required to assess a patient’s bowel activity. For example, having only one sensor to measure gas characteristics and / or measuring only certain components (such as H2) of a patient’s gastrointestinal gas emissions may be desirable.

[0069] Below, this disclosure includes sections illustrating examples of sensing devices and systems, as well as examples of methods and techniques for assessing the likelihood of intestinal diseases. This disclosure also includes an examples section, which provides further details regarding some experiments performed by the inventors, and various example embodiments and use cases.

[0070] Example embodiments of sensing devices and systems

[0071] Now for reference Figure 1A An exploded view of components of one design for an electrochemical sensor 100 is shown. The components include a PCB 102 with an electrode assembly 104, an electrolyte layer 106, and electrical leads connected to the electrode assembly 108. In some embodiments, the electrochemical sensor 100 may operate based on the ammeter principle rather than the metal oxide or chemical resistor principle. Figure 1B As shown, the electrode assembly 104 on the electrochemical sensor 100 includes three electrodes: a counter electrode, a reference electrode, and a working electrode. A design is envisioned that, in addition to... Figure 1BThe shapes, orientations, and positions of electrodes other than the shown electrodes include, for example, electrodes on opposite sides of the substrate, and two, four, or five electrodes. These electrodes work together to make measurements of gas properties (as opposed to being considered individual or complementary sensors). Electrodes can be formed from various conductive compounds and can be attached to or printed onto a substrate such as a PCB. In some examples, one or more of the electrodes can be platinum-based electrodes. In one embodiment, carbon conductive ink is used for the counter and reference electrodes, while another platinum Nafion® carbon ink (Pt-Nafion-Carbon, 1% Nafion, and 3% Pt) is used for the working electrode. Platinum is a catalyst for hydrogen oxidation, a redox reaction that occurs at an open-circuit potential (0 bias). Thus, the platinum in the working electrode provides improved sensitivity and selectivity of the electrochemical sensor for hydrogen measurements. Nafion® is a type of conductive polymer (sulfonated tetrafluoroethylene fluoropolymer copolymer) that is added as an additive to the carbon ink to improve conductivity and / or sensitivity. However, it should be understood that the specific metals, polymers, coatings, inks, etc., used in the inventors' experiments and prototyping are not limiting. The inventors chose carbon ink, platinum, and Nafion for various reasons (cost, complexity, and target application), but it is conceivable that other materials are also suitable for use, such as when it is desired to sense other gaseous components (e.g., hydrogen-based gases), and / or other cost or complexity constraints apply.

[0072] In one example embodiment, PTFE filter paper (e.g., impregnated with sulfuric acid) Figure 1B The paper (shown) was used to create an impregnated absorbent substrate by providing an electrolyte for gas measurements, but other hygroscopic materials / substrate and other absorbent substrates (e.g., other paper, cellulose materials, natural fibers, polymer-based materials and polymer fiber materials (woven or nonwoven), ceramic-like materials, glass fibers, porous materials, sponge-like materials, gels, membranes, etc.) were also envisioned as electrolytes. The paper was impregnated in a strong sulfuric acid solution and then dried at room temperature. Strong sulfuric acid has a very low evaporation rate due to its low vapor pressure (0.001 mm Hg at 25°C); and thus, an invisible layer of acid remains adsorbed onto the filter pad after drying. Furthermore, sulfuric acid is hygroscopic, meaning it can absorb moisture from the environment, thus maintaining high conductivity of the filter pad without any visible liquid. Therefore, a quasi-solid electrolyte was formed, which remained at 0.056 (+ / -0.013, n=6) S cm⁻¹. -1 Its high conductivity, which can be compared with Nafion's conductivity of 0.079–0.2 Scm. -1They are comparable. Even with continuous exposure to air, the high conductivity of the filter pad is maintained for at least one month.

[0073] Carbon, as a material for electrodes, offers certain advantages, such as a stable potential with variations as small as 1 mV per day, even more stable than standard silver chloride electrodes (Ag / AgCl). While carbon electrodes may be sensitive to the concentration of chlorides prevalent in the environment, chlorides are generally absent for gas sensing applications, thus carbon can be used as a quasi-reference electrode. Additionally, silver or silver chloride, as materials for reference electrodes, would be undesirable for use in strongly acidic environments, as it will gradually dissolve in the acid. Although this process is slow, it limits the long-term performance of the sensor.

[0074] In another embodiment, the electrochemical sensor comprises conductive carbon ink with platinum and Nafion as working electrodes, and bare conductive carbon ink as counter and reference electrodes, respectively. In some configurations, the electrodes may be screen-printed on a flat surface of a PCB. In other configurations, alternatives to the PCB may include Kapton or a ceramic substrate. In yet another embodiment, the PCB is not used, and the electrodes are self-supporting or integrated into the housing of the device. In some configurations where a PCB or other substrate is used, the PCB or substrate may form a watertight seal within the housing of the device, and / or may be permeable. The quasi-solid electrolyte is made of cellulose paper, previously wetted with 6 µL of concentrated sulfuric acid (4 mol L⁻¹) and dried at room temperature for at least 20 minutes. A commercially available PTFE membrane is placed on top of the electrode system, and pressure is applied toward the electrodes to maximize conductivity (Figure 1). In some embodiments, the entire electrode system is protected by a secure 3D-printed PETG housing. The housing is sealed by melting two housing components together with acetone.

[0075] During operation, the electrochemical sensor exhibits an electrical output via leads that provide an analog signal to a processor, which converts this signal into data indicating the presence of hydrogen. The electrical output changes substantially linearly with increasing H2 addition acting on the Pt-containing working electrode, thus allowing the sensor to function like a potentiostat. In some embodiments, the specific materials chosen for the electrolyte and electrode, and even the surface area and shape of the electrode, can provide different, even nonlinear, variations in the sensor's electrical output. Consequently, the memory associated with the processor monitoring the output of the sensor leads may include lookup tables or other conversion algorithms for interpreting the presence of H2 from the sensor's output.

[0076] Now for reference Figure 2A conceptual block diagram of a sensing device 200 is shown. Device 200 includes an outer housing 202. The housing 202 defines the size and profile of device 200, as its main components are stored within the housing 202. As shown, the housing is designed to be generally flat, disc-shaped or cylindrical, but other shapes are envisioned (such as wider / thinner shapes), contour shapes designed to conform to the shape of the human body, etc. The housing is designed to be easily attachable to a user's underwear, belt, strap, or skin-friendly adhesive, and may thereby include contours, recesses, or other areas suitable for clips and / or accessories.

[0077] The housing 202 includes at least one opening 204. In some embodiments, the remainder of the housing, except for the opening 204, may be sealed to prevent water or other unwanted intrusion. The opening 204 may directly interface with the electrochemical sensor 206, and its size and / or location may be designed to align with the electrodes of the sensor 206. In other embodiments, the opening 204 may include a breathable membrane, a mesh, or other covering that still allows air permeation. In some embodiments, more than one opening 204 may be utilized, such as on opposing or adjacent surfaces of the housing 202, to facilitate airflow over the sensor 206. Some examples of sensor profiles, dimensions, shapes, orientations, configurations, and housing designs are as follows: Figures 6A-6C As shown; however, these are not intended to be limiting, and housing 202 can be defined in various shapes and sizes, and the configuration of internal components can be influenced by shape factors that are more favorable for positioning, subject comfort, and gastrointestinal gas detection. Similarly, the way such sensors are secured can also vary. For example, some sensors can be housed in a flexible / fabric body that can be adhesively worn by the user, some can be attached to a belt loop, some can be worn with pins to the user's underwear or clothing, or can be secured to clothing with magnetic or compression attachments (e.g., where part of the attachment is on the side of the clothing opposite the main sensor housing), or can simply be part of a belt or other securing device.

[0078] Sensor 206 can be as described above regarding Figure 1A and Figure 1B The sensor described above. In some embodiments, the shape factor of sensor 206 may prevent air, water, and / or foreign matter from transmitting sensor 206 to other components of device 200. For example, the circumference of sensor 206 may mate with housing 202, and / or with gaskets or other sealing devices. Electrodes of sensor 206 (through openings in sensor 206, and / or through a filter / electrolyte pad in the sensor) are positioned to receive air through opening 204. Sensor 206 may be connected to power source 216 to generate potential or other characteristics across electrodes. In other embodiments, sensor 206 may not require a connection to a power source for cross-electrode measurements.

[0079] The electrical output lead of sensor 206 can be connected to processor 208. In some embodiments, the lead may first be connected to other electrical components, such as analog-to-digital converters, filtering components, capacitor elements, etc. Processor 208 may be a microcontroller or other similar computing resource that can read analog or digital data indicating the output of sensor 206. Processor 208 may run software stored in memory, such as memory 210. For example, the software may enable the processor to detect and digitize the electrical output of sensor 206 at a given sampling frequency. In some embodiments, the sampling frequency may be relatively low when readings from the sensor indicate a relatively low hydrogen level (e.g., once every 5s, 10s, 30s, 1m, 1.5m, 2m, 3m, 4m, etc.) and may increase in frequency whenever readings from the sensor indicate a relatively high hydrogen level. Measurements may be stored in memory such as memory 210 and supplemented with timestamp data based on a time counter operated by the processor, which is synchronized with a clock / time counter of an external device to which device 200 may interface. Timestamp data can be correlated with the actual time of day (e.g., based on an identified clock) or it can be correlated with only the time elapsed since a given event (e.g., when a device is turned on).

[0080] As shown in the figure, device 200 includes a single chemical sensor 206 for detecting gas properties. The single sensor 206 allows for highly accurate and sensitive determination of hydrogen levels sufficient to address gastrointestinal gas emissions, without the added complexity of multiple sensors. This capability of using only a single sensor 206 for chemical measurements offers several advantages, such as reduced device size, lower power consumption, and increased reliability.

[0081] In some embodiments, the software stored on memory 210 may also provide further processing of the data generated by sensor 206 by the processor, such that it is enhanced and / or compiled and transformed into a higher level of evaluation. For example, processor 208 may bin the sensor data into time-sequential categories during testing, such as those occurring before or after the consumption of food of interest. Similarly, processor 208 may use calibration data or known output curves to convert the electrical signal of sensor 206 into an estimated hydrogen level for a given time. Processor 208 may also determine the occurrence of individual gastrointestinal gas emissions based on the sensor data. For example, as the hydrogen level increases to a certain elevation level (e.g., 10 times a given baseline level), it can be determined that an emission is likely to occur. Then, if the hydrogen level continues to rise by an amount for a given duration, the processor may classify the detections made during that duration as a single emission. In other words, the processor may be programmed to identify spikes in the hydrogen level for a given duration within a given window and designate them as indicators of gastrointestinal gas emissions. In some embodiments, once an elevated hydrogen level is initially detected, the processor can begin sampling the sensor output on a more frequent basis (e.g., multiple times per second, once per second, etc.) until the hydrogen level returns to or near the baseline level. Thus, if two emissions occur consecutively, the individual peaks of the hydrogen level can be classified as separate occurrences. Based on the identifiers of the individual emissions, the processor can also determine the duration of the first emission, the total number of gastrointestinal gases detected, the emission frequency and time between a given emission, and the individual volumes and intensities of the emissions, as well as the total volume and intensity of the emissions. As another example, data from sensor 206 can be used to monitor the total volume, emission rate, and / or total number of emissions as indicators of gut microbiome activity; thus, when data indicates elevated gut microbiome activity, various identifiers related to an individual's gut condition or susceptibility can be made, as described herein.

