Methods and systems for collecting and analyzing gastrointestinal samples

Abstract

A system may receive, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule configured to collect a sample from a gastrointestinal (GI) tract. A system may determine a collection time for the sample. A system may receive, from the user device, subject data associated with a user input, wherein the subject data comprises the first identifier, a timestamp, and a description of the user input. A system may associate the subject data with the sample collected by the ingestible capsule. A system may receive, from an analysis system, sample data associated with the sample collected by the ingestible capsule, wherein the sample data comprises the second identifier and biological data comprising gastrointestinal (GI) fluid data. A system may analyze the subject data and the sample data to evaluate the biological data in view of the user input.

Description

METHODS AND SYSTEMS FOR COLLECTING AND ANALYZING GASTROINTESTINAL SAMPLESBACKGROUND

[0001] The gastrointestinal (Gl) tract is a series of joined, hollow organs substantively in the form of a long and twisting tube from the mouth to the anus. The contents in the Gl tract such as tissues, mucosal cells, microbiota, molecules, and the like contain information regarding the condition of the Gl tract. Samples of Gl contents may be collected via stool sampling. However, the spatial and temporal information of the Gl contents is poorly preserved in stool samples. Therefore, it is desirable in many situations to collect samples of Gl contents directly from the interior of the Gl tract by using a suitable sampling tool. Different organs of the Gl tract have varying degrees of accessibility. For example, the small intestine is a deep and long organ which is difficult to access using transoral or transnasal catheters. Therefore, various sampling tools and methods, including, but not limited to, tubes with endoscopic-assisted aspiration or biopsy means and micro-electro- mechanical system (MEMS)-based capsules, have been developed for accessing the Gl tract and sampling therein.

[0002] The composition and role of gut microbiota vary along the gastrointestinal tract. Sampling of the gut microbiota is generally restricted to stool sampling or endoscopic retrieval.

[0003] Stool sampling is the most commonly practiced, however the composition of the stool does not provide an accurate proxy for the composition or function of the small intestine. Furthermore, the stool sample cannot be temporally matched to a particular event whose impact is intended to be investigated on the gut microbiota. For example, a change in diet, the ingestion of a drug, or a particular symptom or mood.

[0004] In contrast, an endoscopic sample can retrieve and preserve a sample at any moment in time, enabling the investigation of complex behaviours. However, an endoscopic procedure carries risk and introduces complexity to a procedure. For example, often the bowel must be prepped, or the scope cannot reach past the proximal region. Further, the endoscope introduces contamination to the sample.

[0005] Sampling of the intestinal fluid requires processing of the sample to turn the sample into biological outputs (e.g. sequencing, bioinformatics). It is not performed at the time of sampling.

[0006] It is therefore desired to have a convenient procedure to sample the intestinal tract and relate the sample output to a patient experienced or initiated event.SUMMARY

[0007] In some aspects, implementations of the present disclosure include a computer- implemented method for analysis of intestinal data including: receiving, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject; determining a collection time for the sample; receiving, from the user device, subject data associated with a user input, wherein the subject data includes the first identifier, a timestamp, and a description of the user input; associating the subject data with the sample collected by the ingestible capsule; receiving, from an analysis system, sample data associated with the sample collected by the ingestible capsule, wherein the sample data includes the second identifier and biological data including gastrointestinal (G I) fluid data; and analyzing the subject data and the sample data to evaluate the biological data in view of the user input.

[0008] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the collection time for the sample is determined based on information received from the ingestible capsule.

[0009] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the collection time for the sample is determined based on information received from the user device.

[0010] In some aspects, implementations of the present disclosure include a computer- implemented method for analysis of intestinal data including: receiving, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject; receiving, from the user device, first data including the first identifier, the second identifier, and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determining a collection time for the sample based on the first data; receiving, from the user device, second data associated with a user input, wherein the second data includes the first identifier, a second timestamp, and a description of the user input; associating the second data with the sample collected by the ingestible capsule; receiving, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data includes the second identifier and biological data including gastrointestinal (G I) fluid data; and analyzing the second data and the third data to evaluate the biological data in view of the user input.

[0011] In some aspects, implementations of the present disclosure include a computer- implemented method, further including associating the second identifier with the first identifier.

[0012] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein associating the second data with the sample collected by the ingestible capsule includes temporally relating the second timestamp to the collection time.

[0013] In some aspects, implementations of the present disclosure include a computer- implemented method, further including receiving a plurality of second identifiers, each of the plurality of second identifiers being associated with a respective ingestible capsule.

[0014] In some aspects, implementations of the present disclosure include a computer- implemented method, further including associating each of the plurality of second identifiers with the first identifier.

[0015] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the user input is an action by the subject.

[0016] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the action by the subject is ingestion of a drug, compound, or pharmaceutical.

[0017] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the action by the subject is ingestion of a food or liquid.

[0018] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the user input is a symptom of the subject.

[0019] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the user input is a mood of the subject.

[0020] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the collection time is within a window about 1.5 to 6 hours after the first timestamp.

[0021] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the collection time is about 3 hours after the first timestamp.

[0022] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the biological data includes omic data.

[0023] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the omic data is genomic data, metabolomic data, proteomic data, or transcriptomic data.

[0024] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the biological data includes multiomic data.

[0025] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein analyzing the second data and the third data to evaluate thebiological data in view of the user input includes determining a response of the subject to ingestion of a drug, compound, or pharmaceutical.

[0026] In some aspects, implementations of the present disclosure include a computer- implemented method, further including: transmitting, to the user device, a first instruction to ingest the ingestible capsule; and transmitting, to the user device, a second instruction to ingest the drug, compound, or pharmaceutical.

[0027] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the first instruction and the second instruction are transmitted at predetermined times relative to each other.

[0028] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein analyzing the second data and the third data to evaluate the biological data in view of the user input includes determining a response of the subject to ingestion of a food or liquid or to the recording of a mood or symptom.

[0029] In some aspects, implementations of the present disclosure include a computer- implemented method, further including: transmitting, to the user device, a first instruction to ingest the ingestible capsule; and transmitting, to the user device, a second instruction to ingest the food or liquid.

[0030] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the first instruction and the second instruction are transmitted at predetermined times relative to each other.

[0031] In some aspects, implementations of the present disclosure include a computer- implemented method, further including recommending a health-related action for the subject based on the analysis of the second data and the third data.

[0032] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the health-related action is a dietary recommendation, a fluid intake recommendation, or a sleep recommendation.

[0033] In some aspects, implementations of the present disclosure include a computer- implemented method, further including transmitting, to the user device, the health-related action.

[0034] In some aspects, implementations of the present disclosure include a computer- implemented method, further including providing a diagnosis, prognosis, or treatment recommendation for the subject based on the analysis of the second data and the third data.

[0035] In some aspects, implementations of the present disclosure include a computer- implemented method, further including transmitting, to the user device, the diagnosis, prognosis, or treatment recommendation.

[0036] In some aspects, implementations of the present disclosure include a method as described herein, further including administering a treatment to the subject based on the diagnosis, prognosis, or treatment recommendation.

[0037] In some aspects, implementations of the present disclosure include a system for analysis of intestinal data including: at least one processor; and at least one memory operably coupled to the at least one processor, wherein the at least one memory has computer-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to: receive, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject; receive, from the user device, first data including the first identifier, the second identifier, and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determine a collection time for the sample based on the first data; receive, from the user device, second data associated with a user input, wherein the second data includes the first identifier, a second timestamp, and a description of the user input; associate the second data with the sample collected by the ingestible capsule; receive, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data includes the second identifier and biological data including gastrointestinal (G I) fluid data; and analyze the second data and the third data to evaluate the biological data in view of the user input.

[0038] In some aspects, implementations of the present disclosure include a computer- implemented method for analysis of intestinal data including: receiving, from a user interface, an identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of a subject; receiving, from the user interface, first data including the identifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determining a collection time for the sample based on the first data; receiving, from the user interface, second data associated with a user input, wherein the second data includes a second timestamp and a description of the user input; associating the second data with the sample collected by the ingestible capsule; receiving, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data includes the identifier and biological data including gastrointestinal (G I) fluid data; and analyzing the second data and the third data to evaluate the biological data in view of the user input.

[0039] In some aspects, implementations of the present disclosure include a computer- implemented method for collection of intestinal samples including: receiving, from a user interface, an identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of a subject; receiving, from the user interface, first data including theidentifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determining a collection time for the sample based on the first data; receiving, from the user interface, second data associated with a user input, wherein the second data includes a second timestamp and a description of the user input; and associating the second data with the sample collected by the ingestible capsule.

[0040] In some aspects, implementations of the present disclosure include a computer- implemented method, further including: displaying, on the user interface, a first instruction to ingest the ingestible capsule; and displaying, on the user interface, a second instruction to ingest a drug, compound, or pharmaceutical.

[0041] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the first instruction and the second instruction are displayed at predetermined times relative to each other.

[0042] In some aspects, implementations of the present disclosure include a computer- implemented method, further including: displaying, on the user interface, a first instruction to ingest the ingestible capsule; and displaying, on the user interface, a second instruction to ingest a food or liquid.

[0043] In some aspects, implementations of the present disclosure include a computer- implemented method, wherein the first instruction and the second instruction are transmitted at predetermined times relative to each other.

[0044] In some aspects, implementations of the present disclosure include a system for collection of intestinal samples including: an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of a subject; and a computing device including at least one processor and at least one memory operably coupled to the at least one processor, wherein the at least one memory has computer-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to: receive, at a user interface of the computing device, an identifier associated with the ingestible capsule; receive, at the user interface of the computing device, first data including the identifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determine a collection time for the sample based on the first data; receive, at the user interface of the computing device, second data associated with a user input, wherein the second data includes a second timestamp and a description of the user input; and associate the second data with the sample collected by the ingestible capsule.

[0045] In some aspects, implementations of the present disclosure include a method including: administering a user intervention; administering an ingestible sampling capsule at a timeinterval before or after the user intervention; and receiving sample data collected by the ingestible capsule.

[0046] In some aspects, implementations of the present disclosure include a method, where the ingestible sampling capsule targets the small intestine.

[0047] In some aspects, implementations of the present disclosure include a method, where the ingestible sampling capsule is ingested after a fast of at least 4 hours.

[0048] In some aspects, implementations of the present disclosure include a method, where the sample data relates to the user intervention.

[0049] In some aspects, implementations of the present disclosure include a method any one, where a blood sample is collected at a time interval related to the ingestion of the sampling capsule.

[0050] In some aspects, implementations of the present disclosure include a method, wherein the user intervention is a test product.

[0051] In some aspects, implementations of the present disclosure include a method, wherein the time interval is between 30-90 minutes.

[0052] In some aspects, implementations of the present disclosure include a method, wherein the time interval is 60 minutes.

[0053] It should be understood that the above-described subject matter may also be implemented as a computer-controlled apparatus, a computer process, a computing system, or an article of manufacture, such as a computer-readable storage medium.

[0054] Other systems, methods, features and / or advantages will be or may become apparent to one with skill in the art upon examination of the following drawings and detailed description. It is intended that all such additional systems, methods, features and / or advantages be included within this description and be protected by the accompanying claims.BRIEF DESCRIPTION OF THE DRAWINGS

[0055] The components in the drawings are not necessarily to scale relative to each other. Like reference numerals designate corresponding parts throughout the several views.

[0056] FIGURE 1 is a flowchart of an example method for analysis of intestinal data according to an implementation described herein.

[0057] FIGURE 2 is a flowchart of another example method for analysis of intestinal data according to an implementation described herein.

[0058] FIGURE 3 is a flowchart of an example method for collection of intestinal samples according to an implementation described herein.

[0059] FIGURE 4 illustrates an example computing device.

[0060] FIG. 5 illustrates an example configuration of an implementation of the present disclosure traveling along a gastrointestinal tract.

[0061] FIGS. 6 and 7 illustrate bar plots of genus-level 16S microbiome sequencing profiles, according to a study of an example implementation of the present disclosure.

[0062] FIGS. 8A-8D illustrate in situ x-ray images of an example implementation of the present disclosure in sealed and released configurations, according to implementations of the present disclosure.

[0063] FIGS. 9A and 9B illustrate differences between experimental groups, according to a study of an example implementation of the present disclosure.

[0064] FIGS. 10A-10D illustrate a summary of pairwise group comparisons by machine learning classifiers, according to a study of an example implementation of the present disclosure.

