CN111247598A

CN111247598A - Methods and systems for characterizing appendix-related conditions associated with microbial organisms

Info

Publication number: CN111247598A
Application number: CN201880060376.8A
Authority: CN
Inventors: Z·阿普泰; J·里奇曼; D·阿尔莫纳西德; R·奥尔蒂斯; C·瓦尔迪维亚; I·佩德罗索; P·塔皮亚
Original assignee: Prosomegen
Current assignee: Prosomegen; uBiome Inc
Priority date: 2017-07-18
Filing date: 2018-07-18
Publication date: 2020-06-05
Also published as: JP2020528285A; SG11202000340SA; WO2019018580A1; KR20200051587A; AU2022202660A1; AU2018302175A1; EP3655971A1

Abstract

Embodiments of methods and/or systems for characterizing one or more appendix-related conditions can include determining a microbial dataset associated with a group of subjects based on the microbial dataset; and/or performing a characterization process associated with the one or more appendix-related conditions, wherein performing the characterization process can additionally or alternatively comprise performing the appendix-related characterization process for the one or more appendix-related conditions, and/or determining one or more treatments.

Description

Methods and systems for characterizing appendix-related conditions associated with microbial organisms

Cross Reference to Related Applications

This application claims the benefit of U.S. provisional application serial No. 62/533,816, filed 2017, month 7, and day 18, the entire contents of which are incorporated herein by reference.

This application is related to U.S. application Ser. No. 15/606,743 filed on 26/5/20157, filed on 21/10/2015, and to a continuation of U.S. application Ser. No.14/919,614, which requires a provisional application Ser. No. 62/066,369 filed on 21/10/2014, a provisional application Ser. No. 62/066,369 filed on 4/12/2014, a provisional application Ser. No. 62/087,551 filed on 17/12/2014, a provisional application Ser. No. 62/092,999, a provisional application Ser. No. 62/147,376 filed on 14/2015, a provisional application Ser. 62/147,212 filed on 14/4/2015, a provisional application Ser. No. 62/147,362, a provisional application Ser. No. 2015 4/13/2015, a provisional application Ser. 62/146,855, a provisional application Ser. No. 62/146,855, The benefit of U.S. provisional application serial No. 62/206,654, filed on 18/8/2015, which is incorporated herein by reference in its entirety.

Technical Field

The present invention relates generally to genomics and microbiology.

Background

The microbiome may include commensal (commensal), symbiotic (symbian), and pathogenic microbial ecocommunities associated with the organism. The characterization of the human microbiome is a complex process. The human microbiome includes more than 10 times more microbial cells than human cells, however, characterization of the human microbiome is still in an emerging stage due to limitations in sample processing techniques, genetic analysis techniques, and resources used to process large amounts of data. Current knowledge has clearly established the relationship of microbiome to a variety of health conditions and has increasingly become a vehicle for host genetic and environmental factors in the development of human diseases. The microbiome is suspected to play at least a partial role in a number of health/disease-related states. In addition, the microbiome may mediate the effects of environmental factors on human, plant and/or animal health. In view of the profound significance of microbiome in affecting user health, efforts should continue to determine microbiome characterization, generate insight (insight) from the characterization, and develop therapies for correcting dysbiosis states. However, there are many problems that remain unanswered by methods and systems for analyzing the human microbiome and/or providing therapeutic measures based on the obtained insights.

Accordingly, there is a need in the field of microbiology for a new and useful method and/or system for characterizing, monitoring, diagnosing and/or intervening in one or more microorganism-associated health conditions, such as enabling personalized and/or population-wide (population-wide) use.

Drawings

FIGS. 1A-1B are flow diagrams of variations of one embodiment of a method;

FIG. 2 depicts one embodiment of a method and system;

FIG. 3 depicts a variation of the process for generating a characterization model in one embodiment of the method;

fig. 4 depicts a variation of the mechanism of operation of a probiotic-based therapy in an embodiment of a method;

FIG. 5 depicts a variation of sample processing in one embodiment of a method;

FIG. 6 depicts an example of notification specification;

FIG. 7 depicts a schematic diagram of a variation of one embodiment of the method;

8A-8B depict variations of using a model to perform a characterization process;

FIG. 9 depicts facilitating treatment in one embodiment of a method.

Detailed Description

The following description of the embodiments is not intended to limit the embodiments, but rather to enable any person skilled in the art to make and use the embodiments.

1. Overview

As shown in FIGS. 1A-1B, an embodiment of a method 100 for characterizing one or more appendiceal-related conditions can include: determining that a dataset of microorganisms (e.g., a microbial sequence dataset, a microbiome composition diversity dataset, e.g., based on a microbial sequence dataset, a microbiome functional diversity dataset, e.g., based on a microbial sequence dataset, etc.) is associated with a set of users (e.g., determining a microbial dataset based on a sample from a set of subjects) S110; and/or based on the microbiome dataset (e.g., based on microbiome composition features and/or microbiome functional features derived from the microbiome dataset and associated with one or more appendix-related conditions; etc.), performing characterization processing (e.g., preprocessing, feature determination, feature processing, appendix-related characterization model processing, etc.) associated with the one or more appendix-related conditions S130, wherein performing the characterization process can additionally or alternatively include performing the appendix-related characterization process for one or more appendix-related conditions S135, and/or determining one or more treatments (e.g., determining to prevent, ameliorate, reduce risk, and/or otherwise ameliorate one or more appendix-related conditions, etc.) S140.

Embodiments of the method 100 may additionally or alternatively include one or more of: processing supplemental data (e.g., informative; descriptive; indicative; related to; etc.) associated with one or more appendix-related conditions S120; processing one or more biological samples associated with a user (e.g., a subject, a human, an animal, a patient, etc.) S150; determining appendix-related characterizations of one or more appendix-related conditions for the user via one or more characterization processes based on a user microbiome dataset associated with a biological sample of the user (e.g., a user microbiome sequence dataset; a user microbiome composition dataset; a user microbiome function dataset; a user microbiome characteristic derived from the user microbiome dataset, wherein the user microbiome characteristic may correspond to a characteristic value of the microbiome characteristic determined by the one or more characterization processes; etc.) (S160); facilitating therapeutic intervention (e.g., based on the appendix-related characteristics and/or the therapeutic model) for the user for the one or more appendix-related conditions S170; monitoring the effectiveness of one or more treatments and/or monitoring other suitable components (e.g., microbiome characteristics, etc.) for the user (e.g., based on processing a series of biological samples from the user) over time (e.g., evaluating a user microbiome characteristic for the user over time, such as a user microbiome composition characteristic and/or a functional characteristic associated with the treatment, etc.) (S180); and/or any other suitable treatment.

In a particular example, the method 100 can include determining a microbial sequence dataset associated with a set of subjects (e.g., including subjects with an appendix-related condition; including subjects without an appendix-related condition, wherein samples and/or data associated with the subjects can be used as controls; etc.), based on microbial nucleic acids in samples associated with the set of subjects, wherein the samples include at least one sample associated with one or more appendix-related conditions; collecting supplemental data associated with one or more appendix-related conditions for the group of subjects; determining a set of microbiome features based on the microbiome sequence dataset, the set of microbiome features including at least one of a set of microbiome composition features and a set of microbiome functional features; generating an appendix-related characterization model based on the supplemental data and the set of microbiome features, wherein the appendix-related characterization model is associated with one or more appendix-related conditions; determining an appendix-related characterization of one or more appendix-related conditions for the user based on the appendix-related characterization model; and promoting therapeutic intervention for the user for the one or more appendix-related conditions based on the appendix-related characterization (e.g., providing treatment to the user to promote amelioration of the one or more appendix-related conditions, etc.).

In a particular example, the method 100 can include collecting a sample from a user (e.g., by provision and collection of a sample kit, etc.), wherein the sample includes a microbial nucleic acid corresponding to a microbe associated with one or more appendix-related conditions; determining a microbial dataset associated with the user based on microbial nucleic acids of the sample (e.g., based on sample preparation and/or sample ordering, etc.); determining a user microbiome characteristic (e.g., including at least one of a user microbiome composition characteristic and a user microbiome functional characteristic, etc.) based on the microbiome dataset, wherein the user microbiome characteristic is associated with one or more appendix-related conditions; determining an appendix-related characterization of one or more appendix-related conditions for the user based on the user microbiome characteristics; and/or facilitating a treatment-related therapeutic intervention for the user based on the appendix-related characterization to facilitate amelioration of one or more appendix-related conditions (e.g., promoting treatment to the user; etc.).

Embodiments of method 100 and/or system 200 can be used to characterize (e.g., assess, evaluate, diagnose, describe, etc.) one or more appendix-related conditions (e.g., characterize the appendix-related condition itself, e.g., determine a microbiome characteristic associated with, and/or otherwise associated with, the appendix-related condition; characterize one or more appendix-related conditions for one or more users, e.g., determine a predisposition metric (specificity metric) for one or more appendix-related conditions for one or more users, etc.) and/or characterize one or more appendix-related conditions for one or more users.

Additionally or alternatively, embodiments of method 100 and/or system 200 can be used to identify a microbiome signature and/or other suitable data associated with (e.g., positively correlated, negatively correlated, etc.) one or more appendix-related conditions, e.g., for use as a biomarker (e.g., for a diagnostic procedure, for a therapeutic procedure, etc.).

In an example, the appendix-related characterization can be associated with at least one or more of microbiome composition (e.g., microbiome composition diversity, etc.), microbiome function (e.g., microbiome functional diversity, etc.), and/or other suitable, microbiome-related aspects. In one example, a microbial signature (e.g., describing the composition, function, and/or diversity of identifiable patterns, such as relating to the relative abundance of microbes present in a user's microbiome, such as for a subject exhibiting one or more appendix-related conditions; etc.), and/or a microbial data set (e.g., from which a microbiome signature can be derived, etc.) that can be used for characterization (e.g., diagnosis, risk assessment, etc.), facilitation of therapeutic intervention, monitoring, and/or other suitable purposes, such as by using bioinformatic conduits, analytical techniques, and/or other suitable methods described herein. Additionally or alternatively, embodiments of method 100 and/or system 200 can be used to perform cross-conditioning analysis (e.g., characterize multiple appendix-related conditions, such as determining correlations, covariances, comorbidities, and/or other suitable relationships between different appendix-related conditions, etc.) for multiple appendix-related conditions, such as characterization in context (e.g., diagnosis; providing information related thereto; etc.) and/or treating a user.

Additionally or alternatively, embodiments can be used to facilitate therapeutic intervention (e.g., treatment selection, treatment promotion and/or provision, treatment monitoring, treatment assessment, etc.) for one or more appendix-related conditions, such as through promotion of related treatment (e.g., with a particular body site, such as an intestinal site, a skin site, a nasal site, an oral site, a genital site, other suitable body site, other collection site, determining treatment via a treatment model, etc.). Additionally or alternatively, embodiments can be used to generate models (e.g., appendix-related characterization models, such as for predicting phenotypes, treatment models, such as for determining treatment, machine learning models, such as for processing characteristics, etc.), such models can be used to characterize and/or diagnose a user based on the user's microbiome (e.g., user microbiome characteristics; as clinically diagnosed; as concomitantly diagnosed; etc.) and/or can be used to select and/or provide treatment to a subject associated with one or more appendix-related conditions. Additionally or alternatively, embodiments may perform any suitable function described herein.

Thus, data from a population of users (e.g., a population of subjects associated with one or more appendix-related conditions; positively or negatively associated with one or more appendix-related conditions; etc.) can be used to characterize subsequent users, e.g., to indicate a microbiologically-related health condition, and/or where to improve, and/or to facilitate therapeutic intervention (e.g., to facilitate one or more treatments; to facilitate modulation of the compositional and/or functional diversity of a user's microbiome to one or more of a set of desired equilibrium states, e.g., states associated with improved health states associated with one or more appendix-related conditions; etc.), e.g., associated with one or more appendix-related conditions. Variations of the method 100 can further facilitate selecting, monitoring (e.g., efficacy monitoring, etc.), and/or adjusting the treatment provided to the user, e.g., by collecting and analyzing (e.g., using an appendix-related characterization model) additional samples from the user, across body parts (e.g., across a user's sample collection sites, e.g., corresponding to collection sites associated with a particular body part type, e.g., intestinal sites, oral sites, nasal sites, skin sites, genital sites, etc.), additionally or alternatively processing supplemental data over time, e.g., for one or more appendix-related conditions, over time (e.g., over the course of an entire treatment regimen, through the user's experience of an appendix-related condition, etc.). However, any suitable portion of embodiments of method 100 and/or system 200 may use data from populations, subgroups, individuals, and/or other suitable entities for any suitable purpose.

Embodiments of method 100 and/or system 200 can preferably determine and/or promote (e.g., provide; present; notify; etc.) one or more characterizations and/or treatments of the appendix-related condition, and/or can perform any suitable portion of embodiments of method 100 and/or system 200 with respect to the appendix-related condition. The appendix-related condition can include one or more of the following: appendicitis (e.g., acute appendicitis; suspected appendicitis; etc.), appendicitis (aponexinmigration), appendiceal cancer (e.g., appendiceal tumor), carcinoids, carcinoid syndromes, coprolites, ovarian mucinous tumors, crohn's disease (e.g., appendiceal crohn's disease), lymphoid hyperplasia, congenital malformations (e.g., congenital deletion; repeat appendices; etc.), appendiceal endometriosis, peritoneal salpingosis, vasculitis (e.g., appendicitis, etc.), neural hyperplasia (e.g., neural hyperplasia, etc.), interstitial tumors, non-myogenic tumors, lymphomas, irritable bowel syndrome, mononucleosis, measles, gastrointestinal infections, intussusception, adenomas, diverticular disease, intestinal immune-related disorders, infections; comorbidities, and/or any other suitable condition associated with the appendix.

Additionally or alternatively, the appendix-related condition can include one or more of: diseases, symptoms (e.g., blood flow blockage, tissue death, overproduction of cells, blunt pain proximal appendices, loss of appetite, tissue rupture, pain, appendiceal swelling, abdominal swelling, stuffiness, urination pain, severe pain, cramping, nausea, vomiting, fever, rebound pain, swelling of body parts such as the abdomen, back pain, constipation, diarrhea, peritonitis, abscess, organ failure, muscle stiffness, obstipation, scar tissue, and the like), causes (e.g., triggers, compact fecal matter, lymphoproliferation, e.g., blockage due to stool, parasites, growth, abdominal trauma, and the like), diseases, associated risks (e.g., a predisposition score, etc.), associated severity, behaviors (e.g., physical exercise behavior, alcohol consumption, smoking behavior, pressure-related characteristics, other psychological characteristics, diseases, social behavior, caffeine consumption, alcohol consumption, habitual sleep, other habits, dietary phase, and food phase Off-behaviors such as fiber intake, fruit intake, vegetable intake; meditation and/or other relaxation behaviors; lifestyle conditions associated with appendiceal related disorders; lifestyle conditions that are informative, associated with, indicative, promoting, and/or otherwise associated with diagnosis and/or therapeutic intervention of an appendix-related condition; behaviors that affect and/or are otherwise associated with the appendix and/or appendix-related condition; etc.), environmental factors, demographics-related characteristics (e.g., age, weight, race, gender, etc.), phenotype (e.g., a phenotype measurable on humans, animals, plants, fungi; phenotypes associated with the appendix and/or other related aspects, etc.), and/or any other suitable aspect associated with an appendix-related condition. In one example, one or more appendiceal related conditions can interfere with normal physical, psychological, social, and/or emotional function. In examples, one or more appendix-related conditions can be characterized and/or diagnosed by computed tomography (CT scan), ultrasound, colonoscopy, biopsy, blood test, abdominal examination (e.g., to detect inflammation, etc.), urinalysis (e.g., to detect infection; etc.), diagnostic imaging, medical inquiry, medical history, surveys, sensor data, and/or by any suitable technique (e.g., techniques useful for diagnosing appendix-related conditions, etc.).

Embodiments of method 100 and/or system 200 can be performed on a single user, for example in connection with applying one or more sample processing procedures and/or characterization processes, to process one or more biological samples from the user (e.g., collected across one or more collection sites, etc.), for characterization in connection with the appendix, to facilitate therapeutic intervention, and/or for any other suitable purpose. Additionally or alternatively, embodiments can be implemented on a population of subjects (e.g., including users, excluding users), where the population of subjects can include subjects similar and/or dissimilar to any other subject for any suitable type of feature (e.g., related to appendix-related conditions, demographic characteristics, behavior, microbiome composition and/or function, etc.); to a subset of users (e.g., share characteristics, such as characteristics that affect appendix-related characteristics and/or treatment determinations; etc.); for plants, animals, microorganisms, and/or any other suitable entity. Thus, information derived from a group of subjects (e.g., a population of subjects, a group of subjects, a subset of users, etc.) can be used to provide additional insight to subsequent users. In one variant, a set of biological samples is preferably associated with a wide variety of subjects, and the subjects are processed, for example, including one or more of: different demographic characteristics (e.g., gender, age, marital status, ethnicity, nationality, socioeconomic status, sexual orientation, etc.), different appendix-related conditions (e.g., health and disease status, different genetic predisposition; etc.), different living conditions (e.g., living alone, living with pets, living with important others, living with children, etc.), different dietary habits (e.g., miscellaneous food, vegetarian food, strict vegetarian food, sugar consumption, acid consumption, caffeine consumption, etc.), different behavioral predisposition (e.g., levels of physical exercise, drug use, alcohol use, etc.), different levels of mobility (e.g., related to distance traveled in a given time period), and/or any other suitable characteristic (e.g., characteristics that are influenced, associated, and/or otherwise associated with the composition and/or function of a microbiome, etc.). In an example, as the number of subjects increases, the predictive power of the processes performed by the embodiment portions of method 100 and/or system 200 may increase, such as based on the microbiome of the subsequent user (e.g., for different collection sites of a user sample, etc.), associated with characterizing the subsequent user (e.g., with varying characteristics, etc.). However, portions of embodiments of method 100 and/or system 200 may be performed and/or configured in any suitable manner for any suitable entity (entity) or entities (entities).

The data (e.g., microbiome characteristics, microbiome dataset, model, appendix-related characterization, supplemental data, notifications, etc.) described herein can be associated with any suitable time index (e.g., seconds, minutes, hours, days, weeks, etc.), including one or more of: time indicators indicating when data was collected (e.g., time indicators indicating sample collection; etc.), determined, transmitted, received, and/or otherwise processed; providing a contextual time indicator of the data (e.g., a time indicator associated with the appendix-related trait, e.g., the appendix-related trait describes the condition associated with the appendix and/or the microbiome status of the user at a particular time; etc.); changes in the time index (e.g., changes in the appendix-related characteristic over time, such as response to receiving treatment; time delay between sample collection, sample analysis, providing the appendix-related characteristic or treatment user; and/or other suitable portions of embodiments of method 100; etc.); and/or other suitable time-related indicators.

Additionally or alternatively, parameters, metrics, inputs, outputs, and/or other suitable data may be associated with the following numerical types, including: scores (e.g., a predisposition score for an appendix-associated condition; a feature association score; a relevance score, a covariance score, a microbiome diversity score, a severity score, etc.), individual values (e.g., an individual score for an appendix-associated condition, e.g., a predisposition score for a condition at a different collection site, etc.), aggregate values (e.g., an overall score for a single microbiome-associated score at a different collection site, etc.), binary values (e.g., the presence or absence of a microbiome feature; the presence or absence of an appendix-associated condition; etc.), relative values (e.g., relative taxonomic group abundance, relative microbiome functional abundance, relative feature abundance, etc.), classifications (e.g., classification of an appendix-associated condition and/or diagnosis for a user; feature classification; behavioral classification; demographic feature classification; etc.), confidence (e.g., associated with a microbiome sequence dataset, associated with a microbiome diversity score, associated with other appendix-related characterizations, associated with other outputs; etc.), identifiers, values on a map, and/or any other suitable type of value. Any suitable type of data described herein may be used as input (e.g., for different analysis techniques, models, and/or other suitable components described herein), generated as output (e.g., different analysis techniques, models, and so forth), and/or operated in any suitable manner on any suitable components associated with method 100 and/or system 200.

One or more instances and/or portions of embodiments of the methods 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), simultaneously (concurrently), simultaneously (e.g., parallel data processing; simultaneous cross-condition analysis; multiplex sample processing, e.g., multiplex microbial nucleic acid fragment amplification, the microbial nucleic acid fragments corresponding to target sequences associated with appendix-related conditions; sample processing and analysis to evaluate a set of appendix-related conditions substantially simultaneously; computationally determining microbial datasets, microbiome characteristics, and/or characterizing appendix-related conditions in parallel for multiple users; e.g., parallel computation on different threads simultaneously to increase system throughput; etc.), in temporal relation (e.g., substantially simultaneously, etc.) to a triggering event (e.g., partially manifested by the method 100) Responsive, sequential, preceding, succeeding, etc.), and/or in any other suitable order, at any suitable time and frequency, by and/or using one or more instances of the systems 200, components, and/or entities described herein. In an example, the method 100 can include bridge amplifying a substrate based on a next generation sequencing platform (and/or other suitable sequencing system) of a sample processing system utilized, processing microbial nucleic acids of one or more biological samples to generate a microbial data set, and determining a microbiome characteristic and a microbiome functional diversity characteristic on a computing device in communication with the next generation sequencing platform. Of course, the method 100 and/or system 200 may be configured in any suitable manner.

2. Examples are given.

Microbiome analysis can enable accurate and/or effective characterization and/or therapeutic provision of appendiceal-related conditions caused by microorganisms associated therewith and/or otherwise associated (e.g., as part of an embodiment of method 100, etc.). Particular examples of this technique can overcome several challenges faced by traditional approaches in characterizing appendically-related conditions and/or facilitating therapeutic intervention. First, conventional approaches may require the patient to visit one or more care providers to receive characterization and/or treatment recommendations for the appendix-related condition (e.g., via a diagnostic medical procedure), which may result in inefficiencies and/or health risks associated with the passage of time before diagnosis and/or treatment, associated with inconsistent quality of healthcare, and/or associated with other aspects of the care provider. Second, conventional gene sequencing and analysis techniques for human genome sequencing may be incompatible and/or inefficient when applied to the microbiome (e.g., where the number of microbial cells of the human microbiome may be more than 10 times greater than human cells; where the available analysis techniques and the manner in which the analysis techniques are utilized may differ; where the optimal sample processing techniques may differ (e.g., reduce amplification bias); where different methods of appendix-related characterization may be employed; where the type and relevance of the condition may differ; the cause of the associated condition and/or the available treatment of the associated condition may differ; where the sequence reference database may differ; where the microbial population may differ in different body regions of the user (e.g., at different collection sites), etc.). Third, the rise of sequencing technologies (e.g., next generation sequencing, related technologies, etc.) has raised technical issues (e.g., data processing and analysis issues for over-generated sequence data; processing multiple biological samples in a multiplexed manner; information display issues; treatment prediction issues; treatment provision issues, etc.), but these issues will not exist due to the unprecedented development in speed and data generation associated with sequencing of genetic material. Particular examples of the method 100 and/or system 200 may give a solution to the technology for at least the above challenges.

First, specific examples of the technology may transform an entity (e.g., a user, a biological sample, a therapy facilitation system including a medical device, etc.) into a different state or thing. For example, the techniques can convert a biological sample into components that can be sequenced and analyzed to generate a microbiome dataset and/or microbiome signature that can be used to characterize a user associated with one or more appendix-related conditions (e.g., such as by using a next generation sequencing system, a multiplex amplification procedure; etc.). In another example, the techniques can identify, hinder, and/or promote (e.g., manifest, suggest, provide, administer, etc.), treat (e.g., personalized treatment based on appendix-related characterizations; etc.), and/or otherwise facilitate therapeutic intervention (e.g., facilitate alteration of microbiome composition, microbiome function, etc.) so that one or more appendix-related conditions can be prevented and/or improved, e.g., so as to alter the microbiome and/or the health of the patient (e.g., improve a health condition associated with the appendix-related condition; etc.), e.g., apply one or more microbiome characteristics (e.g., apply an association, relationship, and/or other suitable association between the microbiome characteristics and the one or more appendix-related conditions; etc.). In another example, the techniques may alter microbiome composition and/or function at one or more different body parts of the user (e.g., one or more different collection sites, etc.), for example, targeting and/or altering microbes associated with the microbiome of the gut, nose, skin, mouth, and/or genitals (e.g., by facilitating therapeutic intervention associated with one or more site-specific therapies, etc.). In another example, the techniques may control a therapy facilitation system (e.g., a diet system, an automatic medication dispenser, a behavior modification system, a diagnostic system, a disease therapy facilitation system, etc.) to facilitate therapy (e.g., performed by generating control instructions for the therapy facilitation system, etc.), thereby modifying the therapy facilitation system.

Second, specific examples of the technology can improve computer-related technology (e.g., increase computational efficiency in the storage, retrieval, and/or processing of microbe-related data for appendix-related conditions; computational processing associated with biological sample processing, etc.), for example, by facilitating a computer to perform functions that previously could not be performed. For example, the techniques can apply a set of analytical techniques to non-universal microbial datasets and/or microbiome profiles (e.g., as sample processing techniques and sequencing techniques advance, these techniques can be generated and/or made available today, etc.) in a non-universal manner to improve appendix-related profiles and/or facilitate therapeutic intervention for appendix-related conditions.