[0082] In a further embodiment, an optional additional sensor 214 may be included in the device 200 for the purpose of assessing whether a patient has adhered to a prescribed duration of wearability of the sensing device 200. Thus, the optional sensor 214 may include an accelerometer or other motion sensor, a temperature sensor, or a humidity sensor. The processor 208 may be programmed to supplement the gas sensor data 206 with an indication from the additional sensor output 214 whether the user is wearing the sensor 200 at the time a given hydrogen reading occurs. For example, if the temperature sensor 214 indicates that the device 200 may be touched or worn by the user (e.g., near surface skin temperature) during a given time frame (e.g., two or more consecutive temperature samples), the gas sensor data measured during that time frame can be augmented with an indicator that the data is a valid measurement, and vice versa. Similarly, if the motion or humidity sensor 214 provides a measurement indicating that the device 200 is being worn during a given time frame, the gas data acquired during that time frame can be marked as valid, and vice versa.

[0083] Device 200 may also include an onboard power source 216 and a communication module 212. The power source 216 may include lithium-ion or other rechargeable power sources or rechargeable battery types, low-cost coin cells, supercapacitors, and energy storage capacitors (electrolytic double-layer capacitors (EDLCs)). The communication module 212 may be wireless, such as a Bluetooth transceiver, a local WLAN connection, or a customizable RF transceiver.

[0084] Now for reference Figure 3 System 300 is illustrated in the conceptual diagram. Sensor device 304 performs gas measurements on a given individual during a specific time frame, such as a prescribed test, a given 24-hour period, an hour of wakefulness only, an hour of sleep only, or a specific post-meal period. Device 304 may include, for example, Figure 2 Device 200 or similar devices. Data 302 generated by device 304 indicates the gaseous characteristics of the gas emitted by the wearer's gastrointestinal gases. As described above, the gas sensing data 302 can be enhanced, supplemented, segmented, or extracted into other forms of data. For example, in one embodiment, the gas sensing data may include a corresponding timestamp for each measurement. The gas sensing data 302 can be transmitted to a remote computing resource such as a remote computer 310. The transmission of the gas sensing data 302 may occur via a local Bluetooth connection, or via an Internet connection to a remote service, or other communication network 320.

[0085] In addition to gas sensing data 302, system 300 may optionally include recording and transmitting additional user data 306. For example, additional user data 306 may be input into the patient's mobile device or into the patient's EMR or other user interface. Data 306 may include user information such as age, gender, medical history, or other medical record information. Data 306 may also include information input by the wearer or healthcare professional to indicate what food / compound was consumed and when. For example, data 306 may be extracted from a food diary kept by the wearer or from medical records indicating when and what the patient ate. In other embodiments, additional user data 306 may include the start time of fasting, the duration of fasting, the time the user spent in the bathroom or on the toilet, and the time when various interventions (such as antibiotics, probiotics, elimination diets, etc.) were administered.

[0086] The computing device 310 includes a processor 312, a memory 314, a communication system 316, a user input 318, and a display 320. In some embodiments, additional user data 306 may be directly input into the computing device 310 by the user. The memory 314 may contain software that generates certain determinations based on gas sensing data 302 and additional user data 304. For example, emission information such as the time, frequency, and intensity of gastrointestinal gas emissions can be determined from the gas sensing data 302. At a higher level, according to the methods described below, the likelihood of various intestinal conditions can also be determined from the sensing data 302. The output of such processing can be displayed to a user, such as to a healthcare professional monitoring a wearable device, via the display 320 or a screen.

[0087] Figure 4 This is a flowchart illustrating an example process 400 for determining intestinal condition according to some aspects of this disclosure. As described below, certain implementations may omit some or all of the illustrated features / steps, may be implemented in different orders in some embodiments, and some illustrated features may not be required to implement all embodiments. In some examples, combined with... Figure 2 and Figure 3 The means (e.g., device 200 / 304, processor 208 / 312, and memory 210 / 314, etc.) may be used to perform all or part of the example process 400. However, it should be understood that other suitable processing hardware for performing the operations or features described below may execute process 400.

[0088] At step 402, process 400 receives gas sensing data from a gas sensor. As described with respect to the various hardware implementations herein, the gas sensor may be a wearable gastrointestinal gas sensor device having sensors for detecting the presence and / or concentration of gastric gas components, such as via electrochemical sensing or metal-oxide-semiconductor-based sensing. For example, the gas sensor may generate data or implement changes in electrical properties that can be processed into gas sensing data indicating the concentration of gas components in gastrointestinal gas emissions, such as, for example, hydrogen (H2), carbon dioxide (CO2), nitric oxide (NO), hydrogen sulfide (H2S), and other gases. In some examples, the gas sensing data may include a time series of measurements indicating the concentration of gases in a subject's gastrointestinal gas emissions during a given time period. Thus, the gas sensing data may be correlated with timestamps, counter increments, or other time data to form a supplementary dataset or stream of timed gas sensing data.

[0089] The time series of measurements can be continuous, periodic, on-demand, or triggered by external standards, and can have a constant or dynamic sampling rate. For example, if the amplitude or value of the gas sensing data remains relatively stable at a baseline, the sampling rate can be kept at a relatively low rate. However, if an increased reading is obtained, the sampling rate may be dynamically increased to generate more data for the relevant gastrointestinal gas events and time periods, and vice versa. Similarly, the time periods during which measurements are acquired can vary depending on the purpose of using process 400. For example, in the case where process 400 is a challenging study in which a given food will be given in a controlled environment to measure its effects on gastrointestinal activity (as determined by the various determinations described herein), the periodicity of the time series of measurements and the duration of the measurement periods can be adjusted to correspond to the duration and parameters of the study (e.g., whether baseline measurements will be obtained, or only post-consumption measurements will be obtained, the duration of the entire study, the necessary time fidelity of the gastrointestinal gas emission measurements, etc.).

[0090] At step 404, process 400 optionally receives compliance data indicating whether the subject is wearing the sensor. In some examples, compliance data may indicate whether the sensor is turned on and operating correctly, and / or whether the subject is wearing the sensor when some or all of the measurements in the time-series gas sensing data are acquired. For example, as described in more detail below, compliance data may include signals corresponding to the sensor or one or more accelerometers, temperature sensors, humidity sensors, external "touch"-based electrodes, etc., attached to the sensor or clothing. The signals may be compared to a threshold dataset that may indicate whether the sensor is being worn properly by the subject. In some embodiments, compliance data may be acquired continuously only when gas sensing data is acquired, or at a different frequency than the gas sensing data (e.g., only once every 5 minutes, 15 minutes, 1 hour, 2 hours, etc., while the gas sensing data is acquired once every 0.25 seconds, 0.5 seconds, 1 second, 1.5 seconds, 2 seconds, 5 seconds, 10 seconds, 1 minute, etc.). In a further embodiment, the user may be given the option to self-record compliance data, such as going to the toilet, taking a shower, changing clothes, etc., and may also have the option to self-report gastrointestinal gas information during the period when the sensor is not in place.

[0091] At step 406, process 400 optionally receives user data. In some examples, the user data may indicate the time (which may be a given time / date, or a signal that initiates or increments a counter relative to a time-series gas sensing measurement) when: the intervention of interest occurred; the time when the food of interest was consumed; the time of images or data regarding regular food consumption; the time when the diet regimen began; the time when the fasting period began; and so on. For example, in some examples, the user data may indicate the time when the subject consumed a specific type of "challenge" food (such as carbohydrates or amino acids). For example, the subject may enter the time when they consumed the food and the type of food consumed. In some examples, the time may indicate the period during which the subject fasted and / or abstained. In further embodiments, the user data may include data entries such as confirmation of subsequent protocols, confirmation of detected gastrointestinal gas emissions (for validation purposes), etc.

[0092] At step 408, process 400 optionally filters gas sensing data to remove measurements acquired when the subject is not wearing the sensor or when other invalid situations occur (e.g., the user notices they are not adhering to a fasting or diet plan). In some examples, removing the gas sensing data can ensure that the determination (described below) directly corresponds to the relevant gas sensing data acquired by the subject during the desired time frame.

[0093] At step 410, process 400 identifies time-series gas sensing data that may correspond to the occurrence of gastrointestinal gas emissions. For example, the gas sensing data indicates a relatively low value or baseline, then increases to a peak value exceeding a threshold over a certain time period (such as...). Figure 8 As shown in the graph, if the peak then drops to a lower / baseline value, process 400 can identify the peak as a confirmed gastric gas emission. In some examples, process 400 can mark one or more gastric gas emissions occurring throughout the duration of the gas sensing data, or it can stop when the first gastric gas data is detected. For example, process 400 can determine the total number of gastric gas emissions during a specific time period, the intensity of the emissions(s), and the volume associated with each emission. In some examples, gastric gas emissions can be detected by monitoring the temperature and intensity contained in the gas sensing data.

[0094] At step 412, process 400 determines the emission time corresponding to the occurrence of gastrointestinal gas. In some examples, this time can be used to determine (as received at step 406) the elapsed time between food consumption and gastrointestinal gas emission. Furthermore, in some examples, this time can be used to determine the frequency of gastrointestinal gas emission.

[0095] At step 414, process 400 outputs an indicator of the likelihood that the subject has a gut condition. For example, process 400 may use gas sensing data, alone or in combination with user data received at step 406, to determine the likelihood that the subject has carbohydrate malabsorption, small intestinal bacterial overgrowth (SIBO), diarrhea-dominant inflammatory bowel syndrome (IBS-D), and other gastrointestinal conditions.

[0096] Methods of using human assessment

[0097] In some aspects, this disclosure implements various assessment and sensing methods that can provide data relevant to use by healthcare professionals when considering diagnoses of malabsorption, SIBO, or other intestinal disorders. For example, the sensors, devices, and systems described above can be used to measure the production of intestinal microbial gases, and the resulting data can be provided to healthcare professionals in various ways.

[0098] Now for reference Figure 5 Example protocol method 500 is illustrated in the form of a flowchart. Figure 5 The process 500 illustrated in the diagram can be used as a protocol framework for monitoring and diagnosing intestinal conditions associated with a given food or substance of interest using wearable gastrointestinal sensors. However, as further described below, the protocol method 500 can be adapted for other intestinal condition monitoring and diagnosis, and / or for general tracking of a user's gut response to a typical diet over time.