[0065] FIG. 11 illustrates Pirate plots showing a selection of most abundant taxa, according to a study of an example implementation of the present disclosure.

[0066] FIG. 12 illustrates an example method of sampling showing that capsules according to implementations of the present disclosure can consistently pass through the stomach and enter the small intestine within one hour to begin sampling within the small intestine.

[0067] FIG. 13 illustrates an example method of sampling including configuring the capsule to be ingested 60 minutes before a liquid intervention.DETAILED DESCRIPTION

[0068] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. Methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present disclosure. As used in the specification, and in the appended claims, the singular forms "a," "an," "the" include plural referents unless the context clearly dictates otherwise. The term "comprising" and variations thereof as used herein is used synonymously with the term "including" and variations thereof and are open, non-limiting terms. The terms "optional" or "optionally" used herein mean that the subsequently described feature, event or circumstance may or may not occur, and that the description includes instances where said feature, event or circumstance occurs and instances where it does not. Ranges may be expressed herein as from "about" one particular value, and / or to "about" another particular value. When such a range is expressed, an aspect includes from the one particularvalue and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent "about," it will be understood that the particular value forms another aspect. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint.

[0069] As used herein, the terms "about" or "approximately" when referring to a measurable value such as an amount, a percentage, and the like, is meant to encompass variations of ±20%, ±10%, ±5%, or ±1% from the measurable value.

[0070] "Administration" of "administering" to a subject includes any route of introducing or delivering to a subject an agent. Administration can be carried out by any suitable means for delivering the agent. Administration includes self-administration and the administration by another.

[0071] The term "subject" is defined herein to include animals such as mammals, including, but not limited to, primates (e.g., humans), cows, sheep, goats, horses, dogs, cats, rabbits, rats, mice and the like. In some embodiments, the subject is a human.

[0072] The term "gastrointestinal fluid" or "Gl fluid" is defined herein to include luminal fluid, mucosal fluid, or a combination of luminal and mucosal fluid from the gastrointestinal tract, as well as any semi-solid or solid substances contained in the fluid. The functionality of the gastrointestinal tract also leads to its Gl fluid carrying semi-solid or solid substances, which may originate from food, shedding cells from the body, or metabolic byproducts of intestinal microbes. These substances undergo continuous transformation within the dynamic environment of the gastrointestinal tract.

[0073] The terms "stool sampling" and "fecal sampling" are used interchangeably herein.

[0074] I ngestible sampling capsules offer an improved techniques for collecting samples from the small intestine. They are convenient and cost effective and can be deployed from the home. They passively transit the Gl tract and open and close via a variety of mechanisms. If a preservant is used to stabilize the sample, it is possible to capture and preserve small intestinal samples and relate the samples to different time intervals.

[0075] This can be used to improve research studies, and / or to diagnose a particular condition. In an example method, a patient can be instructed to take one capsule after taking their medication and then evaluate the response of the medication in the small intestine (e.g. insulin or glucose reaction). Or alternatively or additionally, a patient can be instructed to take one capsule in timed coordination with a meal to evaluate an allergic response. Alternatively or additionally, a patient can be instructed to take one capsule at various time points coordinated with the ingestion of protein, for example a protein shake, and then evaluate the rate and efficiency of protein digestion. Optionally you could instruct a patient to take multiple capsules at different times in orderto evaluate response over a period of time. This type of data can be used to improve data within a clinical study, for example studies to evaluate the impact of a specific user action on the gut microbiota. Alternatively, it could be used in patient care to evaluate a user's specific response to a specific action. This information could be used to guide a dose of a pharmaceutical, or recommend a treatment pathway etc. Optionally , it could be used to guide or inform animal care, including pet care.

[0076] As used herein, the term "user action" refers to any action or inaction of a human or animal subject. User actions can optionally include patterns of behavior (e.g., measurements of behavior over time) in addition to single discrete actions. Example user actions include, but are not limited to: alcohol consumption, use of any type of drug or medication, diet (e.g., diet history or schedule), food consumption, fluid consumption, exercise or exercise schedule, sleep or sleep schedule, use of any type of supplement (e.g., vitamins, minerals), and / or any other therapeutic. Furthermore, the term "user action" herein can further encompass recording any of the user actions. As a non-limiting example, both the consumption of a drug, and recording the consumption of the drug, can be "user actions." As another non-limiting example, both the consumption of food, and recording the consumption of the food, can be "user actions."

[0077] As used herein, the term "user symptoms" includes both psychological and physiological symptoms. As non-limiting examples, the user symptoms referred to herein can include pain, fever, fatigue, as well as indications of user mood.

[0078]

[0079] However, existing technologies cannot directly correlate the sampling with the event for the purpose of providing the required information. It would not be convenient, for the patient for the purpose of analysis and many critical errors could occur. In the case of stool sampling, which is typically used for example to assess gut microbiota, the timing of the passage of the stool is highly variable and unassociated with the user action unless interventions such as strong laxatives are used which can alter the sample. In addition, the measurement of mechanisms of action that occur within the small intestine and are then absorbed would be lost.

[0080] In some implementations described herein, a diagnostic device comprising a sampling capsule and a digital device with a user interface are described. The sampling capsule is designed to collect a sample within a recorded time window and the user interface is designed to relate the sampling window with patient events. Additionally, the user interface can be used to deliver specific instructions related to capsule ingestion.

[0081] Fig. 1 is a flowchart of an example method for analysis of intestinal data according to an implementation described herein. It should be understood that the logical operations described with regard to Fig. 1 can be performed using a computing device, e.g. the computing device of Fig. 4.

[0082] At step 110, the method includes receiving, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject. Optionally, the user device can be a personal electronic device such as a smartphone, tablet, or other mobile computing device. Optionally, the user device can be distributed to the subject along with the ingestible capsule, and the user device may be configured to automatically identify the second identifier of capsule. Ingestible apparatuses for collecting Gl fluid samples are described in U.S. 2023 / 0061826, which is incorporated herein by reference in its entirety. It should be understood that the ingestible apparatuses described in U.S. 2023 / 0061826 are provided only as examples. Additionally, in some implementations, the subject ingests a single capsule. In other implementations, the subject ingests a plurality of capsules, for example at different times. In this implementation, each of the plurality of capsules is associated with a respective second identifier. The techniques described herein facilitate associating each of one more capsules with the subject and also associate user input data with the appropriate sample analysis data.

[0083] This disclosure contemplates that the first identifier can be unique to the subject. Additionally, the second identifier can be unique to the ingestible capsule. Optionally, the second identifier can be provided as machine readable data on the ingestible apparatus (e.g. a bar code, QR code, plain text, etc.), which can be read by the user device. Alternatively, the second identifier can be provided in a microchip (e.g. radio frequency identification (RFID)) of the ingestible apparatus, which can be transmitted to and read by the user device. Alternatively, the user can enter the second identifier, e.g. via a user interface, into the user device. In some implementations, the user is the subject. In other implementations, the user is not the subject. For example, the user may be a family member, caregiver, medical professional, etc. assisting the subject.

[0084] At step 120, the method includes determining a collection time for the sample. In some implementations, the collection time is the time that the ingestible capsule starts to collect a sample of the Gl Fluid (see e.g. Sample Start below). In other implementations, the collection time is the time that the ingestible capsule ends collection of the Gl Fluid (see e.g. Sample End below). In yet other implementations, the start and end of the sampling time by the ingestible capsule is long (e.g. over 20, 30, 40, 50, 60 minutes) and then an average time is used to identify the window of the collection time. In some aspects, the collection time for the sample is determined based on information received from the user device. For example, the user may input a time when theingestible capsule was ingested by the subject. As described herein, the collection time for the sample can be determined based on the ingestion time. For example, the collection time may be within a window about 1.5 to 6 hours after the ingestion time. Optionally, the collection time is about 3 hours after the ingestion time. The capsule is designed to collect a sample from and only from a target area of the subject's Gl tract. The relationship between collection time and ingestion time has been found to be consistent across subjects. For example:Sample Start: avg = 2.18 hours, min = 0.68 hours, max = 6.81 hours, inter-quartile range = + / - 1.16Sample End: avg = 3.53 hours, min = 1.61 hours, max = 6.81 hours, inter-quartile range = + / - 1.01In other words, the capsule collects the sample from the target area of the Gl tract at a relatively fixed time relative to ingestion time. The consistency of these collection times are in contrast to the overall transit time of a capsule, which can be used to compare the relative consistency of stool sampling. For example, the majority of capsules will collect a sample within a window of 2 to 3 hours from ingestion. However the full Gl transit time for these capsules is between 24 and 96 hours. This allows collection time to be reliably related to ingestion time.

[0085] In some aspects, the collection time for the sample is determined based on information received from the ingestible capsule. For example, the collection time may be relayed to the user device by a microchip (e.g. radio frequency identification (RFID)) of the ingestible apparatus. In some aspects, the collection time for the sample is determined based on external inputs, such as x-ray or ultrasound. For example, x-ray can be used to observe the start and / or end of sampling using specially designed radiographic labeling. For example a small floating marker can be placed inside the dissolvable shell of an otherwise radiographically labeled device. When the shell dissolves and sampling starts the marker will be seen to float away from the device and sampling can be determined to have started. In some aspects, a radiographically visible spring can be observed to becompressed or extended indicating whether sampling is in progress or completed. These inputs can be used to determine the collection time.

[0086] At step 130, the method includes receiving, from the user device, subject data associated with a user input, wherein the subject data comprises the first identifier, a timestamp, and a description of the user input. In some aspects, the user input is an action such as ingestion of a drug, compound, a food, a substance such as alcohol, or pharmaceutical. In other aspects, the user input is a symptom or mood. In other aspects, the subject data is the onset of a biological process, for example a menstral cycle, an arythmia or a user action such as exercise or sleep onset.

[0087] At step 140, the method includes associating the subject data with the sample collected by the ingestible capsule. In some aspects, the step of associating the subject data with the sample collected by the ingestible capsule includes temporally relating the timestamp to the collection time.

[0088] At step 150, the method includes receiving, from an analysis system, sample data associated with the sample collected by the ingestible capsule, wherein the sample data comprises the second identifier and biological data comprising gastrointestinal (G I) fluid data. It should be understood that the ingestible capsule is collected after transiting the Gl tract (i.e. collected in feces), and it is then shipped to a remote facility (e.g. laboratory) for analysis. The sample data is therefore obtained later in time relative to collection and then associated with the ingestible capsule and the subject based on the second identifier and first identifier, respectively. In an alternative method, the method includes obtaining another biological sample relative to the time of the sample collection by the ingestible capsule. For example, obtaining a blood sample at the predicted time of sample collection by the ingestible capsule, or based on the observed timing of sample collection under image guidance.

[0089] At step 160, the method includes analyzing the subject data and the sample data to evaluate the biological data in view of the user input. In some aspects, the biological data is any data generated from an analysis of the Gl fluid constituents. For example, the luminal fluid, mucosal fluid, or a combination of luminal and mucosal fluid from the gastrointestinal tract, as well as any semisolid or solid substances contained in the fluid. This can include particles that may originate from food, shedding cells from the body, or metabolic byproducts of intestinal microbes. In some aspects, it can include non-biological data for example detection of particles or substances related to the food or other ingested substance, for example a probiotic, prebiotic, a protein, a post biotic, an enzyme, a known or suspected allergen, an insulin spiking foodor a medicinal compound. In some aspects, the third data relates specifically to the user action, for example the detection of a probiotic that was ingested at a particular time point. Optionally, the biological data includes omic ormultiomic data (e.g. genomic, metabolomic, proteomic, and / or transcriptomic data). Optionally, the analysis is used to evaluate the biological data in view of the user input, for example, to determine a response of the subject to ingestion of a drug, compound, or pharmaceutical. Optionally, the analysis includes the evaluation of the state of transformation of an ingested compound, for example the ratio of digested to undigested proteins following the ingestion of a protein.Optionally, the method includes recommending a health-related action (e.g. a dietary recommendation, a fluid intake recommendation, or a sleep recommendation) for the subject based on the analysis. Alternatively or additionally, the method includes providing a diagnosis, prognosis, or treatment recommendation for the subject based on the analysis. Optionally, the method includes administering a treatment to the subject based on the diagnosis, prognosis, or treatment recommendation.

[0090] Fig. 2 is a flowchart of another example method for analysis of intestinal data according to an implementation described herein. It should be understood that the logical operations described with regard to Fig. 2 can be performed using a computing device, e.g. the computing device of Fig. 4.