Third, specific examples of this technique can lead to improvements in treatment speed, appendix-related characterization, accuracy, determination and generalization of microbiome-related treatments, and/or other suitable aspects associated with appendix-related conditions. For example, the techniques can utilize a non-universal microbiome dataset to determine, select, and/or otherwise process microbiome features that are particularly relevant to one or more appendix-related conditions (e.g., a processed microbiome feature associated with an appendix-related condition; a plurality of cross-conditional microbiome features associated with an appendix-related condition, etc.), this can facilitate accuracy (e.g., by using the most relevant microbiome features; by utilizing customized analysis techniques, etc.), processing speed (e.g., by selecting a subset of relevant microbiome features, by performing dimension reduction techniques, by utilizing customized analysis techniques, etc.), and/or other computational improvements associated with phenotypic prediction (e.g., indications of appendix-related conditions, etc.), other suitable characterization, therapeutic intervention facilitation, and/or other suitable objectives. In a particular example, the techniques can apply feature selection rules (e.g., microbiome feature selection rules for composition, function; supplemental features for extraction from a supplemental dataset; etc.) to select a particular optimized subset (e.g., microbiome functional features associated with one or more appendix-related conditions; microbiome composition diversity features, e.g., reference relative abundance features, indicating the health, presence, absence, and/or other suitable range of taxa associated with the appendix-related conditions; user relative abundance features that can be compared to reference relative abundance features associated with the appendix-related conditions, and/or therapeutic responses; etc.) from a vast pool of potential features (e.g., that can be extracted from a large number of microbiome data, e.g., sequence data; identifiable by a univariate statistical test) to generate a profile for use in the analysis of the characteristics of the appendix-related conditions, Applying and/or otherwise facilitating characterization and/or treatment (e.g., via a model, etc.). The potential scale of microbiota (e.g., human microbiota, animal microbiota, etc.) can be translated into a vast amount of data, raising the problem of how to process and analyze the vast amount of data to generate viable microbiological insights associated with appendiceal-related conditions. However, the feature selection rules and/or other suitable computer-executable rules may implement one or more of: shorter generation and execution times (e.g., for generating and/or applying models; for determining appendix-related characteristics and/or associated treatments; etc.); optimized sample processing techniques (e.g., other sample processing components identified by using primer types, other biomolecules, and/or by computational analysis of taxonomic groups, sequences, and/or other suitable data associated with the annex correlation conditions, e.g., improving the transformation of microbial nucleic acids from a biological sample while optimizing to improve specificity, reduce amplification bias, and/or other suitable parameters); the model is simplified, and effective interpretation results are promoted; reducing overfitting; the network effects of appendix-related characterizations are generated, stored, and applied over time by multiple users associated with the appendix-related condition (e.g., by collecting and processing more and more microbiome-related data associated with more and more users to improve the predictability and/or treatment determination of the appendix-related characterizations; etc.); improvements in data storage and retrieval (e.g., storing and/or retrieving appendix-related characterization models, storing specific models, e.g., associated with different appendiceal-related conditions, associated with different users and/or a group of users, storing microbial data sets associated with user accounts, storing treatment monitoring data associated with one or more treatments and/or users receiving treatment, storing characteristics, appendix-related characteristics, and/or other suitable data associated with a user, a group of users, and/or other entities to improve personalized characterization and/or inter-appendix-treatment data delivery (vereldery), etc.), and/or other suitable improvements to the art.

Fourth, specific examples of this technique can correspond to inventive functional distribution across components including a sample processing system, an appendix-related characterization system, and a plurality of users, where the sample processing system can process biological sample processes from the plurality of users substantially simultaneously (e.g., in a multiplexed manner) that can be utilized by the appendix-related characterization system to generate personalized characterizations and/or treatments for appendix-related conditions (e.g., tailored to a user's microbiome, such as relating to user eating behavior, probiotic behavior, medical history, demographic characteristics, other behaviors, preferences, etc.).

Fifth, specific examples of the technology can improve at least the technical fields of genomics, microbiology, microbiome-related computing, diagnostics, therapeutics, microbiome-related digital medicine, general digital medicine, modeling, and/or other related fields. In one example, the techniques can model and/or characterize different appendix-related conditions, e.g., by computationally identifying relevant microbial features (e.g., which can be used as biomarkers for use in diagnosis, to facilitate therapeutic intervention, etc.). In another example, the techniques can perform a cross-disorder analysis to identify and assess cross-disorder microbiome features associated with (e.g., shared cross, correlated cross, etc.) multiple appendix-related disorders (e.g., diseases, phenotypes, etc.). Such identification and characterization of microbiome features can facilitate improved healthcare practices (e.g., at the population and personal level, such as by facilitating diagnostic and therapeutic interventions, etc.) by reducing the risk and prevalence of complications and/or multi-modal appendix-related conditions (e.g., that may be associated with environmental factors, and thus microbiome, etc.). In a particular example, the techniques may apply non-conventional processing (e.g., sample processing procedures; computational analysis processing, etc.), such as improvements in the art.

Sixth, the techniques can utilize specialized computing devices (e.g., devices associated with sample processing systems such as next generation sequencing systems; appendix-related characterization systems; treatment facilitation systems; etc.) to perform the appropriate portions associated with embodiments of method 100 and/or system 200.

However, specific examples of this technique may provide any suitable improvement for appendix-related characterization, microbiome regulation, and/or performing suitable portions of embodiments of method 100 using non-generic components and/or suitable components of embodiments of system 200. 3. Provided is a system.

As shown in FIG. 2, embodiments of the system 200 (e.g., for characterizing appendically related conditions) can include any one or more of the following: a processing system (e.g., sample processing system, etc.) 210 operable for collecting and/or processing a biological sample (e.g., collected by a user and included in a container comprising a pretreatment reagent; etc.) from one or more users (e.g., human subjects, patients, animal subjects, environmental ecosystems, care providers, etc.) for facilitating determination of a microorganism data set (e.g., a microorganism gene sequence; a microorganism sequence data set; etc.); an appendix-related characterization system 220 operable for determining a microbiome characteristic (e.g., a microbiome composition characteristic; a microbiome functional characteristic; a diversity characteristic; a relative abundance range; e.g., based on a microbiome dataset and/or other suitable data; etc.), determining an appendix-related characterization (e.g., a characterization of an appendix-related condition, a treatment-related characterization, a characterization of a user, etc.); and/or a treatment facilitation system 230 that is operable to facilitate therapeutic intervention (e.g., facilitate treatment, etc.) for one or more appendix-related conditions (e.g., based on one or more appendix-related conditions; for ameliorating one or more appendix-related conditions; etc.).

Embodiments of the system 200 can include one or more processing systems 210 that can be used to receive and/or process (e.g., fragment, amplify, sequence, generate an associated data set, etc.) a biological sample to convert microbial nucleic acids and/or other components of the biological sample into data (e.g., genetic sequences that can be subsequently aligned and analyzed; microbial data sets; etc.) to facilitate the generation of appendix-related characterizations and/or therapeutic intervention. The processing system 210 may additionally or alternatively be used to provide the sample kit 250 (e.g., including sample containers, instructions for collecting samples from one or more collection sites, etc.) to a plurality of users (e.g., in response to purchase orders for the sample kit 250), such as through a mail delivery system. The processing system 210 can include one or more sequencing systems 215 (e.g., next generation sequencing systems, sequencing systems for targeted amplicon sequencing, macrotranscriptome sequencing, metagenomic sequencing, sequencing-by-synthesis techniques, capillary sequencing techniques, sanger sequencing, pyrosequencing techniques, nanopore sequencing techniques, etc.) for sequencing one or more biological samples (e.g., sequencing microbial nucleic acids from a biological sample, etc.) such as in generating microbial data (e.g., microbial sequence data, other data of a microbial dataset, etc.). The next-generation sequencing system (e.g., next-generation sequencing platform, etc.) may include any suitable sequencing system (e.g., sequencing platform, etc.) for one or more of high-throughput sequencing (e.g., facilitated by high-throughput sequencing; massively parallel sequencing techniques, polymerase clone sequencing, 454 pyrosequencing, Illumina sequencing, SOLiD sequencing, ion torrent semiconductor sequencing, DNA nanosphere sequencing, helliscope single molecule sequencing, single molecule real-time (SMRT) sequencing, nanopore DNA sequencing, etc.), any generation of sequencing techniques (e.g., second generation sequencing techniques, third generation sequencing techniques, fourth generation sequencing techniques, etc.), sequencing associated with amplicons (e.g., targeted amplicon sequencing), sequencing associated with metagenomics (e.g., macrotranscriptome sequencing, metagenomics sequencing, etc.), sequencing-by-synthesis (sequencing-by-synthesis), and sequencing-by-synthesis (e.g., sequencing-by-sequencing-by-synthesis), etc Tunnel current sequencing, hybridization sequencing, mass spectrometry sequencing, microscopy-based techniques, and/or any suitable next generation sequencing technique. Additionally or alternatively, the sequencing system 215 may perform one or more of capillary sequencing, sanger sequencing (e.g., microfluidic sanger sequencing, etc.), pyrosequencing, nanopore sequencing (oxford nanopore sequencing, etc.), and/or any other suitable type of sequencing facilitated by any suitable sequencing technique.

Additionally or alternatively, the processing system 210 can include a library preparation system (library preparation system) operable to automatically prepare biological samples in a variety of ways (e.g., fragmentation and amplification using primers compatible with genetic targets associated with the appendix-related condition) for sequencing by a sequencing system; and/or any suitable components. The processing system 210 may perform any suitable sample processing techniques described herein. However, the processing system 210 and associated components may be configured in any suitable manner.

Embodiments of system 200 can include one or more appendix-related characterization systems 220, where the characterization systems 220 can be used to determine, analyze, characterize, and/or otherwise process a microbial data set (e.g., based on a biological sample processed to obtain a microbial genetic sequence; an alignment to a reference sequence; etc.); microbiome characteristics (e.g., single variables; variable sets; characteristics for statistical description relevant to phenotypic prediction; variables associated with samples obtained from individuals; variables associated with appendiceal-related conditions; variables describing, in relative or absolute quantities, in whole or in part, microbiome composition and/or function of samples; etc.), models, and/or other suitable data for facilitating appendiceal-related characterization and/or therapeutic intervention. In an example, the appendix-related characterization system 220 can identify data associated with characteristic information that statistically describes differences between samples associated with one or more appendix-related conditions (e.g., presence, absence, risk, predisposition, and/or other aspects associated with the appendix-related condition, etc.), e.g., where different analyses can provide a supplemental view of distinguishing characteristics of different samples (e.g., distinguishing subgroups associated with presence or absence of a condition, etc.). In particular examples, a single predictor variable, a particular biological process, and/or statistically inferred latent variables may provide supplemental information at different data complexity levels to facilitate various downstream opportunities (opportunities) related to characterization, diagnosis, and/or treatment. In another particular example, the appendix-related characterization system 220 processes supplemental data for performing one or more characterization processes.

The appendix-related characterization system 220 can include, generate, apply, and/or otherwise process appendix-related characterization models, which can include any one or more of appendix-related condition models to characterize one or more appendix-related conditions (e.g., to determine a predisposition of one or more appendix-related conditions of one or more users, etc.), treatment models for determining treatment, and/or any other suitable models for any suitable purpose associated with embodiments of the system 200 and/or method 100. In a particular example, the appendix-related characterization system 220 can generate and/or apply a treatment model (e.g., based on a cross-disorder analysis, etc.) to identify and/or characterize a treatment for treating one or more appendix-related disorders. Different appendix-related characterization models (e.g., different combinations of appendix-related characterization models; different models applying different analysis techniques; different input and/or output types; applied in different ways (e.g., time and/or frequency dependent), etc.), which may be applied (e.g., executed, selected, retrieved, stored, etc.) based on one or more of: appendix-related conditions (e.g., different appendix-related characterization models are used depending on the appendix-related condition(s) being characterized, e.g., different levels of applicability for data processing associated with different appendix-related conditions and/or condition combinations, etc.), users (e.g., based on different users' data and/or characteristics, demographic characteristics, genetics, environmental factors, different appendix-related characterization models, etc.), appendix-related characterizations (e.g., different appendix-related characterization models for different types of characterizations, e.g., treatment-related characterizations for diagnosis-related characterizations, e.g., a predisposition score for identifying relevant microbiome composition for determining appendix-related condition; etc.), treatments (e.g., different appendix-related characterization models for monitoring different treatment efficacy, etc.), body part (e.g., for processing different appendix-related characterization models than the microbial datasets corresponding to biological samples from different sample acquisition sites; etc.), supplemental data, and/or any other suitable component. However, the appendix-related characterization model can be customized and/or used in any suitable manner to facilitate appendix-related characterization and/or therapeutic intervention.

The appendix-related characterization system 220 can preferably be used to determine site-specific appendix-related characterizations (e.g., site-specific analysis). In an example, the appendix-related characterization system 220 can generate and/or apply different site-specific appendix-related characterization models. In particular examples, different site-specific appendix-related characterization models can be generated and/or applied based on different microbiome characteristics, such as site-specific characteristics associated with one or more body sites associated with the site-specific appendix-related characterization model (e.g., derived from a sample taken at a subject's intestinal collection site using the intestinal site-specific characteristics, and associated with one or more appendix-related conditions, such as for generating the intestinal site-specific appendix-related characterization model, which can be based on a user sample taken at a user intestinal collection site for determining a characterization; and so forth). Site-specific appendix-related characterization models, site-specific features, samples, site-specific treatments, and/or other suitable entities (e.g., capable of being associated with a body part, etc.), preferably associated with at least one body part (e.g., corresponding to a sample collection site; etc.), including one or more intestinal sites (e.g., characterized based on a stool sample, etc.), skin sites, nasal sites, genital sites (e.g., sites associated with the genitalia, reproductive system), oral sites, and/or any suitable body part. In an example, different appendix-related characterization models can be customized to different types of inputs, outputs, appendix-related characterizations, appendix-related conditions (e.g., different phenotypic indicators that need to be characterized), and/or any other suitable entity. However, the site-specific characterization can be configured in any manner and can be determined in any manner by appendix-related characterization system 220 and/or other suitable components.

The appendix-related characterization models, other components of embodiments of system 200, and/or appropriate portions of embodiments of method 100 (e.g., characterization processes, determining microbiome characteristics, determining appendix-related characterizations, etc.) can employ analytical techniques that include one or more of: univariate statistical tests, multivariate statistical tests, dimension reduction techniques, artificial intelligence methods (e.g., machine learning methods, etc.), performing pattern recognition on data (e.g., identifying correlations between appendix-related conditions and microbiome features), fusing data from multiple sources (e.g., based on microbiome data and/or supplemental data from multiple users associated with one or more appendix-related conditions, e.g., based on microbiome features extracted from the data, generating a characterization model, etc.), combinations of values (e.g., averages, etc.), compression, conversion (e.g., digital-to-analog conversion, analog-to-digital conversion), statistical estimation of data (e.g., normal least squares regression, non-negative least squares regression, principal component analysis, ridge regression, etc.), wave modulation, normalization, updating (e.g., a characterization model and/or a treatment model over time based on the processed biological sample; etc.), ranking (e.g., microbiome characteristics; treatment; etc.), weighting (e.g., microbiome characteristics; etc.), validation, filtering (e.g., for baseline correction, data clipping, etc.), noise reduction, smoothing, padding (e.g., gap-filling), calibration, model fitting, binning, windowing, clipping, transformation, mathematical operations (e.g., derivatives, moving averages, summations, subtractions, multiplications, divisions, etc.), data association, multiplexing (multiplexing), demultiplexing (demultiplexing), interpolation, extrapolation, clustering, image processing techniques, other signal processing operations, other image processing operations, visualization, and/or any other suitable processing operation. The artificial intelligence method may include one or more of: supervised learning (e.g., using logistic regression, using back-propagation neural networks, using random forests, decision trees, etc.), unsupervised learning (e.g., using a priori algorithm, using K-means clustering), semi-supervised learning, deep learning algorithms (e.g., neural networks, constrained boltzmann machine, deep belief network methods, convolutional neural network methods, recursive neural network methods, stacked autoencoder methods, etc.), reinforcement learning (e.g., using Q learning algorithm, using time difference learning), regression algorithms (e.g., ordinary least squares, logistic regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatter plot smoothing, etc.), example-based methods (e.g., K nearest neighbors, learning vector quantization, self-organizing maps, etc.), regularization methods (e.g., ridge regression, least absolute shrinkage, selection of operators, elastic networks, etc.), decision tree learning methods (e.g., classification and regression trees, iterative dichotomy 3, C4.5, chi-squared automated interaction detection, decision stumps, random forests, multivariate adaptive regression splines, gradient pushers, etc.), bayesian methods (e.g., na iotave bayes, mean single dependence estimators, bayesian belief networks, etc.), kernel methods (e.g., support vector machines, radial basis functions, linear discriminant analysis, etc.), clustering methods (e.g., k-means clustering, expectation maximization, etc.), association rule learning algorithms (e.g., a priori algorithm, Eclat algorithm, etc.), artificial neural network models (e.g., perceptron method, back propagation method, Hopfield network method, self-organizing map method, learning vector quantization method, etc.), integration methods (e.g., enhancement methods, etc.) Enhanced aggregation, adaptive enhancement, stacked generalization, gradient enhancement machine methods, random forest methods, etc.) and/or any suitable artificial intelligence method. However, data processing may be used in any suitable manner.

The appendix-related characterization system 220 can perform cross-conditioning analysis for multiple appendix-related conditions (e.g., based on the output of different appendix-related characterization models, such as multi-condition microbiome features, generating multi-condition characterizations; etc.). For example, the appendix-related characterization system can characterize a relationship between appendix-related conditions based on the microbiological data, the microbiome characteristics, and/or microbiome characterizations (e.g., diagnosed, characterized, etc.) associated with other suitable user-related conditions. In a particular example, cross-disorder analysis can be performed based on a characterization of each appendix-related disorder (e.g., an output of a appendix-related characterization model for each appendix-related disorder, etc.). Cross-disorder analysis can include identification of particular disorder features (e.g., associated with only a single appendix-related disorder, etc.), multiple disorder features (e.g., associated with two or more appendix-related disorders, etc.), and/or any other suitable type of feature. Cross-disorder analysis can include determining parameters of report relevance, consistency, and/or other similar parameters that describe a relationship between two or more appendix-related disorders, such as by assessing different pairs of appendix-related disorders. However, the appendix-related characterization system and/or other suitable components can be configured in any suitable manner to facilitate cross-disorder analysis (e.g., applying analysis techniques for cross-disorder analysis purposes; generating cross-disorder characterizations, etc.).

The appendix-related characterization system 220 preferably comprises a remote computing system (e.g., for applying the appendix-related characterization model, etc.), however, can additionally or alternatively comprise any suitable computing system (e.g., a local computing system, a user device, a processing system component, etc.). However, the appendix-related characterization system 220 can be configured in any suitable manner.

Embodiments of the system 200 can include one or more treatment facilitation systems 230 that can be used to facilitate therapeutic intervention (e.g., promote one or more treatments, etc.) for one or more appendix-related conditions (e.g., facilitate modulation of the user's microbiome composition and functional diversity, to improve the user's condition associated with the one or more appendix-related conditions, etc.). Treatment facilitation system 230 can facilitate therapeutic intervention for any number of appendix-related conditions associated with any number of body parts (e.g., corresponding to any suitable number of sample collection sites; etc.), e.g., based on site-specific characterizations (e.g., multi-site characterizations associated with multiple body parts; etc.), multi-condition characterizations, other characterizations, and/or any other suitable data. The therapy facilitation system 230 can include any one or more of: a communication system (e.g., to communicate treatment recommendations, selections, discouraging and/or other suitable treatment-related information to a computing device (e.g., a user device and/or a caregiver device; a mobile device; a smart phone; a desktop computer; in a website, web application and/or mobile application accessed by a computing device; etc.); to enable telemedicine between a caregiver and a subject of an appendix-related condition; etc.), an application executable on the user device (e.g., to indicate a user microbiome composition and/or function; etc.), medical devices (e.g., biological sampling devices, such as for collecting samples from different collection sites; drug delivery devices; surgical systems; etc.), user devices (e.g., biometric sensors), and/or any other suitable components. One or more treatment facilitation systems 230 can be controllable, in communication with, and/or otherwise associated with appendix-related characterization system 220. For example, the appendix-related characterization system 220 can generate a characterization of one or more appendix-related conditions to cause the treatment facilitation system 230 to present (e.g., transmit, communicate, etc.) to a respective user (e.g., at interface 240, etc.). In another example, the treatment facilitation system 230 can update and/or otherwise modify other software of the application and/or device (e.g., a user's smartphone) to facilitate treatment (e.g., promote lifestyle changes in a to-do list application, to improve a user status associated with one or more appendix-related conditions, etc.). However, the therapy facilitation system 230 may be configured in any other manner.

As shown in FIG. 9, embodiments of the system 200 can additionally or alternatively include an interface 240, which interface 240 can improve presentation of the microbiome characteristics, information of the appendix-related condition (e.g., predisposition metric; treatment recommendation; comparison with other users; other characteristics; etc.), and/or specific information (e.g., any suitable data described herein) associated with (e.g., included in, related to, derived from, etc.) one or more of the appendix-related characteristics. In an example, the interface 240 can present information of appendix-related conditions, including microbiome composition (e.g., taxa; relative abundance; etc.), functional diversity (e.g., relative abundance of genes associated with a particular function and a predisposition metric for one or more appendix-related conditions, e.g., relative to a population of users sharing demographic characteristics (e.g., smokers, exercisers, users of different dietary regimens, consumers of probiotics, users of antibiotics, populations undergoing special treatment, etc.)). However, the interface 240 may be configured in any suitable manner.

Although the components of embodiments of system 200 are generally described as distinct components, they may be physically and/or logically integrated in any manner. For example, a computing system (e.g., a remote computing system, a user device, etc.) can implement a portion and/or all of appendix-related characterization system 220 (e.g., applying a microbiome-related condition model, generating a characterization of an appendix-related condition for a user, etc.) and treatment facilitation system 230 (e.g., facilitating treatment intervention by presenting insights related to microbiome composition and/or function, presenting treatment recommendations and/or information, scheduling daily events on a smartphone's calendar application to inform the user about improving appendix-related treatment, etc.). In one example, embodiments of system 200 may omit treatment facilitation system 230. However, the functionality of embodiments of system 200 may be distributed among any suitable system components in any suitable manner. However, the components of embodiments of system 200 may be configured in any suitable manner.

4.1 determination of microbial data sets.

Embodiments of the method 100 can include a block S110, which block S110 can include determining a microorganism dataset (e.g., a microorganism sequence dataset, a microbiome composition diversity dataset, e.g., based on a microorganism sequence dataset, a microbiome functional diversity dataset, e.g., based on a microorganism sequence dataset) associated with a set of users S110. Block S110 can be used to process samples (e.g., biological samples; non-biological samples; sample sets associated with a population of subjects, a subset of subjects sharing demographic characteristics, and/or other suitable characteristics; user samples; etc.) to determine components, functions, pharmacogenomics, and/or other suitable aspects associated with the respective microbiome, such as those associated with one or more appendix-related conditions. The compositional and/or functional aspects can include one or more aspects in the level of the microorganism (and/or other suitable dimensions), including parameters related to the distribution of the microorganism between different kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable sub-taxonomic units (e.g., as measured by the total abundance of each group, the relative abundance of each group, the total number of groups represented, etc.). Compositional and/or functional aspects may also be represented by an Operational Taxonomy Unit (OTU). The compositional and/or functional aspects may additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multisite sequence typing, 16S sequences, 18S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.). Compositional and functional aspects may include the presence or absence or quantity of genes associated with a particular function (e.g., enzymatic activity, transport function, immunological activity, etc.). Thus, the output of block S110 can be used to facilitate determination of microbiome features (e.g., generation of a microbiome sequence dataset, which can be used to identify microbiome features; etc.) for use in characterization processing of block S110 and/or other suitable portions of the method 100 embodiments (e.g., an output of a microbiome composition dataset, a microbiome functional dataset, and/or other suitable microbiome datasets from which the microbiome features can be extracted can be obtained at block S110, etc.), where the features can be microbiome-based (e.g., presence of a bacterial genus), genetically based (e.g., based on a representation of a particular genetic region and/or sequence), function based (e.g., presence of a particular catalytic activity), and/or any other suitable microbiome features.

In one variant, block S110 can include a biological marker protein derived from a bacterial and/or archaebacterium associated with one or more associated gene families, ribosomal protein S2, ribosomal protein S3, ribosomal protein S5, ribosomal protein S7, ribosomal protein S8, ribosomal protein S9, ribosomal protein S10, ribosomal protein S11, ribosomal protein S12/S23, ribosomal protein S13, ribosomal protein S15P/S13e, ribosomal protein S17, ribosomal protein S19, ribosomal protein L1, ribosomal protein L2, ribosomal protein L3, ribosomal protein L4/L1e, ribosomal protein L5, ribosomal protein L6, a ribosomal protein L6, a phenylalanine L6, a protein L6, a protein L, a protein L, a protein L.

Thus, the microbiome composition and/or functional aspects characterizing a collection of biological samples preferably include a combination of sample processing techniques (e.g., wet laboratory techniques; as shown in FIG. 5), including, but not limited to, amplicon sequencing (e.g., 16S, 18S, ITS), UMI, 3-step PCR, CRISPR, metagenomic methods, macrotranscriptomics, use of random primers, and computational techniques (e.g., using bioinformatic tools) to quantitatively and/or qualitatively characterize the microbiome and functional aspects associated with each biological sample from a subject or population of subjects. For example, determining a microbial dataset (e.g., a microbial sequence dataset, etc.) can include determining at least one of a metagenomic library and a macrotranscriptome library based on microbial nucleic acids of one or more samples (e.g., at least a subset of microbial nucleic acids present in the sample; etc.), and in determining a set of microbiome features, can be based on at least one of the metagenomic library and the macrotranscriptome library.

In a variation, the sample processing in block S110 may include one or more of: lysing the biological sample, disrupting the membrane in the cells of the biological sample, separating unwanted elements (e.g., RNA, protein) from the biological sample, purifying nucleic acids (e.g., DNA) from the biological sample, amplifying nucleic acids from the biological sample, further purifying the amplified nucleic acids from the biological sample, and sequencing the amplified nucleic acids from the biological sample. In one example, block S110 may include: a biological sample is collected from a group of users (e.g., a biological sample collected by a user using a sampling kit that includes a sample container, etc.), wherein the biological sample includes a microbial nucleic acid associated with an appendix-related condition (e.g., a microbial nucleic acid that includes a target sequence associated with the appendix-related condition; etc.). In another example, block S110 can include providing a set of sampling kits to a set of users, each sampling kit in the set of sampling kits including a sample container (e.g., including a pretreatment reagent, such as a lysis reagent; etc.) operable to receive a biological sample from one of the set of users.