[0099] At box 502, process 500 may include instructing the user to begin an initial dietary plan. In some embodiments, a software application running on the user's device (such as, for example, a mobile phone) may provide the user with software instructions on how to follow the initial dietary plan. This may involve instructing the user on the following: which types of food to avoid, when to eat or not eat them during the day, total food intake, certain types of food that must be eaten or strictly prohibited, and graphical depictions of example foods. The application may also instruct the user on the duration for which the initial dietary plan should be followed and when the initial dietary plan will end. In some embodiments, the initial dietary plan may be tailored to the type of study to be conducted on the user, which may be user-defined, selected by the user from available pre-planned studies, or defined by a healthcare provider through EMR integration or a healthcare provider portal. For example, based on the goals of the study, the user may be instructed to avoid high-fiber foods, and the user may be presented with examples of foods to avoid and / or images of such foods. As another example, the user may be instructed to avoid gluten, sugar alcohols, or other dietaryly sensitive ingredients. In some embodiments, the application may monitor user compliance with the plan through self-reporting or integration with external diet tracking tools. Furthermore, in alternative implementations of Process 500, the initial dietary plan may be unnecessary, such as when users confirm that they have not eaten certain avoided foods in the past 24 hours (or other time periods), or when general monitoring of a regular diet is to be performed.

[0100] At box 504, the process may include instructing the user to begin a fasting period. This instruction may be delivered via the same user interface or application described in box 502. In some embodiments, the system may provide reminders and alerts as the fasting period approaches. For example, a mobile application may generate notifications indicating when to stop eating or drinking, and may generate warnings if the fasting period is not initiated on time. The duration of the fasting period may be preset by the system or customized based on the type of study being performed and / or the target carbohydrates or amino acids to be fasted. Fasting periods may vary between different protocols, such as shorter periods for general gut health assessments or longer periods for studies requiring a significant baseline reduction in gut activity, or even be optional in some implementations.

[0101] At box 506, process 500 may include sending a notification or reminder to the user about an upcoming fasting period, and / or requesting information for confirmation of adherence to the initial dietary protocol. In some embodiments, this may involve presenting the user with a checklist or questionnaire to verify adherence to dietary restrictions. For example, the user may confirm whether they have avoided specific foods or followed the required eating pattern. In some examples, process 500 may also provide dynamic feedback or suggestions if the user reports partial adherence (such as extending the preparation period or adjusting the fasting protocol). In some embodiments, notifications, reminders, and / or requests for confirmation may be delivered to the user via graphical, text, or audio prompts, such as via software running on or integrated with the user's mobile or wearable device. In other embodiments, data concerning the user's food consumption may be obtained from other sources, including: food entries in a hospitalized patient's medical chart; and environmental monitoring devices for independently verifying dietary adherence, such as via a blood glucose monitor or a smart device that tracks food intake.

[0102] At box 508, process 500 may prompt the user to prepare or obtain a designated study food. For example, as the fasting period nears its end, the user may be prompted to prepare the designated study food. This step may involve detailed instructions provided through the user interface, including recipes, ingredient lists, and preparation techniques. In some embodiments, the study food may be pre-packaged and provided to the user along with preparation instructions for its use in the study. For example, the system may recommend specific carbohydrate or protein sources designed to stimulate bowel activity for diagnostic purposes. The application may also provide timers or visual guidance to help the user prepare the food correctly. Additionally, the system may verify the user's adherence to the fasting and preparation instructions through questionnaires, user-uploaded photos, or other compliance verification tools.

[0103] At box 510, the user can be instructed to consume prepared research food and confirm the proper placement of the wearable gastrointestinal gas sensor. The sensor can be positioned on or near the user's underwear, and the system can provide visual or video guidance to ensure correct placement. In some embodiments, the wearable sensor may include self-checking features, such as notifications confirming proper positioning based on data quality or environmental parameters. The system can also instruct the user on how to securely hold the sensor to prevent displacement during the research period. If the sensor is removed or improperly aligned, a notification can alert the user. In further embodiments, compliance sensing may include using temperature sensor data, accelerometer / motion sensor signals, etc., as described herein.

[0104] At box 512, process 500 may acquire sensor measurement data. For example, a wearable sensor device (such as those described above) measures gastrointestinal gas emissions during a study period. This may include continuous or interval-based measurements of electrical values ​​from the sensor that indicate the presence of specific gaseous components such as hydrogen-based compounds, nitrogen-based compounds, various sulfides, ammonia, methane, VOCs, or other diagnostic markers. For example, the sensor may include an electrochemical sensor as described above for detecting the total nitrogen or total hydrogen content of the sensed gas. The system may also record the time and frequency of emissions to establish temporal relationships and / or patterns. In some embodiments, the sensor may be combined with additional environmental sensors (such as accelerometers or temperature monitors) to verify that it is worn correctly and operates within expected parameters. Data collected by the sensor may be stored locally or transferred to a cloud-based system for real-time analysis and storage.

[0105] At box 514, process 500 may optionally detect indications of sensor noncompliance or inappropriate operation. For example, if no data is received for an extended period, or if the recorded data deviates significantly from the expected pattern (e.g., due to duration or atmospheric conditions resulting from high gas content, failing to constitute a sustained high nitrogen or hydrogen reading for gastrointestinal emissions), the system may alert the user to verify sensor placement, perform diagnostics, seek technical support, or verify protocol compliance. The notification may be delivered via software application, email, or text message and may include troubleshooting steps or contact options for technical support. In some embodiments, the system may utilize trained machine learning algorithms to identify anomalies and differentiate between user noncompliance and potential technical problems with the sensor.

[0106] At box 516, process 500 allows sensing devices to output data acquired during the study to a user interface and / or integrate it into an electronic medical record (EMR). The user interface can present the data in a visually intuitive format, such as a graph or chart showing changes in gastrointestinal gas activity over time. In some embodiments, the data may include higher-order interpretations, such as probability scores for specific intestinal conditions. Integration with the EMR allows healthcare providers to review the results and use them in conjunction with other diagnostic information. Data privacy and security measures, such as encryption and access controls, can be implemented to ensure compliance with healthcare regulations.

[0107] Therefore, the procedures derived from Process 500 can provide a comprehensive and customizable approach for collecting and analyzing intestinal gas emissions. By incorporating multi-layered user guidance, compliance verification, and data analysis, the system ensures high-quality data acquisition for diagnosing conditions such as small intestinal bacterial overgrowth (SIBO), diarrhea-predominant irritable bowel syndrome (IBS-D), and carbohydrate malabsorption. Furthermore, by integrating the user interface into a patient-accessible device (e.g., a mobile device) and allowing sensors to be mounted inside the patient's clothing, research and monitoring can be performed by the patient themselves in their own home / workplace, during normal daily activities, and at their own pace—benefits that were not possible with previous methods similar to breath tests.

[0108] In light of the overall highlights of Process 500, we will now describe various specific implementations and example studies.

[0109] For some approaches, initial data collection studies can be performed to train machine learning models and / or adjust various thresholds and / or determine metrics for specific dietary items, individual / patient categories, or even specific patients (e.g., in precise or personalized treatment contexts). For example, several protocols for developing training data to adjust various thresholds are further described below. In one such example, two groups of individuals may participate in data collection. One group is known to have a condition such as SIBO, IBS-D, or other causes of malabsorption for a given type of food item. The other group is known not to have such a condition. These groups are randomly assigned and either given a placebo or a food item known to cause intestinal discomfort for the condition group. The time of the first gastric gas expulsion, the total number of expulsions, the volume / intensity of each expulsion, the microbiome activity index, and / or the total volume / intensity of expulsions during a given time period (e.g., 4 hours) can be measured (more details about specific protocols are described below). Regression, averaging, or machine learning algorithms can then be applied to determine the likelihood that an individual will exhibit symptoms of the condition of interest based on their measured gastric gas activity. In another example, a more simplified protocol can be performed to adjust one or more thresholds. In this type of example, FODMAP, which is always poorly absorbed by humans, was chosen. A group of participants were given FODMAP to consume, and for that group, the time of the first gastrointestinal gas, the total number of gastrointestinal gas exhalations, the volume / intensity of each gastrointestinal gas, and the total volume / intensity of gastrointestinal gas exhalations over a given time period were measured. Based on this, a threshold can be determined such that if a given individual exhibits certain gastrointestinal gas activity when consuming another given FODMAP, they may be sensitive to that FODMAP or may have a condition such as SIBO, IBS-D, or other causes of malabsorption.

[0110] In one implementation, methods can be performed to measure the characteristics of a patient's gastrointestinal gases, providing insight into the likelihood of malabsorption, such as malabsorption of dietary carbohydrates. The characteristics determined by measuring gastrointestinal gases can be interpreted to gain insight into the production of gut microbial gases. For example, in one approach, the patient can be instructed to avoid foods containing fermentable oligosaccharides, disaccharides, oligosaccharides, and polyols (FODMAP) for at least 24 hours prior to the start of the measurement. For some patients, this may be extended to 72 hours, depending on background results from previous measurements. Patients can receive instructions on how to complete the test via a smartphone app or printed brochure. Alternatively, healthcare professionals can be given instructions on what the patient can or cannot eat via electronic medical records or similar notification methods (e.g., in the case of inpatient care). In further examples, specific foods can be recommended or prescribed, known to be unlikely to cause excessive production of gases of interest during the measurement.

[0111] Next, before starting the measurement, the patient may be instructed or instructed to fast for 8–12 hours. In some cases, depending on background results from previous measurements and patient input, fasting may be extended to 24 hours. For example, if the patient continues to report bloating after 8 hours, the fasting period may be extended, or other interventions may be taken to reduce the presence of gas in the patient's intestines. At the end of the fasting period (or earlier, if desired), a wearable sensor, such as that described above, may be attached to the patient's underwear via any of the attachment options disclosed herein or other suitable methods.

[0112] The wearable device can then begin acquiring sensor measurements. These measurements may include, but are not limited to, signals corresponding to the total concentration of hydrogen, volatile sulfur compounds, and volatile organic compounds present in gastrointestinal gas. Such compounds include, but are not limited to, hydrogen, ammonia, indole, skatole, methane, carbon dioxide, hydrogen sulfide, methyl mercaptan (methanethiol), ethanethiol, dimethyl sulfide, trimethyl sulfide, dimethyl disulfide, and dimethyl trisulfide. The device may also include sensors for measuring patient compliance with the wearer. For example, temperature sensing, humidity sensing, and / or motion sensing (e.g., via a 6-axis accelerometer or a 9-axis inertial measurement unit) may be performed to determine when the patient should properly wear the smart underwear device. For example, sensor measurements of the gas composition can be considered “valid” data for assessing gastrointestinal gas, provided that the temperature measurement remains within a range consistent with the sensors attached to the patient’s underwear (e.g., typically similar to the surface skin temperature of a relevant area of ​​the patient’s body), and / or as long as the humidity measurement remains within a given range, and / or as long as motion is detected by a motion sensor within a given period. In contrast, if the temperature suddenly drops (e.g., to roughly ambient temperature), and / or movement ceases for a given period of time, the data acquired by the gas sensor can be flagged as potentially irrelevant or invalid. Similarly, a notification can be sent to the user to reactivate the sensor, or a notification can be sent to the healthcare provider indicating that compliance with wearing the sensor is not optimal.

[0113] At some point after the patient begins wearing the device, the patient can be instructed or prompted to consume a food or compound selected for assessment. This time period can be predetermined (e.g., 1 hour) or can be dynamically assessed based on the quality of the initial measurement. A time period can also be selected at the time of testing, based on the confidence level that the patient has adhered to the healthcare provider's dietary restrictions.