[0091] At step 210, the method includes receiving, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (Gl) tract of the subject. Optionally, the user device can be a personal electronic device such as a smartphone, tablet, or other mobile computing device. Optionally, the user device can be distributed to the subject along with the ingestible capsule, and the user device may be configured to automatically identify the second identifier of capsule. Ingestible apparatuses for collecting Gl fluid samples are described in U.S. 2023 / 0061826, which is incorporated herein by reference in its entirety. It should be understood that the ingestible apparatuses described in U.S. 2023 / 0061826 are provided only as examples. Additionally, in some implementations, the subject ingests a single capsule. In other implementations, the subject ingests a plurality of capsules, for example at different times. In this implementation, each of the plurality of capsules is associated with a respective second identifier. The techniques described herein facilitate associating each of one more capsules with the subject and also associate user input data with the appropriate sample analysis data. In implementations with multiple capsules, the method further includes receiving a plurality of second identifiers, each of the plurality of second identifiers being associated with a respective ingestible capsule. Additionally, the method further includes associating each of the plurality of second identifiers with the first identifier.

[0092] This disclosure contemplates that the first identifier can be unique to the subject. Additionally, the second identifier can be unique to the ingestible capsule. In some aspects, themethod further includes associating the second identifier with the first identifier. Optionally, the second identifier can be provided as machine readable data on the ingestible apparatus (e.g. a bar code, QR code, plain text, etc.), which can be read by the user device. Alternatively, the second identifier can be provided in a microchip (e.g. radio frequency identification (RFID)) of the ingestible apparatus, which can be transmitted to and read by the user device. Alternatively, the user can enter the second identifier, e.g. via a user interface, into the user device. In some implementations, the user is the subject. In other implementations, the user is not the subject. For example, the user may be a family member, caregiver, medical professional, etc. who assists the subject.

[0093] At step 220, the method includes receiving, from the user device, first data comprising the first identifier, the second identifier, and a first timestamp that indicates when the ingestible capsule was ingested by the subject. For example, the user may input a time (e.g. the first timestamp) when the ingestible capsule was ingested by the subject. As described below, the collection time for the sample can be determined based on the first timestamp.

[0094] At step 230, the method includes determining a collection time for the sample based on the first data. In some implementations, the collection time is the time that the ingestible capsule starts to collect a sample of the Gl Fluid (see e.g. Sample Start below). In other implementations, the collection time is the time that the ingestible capsule ends collection of the Gl Fluid (see e.g. Sample End below). In yet other implementations, the start and end of the sampling time by the ingestible capsule is long (e.g. over 20, 30, 40, 50, 60 minutes) and then an average time is used to identify the window of the collection time. For example, the collection time may be within a window about 1.5 to 6 hours after the first timestamp. Optionally, the collection time is about 3 hours after the first timestamp. The capsule is designed to collect a sample from and only from a target area of the Gl tract. The relationship between collection time and first timestamp has been found to be consistent across subjects. For example:Sample Start: avg = 2.18 hours, min = 0.68 hours, max = 6.81 hours, inter-quartile range = + / - 1.16Sample End: avg = 3.53 hours, min = 1.61 hours, max = 6.81 hours,inter-quartile range = + / - 1.01In other words, the capsule collects the sample from the target area of the Gl tract at a relatively fixed time relative to ingestion time. The consistency of these collection times are in contrast to the overall transit time of a capsule, which can be used to compare the relative consistency of stool sampling. For example, the majority of capsules will collect a sample within a window of 2 to 3 hours from ingestion. However the full Gl transit time for these capsules is between 24 and 96 hours.

[0095] At step 240, the method includes receiving, from the user device, second data associated with a user input, wherein the second data comprises the first identifier, a second timestamp, and a description of the user input. In some aspects, the user input is an action by the subject. Optionally, the action by the subject is ingestion of a drug, compound, or pharmaceutical. In some aspects, the action by the subject is ingestion of a food or liquid. Alternatively or additionally, the user input is a symptom of the subject or a mood of the subject.

[0096] At step 250, the method includes associating the second data with the sample collected by the ingestible capsule. In some aspects, the step of associating the second data with the sample collected by the ingestible capsule includes temporally relating the second timestamp to the collection time. As described above at step 230, the collection time can be reliably established based on the first timestamp. Accordingly, the second data can similarly be associated with the sample collected by the ingestible capsule using the second timestamp. In implementations with multiple capsules, the second timestamp can be used to associate the second data with the appropriate sample.

[0097] At step 260, the method includes receiving, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data comprises the second identifier and biological data comprising gastrointestinal (Gl) fluid data. In some aspects, the biological data is any data generated from an analysis of the Gl fluid constituents. For example, the luminal fluid, mucosal fluid, or a combination of luminal and mucosal fluid from the gastrointestinal tract, as well as any semi-solid or solid substances contained in the fluid. This can include particles that may originate from food, shedding cells from the body, or metabolic byproducts of intestinal microbes. In some aspects, it can include non-biological data for example detection of particles or substances related to the food or other ingested substance, for example a probiotic, prebiotic, post biotic or a medicinal compound. In some aspects, the third data relates specifically to the user action, for example the detection of a probiotic that was ingested at a particular time point.

[0098] In some aspects, the biological data includes omic data. For example, the omic data can be genomic data, metabolomic, proteomic, or transcriptomic data. In some aspects, the biological data includes multiomic data.

[0099] At step 270, the method includes analyzing the second data and the third data to evaluate the biological data in view of the user input.

[0100] In some aspects, the step of analyzing the second data and the third data to evaluate the biological data in view of the user input includes determining a response of the subject to ingestion of a drug, compound, or pharmaceutical. Optionally, the method further includes: transmitting, to the user device, a first instruction to ingest the ingestible capsule; and transmitting, to the user device, a second instruction to ingest the drug, compound, or pharmaceutical. The first instruction and the second instruction can be transmitted at predetermined times relative to each other.

[0101] In some aspects, the step of analyzing the second data and the third data to evaluate the biological data in view of the user input includes determining a response of the subject to ingestion of a food or liquid. Optionally, the method further includes: transmitting, to the user device, a first instruction to ingest the ingestible capsule; and transmitting, to the user device, a second instruction to ingest the food or liquid. The first instruction and the second instruction can be transmitted at predetermined times relative to each other.

[0102] In some aspects, the method further includes recommending a health-related action for the subject based on the analysis of the second data and the third data. For example, the health- related action can be a dietary recommendation, a fluid intake recommendation, or a sleep recommendation. In some aspects, the method further includes instructing a next ingestion of a capsule based on the analysis of the second and the third data. Optionally, the method further includes transmitting, to the user device, the health-related action or the further instruction to ingest.

[0103] In some aspects, the method further includes providing a diagnosis, prognosis, or treatment recommendation for the subject based on the analysis of the second data and the third data. Optionally, the method further includes transmitting, to the user device, the diagnosis, prognosis, or treatment recommendation.

[0104] In some aspects, the techniques described herein a method for treating a subject including: analyzing intestinal data as described herein with regard to Figs. 1 or 2; providing a diagnosis, prognosis, or treatment recommendation for the subject based on the analysis; and administering a treatment to the subject based on the diagnosis, prognosis, or treatment recommendation.

[0105] Fig. 3 is a flowchart of an example method for collection of intestinal samples according to an implementation described herein. It should be understood that the logical operations described with regard to Fig. 3 can be performed using a computing device, e.g. the computing device of Fig. 4.

[0106] At step 310, the method includes receiving, from a user interface, an identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (Gl) tract of a subject. Optionally, the user device can be a personal electronic device such as a smartphone, tablet, or other mobile computing device. Optionally, the user device is provided to the subject with the ingestible capsule and is specially configured for detection of the second identifier of capsule. Ingestible apparatuses for collecting Gl fluid samples are described in U.S. 2023 / 0061826, which is incorporated herein by reference in its entirety. It should be understood that the ingestible apparatuses described in U.S. 2023 / 0061826 are provided only as examples. Additionally, in some implementations, the subject ingests a single capsule. In other implementations, the subject ingests a plurality of capsules, for example at different times. In this implementation, each of the plurality of capsules is associated with a respective identifier. The techniques described herein facilitate associating each of one more capsules with the subject and also associate user input data with the appropriate sample analysis data.

[0107] This disclosure contemplates that the identifier can be unique to the ingestible capsule. Optionally, the identifier can be provided as machine readable data on the ingestible apparatus (e.g. a bar code, QR code, plain text, etc.), which can be read by the user device. Alternatively, the identifier can be provided in a microchip (e.g. radio frequency identification (RFID)) of the ingestible apparatus, which can be transmitted to and read by the user device. Alternatively, the user can enter the identifier, e.g. via a user interface, into the user device. In some implementations, the user is the subject. In other implementations, the user is not the subject. For example, the user may be a family member, caregiver, medical professional, etc. who assists the subject.

[0108] At step 320, the method includes receiving, from the user interface, first data comprising the identifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject. For example, the user may input a time (e.g. the first timestamp) when the ingestible capsule was ingested by the subject. As described below, the collection time for the sample can be determined based on the first timestamp.

[0109] At step 330, the method includes determining a collection time for the sample based on the first data. In some implementations, the collection time is the time that the ingestible capsule starts to collect a sample of the Gl Fluid (see e.g. Sample Start below). In other implementations, thecollection time is the time that the ingestible capsule ends collection of the Gl Fluid (see e.g. Sample End below). In yet other implementations, the start and end of the sampling time by the ingestible capsule is long (e.g. over 20, 30, 40, 50, 60 minutes) and then an average time is used to identify the window of the collection time. For example, the collection time may be within a window about 1.5 to 6 hours after the first timestamp. Optionally, the collection time is about 3 hours after the first timestamp. The capsule is designed to collect a sample from and only from a target area of the Gl tract. The relationship between collection time and first timestamp has been found to be consistent across subjects. For example:Sample Start: avg = 2.18 hours, min = 0.68 hours, max = 6.81 hours, inter-quartile range = + / - 1.16Sample End: avg = 3.53 hours, min = 1.61 hours, max = 6.81 hours, inter-quartile range = + / - 1.01In other words, the capsule collects the sample from the target area of the Gl tract at a relatively fixed time relative to ingestion time. The consistency of these collection times are in contrast to the overall transit time of a capsule, which can be used to compare the relative consistency of stool sampling. For example, the majority of capsules will collect a sample within a window of 2 to 3 hours from ingestion. However the full Gl transit time for these capsules is between 24 and 96 hours.

[0110] At step 340, the method includes receiving, from the user interface, second data associated with a user input, wherein the second data comprises a second timestamp and a description of the user input. In some aspects, the user input is an action by the subject. Optionally, the action by the subject is ingestion of a drug, compound, or pharmaceutical. In some aspects, the action by the subject is ingestion of a food or liquid. Alternatively or additionally, the user input is a symptom of the subject or a mood of the subject.

[0111] At step 350, the method includes associating the second data with the sample collected by the ingestible capsule. In some aspects, the step of associating the second data with the sample collected by the ingestible capsule includes temporally relating the second timestamp to thecollection time. As described above at step 330, the collection time can be reliably established based on the first timestamp. Accordingly, the second data can similarly be associated with the sample collected by the ingestible capsule using the second timestamp.

[0112] In some aspects, the method further includes: displaying, on the user interface, a first instruction to ingest the ingestible capsule; and displaying, on the user interface, a second instruction to ingest a drug, compound, or pharmaceutical. The first instruction and the second instruction can be displayed at predetermined times relative to each other.

[0113] In some aspects, the method further includes: displaying, on the user interface, a first instruction to ingest the ingestible capsule; and displaying, on the user interface, a second instruction to ingest a food or liquid. The first instruction and the second instruction can be transmitted at predetermined times relative to each other.

[0114] It should be appreciated that the logical operations described herein with respect to the various figures may be implemented (1) as a sequence of computer implemented acts or program modules (i.e., software) running on a computing device (e.g., the computing device described in Fig. 4), (2) as interconnected machine logic circuits or circuit modules (i.e., hardware) within the computing device and / or (3) a combination of software and hardware of the computing device. Thus, the logical operations discussed herein are not limited to any specific combination of hardware and software. The implementation is a matter of choice dependent on the performance and other requirements of the computing device. Accordingly, the logical operations described herein are referred to variously as operations, structural devices, acts, or modules. These operations, structural devices, acts and modules may be implemented in software, in firmware, in special purpose digital logic, and any combination thereof. It should also be appreciated that more or fewer operations may be performed than shown in the figures and described herein. These operations may also be performed in a different order than those described herein.