In a variant, lysing the biological sample and/or disrupting the membrane in the cells of the biological sample preferably comprises physical methods (e.g., bead beating, nitrogen depressurization, homogenization, sonication) which omit certain reagents which, when sequenced, produce deviations in the representation of certain bacterial populations. Additionally or alternatively, the lysis or destruction in block S110 may involve chemical methods (e.g., using detergents, using solvents, using surfactants, etc.). Additionally or alternatively, the lysis or disruption in block S110 may involve a biological method. In variants, the isolation of the undesired elements may comprise the removal of RNA using RNase and/or the removal of proteins using protease. In variants, purification of the nucleic acid may include one or more of the following: precipitating nucleic acids from a biological sample (e.g., using an alcohol-based precipitation method), liquid-liquid based purification techniques (e.g., phenol-chloroform extraction), chromatography-based purification techniques (e.g., column adsorption), purification techniques involving the use of a portion of bound particles (e.g., magnetic beads, floating beads, beads with a size distribution, ultrasonically responsive beads, etc.) configured to bind nucleic acids and configured to release nucleic acids in an elution environment (e.g., with an elution solution, providing a change in pH, providing a change in temperature, etc.), and any other suitable purification technique.

In variants, amplification of the purified nucleic acid may include one or more of: polymerase Chain Reaction (PCR) -based techniques (e.g., solid phase PCR, RT-PCR, qPCR, multiplex PCR, touchdown PCR, nanoPCR, nested PCR, hot start PCR, etc.), helicase-dependent amplification (HDA), loop-mediated isothermal amplification (LAMP), self-sustained sequence replication (3SR), nucleic acid sequence-based amplification (NASBA), Strand Displacement Amplification (SDA), Rolling Circle Amplification (RCA), Ligase Chain Reaction (LCR), and any other suitable amplification technique. In amplification of purified nucleic acids, the primers used are preferably selected to prevent or minimize amplification bias, and are configured to amplify nucleic acid regions/sequences (e.g., 16S region, 18S region, ITS region, etc.), which are informative in informatics, phylogenetics, for diagnostic, formulation (e.g., probiotic formulation), and/or any other suitable purpose. Thus, in amplification, universal primers configured to avoid amplification bias (e.g., F27-R338 primer set for 16S RNA, F515-R806 primer set for 16S RNA, etc.) may be used. Additionally or alternatively, including bound barcode sequences and/or UMIs specific to the biological sample, the user, appendix-associated characterization, taxa, target sequences, and/or any other suitable component (which can facilitate post-sequencing identification processes, e.g., for mapping sequence reads to microbiome composition and/or microbiome functional aspects; etc.). The primers used in variants of Block S110 may additionally or alternatively include primers configured to mate with sequencing technologies involving complementary adaptors (e.g., Illumina sequencing). Additionally or alternatively, Block S110 may implement any other steps configured to facilitate processing (e.g., using Nextera kit). In one particular example, performing amplification and/or sample processing operations can be performed in a multiplexed manner (e.g., for a single biological sample, for multiple biological samples across multiple users; etc.). In another specific example, performing amplification can include a normalization step to balance all amplicons in the library and the detection mixture independent of the amount of starting material, such as 3-step PCR, magnetic bead-based normalization, and/or other suitable techniques.

In variants, sequencing of purified nucleic acids may include methods involving targeted amplicon sequencing, macrotranscriptome sequencing, and/or metagenomic sequencing, implementing techniques including one or more of: sequencing-by-synthesis techniques (e.g., Illumina sequencing), capillary sequencing techniques (e.g., sanger sequencing), pyrosequencing techniques, and nanopore sequencing techniques (e.g., using oxford nanopore techniques).

In one particular example, the nucleic acid amplification and sequencing of the biological sample from the set of biological samples comprises: solid phase PCR involving bridging (bridge) DNA fragments of a biological sample on a substrate using oligonucleotide adaptors, wherein amplification involves primers having a forward index sequence (e.g., Illumina forward index corresponding to the MiSeq/NextSeq/HiSeq platform), a forward barcode sequence, a transposase sequence (e.g., transposase binding site corresponding to the MiSeq/NextSeq/HiSeq platform), an adaptor (e.g., a zero, one, or two base fragment configured to reduce homogeneity and improve sequence outcome), other random bases, UMI, a sequence for targeting a particular target region (e.g., 16S region, 18S region, ITS region), a reverse index sequence (e.g., Illumina reverse index corresponding to the MiSeq/HiSeq platform), and a reverse barcode sequence. In this particular example, sequencing may include Illumina sequencing using sequencing-by-synthesis techniques (e.g., using the HiSeq platform, using the MiSeq platform, using the NextSeq platform, etc.). In another particular example, the method 100 may include: identifying one or more primer types that are compatible with one or more genetic targets associated with one or more appendix-associated conditions (e.g., one or more biomarkers for, positively correlated with, negatively correlated with, causative of, etc.); determining a microorganism data set (e.g., a microorganism sequence data set; e.g., using a next generation sequencing system; etc.) for one or more users (e.g., a group of subjects) based on one or more primer types (e.g., based on primers corresponding to the one or more primer types, and microorganism nucleic acid contained in a collected biological sample), e.g., single-and/or multiple-amplification of fragmented microorganism nucleic acid by fragmenting microorganism nucleic acid, and/or based on one or more identified primer types (e.g., primers corresponding to the primer types, etc.) that are compatible with one or more genetic targets associated with the appendix-associated condition; and/or promoting (e.g., providing) treatment of a condition of the user based on appendix-related characterizations derived from the microbial dataset (e.g., appendix-related condition; being capable of selectively modulating the microbiome of the user associated with at least one of a population size of a desired taxonomic group and a desired microbiome function). In one particular example, determining the microbial dataset can include generating amplified microbial nucleic acids by at least one of a single amplification process and a multiplex amplification process of microbial nucleic acids; a microbial data set is determined based on the amplified microbial nucleic acids using a next generation sequencing system.

In an example, the biological sample may correspond to one or more acquisition sites including at least one of an intestinal acquisition site (e.g., a body part type corresponding to an intestinal site), a skin acquisition site (e.g., a body part type corresponding to a skin site), a nasal acquisition site (e.g., a body part type corresponding to a nasal site), an oral acquisition site (e.g., a body part type corresponding to an oral site), and a genital acquisition site (e.g., a body part type corresponding to a genital site). In one particular example, determining a microbial dataset (e.g., a microbial sequence dataset, etc.) can include identifying a first primer type that is compatible with one or more appendix-associated conditions and a first genetic target associated with a first acquisition site of the set of acquisition sites; identifying a second primer type that is compatible with the one or more appendix-associated conditions and a second genetic target associated with a second collection site of the set of collection sites; a microbial data set is generated for a group of subjects based on microbial nucleic acids, a first primer corresponding to a first primer type, and a second primer corresponding to a second primer type.

In variants, the primers used in block S110 and/or other suitable portions of the method 100 embodiments (e.g., primer types corresponding to primer sequences; etc.) may include primers associated with protein genes (e.g., encoding conserved protein gene sequences across multiple taxa, e.g., capable of multiple amplifications of multiple targets and/or taxa; etc.). The primers can additionally or alternatively be compatible with an appendix-associated condition (e.g., primers compatible with genetic targets including microbial sequence biomarkers of microbes associated with an appendix-associated condition, etc.), a microbiome composition characteristic (e.g., the identified primers are compatible with genetic targets corresponding to a microbiome composition characteristic associated with a set of taxa associated with an appendix-associated condition; genetic sequences from which a relative abundance characteristic is derived, etc.), a functional diversity characteristic, a complementary characteristic, and/or other suitable characteristics and/or data. The primers (and/or other suitable molecules, markers, and/or biological materials described herein) can be of any suitable size (e.g., sequence length, number of base pairs, conserved sequence length, variable region length, etc.). Additionally or alternatively, any suitable number of primers can be used in sample processing for characterization (e.g., appendix-related characterization; etc.), to improve sample processing (e.g., by reducing amplification bias, etc.), and/or for any suitable purpose. The primer can be associated with any suitable number of targets, sequences, taxa, disorders, and/or other suitable aspects. Primers used in block S110 and/or other suitable portions of the method 100 embodiment may be selected by the processes described in block S110 (e.g., primer selection based on parameters used to generate the classification database), and/or any other suitable portion of the method 100 embodiment. Additionally or alternatively, the primers (and/or treatments associated with the primers) may include and/or be similar to those described in U.S. application No.14/919,614 (incorporated herein by reference in its entirety), filed 10, 21/2015. However, the identification and/or use of the primers may be configured in any suitable manner.

Certain variants of sample processing may include further purification of the amplified nucleic acid (e.g., PCR products) prior to sequencing, which function is to remove excess amplification elements (e.g., primers, dntps, enzymes, salts, etc.).

In an example, any one or more of the following may be used to facilitate additional purification: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and/or any other suitable purification technique.

In a variant, the calculation process in block S110 may include any one or more of the following: identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (e.g., aligning fragmented sequences using one or more of single-ended alignment, gapless alignment, gapped alignment, pairing), and generating features associated with (e.g., derived from) compositional and/or functional aspects of a microbiome associated with a biological sample.

Identification of a microbiome-derived sequence may include mapping sequence data from sample processing to a Reference Genome of the subject (e.g., provided by a Genome Reference Consortium) in order to remove subject Genome-derived sequences. The unidentified sequences remaining after mapping the sequence data to the subject's reference genome can then be further clustered into Operable Taxonomic Units (OTUs) based on sequence similarity and/or reference-based methods (e.g., using VAMPS, using MG-RAST, using QIIME database), aligned (e.g., using genome hashing methods, using Needleman-Wunsch algorithms, using Smith-Waterman algorithms), mapped to the reference bacterial genome (e.g., provided by National center for Biotechnology Information) using alignment algorithms (e.g., basic local alignment tools, FPGA accelerated alignment tools, BWT indexing using BWA, BWT indexing using SOAP, BWT indexing using Bowtie, etc.). The mapping of the unrecognized sequence may additionally or alternatively include mapping to a reference archaeal genome, a viral genome, and/or a eukaryotic genome. Furthermore, the mapping of the taxonomy units may be performed with respect to existing databases and/or with respect to custom generated databases.

However, processing the biological sample, generating the microbial data set, and/or other aspects associated with block S110 may be performed in any suitable manner.

4.2 processing the supplementary data.

Embodiments of the method 100 can additionally or alternatively include block S120, which can include processing (e.g., receiving, collecting, transforming, determining, ranking, identifying relevance, etc.) supplemental data (e.g., one or more supplemental data, etc.) associated with (e.g., providing information; describing; indicating; relating to; etc.) one or more appendix-related conditions, one or more users, and/or other suitable entities. Block S120 can be used to process data that supplements the microbial dataset, the microbiome characteristics (e.g., associated with determining appendix-related characteristics and/or facilitating therapeutic intervention, etc.), and/or can be used to supplement any suitable portion of method 100 and/or system 200 (e.g., to process supplemental data that facilitates one or more characterization processes; e.g., in block S130; e.g., to facilitate training, validation, generation, determination, application, and/or otherwise processing appendix-related characterization models, etc.). In an example, the supplemental data can include at least one of survey-derived data, user data, site-specific data, and device data (and/or other suitable supplemental data), wherein an example of the method 100 can include determining a set of supplemental features based on at least one of the survey-derived data, user data, site-specific data, and device data (and/or other suitable supplemental data); and generating one or more appendix-related characterization models based on the supplemental characteristics, the microbiome characteristics, and/or other suitable data.

The supplemental data may include one or more of the following: survey derived data (e.g., response data from one or more surveys investigated for one or more appendix-related conditions, data of any suitable type described herein; etc.); site-specific data (e.g., data that provides information to different collection sites, such as a priori biological knowledge indicating the association of microbiota between a particular collection site and one or more appendix-related conditions; etc.); data for appendix-related conditions (e.g., data that provides information about various appendix-related conditions, e.g., related to microbiome characteristics, treatment, user, etc.); device data (e.g., sensor data; contextual sensor data associated with the appendix; wearable device data; medical device data; user's device data, such as mobile phone application data; network application data, etc.); user data (e.g., current user medical data and historical medical data, such as historical treatment, historical medical examination data, medical device-derived data, physiological data, data related to medical examinations, social media data, demographic data, family history data, behavioral data describing behaviors, environmental factor data describing environmental factors, diet-related data, such as data from food service registrations, data from spectrophotometric analysis, data input by a user, nutritional data associated with probiotics and/or prebiotic food, food consumption type, food consumption, caloric data, diet regimen data, and/or other suitable data related to diet; etc.); prior biological knowledge (e.g., informative for the appendix-related condition, the microbiome signature, the association between the microbiome signature and the appendix-related condition, etc.); and/or any other suitable type of supplemental data.

In variations, processing the supplemental data may include processing survey-derived data, where the survey-derived data may provide physiological data, demographic data, behavioral data, environmental factor data (e.g., describing environmental factors, etc.), other types of supplemental data, and/or any other suitable data. The physiological data may include information related to physiological characteristics (e.g., height, weight, body mass index, body fat percentage, body hair level, medical history, etc.). Demographic data may include information related to demographic characteristics (e.g., gender, age, race, marital status, number of siblings, socioeconomic status, sexual orientation, etc.). The behavior data may describe one or more of the following behaviors: health-related conditions (e.g., health and disease states), eating habits (e.g., alcohol consumption, caffeine consumption, miscellaneous foods, vegetarian foods, strict vegetarian foods, sugar consumption, acid consumption, wheat, egg, soy, nuts, peanuts, shellfish consumption, food preferences, allergy profiles, consumption and/or avoidance of other food items, etc.), behavioral tendencies (e.g., workout levels, medication use, alcohol use, habit development, etc.), varying degrees of activity (e.g., amount of exercise, e.g., low, medium, and/or extreme workout activities; related to distance traveled over a given period of time; indicated by a motion sensor, e.g., a motion and/or location sensor; etc.), varying levels of sexual activity (e.g., related to the number and sexual orientation of the partner), and any other suitable behavioral data. The survey-derived data may include quantitative data, qualitative data, and/or other suitable types of survey-derived data, for example, the qualitative data may be converted to quantitative data (e.g., using severity ratings, mapping qualitative score responses to quantitative scores, etc.). Processing the survey-derived data can include facilitating collection of the survey-derived data, for example, by providing one or more surveys to one or more users, subjects, and/or other suitable entities. The survey may be provided in person (e.g., in coordination with the provision of the sampling kit and/or the receipt of the sample; etc.), electronically (e.g., during account setup; an application executing on the subject's electronic device, at a web application and/or website accessible via an internet connection; etc.), and/or in any other suitable manner.

Additionally or alternatively, processing the supplemental data can include processing sensor data (e.g., a sensor of the appendix-related device, a wearable computing device, a mobile device; a biometric sensor associated with the user, such as a biometric sensor of a user smartphone; etc.). The sensor data may include one or more of the following: physical exercise and/or physical activity related data (e.g., accelerometer data, gyroscope data, location sensor data, such as GPS data and/or other mobility sensor data from one or more devices, such as a mobile device and/or wearable electronic device, etc.), sensor data describing environmental factors (e.g., temperature data, altitude data, climate data, lighting parameter data, pressure data, air quality data, etc.), biometric sensor data (e.g., blood pressure data, temperature data, pressure data associated with swelling, heart rate sensor data, fingerprint sensor data, optical sensor data, such as facial images and/or video, data recorded by sensors of a mobile device, data recorded by a wearable device or other peripheral device, etc.), and/or any other suitable data associated with the sensors. Additionally or alternatively, the sensor data may include data sampled at one or more of: optical sensors (e.g., image sensors, light sensors, cameras, etc.), audio sensors (e.g., microphones, etc.), temperature sensors, volatile compound sensors, air quality sensors, weight sensors, humidity sensors, depth sensors, location sensors (GPS receivers; beacons; indoor positioning systems; compasses, etc.), motion sensors (e.g., accelerometers, gyroscopes, magnetometers, motion sensors integrated by a device worn by the user), biometric sensors (e.g., heart rate sensors for monitoring heart rate, fingerprint sensors, facial recognition sensors, bioimpedance sensors, etc.), pressure sensors, proximity sensors (e.g., for monitoring motion and/or other aspects of third party objects associated with the user's appendix period; etc.), a flow sensor, a power sensor (e.g., a hall effect sensor), a sensor associated with virtual reality, a sensor associated with augmented reality, and/or any other suitable type of sensor.

Additionally and alternatively, the supplemental data may include medical record data and/or clinical data. As such, portions of the supplemental data set may be derived from one or more Electronic Health Records (EHRs). Additionally or alternatively, the supplemental data may include any other suitable diagnostic information (e.g., clinical diagnostic information). Any suitable supplemental data (e.g., in the form of extracted supplemental features, etc.) may be combined with the microbiome features and/or other suitable data and/or used to perform portions of embodiments of method 100 and/or system 200 (e.g., perform a characterization process, etc.). For example, supplemental data associated with (e.g., derived from) a computed tomography scan (CT scan), ultrasound, colonoscopy, biopsy, blood test, abdominal examination (e.g., to detect inflammation, etc.), urinalysis (e.g., to detect infection, etc.), diagnostic imaging, other suitable diagnostic procedures associated with appendiceal-related conditions, survey-related information, and/or any other suitable test may be used to supplement (e.g., for any suitable portion of an embodiment of method 100 and/or system 200).

Additionally or alternatively, the supplemental data may include treatment-related data including one or more of: treatment regimen, type of treatment, recommended treatment, treatment used by the user, treatment compliance, and/or other treatment-related suitable data. For example, the supplemental data can include user compliance metrics (e.g., medication compliance, probiotic compliance, physical exercise compliance, diet compliance, etc.) related to one or more treatments (e.g., recommended treatments, etc.). However, processing the supplemental data may be performed in any suitable manner.

4.3 perform the characterization process.

Embodiments of method 100 can include block S130, which block S130 can include performing characterization processing (e.g., preprocessing; feature generation; feature processing; site-specific characterization, e.g., characterization specific to one or more specific body parts, e.g., samples taken at acquisition sites corresponding to body parts, e.g., multi-site characterization of multiple body parts; cross-disorder analysis of multiple appendix-related disorders; model generation, etc.) associated with one or more appendix-related disorders, e.g., S130 based on a microorganism data set (e.g., derived from block S110, etc.) and/or other suitable data (e.g., a supplemental data set; etc.). Block S130 can be used to identify, determine, extract, and/or otherwise process features and/or combinations of features that can be used to determine a user or a set of user appendix-related characterizations based on user microbiome composition (e.g., microbiome composition diversity features, etc.), function (e.g., microbiome functional diversity features, etc.), and/or other suitable microbiome features (e.g., by generating and applying a characterization model, for determining appendix-related characterizations, etc.). As such, the characterization process can be used as a diagnostic tool that can characterize a subject based on microbiome composition and/or functional characteristics associated with one or more health conditions states (e.g., the state of an appendix-related condition), behavioral characteristics, medical conditions, demographic characteristics, and/or any other suitable characteristics. Such characterization may be used to determine, recommend, and/or provide treatment (e.g., personalized treatment as determined by a treatment model, etc.), and/or otherwise facilitate therapeutic intervention.

Performing characterization process S130 can include preprocessing the microbiome dataset, microbiome features, and/or other suitable data to facilitate downstream processing (e.g., determining appendix-related characterizations, etc.). In an example, performing the characterization process may include: filtering the microorganism data set (e.g., filtering the microorganism data set, e.g., prior to determining a microbiome characteristic using a set of analytical techniques, etc.), by at least one of: a) removing first sample data (e.g., associated with one or more appendix-related conditions, etc.) corresponding to a first sample outlier of a set of biological samples, determining the first sample outlier, e.g., by at least one of principal component analysis, dimension reduction techniques, and multivariate methods; b) removing second sample data corresponding to a second sample outlier of the set of biological samples, wherein the second sample outlier may be determined based on a corresponding data quality of the set of microbiome features (e.g., below a threshold condition, removing samples corresponding to some microbiome features having high quality data, etc.); c) removing one or more microbiome features from the microbiome feature based on a number of samples for which the number of samples of the microbiome feature does not satisfy the threshold number of samples condition, wherein the number of samples corresponds to a number of samples associated with high quality data for the microbiome feature. However, the pre-processing may be performed using analytical techniques in any suitable manner.

In performing the characterization process, block S130 can characterize the subject as exhibiting characteristics associated with one or more appendix-related conditions (e.g., characteristics of a group of users having one or more appendix-related conditions, etc.) using computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) (e.g., where user microbiome characteristics are determined, can include identifying characteristic values of the microbiome characteristics by a characterization process that is associated with or otherwise associated with one or more appendix-related conditions, etc.).

As shown in FIG. 3, performing the characterization process can include determining one or more microbiome characteristics associated with the one or more appendix-related conditions (e.g., determining the microbiome characteristic most relevant to the one or more appendix-related conditions; determining a user microbiome characteristic, e.g., the presence, absence, and/or value of a user microbiome characteristic corresponding to the identified microbiome characteristic, etc., wherein the identified microbiome characteristic is associated with the one or more appendix-related conditions), for example, by applying one or more analysis techniques. In an example, determining a microbiome characteristic (e.g., a microbiome composition characteristic, a microbiome functional characteristic, etc.) can apply a set of analysis techniques, including at least one of univariate statistical tests, multivariate statistical tests, dimension reduction techniques, and artificial intelligence methods, such as based on a microbiome data set (e.g., a microbiome sequence data set, etc.), and wherein the microbiome characteristic can be configured to improve a function associated with a computing system associated with determining an appendix-related condition for a user (e.g., associated with accuracy, error reduction, processing speed, scaling, etc.). In one example, determining the microbiome characteristic (e.g., the user microbiome characteristic, etc.) may include applying a set of analysis techniques, determining at least one of a presence of at least one of a microbiome composition diversity characteristic and a microbiome functional diversity characteristic, an absence of at least one of a microbiome composition diversity characteristic and a microbiome functional diversity characteristic, a relative abundance characteristic describing a relative abundance of different taxonomic groups associated with the first appendix-related condition, a ratio characteristic describing a ratio between at least two microbiome characteristics, an interaction characteristic describing an interaction between different taxonomic groups, a phylogenetic distance characteristic describing a phylogenetic distance between different taxonomic groups, e.g., based on a microbiome data set, and wherein the set of analysis techniques may include a univariate statistical test, a multivariate test, a test, Dimension reduction technology and artificial intelligence method.

In a variant, generating features associated with (e.g., derived from) the microbiome composition and functional aspects associated with the biological sample may be performed once a representative group of microorganisms of the microbiome associated with the biological sample is identified. In one variation, generating the features may include generating the features based on multi-site sequence typing (MSLT) to identify markers that may be used for characterization in subsequent blocks of the method 100. Additionally or alternatively, generating the features may include generating features that describe the presence or absence of certain microbiome classification groups, and/or the ratios between displayed microbiome classification groups. Additionally or alternatively, generating the features may include generating features describing one or more of: the number of representative taxonomic groups, the network of representative taxonomic groups, the representative associations of different taxonomic groups, the interactions between different taxonomic groups, the products produced by different taxonomic groups, the interactions between the products produced by different taxonomic groups, the ratio between dead and live microorganisms (e.g., for different representative taxonomic groups based on RNA analysis), the phylogenetic distance (e.g., according to the Kantorovich-Rubinstein distance, Wasserstein distance, etc.), any other suitable taxonomic group-related characteristics, any other suitable genetic or functional aspect.

Additionally or alternatively, generating the features may include generating features that describe the Relative Abundance of different groups of microorganisms, for example, using the sparCC method, using the Genome Relative Abundance and Average size (GAAS) method, and/or using the Genome Relative Abundance using mixed Model theory (GRAMMy) method that uses sequence similarity data for maximum likelihood estimation of the Relative Abundance of one or more groups of microorganisms. Additionally or alternatively, generating the feature may include generating a statistical measure of the taxonomic variation derived from the abundance metric. Additionally or alternatively, generating the feature may include generating a feature associated with (e.g., derived from) the relative abundance factor (e.g., associated with a change in abundance of one taxon that affects the abundance of the other taxon). Additionally or alternatively, generating the features may include generating qualitative features that describe one or more taxonomic groups in isolation and/or in combination. Additionally or alternatively, generating the features may include feature generation associated with genetic markers (e.g., representative 16S, 18S and/or ITS sequences) characterizing the microorganisms of the microbiome associated with the biological sample. Additionally or alternatively, generating a feature may include feature generation related to a particular gene and/or a biofunctional association with a particular gene. Additionally or alternatively, generating the feature may include feature generation related to pathogenicity of the taxon and/or a product attributed to the taxon. However, block S130 may include any other suitable feature determination that determines sequencing and mapping derived from biological sample nucleic acids. For example, one or more features may be combined (e.g., relating to two groups, three groups), correlated (e.g., correlating with correlations between different features), and/or correlated with changes in the features (e.g., temporal changes, changes across sample sites, etc., spatial changes, etc.). However, the microbiome characteristic may be determined in any suitable manner.

In a variation, performing the characterization process can include performing one or more multi-site analyses associated with the plurality of acquisition sites (e.g., using an appendix-related characterization model; generating a multi-site characterization, etc.), such as performing an appendix-related characterization based on a set of site-specific features including a first subset of site-specific features associated with a first body site and a second subset of site-specific features associated with a second body site. However, the multi-site analysis may be performed in any suitable manner.

In variations, performing the characterization process can include performing one or more cross-disorder analyses for a plurality of appendix-related disorders (e.g., using an appendix-related characterization model, etc.). In one example, performing the cross-disorder analysis can include determining a set of cross-disorder features (e.g., as part of determining the microbiome features, etc.) associated with a plurality of appendix-related disorders (e.g., a first appendix-related disorder and a second appendix-related disorder, etc.) based on one or more analysis techniques, wherein determining the appendix-related characterization can include determining, for the user, appendix-related characterizations of the plurality of appendix-related disorders (e.g., the first and second appendix-related disorders, etc.) based on one or more appendix-related characterization models, and the set of cross-disorder features can be configured to improve a computing system-related function associated with determining, for the user, appendix-related characterizations of the plurality of appendix-related disorders. Performing the cross-disorder analysis can include determining a correlation metric for the cross-disorder (e.g., correlation and/or covariance between data corresponding to different appendix-related disorders, etc.) and/or other suitable metrics associated with the cross-disorder analysis. However, cross-disorder analysis may be performed in any suitable manner.