[0114] In some embodiments, the food or compound may be a carbohydrate of interest formulated into a beverage, capsule, or contained in a food. Carbohydrates include one or a combination of the following: monosaccharides, disaccharides, oligosaccharides, polysaccharides, or polyols (sugar alcohols), including but not limited to: glucose, galactose, fructose, fucose, arabinose, xylose, maltose, sucrose, lactose, fructooligosaccharides (FOS), galactooligosaccharides (GOS), maltodextrin, inulin, chicory root extract, sorbitol, mannitol, erythritol, maltitol, lactitol, lactulose, and / or xylitol. In other methods, the food or compound of interest may consist substantially of (or include) amino acids, such as a solution containing cysteine, methionine, tryptophan, or any other amino acid, or a mixture of amino acids.

[0115] After a patient has consumed the food or compound of interest, the device can acquire continuous measurements over a measurement period. The measurement period can depend on the type of food / compound consumed and / or the patient's age, health, and previous measurement history. In other embodiments, the measurement period can be dynamically determined. For example, a processor associated with the wearable device can determine whether a sufficient amount of gastrointestinal gas has been measured and can send a notification to the patient or healthcare provider indicating when the test can be completed. In other examples, the measurement period can be dynamically determined by waiting until a gastrointestinal gas emission (one or more) with specific characteristics is detected and continues thereafter for a given period of time. In some embodiments, data can be acquired up to 3 hours from the time the food / compound was consumed, up to 3 hours from the time the first qualified gastrointestinal gas emission was detected, or other durations such as 8, 9, 10, 11, 12, 14, 18, or 24 hours (whether from the time the device was turned on, from the time the food / compound was consumed, or from the time gastrointestinal gas emission was detected).

[0116] During sensing, the wearable device can transmit sensor data to a separate processor, or the data can be stored and processed locally on the device itself or on a device physically coupled to the wearable sensing device. The hardware and connection types used for transmitting and / or storing data can be as described above. All data can be timestamped with a clock / time synchronized with that used by the patient and / or healthcare provider to more accurately assess the time difference between the time the food / compound of interest is consumed and / or emitted.

[0117] The acquired data can be processed (either in real time during evaluation or through subsequent analysis) to determine certain characteristics of the patient's gastrointestinal gas emissions during the test. For example, temperature, humidity, and / or motion data can be used to bind gas sensor data to determine which time periods represent valid data, such as when the user is wearing the sensors. Similarly, other sensor data can be tagged or bound to reflect data acquired before and after the consumption of the food / compound of interest, time of day, etc. Thus, data related to gastrointestinal gas emissions can include gas sensor data (e.g., electrochemical signals), but may also include supplementary data and / or annotations, such as time data, and various other markers corresponding to the timing of the event sequence and / or the test procedure itself.

[0118] Annotated / labeled gas sensor data can be analyzed to determine the quantity, total volume, intensity, concentration, general / total emission occurrence, and emission characteristics during the measurement period. These will be relevant to determining the likelihood of intestinal disorders, such as malabsorption of the consumed food / compound. For example, the likelihood of malabsorption can be determined if at least two gastrointestinal gas emissions are detected within 3 hours of consuming the food / compound of interest. Gastrointestinal gas emissions can be determined from sensor data by comparison with baseline values: for example, baseline gas sensor detection values ​​of 5, 7, 8, 10, 15, or other multiples can be labeled as including individual gastrointestinal gas emissions. (Baseline values ​​can be determined via various methods, such as those described herein). Furthermore, the degree or importance of intestinal disorders (e.g., malabsorption) can be assessed by the combination of the total number and volume of intestinal microbial gases detected as gastrointestinal gases.

[0119] In some cases, a test may be flagged as defective if gastric gas is detected less than one hour (or other threshold) after consuming the food / compound of interest. A negative determination may be generated if no gastric gas is reported or the amount is minimal within a given window (e.g., a 3-hour window after consuming the food / compound of interest) (e.g., the patient's gut microbiome gas does not reflect the possibility of poor absorption of the food / compound). If unusual or indeterminate gas quantities, volumes, etc., are detected, the test may be flagged as invalid and / or a repeat test may be necessary.

[0120] As another example, the systems and devices described herein can be utilized in methods for assessing the likelihood of SIBO in patients. As described above in the methods for assessing the likelihood of malabsorption, patients wearing the sensor device fast and then consume a food / compound of interest in a challenge study (e.g., a carbohydrate challenge consuming carbohydrates, which are potential trigger foods that may lead to malabsorption or other elevated gut microbiome activity, used to test whether patients exhibit increased gastrointestinal / gas emission activity due to consumption of the food of interest as the challenge object). Data measurements are performed in the same manner as described in the foregoing methods. However, the data are then processed using additional or alternative analyses. The likelihood of SIBO can be determined using time-weighted gas generation data. The more gastrointestinal gas detected early in the measurement window (e.g., at the end of a 3-hour measurement), the greater the likelihood that the patient has SIBO.

[0121] In further example methods, initial baseline measurements can be obtained before fasting and consuming the food / compound of interest. For example, in a method for determining the likelihood of IBS-D or SIBO, the patient is instructed to wear the sensing device for a longer period of time during which a baseline of the frequency, volume, and timing of the patient's gut microbial gas production is established. In some embodiments, a mobile app, website, brochure, instruction manual, or other means of delivering information to the patient instructs the patient when to wear the device. Patients may also be instructed to keep a food diary with precise timing of each food consumption. Patients may even be given specific dietary plans and instructed about which foods to eat at which times or on which days. The initial baseline recording window can be one day, two days, or any other number of days, such as up to seven days. Recording can be "continuous," such as being done as frequently as possible on a constant basis (always measuring at the fastest sampling rate), on a constant basis at a given period or sampling rate, on a continuous daily basis at certain times of day, only at a periodic rate during certain hours of day (e.g., wake / sleep hours), and so on. Based on these measurements, baseline levels of gastric gas emissions can be determined using statistical feature extraction or more advanced statistical methods. Statistical features include the average amount of emissions per day (or hourly, weekly, etc.), total volume or intensity of emissions, median or quartile emissions, and mean or median microbiome activity index. More advanced statistical methods include exponential smoothing trend decomposition, Bayesian estimation, and Fourier transform. Data acquired during this baseline period can be used for comparison with future studies and / or can be transmitted to one or more healthcare professionals monitoring the tests and / or for interpreting summary data of the acquired data (e.g., the amount, volume, frequency, composition, etc. of gastric gas emissions). Such methods can be used to diagnose SIBO, IBS-D, inflammatory malabsorption, and similar conditions, and to estimate gut microbiome activity.

[0122] In additional example methods, sensing devices may be utilized in modified versions of the methods described above to assess the efficacy of treatment for IBS-D / SIBO by measuring the production of gut microbial gases excreted in gastrointestinal gas. Such methods may include obtaining baseline measurements of gut microbial gas production as described above (e.g., for 1–7 days). This may occur before or after the treatment regimen. In some cases, a baseline is obtained, and the patient is then instructed to subsequently take prescribed interventions, such as antibiotics (e.g., rifaximin / cefaxine), or probiotics or prebiotics, or dietary interventions (e.g., FODMAP avoidance, elimination diets, etc.). Shortly before or after the start of the intervention, another measurement cycle is initiated, in which data are acquired for another period (e.g., another 1–7 days), and the patient is given appropriate instructions regarding device wearing, recording intervention adherence, and / or keeping a food diary with consumption times. In some methods, another baseline measurement cycle may be performed after the intervention period is completed to reassess post-intervention symptoms.

[0123] In a further example, a method can be used to detect the utilization of dietary fiber or fiber supplements by gut microbiota by measuring the production of gut microbial gases excreted in gastrointestinal gas. Dietary fiber consumption has many benefits, and most people consume far less than the recommended daily intake. One benefit of fiber is that gut microbiota can ferment it into beneficial short-chain fatty acids (SCFAs). Using the systems and techniques described above, a method can be performed to determine whether an individual's microbiome is capable of fermenting dietary fiber or fiber supplements (and to what extent) by measuring the production of gut microbial gases excreted in gastrointestinal gas. This determination can be based on the premise that the more gut microbial gases detected via gastrointestinal gas, the better the gut microbiota can utilize dietary fiber or fiber supplements. This method can be managed and monitored by clinicians or can be performed directly by consumers via instructions from diet / nutrition companies, such as through a smartphone app.

[0124] In such methods, participants may be instructed to consume dietary fiber or dietary fiber supplements, with the consumption time recorded in an app, diary, website, EMR, etc. Participants should also avoid other fermentable foods (which will be listed in the patient instructions or app, or directly instructed by a clinician / dietologist), or simply fast for a given period (the fasting or avoidance period can be described as in the methods above). Participants are then instructed to wear a sensing device (such as those disclosed herein) for at least 3 hours after consuming the dietary fiber, but ideally 6–12 hours. In some methods, participant compliance with wearing the sensing device and the device's operability (e.g., being turned on and sending valid data) can be monitored. During the measurement period, the extent of gut microbial gas production in the gastrointestinal tract is determined via the output of the sensing device. The detected level of gastrointestinal gas can be used to interpret fiber utilization: the more gastrointestinal gas detected, the higher the fiber is likely being utilized. The detected level of gastrointestinal gas can be measured by one or more of the following indicators: the number of individual gastrointestinal gas emissions, the duration of detected emissions, the magnitude of emissions, and the total volume of each / all emissions. These measurements can be derived from the output of the sensing device and reported to the user or clinician (the derived measurements can be determined by the processor of the sensing device itself, or by a separate computing device that receives the raw data from the sensing device). The method can then provide an estimated utilization rate or range based on cumulative data from study participants. In a further example, the process can be repeated with alternative fiber compositions if desired.

[0125] In another example, a method could be implemented that provides personalized nutritional advice to users as a way to reduce IBS-D / SIBO symptoms caused by excessive gut microbial gas production. One approach patients with IBS-D / SIBO attempt to alleviate their symptoms is through dietary interventions. However, their methods are often anecdotal and depend on the individual's ability to accurately recall / record how they felt after a period of time following the interventional diet. However, the methods and systems presented in this paper can objectively measure gut microbial gas production and correlate it with diet to make evidence-based and personalized recommendations that avoid foods that cause excessive symptoms while maintaining maximum dietary diversity.

[0126] Smartphone apps or other user interfaces can provide users with a way to input the type and quantity of food consumed, as well as the time when the food was consumed. In this way, a timed food diary is maintained. During time synchronization, data is collected from sensing devices, as described herein, connected to the smartphone. When the smartphone app records data and determines that abnormal, rapid, or excessive flatulence is detected, the app can correlate the event with the food eaten. Using a database of the components of the eaten food (e.g., the FODMAP content of the food), the app can begin to correlate the diet with gut microbiome gas production. Thus, the app can generate personalized recommendations of "trigger" foods that cause excessive gut microbiome gas production based on data from the smart underwear device. Furthermore, the app can allow users to query whether a given food item is likely or unlikely to cause gut discomfort based on its similarity to other foods known by the app to cause elevated gut microbiome activity or other gastrointestinal discomfort. Similarly, the app can generate personalized food recommendations that will not cause symptoms based on data from the smart underwear device.