[0115] Referring to Fig. 4, an example computing device 500 upon which the methods described herein may be implemented is illustrated. It should be understood that the example computing device 500 is only one example of a suitable computing environment upon which the methods described herein may be implemented. Optionally, the computing device 500 can be a well-known computing system including, but not limited to, personal computers, servers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, network personal computers (PCs), minicomputers, mainframe computers, embedded systems, and / or distributed computing environments including a plurality of any of the above systems or devices. Distributed computing environments enable remote computing devices, which are connected to a communication network or other data transmission medium, to perform various tasks. In thedistributed computing environment, the program modules, applications, and other data may be stored on local and / or remote computer storage media.

[0116] In its most basic configuration, computing device 500 typically includes at least one processing unit 506 and system memory 504. Depending on the exact configuration and type of computing device, system memory 504 may be volatile (such as random access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in Fig. 4 by box 502. The processing unit 506 may be a standard programmable processor that performs arithmetic and logic operations necessary for operation of the computing device 500. The computing device 500 may also include a bus or other communication mechanism for communicating information among various components of the computing device 500.

[0117] Computing device 500 may have additional features / functionality. For example, computing device 500 may include additional storage such as removable storage 508 and nonremovable storage 510 including, but not limited to, magnetic or optical disks or tapes. Computing device 500 may also contain network connection(s) 516 that allow the device to communicate with other devices. Computing device 500 may also have input device(s) 514 such as a keyboard, mouse, touch screen, etc. Output device(s) 512 such as a display, speakers, printer, etc. may also be included. The additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 500. All these devices are well known in the art and need not be discussed at length here.

[0118] The processing unit 506 may be configured to execute program code encoded in tangible, computer-readable media. Tangible, computer-readable media refers to any media that is capable of providing data that causes the computing device 500 (i.e., a machine) to operate in a particular fashion. Various computer-readable media may be utilized to provide instructions to the processing unit 506 for execution. Example tangible, computer-readable media may include, but is not limited to, volatile media, non-volatile media, removable media and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. System memory 504, removable storage 508, and non-removable storage 510 are all examples of tangible, computer storage media. Example tangible, computer-readable recording media include, but are not limited to, an integrated circuit (e.g., field-programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory orother memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.

[0119] In an example implementation, the processing unit 506 may execute program code stored in the system memory 504. For example, the bus may carry data to the system memory 504, from which the processing unit 506 receives and executes instructions. The data received by the system memory 504 may optionally be stored on the removable storage 508 or the non-removable storage 510 before or after execution by the processing unit 506.

[0120] It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination thereof. Thus, the methods and apparatuses of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computing device, the machine becomes an apparatus for practicing the presently disclosed subject matter. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and non-volatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the presently disclosed subject matter, e.g., through the use of an application programming interface (API), reusable controls, or the like. Such programs may be implemented in a high level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language, if desired. In any case, the language may be a compiled or interpreted language and it may be combined with hardware implementations.

[0121] Examples

[0122] The following examples are put forth so as to provide those of ordinary skill in the art with a complete disclosure and description of how the compounds, compositions, articles, devices and / or methods claimed herein are made and evaluated, and are intended to be purely exemplary and are not intended to limit the disclosure. Efforts have been made to ensure accuracy with respect to numbers (e.g., amounts, temperature, etc.), but some errors and deviations should be accounted for. Unless indicated otherwise, parts are parts by weight, temperature is in °C or is at ambient temperature, and pressure is at or near atmospheric.

[0123] Example 1

[0124] A study was performed using an example implementation of the present disclosure including a capsule and methods for collecting and preserving DNA samples from the small intestine using the capsule.

[0125] Methods. Individuals with previously diagnosed IBS (Rome 4 criteria, diagnosed by a gastroenterologist) and healthy / control volunteers were recruited from clinical practice, recruitment posters at the University of Calgary-affiliated gastroenterology clinics. Recruited participants were between 18 - 70 years of age, and selected based on the following criteria; 1) did not have prior gastrointestinal disease, surgery, or radiation treatment; 2) did not use any medications a week prior to the study that would affect Gl motility or acidity; 3) if female, were not pregnant, not breastfeeding, and practicing birth control; 4) did not take antibiotics, colon cleanses / colonics, or bowel preparation for colonoscopy within 2 weeks prior to recruitment; 5) if in the control group, did not have fewer than 2 bowel movements a week.

[0126] All participants ingested two capsules at two separate visits separated by 7 to 21 days. In the first visit, the two capsules were ingested with the participants in a fasted state (minimum 8 hour fast) with water. For this study a small radiopaque bead was inserted between the external coating and the capsule; when the bead became detached from the capsule on X-ray it indicated that the external coating had sloughed off and that the capsule had started collecting content. Capsules have a mechanical closure mechanism which can be assessed by X-ray to be open (i.e. collecting content) or approximately every 15 - 45 minutes until capsules completed sampling) to document sample collection start-, end-point locations, sampling durations and timing from ingestion to start- and end-point sampling. After completing their X-ray visit, participants returned home with instructions and materials for retrieving the capsules by screening each bowel movement until they were retrieved. When a capsule was found, subjects also collected a separate fecal sample from the same stool.

[0127] Between 7 and 21 days following the initial X-ray visit, fasting participants underwent an esophagogastroduodenoscopy (EGD) procedure to collect a duodenal aspirate, duodenal cytological brush, and saliva sample. Saliva was collected prior to the endoscopy. Standard conscious sedation with fentanyl and midazolam was used, and oral spray anesthetic was not used. The gastroscope was intubated as far as possible into the duodenum (typically to the fourth part; at least third part in all cases). Aspirate was taken first using sterile technique around scope handling and particularly regarding the scope biopsy channel. A sterile aspiration catheter was used to collect a fluid sample from the distal duodenum. Duodenal mucosal brushing was then taken. All EGD evaluations were visually normal.

[0128] The day after endoscopy, two further capsules were ingested by the participants. Identical with the first phase of the study, capsules were ingested with water on a fasted stomach and collected along with matched fecal samples at home. Collected capsules and fecal samples were returned via courier immediately and processed within 24 hours upon receipt using sterile technique.

[0129] Samples were received, extracted, and prepared for 16S sequencing and metabolomics analysis. A subset of the remaining DNA samples were prepared for shotgun metagenomic sequencing to further assess the DNA quality.

[0130] RESULTS. The ingestible capsules were evaluated for their ability to reliably and reproducibly collect small intestinal luminal samples both by physical assessment and comparison of 16S rRNA gene amplicon sequencing and metabolomics data to samples collected from patient matched feces and endoscopy aspirate. A total of 30 participants were recruited, comprising 10 reference healthy controls and 20 with impaired Gl motility (8 IBS-Constipation or IBS-C, 10 IBS- Diarrhea or IBS-D, and 2 IBS-Mixed or IBS-M), with a median age 43 years (min 23; max 67; IQR 32 to 53), and male / female ratio of 12 / 18.

[0131] Of the 120 total capsule ingestions performed, all capsules were confirmed to have passed spontaneously, with 93% (112 / 120) being successfully retrieved and returned (58 / 60 following X-Ray and 54 / 60 following Endoscopy). There was no participant dropout between visits, and no serious adverse events were reported that concern the safety of the capsule. Minimal adverse events were reported and were not device-related: 1 event of prolonged capsule retention (> 7 days) in an IBS patient likely associated with opioid used at endoscopy.

[0132] X-ray tracking of capsule sampling endpoints indicated a high targeting accuracy of the capsule to the SI. X-rays were read by an expert radiologist blinded to any other subject information other than time post-ingestion. With an X-ray sampling interval of 15-45 minutes, 41 of the 60 capsules ingested had confirmed sampling start locations (start was observed by the marker displacement floating away once the capsule shell dissolved) and 47 had confirmed sampling end locations (end being observation of spring deployment). Sampling location was deemed determinate if it could be confidently assigned by the radiologist and if capsule opening or closing events were seen at least one observation interval after capsule Gl-regional transit events. All 41 determinately tracked capsules for start-point initiated sampling in the SI, which also included 3 / 4 capsules that were observed to remain in the epigastric region for longer than 2 hours, and of these, one capsule remained in the epigastric region without observed shell dissolution, indicating no impact of gastric pH on outer shell integrity. Of the determinate tracked capsules for capsule closure, 44 / 49 (~90 %) were observed to complete sampling in the distal SI, with 15 sampling in the jejunum region and 29in the ileum region. No significant difference in sampling end location was observed across participant motility groups (Chi-squared goodness of fit test p-value ~ 0.41). The remaining 5 also sampled primarily in the distal SI but final sealing was observed in the proximal colon. The median capsule sampling duration from sampling start to end was ~1.5 hours (min = 1.04 hours; max = 2.02 hours) and was also not found to significantly differ by sampling end location (SI vs. Colon) or participant motility group (Kruskal-Wallis rank-sum test p-value ~0.78). The median capsule total Gl transit time was ~46.1 hours and was only found to significantly differ between control and IBS-C groups (median transit time control vs. IBS-C = 29.7 hours vs. 54.7 hours; two-sided Mann-Whitney test p-value ~ 0.006). Excluding one participant where dual-capsules remained in the gastric region for the duration of X-ray observation, X-ray tracking was able to definitively discern that the capsule successfully collected a SI sample, with either a determinate starting or end location in the SI, for 29 / 29 ~ 100% of study participants.

[0133] To evaluate the efficacy of the capsule in capturing and preserving an uncontaminated sample of the SI, microbiome and metabolomics profiles of key Gl metabolites (BAs and SCFAs) were compared between capsules and matched fecal samples. An examination of 16S microbiome taxa plots revealed distinct microbiome compositions between capsule and fecal samples, with the former dominated by bacteria of the genera Streptococcus, and the latter dominated a more diverse profile of Blautia, Bacteroidetes, and Fecalibacterium, consistent with previous findings [4]. Principal component analysis of microbiome beta-diversities (weighted Unifrac distance) confirmed a significant difference in microbiome composition between capsule and fecal samples (PERMANOVA P-value ~ 0.001, n = 66 samples).

[0134] To have a better assessment of the quality of the DNA extracted from the capsules, DNA samples from 16 participants (For each participant, 1 capsule, 1 matched endoscopic aspirate, 1 cytological brush, 1 saliva, and 1 feces sample) for a follow-up shotgun metagenomic sequencing. Five samples (1 / 16 saliva samples, 0 / 16 brush samples, 2 / 16 aspirate samples, and 1 / 16 capsule samples) failed library preparation and in total 76 samples were sequenced. Good sequencing data was obtained for all samples with an average of 38.5 M read pairs per sample and a minimum of 32.9 M read pairs. The majority of the reads (on average 99.6%) were of high quality. Cytological brush and aspirate samples contained high proportions of host (human) DNA (93.1 - 96.4 % of all reads) which decreases the proportion of microbial DNA available for microbiome analysis. Cytological brush samples had, on average, only 0.04 M read pairs per sample that could be mapped to the gene catalog. For capsule and fecal samples we obtained low proportions of host DNA and high numbers of read pairs mapping to the gene catalog. The small intestine microbiome profiles from the shotgun metagenomic testing look similar with what we observed from the 16s sequencingresults: Streptococcus (n=192) appears among the top genus in the capsule samples. The high quality non-human reads of the capsules (average 32.06M per sample, or 88.01% of 36.53 Million reads) is comparable with those of the fecal samples (average 37.35M per sample, or 85.25% of 43.74 Million reads) reflected the quality of the data collection with the fiber construct.

[0135] The differences between capsule and fecal sampling were also shown by the concentrations of key Gl metabolites. For example, the concentrations of all SCFAs examined were found to be significantly increased in fecal samples compared to capsules (two-sided Mann-Whitney test p-value range: 1.65 xlO14- 2.29 x 1012). Furthermore, a striking difference was observed between conjugated and deconjugated BAs, which were nearly all exclusively associated with capsule and fecal samples, respectively. The only exception was the presence of the primary cholic acid (CA) in feces, which was most likely the result of production via alternate colonic microbial deconjugation pathways. Together these results confirm that capsules capture a distinct microbiome and metabolomic profile from the SI that is effectively preserved against fecal contamination during Gl transit and performs robustly under different gut motility conditions.