In a variation, the characterization can be based on features that are associated with (e.g., derived from) a statistical analysis (e.g., analysis of a probability distribution) of similarity and/or dissimilarity between a first group of subjects exhibiting a target state (e.g., appendix-related condition) and a second group of subjects not exhibiting a target state (e.g., a "normal" state). In practicing this variant, one or more of the Kolmogorov-Smirnov (KS) test, the displacement test, the Cram mer-von Mises test, any other statistical test (e.g., t-test, z-test, chi-square test, distribution-related test, etc.), and/or other suitable analytical techniques may be used. In particular, one or more such statistical hypothesis tests may be used to evaluate a set of features having different abundances in a first group of subjects exhibiting a target state (e.g., a diseased state) and a second group of subjects not exhibiting the target state (e.g., having a normal state). . In more detail, the set of features evaluated may be constrained to increase or decrease the confidence of the characterization based on the percentage of abundance associated with the first and second sets of subjects and/or any other suitable parameter related to diversity. In a particular implementation of this example, the features can be derived from a taxon of bacteria that is abundant in a proportion of the first and second groups of subjects, wherein the relative abundance of the taxon between the first and second groups of subjects can be determined by the KS test and has a significance indication (e.g., expressed in a p-value). Thus, the output of block S130 can include a normalized relative abundance value with a significance indication (e.g., a p-value of 0.0013) (e.g., between a diseased subject and a healthy subject, the abundance of a subject with the appendix-related disorder is increased by 25% compared to a subject without the appendix-related disorder). The variant of feature generation may additionally or alternatively be implemented or derived from a functional or metadata feature thereof (e.g., a non-bacterial marker). Additionally or alternatively, any suitable microbiome characteristic may be derived based on statistical analysis (e.g., applied to a microbiome sequence dataset and/or other suitable microbiome dataset, etc.), including any one or more of the following: predictive analysis, multi-hypothesis testing, random forest testing, principal component analysis, and/or other suitable analysis techniques.

In performing the characterization process, block S130 may additionally or alternatively convert the input data from at least one of a microbiome composition diversity dataset and a microbiome functional diversity dataset into feature vectors, which may be tested for predicting efficacy of the characterization of the population of subjects. Data from the supplemental data set may be used to provide an indication of one or more tokens of a set of tokens, where the token processing is trained using the candidate features and a training data set of candidate classifications to identify features and/or feature combinations with high features (or low degree) predictive power to accurately predict classifications. . In this way, a feature set (e.g., of subject features, feature combinations) that is highly correlated with a particular classification of a subject is identified using a refinement of the characterization process of the training data set.

In one variation, the feature vector (and/or any suitable set of features) that effectively predicts the classification of the characterization process may include features that are associated with one or more terms: a microbiome diversity metric (e.g., associated with distribution across a microbiome, associated with distribution in an archaeal, bacterial, viral, and/or eukaryotic population), the presence of a microbiome in a microbiome, a representation of a particular genetic sequence (e.g., a 16S sequence) in a microbiome, the relative abundance of a microbiome in a microbiome, a microbiome stress resistance index (e.g., corresponding to perturbations determined from a supplemental dataset), the abundance of genes encoding proteins or RNAs (enzymes, transporters, proteins in the immune system, hormones, interfering RNAs, etc.) with a given function, and other suitable characteristics associated with (e.g., derived from) a microbiome diversity dataset and/or a supplemental dataset. In a variant, the microbiome characteristic may be associated with (e.g., include, correspond to, represent, etc.) at least one of: the presence of a microbiome characteristic from a microbiome characteristic (e.g., a user microbiome characteristic, etc.), the absence of a microbiome characteristic from the microbiome characteristic, the relative abundance of different taxonomic groups associated with the appendix-related condition; a ratio between at least two microbiome features associated with different taxonomic groups, an interaction between different taxonomic groups, and a phylogenetic distance between different taxonomic groups. In a particular example, the microbiome features can include relative abundance features associated with at least one of microbiome composition diversity features (e.g., relative abundances associated with different taxa, etc.) and microbiome functional diversity features (e.g., relative abundances of sequences corresponding to different functional features; etc.). The relative abundance features and/or other suitable microbiome features (and/or other suitable data described herein) may be extracted and/or otherwise determined based on: normalization, feature vectors derived from at least one of linear latent variable analysis and non-linear latent variable analysis, linear regression, non-linear regression, kernel methods, feature embedding methods, machine learning methods, statistical inference methods, and/or other suitable analysis techniques. Additionally or alternatively, combinations of features can be used in the feature vector, where the features can be grouped and/or weighted to provide combined features as part of the feature set. For example, a feature or set of features may comprise a weighted combination of: the number of representative classes of bacteria in a microbiome, the presence of a particular genus of bacteria in a microbiome, a representation of a particular 16S sequence in a microbiome, and the relative abundance of bacteria of the first phylum over bacteria of the second phylum. However, the feature vectors may additionally or alternatively be determined in any other suitable manner.

In a variation, the characterization process may be generated and trained according to a Random Forest Predictor (RFP) algorithm that combines bagging (e.g., guided aggregation) and randomly selected feature sets from a training data set to construct a decision tree T that is associated with random feature sets. When using a random forest algorithm, N cases are randomly drawn from the decision tree set for replacement to create a subset of the decision tree, and for each node, m predicted features are selected from all the predicted features for evaluation. Predictive features of an optimal segmentation (split) are provided at a node (e.g., according to an objective function) for performing segmentation (e.g., bifurcation at the node and trifurcation at the node). By sampling multiple times from a large data set, the strength of the characterization process can be greatly increased when identifying features that are strong in predictive classification. In this variation, measures to prevent bias (e.g., sampling bias) and/or account for the amount of bias may be included during processing, for example, to increase the robustness of the model.

In a variation, block S130 and/or other portions of embodiments of method 100 may include applying computer-implemented rules (e.g., models, feature selection rules, etc.) to process data at a population level, however may additionally or alternatively include applying computer-implemented rules to process microbiome-related data on a demographic-specific basis (e.g., a subset that shares one or more demographic characteristics, such as a treatment regimen, a dietary regimen, a physical exercise regimen, a race, an age, a gender, a weight, a behavior, etc.), a basis for a particular condition (e.g., shows a subset of a particular appendix-related condition, a combination of appendix-related conditions, triggers for appendix-related conditions, related symptoms, etc.), a particular type of sample basis (e.g., applying different computer-implemented rules to process microbiome data derived from different collection sites, etc.), user base (e.g., rules executed by different computers for different users; etc.), and/or on any other suitable basis. Thus, block S130 may include assigning users in the user population to one or more subgroups; and apply different computer-implemented rules to determine different subsets of features (e.g., using the set of feature types; types of characterization models generated from the features; etc.). However, applying the computer implemented rules may be performed in any suitable manner.

In another variation, block S130 can include processing (e.g., generating, training, updating, executing, storing, etc.) one or more appendix-related characterization models (e.g., appendix-related condition models, treatment models, etc.) of one or more appendix-related conditions (e.g., for outputting to a user a characterization that describes a user microbiome characteristic associated with the appendix-related condition; for outputting a treatment determination treatment model for the one or more appendix-related conditions; etc.). The characterization model preferably utilizes the microbiome features as inputs and preferably outputs appendix-related features and/or any suitable components thereof, although the characterization model may use any suitable inputs to generate any suitable outputs. In one example, block S130 can include transforming the supplemental data, the microbiome composition diversity characteristic, and the microbiome functional diversity characteristic, the other microbiome characteristics, the output of the appendix-related characterization model, and/or other suitable data into one or more characterization models for one or more appendix-related conditions (e.g., training the appendix-related characterization model based on the supplemental data and the microbiome characteristics; etc.). In another example, the method 100 may include: determining a population microbial sequence data set for a population of users associated with one or more appendix-related conditions (e.g., comprising microbial sequence outputs of different users of the population; etc.) based on a set of samples from the population of users (and/or based on one or more primer types associated with the appendix-related condition; etc.); collecting a complementary data set associated with diagnosis of one or more appendix-associated conditions in a population of subjects; and generating a appendix-associated characterization model based on the cluster microbe sequence dataset and the supplemental dataset. In one example, the method 100 can include determining a set of user microbiome features for a user based on a sample from the user, wherein the set of user microbiome features is associated with a set of microbiome features associated with a set of subjects (e.g., determining that the microbiome features are associated with one or more appendix-related conditions based on processing a biological sample corresponding to a set of subjects associated with the one or more appendix-related conditions; a set of microbiome composition features and the set of microbiome functional features; etc.); determining an appendix-related characterization comprising determining for the user a treatment for one or more appendix-related conditions based on the treatment model and the set of user microbiome characteristics; provide therapy (e.g., on a computing device associated with the user, provide therapy recommendations to the user, etc.) and/or otherwise facilitate therapeutic intervention.

In another variation, as shown in FIGS. 8A-8B, different appendix-related characterization models and/or other suitable models can be generated (e.g., generated with different algorithms, different feature sets, different input and/or output types, applied in different ways, e.g., related to time, frequency, components of the applied models, etc.), for different appendix-related conditions, different user demographics (e.g., based on age, gender, weight, height, race), different body parts (e.g., bowel part models, nasal part models, skin part models, oral part models, genital part models, etc.), individual users, supplemental data (e.g., models incorporating prior knowledge of microbiome features, appendix-related conditions, and/or other suitable components; features associated with biometric sensor data and/or survey response data versus independent models Models of supplemental data, etc.), and/or other suitable criteria. In a particular example, the method 100 may include acquiring a first site-specific sample associated with a first body site (e.g., an intestinal site; a sample acquired by a user at a body acquisition site corresponding to the first body site; one or more suitable body sites; etc.); determining a microbial dataset based on the site-specific sample; determining a first site-specific microbiome characteristic (e.g., a site-specific combinatorial characteristic; a site-specific functional characteristic; a suitable microbiome characteristic described herein that is associated with an appendix-related condition; a characteristic associated with the first body site; etc.) based on the microbiome data set, determining a first site-specific appendix-related characterization model (e.g., a gut site-specific appendix-related characterization model; etc.) based on the first site-specific microbiome characteristic; and determining a condition associated with the appendix-related condition for the user based on the first site-specific appendix-related characterization model (e.g., using the first site-specific appendix-related characterization model to process a user microbiome characteristic, such as a user site-specific microbiome characteristic, derived based on a user sample taken at a user body collection site corresponding to the first body site, etc.). In a particular example, the method 100 may include acquiring a second site-specific sample associated with a second body site (e.g., at least one of a skin site, a genital site, an oral site, and a nasal site; one or more suitable body sites; etc.); determining a second site-specific microbiome characteristic (e.g., site-specific compositional characteristics; site-specific functional characteristics; characteristics associated with a second body site; etc.); generating a second site-specific appendix-related characterization model (e.g., associated with a second body site; etc.) based on the second site-specific compositional features; collecting a user sample from an additional user, the user sample being associated with a second body part (e.g., by the additional user collecting at a collection site corresponding to the second body part; etc.); and determining additional relevant representations of the appendix-related condition for additional users based on the second site-specific appendix-related representation model (e.g., from a set of site-specific appendix-related representation models, based on an association between the user sample and the body part, selecting a second site-specific appendix-related representation model application, e.g., selecting a skin site-specific appendix-related representation model application based on the user sample taken at the user skin collection site, etc.).

In variations, determining the appendix-related trait and/or any other suitable characteristic can include determining a site-specific appendix-related trait (e.g., a site-specific analysis) that includes appendix-related traits that are associated with a particular body site (e.g., gut, healthy gut, skin, nose, mouth, genitalia, other suitable body site, other sample collection site, etc.), for example, by any one or more of: determining an appendix-related characterization (e.g., defining a correlation between an appendix-related condition and microbiome features associated with one or more body parts) based on an appendix-related characterization model derived from the part-specific data; the appendix-related characterization is determined based on processing of the user's biological sample taken from one or more body parts and/or any other suitable relevant part. In examples, machine learning methods (e.g., classifiers, depth learning algorithms, SVMs, random forests), parameter optimization methods (e.g., bayesian parameter optimization), validation methods (e.g., cross validation methods), statistical tests (e.g., univariate statistical techniques, multivariate statistical techniques, correlation analyses, such as typical correlation analyses, etc.), dimension reduction techniques (e.g., PCA), and/or other suitable analysis techniques (e.g., as described herein) can be applied to determine site-related (e.g., body-site-related, etc.) characterizations (e.g., using one or more methods for one or more sample acquisition sites, e.g., for each type of sample acquisition site, etc.), other suitable characterizations, treatments, and/or any other suitable output. In a particular example, performing the characterization process (e.g., determining an appendix-related characterization; determining a microbiome signature; based on an appendix-related characterization model; etc.) can include applying at least one of: machine learning methods, parameter optimization methods, statistical tests, dimension reduction methods, and/or other suitable methods (e.g., wherein a microbiome characteristic, such as a set of microbiome composition diversity characteristics and/or a set of microbiome functional diversity characteristics, may be associated with a microorganism collected at least at one of an intestinal site, a skin site, a nasal site, an oral site, a genital site, etc.). In another particular example, characterization processing performed on a plurality of sample acquisition sites may be used to generate individual characterizations that may be combined to determine an overall characterization (e.g., an overall microbiome score, e.g., for one or more conditions described herein, etc.). However, method 100 may include determining any suitable site-specific (e.g., site-specific) output, and/or performing any suitable portion of an embodiment of method 100 (e.g., collecting a sample, processing a sample, determining a treatment) using site-specific and/or other site-specific correlations in any suitable manner.

Characterization of the subject may additionally or alternatively implement the use of high false positive tests and/or high false negative tests to facilitate sensitivity of the analytical characterization process to support the analysis generated according to embodiments of the method 100. However, one or more characterization processes S130 may be performed in any suitable manner.

A appendix-related characterization treatment.

Performing characterization process S130 can include performing appendix-related characterization process S135 (e.g., determining a characterization of one or more appendix-related conditions; determining and/or applying one or more appendix-related characterization models; and the like), for example, for one or more users (e.g., data corresponding to samples from a set of subjects for generating one or more appendix-related characterization models, e.g., where one or more subjects are associated with an appendix-related condition, e.g., a subject diagnosed with one or more appendix-related conditions; e.g., by using one or more appendix-related characterization models, e.g., by applying one or more appendix-related characterization models to a user microbiome sequence data set derived from sequencing a user' S sample, generating a appendix-related characterization for a single user; etc.) and/or for one or more appendix-related conditions.

In a variation, performing the appendix-related characterization process can include determining a microbiome signature associated with the one or more appendix-related conditions. In one example, performing the appendix-related characterization process can include applying one or more analysis techniques (e.g., statistical analysis) to identify a microbiome feature set (e.g., microbiome composition feature, microbiome composition diversity feature, microbiome functional diversity feature, etc.) that has a highest correlation (e.g., positive correlation, negative correlation, etc.) with one or more appendix-related conditions (e.g., a feature associated with a single appendix-related condition, a cross-condition feature associated with multiple appendix-related conditions, and/or other suitable appendix-related conditions, etc.). In a particular example, determining a set of microbiome features (e.g., associated with and/or otherwise associated with one or more appendix-related conditions; for generating one or more appendix-related characterization models, etc.) can include applying a set of analysis techniques based on the microbiome sequence data to determine at least one of: the method may further comprise the step of determining a set of characteristics of at least one of a microbiome composition diversity characteristic, a presence of at least one of a microbiome composition diversity characteristic, a microbial functional diversity characteristic, a relative abundance characteristic describing a relative abundance of different taxonomic groups associated with the appendix-related condition, a ratio characteristic describing a ratio between at least two microbiome characteristics associated with the different taxonomic groups, an interaction characteristic describing an interaction between the different taxonomic groups, and a phylogenetic distance characteristic describing a phylogenetic distance between the different taxonomic groups, and/or wherein the set of analysis techniques may comprise at least one of a univariate statistical test, a multivariate statistical test, a dimension reduction technique, and an artificial intelligence method.

In a particular example, performing the appendix-related characterization process can facilitate therapeutic intervention of the one or more appendix-related conditions, such as by facilitating an intervention associated with a treatment that has a positive impact on a state of one or more users associated with the one or more appendix-related conditions. In another particular example, performing appendix-related characterization processing (e.g., determining features having the highest correlation with one or more appendix-related conditions, etc.) can be based on a random forest prediction algorithm that is trained using a training dataset derived from a subset of a population of subjects (e.g., subjects having one or more appendix-related conditions; subjects not having one or more appendix-related conditions; etc.) and validated using a validation dataset derived from the subset of the population of subjects. However, the microbiome characteristic and/or other suitable aspect associated with one or more appendix-related conditions can be determined in any suitable manner.

In variations, performing the appendix-related characterization procedure can include performing an appendix-related characterization procedure for an appendix-related condition that includes the absence of an appendix (e.g., a resected appendix; etc.) and/or inflammation associated with the appendix (e.g., appendicitis; inflammation of portions of the large intestine proximal to the area of the appendix; etc.). In an example, performing the appendix-related characterization process can include identifying a microbiome signature that is correlated (e.g., has the highest correlation, positive correlation, negative correlation, etc.) with the absence of the appendix and/or inflammation associated with the appendix (and/or other suitable appendix-related condition, e.g., one or more treatments that would produce a positive effect), training with a training dataset derived from a subset of a population of subjects based on a random forest prediction algorithm, and validating with a validation dataset derived from a subset of the population of subjects. In a particular example, the appendix-related condition can include a condition that is characterizable (e.g., diagnosable) by one or more of a blood test, a urine test, a diagnostic imaging examination (e.g., ultrasound, CT scan, etc.), and/or other suitable diagnostic procedure (e.g., described herein, etc.).