[0127] In some examples, users can report their stress levels to a software application for correlation with gas sensing data to determine the relationship between mental state and gut microbiome activity or other gut conditions and disorders. (As used herein, "stress" can broadly include mental stress, emotional stress, anxiety state, etc.) In some embodiments, users can respond to standardized anxiety or stress questionnaires such as: the State-Trait Anxiety Inventory (STAI); the Visual Analogue Scale for Anxiety (VAS-A); the Positive and Negative Effects Schedule (PANAS); or a more customized set of questions that asks users to simply rate their stress levels on a numerical or word-based scale. Based on these questions, the software application can generate indicators of the reported stress level, such as no / low / moderate / high / very high stress state. Alternatively, users can simply enter a description of how they feel, and the software application, such as a language model, can generate a category of the reported stress level (e.g., no, low, moderate, high, very high), or simply enter their stress level directly.

[0128] Furthermore, the systems and methods envisioned herein can be configured to determine the temporal relationship between elevated stress levels (e.g., high or very high stress indicators) and changes in gut microbiome activity. For example, in methods for assessing stress-induced intestinal disorders (similar to stress-induced IBS (including IBS-D and IBS-M), stress-induced SIBO, functional dyspepsia, stress-related bowel disorders, stress-induced gut microbiome imbalance, etc.), a user can wear a gastrointestinal gas sensor, such as those described herein, for a given time period (e.g., several hours, days, weeks, etc.). During this time period, the time series of gastrointestinal gas data is recorded as described above, and periodically (via a user interface, such as a software application on a mobile device) prompts the patient to report or otherwise provide an indication of their perceived stress level. In some examples, the prompts can be given on a periodic basis throughout the study period, or can be dynamically increased / decreased at a frequency corresponding to changes in gastrointestinal gas emission activity. As described above, during periods in which the user reports a low-stress or stress-free state, a baseline gastrointestinal gas level or gut microbiome activity index can be determined. Then, for the time periods during which users report elevated pressure, the system or method can correlate gas sensing data before, during, and shortly after these periods. The gastrointestinal gas data associated with these periods of elevated pressure can be compared to a baseline to determine the impact of pressure on intestinal activity. This can include quantifying the magnitude of deviation from the baseline during the reported periods of elevated pressure levels, and various temporal relationships between the occurrence of elevated pressure levels and changes in gut microbiome activity, such as the time lag between the occurrence of elevated pressure and the subsequent gut microbiome—both at and after the occurrence, and throughout and after the duration of elevated pressure; and the duration of elevated gut microbiome activity after the occurrence of pressure. Based on these determinations, a gut-brain reactivity profile can be derived for use in the diagnosis, assessment, and / or treatment of enteroencephalopathy. Example output, user interface, and diagnostics

[0129] As a result of using the aforementioned systems and methods, various types of output and information can be provided to the user. In some embodiments, data on gastrointestinal gas activity (e.g., time of first emission, total number of emissions, time of each emission during the day, volume / intensity of each emission, and total volume / intensity of emissions during a given time period) can be provided to the user. Overall statistics or charts showing the wearer's gastrointestinal gas activity averages and trends can also be provided to the user. Figure 8 The event graph type shown. (e.g.) Figure 8As shown, a graph depicts the normalized, observed changes in the frequency and intensity of flatulence over a week in healthy individuals (left) and individuals suspected of having SIBO (right). In other embodiments, higher-order determinations, such as the probability of malabsorption (e.g., expressed as a percentage or Boolean value with confidence), or a specific diagnosis for a given condition, can be provided to the user. In such cases, the underlying principles for a given probability or diagnosis can also be provided, such as the output of an interpretable AI method or an indication that the wearer's gastric gas activity exceeds certain thresholds, thereby prompting a diagnosis or given probability. For example, if a user experiences their first gastric gas within the expected timeframe for malabsorption after consuming "challenge" of a food of interest in a study, the systems and methods described herein can provide the user with a diagnosis such as "the wearer has malabsorbed the given carbohydrates consumed in the test (X grams)." The quantity and frequency of gastric gas will also be used to confirm the result. However, in many cases, the inventors have determined that the timeframe is more critical for the diagnosis of malabsorption.

[0130] In other methods, a person continuously wears the device and tracks the consumption of all the food they eat in an application. In some embodiments, the person can take photos of the food they eat, and the software application will use an AI food recognition model to determine what the food is and its ingredient profile. In other embodiments, the person can answer a questionnaire or input information about the quantity, type, and / or nutritional information of all the food they eat during the study period. Given that continuous tracking may occur over a long period, inputting the vast majority (e.g., 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, etc.) can be considered to provide information about “all” of the food eaten. In this approach, the systems and methods described herein will use AI to extract information from gastrointestinal gas data to identify which foods may lead to malabsorption. In this case, in addition to an event graph collected over a long period, the user interface can also provide a score based on a food diary reflecting the likelihood of which types of food may be malabsorbed. In a further embodiment, these scores can be updated on a continuous and automatic basis, such as each time a 4-hour window has elapsed after a food item has been consumed. When a new food item is identified as malabsorbable or potentially malabsorbable, an alert and personalized recommendations can be sent to the user.

[0131] In a further embodiment, application developers may (with appropriate patient consent) anonymize data obtained from the use of sensing devices and applications, and train deep learning algorithms to predict which types of food items may be malabsorbed, which food items should be avoided by similar individuals with SIBO, IBS-D, or similar diagnoses, and which food items are generally tolerated by similar individuals. Experimental research and prototypes

[0132] To address the need for a practical, non-invasive tool for measuring gut microbial gas production, the inventors have developed a "smart underwear" device: a wearable sensor system for continuous, autonomous, and longitudinal monitoring of the composition, frequency, and intensity of gastrointestinal gas. The smart underwear prototype device comprises a small sensor module that clips onto the outside of a bra-like garment near the perineum. It combines an electrochemical gas sensor for measuring gases produced by gut microbes excreted in gut gas, with a temperature sensor and an accelerometer for tracking whether the device is being worn. This device allows for novel, non-invasive, real-time measurements of gut gas over extended periods during daily activities, providing unprecedented capabilities for monitoring gut microbial gas production.

[0133] Now for reference Figures 6A-6C This section will discuss various prototype designs. For lithium-ion batteries, the initial design of the smart underwear features an open-source microcontroller (Ambiq Apollo 3 Artemis) attached to the individual's waistband. Sensors are located in the perineal area and connected to a backpack via wires through the underwear, such as... Figure 6C As shown. While the prototype was functional and had the advantage of a high-capacity rechargeable battery, its large size and the use of wires were uncomfortable and unreliable. The sensor was secured to the underwear using double-sided tape, which was not always reliable and required frequent replacement during wash cycles. Subsequent iterations addressed all these issues by reducing the size of the device and using custom-made 3D-printed buckles for attachment.

[0134] Another design concept features a circular shape, in which a button battery is integrated into the sensor itself, and the entire device is secured near the perineum using fasteners through the fabric of the underwear, such as... Figure 6B As shown. The buckle can be standard size, or it can be custom size / molded for user comfort (e.g., molding process, 3D printing, etc.).

[0135] Furthermore, another prototype utilizes the same "single device" design (with a snap-on attachment) and is characterized by a square shape slightly larger than a quarter of a dollar (e.g., approximately 26 × 29 × 9 mm), and is powered by two silver oxide button cells with sufficient capacity to power the device for up to 12 hours (see exploded view of the button cell arrangement design in Figure 7). Power consumption was measured, showing an average of 173 µA in sleep mode and 918 µA in power-on mode (see graph in Figure 7).

[0136] These prototypes may have one or two main sensing components for gas sensing, as well as optional temperature, humidity, and accelerometer sensors for tracking when the device is being worn. Furthermore, the device connects to a mobile phone via Bluetooth for data transmission. Thus, the device envisioned herein may include at least one gas sensor, at least one temperature sensor, at least one humidity sensor, at least one accelerometer, at least one wireless communication module, a processor, and onboard memory.

[0137] Regarding sensing capabilities, the inventors considered utilizing commercially available, off-the-shelf electrochemical sensors. However, one drawback of these sensors compared to other types is their size. After some experimentation, it was determined that this limitation hindered further miniaturization of the device, but the size would negatively impact comfort and user acceptance. Therefore, the inventors set out to develop custom sensors that could provide adequate accuracy while maintaining a comfortable small size.

[0138] Specifically, the inventors found electrochemical sensors particularly useful because they can operate in low-power applications while providing high sensitivity and accuracy across a wide range of gas concentrations at a relatively low cost. Among various electrochemical techniques, the amperometric method was used due to its advantages in miniaturized devices caused by its simplicity and high sensitivity. In this method, an open-circuit potential (0 bias) is maintained while the current is continuously recorded over time. If hydrogen is present, its oxidation at the working electrode generates a current, resulting in a positive peak. The sensor's sensitivity is determined by the capacity of H2 molecules, which can be oxidized at the working electrode, catalyzed by the presence of platinum particles in the carbon black. The electrochemical reaction mechanism can be summarized as follows:

[0139] The transfer of four electrons generates a current that is detected and amplified by a potentiostat.

[0140] To create miniaturized electrochemical sensors, the inventors utilized disposable vinyl templates in some designs. Electrode patterns, characterized by an immersion gold (ENIG) layer, were produced from these templates using an automated dicing machine assembled by PCBway®. These electrode patterns were applied to prefabricated printed circuit boards (PCBs) that served both as a support structure and as a connection interface to the main device. Bare conductive carbon ink could be used to create counter and reference electrodes. The conductive ink (MG Chemicals®) was screen-printed onto the PCB surface through the template and subsequently dried at 150°C using a hot air gun for approximately 1 minute.

[0141] In some examples, 2 µL of conductive Vulcan carbon ink (DURA-ink® Pt40) (containing 3% platinum and 1% Nafion® by weight) can be deposited on the working electrode area and dried at 150°C for approximately 1 minute. Once completely dry, the hot air gun temperature can be increased to 300°C for several seconds, causing the vinyl stencil to melt and detach from the PCB, leaving the desired electrode pattern. Any residual vinyl accumulated at the edges of the PCB can be removed manually.

[0142] To aid electrochemical sensing, in some examples, a solid electrolyte can then be fabricated using a polytetrafluoroethylene (PTFE) membrane, typically used for biological culture sealing, already concentrated in 6 µL (5 mol L). - ¹) The PTFE membrane is pre-wetted with sulfuric acid (Sigma-Aldrich, 95-98%) and allowed to dry at room temperature for at least 1 hour. A visual inspection can be performed before use to ensure it is completely dry. The membrane's conductivity can also be checked before use, falling within the range of 4-100 kΩ·cm. - Within the range of ¹.

[0143] A PTFE membrane (Sigma-Aldrich®) can then be placed on the electrode system, followed by a layer of Teflon tape (PTFE, EverFlow®), which can be wrapped around the PCB with slight pressure to ensure tight contact between the membrane and the electrode. The entire electrode assembly can then be encapsulated in a protective 3D-printed PETG (Overture®) housing, ensuring that the housing does not directly contact the sensor pad to prevent any interference. The housing can be sealed by wetting the top and bottom with acetone to join them together.