[0136] As a final evaluation of capsule performance as an accurate and reliable SI sampling tool, microbiome composition (using 16S rRNA gene sequencing, for its level of completeness) and metabolomic profiles from a second round of capsule ingestions were compared against gold- standard endoscopic aspirate and cytology brush samples from the duodenum. In addition to fecal samples, which served to demonstrate the effective sealing performance of capsules against potential fecal contamination, saliva samples were also included to assess potential oral contamination of endoscopy sampling.

[0137] Principal co-ordinates analysis of 165 microbiome beta-diversities revealed a noticeable degree of overlap between capsule, endoscopic aspirate and cytological brush samples, particularly on the primary-axis of variation (~28.1% total variance explained). Interestingly, the second axis of variation (~9.9% of total variance explained) also indicated a separation between capsules from a subset of endoscopic and saliva samples, likely reflecting biologically relevant differences between duodenal and ileum microbiome communities resulting from underlying physiological pH gradients (see following section). Further PERMANOVA statistical testing (regressing weighted Unifrac beta diversity against sample type as an ordered factor with capsules as the intercept) did reveal a significant difference in 165 microbial composition between capsules and other sampling methods (p-value ~ 0.001, R2~ 0.41). However when broken down by sample type the majority of variation was contributed by fecal and saliva samples (feces PERMANOVA p-value ~ 0.001, R2~ 0.17; saliva PERMANOVA p-value ~ 0.001,5 R2~ 0.13), while endoscopic aspirate and cytology brush contributed a substantially lower effect-size in terms of proportion of varianceexplained (endoscopic aspirate PERMANOVA p-value ~ 0.001, R2~ 0.048; cytology brush PERMANOVA p-value ~ 0.001, R2~ 0.061). Overall, microbiome profiles captured by capsule and endoscopy were both substantially different to fecal samples and to similar degrees (capsule vs. feces median Unifrac distances ~ 0.538; endoscopic aspirate vs. feces ~ 0.554; cytology brush vs. feces ~ 0.541).

[0138] The analysis of targeted metabolomics profiles also demonstrated that both endoscopic aspirates and capsules recapitulated significant differences in BA and SCFA concentrations in comparison to fecal samples. The presence of conjugated BAs was also found to be exclusive to the SI, with capsules showing no significant differences in concentration compared to gold standard endoscopic aspirates (two-sided Mann-Whitney test FDR adjusted p-values: min = 0.194, max = 0.917, mean ~ 0.5). Deconjugated BAs were again exclusively found in feces, except for cholic acid (CA) which are also detected in the Capsule and Aspirate samples.

[0139] SCFA concentrations were also found to be significantly increased in feces in comparison to both capsule and endoscopic aspirates (two-sided Mann-Whitney test FDR-corrected p-values, endoscopic aspirate vs. feces = 2.72 x 10'9to 1.23 x 10'8), similar to previous results from the X-ray visit. Interestingly, SCFA concentrations were also found to be significantly increased in capsules relative to endoscopic aspirates (two-sided Mann-Whitney test FDR-corrected p-values, capsule vs. endoscopic aspirate = 2.72 x 10'9to 3.84 x 10'6). This result suggests an increasing gradient in SCFA production from proximal SI (endoscopy), distal SI (capsule), and colon (feces), and furthermore implies that SCFA production is not an exclusive metabolic function of the colonic microbiome.

[0140] Additional semi-targeted metabolomics analyses of a panel of 85 metabolites also revealed a strong differentiation between capsules and fecal sample and similarity to endoscopic aspirates, as indicated by hierarchical clustering of metabolite profiles. Given the substantial differences in total spectral abundance between fecal and SI samples (feces vs. capsule and endoscopic aspirate median loglO total spectral abundances: ~7.5 vs. 8.3), spectral counts were normalized using the following procedure to aid comparison normalization by median metabolite spectral count; loglO transformation; and sample-wise auto-correlation / unit scaling of metabolite profiles. Additional K-means clustering of loglO transformed metabolite intensities further revealed markedly different patterns of intensity for different sets of metabolites across sample types. Although the majority of metabolites (endoscopy visit) were generally of high intensity in fecal samples and low in saliva (63 / 85 significantly elevated metabolite concentrations in feces by two- sided Mann-Whitney test FDR adjusted p-value <= 0.05), several were identified (10 / 85) that were significantly increased in capsule samples (two-sided Mann-Whitney test FDR adjusted p-valuerange < 0.04 to 1.5 x 10'9), of which (8 / 10) were also significantly increased in endoscopic aspirates. As expected from the targeted metabolomics analyses, the primary bile-acid glycocholate was identified, as well as several amino-acids (L-Arginine, L-Histidine, and L-Cysteine). Taken together, these results demonstrate that the capsule performs on par with endoscopy in sampling the SI, capturing microbiome profiles and broad metabolic profile distributions that are significantly distinct from feces, and identifying metabolic markers associated with important physiological differences between the SI and colon.

[0141] To understand whether the differences in 16S microbiome composition between capsule and endoscopy samples were due to potential oral contamination, or a biologically meaningful difference in community composition due to differences in SI sampling location, a summary of all unique and shared amplicon sequence variants (ASVs) detected across the distinct sample types was generated, representing the biogeographic distribution of bacteria across the Gl tract.

[0142] Although the vast majority of ASVs detected were unique to fecal samples (256 / 446 ~ 57% of ASVs), followed by saliva samples (46 / 446 ~ 10% of ASVs detected), several intersections of interest were found that revealed insights into the distribution of bacterial genera across the Gl tract. The first intersection of particular interest represented ASVs that were only identified in endoscopic aspirate and cytology brush, and saliva samples (34 / 446 ~ 7% of ASVs detected).Notably, this intersection contained several ASVs annotated to the family Prevotellaceae, a known acid-tolerant bacterium present in the oral microbiome, which were frequently detected (>= 50%) across saliva and endoscopic samples (duodenum), but notably absent in capsules (jejunum + ileum). These results would indicate that the differences between capsule and endoscopy likely reflect biologically relevant regional differences in microbiome composition influenced by proximity of sampling location to the stomach. A second substantial intersection included ASVs identified in capsule and endoscopy and saliva samples (39 / 446 ~ 8%), which were relatively increased in relative abundance and prevalence in endoscopy and saliva compared to capsule. The last intersection of interest contained ASVs identified in all sample types (12 / 446 ~ 2%), of which Streptococcus, a keystone genus of the SI was found to be particularly dominant across capsule, endoscopic aspirate, and cytological brush samples (~30% relative abundance) and reduced in saliva (~10%) and feces (< 1%). Taken together, these results further support that the capsule is capturing a sample representative of the SI, specifically the distal region.

[0143] Example 2

[0144] Implementations of the present disclosure address the lack of tools and methods to study Gl regions that are difficult to assess in relation to user actions, events and interventions. Anexample implementation, described herein, includes a fully autonomous and passive sampling method, using a capsule, for convenient, high-quality, and reliable sampling to study the dietmicrobiota interactions in the SI in relation to an ingestion event.

[0145] An example implementation, described herein, includes a fully autonomous and passive sampling method, , for convenient, high-quality, and reliable sampling to study the dietmicrobiota interactions in the SI. The sealing efficacy and microbial DNA preservation capacity of the capsules was first validated through in vitro simulation assays. Then, a clinical study was conducted with 20 healthy participants to validate the in vivo use of the capsules to reliably capture samples for SI microbiome analysis before and after an intervention. Participants ingested the capsules at baseline and 7 days later, with a probiotic capsule containing a blend of L. rhamnosus R0011 and B. longum R0175. Following baseline capsule ingestion, multiple low-dosage x-ray scans were performed to track the sampling location. Fecal samples corresponding with the baseline and intervention capsule were analyzed for comparison. The capsules' performance in vitro demonstrated the potential for contamination-free sampling with preservation of the microbial communities. Within the clinical study, the capsules performed safely and reliably for collection of SI content. X-ray tracking confirmed that 97.2% of the capsules completed sample collection in the SI regions before reaching the colon. Importantly, our data showed that the capsules sampled in the right area of the intestines and that baseline microbiome profile from the capsule collected sample is significantly different from fecal microbiome profile. The capsule successfully detected a concurrent probiotic intervention in the small intestine, which was not detectable using stool samples. The high accuracy of sampling location and sealing efficacy of the capsules makes them potentially useful research tools in clinical trials for studying diet-microbiota interactions in health and disease.

[0146] These chThis is the first report of spatial and temporal accuracy, including use in humans with and without intervention. In this study, we developed a pH-based autonomous and passive Small Intestine MicroBiome Aspiration capsule. The S capsules have unique features of large sampling ports for reliable sampling volume, strong sample sealing performance and embedded microbial DNA preserving agents to ensure sample quality during the capsule transit time and capsule return process. The results of in vitro simulation assays and clinical study presented herein confirm that the capsules are well-tolerated, minimally invasive capsule that passively captures, seals, and preserves small intestine luminal fluid, providing samples that are suitable for downstream microbiome analysis.

[0147] Methods

[0148] The capsule of the example implementation has overall dimensions of 25.4 mm (L) x 8 mm (D), the size of a OOEL capsule. The functional device is contained within a pH sensitive small intestine targeting outer shell. The outer shell is designed to disintegrate at a nearly neutral pH, which is similar to the pH of the proximal small intestine region. The capsule comprises a main body which includes a sampling chamber with sufficient volume (~105 pL) to capture a representative sample of intestinal fluid. The intestinal fluid enters the chamber through four evenly spaced ports which are accessed by the fluid only upon disintegration of the outer shell (FIG. 5).

[0149] The ports are radially facing towards the mucosal layer of the small intestine to collect luminal samples near the mucosa. The ports are sufficiently wide to allow easy inflow of the liquid sample. Hydrophilic fibers are placed in the chamber to wick in and retain the liquid sample. On the top of the sampling chamber, a piston is held in the open position by a latch until it is exposed to small intestine fluid and dissolved in a time-controlled manner after the outer shell dissolution. Then, the sampling chamber is closed and sealed with a compressive spring forced by the piston on its top end.

[0150] A preserving agent is embedded within the sampling chamber to maintain the integrity of the collected sample during the capsule transit time in the gut and the capsule return process. The sampling chamber is closed on the lower end by a cap. After retrieval of the device, the cap is easily removed to access the collected sample for downstream processing and analysis.

[0151] In vitro sealing performance and preserving agent efficacy. To assess the sealing performance, 37 capsules without outer shells were submerged in sterile PBS for 4 hours, automatically triggering the sampling and sealing mechanism in the presence of an aqueous solution. 34 capsules were then transferred to a healthy donor fecal slurry spiked with L. rhamnosus R0011 (~109CFU / mL). Out of these 34 capsules, 3 were manually unsealed as a positive contamination control. The 3 remaining capsules were not exposed to the fecal slurry. All the control capsules and the treatment capsules, while being submerged in the fecal slurry, were anaerobically incubated at 37 °C for 72 hours (to simulate the gut transit environment), then at 4 °C for 72 hours (to simulate a cold shipping condition). Then, the capsules were opened, and samples were recovered by pipetting. Contamination was assessed using a strain-specific SYBR Green qPCR assay targeting R0011.

[0152] Another set of 20 capsules were subjected to the same sealing procedure in PBS followed by a 24 h incubation in ROOll-spiked healthy donor fecal slurry. While these capsules remained in the slurry, 8 capsules were frozen at -20 °C for 24 h or 168 h, and 2 capsules were incubated at 4 °C for the same time points (Table 1). After thawing, capsules were removed from theslurry and then samples were retrieved from the capsules by pipetting and contamination was assessed with a strain-specific SYBR Green qPCR assay targeting R0011.

[0153] For preserving agent efficacy testing, fresh SI endoscopic aspirates from 3 patients collected on the same day from the Intestinal Inflammation Tissue Bank (I ITB), University of Calgary were pooled, homogenized and inoculated into 30 capsules containing the embedded preserving agent and 4 capsules without the preserving agent. The filled capsules were then sealed and incubated at 37 °C for 144 hours (6 days) under anaerobic conditions before sample retrieval for DNA extraction.

[0154] DNA extraction and quantification. Once the capsules were received by the lab, the samples were removed from the capsules using a sterile pipette to avoid crosscontamination by the fecal matters attached to the outer surface of the capsule body. The samples from the capsules and their associated control samples collected at various timepoints were stored at -20 °C for up to 7 days until the scheduled DNA extraction. DNA Extraction of the capsule and fecal samples was performed with the Qiagen QIAamp PowerFecal Pro DNA Kit, following the manufacturer's protocol with modifications for the capsule samples. The Qubit dsDNA HS Assay Kit was used with the Qubit® 2.0 Fluorometer to measure the capsule aspirate and fecal sample DNA concentration.