A microbiome signature (e.g., a microbiome composition signature; a site-specific composition signature associated with one or more body sites; a microbiome functional signature; a site-specific functional signature associated with one or more body sites; etc.) associated with one or more appendix-related conditions (e.g., a signature that describes abundance; a signature that describes relative abundance; a signature that describes a functional aspect; a signature derived therefrom; a signature that describes the presence and/or absence; etc.) may include a signature associated with any combination of one or more taxa, such as a signature associated with one or more body sites (e.g., where a microbiome composition signature may include a site-specific composition signature associated with one or more body sites, for example, wherein the correlation between a compositional characteristic and one or more appendix-related conditions may be specific to one or more body parts, e.g., to the microbiome composition observed at the body part, derived from a sample taken at the corresponding body collection site for that body part; etc.): gemfibrococcus (Gemella) (genus) (e.g., genital region), sarcina atypical (e.g., Veillonella oxypica) (species) (e.g., genital region), parvulus pneumophilus (species) (e.g., genital region), Lactobacillus crispatus (species) (e.g., genital region), Phyllobacteriaceae (family) (e.g., genital region), aquabacter (genus) (e.g., genital region), anaerobe (e.g., probiotic region), and combinations thereofGenital sites), geminianaerobes (species) (e.g., genital sites), Ochrobactrum (Ochrobactrum) (genus) (e.g., genital sites), campylobacter cleaveris (mobiunicus curtisii) (species) (e.g., genital sites), Actinomyces neui (species) (e.g., genital sites), anaerobacter lactis (species) (e.g., genital sites), Lactobacillus johnsonii (Lactobacillus johnsonii) (species) (e.g., genital sites), Verrucomicrobiales (vercomobiles) (order) (e.g., genital sites), verrucomicrobial microsomidae (Verrucomicrobia) (phylum) (e.g., genital sites), Verrucomicrobia (Verrucomicrobiae) (e.g., genital sites), Verrucomicrobia (Verrucomicrobia) (e.g., genital sites), enterobacter (0209) (e.g., proteobacteria sp.), genital area), Corynebacterium freiburgense (species) (e.g., genital area), Lactobacillus Akhmro1(Lactobacillus sp. Akhmro1) (species) (e.g., genital area), anaerobacterium 9401487 (anaerobacterium 9401487) (species) (e.g., genital area), Mesorhizobium (Mesorhizobium) (species) (e.g., genital area), Lactobacillus reuteri (Lactobacillus reuteri) (species) (e.g., genital area), Megasphaera UPII199-6(Megasphaera sp. UPII 199-6) (species) (e.g., genital area), Lactobacillus C30An8(Lactobacillus sp. canu) (species) (e.g., genital area), peptococcus S9 Pr-12(peptococcus sp. s9-12) (species) (e.g., genital area), streptococcus lactis (streptococcus sp. cjudae) (e.g., genital area), enterococcus sp. 9-12) (e.g., neisseria sp. 9-12) (species) (e.g., genital area), streptococcus (streptococcus sp. i) (e.sp. enterobacter sp. enterobacter (e.g., enterobacter sp. enterobacter) thereof (e.g., enterococcus sp. enterobacter sp. enterobacter (e.g., enterococcus (, Neisseria mucinicus (Neisseria mucosae) (species) (e.g., intestinal tract site), Agrobactrobaterium argentatum (species) (e.g., intestinal tract site), Bacteroides simplex (species) (e.g., intestinal tract site), Bacteroides vulgatus (species) (e.g., intestinal tract site), Parabacteroides destructor (species) (e.g., intestinal tract site), Megasphaera (genus) (e.g., intestinal tract site), Proteobacteria (phylum) (e.g., intestinal tract site), Micrococcaceae (Micrococcaceae) (family) (e.g., family)Such as intestinal site), streptococcus thermophilus (streptococcus thermophilus) (species) (e.g. intestinal site), streptococcus paracasei (streptococcus paracaseangualis) (species) (e.g. intestinal site), genus geminiella (Gemella) (e.g. intestinal site), Clostridium (Clostridium) (species) (e.g. intestinal site), Actinomycetales (Actinomycetales) (order) (e.g. intestinal site), Actinomycetales (actinomycetalesceae) (family e.g. intestinal site), β -proteobacteria (Betaproteobacteria) (class) (e.g. intestinal site), measles (geobacter) mobaracteatum) (e.g. intestinal site), Lactobacillus (roh) (e.g. intestinal site), enterobacter (enterobacter sp) (e) (e.g. intestinal site), enterobacter (enterobacter sp) (e) (e.g. intestinal site), enterobacter sp) (e.g. intestinal site), enterobacter (enterobacter sp) (e.g. intestinal site), enterobacter (enterobacter sp) (e) (e.g. intestinal site), enterobacter (enterobacter sp) (enterobacter) (e) (e.g. intestinal site), enterobacter (enterobacter sp) (e.g. intestinal site), enterobacter (intestinal site), enterobacter (enterobacter) (e.g. intestinal site), enterobacter (enteron) (e.g. intestinal site), enteron) (e.g. intestinal site), enterobacter (enterobacter) enteron) (e.g. intestinal site), enterobacter (enteron) (e.g. intestinal site), enterobacter (enteron) (e) (e.g. intestinal site) (e) (e.g. intestinal site), enteron (enterobacter (p. 1064), enteron) (e) (e.g. intestinal site), enterobacter (enteron) (e) (e.g. intestinal site), enteron) (e.g. intestinal site), enterobacter (p. 1064), enterobacter (intestinal site) (enterobacter (enteron) (e) (e.g. intestinal site), enteron (enteron) (e) (e.g. intestinal site), enteron) (e.g. intestinal sitetocccaceae (Family) (e.g., intestinal site), Peptococcaceae (Peptococcaceae) (Family) (e.g., intestinal site), Carnobacteriaceae (Carnobacteriaceae) (Family) (e.g., intestinal site), genus E2_20(Dialister sp.e2_20) (species) (e.g., intestinal site), Neisseriales (Neisseriales) (order) (e.g., intestinal site), Megasphaera C1(Megasphaera genospora.cl) (species) (e.g., intestinal site), Moryella (genus) (e.g., intestinal site), syntrophic phyla (synergystates) (phylum) (e.g., intestinal site), erysipelothrix (Erysipelotrichia) (class) (e.g., intestinal site), erysipelothrix (erysipelogynes) (order) (e.g., intestinal site), clostridium Family (clostridium) (Family, intestinal site), unknown enterotype (clostridium) (Family), enterotoxigenia) (e.g., enteron) (39.g., enteron, 11) Bacteroides D22(bacteroides D22) (species) (e.g., intestinal tract site), syntrophic bacteria (syntista) (class) (e.g., intestinal tract site), syntrophic bacteria (syntistales) (order) (e.g., intestinal tract site), syntrophic bacteria (syntistaceae) (family) (e.g., intestinal tract site), Lactobacillus TAB-22(Lactobacillus sp.tab-22) (species) (e.g., intestinal tract site), flavobacterium (flavobacterium) (genus) (e.g., intestinal tract site), sarteridae (Sutterellaceae) (family) (e.g., intestinal tract site), anaerobacter 5_1_63FAA (anaerobiotides sp.5_1_63FAA) (species) (e.g., intestinal tract site), Streptococcus 2011_ Oral _ MS _ A3(Streptococcus 2011_ orusl _ MS _ A3) (species) (e.g., intestinal tract site), Streptococcus 2011_ orusorusorusl _ MS _ A3) (vsrons D3636.2011 _ 2011_ p.g., intestinal tract site) Large fengolds genus S9 AA1-5(Finegoldia sp.s9 AA1-5) (species) (e.g., intestinal tract site), french bacillus (Fretibacterium) (genus) (e.g., intestinal tract site), staphylococcus 3348O2 (staphyloccocusp.3348o2) (species) (e.g., intestinal tract site), clostridium (genus) (e.g., intestinal tract site), enterobacter (intestinobacter) (genus) (e.g., intestinal tract site), Acinetobacter (Acinetobacter) (genus) (e.g., intestinal tract site), Klebsiella (Klebsiella) (e.g., intestinal tract site), bacteroides thetaiotaomicron) (species) (e.g., intestinal tract site), vibrio butyric acid (butryivibrio) (e.g., intestinal tract site), vibrio (butryivibrio) (e.g., intestinal tract site)Site), clostridium gangreniformis (species) (e.g., intestinal site), helicobacter sp (genus) (e.g., intestinal site), hemifusus serperonospora (species) (e.g., intestinal site), Pediococcus (Pediococcus) (genus) (e.g., intestinal site), pterogoniella (Finegoldia magna) (species) (e.g., intestinal site), Blautia hansenii (species) (e.g., intestinal site), Enterococcus faecium (species) (e.g., intestinal site), lactococcus lactis (species) (e.g., intestinal site), Bacillus (Bacillus) (species) (e.g., intestinal site), clostridium difficile (clostridium difficile) (species) (e.g., intestinal site), clostridium welchii (Enterococcus), clostridium difficile (e.g., intestinal site), clostridium difficile (clostridium difficile) (e.g., intestinal site), clostridium difficile (enterobacter) (e.g., intestinal site), intestinal site), Lactobacillus plantarum (species) (e.g., intestinal site), Lactobacillus paracasei (species) (e.g., intestinal site), Bifidobacterium adolescentis (species) (e.g., intestinal site), Bifidobacterium breve (species) (e.g., intestinal site), Bifidobacterium odonta (Bifidobacterium bifidum) (e.g., intestinal site), Bifidobacterium bifidum (Bifidobacterium bifidum) (e.g., intestinal site), Bifidobacterium animalis (Bifidobacterium animalis) (species) (e.g., intestinal site), Bifidobacterium pseudocatenulatum (Bifidobacterium pseudocatenulatum) (e.g., intestinal site), Bacteroides ovatus (Bacteroides ovatus) (e.g., intestinal site), proteose peptone (polyporus basilicium) (e.g., intestinal site), proteose (polyporus basilicus) (e.g., intestinal site), Lactobacillus acidophilus (Lactobacillus) (e.g., intestinal site), Lactobacillus species) (e.g., intestinal site), Lactobacillus acidophilus (e.g., intestinal site), Lactobacillus species (e.g., intestinal site), Lactobacillus acidophilus (Lactobacillus (e.g., intestinal site), Lactobacillus (e.g., intestinal site, Lactobacillus (Lactobacillus), Lactobacillus strain (Lactobacillus), Lactobacillus (e.g., intestinal site, Lactobacillus), Lactobacillus (, Species (e.g., intestinal site), succinicium (genus) (e.g., intestinal site), Sporobacter (genus) (e.g., intestinal site), vibrio ruminolyticus (species) (e.g., intestinal site), Weissella (genus) (e.g., intestinal site), Bacteroides faecalis (species) (e.g., intestinal site)E.g., intestinal site), lactobacillus rhamnosus (lactobacillus rhamnosus) (species) (e.g., intestinal site), Pantoea (Pantoea) (genus) (e.g., intestinal site), Holdemania (Holdemania) (species) (e.g., intestinal site), thermoanaerobacter (order) (e.g., intestinal site), Bifidobacterium bifidum (Bifidobacterium gallicum) (species) (e.g., intestinal site), Bifidobacterium alba (Bifidobacterium pulullum) (species) (e.g., intestinal site), leuconostoc (leuconostoc) (family) (e.g., intestinal site), egeoviridae (leuconostoc) (family) (e.g., intestinal site), egypterium (eggerella) (species) (e.g., intestinal site), lactobacillus (anaerobium) (e.g., intestinal site), escherichia coli (escherichia) (e.g., intestinal site), lactobacillus faecalis (e.g., intestinal site), bacillus (e.g., enterobacter coli) (e.g., intestinal site), escherichia coli (e.g., intestinal site), lactobacillus (e) (e, lactobacillus species) (e.g., intestinal site), lactobacillus (e, lactobacillus, Pseudofollavonia california (species) (e.g., intestinal site), anaerobacterium (anaerobacterium) (e.g., intestinal site), sarcina (paraspora) (e.g., intestinal site), sarcina paucivorans (paraspora paucivorans) (e.g., intestinal site), oscillatoria (oscillatoria) species (e.g., intestinal site), actinomycetoma (species) (e.g., intestinal site), anaerobacterium (brevibacterium) (e.g., intestinal site), actinomycetoma (species) (e.g., intestinal site), anaerobacterium (thermobacillus) (e.g., intestinal site), brevibacterium (brevibacterium) (e.g., intestinal site), lactobacillus (species) (e.g., CR-609 site), anaerobacterium (thermobacter) (e.g., enterobacter) (e, thermoanaerobacterium (thermoanaerobacterium) (e, enterobacter (e), intestinal site), Gelria (e.g., intestinal site), Acidobacterium (order) (e.g., intestinal site), Bacteroides macerans (species) (e.g., intestinal site), Rhodocyclales (order) (e.g., intestinal site), Rhodotorulales (order) (e.g., intestinal site), human fecal anaerobe (species) (e.g., intestinal site), Alistipes finegoldii (species) (e.g., intestinal site), Oscillatoriaceae (family) (e.g., intestinal site), Peptorphis sp.2002-38328 (species) (e.g., intestinal site), Hespel 2002-peptonelia (e.g., intestinal site), Bacteroides 35AE37(Bacteroides sp.35ae37) (species) (e.g., intestinal site), Marvinbryantia (e.g., intestinal site), anaerobacterium mobilis (species) (e.g., intestinal site), anaerobium (e.g., intestinal site), catarrhium (e.g., intestinal site), aspergillus pratensis (e.g., intestinal site), proteophilus (Proteiniphilum) (e.g., intestinal site), rhizoctonia solani (Roseburiafaecis) (species) (e.g., intestinal site), Streptococcus S16-11(Streptococcus sp.s16-11) (species) (e.g., intestinal site), Bacteroides 4072(Bacteroides sp.4072) (species) (e.g., intestinal site), Alistipes shashiii (e.g., intestinal site), enterobacter (intestinis) (e.g., intestinal site), intestinal site), lactobacillus (species) (e.g., intestinal site), bifidobacterium longum (bifidum tsureumiens) (species) (e.g., intestinal site), doramellium (species) (e.g., intestinal site), Bacteroides xylodegradans (Bacteroides xylanisolvens) (species) (e.g., intestinal site), Cronobacter (Cronobacter) (e.g., intestinal site), isocauda (allosca dordovia) (e.g., intestinal site), isocauda (allosca omnivorans) (species) (e.g., intestinal site), lachnifera (e.g., intestinal site), lactanizofer (e.g., intestinal site), cataceae (e.g., intestinal site), aldehara (adleri) (e.g., intestinal site), aldehara (adleruequuaiae) (e.g., intestinal site), aldehara (adlereus) (e.g., intestinal site), aldehara (aldehara) (e.g., aldehara 2) (e.g., aldehara 6324-8652), intestinal site), Bacteroides EBA5-17(Bacteroides sp. EBA5-17) (species) (e.g., intestinal site), oscillatorius (Oscillibacter) (species) (e.g., intestinal site), goldendorferia parmelinii (species) (e.g., intestinal site), corynebacterium NML05a004 (species) (e.g., intestinal site), human excretory salmonella paratyphi (species) (e.g., intestinal site), phogonella DJF _ RR21 (mitsuokla sp. DJF _ RR21) (species) (e.g., intestinal site), vibrio butyrate (butyclomonas) (e.g., intestinal site), Bifidobacterium bifidum (bifidum bris) (species) (e.g., intestinal site), AlistiA species (e.g., intestinal site), a gordonia species (e.g., intestinal site), an anaerobe species (e.g., intestinal site), a Klebsiella species B12(Klebsiella sp.b12) (e.g., intestinal site), another species of bacteria RMA9912 (e.g., intestinal site), an anaerobe species (e.g., intestinal site), a Bacteroides coprocola species (e.g., intestinal site), a blautita sp 5(blautiasp.ser5) (e.g., intestinal site), a Bacteroides sinense species (e.g., intestinal site), a cholecystobacteria coprocola sp.4_1_30 (e.g., udgut site), a brucelloviridae family (e.g., intestinal site), an Enterobacter sp.g., intestinal site), a Enterobacter species (e.g., intestinal site), a Enterobacter sp.345 (e.g., intestinal site) Bifidobacterium bifidum (Bifidobacterium biavatini) (species) (e.g., intestinal site), Peptophilus sp.1-14 (species) (e.g., intestinal site), Mycobacterium HGB5 (Alisipes sp.HGB5) (e.g., intestinal site), Bacteroides SLC1-38 (species) (e.g., intestinal site), Lactobacillus Akhmro1(Lactobacillus sp.Akhmro1) (species) (e.g., intestinal site), Klebsiella SOR89(Klebsiella sp.SOR89) (species) (e.g., intestinal site), Enterococcus C6I11(Enterococcus sp.6C11) (species) (e.g., intestinal site), Pseudomonas novovorifera (e.g., intestinal site), Bacteroides V9 (Lactobacillus sp.6C11) (species) (e.g., intestinal site), Pseudomonas rhodobacter sp.31 (E.g., Lactobacillus rhodobacter sp.3) (e.g., Escherichia coli sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp., intestinal site), Campylobacter CCUG 45114(Varibaculum sp. CCUG 45114) (species) (e.g., intestinal site), Vibrio butyrate 214-4 (butyricimonassasp.214-4) (species) (e.g., intestinal site), anaerobic rhamnus (species) (e.g., intestinal site), coccus negative S5-A15(Negativicoccus sp.S5-A15) (species) (e.g., intestinal site), [ Collinsella: (Colinula)), [ Colinula (Colinula)), (Colinula), (Col]Masssiliensis (Klebsiella pneumoniae)]Mosaics (species) (e.g., intestinal tract site), Corynebacterium jw37(Corynebacterium sp. jw37) (species) (e.g., intestinal tract site), Roseburia 499(Roseburia sp.499) (species) (e.g., intestinal tract site)Tract site), genus Microbacterium S7MSR5 (Diarister sp.S7MSR5) (species) (e.g., intestine site), genus Anaerococcus S887-3 (Anaerococcus sp.S887-3) (species) (e.g., intestine site), genus Macrogol S8F 7 (Finegoldiasp.S8F 7) (species) (e.g., intestine site), genus Meyer S9 PR-10(Murdochiella sp.S9 PR-10) (species) (e.g., intestine site), genus Peptophilus S9 PR-13 (Peptophilus sp.S9 PR-13) (species) (e.g., intestine site), genus Bacteroides J1511(Bacteroides sp.J1511) (species) (e.g., intestine site), genus Corynebacterium 713182/2012(Corynebacterium sp.3182/2012) (species) (e.g., intestine site), genus Rahnella P18 (Enterobacter sp.18) (species) (e.g., intestine site), genus Peptomonas sp.g., intestinal site), genus Peptophilus sp.g., Enterobacter sp.S 18 (Enterobacter sp.g., Enterobacter site), genus P18 (Enterobacter sp.g., Enterobacter sp.S 3518 (Enterobacter site), genus P., Robinson KNHs210 (species) (e.g., intestinal site), candida cubensis (genus) (e.g., intestinal site), butyric acid bacterium faecalis (species) (e.g., intestinal site), senegalissimalia (genus) (e.g., intestinal site), peptostreptophilus DNF00840(Peptoniphilus sp.dnf00840) (species) (e.g., intestinal site), rombouutosia (genus) (e.g., intestinal site), dusspora (species) (e.g., intestinal site), Moraxellaceae (Moraxellaceae) (family) (e.g., oral site), Moraxella (Moraxella) (e.g., oral site), chikungunya (Eikenella) (genus) (e.g., oral site), Eikenella (eikondens) (roaming species) (e.g., oral site), cercosococcus (genus) (e.g., oral site), Phyllobacterium (Phyllobacterium) (e.g., Veillonella), ceronas (Veillonella) (e.g., Veillonella (Veillonella), oral site), species (e.g., species) of clostridium wadswortheinsis (e.g., oral site), johnsonia lazeri (e.g., oral site), species (e.g., species) of Bacteroides acidiferans (e.g., oral site), chaetomium herringsonii (e.g., oral site), chaetomium sarmentosum (e.g., oral site), chaetobacter sakei (e.g., oral site), chaetobacter sphaeroides AHN9756 (e.g., species) (e.g., oral site), bergey sp. AF14 (bergeylass. 14) (species) (e.g., oral site), arosai F0004 (olsenia sp. F0004) (species) ((species) ("oral site), ande.g. the oral cavity site), Bacteroides D22(Bacteroides sp.d22) (species) (e.g. the oral cavity site), Phyllobacterium T50(Phyllobacterium sp.t50) (species) (e.g. the oral cavity site), Actinomyces ICM47(Actinomyces sp.icm47) (species) (e.g. the oral cavity site), clostridium AS2(Fusobacterium sp.as2) (species) (e.g. the oral cavity site), ciliaceae (Leptotrichiaceae) (family) (e.g. the oral cavity site), Comamonas (Comamonas) (genus) (e.g. the nasal site), Peptostreptococcus (Peptostreptococcus) (genus) (e.g. the nasal site), Actinomyces viscosus (Actinomyces viscosus) (species) (e.g. the nasal site), Actinomyces carinicae (actinosococcus) (species) (e.g. the nasal site), Bifidobacterium (Bifidobacterium) (e) (e.g. the nasal site), Bifidobacterium (Bifidobacterium) (e) (e.g. the rhinococcus (rhodobacter) (e) (e.g. rhodobacter (rhodobacter) or rhodobacter (e), nasal part), bifidobacteria (order) (e.g., nasal part), Roseburia intestinalis (species) (e.g., nasal part), gyrobacterium (genus) (e.g., nasal part), Bifidobacterium longum (species) (e.g., nasal part), agrobacterium (genus) (e.g., nasal part), streptococcus₁₁aTha₁(Streptococcus sp.11aTha1) (species) (e.g., nasal site), Sauteriaceae (Sutterellaceae) (family) (e.g., nasal site), Flavobacterium (Flavobacterium) (genus) (e.g., nasal site), Ochrobactrum (Ochrobactrum) (genus) (e.g., nasal site), Enterobacter sakazakii (species) (e.g., nasal site), Anaerococcus vaginalis (Anerococcus) (species) (e.g., nasal site), Sphingobacteria (Sphingobacteria) (class) (e.g., nasal site), Brucella) (class (e.g., nasal site), Brucella (Brucella) (family) (class (family) (e.g., nasal site), order Sphingobacteriales (order) (e.g., nasal site), order Exxormans (genus) (genus peptone) (e.g., site), genus gptophthora 18A (Photobacterium) (genus), Microbacterium sp.g., Croton (Achromobacter) (class 4) (genus, Microbacterium sp) (e.g., nose site), genus Croton (Achromobacter) (e.g., Croton) (genus Akthrobacter) (genus), nasal site), Corynebacterium jw37 (species) (e.g., nasal site), staphylococcus aureus(Staphylococus aureus) (species) (e.g., nasal site), Brevundimonas (Brevundimonas) (genus) (e.g., nasal site), Aureobasidae (Caulobacter) (family) (e.g., nasal site), Aureobasidioles (Caulobacter) (order) (e.g., nasal site), Alcaligenes diazotrophicus (species) (e.g., nasal site), Anaerobacillus (Anaerobacillus) (genus) (e.g., nasal site), Acinetobacter WB22-23(Acinetobacter WB22-23) (species) (e.g., nasal site), Pseudomonas (Pseudomonas) (genus) (e.g., skin site), Neisseriaceae (Neisseria) (family) (e.g., skin site), Parabacterium (Parabacterium) (species) (e.g., skin site), Prevotella (Prevotella) (e.g., skin site), Pseudomonas (skin site) (e.g., skin site), Pseudomonas sp.g., skin site (skin site), Pseudomonas sp., skin site (, Streptococcus paracoccus (Streptococcus paraguansis) (species) (e.g., skin site), dermatophytes acnes (species) (e.g., skin site), Veillonellaceae (Veillonellaceae) (family) (e.g., skin site), cilium (Leptotrichia) (genus) (e.g., skin site), corynebacterium (phacolactobacterium) (genus) (e.g., skin site), Flavobacteriaceae (Flavobacteriaceae) (family) (e.g., skin site), dalfordia (Delftia) (genus) (e.g., skin site), flavobacterium (flavobacterium) (e.g., skin site), Prevotellaceae (Prevotellaceae) (e.g., skin site), Lachnospiraceae (Lachnospiraceae) (e.g., skin site), Streptococcus (Peptostreptococcaceae) (e.g., skin site), rhodobacter (flavobacterium) (e.g., skin site), flavobacterium (e.g., order), skin site), Neisseriales (order) (e.g., skin site), Parabacteroides (genus) (e.g., skin site), Streptococcus oral taxon G63(Streptococcus sp. oral taxon G63) (e.g., skin site), aminoacidococcaceae (family) (e.g., skin site), Veillonella CM60(Veillonella CM60) (e.g., skin site), Staphylococcus C9I2(Staphylococcus C9I2) (e.g., skin site), cephalosporaceae (leptichiae) (e.g., skin site), neisseriaceae (leptociaceae) (e.g., skin site), bacteroides sp. C2 (Staphylococcus C. C9I2) (e.g., skin site), neisseriaceae (leptociaceae) (e) (e.g., skin site),Saccharophilus species (e.g., skin site), fusiforme genus (e.g., skin site), Staphylococcus 3348O2(Staphylococcus coccus p.3348o2) (species) (e.g., skin site), Parabacteroides faecalis (parabacter merdae) (species) (e.g., skin site), corynebacterium aerogenes (collinesla aerofaciens) (species) (e.g., skin site), bacillus sphingomyelinus (class (spongobacteria) (class) (e.g., skin site), bacillus sphingomyelinensis order (order) (e.g., skin site), peptostreptococcus 1-14 (peptionibacterium sp.1-14) (species) (e.g., skin site), bacillus anaerobicus (anaerobacterium) (e.g., skin site), propionibacterium 184l 4(propionibacterium sp.1844) (species) (e.g., skin site), Staphylococcus (mycobacterium longum) (e.g., Staphylococcus 16), skin site) and/or other suitable classification element (e.g., associated with any suitable body site, etc.).

Additionally or alternatively, the microbiome features associated with the one or more appendix-related conditions can include features (e.g., microbiome composition features, etc.) associated with any combination of one or more of the following taxa (e.g., associated with one or more body parts, etc.): the phylum of the Houttyniae (Firmicutes) (phylum), Enterococcus gossypii (Enterococcus raffinosus) (species), Staphylococcus C9I2(Staphylococcus sp. C9I2) (species), Gemela 933-88 (Gemela sp.933-88) (species), Veillonella (Veillonella) (genus), Gamma-Proteobacteria (Gamma-proteus) (class), Enterococcus SI-4(Enterococcus sp.SI-4) (species), Enterobacteriaceae (Enterobacteriales) (order), Enterobacteriaceae (Enterobacteriaceae) (family), Phascobacterium (Phascobacterium) (genus), Hippophilus (Desymobacteriaceae) (family), Aminococcus (Acminococcus) (family), Phascolecophyceae) (family), Acminococcus (Achromobacter sp) (family), Streptococcus sp 4 (Bifidobacterium sp. 1) (species), Streptococcus (Streptococcus desulfonatiformes sp. 1) (species), Streptococcus sp. faecalis sp.63 (family), Streptococcus sp. faecalis sp.1 (Streptococcus sp. 1. sp.63 (family), Streptococcus sp.5 (Streptococcus sp. faecalis (family), Streptococcus sp. faecalis (family ), Streptococcus sp. faecalis (family 5. faecalis (family), family, Faecalis (genus), delta-proteobacteria (class), burkholderia (family), bacteroides RMA9912 (species), methanobrevibacterium (genus), enterobacter (species), bacteroides HGB5 (species sp. HGB5), geminicoccus (genus), methanobacter (species), methanobacter subulatus (species), methanobacterium smithii (species), intestinomonas (genus), Lactobacillus 7_1_47FAA (species lactobacterium sp.7_1_ 47), methanobacterium (genus), methanobacterium family (genus), bacteroides (genus), rhodobacter sp.sp.sp.7 _1_47FAA (species), methanobacterium family (genus), rhodobacter (species), rhodobacter (genus), rhodobacter (species, rhodobacter (genus), rhodobacter (genus), kluyvera georgiana (species), Kluyvera faecalis (species), Clostridium (species), Lactobacillus longus (species), Raosbai Ribes 11SE39 (species), Bacteroides AR29 (species), Kluyveromyces sp. AR29 (species), Kluyveromyces (genus), Mycobacterium NML05A004 (species), Microbacterium proides L05a004 (species), Microbacterium prorectinum (species), Anaerobiospirillus (species), Lactobacillus (genus), Lactobacillus (species), Corynebacterium 3_2_56FAA (species), Corynebacterium sp.3_2_56FAA (species), Lactobacillus sp.sp.912 (species), Lactobacillus paracasei (species), Lactobacillus paracoccus sp.sp. 89 (species), Lactobacillus paracasei strain, Lactobacillus sp. sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp., Bacteroides vulgatus (species Bacteroides plebeius), Propionibacterium sp09A (species Propionibacterium sp.msp09a), Streptococcus pasteurianus (species Streptococcus pasteurianus), anaerobacterium 765 (species anaerovirus sp.765), muciniphora-ecklonifera (species Akkermansia), actinomycete histoplasma (species), enterobacter sakazakii (species Cronobacter sakazakii), pseudomonas flavum (species), staphylococcus (species), enterococcus aminoacidus (species Acidaminococcus intestini), Propionibacterium praecox (species Propionibacterium), Bacteroides thetaiotaomicron) (species clostridium 21 (species clostridium sp.21), Streptococcus thermophilus (species Streptococcus thermophilus) Streptococcus (species clostridium sp.32), Streptococcus pyogenes (species Streptococcus pyogenes sp.3635), Streptococcus pyogenes (species MFC sp.3635), Streptococcus pyogenes (species mfc.31), Streptococcus pyogenes (species mfc.32) (species), Streptococcus inia sp.31 (species MFC sp.35 (species), Streptococcus pyogenes sp.35 (species MFC) and Streptococcus pyogenes (species MFC) are incorporated herein incorporated by a Fusobacterium ulcerosum (Fusobacterium ulcerans), Morganella morganii (Morganella morganii) (species), Bacteroides SLC1-38 (Bacteroides. SLC1-38) (species), Bacteroides exserohilus (Bacteroides eggerthii) (species), chicken manure bacilli (species), Bacteroides CB57(Bacteroides sp.CB57) (species), Bifidobacterium faecalis (Bifidobacterium stercoris) (species), Veilonella atrocerivii (Veilonella atypus) (species), Clostridium gangreniformis (Fusobacterium necrophorenes) (species), Lactobacillus curvatus (Lactobacillus crispatus) (species), Lactobacillus crispatus (Lactobacillus crispatus) (species), Lactobacillus veitchii MSA12 (species), Lactobacillus paracasei (Lactobacillus paracasei) (species), Lactobacillus paracasei (Escherichia sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp.sp., Propionibacterium (genus), Cronobacterium (genus), Anaerococcus (genus), Enterobacter (genus), Staphylococcus (genus), Bacillus (genus), Pseudoptera (genus), Pediococcus (genus), Morganella (genus), Aminococcus (genus), Lactobacillus (genus), Vibrio succinogenes (genus), Microbacterium (genus), Megasphaera (genus), Asachromospora (genus), Lactobacillus (genus), Fenugueda (genus), Phaeococcus (genus), Anaerococcus (genus), Streptococcus (genus), Staphylococcus (genus), Streptococcus (family), Staphylococcus (family ), family Polyporaceae (family), family Staphylococcus (genus), family (family) and family (family) of the genera), genus (genus, species (genus, clostridiales Family xi, unknown species (Clostridia Family xi, incorteceae), streptococcaceae (Peptostreptococcaceae), vibrio succinicaceae (succinivibrioceae), dermobacteriaceae (dermobacteriaceae) (Family), Corynebacteriaceae (Corynebacteriaceae) (Family), rhodospiraceae (rhodospiraceae) (Family), selenomonas (order), Lactobacillales (lactillales) (order), Clostridiales (clostridium) (order), xanthomonas (xanthomonas) (order), Bacillales (Bacillales) (order), sphaeroides (order), sphaerococcus (order), clostridium (order), pseudomonas (order), actinomycetes (order), clostridium (order), clostridium (order), clostridium (order), clostridium (order), clostridium (order), bifidobacterium (genus), Bifidobacterium (order), oscillatoria (family), and/or other suitable taxonomic units (e.g., associated with any suitable body part, etc.).

The term "gastrointestinal disorder" includes, for example, gastrointestinal disorders "or" intestinal disorders "(e.g., gastrointestinal disorders), gastrointestinal disorders (e.g., gastrointestinal disorders), gastrointestinal disorders), gastrointestinal disorders, including, gastrointestinal disorders, such as gastrointestinal disorders, including, gastrointestinal disorders, including, gastrointestinal disorders, such as, gastrointestinal disorders, including, gastrointestinal disorders, such as, gastrointestinal disorders, and/disorders, gastrointestinal disorders, and metabolic disorders, gastrointestinal disorders, and/diseases, and metabolic disorders, including, gastrointestinal disorders, including, gastrointestinal disorders, such as, gastrointestinal disorders, and/diseases, gastrointestinal disorders, and metabolic pathways (e.g., such as, gastrointestinal disorders, and/diseases, gastrointestinal disorders, and/diseases, gastrointestinal disorders, and/diseases, and/disorders, gastrointestinal disorders, and/diseases, and/disorders, gastrointestinal disorders, and/disorders, including, and/disorders, and/metabolic pathways (e.g., key disorders, and/disorders related disorders, and/disorders related disorders, including, gastrointestinal disorders, and/disorders related disorders, and/disorders related disorders, and/metabolic pathways, such as gastrointestinal disorders, and/metabolic pathways, such as gastrointestinal disorders related disorders, and/metabolic pathways, and/or metabolic pathways, and/disorders related to or metabolic pathways, and/related disorders such as, and/related disorders, and/disorders related disorders such as gastrointestinal disorders, and/metabolic pathways, including, and/metabolic pathways, and/related disorders, and/metabolic pathways (e.g. key-related to or metabolic pathways, and/related to or metabolic pathways, e.g. key (e.g. key-related to or metabolic pathways, and/related to the key (e.g. key-related to the key (e.g. key-related to the key, or related to the key-related to the key or related to the key (e.g. key-related to the key (e.g. key-related to the key or metabolic pathways, or related to the key-related to the key or related to the key or related to the key or related to the key or related to the key or the key.