[0144] In alternative embodiments, the inventors envision utilizing metal oxide-based sensors to replace or complement electrochemical sensors. In such designs, instead of the platinum / Nafion compound deposited to form the working electrode (and the bare carbon ink used to form other electrodes), a metal oxide sensing layer can be used as the working gas sensor. For example, the metal oxide sensor can be electrically connected to receive an applied reference electrical signal using a portion of the sensor (e.g., a semiconductor sensing layer acting as a "sensing element" exposed to gastrointestinal gases, such as exposure to a hydrogen-based gas of interest). In some examples, the gas sensor can be a metal oxide semiconductor sensor. The composition of the metal oxide sensor can be configured such that it exhibits a change in resistance when target gas molecules interact with the sensor. In other words, instead of exhibiting altered electrical properties (e.g., current or voltage) due to redox reactions at the working electrode, as is the case in electrochemical sensors, the metal oxide sensor includes a metal oxide sensing layer that adsorbs gas molecules, altering the material in a manner that affects its resistance.

[0145] Therefore, implementing a detection scheme using a metal oxide sensor involves modifying the interaction between the processor of the sensing device and the gas sensor / electrode. For example, the processor applies a current / voltage at a reference level across the metal oxide sensor via a first lead and then detects the change in resistance or conductance caused by the change in the sensor. This can be achieved, for example, by sensing the electrical properties of the signal after it has been applied to the metal oxide sensor to derive the resistance or conductance and outputting a signal indicating the presence of target gas molecules based on the change in resistance or conductance. Calibration data can be used to correlate the resistance / conductance change with the concentration of the gas of interest. By focusing on the value of the resistance / conductance change rather than its absolute value, any saturation or long-term variations in the metal oxide sensor due to gas adsorption can be overcome.

[0146] In the prototype device, the inventors utilized an analog front-end (AFE) integrated circuit (IC) compatible with chemical and gas sensing applications, referred to as the LMP91000 (Texas Instruments). Regardless of the sensor type conceived herein, the IC can be configured to detect, filter, and process gas sensing signal values ​​to assess the amount, confirmed occurrence, frequency, and / or intensity of gastrointestinal gas emissions. In the prototype, the LMP91000 can be used as a miniaturized version of a regulator by converting and amplifying a small current generated according to a chemical reaction into a measurable voltage, which is then translated into a digital signal by the microcontroller's ADC. Therefore, the gain of the transimpedance amplifier within the LMP91000 directly affects the sensitivity detected by the microcontroller. The inventors investigated sensor performance at different gain levels, showing that higher amplification results in a stronger signal output without a significant positive impact on noise (see Figure 9, comparison of gain levels). This allows for the detection of hydrogen concentration in gastrointestinal gas over a wide range, although excessively high concentrations may saturate the signal. The inventors determined that a suitable compromise design utilizes gains of 2 and 6 (corresponding to transimpedance amplification resistors of 3.5kΩ and 35kΩ) to detect low and high hydrogen concentrations, respectively.

[0147] In experimental testing, sensor sensitivity depends on several factors, including circuit design, amplification, noise reduction techniques, front-end capability, ADC resolution, temperature, humidity, catalyst concentration, and the surface area of ​​the working electrode. In some experiments using electrolytic sensors, electrolyte volume also plays a significant role in gas sensor sensitivity because the gas must dissolve in the electrolyte before reaching the surface of the working electrode. Thus, the inventors determined that electrochemical gas sensors operate better in thin-layer diffusion schemes, as larger electrolyte volumes slow response times, increase ohmic drop, and dilute the analyte. Paradoxically, conventional water-based electrochemical gas sensors require large reservoirs to prevent evaporation. This illustrates a drawback of these types of sensors: they require a relatively large size to house the reservoir compared to other sensor types. This limitation can be partially addressed by using conductive polymers that act as solid electrolytes, which do not evaporate, although these polymers typically still require a humid environment to maintain the desired conductivity. The most common example of such electrolytes is the use of conductive polymers like Nafion, which perform well in high humidity environments but lose conductivity when completely dry. These polymers are relatively expensive, and this cost is reflected in the price of the sensor.

[0148] Consequently, the inventors developed a suitable alternative design using concentrated sulfuric acid dispersed in a porous PTFE membrane as a pseudo-solid electrolyte with good and minimal evaporation, thus enabling the fabrication of custom sensors using inexpensive materials and 3D printing technology. This approach allows for a reduction in the size and cost of the device conceived herein.

[0149] An electrochemical gas sensor was fabricated using carbon conductive ink as the counter and reference electrodes, and platinum-Nafion-carbon ink (1% Nafion and 3% Pt) as the working electrode. Platinum (Pt) served as the catalyst for hydrogen oxidation, a redox reaction that occurs at an open-circuit potential (0 bias) and is primarily responsible for the sensitivity and selectivity of the electrochemical sensor. Nafion, a polyfluorinated conductive polymer, can be used as a solid electrolyte when it has a sufficiently large thickness and can also be used as a binder to improve the mechanical stability of the carbon ink. 43–46 As mentioned earlier, the inventors used PTFE filter paper embedded in a strong sulfuric acid solution. 5 mol L - The concentration, due to its low vapor pressure (0.001 mmHg at 25°C), ensures a very low evaporation rate, so that even after drying, an invisible acid layer remains adsorbed onto the filter pad. Furthermore, the hygroscopic nature of sulfuric acid keeps the filter pad highly conductive. Thus, a quasi-solid electrolyte is formed, maintaining a conductivity of 0.056 ± 0.013 S cm⁻¹. - ¹ (n=6) has high conductivity (Figure xxxx), which is similar to Nafion's conductivity (0.079 – 0.2 S cm⁻¹). - ¹) are comparable. Even when continuously exposed to air, the filter pad maintains high conductivity for at least one month.

[0150] The unconventional use of carbon as a quasi-reference electrode provides a stable potential with a daily variation of less than 1 mV, making it even more stable than standard silver / silver chloride (Ag / AgCl) electrodes. The main reason this electrode is not commonly used is its sensitivity to the ubiquitous chloride concentrations in the environment. However, carbon can be used as a quasi-reference electrode for gas sensing applications where chlorides are not present. This decision also stems from the fact that silver or silver chloride spurs cannot be used in strongly acidic environments because they gradually dissolve in the acid. While this process is slow, it limits the long-term performance of the sensor. The use of bare carbon as a reference electrode has limitations. The potential difference between the working electrode containing Pt particles and the reference electrode can lead to high transient currents at the start of an ammeter measurement, increasing the waiting time for baseline stabilization. To minimize this effect, the amount of Pt deposited on the electrode should be balanced between desired sensitivity and electrode stability. Furthermore, if the transient current is relatively large and the amplifier gain in the analog front end is set too high, this can lead to signal saturation. This is addressed by allowing an initial stabilization time of 10 to 20 minutes. A PTFE tape layer and slight pressure were applied to the electrochemical electrode and electrolyte pad to ensure complete contact across the entire electrode area. This layer provided additional protection, reduced water evaporation, and strengthened the connection between the electrode system and the electrolyte. Finally, the sensor connection was completed using a 3D-printed PETG housing, sealed with acetone to protect the sensor. Figures 10A-10D ). Figure 10E and Figure 10H A voltammogram of a custom carbon electrode with ferrocyanide is shown, using a standard Ag / AgCl electrode as a reference, demonstrating that the conductivity of the carbon is sufficient to perform electrochemical experiments for several reduction and oxidation cycles. A comparison between the Ag / AgCl reference electrode and the carbon quasi-reference electrode shows that the potential when using the carbon quasi-reference electrode is almost identical to that observed using the standard Ag / AgCl electrode. This indicates that the carbon reference electrode behaves similarly to the silver chloride electrode under these conditions. The sensor was evaluated using a benchtop potentiostat (showing a linear range between 0-2000 ppm H₂). Figure 10F and Figure 10I And it is able to detect real gastrointestinal gas, as indicated by the overlapping signal from a device that incorporates both custom and commercial sensors. Figure 10G The effects of temperature and humidity were investigated, showing a significant baseline deviation above 30°C. Figure 10J This will not cause any problems for the device, as the expected wearing temperature should not exceed body temperature (36°C) unless the device is used under extreme environmental conditions. On the other hand, humidity shows a greater contribution to baseline drift. Figure 10MDespite these variations, the sensor operates consistently across a wide humidity range, with sensitivity maximized at the midpoint. Figure 10K This change can be explained by its effect on the electrolyte: at lower humidity, water adsorption in the strongly acidic filter pad decreases, thus affecting the battery's electrochemical conductivity. Figure 10L To address the issue of baseline adjustment and initial stabilization time over long periods, the device software was programmed to recalibrate the baseline threshold every 5 minutes.

[0151] Figures 10A-10C A schematic diagram and images of a custom sensor are shown, featuring a pseudo-solid electrolyte made using strong sulfuric acid adsorbed onto a PTFE filter pad. The electrolyte is deposited onto a three-electrode system, which is screen-printed and attached to a custom PCB for electrical connection. An additional PTFE membrane is applied to minimize moisture evaporation, and a 3D-printed PETG cartridge is sealed around the electrodes for strong mechanical protection, leaving only small pores for gas diffusion. Figure 10D These are photographs of the actual electrodes and complete sensor images that match the design. Figure 10E The figure illustrates the cyclic voltammetry of the fabricated electrode in the case of a ferri / ferricyanide couple, compared to a standard Ag / AgCl electrode. Figure 10H The figure shows a signal comparison of the ferri / ferricyanide couple on a custom carbon electrode relative to a standard Ag / AgCl reference electrode and a carbon pseudo-reference electrode, indicating signal overlap. Figures 10F-10I The figure shows the calibration curve of the custom sensor characterized on a benchtop potentiostat, which illustrates the linear range that increases with the addition of H2. Figure 10G The illustration shows a comparison between commercial and custom sensors, both of which were attached to the same smart underwear and tested on human subjects. Figure 10J The figure illustrates the effects of temperature and (M) relative humidity on a custom sensor, demonstrating how the sensor reacts to changes in relative humidity and minimal changes with temperature. Figure 10K The figure illustrates the effect of relative humidity changes on the sensor baseline, showing the performance of equal H2 concentration at 1660 ppm at different relative humidity levels measured using a benchtop potentiostat. Figure 10L The figure shows measurements of H2 gas concentration (1660 ppm) over 12 different days using smart underwear, illustrating small baseline variations attributable to daily changes in relative humidity and temperature.

[0152] In some examples, a custom sensor can be characterized using a PSTAT 910 potentiostat (Metrohm®) with commercially available NIST-certified gas canisters (Gasco®) containing: 3% hydrogen (H2) in nitrogen (N2), 50 ppm hydrogen sulfide (H2S) in pure (99.99%) nitrogen (N2), carbon dioxide (CO2), 20.9% O2 in air, 2.5% methane (CH4) in N2, 50 ppm nitric oxide (NO) in N2, and 25 ppm nitrogen dioxide (NO2) in N2. Sensor characterization and testing can be performed using a “gastrointestinal gas simulator.” In short, a mass flow regulator and microcontroller are used to release the desired concentration of gas into the measurement chamber where the sensor is being tested. This device can be used for sensor calibration and interference studies. For example, in some examples, the linear range for hydrogen detection was evaluated between 332 and 1992 ppm, and selectivity was tested for gases commonly found in gastrointestinal gases (such as CH4 and CO2) as well as gases at lower concentrations (such as H2S, NO, and NO2).