[0155] 16S library preparation and sequencing for in-vitro preserving agent efficacy testing. For the preserving agent efficacy testing, the 16S rRNA gene V4 variable region was amplified using PCR primers with internal barcodes (primers: F: AATGATACGGCGACCACCGAGATCTACAC-barcode-TATGGTAATTGTGTGCCAGCMGCCGCGGTAA, R: CAAGCAGAAGACGGCATACGAGAT-barcode-AGTCAGTCAGCCGGACTACHVGGGTWTCTAAT) in a 35 cycle PCR using the KAPA HiFi HotStart master mix (Roche Sequencing). The conditions for the thermocycler were as follows: 98 °C for 2 minutes, followed by 35 cycles of 98 °C for 30 seconds, 55 °C for 30 seconds and 72 °C for 20 seconds, after which a final elongation step at 72 °C for 7 minutes. Amplified PCR products were checked in a 1 % agarose gel. The PCR products were then purified using NucleoMag NGS Clean-up and Size Select (Macherey-Nagel) and concentrations normalized using SequalPrep Normalization Plate (Invitrogen). Amplicons were pooled and concentration and quality were determined using the Qubit HS DNA kit (Invitrogen) and the Tapestation D1000 assay (Agilent), respectively. Amplicon sequencing was done on a MiSeq Benchtop DNA sequencer (Illumina) using a V2-500 cycle kit (Illumina Inc). The pooled library was then denatured and prepared for loading on an Illumina MiSeq cartridge with a 5% PhiX Control.

[0156] On the morning of the first day (termed the Baseline day), fasted participants visited the X-ray facility and then ingested two capsules simultaneously. After ingestingthe capsules, low-dose (70-80kVp), multiple X-ray imaging was used to confirm the timing and location of the capsules. The protocol specifies a scanning interval of approximately 30 minutes, and each volunteer is not permitted to undergo more than 12 scans on the same day. Once capsules were seen finished sampling on an X-ray image, participants were provided with capsule and stool collection kits and discharged. Participants were then allowed to leave the clinic and resume normal activities. Participants were asked to resume normal eating 4 hours after the capsule ingestion and maintain a stable diet until the second visit. Participants were asked to monitor their stool for the passing of the capsule, and upon excretion they were asked to collect the capsules and a stool sample from the same bowel movement using the provided retrieval kit and return it promptly in an ice box for analysis.

[0157] After at least 5 days, but no more than 21 days following Baseline day, participants returned for a second visit (termed Intervention day) at the Investigators office or a clinic. Participants were required to fast overnight in advance of this visit. In the morning, they first ingested the probiotic capsule (40 billion CFUs, containing a blend of L. rhamnosus R0011 (71%) and B. longum R0175 (29%)) under instruction and then immediately ingested two capsules under supervision. Participants did not undergo X-ray monitoring on their second visit. The participants were allowed to resume normal activities and were allowed to eat 4 hours after the ingestion of the capsules. Participants were provided with stool and capsule collection kits and were instructed again on procedures for capsule return. Upon excretion of the capsule, patients collected the capsules and a stool sample from the same bowel movement and returned it promptly for analysis.

[0158] 16S library preparation and sequencing for Clinical Samples. The 16S targeted amplicon sequencing library was prepared by amplifying 10 pL of each capsule gDNA extracts (or 25 ng for the fecal extracts) with IX KAPA Hi Fi HotStart ReadyMix (Roche, cat # KK2802) and 200 nM universal 16S primers (forward 5'-CCTACGGGNGGCWGCAG-3' and reverse 5'- GACTACHVGGGTATCTAATCC-3') targeting V3-V4 regions in a 25 pL reaction volume . PCR products were visualized on a 2% agarose precast E-Gel stained with SYBR Safe dye (Invitrogen cat # G72080). Amplicons were purified with Agencourt AMPure beads (Beckman Coulter, cat # A63881) following Illumina 16S Metagenomic sequencing library preparation's protocol. A second round of amplification using 5 pL of the purified amplicon PCR reaction as template, 2.5 pL each of Nextera XT V2 primers sets A and D (Illumina, cat # FC-131-2001 and FC-131-2004) and IX KAPA Hi Fi ReadyMix was performed in 25 pL reactions with the same cycling conditions as the Amplicon PCR except that only 8 cycles were used. PCR reactions were again purified with AMPure beads before individual fluorescent quantification by Quant-iT PicoGreen dsDNA assay (Life Technologies, cat # P7589). Volumes corresponding to 100 ng of each purified Index PCR reaction were pooled using anEpMotion 5075 liquid handling robot (Eppendorf) and this pool was quantified with QuBit Broad Range assay (ThermoScientific, cat # Q32853) following manufacturer's instructions. This pool was also quality controlled for the presence of the desired amplicon (size obtained 630 bp) and the absence of secondary amplification by running a High Sensitivity D1000 TapeStation assay (Agilent, cat # 5067-5584 / 5585). Library was denatured with 0.2 N NaOH and loaded at 8 pM with 5 % PhiX (Illumina, cat # FC-110-3001) on an Illumina MiSeq instrument using MiSeq V3 Reagent Kit (Illumina, cat # MS- 102-3003) for 2x 301 cycles.

[0159] Microbiome analysis. The demultiplexed fastq sequences were imported into QIIME2 (Quantitative Insight Into Microbial Ecology-2) as artefacts and inspected for overall quality (visual inspection of the q-Scores per base plots). The reads were determined to be very high quality on the 40nt->280nt for the forward and 40nt->260nt for the reverse reads. These parameters were used to denoise the paired reads using the Dada2 denoiser (as a QIIME2 encapsulated version). Hence, the reads were clustered into amplicon sequence variants (ASVs). The feature classifier was used to attribute the ASVs to the closest known taxa using QIIME2's taxonomic classification module (linking ASV sequences to known bacterial groups). The taxonomy file was trained on a 99% clustered Silva_138 taxonomic database (V3-V4 subregion of the 16S). The ASV tables were exported as Level-6 (Genus Level) relative abundance tables for downstream analysis.

[0160] The 'core-metrics' module from QIIME2 was also used to generate the Alpha Diversity measures and the PCoA distance matrices. The alpha diversity algorithms include Pielou (Evenness), Faith (Phylogenetic Distance) and Shannon Entropy. For the PCoA, the Weighted UniFrac algorithm was used. The PCoA was viewed interactively with the Emperor module through the QI IM E 2 viewing server (https: / / view.qiime2.org / ) and a collection of images was captured for later reporting. The PCoA and diversity figures were calculated on ASV tables and rooted tree (phylogenetic relation between observed ASV sequences).

[0161] To determine group differences between the treatment groups, QIIME2's encapsulated Machine Learning Sample Classifier was used with the ExtraTreesClassifier algorithm. In order to assess the presence or absence of differences between treatment groups (or Capsules V. Stool samples), the algorithm trains on 2 / 3 of the sample's taxonomic tables at genus level (Training Set) and then test its predictive power on the remaining 1 / 3 of the samples from each group (Testing Set). If the algorithm is able to tell the samples from each other for the Label of interest (the variable used to make the groups, ex. Stool V. Capsule samples), then the groups are determined to be different. The accuracy results of the Test Set results are presented as confusion matrices, with the main classification indicator being the Final Accuracy.

[0162] The Capsule can effectively seal and protect collected samples. The goal of our in vitro validation experiments was to assess the possibility of contamination occurring after completion of the sample collection and sealing of the capsule, simulating the conditions of capsule transit through the Gl tract and potential shipping and storage conditions. For the first experimental design, there was no contamination in 29 out of the 31 test capsules as shown by the absence of detection of L. rhamnosus R0011 in the capsules' cargo. The 2 samples with positive detection of R0011 are the result of one obstructed capsule preventing it from completely closing (which precludes from assessing sealing efficacy) and one capsule that incidentally touched the biosafety cabinet during the sample removal process, the cargo coming into contact with the outside of the capsule. Therefore, the detection of R0011 in these 2 samples was not due to failure of the seal and were excluded from the sealing efficacy evaluation (Table 1). Importantly, no target bacteria were detected in the negative controls, and R0011 was detected between 104and 105in the contamination positive controls (i.e., manually unsealed before immersion in the contaminating slurry).

[0163] In the second set of sealing efficacy testing (Table 1), all 16 frozen samples were negative for R0011 after thawing, regardless of the time spent at -20 °C. This temperature was tested to ensure that the spring-based mechanism and sealing capacity of the capsules were able to withstand freezing stress causing an expansion of the cargo. The 4 samples kept at 4 °C were also negative for R0011. Overall, the sealing efficacy of the capsule was conservatively evaluated at 2 positives / 51 tested capsules (96.1 % efficacy) (Table 1).

[0164] Table 1:

[0165] Most of the 16S microbiome sequencing profiles of the endoscopic aspirate samples stored in the capsules with the preserving agent (Group P, N=30) are similar to the TO control (FIG. 6), while the 4 samples without the preserving agent (Group NP, N=4) are characterized by a notable dominance of Pasteurellaceae. The absence of preservative allows for the overgrowth of some species, which changes the relative abundance in the sample. Among samples with the preserving agent, 5 / 30 capsules showed a variable amount of staphylococcus contamination after 6 days at 37 °C. After removal of Staphylococci by bioinformatics filtering, the 5 samples displayed a 16S profile similar to the other samples and TO (FIG. 7).

[0166] The Capsule Performs Safely and Reliably for Collection of Small IntestineLuminal Fluid in vivo. The clinical study results are summarized in Table 2. This single arm study enrolled 20 healthy volunteers (mean age (±SD): 38 ± 11, range 18-58 years; mean BMI 25.6 ± 4.7; 12 females and 8 males) recruited at the Foothills Hospital in Calgary Alberta. The capsules were well tolerated by participants (N = 20), with no adverse events or capsule retention, and only 5 out of the 40 incidences (2 capsule retrievals by each of the 20 participants) where participants judged the first-time retrieval of the capsules from stool in the baseline round was "difficult", 18 / 40 "Neutral", 13 / 40 "easy", 4 / 40 "very easy", and 1 / 40 "Lost". In the probiotic round, the difficulty of capsule retrieval was reported as follows: 5 / 40 "difficult", 11 / 40 "Neutral", 18 / 40 "easy", 3 / 40 "very easy", and 1 / 40 "Lost". Overall, 78 / 80 (97.5%) capsules (39 baseline + 39 probiotic intervention) were retrieved by participants, after a median number of 2 stools (range 1-7) and with total median transit time (ingestion to expulsion) of 30 hours (IQR 23-48). The two missing capsules were established as lost in feces in two participants through follow-up x-ray scans confirming capsule clearance.

[0167] Table 2:

[0168] Baseline capsules were monitored by X-ray (2 capsules per participant) following ingestion to determine the sampling time and region. FIG. 8A and 8B show examples of the full abdominal X-ray images taken from one participant at two different timepoints. FIG. 8C highlights the region of interest (ROI) in which the two capsules were still open (sampling); FIG. 8D shows the ROI in which the two capsules were closed (sampling completed). A radiopaque marker was attached on one of the two capsules ingested together in order to distinguish the two capsules in the X-ray images. In total, 35 / 40 (87.5%) capsules were confirmed as having completed sample collection in the targeted region of the small intestine. The only failed case was a capsule that was seen completing collection in the stomach before reaching the small intestine. Four capsules were classified as indeterminate due to the X-ray scanning frequency or the overall duration of the X-ray scanning period. Of these four capsules, two were still in the stomach at the end of the X-ray schedule, although sampling collection was not completed. Two other capsules were last seen open in the small intestine in one X-ray scan and were first seen closed on the next scan but had already reached the colon. If these four cases were excluded from the sampling location analysis, 35 / 36 (97.2 %) capsules were observed collecting samples in the small intestine before reaching the colon, with 1 / 36 (2.8 %) completing its collection while still in the stomach. Overall, 38 / 40 capsules completed sample collection within 210 minutes following ingestion (IQR 150-180 minutes).