In variations, a site-specific appendix-related characterization model (e.g., for determining an appendix-related characterization based on processing user site-specific microbiome features associated with one or more body parts that are also associated with the site-specific appendix-related characterization model) and/or an appendix-related characterization (e.g., associated with a body part; etc.) may be determined based on site-specific microbiome features (e.g., associated with one or more body parts; etc.) (e.g., site-specific constituent features; site-specific functional features; etc.) described herein. In an example, the method 100 can include determining a user microbiome signature (e.g., for a user who can determine and/or promote appendix-related traits and/or treatment; determining a value of a characteristic of a microbiome signature for a user, wherein the microbiome signature is determined to be associated with, e.g., correlated with, one or more appendix-related conditions; etc.), the user microbiome signature comprising site-specific microbiome signatures associated with one or more body sites.

In variations, the appendix-related characterization model and/or appendix-related characterization can be determined based on a microbiome signature (e.g., associated with one or more appendix-related conditions; etc.) that includes a microbiome composition signature (e.g., site-specific composition signature; etc.) and a microbiome functional signature (e.g., site-specific functional signature). In one example, the method 100 may include determining site-specific compositional features (e.g., associated with the gut site; compositional features described herein; etc.) and site-specific functional features (e.g., associated with the gut site; functional features described herein; etc.); and generating a site-specific appendix-related characterization model (e.g., associated with the bowel site; for processing data derived from samples taken at the bowel collection site; etc.) based on the site-specific compositional characteristics, the site-specific functional characteristics, and/or other suitable data (e.g., supplemental data, etc.); and/or determining one or more appendix-related characterizations for one or more users based on the site-specific appendix-related characterization model and the user microbiome characteristics (e.g., derived from a user sample taken at the bowel harvest site).

In particular examples, a microbiome composition characteristic described herein (e.g., including a site-specific composition characteristic, etc.), a microbiome functional characteristic described herein, and/or other suitable microbiome characteristic may be determined based on one or more microbiome datasets (e.g., a microbiome sequence dataset, etc.), the microbial dataset is determined based on a sample (e.g., sequencing microbial nucleic acids in a sample, etc.) from a group of subjects associated with an appendix-related condition (e.g., a group of subjects includes subjects having an appendix-related condition, e.g., absence of an appendix and/or other suitable appendix-related condition; including subjects without an appendix-related condition, e.g., subjects having an appendix, where such sample and/or related data can serve as a control; a population of subjects; etc.).

In variations, any suitable combination of the microbiome features described herein can be used in an appendicitis characterization procedure (e.g., determining and/or applying an appendicitis characterization model for diagnosis and/or proper characterization of an appendicitis condition; facilitating determination and/or application of a treatment model and/or treatment for an appendicitis condition; etc.). In one example, the combination of microbiome features can predict the likelihood of appendicitis in an individual based on his/her own gut microbiome sample, including the presence, absence, relative abundance, or any other microbiome feature derived from analysis of the gut sample.

In variations, any suitable combination of the microbiome features described herein can be used for prevention, treatment, and/or promotion of suitable therapeutic intervention for one or more appendix-related conditions associated with the microbiome, such as for restoring the gut microbiome (microbiota) to a healthy line (e.g., improving microbiome diversity), including, for example, modulating the presence, absence, or relative abundance of microorganisms in the human gut microbiome and/or other suitable microbiome associated with a suitable body site (e.g., for a target microbiome composition and/or function associated with a user who has an appendix without appendiceal symptoms associated with inflammatory bowel disease and/or with other suitable appendix-related conditions). However, the microbiome characteristic associated with the appendix-related condition can be applied in any suitable manner to the prevention, treatment, and/or promotion of suitable therapeutic intervention of one or more appendix-related conditions.

In an example, the method 100 may include: based on the first set of compositional features (e.g., including at least one or more of the microbiome features described above in connection with the first variant; including any suitable combination of microbiome features; etc.), the first appendix-related characterization model, the second set of compositional features (e.g., including at least one or more of the microbiome features described above in connection with the second variant; including any suitable combination of microbiome features; etc.), and the second appendix-related characterization model, the user is determined an appendix-related characterization of the first appendix-related condition and the second appendix-related condition, wherein the first appendix-related characterization model is associated with the first appendix-related condition (e.g., the first appendix-related characterization model determines a characterization of the first appendix-related condition, etc.), and wherein the second appendix-related characterization model is associated with the second appendix-related condition (e.g., wherein the second appendix-related characterization model determines a characterization of the second appendix-related condition, and so on). In this example, determining the user microbiome feature may include determining a first user microbiome functional feature associated with a first function from at least one of a homologous homology Clustering (COG) database and a Kyoto Encyclopedia of Genes and Genomes (KEGG) database, wherein the first user microbiome functional feature is associated with a first appendix-related disorder; determining a second user microbiome functional feature associated with a second function from at least one of the COG database and the KEGG database, wherein the second user microbiome functional feature is associated with a second appendix-related condition, wherein determining the appendix-related characterization can include determining a relevant characterization of the first appendix-related condition and the second appendix-related condition for the user based on the first set of component features, the first user microbiome functional feature, the first appendix-related characterization model, the second set of component features, the second user microbiome functional feature, and the second appendix-related characterization model. Additionally or alternatively, any combination of microbiome features can be used with any suitable number and type of appendix-related characterization models to determine a relevant characterization of one or more appendix-related conditions in any suitable manner.

In an example, the method 100 can include generating one or more appendix-related characterization models based on any suitable combination of the microbiome features described above and/or herein (e.g., based on a set of microbiome composition features including a characteristic associated with at least one of the taxa described herein; and/or based on a microbiome functional feature described herein, e.g., corresponding to a function of a database described herein). In one example, performing a characterization process on the user can include characterizing the user as having one or more appendix-related conditions, e.g., based on the detection of, corresponding values to, and/or other aspects related to the microbiome features described herein (e.g., the microbiome features described above, etc.), as well as in addition to (e.g., in addition to, complementary to, etc.) or in the alternative, e.g., in a typical diagnostic approach, other characterization (e.g., characterization related to treatment), treatment, monitoring, and/or other suitable approach associated with appendix-related conditions. In variations, the microbiome signature can be used for diagnostic, other characterization, treatment, monitoring, and/or any other suitable purpose and/or method associated with the appendix-related condition. However, determining one or more appendix-related characterizations can be performed in any suitable manner.

B determination of treatment.

Performing characterization process S130 (e.g., performing an appendix-related treatment) can include block S140, which can include determining one or more treatments (e.g., treatments configured to modulate microbiome composition, function, diversity, and/or other suitable aspects, e.g., to improve one or more aspects associated with an appendix-related condition, such as characterizing in a user based on one or more characterization processes, etc.). Block S140 may be used to identify, select, sort, prioritize, predict, discourage, and/or otherwise determine treatment (e.g., facilitate determination of treatment, etc.). For example, block S140 can include determining one or more of a probiotic-based therapy, a phage-based therapy, a small molecule-based therapy, and/or other suitable therapy, e.g., can transform the subject 'S microbiome composition, function, diversity, and/or other characteristics (e.g., microbiome at any suitable site, etc.) into a desired state (e.g., a balanced state, etc.) to promote the user' S health, thereby ameliorating one or more appendix-related conditions, and/or for other suitable purposes.

Treatment (e.g., appendix-related treatment, etc.) may include any one or more of the following: consumables (e.g., probiotic therapy, prebiotic therapy, pharmaceuticals, such as antibiotics, allergy or cold drugs, phage-based therapy, consumables for basal conditions, small molecule therapy, etc.); device-related therapy (e.g., monitoring devices; sensor-based devices; medical devices; implantable medical devices, etc.); surgical procedures (e.g., appendectomy, prophylactic appendectomy, abdominal surgery, laparoscopic surgery, incision surgery, etc.); psychologically-related therapies (e.g., cognitive behavioral therapy, anxiety therapy, conversational therapy, psychokinetic therapy, action-oriented therapy, rational emotional behavioral therapy, interpersonal psychological therapy, relaxation training, deep breathing techniques, progressive muscle relaxation, appendiceal restriction therapy, meditation, etc.); behavioral modification therapy (e.g., avoidance of pain relievers, antacids, laxatives, heating pads, and/or other suitable therapies and/or activities; physical exercise recommendations, e.g., increased exercise; dietary recommendations, e.g., decreased sugar intake, increased vegetable intake, increased fish intake, decreased caffeine consumption, decreased alcohol consumption, decreased carbohydrate intake; smoking recommendations, e.g., decreased tobacco intake; weight-related recommendations; sleep habit recommendations, etc.); topically administered treatments (e.g., topical probiotics, prebiotics, and/or antibiotics; phage-based treatments); an environmental factor modification therapy; any other suitable aspect of modification associated with one or more appendiceal related conditions; and/or any other suitable treatment (e.g., for improving a health condition associated with one or more appendix-related conditions, e.g., for improving one or more appendix-related conditions, for reducing the risk of one or more appendix-related conditions, etc.). In an example, the type of treatment may include any one or more of the following: probiotic therapy, phage-based therapy, small molecule-based therapy, cognitive/behavioral therapy, physical rehabilitation therapy, clinical therapy, drug-based therapy, diet-related therapy, and/or any other suitable therapy designed to operate in any other suitable manner to promote the health of a user.

In variations, treatment may include site-specific treatment associated with one or more body parts, e.g., for one or more different body parts of the user (e.g., one or more different collection sites), facilitating modification of microbiome composition and/or function, e.g., targeting and/or transforming microbes associated with intestinal, nasal, skin, oral, and/or genital parts (e.g., by facilitating therapeutic intervention associated with one or more treatments configured to be specific to one or more user body parts, e.g., microbiome at one or more user body parts, etc.), e.g., for facilitating improvement of one or more appendix-related conditions (e.g., by modifying a user microbiome composition and/or function for a particular user body part to a target microbiome composition and/or function, microbiome composition and/or function, e.g., at a specific body site, and which is associated with a healthy appendiceal state and/or lack of one or more appendiceal-related conditions; etc.). Site-specific therapies may include any one or more consumables (e.g., microbiome targeted to intestinal sites and/or microbiome associated with any suitable body site; etc.); topical treatment (e.g., for modifying the skin microbiome, nasal microbiome, oral microbiome, genital microbiome, etc.); and/or any other suitable type of treatment. In an example, the method 100 may include collecting a sample from a user associated with a first body site (e.g., including at least one of an intestinal site, a skin site, a genital site, an oral site, and a nasal site, etc.); determining a site-specific compositional feature associated with a first body site; determining an appendix-related characterization of the appendix-related condition for the user based on the site-specific compositional features; and facilitating a therapeutic intervention associated with a first site-specific treatment of the user (e.g., providing the first site-specific treatment to the user, etc.) based on the appendix-related characterization to facilitate amelioration of the appendix-related condition, wherein the first site-specific treatment is associated with the first body site. In an example, the method 100 can include, after facilitating a post-treatment outcome associated with a first site-specific treatment (e.g., after providing the first site-specific treatment; etc.), collecting a post-treatment sample from the user, wherein the post-treatment sample is associated with a second body site (e.g., including at least one of an intestinal site, a skin site, a genital site, an oral site, and a nasal site; etc.); determining a post-treatment appendix-related characteristic of the appendix-related condition for the user based on the site-specific characteristic associated with the second body site; and facilitating a therapeutic intervention associated with a second site-specific treatment for the user (e.g., providing the user with the second site-specific treatment, etc.) based on the post-treatment appendix-related characterization to facilitate amelioration of the appendix-related condition, wherein the second site-specific treatment is associated with the second body site.

In a variation, the treatment can include one or more phage-based treatments (e.g., in the form of consumables, in the form of topical administration treatments, etc.), wherein one or more phage populations (e.g., in colony forming units) specific to certain bacteria (or other microorganisms) representative in the subject can be used to down-regulate or otherwise eliminate certain bacteria populations. As such, phage-based therapies can be used to reduce the size of an undesirable population of representative bacteria in a subject. Additionally or alternatively, phage-based therapy can be used to increase the relative abundance of targeted bacterial populations of unused phage. However, phage-based therapy may be used in any suitable manner to modulate a characteristic of the microbiome (e.g., microbiome composition, microbiome function, etc.), and/or may be used for any suitable purpose.

In variations, treatment can include one or more probiotic treatment and/or prebiotic treatment associated with (e.g., including any combination of one or more, in any suitable amount and/or concentration, such as any suitable relative amount and/or concentration; and the like) and/or one or more of at least one or more of any suitable taxa described herein (e.g., microbiome composition characteristics associated with one or more appendix-related conditions, and the like): enterococcus gossypii (Enterococcus raffinosus), Staphylococcus C9I2(Staphylococcus sp.C9I2), Gephyrococcus 933-88(Gemella sp.933-88), Enterococcus SI-4(Enterococcus sp.SI-4), Choerophilus 4_1_30(Bilophila sp.4_1_30), Corynebacterium anaerobium 5_1_63FAA (Anaerospermus sp.5_1_63FAA), Bacillus coprinus (Phascolatobacter faecium), Mycobacterium RMA9912 (Alisteles sp.RMA9912), Clinobacter visceral, Mycobacterium HGB5 (Alisteship HGB5), Bacillus coprinus varioticus var, Methylobacterium Shigella (Methanobacterium cellulophilus sp.11sp.), Lactobacillus plantarum 7 (Klebsiella sp.11. sp.11), Lactobacillus sp.31. sp.31, Lactobacillus sp.31. sp.31, Lactobacillus sp, Lactobacillus sp.31. sp.31, Lactobacillus sp.31. sp.sp.31. sp.31. sp.sp.sp.sp.sp.sp.sp.sp., Prevotella mongolica, Achromobacter sp.3_2_56FAA (Anaerosperms sp.3_2_56FAA), Klebsiella SoR89(Klebsiella sp.SOR89), Macrosphaera DNF00912(Megasphaera sp.DNF00912), Veillonella dispar (Veilonella dispar), Lactobacillus mucosae (Lactobacillus mucosae), Bacteroides fragilis (Bacteroides fragilis), Streptococcus equi (Streptococcus equinus), Bacteroides vulgatus (Bacteroides plexius), Propionibacterium sp.09 09A (Propionibacterium sp.MSP09A), Streptococcus pasteurella (Streptococcus pasteurella pasteurianus), Acidovibrio 765 (Anaerobiospiricus sp.63765), Myxophycin-Achromobacter (Streptococcus sp.Saponensis), Streptococcus sp.faecalis (Streptococcus sp. 21), Micrococcus pyogenes (Corynebacterium parvus), Micrococcus pyogenes sp.36 1, Micrococcus pyogenes (Corynebacterium parvus), Streptococcus sp. 21), Streptococcus sp.sp.sp.sp.sp.sp. 21), Streptococcus sp.sp.sp.sp., Megasphaeragenom sp.Cl, Streptococcus BS35 (Streptococcus sp.BS35a), Streptococcus thermophilus (Streptococcus thermophilus), Clostridium ulcerosa (Fusobacterium ulcerans), Morganella morganii (Morganella morganii), Bacteroides SLC1-38(Bacteroides sp.SLC1-38), Bacteroides exserohilus (Bacteroides seggrii), Corynebacterium gallinarum, Bacteroides CB57(Bacteroides sp.CB57), Bifidobacterium faecalis (Bifidobacterium stercosporis), Veononella amylovora typica (Veononella amylovora), Clostridium gangreniformis (Fusobacterium necropoides), Lactobacillus curvatus (Lactobacillus crispus), Vegonococcus veillantus 12 (Lactobacillus veillantus 12), Lactobacillus paracasei (Lactobacillus paracasei, Lactobacillus parac, e.g., microbiome characteristic related; a classification unit associated with a functional feature described herein, etc.). The microorganisms and/or any suitable combination of microorganisms associated with a given taxon can be provided at a dose of 10 ten thousand to 100 billion CFUs and/or any suitable amount for one or more probiotic treatments and/or other suitable treatments (e.g., as determined from a treatment model that can predict positive modulation of a patient's microbiome in a treatment response; different amounts for different taxons; same or similar amounts for different taxons; etc.). In one example, a subject may be instructed to ingest a capsule comprising a probiotic formulation according to his/her one or more of the following customized regimens: physiology (e.g., body mass index, weight, height), demographic characteristics (e.g., gender, age), severity of malnutrition, sensitivity to drugs, and any other suitable factors. In an example, probiotic and/or prebiotic therapy can be used to modulate a user's microbiome (e.g., related to composition, function, etc.) to promote amelioration of one or more appendiceal-related conditions. In an example, promoting therapeutic intervention can include promoting (e.g., recommending, informing the user about, providing, administering, promoting a benefit to, etc.) one or more probiotic treatments and/or prebiotic treatments to the user, e.g., to promote amelioration of one or more appendix-related conditions.

In a specific example of a probiotic treatment, as shown in fig. 4, a candidate treatment of the treatment model may perform one or more of the following: by providing a physical barrier (e.g., by resistance to colonization), preventing pathogen entry into epithelial cells), by stimulating goblet cells to induce the formation of a mucus barrier, enhancing the integrity of apical tight junctions between epithelial cells in a subject (e.g., by promoting upregulation of zona-occludins 1, by preventing redistribution of tight junction proteins), producing antibacterial factors, stimulating the production of anti-inflammatory cytokines (e.g., by signaling by dendritic cells and induction of regulatory T cells), triggering an immune response, and performing any other suitable function that can modulate a subject's microbiome away from a malnourished state. However, the probiotic treatment and/or the prebiotic treatment may be configured in any suitable manner.

In another particular example, the treatment may include a medical device-based treatment (e.g., associated with human behavior modification, associated with treatment of a disease-related condition, etc.).

In a variant, the treatment model is preferably based on data from a large population of subjects, which may include a population of subjects derived from the microbiome diversity dataset of block S110, wherein the microbiome composition and/or functional characteristics or health condition are well characterized both before and after exposure to various therapeutic measures. Such data can be used to train and validate treatment delivery models to identify treatment options that provide a desired outcome to a subject based on different appendix-related characterizations. In a variation, a support vector machine, as a supervised machine learning algorithm, may be used to generate the therapy delivery model. However, any other suitable machine learning algorithm described above may facilitate generation of the therapy delivery model.

Additionally or alternatively, the treatment model may be derived in association with identifying "normal" or baseline microbial composition and/or functional characteristics, such as subjects assessed from a population of subjects identified as in good health. Upon identifying a subset of subjects in the population of subjects characterized as good health (e.g., using the features of the characterization process), adjusting the microbial composition and/or functional features to those treatments in good health subjects can be generated in block S140. Block S140 can thus include identifying one or more baseline microbiome compositions and/or functional characteristics (e.g., one baseline microbiome for each of a set of demographic characteristics), and potential treatment modalities and treatment protocols that can transform a microbiome of a subject in a malnutrition state to one of the identified baseline microbiome compositions and/or functional characteristics. However, the therapy model may be generated and/or improved in any other suitable manner.

The microbial composition associated with probiotic treatment and/or prebiotic treatment (e.g., associated with probiotic treatment as determined by a treatment model applied by a treatment facilitation system, etc.) can include microbes that are culturable (e.g., can be expanded to provide scalable treatment) and/or non-lethal (e.g., whose required therapeutic dose is non-lethal). In addition, the microbial composition may include a single type of microorganism that has an acute or palliative effect on the microbiome of the subject. Additionally or alternatively, the microbial composition may include a balanced combination of multiple types of microbes configured to cooperate in driving the microbiome of the subject to a desired state. For example, a combination of multiple bacterial types in probiotic treatment may include a first type of bacteria that produces a product for a second type of bacteria that has a strong effect in positively affecting the microbiome of the subject. Additionally or alternatively, in probiotic treatment, a combination of multiple types of bacteria may include some types of bacteria that produce proteins with the same function that may positively affect the microbiome of the subject.

The probiotic and/or prebiotic composition may be naturally or synthetically derived. For example, in one application, the probiotic composition may be naturally derived from stool or other biological matter (e.g., one or more subjects identified using characterization treatments and treatment models having baseline microbiome composition and/or functional characteristics). Additionally or alternatively, the probiotic composition may be synthetically derived (e.g., derived using a bench mark method) based on baseline microbiome composition and/or functional characteristics. In a variant, the microbial agent that may be used in probiotic treatment may include one or more of: yeasts (e.g., Saccharomyces boulardii), gram-negative bacteria (e.g., escherichia coli), gram-positive bacteria (e.g., bifidobacterium bifidum, bifidobacterium infantis (bifidobacterium infantis), Lactobacillus rhamnosus (Lactobacillus rhamnous), Lactobacillus lactis (Lactobacillus lactis), Lactobacillus plantarum (Lactobacillus plantarum), Lactobacillus acidophilus (Lactobacillus acidophilus), Lactobacillus casei (Lactobacillus casei), Bacillus fermentum (Bacillus polyfermenticus), and the like), and any other suitable type of microbial preparation. However, probiotic treatment, prebiotic treatment, and/or other suitable treatment may include any suitable combination of microorganisms associated with any suitable taxa described herein, and/or the treatment may be configured in any suitable manner.

Block S140 may include executing, storing, retrieving, and/or otherwise processing one or more therapy models used to determine one or more therapies. The treatment of the one or more treatment models is preferably based on microbiome characteristics. For example, generating a treatment model can be based on microbiome characteristics associated with one or more appendix-related conditions, treatment-related aspects (e.g., treatment efficacy associated with the microbiome characteristics, and/or other suitable data). Additionally or alternatively, the treatment therapy model may be based on any suitable data. In one example, processing the therapy model may include determining one or more therapies for the user based on one or more therapy models, user microbiome characteristics (e.g., inputting user microbiome characteristic values to one or more therapy models, etc.), supplemental data, (e.g., prior knowledge associated with the therapy, such as relating to microorganism-related metabolism, user medical history, user demographic data, such as describing demographic characteristics, etc.), and/or any other suitable data. However, the treatment therapy model may be based on any suitable data in any suitable manner.

The appendix-associated characterization model can include one or more treatment models. In one example, determining one or more appendix-related characterizations (e.g., for one or more users, for one or more appendix-related conditions, etc.) can include determining one or more treatments, e.g., based on one or more treatment models, e.g., applying one or more treatment models, etc., and/or other suitable data (e.g., microbiome characteristics, e.g., user microbiome characteristics, a microbiome dataset, e.g., a user microbiome dataset, etc.). In a particular example, determining one or more appendix-related traits can include determining a first appendix-related trait for a user (e.g., describing a predisposition to one or more appendix-related conditions; etc.); and determining a second appendix-related characteristic for the user based on the first appendix-related condition (e.g., determining one or more treatments, e.g., recommendations to the user, based on a predisposition for one or more appendix-related conditions; etc.). In a particular example, appendix-related characterizations can include tendency-related data (e.g., diagnostic data; associated microbiome composition, function, diversity, and/or other characteristics; etc.) and treatment-related data (e.g., recommended treatments, potential treatments; etc.). However, the appendix-related characterization can include any suitable data (e.g., any combination of the data described herein, etc.).

Processing the therapy model may include processing a plurality of therapy models. For example, different therapy models may be treated for different therapies (e.g., different models for different individual therapies, different models for different combinations and/or categories of therapies, such as a first therapy model for determining consumable therapies and a second therapy model for determining psychologically-related therapies, etc.). In one example, different treatment models can be tailored for different appendix-related conditions, e.g., different models for different individual appendix-related conditions; different models for different combinations and/or categories of appendix-related conditions, etc.). Additionally or alternatively, processing multiple therapy models may be performed (e.g., based thereon; processing different therapy models therefor; etc.) for any suitable type of data and/or entity. However, processing the plurality of therapy models may be performed in any suitable manner, and determining and/or applying one or more therapy models may be performed in any suitable manner.

4.4 processing the user biological sample.

Embodiments of the method 100 may additionally or alternatively include block S150, which block S150 may include processing one or more biological samples from the user (e.g., biological samples from different collection sites of the user, etc.). Block S150 can be used to facilitate user generation of a microbial data set, such as for input to derive characterization processes, such as, for example, by applying one or more appendix-related characterization models, generating appendix-related characterizations for a user, and so forth). As such, block S150 may include receiving, processing, and/or analyzing one or more biological samples from one or more users (e.g., multiple biological samples of the same user, different biological samples of different users, etc., over time). In block S150, a biological sample is generated from the user and/or the user' S environment, preferably in a non-invasive manner (non-invasive manner). In a variant, the non-invasive manner of sample reception may use any one or more of the following: a permeable substrate (e.g., a swab configured to wipe a user's body area, toilet paper, sponge, etc.), an impermeable substrate (e.g., a slide, tape, etc.), a container configured to receive a sample from a user's body area (e.g., a bottle, test tube, bag, etc.), and any other suitable sample receiving element. In a particular example, the biological sample can be collected in a non-invasive manner (e.g., using a swab and a bottle) from one or more of the nose, skin, genitalia, mouth, and intestines of the user (e.g., via a fecal sample, etc.). However, the biological sample may additionally or alternatively be received semi-invasively (semi-invasive) or invasively (invasive). In a variant, the invasive manner of sample reception may use any one or more of the following: needles, syringes, biopsy elements, lancets, and any other suitable instrument for collecting samples in a semi-invasive or invasive manner. In particular examples, the sample may include a blood sample, a plasma/serum sample (e.g., to enable extraction of cell-free DNA), and a tissue sample.