[0153] Figures 11A-11B The figures show calibration curves of the inventor's sensor design relative to commercial sensors. Data was collected using a gastrointestinal gas simulator. These figures illustrate the linear range of hydrogen concentration increases. Figure 11C The graph shows the sensor response time, indicating that 90% of the total signal is reached in approximately 18 seconds, while the maximum value is reached after 42 seconds. The release time is within the same range and lasts an average of approximately 23 seconds to recover to the previous baseline at 22°C and 52% relative humidity. Figure 11D The figure illustrates interference from other gases commonly found in gastrointestinal gas. The signal was normalized with respect to hydrogen. The sensor showed good selectivity toward H2, and significant interference was observed only with hydrogen sulfide (H2S). However, the concentration of this gas is typically absent or very low in healthy individuals, and significant concentrations were reported only in patients suffering from inflammation in the intestines. Figures 11E-11H The figure illustrates the same experiment conducted using a custom sensor for comparison, where only significant interference from nitric oxide (NO) was detected; however, like H2S, this gas was only reported in very low concentrations in patients with severe inflammation.

[0154] Hydrogen is one of the most abundant gases in the gastrointestinal tract, and interference from other gases is expected to be negligible. However, selectivity was evaluated, and data related to the hydrogen signal were plotted. The results indicated that hydrogen sulfide was less sensitive in the custom sensor than in a commercial sensor. No significant levels of carbon dioxide, methane, or nitrogen dioxide were measured in the custom sensor; however, significant interference from nitric oxide was detected. This gas is typically absent in healthy individuals but may occur as a result of intestinal inflammation, such as in patients with inflammatory bowel disease (IBD), celiac disease, or those who have recently undergone chemotherapy. This means that in such patients, the signal from hydrogen may be overestimated, but gastrointestinal gas detection would still be possible.

[0155] The custom sensor has a limit of detection (LOD) of 58 ppm and a limit of quantitation (LOQ) of 254 ppm, with a sensitivity of 32 nA ppm. - ¹ These values ​​are sufficient for the desired smart underwear applications.

[0156] Wearable Detection Algorithm: Based on research findings from the inventors' experiments, temperature was determined to be the most effective indicator of device compliance / usage; however, the inventors presuppose that it should not be used as the sole indicator of compliance. This can be seen from the arrow in Figure 12, where the temperature is not elevated, but the accelerometer indicates that the person is wearing the device. The accelerometer is configured in a low-power mode with sniff mode characteristics, allowing it to operate independently of the main microcontroller. If movement is detected, it triggers an interrupt, and the state is saved in non-volatile (NOR flash) memory at 5-minute intervals. This method is designed to save power and optimize battery life.

[0157] Figure 12A The diagram shows the output of the temperature and accelerometer. The arrows indicate gaps in the accelerometer readings, which correspond to moments when the temperature reading is below a reference threshold (arbitrarily chosen as room temperature). Figure 12B The figure shows the results calculated using a corresponding wearable algorithm based on combined readings from an accelerometer and a temperature sensor. Figure 12C The diagram illustrates the wearing time of the hand recorder, highlighting the necessity of an accelerometer (black arrow) for accurately assessing whether the device is being worn.

[0158] The algorithm used in the inventors' experiments detects whether a device is being worn by combining two sensor inputs: temperature and accelerometer data. The method is based on a specified probability score for temperature deviation from a baseline, and further enhanced by an accelerometer score if movement is detected within a specific timeframe. The baseline temperature is set at the ambient level, and the probability of wearing the device is calculated based on how much the recorded temperature exceeds this baseline. Higher temperatures indicate closer contact with the body, suggesting the device is being worn. Depending on the temperature, the probability score ranges from 0 to 90.

[0159] To enhance detection accuracy, accelerometer data is incorporated. An accelerometer score is added only when movement is detected within a specific time window: this requires movement to be detected before, during, and after the current time step. If this condition is met, an additional score is added, reflecting the assumption that movement indicates the device is in use. The accelerometer contributes 10% to the overall wear probability. This ensures that temperature remains the primary factor, while movement acts as a secondary indicator.

[0160] The final probability for the wearable device is the sum of the probability of temperature and the accelerometer score, where the accelerometer score is only considered if the movement condition is met. This logic is applied to each data point in the input file, and the results are saved in a new output file.

[0161] Microbiome Activity Index: High gut microbiome activity is closely associated with hydrogen production, reflected in both increased frequency of gastric gas and hydrogen concentration. Considering both variables together is essential. Accumulated gas can be released in a few high-intensity events or through a series of smaller gastric gas events. Therefore, relying solely on gastric gas counts or simply measuring sensor output does not provide a complete picture of gut microbiome activity. Additionally, high concentrations of hydrogen in the gut have been found to sometimes saturate the sensor. To address this, the absolute value of the first derivative of the sensor signal is used to provide a more accurate assessment of gastric gas intensity. By measuring the rate of change rather than the signal output, baseline contribution is reduced, and gastric gas is more accurately identified. While this method provides a clearer graphical representation, it may overlook situations where low but persistent gastric gas also indicates high microbiome activity. To capture this aspect, a new metric, defined as the microbiome activity index, is introduced. Mathematically, gastric gas time and counters can be defined as: ,in, Δ is the absolute value of the first derivative of the electrochemical sensor signal, W(x) is a binary function that takes the value 1 when x≥B (where B is the dynamic baseline threshold; gastrointestinal gas is not considered below this threshold), Δ t i It refers to the discrete time interval between continuous data points and the microbiome activity index.

[0162] The physical meaning of the microbiome activity index is the number of events whose signals exceed a baseline threshold. By focusing on the number of data points collected rather than intensity values, it provides a better representation of overall microbiome activity. This empirical metric allows for a balance between moments of slight gas release and large-scale exhaust events that might otherwise be overlooked. This method was tested on 38 participants in the GUMDROP study described below.

[0163] The GUMDROP study: A single-blind, crossover study comparing inulin-containing gumdrop candies with placebo gumdrops revealed significant differences in results, both within individual participants and across pooled data from 38 participants. Inulin was chosen because it is a universally non-absorbed form of FODMAP, thus ensuring that everyone was prepared for its poor absorption.

[0164] Statistical analysis was performed using a one-sided Wilcoxon signed-rank test, with a null hypothesis (H0) indicating that the microbiome activity index from the placebo gummy group was lower than that from the inulin gummy group. The results supported the rejection of H0, with a p-value of 3.6 × 10⁻⁶. - ¹¹, and the statistical test value was 5.0, indicating a significant effect of inulin on the microbiome activity index (Figure 13). Overall, 94.7% of participants exhibited higher microbiome activity when consuming inulin gummies, strongly suggesting that the device can be used to diagnose carbohydrate malabsorption. The remaining 5.3% of participants who did not follow the expected trend were attributed to poor adherence to a low-FODMAP diet or a very long transit time that may have delayed the onset of gastrointestinal gas within the expected 8 hours of wearing the device. One participant reported mild diarrhea after consuming the placebo gummies, while five participants reported the same symptoms after consuming the inulin gummies, suggesting that not all gastrointestinal gas events may have been captured. Surprisingly, 31.6% of participants also reported experiencing bloating, diarrhea, or constipation after consuming the placebo gummies, while 65.8% of participants reported similar feelings after consuming one packet of inulin gummies. These results indicate that inulin gummies significantly alter gut microbiome activity and thus alter gut perception of symptoms associated with bloating, diarrhea, or constipation. The findings also highlight the potential unreliability of the survey, as the likelihood that 31.6% of participants actually experienced symptoms after consuming only six regular gummy candies is very low. This figure could be attributed to fear and anticipation of gastrointestinal discomfort after consuming the gummy candies, which could lead to an overestimation of perceived symptoms after the first bag of placebo gummy candies.

[0165] Notably, even under low FODMAP dietary restrictions, the device still detected low, but quantifiable, microbiome activity index values ​​(by...) after consuming the placebo gummy. Figure 13D (The blue bars in the image indicate this). This result suggests that either the sugar content of the gummy candies exceeds the intestines' absorption capacity, or a more stringent dietary regimen would be needed to completely eliminate the availability of fermentable foods that cause flatulence. This helps prevent false positives, especially when testing individual carbohydrates without using placebo gummy candies for baseline comparison.

[0166] By setting the microbiome activity index threshold to 256, only 7 out of 38 participants had a microbiome activity index above this threshold after consuming the placebo gummy, while 35 out of 38 participants exceeded the threshold after consuming the inulin gummy. This corresponds to an expected specificity of 81.6% and a sensitivity of 91.1%. These results highlight the effectiveness of this threshold, which will be further refined and validated in future studies to further enhance diagnostic accuracy.

[0167] Figure 13A The figure shows an example from participants in the GUMDROP study, illustrating the gas intensity across two groups: first, the gas intensity of the placebo gummy group (blue line), and then the gas intensity of the inulin gummy group (orange line). Figure 13B The figure shows a wearable tracker, indicating that the device was effectively worn (threshold probability ≥ 51%) during both groups. Figure 13C The figure shows the microbiome activity index calculated for each participant, indicating high microbiome activity during the inulin group in 94.7% of the cases (n=38). Figure 13D This is an illustrative example of the GUMDROP study. Participants avoided high-fiber foods for two days before the start of the study and throughout the study period. Figure 13E The figure shows the Wilcoxon one-tailed signed-rank test, with a p-value of 3.6 x 10⁻⁶. - ¹1, and the statistical test value is 5.0.

[0168] This work introduces a novel device and method for real-time measurement of gut microbiome activity by detecting hydrogen gas produced during bacterial fermentation and expelled as gastrointestinal gas. The device has broad potential applications, including the clinical diagnosis of malabsorption, early detection of gastrointestinal disorders, and monitoring the efficacy of treatments for bloating and excessive flatulence. Notably, it promises to revolutionize drug development in the field of gas relief and bloating by providing quantitative measurements of the efficacy of medications and supplements, replacing the current reliance on subjective investigations that dominate research. Additionally, the device offers a compelling alternative to traditional breath tests, which are rarely performed due to their lack of sensitivity and the inconvenience they cause to patients. In contrast, the device described herein is characterized by its built-in ability to be used comfortably at home for tracking user compliance with necessary dietary preparations. Significant progress has also been made in the fabrication of custom electrochemical gas sensors, resulting in reduced costs and smaller size compared to commercial counterparts. In some examples, the validation of malabsorption can be extended to include more types of carbohydrates, such as lactose and fructose.

[0169] Several device prototypes have been developed to reduce size and improve comfort and reliability. In some examples, the device can be comfortably clipped onto the outside of the participant's underwear, close to the rectum in the perineal area. This placement allows the device to be directly in the flow of gastric gas, enabling passive measurement of the frequency, volume, and composition of the gastric gas. The smart underwear is compatible with almost all types of underwear by allowing users to choose between four buckle sizes that adjust for different underwear thicknesses.

[0170] The device estimates the probability of being worn and whether it is being worn by combining readings from an accelerometer and a temperature sensor. In some examples, all the electronics can be housed in a custom four-layer PCB (assembled by PCBway). The device can be powered by two silver oxide batteries, typically used in earbuds, avoiding the use of lithium-ion batteries due to their known hazards. The custom battery holder can be made of brass and cut from a single 0.02-inch thick foil using a metal adhesive jetting system, with the two batteries separated by a custom 3D-printed PETG separator.