[0169] The mean sample weight per capsule was 89 ± 1 mg (range 15-130 mg). In total, 87.2% (68 / 78) of the capsules collected more than 20 mg of sample, which is the threshold weight we set to assess the sample collection efficacy. However, the sample collection rate increased to 97.5% (39 / 40) by ingestion under the current dual-capsule ingestion protocol in this study. DNA extraction was performed on all 38 / 38 baseline capsule samples (1 baseline capsule and 1 post-intervention capsule samples were lost in feces), 28 / 40 post-intervention capsule samples and all fecal samples. 66 samples, including at least one from each ingestion time point, were allocated for 165 sequencing and a remaining 12 capsule samples were stored at -80°C for future analysis. 65 / 66 capsules allocated for 165 sequencing had sufficient DNA of suitable quality for downstream analyses. In the 100 pL of the DNA elution, the median DNA concentration of baselinesmall intestine sample collected from the capsules was 0.058 ng / pL (IQR 0.039 - 0.082 ng / pL). The median DNA amount of post-intervention small intestine sample collected from the capsules was 0.557 ng / pl (IQR 0.153 - 1.41 ng / pl), which represents the high concentration of probiotics in the capsules. In contrast, the fecal samples contain much more concentrated DNA: median 561.13 ng / pL (IQR 491.28-688.34 ng / pL) for the baseline fecal DNA concentration and 629.20 ng / pL (IQR 519.38- 698.11 ng / pL) for post-intervention fecal DNA concentration.

[0170] Microbiota composition is different between small intestinal capsule samples and stool samples. Samples were analysed using a PCoA with an unsupervised Weighted UniFrac algorithm revealing 3 main clusters based on spatial disposition (FIG. 9A). The orange (Baseline) and green (Probiotics) samples are concentrated on the left-center of the PCoA space and correspond to stool samples. The capsule samples are located on the right side and separated along the PC2 axis, suggesting group differences between stool samples and capsules, and between Capsules-Probiotics (red, bottom-right) and the Capsules-Baseline (blue, top-right). These differences are also visible on the grouped taxonomic bar plots (FIG. 9B), where the apparent difference between Capsules- Baseline and Capsules-Probiotics is caused by the high number of Lactobacilli and Bifidobacteria from the probiotic co-ingestion. Indeed, the removal of Lactobacilli and Bifidobacteria from the grouped bar plot analysis by bioinformatic filtering increased the similarity between the Capsules- Probiotics and the Capsules-Baseline in a manner similar to stool samples.

[0171] As expected for samples originating from the small intestine, alpha diversity measures were lower in the capsules compared to the stool samples, while the Capsules-Probiotics showed an even lower diversity than the Capsules-Baseline that is most likely due to the presence of high amounts of probiotic Lactobacilli and Bifidobacteria.

[0172] Comparisons between groups by machine learning confirms the difference between the capsule and stool samples

[0173] The overall differences between the stool and capsule samples were also assessed by machine learning group comparisons (FIG. 10A) with 3 comparisons. The first comparison (Comp 1) included all the stool vs. capsules samples without consideration for the probiotic intervention. The very high final accuracy at 100% means that from training on genus-level taxonomic tables, the algorithm was always able to distinguish between the stool and capsules samples (i.e. the group differences were clear and the capsules are consistently different from the stool samples) (FIG. 10B). In comparison 2 (Comp 2), a final accuracy of 45% (accuracy ratio of 0.83) means that the algorithm could not distinguish between stool-baseline or stool-probiotics very efficiently (FIG. 10C). In comparison 3 (Comp 3), a final accuracy of 100 % shows that the algorithm was able to clearly distinguish between the capsule-baseline and capsule-probiotics (FIG. 10D).

[0174] The main group classifiers are representative of their sampling region.

[0175] Among the main classifiers identified by the machine learning algorithm(comparing Baseline stool samples versus Baseline capsule samples), several taxa previously associated with the SI microbiome were identified as enriched in the capsules while taxa known to be associated with the colon were enriched in the stool samples (FIG. 11). For example, Streptococcus, Veillonella, Actinomyces, Gemella and TM7x were enriched in the capsule baseline sample, while Bacteroides, Blautia, Faecalibacterium, Dorea and Anaerostipes were enriched in the stool baseline samples.

[0176] Discussion

[0177] The study introduced the Small Intestine MicroBiome Aspiration capsule, a minimally invasive device for collecting small intestine luminal fluid for microbiome analysis. The capsules will allow to address a critical research gap in gastrointestinal microbiome studies, where the upper gastrointestinal tract's unique characteristics are often overlooked due to the challenges of accessing and sampling this region.

[0178] In vitro, the capsules displayed an excellent sealing efficacy under conditions mimicking those encountered during a clinical trial (i.e., gut transit at 37 °C in fecal slurry and storage / shipping at 4 °C or -20 °C). For the preserving agent efficacy, our in vitro testing was very challenging, designed as a worst-case scenario with 6 days at 37 °C after manipulations for manual inoculations that are an unusual capsule usage. We observed that only 5 capsules out of 30 displayed a variable amount of staphylococcus contamination. However, it was clear with the negative controls that the preserving agent was successful at maintaining the community architecture of most samples in the absence of an external contamination. In a real-life setting, the capsule parts inside of their pH-sensitive outer shell are sterile and undergo DNA removal process during the manufacturing process. In the current experimental setting, it is likely that the capsules were contaminated with various amounts of Staphylococci during the inoculation or extraction of the cargo by pipetting, before the 16S amplification.

[0179] In the clinical study, the capsule showed a remarkable ability to collect samples from the small intestine using the multiple X-ray tracking method, 97.2% (35 / 36) completing sample collection in the targeted region. The companion X-ray tracking method provides a relatively affordable and minimally invasive approach to validate the timing and location of sampling with our passive sampling capsule technology.

[0180] Microbiome analysis revealed significant differences between the small intestine microbiome profiles obtained with the small intestinal and fecal microbiome profiles. Notably, the intervention was detectable within the capsules and not detectable within the stool.This highlights the importance of directly sampling the small intestine for a more accurate understanding of diet-microbiota interactions, as well as the ability to time the collection of the samples in relation to an ingestion. This technology possesses the potential to transform our comprehension of the gut microbiome and its implications in health and disease. Furthermore, it facilitates interventional studies for monitoring both the immediate and prolonged impacts on the small intestine microbiome resulting from various medications and nutraceutical products, including prebiotics, probiotics, and postbiotics. The capsule captured the co-ingested intervention, here a probiotic, which took a lot of space in the capsule and appeared clearly in the bar plots of the microbiome analysis. This is interesting because probiotic interventions are reputably difficult to monitor after one dose using stool samples; this study proved that the probiotic bacteria reached the small intestine. In future probiotics studies, care should be given to allow enough time for washout of the probiotic bolus before ingesting the capsules in order to study the effects of repeated doses on the SI microbiome. We have succeeded in removing the probiotic signal by filtering out all the Bifidobacteria and Lactobacilli, but this is an artificial analysis shown here as a proof-of-concept and the validity of using this approach to further analyze microbiome composition after filtering the samples should be determined.

[0181] When analyzing the baseline capsule and stool samples, there was a clear difference in microbiome composition between the 2 sampling locations, and the main genera enriched in the capsule converge with the published microbiome composition of small intestine endoscopy samples. There is still a lack of direct comparison with small intestine samples collected by other means, which is a limitation of this study.

[0182] The capsules may offer a minimally invasive alternative to endoscopic aspiration, making it more comfortable for study participants while providing comprehensive spatial representation along the gastrointestinal tract. Its embedded preservative agent ensures the retention of time-stamped microbiome snapshots, crucial for studying dynamic effect of a concurrent intervention to the small intestine microbiome, either pharmaceutical or dietary.

[0183] Conclusions

[0184] By addressing the need for minimally invasive devices to collect, seal and preserve the small intestine sample, the capsule opens new avenues for research into various digestive conditions, such as small intestine bacterial overgrowth, irritable bowel syndrome, obesity, metabolic diseases, and cancer.

[0185] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matterdefined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

[0186] Example 3

[0187] An example implementation of the present disclosure includes methods of using the capsules described herein. In the example method, capsules are swallowed following a fast, between 4 and 12 hours, preferably at least 8 hours.

[0188] 30 minutes prior to a first capsule ingestion, the participants drink an amount of water. A series of capsules are ingested at an intervals in advance of ingestion of a test product. For example, the intervals can be between 15 and 60 minutes, ideally they are at 30 min.

[0189] As a non-limiting example, the capsules can be ingested at 90, 60 and 30 minutes before the test product. If only one ingestion, it can be at 60 minutes prior to the ingestion of the test product.

[0190] The test product can be a food, a medication substance, etc. It can be liquid or solid. It can be anything that modifies the gastric emptying or pH of the stomach.

[0191] The capsules can be marked to distinguish which capsule was ingested at what time point, in order to correlate the analysis.

[0192] The user can record the ingestion time of the capsules and the ingestion time of the test product. The test product is consumed when the capsules have already left or are close to leaving the stomach. The sampling duration is long enough to collect the sampling of the test product as it transits the stomach and collect the test product within the small intestine.

[0193] Optionally, the ingestion is combined with a blood sample taken at intervals to coordinate with the sampling time, x minutes following the capsule ingestion.

[0194] Optionally , an x-ray can be used to more precisely time the blood sampling with the capsule ingestion.

[0195] Example 4: Sample timing Implementations of the present disclosure include methods of scheduling capsule delivery. X-ray tracking of the capsule (over 150 cases), shows that due to its small size, most of the capsules can consistently pass through the stomach and enter the small intestine within one hour (FIG. 12). After this, its sampling time, even if passive, can accurately complete sampling approximately three hours after ingestion.

[0196] Based on these observations, implementations of the present disclosure include methods of scheduling the capsule intake for different scenarios, such as ingestion in non-fasted states to detect different types of food interventions. For example, to detect intervention in the small intestine in a non-fasted state, as analyzed in FIG. 13, the method can include configuringthe capsule to be ingested 60 minutes before a liquid intervention. This allows the capsule to pass through the stomach and enter the small intestine before the intervention, and then slowly move within the small intestine for about 120 to 150 minutes, waiting for the liquid intervention to enter the small intestine and sample it from the small intestine contents.

[0197] Example 5

[0198] Additional example systems and methods are described herein, according to various example implementations of the present disclosure.

[0199] In an example implementation, the system includes: a user interface to require patient event inputs - for example the ingestion and timing of a drug; a user interface to record the timing of the ingestion of a Gl sampling capsule; a Gl sampling capsule that is tuned to collect a sample within a specified window; a means of identifying the sampling capsule with the recorded input events; an analysis system to receive the sampling outputs from the sampling capsule (e.g. sequencing inputs) and the timing inputs for the purpose of providing information on the patient (i.e. a diagnosis).

[0200] The sampling capsule could also be a monitored capsule capable of returning the information to the device, or as an input to the analysis system.

[0201] The user interface instructs the user how to initiate the sample collection for good sample quality, and provides a means for the user compliance to be monitored in relation to the final sample (i.e., the user is asked to fast, the user confirms fasting; The user is requested to drink an amount of fluid 30 min prior to ingestion; the user confirms the volume and timing of the ingestion).

[0202] Integration of user input for temporal correlation with intestinal data. In an example implementation of the present disclosure, a companion device (e.g., a smartphone or other mobile device) is used. The companion device can be configured for user input regarding the time of intestinal data collection, user actions, and / or user symptoms. The intestinal data collection with the companion device can be performed with the capsules described according to implementations of the present disclosure. For example, the capsule can be ingested at a certain time before the collection and the transit time of the capsule is relatively unchanged under different motility conditions. The collection can optionally occur in vivo, and / or within 4 hours.

[0203] A system for temporal analysis of intestinal data - capsule. In another example implementation of the present disclosure, a system includes a collection apparatus and a companion device which temporally associates the collection of a biological fluid with user inputs. The collection apparatus can be an ingestible capsule which collects a biological fluid from within the gastrointestinal tract (e.g., the small intestine).

[0204] The system can optionally implement a method including: a patient ingests a capsule (e.g., based on instructions from the companion device). Collection apparatus collection can optionally occur within 4 hours. The companion device can optionally implement methods of recording user inputs by a user interface, where the user interface is configured to receive a time of collection apparatus ingestion, user actions (e.g., user actions recorded and self-reported by the user), and / or user symptoms. Optionally, the method can include analyzing the biological fluid collected by the collection apparatus by multiomic sequencing. Optionally, the collection apparatus can be excreted by the user. Optionally, the method can include sending the collection apparatus to a remote location for analysis.