In the above variations and examples, the biological sample may be obtained from the body of the user without the assistance of another entity (e.g., a caregiver associated with the user, a healthcare professional, an automated or semi-automated sample acquisition device, etc.), or may alternatively be obtained from the body of the user with the assistance of another entity. In one example, the sample preparation kit may be provided to a user without the assistance of other entities in obtaining a biological sample from the user during sample extraction. In this example, the kit may include one or more swabs for obtaining a sample, one or more containers configured to receive the swabs for storage, instructions for sample preparation and setting up a user account, elements (e.g., barcode identifiers, tags, etc.) configured to associate the sample with a user, a receiver that allows the sample from the user to be delivered to a sample processing operation, e.g., via a mail delivery system). In another example, where a biological sample is taken from a user with the assistance of another entity, one or more samples may be collected from the user in a clinical or research setting (e.g., during a clinical appointment). However, the biological sample may be received from the user in any other suitable manner.

Further, processing and analysis of biological samples from a user (e.g., to generate a user microbial data set, etc.) is preferably performed in a manner similar to one of the embodiments, variations, and/or the sample receiving examples described above in connection with block S110, and/or any other suitable portion of the method 100 and/or system 200 embodiments. In this way, the receiving and processing of the biological sample in block S150 may be performed for the user using processes similar to those used to receive and process the biological sample used to perform the characterization process of method 100, e.g., to provide consistency of processing. However, the receiving and processing of the biological sample in block S150 may additionally or alternatively be performed in any other suitable manner.

4.5 appendix-related characterisation was determined.

Embodiments of method 100 can additionally or alternatively include block S160, which block S160 can include determining an appendix-related characterization for the user using one or more characterization processes (e.g., the one or more characterization processes associated with block S130, etc.), for example, based on processing one or more microbiome datasets derived from a biological sample of the user (e.g., a user microbiome sequence dataset, a microbiome composition dataset, a microbiome functional diversity dataset; processing the microbiome dataset to extract a user microbiome feature (e.g., extract feature values, etc.), which can be used to determine the appendix-related characterization (S), etc.). Block S160 can be used to characterize one or more appendix-related conditions for the user, such as by extracting features from microbiome-derived data of the user, and using the features as input to an embodiment, variant, or example of the characterization process described above in block S130 (e.g., using values of user microbiome features as input to a characterization model of a microbiome-related condition, etc.). In an example, block S160 can include generating an appendix-related characterization for the user based on the user microbiome features and the appendix-related condition model (e.g., generated in block S130). The appendix-related characterizations can be for any number and/or combination of appendix-related conditions (e.g., a combination of appendix-related conditions, a single appendix-related condition and/or other suitable appendix-related condition; etc.), user, site of collection, and/or other suitable entity. Appendix-related characterizations can include one or more of the following: diagnosis (e.g., presence or absence of appendiceal-related disorder; etc.); risk (e.g., a risk score for the occurrence and/or presence of an appendix-related condition, information about appendix-related characterizations (e.g., symptoms, signs, triggers, related conditions, etc.), comparisons (e.g., comparisons with other subgroups, populations, users, historical health conditions of users, such as historical microbiome composition and/or functional diversity, comparisons associated with appendix-related conditions, etc.), treatment determinations, other suitable outputs associated with characterization processes, and/or any other suitable data.

In another variation, the appendix-associated characterization can include a microbiome diversity score (e.g., related to microbiome composition, functionally, etc.) that is associated with (e.g., related to; negatively related to; positively related to; etc.) a microbiome diversity score associated with one or more appendix-associated conditions. In an example, the appendix-related characterization can include a microbiome diversity score over time (e.g., calculating a plurality of biological samples of the user taken over time), a comparison of microbiome diversity scores of other users, and/or any other suitable type of microbiome diversity score. However, the microbiome diversity score may be processed in any suitable manner (e.g., determining a microbiome diversity score; using a microbiome diversity score to determine and/or provide treatment; etc.).

Determining the appendix-related characterization in block S160 preferably includes determining features and/or combinations of features associated with the microbiome composition and/or functional features of the user (e.g., determining feature values associated with the user, the feature values corresponding to the microbiome features determined in block S130, etc.), inputting the features to a characterization process, and receiving an output characterizing the user as one or more of: behavioral groups, gender groups, diet groups, disease status groups, and any other suitable group that can be identified by the characterization process. Block S160 may additionally or alternatively include generation and/or output of a confidence level associated with the user characterization. For example, the confidence level may be derived from the number of features used to generate the characterization, the relative weights or grades of the features used to generate the characterization, a measure of deviation in the characterization process, and/or any other suitable parameter associated with an aspect of the characterization process. However, the user microbiome signature can be utilized in any suitable manner to generate any suitable appendix-related signature.

In some variations, features extracted from the user' S microbial dataset may be supplemented with feature supplements (e.g., extracted from supplemental data collected for the user; e.g., survey-derived features, medical history-derived features, sensor data, etc.), wherein such data, user microbiota data, and/or other suitable data may be used for further refined characterization processing of block S130, block S160, and/or other suitable portions of embodiments of method 100.

Determining the appendix-related characterization preferably includes extracting and applying user microbiome features (e.g., user microbiome composition diversity features; user microbiome functional diversity features; extracting feature values, etc.) for the user (e.g., based on the user microbiome dataset), the feature model, and/or other suitable components, e.g., by employing the process described in block S130, and/or by employing any suitable method described herein.

In a variation, as shown in FIG. 6, block S160 can present the appendix-related characterization (e.g., information extracted from the characterization; as part of facilitating a therapeutic intervention; etc.), for example, on a network interface, a mobile application, and/or any other suitable interface, however, the presentation of the information can be performed in any suitable manner. However, the user microbiological data set may additionally or alternatively be used in any other suitable manner to enhance the model of the method 100, and block S160 may be performed in any suitable manner.

4.6 facilitating therapeutic intervention.

As shown in fig. 9, embodiments of the method 100 can additionally or alternatively include block S170, which can include facilitating therapeutic intervention (e.g., promoting treatment, providing treatment, facilitating provision of treatment, etc.) for one or more users (e.g., based on the appendix-related features and/or the treatment model) for one or more appendix-related conditions. Block S170 can be employed to recommend, promote, provide, and/or otherwise facilitate one or more treatment-related therapeutic interventions for the user, such as to transform the user' S microbiome composition and/or functional diversity into a desired equilibrium state (and/or otherwise improve the state of one or more appendix-related conditions, etc.) that is associated with the one or more appendix-related conditions. Block S170 may include providing the customized therapy to the user based on the microbiome composition and functional characteristics of the user, wherein the customized therapy may include a microbial preparation configured to correct a characteristic of the user (which has the identified characteristic) malnutrition. In this way, based on the trained treatment model, the output of block S140 can be used to directly promote customized treatment formulations and protocols (e.g., dosages, instructions for use) to the user. Additionally or alternatively, the provision of treatment may include recommending available treatment measures (which are configured to transform the microbiome composition and/or functional characteristics into a desired state). In a variant, the treatment may include any one or more of the following: consumables, topical treatments (e.g., lotions, ointments, preservatives, etc.), medications (e.g., medications associated with any suitable type and/or dosage of medication, etc.), bacteriophages, environmental treatments, behavioral modification (e.g., diet modification treatments, stress-relief treatments, physical exercise-related treatments, etc.), diagnostic procedures, other medically-related procedures, and/or any other suitable treatment associated with the appendix-related condition, consumables, can include any one or more of the following: food and/or beverage products (e.g., probiotic and/or prebiotic food and/or beverage products, etc.), nutritional supplements (e.g., vitamins, minerals, fibers, fatty acids, amino acids, prebiotics, probiotics, etc.), consumable drugs, and/or any other suitable therapeutic measures. In an example, providing one or more treatments and/or otherwise facilitating therapeutic intervention may include providing a recommendation of one or more treatments to one or more users on one or more computing devices associated with the one or more users (e.g., on a user interface such as a web application, presented at the computing device, etc.).

For example, a combination of commercially available probiotic supplements may include appropriate probiotic treatment for the user, depending on the output of the treatment model. In another example, the method 100 can include determining an appendix-related condition risk for the appendix-related condition for the user based on the appendix-related condition model (e.g., and/or the user microbiome characteristics); and promoting treatment to the user based on the appendiceal related condition risk.

In one variation, facilitating therapeutic intervention can include promoting a diagnostic procedure (e.g., to facilitate detection of an appendix-related condition, which can facilitate subsequent promotion of other treatments, such as adjusting a user's microbiome to improve a user's health associated with one or more appendix-related conditions; etc.). The diagnostic procedure may include any one or more of the following: a medical history analysis, an imaging examination, a cell culture assay, an antibody assay, a skin prick assay, a patch assay, a blood assay, a challenge assay, performing portions of the embodiments of the method 100, and/or any other suitable procedure that facilitates detection (e.g., observation, prediction, etc.) of an appendix-related condition. Additionally or alternatively, diagnostic device-related information and/or other suitable diagnostic information may be processed as part of the supplemental data set (e.g., as associated with block S120, where such data may be used to determine and/or apply characterization models, treatment models, and/or other suitable models; etc.), and/or collected, used, and/or otherwise processed in association with any suitable portion of the embodiments of method 100 (e.g., to administer diagnostic procedures for monitoring treatment efficacy associated with block S180; etc.).

In another variation, block S170 can include promoting phage-based therapy. In more detail, one or more populations of bacteriophage (e.g., in terms of colony forming units) specific to certain representative bacteria (or other microorganisms) in a user may be used to down-regulate or otherwise eliminate certain bacterial populations. In this way, phage-based therapy can be used to reduce the size of an undesirable population of representative bacteria in a user. Complementarily, phage-based therapies can be used to increase the relative abundance of unused phage-targeted bacterial populations.

In another variation, facilitating a therapeutic intervention (e.g., providing a treatment, etc.) can include providing a notification to the user regarding a recommended treatment, a form of other treatment, appendix-related characteristics, and/or other suitable data. In a particular example, providing treatment to the user can include providing treatment recommendations (e.g., substantially concurrently with providing the user with information derived from appendix-related characterizations) and/or other suitable treatment-related information (e.g., efficacy; comparison to other individual users, subgroups of users, and/or groups of users; treatment comparisons; historical treatment and/or treatment-related information; psychotherapeutic guidance, such as cognitive behavioral treatment; etc.), such as by presenting notifications on a network interface (e.g., via a user account associated with the user and identifying the user, etc.). Notifications can be provided to a user by executing an electronic device (e.g., personal computer, mobile device, tablet, wearable, head-mounted wearable computing device, wrist-mounted wearable computing device, etc.) configured as an application, web interface, and/or information client for providing notifications. In one example, a network interface of a personal computer or laptop associated with the user can provide the user with access to a user account via the user, wherein the user account includes information regarding appendix-related characterizations of the user, detailed characterizations of aspects of the user' S microbiome (e.g., related to the relevance of appendix-related characterizations; etc.), and/or notifications regarding suggested treatment (e.g., notifications generated in blocks S140 and/or S170, etc.). In another example, an application executing on a personal electronic device (e.g., smartphone, smartwatch, head-mounted smart device) may be configured to provide a notification (e.g., on a display, in a tactile manner, in an audible manner, etc.) about a treatment recommendation generated by the treatment model of block S170. The notification and/or probiotic treatment may additionally or alternatively be provided directly by an entity associated with the user (e.g., a caregiver, spouse, important others, healthcare professional, etc.). In some additional variations, a notification may additionally or alternatively be provided to an entity (e.g., a healthcare professional) associated with the user, for example, where the entity can facilitate the provision of therapy (e.g., by prescription, by conducting a conference of therapy, by a digital telemedicine conference using optical and/or audio sensors of a computing device, etc.). However, providing notification and/or otherwise facilitating treatment is performed in any suitable manner.

4.7 monitor the effect of the treatment.

As shown in fig. 7, the method may additionally or alternatively include block S180, the block S180 may include: over time, the user is monitored (e.g., based on processing a series of biological samples from the user) for the effect of one or more treatments and/or for other suitable components (e.g., microbiome characteristics, etc.). Block S180 may be used to collect additional data regarding the positive, negative, and/or lack of effect of one or more treatments (e.g., as suggested by a treatment model for a given characterized user, etc.), and/or monitoring microbiome characteristics (e.g., assessing microbiome composition and/or functional characteristics for a user at a set of time points, etc.).

Thus, during the course of treatment facilitated by the treatment model, the user is monitored (e.g., by receiving and analyzing a biological sample from the user throughout the course of treatment, by receiving survey-derived data from the user throughout the course of treatment) to generate a treatment effect model for each of the representations provided by the characterization process of block S130 and each of the recommended treatment measures provided in blocks S140 and S170.

In block S180, the user may be prompted to provide additional biological samples, supplemental data, and/or other suitable data at one or more key points in time of a treatment regimen in conjunction with the treatment, and the additional biological samples may be processed and analyzed (e.g., in a manner similar to that described in connection with block S120) to generate metrics characterizing modulation of the user' S microbiome composition and/or functional characteristics. For example, metrics relating to one or more of the following: at an earlier point in time, the relative abundance of a representative one or more taxonomic groups in the user microbiome changes, the representation of a particular taxonomic group in the user microbiome changes, the ratio between the abundance of a first taxonomic group of bacteria and the abundance of a second taxonomic group of bacteria in the user microbiome changes, the relative abundance of one or more functional populations in the user microbiome changes, and any other suitable metric that can be used to assess the effect of a treatment from the microbiome composition and/or functional characteristic changes. Additionally or alternatively, survey-derived data from the user relating to the user' S experience with the treatment (e.g., side effects experienced, assessment of personal improvement, behavioral changes, symptom improvement, etc.) may be used to determine the effect of the treatment in block S180. For example, the method 100 may include receiving a post-treatment biological sample from a user; collecting a supplemental data set from a user, wherein the supplemental data set describes user compliance with a therapy (e.g., determined and promoted therapy) and/or other suitable user characteristics (e.g., behavior, condition, etc.); generating a post-treatment appendix-related characterization of the first user that is associated with the appendix-related condition based on the appendix-related characterization model and the post-treatment biological sample; the updated treatment for the appendix-related condition is promoted to the user based on the post-treatment appendix-related characterization (e.g., based on a comparison between the post-treatment appendix-related characterization and the pre-treatment appendix-related characterization; etc.) and/or the user's compliance with the treatment (e.g., based on a positive or negative outcome of the user's microbiome associated with the appendix-related condition to modify the treatment; etc.). Additionally or alternatively, other suitable data (e.g., supplemental data describing user behavior associated with one or more appendix-related conditions; supplemental data describing appendix-related conditions, such as observed symptoms; etc.) can be used to determine post-treatment characterizations (e.g., degree of change from pre-treatment to post-treatment associated with an appendix-related condition; etc.), updated treatments (e.g., based on efficacy and/or compliance with the promoted treatment, updated treatments, etc.).

In an example, the method 100 may include: collecting supplemental data (e.g., survey derived data; informing of appendix-related condition status, such as relating to symptom severity; etc.); determining a user's appendix-related characterization based on the user microbiome characteristics and the supplemental data; facilitating a therapeutic intervention related to the treatment of the appendix-related condition (e.g., promoting treatment to a user, etc.) based on the appendix-related characterization; collecting a post-treatment biological sample from the user (e.g., after facilitating a therapeutic intervention; etc.); collecting subsequent supplemental data (e.g., including at least second survey-derived data and equipment data; etc.); a post-treatment appendix-related characterization of the appendix-related condition is determined for the user based on the subsequent supplemental data and the post-treatment user microbiome characteristics associated with the post-treatment biological sample. In this example, the method 100 may include: based on the post-treatment appendix-related characteristic, a treatment intervention associated with an updated treatment (e.g., a modification of the treatment; a different treatment; etc.) is provided to the user to ameliorate the appendix-related condition, for example, wherein the updated treatment can include at least one of: consumables, device-related therapies, surgical procedures, psychologically-associated therapies, behavioral modification therapies, and environmental factor modification therapies. In this example, determining the post-treatment appendix-related characterization can include: based on the post-treatment microbiome characteristics, a comparison is determined between the user's microbiome characteristics and reference microbiome characteristics corresponding to a subset of users that share at least one of behavioral and environmental factors (and/or other suitable characteristics) associated with the appendix-related condition, and wherein facilitating therapeutic intervention related to the updated treatment can include: the comparison results are presented to the user to facilitate at least one of a behavior modification therapy and an environmental modification therapy and/or other suitable therapy. However, block S180 may be performed in any suitable manner in relation to additional biological samples, additional supplemental data, and/or other suitable additional data.

The treatment effect, processing of additional biological samples (e.g., determining additional appendix-related characterizations, treatments, etc.), and/or other suitable aspects associated with the continuous collection, processing, and analysis of appendix-related condition-related biological samples, may be performed at any suitable time and frequency to generate, update, and/or otherwise process models (e.g., characterization models, treatment models, etc.) and/or for any other suitable purpose (e.g., as an input associated with other portions of the method 100 embodiments). Of course, block S180 may be performed in any suitable manner.

Of course, embodiments of the method 100 may include any other suitable blocks or steps configured to facilitate receiving a biological sample from a subject, processing the biological sample from the subject, analyzing data derived from the biological sample, and generating a model that may be used to provide customized diagnosis and/or probiotic-based therapy based on the particular microbiome composition and/or functional characteristics of the subject.

Embodiments of method 100 and/or system 200 may include each combination and permutation of the various system components and the various method processes, including any variations (e.g., embodiments, variations, examples, specific examples, figures, etc.), where portions and/or processes of embodiments of method 100 may be performed asynchronously (e.g., sequentially), simultaneously (e.g., in parallel), or in any other suitable order by and/or using one or more examples, elements, components and/or other aspects of system 200 and/or other entities described herein.

Any variations described herein (e.g., embodiments, variations, examples, specific examples, figures, etc.) and/or any portions of variations described herein may additionally or alternatively be combined, aggregated, eliminated, used, performed serially, performed in parallel, and/or otherwise applied.

Some portions of embodiments of method 100 and/or system 200 may be embodied and/or carried out, at least in part, as a machine configured to receive a computer-readable medium storing computer-readable instructions. The instructions may be executed by a computer-executable component integrated with the system. The computer readable medium may be stored on any suitable computer readable medium, such as RAM, ROM, flash memory, EEPROM, optical devices (CD or DVD), hard drives, floppy drives or any suitable device. The computer-executable components may be general-purpose processors or special-purpose processors, however, any suitable special-purpose hardware or hardware/firmware combination may alternatively or additionally execute instructions.

As those skilled in the art will recognize from the foregoing detailed description and from the accompanying drawings and claims, modifications and variations can be made to the embodiments of the method 100, the system 200 and/or variations without departing from the scope as defined in the claims.

Claims

1. A method for characterizing an appendix-related condition associated with a microbial organism, the method comprising:

determining a microbial sequence dataset associated with a group of subjects based on microbial nucleic acids from samples associated with the group of subjects, wherein the samples include at least one sample associated with an appendix-related condition;

collecting supplemental data associated with the appendix-associated condition for the group of subjects;

determining a set of microbiome features based on the microbiome sequence dataset, the set of microbiome features including at least one of a set of microbiome composition features and a set of microbiome functional features;

generating an appendix-related characterization model based on the supplemental data and the set of microbiome features, wherein the appendix-related characterization model is associated with the appendix-related condition;

determining a relevant representation of the appendix-related condition for the user based on the appendix-related representation model; and

providing to the user a therapy that facilitates amelioration of an appendix-related condition based on the appendix-related characterization.

2. The method according to claim 1, wherein the sample comprises A first site-specific sample associated with an intestinal site, wherein the group of microorganisms comprises an enterobacter (Lactobacillus), A Lactobacillus paracasei (Lactobacillus paracasei), A Lactobacillus paracasei (Lactobacillus paracasei), A (Lactobacillus paracasei), A (Lactobacillus paracasei), Lactobacillus paracasei (Lactobacillus paracasei), Lactobacillus-Lactobacillus paracasei), Lactobacillus paracasei (Lactobacillus-12), Lactobacillus (Lactobacillus-Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus-Eschericidocellio), Lactobacillus (Lactobacillus), Lactobacillus-Eschericidocellio-Escherices (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus-Everoni (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus-Escherisoni (Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus-Escherisoni (Lactobacillus-Escherisoni), Lactobacillus (Lactobacillus), Lactobacillus-EscherisporA), Lactobacillus-Escherisoni), Lactobacillus (Lactobacillus), Lactobacillus-Escherisoni), Lactobacillus (Lactobacillus-Escherisonigraciliobacter), Lactobacillus-Escherisoni (Lactobacillus), Lactobacillus-Escherisoni), Lactobacillus-EscherisporA (Lactobacillus), Lactobacillus-Escherisoni (Lactobacillus-EscherisporA (EscherisporA), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus-Escherischobacterium (EscherisporA), Lactobacillus (EscherisporA (Lactobacillus), Lactobacillus-Escherischoides (Lactobacillus-EscherisporA), Lactobacillus (Lactobacillus), Lactobacillus (Escherisoni (Lactobacillus), Lactobacillus (EscherisporA), Lactobacillus (EscherisporA), Lactobacillus (Escherischoidei), Lactobacillus (Escherischoiei), Lactobacillus (Escherischoides), Lactobacillus (Lactobacillus-Escherischoidei), Lactobacillus-Spirochavictori), Lactobacillus (Escherischoiedoides (EscherischoisrA), Lactobacillus (Escherischoiedoides (Escherischoiei), Lactobacillus (Escherischoiedoides), Lactobacillus (Lactobacillus), Lactobacillus-Escherischoiedoides (Escherischoiedor), Lactobacillus-Escherischoiedor), Lactobacillus-Escherischoides), Lactobacillus-Escherischoiedoides (Escherischoides), Lactobacillus (EscherisporA), Lactobacillus-Microbacterium (Escherischoiedoides), Lactobacillus (Escherischoiedoides), Lactobacillus (Escherischoides (EscherischoislA), Lactobacillus-Microbacterium (Escherischobacterium (EscherischoislA), Lactobacillus (Escherischoidi), Lactobacillus (Escherischoiei), Lactobacillus (Escherischoiedoides (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischobacterium (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischobacterium (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (EscherischoislA), Lactobacillus (Escherischoides), Lactobacillus (E), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides (Evoides), Lactobacillus (Evoides (Escherischoides), Lactobacillus (Escherischoides), Lactobacillus (Escherischoides (Evobacterium (Escherischoides), Lactobacillus (EscherisporA.

3. The method of claim 2, wherein determining the set of microbiome characteristics comprises determining the set of microbiome characteristics based on the microbiome sequence dataset, wherein the set of microbiome functional characteristics comprises gut site and neurodegenerative disease, signaling molecules and interactions, biodegradation and metabolism of xenobiotics, ascorbic acid and aldehyde acid metabolism, Huntington's chorea, phosphoinositide metabolism, propionate metabolism, starch and sucrose metabolism, caprolactam degradation, cell motility and secretion, valine, leucine and isoleucine degradation, tryptophan metabolism, type I diabetes, phenylalanine metabolism, selenium compound metabolism, lysine degradation, polycyclic aromatic hydrocarbon degradation, biosynthesis and metabolism of polysaccharides, renal cell carcinoma, butyrate metabolism, carbon fixation pathways in prokaryotes, citric acid cycle (TCA cycle), biosynthesis of lipopolysaccharide, RNA transport, thiamine metabolism, 1,1, 1-trichloro-2, 2-bis (4-chlorophenyl) ethane (DDT) degradation, electron transfer vectors, Amyotrophic Lateral Sclerosis (ALS), prion sclerosis, and toluene degradation, wherein the functional characteristics are associated with at least one of the specific site, wherein the functional characteristics complement a model for the first specific site of linolenic acid, and linolenic acid, wherein the first specific site is characterized based on the first specific site, and the second characteristic.

4. The method of claim 2, further comprising:

collecting a second site specific sample associated with at least one of a skin site, a genital site, an oral site, and a nasal site;

determining a second site-specific compositional feature associated with at least one of a skin site, a genital site, an oral site, and a nasal site, wherein the second site-specific compositional feature is associated with a genus of Gemini (genus), a species of Wedelia atypical, a species of Microbacterium pneumophila, a species of Lactobacillus curvatus, a Follobacteriaceae (family), a genus of Microbacterium, an anaerobe (genus), a species of Anaerobiosis, a genus of Xanthium, a species of Campylobacter Klebsiella, an actinomyces neucleae (species), an anaerobacter lactolyticus (species), a species of Lactobacillus johnsonii (genus), a order of verrucomica, a phylum (phylum), a class of Microbacterium (family), a family of Microbacterium succinicides (species), a genus F0209 (species), a corynebacterium felis (species), a genus Akhmro1 (species), an anaerobacter 9401487 (species), a genus Mesorhizobium (genus), a genus, Lactobacillus reuteri (species), Lactobacillus UPII 199-6 (species), Lactobacillus C30An8 (species), Pediococcus S9Pr-12 (species), Streptococcus marinus (species), Moraxeridae (family), Moraxella (species), Eikeium (species), Echinogyne (species), Rogococcus (species), Propionibacterium (species), Veillonella (species), Waldstella (species), Idiopsis (species), Pediobacter acidogenic Bacteroides (species), ciliella hernalis (species), Cellulosia arenicola (species), Carbonocytophaga N AH9756 (species), Bergey AF14 (species), Erersonia F0004 (species), Bacteroides D22 (species), Phyllobacterium T50 (species), Actinomyces 47 (species), Clostridium AS2 (species), Mycoplasmataceae (species), Streptococcus digesta (species), Streptococcus (species), Lactobacillus kephalospirilluscatopteria (species), Echinococcus (genus Eisenia (species), Echinococcus) and Eisenia (species), Actinomyces viscosus (species), Actinomyces saprodontii (species), Bifidobacterium (genus), Bifidobacterium (family), Rhodospirillaceae (family), Bifidobacterium order (order), Roseburia enterobacter (species), Helicoveromyces (genus), Bifidobacterium longum (species), Bacillus (genus), Streptococcus 11aTha1 (species), Sauteriaceae (family), Flavobacterium (genus), Enterobacter sakazakii (species), Annococcus vaginalis (species), Coleobacterium (class), Brucellaceae (family), Coleobacterium order (order), Exendiman (genus), Hyphomyces gpaco18A (species), Citrobacter BW4 (species), Cronobacter (genus), Corynebacterium jw37 (species), Staphylococcus aureus (species), Brevundimonas (genus), Aureobacterium family (family), Aureobacterium order, Alcaligenes (species), Alcaliphilus diazotrophomonas (species), Peptobacterium (order), Pe, Anaerobic Bacillus (genus), Acinetobacter WB22-23 (species), Pseudomonas (genus), Neissiaceae (family), Parabacteroides dieselae (species), Prevotella (genus), Clostridium pralatum (species), Streptococcus paracasei (species), Dermacocephalus acnes (species), Veillonaceae (family), cilium (genus), Coorabacter (genus), Flavobacteriaceae (family), Delftia (genus), Flavobacterium (class), Prevoteriaceae (family), Lachnaceae (family), Pectitaceae (family), Pectinomycetaceae (family), Dorteriaceae (genus), Dorteriales (genus), Flavobiales (order), Neissales (order), Parabacteroides (genus), Streptococcus oral Classification G63 (species), Aminococcaceae (family), Veillonella CM60 (species), Vitis C9I2 (species), Glycrophilophilus (species), Fusicatenibacter (genus), Staphylococcus 3348O2 (genus), Parabacter faecalis (genus), Coprinus aerogenes (genus), Peptonophilus 1-14 (genus), Propionibacterium KPL1844 (genus), Methylobacterium longissimum (genus), and Staphylococcus C5I16 (genus);

generating a second site-specific appendix-related characterization model based on the second site-specific compositional features;

collecting a user sample from another user, the user sample associated with at least one of a skin site, a genital site, an oral site, and a nasal site; and

based on the second site-specific appendix-related characterization model, relevant characterizations of appendix-related conditions are determined for other users.