[0171] A prototype smart underwear device utilizes a microcontroller (Ambiq Apollo 3 Artemis, Sparkfun®) with Bluetooth Low Energy (BLE) capability, which facilitates connectivity between the device and a mobile app used to retrieve data. The system also includes low-power serial flash memory (IS25LP064D, ISSI®), ferroelectric random access memory (MB85RC64TA, FRAM, Fujitsu®), and two electrochemical sensors (EC-Sense®) connected to two configurable analog front-ends (LMP91000, Texas Instruments®) that act as small voltage regulators. These sensors transmit analog signals converted by an embedded analog-to-digital converter (ADC) within the Artemis microcontroller. Additionally, a temperature sensor and an accelerometer (MC3630, Memsic®) are used for wear detection.

[0172] The microcontroller spends most of its time in a low-power sleep mode, waking up approximately every five minutes to save a baseline signal, or whenever a gas event causes an interrupt to the ADC system via its built-in direct memory access (DMA) feature. The accelerometer can also trigger an interrupt every two sleep and power-on cycles to assist in wearable detection. The device is powered by two silver oxide batteries (357 / 303 Energizer®). Power consumption is measured using a Power Profiler Kit II (Nordic Semiconductor®).

[0173] Based on the inventors' work (including various experiments and designs for smart underwear devices), it is evident that the systems and methods described herein offer significant advantages for clinical applications, including the diagnosis of malabsorption, early detection of gastrointestinal disorders, and monitoring of therapeutic effects associated with bloating and excessive flatulence. Additionally, such systems and methods are envisioned for use in certain drug discovery processes, such as for treating gas and bloating by providing objective, quantitative data (a significant improvement over subjective surveys currently used in research). In some examples, these systems and methods can also replace or complement traditional respiratory testing, providing patients with a more comfortable and accurate alternative, enabling long-term, home-based monitoring, and improving user compliance and comfort. Moreover, the systems and methods presented herein can be utilized to unlock personalized, self-controlled food studies, which individuals can conduct themselves to determine their sensitivities to various foods, ingredients, and their components.

Claims

1. An apparatus comprising: A housing having a contour that allows the housing to be secured to the underwear when the user's underwear is worn, near the user's perineal area; A processor, the processor being positioned within the housing; A gas sensor, located within the housing, includes at least one sensing element, wherein a composition of the at least one sensing element interacts with at least one gaseous component in the gastrointestinal gas emissions from the user to generate an electrical signal indicating the presence or concentration of the gaseous component; A communication module, located within the housing and connected to the processor, for transmitting data from the processor to a remote computing device; and A memory located within the housing and having an instruction set stored thereon, which, when executed by the processor, causes the processor to: Read the electrical signal generated by the gas sensor; Record gastrointestinal gas data based on the aforementioned electrical signals; The gastrointestinal gas data is correlated with time data corresponding to the time when the electrical signal was generated to generate timed gastrointestinal gas data; and The timed gastrointestinal gas data is output via the communication module.

2. The device of claim 1, further comprising a power source located within the housing and connected to provide power to the processor, the communication module, and the memory throughout a study conducted via the device for a duration of at least 12 hours.

3. The device according to claim 2, wherein, The power source includes an electrolytic double-layer capacitor (EDLC) connected to provide rechargeable power to the device.

4. The device according to claim 1, wherein, The gas sensor is positioned aligned with an opening in the housing near the user's perineal area, the opening being configured to allow gas to permeate into the at least one sensing element.

5. The device of claim 1, comprising a temperature sensor and a motion sensor, both disposed within the housing and connected to provide signals to the processor; and in, The instruction set further enables the processor to: An indication of whether the device is being worn by the user is generated based on temperature sensor signals and motion sensor signals.

6. The device according to claim 1, wherein, The gas sensor is an electrochemical sensor, which includes a reference electrode and a counter electrode, wherein the at least one sensing element is a working electrode comprising a material that exhibits a change in current due to a redox reaction when exposed to a hydrogen-based gas.

7. The device according to claim 6, wherein, The working electrode is a platinum-based electrode, and the reference electrode and the counter electrode comprise conductive carbon.

8. The device according to claim 1, wherein, The gas sensor is a metal oxide semiconductor sensor, and the at least one sensing element includes a metal oxide sensing layer of the metal oxide semiconductor sensor, wherein the metal oxide semiconductor sensor exhibits a change in resistance in response to the adsorption of hydrogen-based gas at the metal oxide sensing layer.

9. The device of claim 6, further comprising a quasi-solid electrolyte configured to contact the working electrode, the reference electrode, and the counter electrode.

10. The device according to claim 9, wherein, The quasi-solid electrolyte includes an absorbent substrate impregnated with a hygroscopic substance.

11. A system for determining the probability of an intestinal condition, the system comprising: processor; Communication module; as well as A memory, which communicates with the processor and stores software instructions that cause the processor to: Gas sensing data is received from a gas sensor, the gas sensing data including a time series of measurements indicating the concentration of gas in the gastrointestinal gaseous excrement of a subject during a given time period; Receive compliance data, which indicates whether the subject was wearing the gas sensor when the time-series measurements were acquired; Receive additional user data, which indicates the time when the food of interest was consumed relative to the time series of the measurement; Perform at least one of the following: The time of the first gastrointestinal gas emission after the consumption of the food of interest is determined based on the gas sensing data. Determine the microbiome activity index; or Determine the total number of gastrointestinal gas emissions during the given time period following the consumption of the food of interest; as well as Output an indicator to the user regarding the effect of the food consumption on the subject.

12. The system according to claim 11, wherein, The software instructions further enable the processor to: receive, in association with receiving the additional user data, the time when the subject consumed food comprising the target carbohydrates or amino acids after fasting.

13. The system according to claim 11, wherein, The software instructions further enable the processor to: output to the user an indicator of the likelihood that the subject has a gut condition including carbohydrate malabsorption.

14. The system according to claim 11, wherein, The given time period is at least three hours.

15. The system according to claim 11, wherein: The given time period is at least one week; The time series of the measurements provides gastrointestinal gas monitoring throughout the user's daily activities; as well as The microbiome activity index is calculated relative to all the food the user eats within the given time period.

16. The system according to claim 15, wherein, The additional user data indicates all the food consumed by the user during the given time period, and wherein the software instructions further cause the processor to identify trigger food that, when consumed by the user, causes the microbiome activity index to increase by a threshold amount compared to the user's baseline microbiome activity index.

17. The system according to claim 16, wherein, The additional user data indicating all the food the user eats is determined via a user interface that allows the user to upload photos of the food eaten for processing by a food recognition AI model, or to input nutritional information about the food eaten; and wherein the software instructions further enable the processor to determine the components in the food eaten that may cause increased gastrointestinal activity.

18. The system according to claim 11, wherein, The compliance data is derived from at least one of the following: the output of a temperature sensor disposed within the housing containing the gas sensor; or the output of a motion sensor disposed within the housing containing the gas sensor.

19. The system of claim 11, further comprising a display screen of a mobile device, wherein, The software instructions further cause the processor to: The display screen prompts the user to ensure that the gas sensor is worn close to the perineal area; The user is prompted to begin the diet plan; as well as The user is prompted to consume the food of interest at a given time after the start of the diet plan.

20. The system according to claim 11, wherein, The software instructions further enable the processor to monitor peak values ​​in the gas sensing data that indicate gastrointestinal gas emissions, and to determine that a confirmed gastrointestinal gas emission has occurred when the signal value increases from a baseline level to a peak exceeding a threshold within a given time period and then falls back to the baseline level.

21. The system according to claim 11, wherein, The microbiome activity index indicates the number of events in which the gas sensing data exceeds a threshold, and is calculated using the absolute value of the first derivative of the time series of the measurement.

22. A method for diagnosing intestinal diseases, comprising: Subjects were required to fast for the initial fasting period; Provide a wearable gastrointestinal gas sensor for the subject to wear; The subject was given a dietary intervention and the time it took to consume the dietary intervention was recorded. Gastrointestinal gas data from the wearable gas sensor are continuously recorded throughout the measurement period, which includes at least one hour; The gastrointestinal gas activity is determined based on the gastrointestinal gas data, which includes at least one of the following: the time elapsed from the consumption of the dietary intervention to the first gastrointestinal gas emission, the total number of gastrointestinal gas emissions, the frequency of gastrointestinal gas emissions, the intensity of gastrointestinal gas emissions, the temporal distribution of gastrointestinal gas emissions, the volume of gastrointestinal gas emissions, and the total volume of all gastrointestinal gas emissions. as well as The diagnosis of the intestinal condition is provided based on the time consumed by the gastrointestinal gas activity and the dietary intervention.

23. The method according to claim 22, wherein, The dietary intervention is carbohydrates, and the intestinal symptom is malabsorption of the carbohydrates.

24. The method according to claim 22, wherein, The intestinal condition is at least one of small intestinal bacterial overgrowth (SIBO) or diarrhea-dominant irritable bowel syndrome (IBS-D), and the diagnosis is made by providing information on gastrointestinal gas activity and related dietary interventions to a trained machine learning algorithm configured to determine the likelihood of SIBO or IBS-D based on training data from patients with a history of SIBO or IBS-D and patients without a history of SIBO and IBS-D.

25. The method according to claim 22, wherein, The dietary intervention includes a carbohydrate challenge using at least one of glucose or fructose, and the intestinal condition is secondary malabsorption in an individual suffering from at least one of the following: recent chemotherapy, celiac disease, or inflammatory bowel disease.

26. The method according to claim 22, wherein, The wearable gastrointestinal gas sensor includes the device according to any one of claims 1-10.

27. The system according to claim 11, wherein, The gas sensor is the device according to any one of claims 1-10.

28. The system according to claim 19, wherein, The processor, the communication module, and the memory are part of the mobile device, the gas sensor is the device according to any one of claims 1-10, and the system allows subjects to assess the effects of food outside of a medical clinic without the involvement of the medical clinic.

29. A method for assessing intestinal disorders, comprising: Provide a wearable gastrointestinal gas sensor for subjects to wear during the study period; Record gastric gas data from the wearable gastric gas sensor during the study period; Receive the subject's reported stress level during the study period; A baseline gut microbiome activity index was established by analyzing gastrointestinal gas data during periods of low or no reported stress levels. Changes in gut microbiome activity induced by stress were measured by comparing gastrointestinal gas data during periods of elevated reported stress levels with baseline gut microbiome activity indices. The magnitude of the deviation from baseline during the period of elevated reported stress levels; Determine the temporal relationship between the occurrence of elevated stress levels and changes in gut microbiome activity, including the time delay between stress occurrence and gut microbiome response, and the duration of elevated gut microbiome activity following stress occurrence; as well as Gut-brain reactivity curves are generated based on the following: the magnitude of changes in gut microbiome activity during periods of elevated reported stress levels; the time delay between stress occurrence and gut microbiome response; and the duration of elevated gut microbiome activity following a stress event.

30. The method according to claim 29, wherein, The wearable gastrointestinal gas sensor includes the device according to any one of claims 1-10.

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