[0205] Optionally, the analysis can be temporally associated with user inputs to identify determining factors that can affect the biological fluid composition

[0206] Optionally, the results are relayed back to the user via the device

[0207] Optionally, the user makes informed health decisions based on the analysis

[0208] A system for temporal analysis of intestinal data - remote. Another example implementation of the present disclosure includes a system including a collection apparatus (e.g., an ingestible capsule as described herein) which collects fluid from a region of the gastrointestinal tract and a companion device (e.g., a smartphone or other mobile computing device including a user interface) which temporally associates the collection of a biological fluid (e.g., a fluid from the small intestine) with user inputs. The companion device can implement a method including: recording user inputs by a user interface, where the user inputs can include: the time of device ingestion, user actions, and / or user symptoms.

[0209] Optionally, the capsule can be excreted and sent away for off-site analysis (e.g., remote from the user). Alternatively or additionally, the biological fluid is processed and then analyzed in combination with the user inputs. Optionally, the processing can include multiomic sequencing

[0210] Optionally, the analysis can be temporally associated with user inputs to identify determining factors which affect the biological fluid composition. Optionally the temporal association is based on the ingestion of a collection apparatus.

[0211] In some implementations, the results are relayed back to the user via the device. Optionally ,the user can make informed health decisions based on the analysis

[0212] System to evaluate intestinal conditions in association with user factors. Another example implementation includes an analysis platform for integrating intestinal data with user- inputted data including the time of capsule ingestion, user actions, and / or user symptoms.

[0213] In some implementations, the time of data collection is inferred from the time of capsule ingestion. Alternatively or additionally, the analysis platform (e.g., one or more computing devices) can be configured to generate comprehensive reports. The comprehensive reports can indicate correlations between user actions, user symptoms, temporal factors and / or small intestine microbiota composition. Optionally, the comprehensive reports can be provided to users (e.g., output by the one or more computing devices). The comprehensive reports can be used by users to inform health decisions and by health practitioners to inform research and health initiatives. Optionally, providing the comprehensive reports to the users can include transmitting them to a user computing device and / or outputting them for display on a user computing device (e.g., a smartphone or other mobile computing device).

[0214] Feedback system for user recommendations. Implementations of the present disclosure further include feedback systems for user recommendations. The feedback system can be implemented using one or more computing devices (e.g., mobile computing devices like smartphones). The feedback system can include a user interface to provide health recommendations to the user based on user inputs and microbiome analysis. The health recommendations can include recommended user actions. Some non-limiting examples of recommendations include dietary recommendations, sleep recommendations, and fluid intake recommendations, but it should be understood that any of the user actions described herein can be recommended by systems according to implementations of the present disclosure. The recommendations can be configured to enable the user to make informed health decisions and / or alter daily habits. In some implementations, the system integrates user preferences and historical data to refine and enhance the accuracy of recommendations over time.

[0215] Secure data storage and privacy protection. A companion system to the device comprising a secure cloud-based storage system for housing microbiota data and user inputs. For example, a user device (e.g., a mobile computing device or other computing device) can include a user interface, and can be in networked communication with the remote companion system. The networked communication can include encryption protocols to safeguard sensitive user data (e.g., any of the data described herein, e.g., data is associated with small intestine microbiota samples). The companion system can be configured to implement any privacy regulations and / or standards to provide confidentiality and data protection.

[0216] Compatibility with healthcare systems and research platforms. Implementations of the present disclosure can include a system within the device equipped with integration capabilities allowing data sharing with health providers or research institutions.

[0217] For example, a user device (e.g., a mobile computing device or other computing device) can include a user interface, and can be in networked communication with a remote research platform or healthcare system. The networked communication can be configured to transmit any of the data described herein to healthcare systems to improve data exchange and collaboration in the healthcare ecosystem. For example, any of the data described herein can be incorporated into patient records.

[0218] Non-limiting examples of the data that can be transmitted include: gut microbiota data; user-inputted data, (e.g., the time of capsule ingestion, user actions like user medication intake, user fluid intake, and / or user diet, user sleep schedule); and / or user symptoms (e.g., user mood, user symptoms).

[0219] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.

Claims

WHAT IS CLAIMED:

1. A computer-implemented method for analysis of intestinal data comprising: receiving, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject; determining a collection time for the sample; receiving, from the user device, subject data associated with a user input, wherein the subject data comprises the first identifier, a timestamp, and a description of the user input; associating the subject data with the sample collected by the ingestible capsule; receiving, from an analysis system, sample data associated with the sample collected by the ingestible capsule, wherein the sample data comprises the second identifier and biological data comprising gastrointestinal (G I) fluid data; and analyzing the subject data and the sample data to evaluate the biological data in view of the user input.

2. The computer-implemented method of claim 1, wherein the collection time for the sample is determined based on information received from the ingestible capsule.

3. The computer-implemented method of claim 1, wherein the collection time for the sample is determined based on information received from the user device.

4. A computer-implemented method for analysis of intestinal data comprising: receiving, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject; receiving, from the user device, first data comprising the first identifier, the second identifier, and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determining a collection time for the sample based on the first data; receiving, from the user device, second data associated with a user input, wherein the second data comprises the first identifier, a second timestamp, and a description of the user input; associating the second data with the sample collected by the ingestible capsule;receiving, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data comprises the second identifier and biological data comprising gastrointestinal (Gl) fluid data; and analyzing the second data and the third data to evaluate the biological data in view of the user input.

5. The computer-implemented method of claim 4, further comprising associating the second identifier with the first identifier.

6. The computer-implemented method of claim 4 or 5, wherein associating the second data with the sample collected by the ingestible capsule comprises temporally relating the second timestamp to the collection time.

7. The computer-implemented method of claim 4, further comprising receiving a plurality of second identifiers, each of the plurality of second identifiers being associated with a respective ingestible capsule.

8. The computer-implemented method of claim 7, further comprising associating each of the plurality of second identifiers with the first identifier.

9. The computer-implemented method of any one of claims 4-8, wherein the user input is an action by the subject.

10. The computer-implemented method of claim 9, wherein the action by the subject is ingestion of a drug, compound, or pharmaceutical.

11. The computer-implemented method of claim 9, wherein the action by the subject is ingestion of a food or liquid.

12. The computer-implemented method of any one of claims 4-8, wherein the user input is a symptom of the subject.

13. The computer-implemented method of any one of claims 4-8, wherein the user input is a mood of the subject.

14. The computer-implemented method of any one of claims 4-13, wherein the collection time is within a window about 1.5 to 6 hours after the first timestamp.

15. The computer-implemented method of any one of claims 4-13, wherein the collection time is about 3 hours after the first timestamp.

16. The computer-implemented method of any one of claims 4-15, wherein the biological data comprises omic data.

17. The computer-implemented method of claim 16, wherein the omic data is genomic data, metabolomic data, proteomic data, or transcriptomic data.

18. The computer-implemented method of any one of claims 4-15, wherein the biological data comprises multiomic data.

19. The computer-implemented method of any one of claims 4-18, wherein analyzing the second data and the third data to evaluate the biological data in view of the user input comprises determining a response of the subject to ingestion of a drug, compound, or pharmaceutical.

20. The computer-implemented method of claim 19, further comprising: transmitting, to the user device, a first instruction to ingest the ingestible capsule; and transmitting, to the user device, a second instruction to ingest the drug, compound, or pharmaceutical.

21. The computer-implemented method of claim 20, wherein the first instruction and the second instruction are transmitted at predetermined times relative to each other.

22. The computer-implemented method of any one of claims 4-18, wherein analyzing the second data and the third data to evaluate the biological data in view of the user input comprises determining a response of the subject to ingestion of a food or liquid or to the recording of a mood or symptom.

23. The computer-implemented method of claim 22, further comprising: transmitting, to the user device, a first instruction to ingest the ingestible capsule; andtransmitting, to the user device, a second instruction to ingest the food or liquid.

24. The computer-implemented method of claim 23, wherein the first instruction and the second instruction are transmitted at predetermined times relative to each other.

25. The computer-implemented method of any one of claims 4-24, further comprising recommending a health-related action for the subject based on the analysis of the second data and the third data.

26. The computer-implemented method of claim 25, wherein the health-related action is a dietary recommendation, a fluid intake recommendation, or a sleep recommendation.

27. The computer-implemented method of claim 25 or 26, further comprising transmitting, to the user device, the health-related action.

28. The computer-implemented method of any one of claims 4-27, further comprising providing a diagnosis, prognosis, or treatment recommendation for the subject based on the analysis of the second data and the third data.

29. The computer-implemented method of claim 28, further comprising transmitting, to the user device, the diagnosis, prognosis, or treatment recommendation.

30. A method of treatment comprising: performing the analysis of intestinal data according to claim 28 or 29; and administering a treatment to the subject based on the diagnosis, prognosis, or treatment recommendation.

31. A system for analysis of intestinal data comprising: at least one processor; and at least one memory operably coupled to the at least one processor, wherein the at least one memory has computer-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to:receive, from a user device, a first identifier associated with a subject and a second identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of the subject; receive, from the user device, first data comprising the first identifier, the second identifier, and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determine a collection time for the sample based on the first data; receive, from the user device, second data associated with a user input, wherein the second data comprises the first identifier, a second timestamp, and a description of the user input; associate the second data with the sample collected by the ingestible capsule; receive, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data comprises the second identifier and biological data comprising gastrointestinal (G I) fluid data; and analyze the second data and the third data to evaluate the biological data in view of the user input.

32. A computer-implemented method for analysis of intestinal data comprising: receiving, from a user interface, an identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (G I) tract of a subject; receiving, from the user interface, first data comprising the identifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determining a collection time for the sample based on the first data; receiving, from the user interface, second data associated with a user input, wherein the second data comprises a second timestamp and a description of the user input; associating the second data with the sample collected by the ingestible capsule; receiving, from an analysis system, third data associated with the sample collected by the ingestible capsule, wherein the third data comprises the identifier and biological data comprising gastrointestinal (Gl) fluid data; and analyzing the second data and the third data to evaluate the biological data in view of the user input.

33. A computer-implemented method for collection of intestinal samples comprising: receiving, from a user interface, an identifier associated with an ingestible capsule that is configured to collect a sample from a gastrointestinal (Gl) tract of a subject; receiving, from the user interface, first data comprising the identifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject;determining a collection time for the sample based on the first data; receiving, from the user interface, second data associated with a user input, wherein the second data comprises a second timestamp and a description of the user input; and associating the second data with the sample collected by the ingestible capsule.

34. The computer-implemented method of claim 33, further comprising: displaying, on the user interface, a first instruction to ingest the ingestible capsule; and displaying, on the user interface, a second instruction to ingest a drug, compound, or pharmaceutical.

35. The computer-implemented method of claim 34, wherein the first instruction and the second instruction are displayed at predetermined times relative to each other.

36. The computer-implemented method of claim 33, further comprising: displaying, on the user interface, a first instruction to ingest the ingestible capsule; and displaying, on the user interface, a second instruction to ingest a food or liquid.

37. The computer-implemented method of claim 36, wherein the first instruction and the second instruction are transmitted at predetermined times relative to each other.

38. A system for collection of intestinal samples comprising: an ingestible capsule that is configured to collect a sample from a gastrointestinal (Gl) tract of a subject; and a computing device comprising at least one processor and at least one memory operably coupled to the at least one processor, wherein the at least one memory has computer-executable instructions stored thereon that, when executed by the at least one processor, cause the at least one processor to: receive, at a user interface of the computing device, an identifier associated with the ingestible capsule; receive, at the user interface of the computing device, first data comprising the identifier and a first timestamp that indicates when the ingestible capsule was ingested by the subject; determine a collection time for the sample based on the first data; receive, at the user interface of the computing device, second data associated with a user input, wherein the second data comprises a second timestamp and a description of the user input; andassociate the second data with the sample collected by the ingestible capsule.

39. A method comprising: administering a user intervention; administering an ingestible sampling capsule at a time interval before or after the user intervention; and receiving sample data collected by the ingestible capsule.

40. The method of claim 39, wherein the ingestible sampling capsule targets the small intestine.

41. The method of claim 39 or claim 40, wherein the ingestible sampling capsule is ingested after a fast of at least 4 hours.

42. The method of any one of claims 39-41, wherein the sample data relates to the user intervention.

43. The method of any one of claims 39-42, wherein a blood sample is collected at a time interval related to the ingestion of the sampling capsule.

44. The method of any one of claims 39-43, wherein the user intervention is a test product.

45. The method of any one of claims 39-44, wherein the time interval is between 30-90 minutes.

46. The method of any one of claims 39-45, wherein the time interval is 60 minutes.

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