5. The method of claim 1, wherein the sample comprises a site-specific sample associated with a skin site; wherein determining the set of microbiome characteristics comprises determining the set of microbiome composition characteristics comprising the skin site and the pseudomonas (genus), neisseriaceae (family), parapacteroides diesei (species), prevotella (genus), clostridium (species), streptococcus paracasei (species), dermatophyte acnes (species), veillonellaceae (family), cilium (genus), corallobacter (genus), flavobacteriaceae (family), dalbergia (genus), flavobacterium (family), flavobacterium (class), prevotellaceae (family), lachnospiraceae (family), peptostreptococcaceae (family), dorferia (genus), flavobacterium (order), neisseriaceae (order), paraperiobacter (genus), streptococcus oral classification G63 (species), aminoacidococcaceae (family), veillonellaceae (family), veillonella 60 (species), At least one associated site-specific compositional feature of staphylococcus C9I2 (species), mucomyidae (family), saccharophilus (species), Fusicatenibacter (genus), staphylococcus 3348O2 (species), parabacteroides faecalis (species), corynebacterium aeroginosum (species), sphingolipid bacillaceae (class), sphingolipid bacillaceae (order), peptophilus 1-14 (species), anaerobic bacillus (genus), propionibacterium KPL1844 (species), methanobacterium longum (species), staphylococcus C5I16 (species); wherein generating the appendix-related characterization model based on the supplemental data and the site-specific compositional features comprises generating the site-specific appendix-related characterization model; wherein said determining the appendix-related trait comprises: and determining the relevant characteristics of the appendix-related condition for the user based on the appendix-related characteristic model of the specific part.

6. The method of claim 1, wherein the sample comprises a site-specific sample associated with a genital site; wherein determining the set of microbiome characteristics comprises determining the set of microbiome composition characteristics comprising the genital area and the twinning coccus (genus), veillonella typica (species), parvula pneumophila (species), lactobacillus curvatus (species), phyllobacteriaceae (family), corynebacterium aquaticum (genus), anaerobe (genus), paraanaerobe (species), ochrobactrum (genus), campylobacter clenbergii (species), actinomyces neuclenii (species), anaerococcus lactis (species), lactobacillus johnsonii (order), verruculomycetales (order), microbiales (phyla), verrucomicrobactes (class), verrucomiciaceae (family), succinobacterium (species), mirabilis F0209 (species), corynebacterium freudenro (species), lactobacillus Akhmro1 (species), anaerobacter 9401487 (species), mesorhizobium, lactobacillus reuteri (genus), lactobacillus reuteri (species), At least one associated site-specific compositional feature of the genus megasphaera UPII 199-6 (species), lactobacillus C30An8 (species), peptococcus S9Pr-12 (species), streptococcus iniae (species); wherein generating the appendix-related characterization model comprises: generating a site-specific appendix-related characterization model based on the supplemental data and site-specific compositional features; wherein determining the appendix-related trait comprises: and determining the relevant characteristics of the appendix-related condition for the user based on the appendix-related characteristic model of the specific part.

7. The method of claim 1, wherein the sample comprises a site-specific sample associated with an oral site; wherein determining the set of microbiome features comprises determining the set of microbiome composition features comprising site-specific composition features associated with the oral site and at least one of moraxellaceae (family), moraxella (genus), akenlium (genus), arkinson (species), nomadicococcus (genus), phyllobacterium (genus), veillonella dispar (species), gordonia (species), waldascinia (species), lazy johnson (species), bacteroides acidoginis (species), cilium hirsutella (species), cilium sarmentosum (species), capnocytophaga AHN9756 (species), burgeria AF14 (species), eusenia F0004 (species), bacteroides D22 (species), phyllotium T50 (species), actinomyces 47 (species), clostridium AS2 (species), and ciliaceae (family); wherein the generating of the appendix-related characterization model comprises: generating a site-specific appendix-related characterization model based on the supplemental data and the compositional features of the specific site; wherein determining the appendix-related trait comprises: based on the site-specific appendix-related characterization model, relevant characterizations of appendix-related conditions are determined for the user.

8. The method of claim 1, wherein the sample comprises a site specific sample associated with a nasal site; wherein determining the microbiome signature comprises determining the microbiome composition signature comprising the nasal location and the bacterial species of the genus comamonas, the genus peptostreptococcus, the genus actinomyces, the species actinomyces carinii, the genus bifidobacterium, the family rhodospirillaceae, the family bifidobacterium order, the genus rosporium, the genus helichrysum, the genus bifidobacterium longum, the genus aggregatibacter, the genus streptococcus 11aTha1, the family satcheliaceae, the genus flavobacterium, the genus ochrobactrum, the genus enterobacter sakazakii, the genus anovulgare, the genus sphingolipid bacilli, the family bruceae, the order sphingomyxobacterium, the genus akkermansia, the genus peptostrepta gpaco18A, At least one associated site-specific compositional feature of at least one of Citrobacter BW4 (species), Cronobacter (genus), Corynebacterium jw37 (species), Staphylococcus aureus (species), Brevundimonas (genus), Aureobacidaceae (family), Aureobacidaceae (order), Alcaligenes diazotrophomonas (species), Anaerobacillus (genus), and Acinetobacter WB22-23 (species); wherein generating the appendix-related characterization model comprises: generating a site-specific appendix-related characterization model based on the supplemental data and the compositional features of the specific site; wherein determining the appendix-related trait comprises: based on the site-specific appendix-related characterization model, a relevant characterization of the appendix-related condition is determined for the user.

9. The method of claim 1, wherein determining the set of microbiome characteristics comprises determining the identity of enterococcus gossypii (species), staphylococcus C9I2 (species), diplococcus 933-88 (species), veillonella (genus), gamma-proteobacteria (class), enterococcus SI-4 (species), enterobacteriales (order), enterobacteriaceae (family), coleoptera (genus), osmylobacteria (genus), ruminobacteriaceae (family), aminoacidococcaceae (family), cholecystobacteria 4_1_30 (species), anaerobic corynebacteria 5_1_63FAA (species), devulcanidae (family), coprocolla (species), devulcanidae (class), coprobacteria (class), delta-proteobacteria (class), burkedleridae (family), mycobacterium RMA 9912 (species), methanobrevibacterium (genus), and, Clinobacterium viscidum (species), Vibrio HGB5 (species), Gemini (genus), Subdoligurum variabile (species), Methanobacterium smithii (species), Intestimanas (genus), Lactobacillus 7_1_47FAA (species), Methanobacteriaceae (family), Choerophilus (genus), Methanobacterium order (order), Clostridiaceae (family), Guanugomycota (phylum), Methanobacterium (class), Aspergillus pratensis (species), Carnobacterium (family), Kluyveromyces (genus), Kluyveromyces (species), Strongylobacter (species), Lactobacillus elongatus (species), Roseburia 11SE39 (species), Bacteroides AR29 (species), Coriolus (species), Vibrio NML05A004 (species), Protovorax (species), Corynebacterium parvus (species), Anaerococcus (Lactobius (species), Corynebacterium anaerobacter 56) and Corynebacterium 3_ Fa (species), Corynebacterium 3_ Fa _ 2A (species), Rhodobactridae (family), Klebsiella SOR89 (species), Macrosphaera DNF00912 (species), veillonella dispar (species), Lactobacillus mucosae (species), Bacteroides fragilis (species), Streptococcus equi (species), Bacteroides vulgatus (species), Propionibacterium MSP09A (species), Streptococcus pasteurii (species), Anaerococcus 765 (species), muciniphila-Ekermansia (species), Actinomyces Tourette subsp (species), Enterobacterium sakazakii (species), Pseudomonas aeruginosa (species), Staphylococcus (species), enterococcus aminoacids (species), Propionibacterium granulum (species), Bacteroides thetaiotaomicron (species), Clostridium CM21 (species), Pediococcus MFC1 (species), Pseudomonas aeruginosa (species), Pediococcus octodes (species), Micrococcus macrosphaera C1 (species), Streptococcus BS35a (species), Streptococcus thermophilus (species), Clostridium ulcerosa (species), Clostridium (species), Morganella morganii (species), Bacteroides SLC1-38 (species), Bacteroides exserohilus (species), chicken manure bacilli (species), Bacteroides CB57 (species), Bifidobacterium coprinus (species), Veillonella sarmentosa (species), Clostridium gangrenosum (species), Lactobacillus curvatus (species), Velereria MSA12 (species), Asaccharospora irguraria (species), Clostridium ramorum (species), Lactobacillus TAB-22 (species), human excretory Parasalmonella (species), Lactobacillus C4I2 (species), Bacteroides 157 (species), Klebsiella (species), Pseudosciaena (species), Streptococcus (species), Propionibacterium (species), Cronobacter (species), anaerobic arc (species), Enterobacter (species), Staphylococcus (species), Lactobacillus (species), Bacillus thuringiensis (species), Prevotella (species), Peptorella (species), Pediococcus (species), and Bacillus (species), Morganella (genus), Aminococcus (genus), Vibrio (genus), Microbacterium (genus), Macrococcus (genus), Pyrococcus (genus), Asaccharospora (genus), Vibrio (genus), Dafengoldfora (genus), Anaerococcus (genus), Streptococcus (family), Propioniaceae (family), Weironellaceae (family), Staphylocoaceae (family), Colostridaceae (family), Clostridiaceae (family), unknown species (family), Pectinomycetaceae (family), Vibrionaceae (family), Pectinomyceceae (family), Corynebacteriaceae (family), Rhodospirillaceae (family), Selenomonas (order), Lactobacillales (order), Clostridiales (order), Xanthomonas (order), Bacillales (order), Pleurococcales (order), Aeromonas (order), Pseudomonas (order), Bacillus (order), Spirillus (order), Clostridia (order), Aeromonas (order), Pseudomonas (order), Mycospora, A microbiome composition characteristic associated with at least one of Negativicultes (class), Clostridia (class), Proteobacteria (phylum), cyanobacteria (phylum), Bacteroides (species), Alisiprescutendinis (species), Actinomycetes (class), Lactobacillaceae (family), Bifidobacteriaceae (family), Bifidobacterium (genus), Bifidobacteriales (order), and Oscillatoriaceae (family); wherein generating the appendix-related characterization model comprises: generating the appendix-associated characterization model based on the supplemental data and the set of microbiome composition features.

10. The method of claim 1, wherein determining the microbial sequence data set comprises: determining at least one of a metagenomic library and a macrotranscriptome library based on at least a subset of the microbial nucleic acids; wherein determining the set of microbiome characteristics comprises: the set of microbiome features is determined based on at least one of the metagenomic library and the macrotranscriptome library.

11. The method of claim 1, wherein determining the set of microbiome features based on the microbiome sequence dataset comprises using a set of analytical techniques to determine at least one of a microbiome composition diversity feature and a microbiome functional diversity feature of its presence, at least one of a microbiome composition diversity feature and a microbiome functional diversity feature of its absence, a relative abundance feature describing a relative abundance of different taxonomic groups associated with the appendix-related disorder, a ratio feature describing a ratio between at least two microbiome features associated with different taxonomic groups, an interaction feature describing an interaction between different taxonomic groups, and a phylogenetic distance feature describing a phylogenetic distance between different taxonomic groups; wherein the set of analysis techniques includes at least one of univariate statistical testing, multivariate statistical testing, dimension reduction techniques and artificial intelligence methods.

12. The method of claim 1, wherein the treatment comprises at least one of a consumable, a device-related treatment, a surgical procedure, a psychologically-associated treatment, and a behavioral modification treatment; wherein providing the treatment comprises providing a recommendation of treatment to the user on a computing device associated with the user.

13. A method for characterizing an appendix-related condition associated with a microbial organism, the method comprising:

collecting a sample from a user, wherein the sample comprises microbial nucleic acid corresponding to a microbe associated with the appendix-related condition;

determining a microbial dataset associated with a user based on microbial nucleic acids of the sample;

determining a user microbiome characteristic based on the microbiome dataset, the user microbiome characteristic comprising at least one of a user microbiome composition characteristic and a user microbiome functional characteristic; wherein the user microbiome characteristic is associated with an appendix-related condition;

determining a relevant characterization of the appendix-related condition for the user based on the user microbiome features; and

based on the appendix-related characterization, facilitating a therapeutic intervention associated with the treatment of the user to facilitate amelioration of the appendix-related condition.

14. The method according to claim 13, wherein the characteristics of the user microbiome include the characteristics of the user microbiome composition including the genus Lactobacillus (Lactobacillus), the genus Paralichenidae), the genus Lactobacillus (Lactobacillus), the genus Paralichenicilli (Lactobacillus), the genus Paralicheniformis (Lactobacillus), the genus Lactobacillus (Lactobacillus), the genus Paralichenidae), the genus Lactobacillus (Lactobacillus), the genus Paralicheniformis (Lactobacillus), the genus Lactobacillus (Lactobacillus), the genus Paralichenicillium), the genus Lactobacillus (Lactobacillus), the genus Lactobacillus (Lactobacillus), the family Lactobacillus), the genus Lactobacillus), the family Lactobacillus (Lactobacillus), the order Paralichenicillium), the genus Lactobacillus (Lactobacillus), the order Paralichenicillium (Lactobacillus), the genus Lactobacillus (Lactobacillus), the order Paralichtheliopsilliferae), the genus Lactobacillus (Lactobacillus), the order Paralichtheliopsilliferae), the genus Lactobacillus (Lactobacillus), the order), the genus Lactobacillus), the order), the genus Lactobacillus (Lactobacillus), the order), the genus Lactobacillus (Lactobacillus), the order Eschenociceps (Lactobacillus), the order Eschenociceps (Lactobacillus), the order Eschenociceps-rhodobacter), the order Eschenoclaviophylobacter (order), the order Eschenoclaviophylobacter (Lactobacillus (order), the order Escherisdorsiferidiomycetaledorsiferiphylobacter (order), the order Eschenoclaviophylobacter (order), the order EscherisporA (order), the order), the order EscherisporA (order), the order), the order (order, the order), the order (order Escherisdorsiferidiomycetaledorsiferidiomycetaledorsiferiphylobacter (order), the order), the order, order), the order), the order (order, the order), the order (order, the order), the order), the order (order ), the order), the order, the order, order), the order (order, the order), the order), the order), the order (order, the order.

15. The method of claim 13, wherein determining the user microbiome characteristic comprises: determining the user microbiome composition characteristic based on the microbiome dataset, the user microbiome composition characteristic comprising a skin site and pseudomonas (genus), neisseriaceae (family), parabacteroides diesei (species), prevotella (genus), clostridium pratensis (species), streptococcus paracasei (species), dermatophyte acnes (species), veillonellaceae (family), cilium (genus), corallobacter (genus), flavobacteriaceae (family), daltephrix (genus), flavobacterium (class), prevotella (family), lachnospiraceae (family), peptostridiaceae (family), dolichia (genus), xanthobacter (order), neisseriaceae (order), parabacteroides (genus), streptococcus oral taxonomic group G63 (species), aminoacidococcaceae (family), veillonella (species), veillonella 60 (species), At least one site-specific compositional feature of interest in Staphylococcus C9I2 (species), Citrobacter (family), Glycophilobacter (species), Fusicatenibacter (genus), Staphylococcus 3348O2 (species), Bacteroides coprinus (species), Coprinus aerogenes (species), Coleobacterium (class), Coleobacterium (order), Pectinophilus 1-14 (species), anaerobic Bacillus (genus), Propionibacterium KPL1844 (species), Methylobacterium longum (species), Staphylococcus C5I16 (species); wherein determining the appendix-related trait comprises: based on the site-specific compositional features, relevant characterizations of appendix-related conditions are determined for the user.

16. The method of claim 13, wherein determining the user microbiome characteristic comprises: determining the user microbiome composition characteristics including genital area and twinning coccus (genus), veillonella typica (species), parvulus pneumophila (species), lactobacillus curvatus (species), phyllobacteriaceae (family), corynebacterium (genus), anaerobe (genus), geminiform anaerobe (species), ochrobactrum (genus), campylobacter closterii (species), actinomyces neuclei (species), lactoanaerobacter lactis (species), lactobacillus johnsonii (species), verruciformes (order), verrucomicrobia (phylum), verrucomicrobacteridae (family), succinolyticus (species), kiwium F0209 (species), corynebacterium freudenreichii (species), lactobacillus Akhmro1 (species), anaerobacter 9401487 (species), mesorhizoma (genus), lactobacillus reuteri (species), and, At least one of the related site-specific compositional features of Macrococcus UPII 199-6 (species), Lactobacillus C30An8 (species), Pediococcus S9Pr-12 (species), Streptococcus marinus (species); wherein determining the appendix-related trait comprises: based on the site-specific compositional features, a relevant characterization of the appendix-related condition is determined for the user.

17. The method of claim 13, wherein determining the user microbiome characteristic comprises: determining the user microbiome composition characteristics based on the microbiome data set, the user microbiome composition characteristics including site-specific composition characteristics of an oral site associated with at least one of moraxellaceae (family), moraxella (genus), akenlium (genus), arkinsonia (species), roamococcus (genus), phyllobacterium (genus), veillonella dispar (species), gordonia (species), lazy johnson (species), bacteroides acidalis (species), cilium hertzeri (species), cilium arenicoli (species), capnocytophaga AHN9756 (species), bergey AF14 (species), eusenia F0004 (species), bacteroides D22 (species), phyllobacterium T50 (species), actinomyces 47 (species), clostridium AS2 (species), and ciliaceae (family); wherein determining the appendix-related trait comprises: based on the site-specific compositional features, a relevant characterization of the appendix-related condition is determined for the user.

18. The method of claim 13, wherein determining the user microbiome characteristic comprises: determining the user microbiome composition characteristics based on the microbial dataset, the user microbiome composition characteristics including nasal location and comamonas (genus), peptostreptococcus (genus), actinomyces viscosus (species), actinomyces carinii (species), bifidobacterium (genus), bifidobacterium (family), rhodospirillaceae (family), order of bifidobacteria (order), rosporia enterobacter (species), gyrobacterium (genus), bifidobacterium longum (species), aggregatibacter (genus), streptococcus 11aTha1 (species), sauteiaceae (family), flavobacterium (genus), xanthobacter (genus), ochrobactrum (genus), enterobacter sakazakii (species), anococcus vaginalis (species), class of sphingolipid bacillaceae (class), order of sphingolipid bacillaceae (order), akkermansia (genus), peptophilus gpaco18A (species), citrobacter BW4 (species), bactenoid (species), bacteroides serpinicollis (species), bactor, (ii) at least one associated site-specific compositional feature of Cronobacter (genus), Corynebacterium jw37 (species), Staphylococcus aureus (species), Brevundimonas (genus), Aureobacidaceae (family), Aureobaciales (order), Alcaligenes diazotrophomonas (species), anaerobic Bacillus (genus) and Acinetobacter WB22-23 (species); wherein determining the appendix-related trait comprises: based on the site-specific compositional features, relevant characterizations of appendix-related conditions are determined for the user.

19. The method of claim 13, wherein determining the user microbiome characteristics comprises determining the functional activity of a protein transporter, a protein transporter, a protein transporter, a protein transporter, a protein transporter protein, a protein transporter protein, a protein transporter, a protein transporter.

20. The method of claim 13, wherein the treatment comprises at least one of a probiotic treatment and a prebiotic treatment; wherein facilitating therapeutic intervention comprises promoting the at least one of a probiotic treatment and a prebiotic treatment to the user to facilitate amelioration of the appendix-related condition; wherein at least one of probiotic treatment and prebiotic treatment is selected from the group consisting of enterococcus raffinose (species), enterococcus C9I2 (species), enterococcus 933-88 (species), enterococcus SI-4, Chorda 4_1_30, Corynebacterium anaerobicum 5_1_63FAA, Bacillus coprolacus, Mycobacterium RMA 9912, Clerodendrobacillus viscera, Mycobacterium HGB5, Subdoligranum variabile, Methanobacterium smithii, Lactobacillus 7_1_47FAA, Aspergillus pratensis, Kluyveromyces zogenes, Brucella coprinus, Clostridium prasudahlii, Lactobacillus elongatus, Roseburia 11SE39, Bacteroides AR29, Mycobacterium NML05A004, Proteus elevator, Corynebacterium anaerobium 3_2_56FAA, Klebsiella SOR89, Lactobacillus megaterium F912, Lactobacillus sanctus, Lactobacillus crispatus, Streptococcus faecalis, and Lactobacillus, Propionibacterium MSP09A, Streptococcus pasteuris, Anaerobacter 765, muciniphilic-Ackermansia, Actinomycete Toxoplasma subspecies, Enterobacter sakazakii, Pseudomonas luteo-virens, Staphylococcus, enterococcus aminoacidum, Propionibacterium granulum, Bacteroides thetaiotaomicron, Clostridium CM21, Pediococcus MFC1, Pseudomonas aeruginosa, Sarcina, Megasphaera genomo Cl, Streptococcus BS35a, Streptococcus thermophilus, Clostridium ulcerosa, Morganella morganii, Bacteroides 1-38, Bacteroides Ehrlichia, Chicken manure Bacteroides CB57, Bifidobacterium faecalis, atypical veillonella, Clostridium necropis, Lactobacillus curvatus, Veillonella MSA12, Asaccharospora regius, Clostridium ramosum, Lactobacillus-22, Salmonella typhimurium excreta, Lactobacillus paracasei, Lactobacillus C4I2, Pseudomonas purotis, Pseudomonas sp, Alcaligenes 157, and Alinidipes.

21. A method for characterizing an appendix-related condition associated with a microbial organism, the method comprising:

determining a user microbiome characteristic based on the microbiome dataset, wherein the user microbiome characteristic is associated with an appendix-related condition; and

based on the user microbiome characteristics, a relevant characterization of the appendix-related condition is determined for the user.

22. The microorganism includes Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, MicrophylNo, Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Microphylobacter, Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (No, Lactobacillus), Lactobacillus (No, Lactobacillus (No, Lactobacillus), Lactobacillus.

23. The method of claim 22, wherein the first and second portions are selected from the group consisting of,

wherein the sample is associated with a first body part comprising at least one of an intestinal region, a skin region, a genital region, an oral region, and a nasal region,

wherein the user microbiome composition characteristics comprise site-specific composition characteristics, each site-specific composition characteristic associated with a first body site,

wherein determining the appendix-related trait comprises determining a relevant trait of an appendix-related condition for a user based on site-specific compositional characteristics, and

wherein the method further comprises providing a first site-specific treatment to the user based on the appendix-related characterization to promote amelioration of the appendix-related condition, wherein the first site-specific treatment is associated with the first body site.

24. The method of claim 23, further comprising:

after providing the first site-specific treatment, collecting a post-treatment sample from a user, wherein the post-treatment sample is associated with a second body site, the second body site comprising at least one of an intestinal site, a skin site, a genital site, an oral site, and a nasal site;

determining an appendix-related condition for the user for the treated appendix-related characterization based on the site-specific feature associated with the second body site; and

based on the post-treatment appendix-related characterization, providing a second site-specific treatment to the user to promote amelioration of the appendix-related condition, wherein the second site-specific treatment is associated with a second body site.

25. The method according to claim 21, wherein the characteristics of the microbiome include determination of the intestinal site of the microorganism (NeisseriA), NeisseriA (family), NeisseriA (genus), MyxoplasmA (genus), Lactobacillus (Lactobacillus), Lactobacillus paracasei (genus), Lactobacillus paracasei (genus Lactobacillus (genus), Lactobacillus paracasei (genus Lactobacillus), Lactobacillus sp), Lactobacillus (genus Lactobacillus-11), Lactobacillus (genus), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus paracasei), Lactobacillus (genus Lactobacillus), Lactobacillus paracasei), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus paracasei), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus paracasei), Lactobacillus (genus Lactobacillus paracasei), Lactobacillus sp), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus sp), Lactobacillus-Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus-Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus (genus), Lactobacillus (genus Lactobacillus), Lactobacillus (genus Lactobacillus-Lactobacillus), Lactobacillus (genus Lactobacillus), Lactobacillus (family), Lactobacillus-Lactobacillus), Lactobacillus (family), Lactobacillus (genus Lactobacillus (family EscherEscherEscherNo (family), Lactobacillus (family), Lactobacillus (genus Lactobacillus), Lactobacillus (family), Lactobacillus (order), Lactobacillus (genus Lactobacillus (family), Lactobacillus (family EscherNo (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family EscherNo (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus-11 (family), Lactobacillus (family), Lactobacillus-EscherNo (family), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus-Spirochavictoriae), Lactobacillus (family EscherNo (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family, phylobacter (family), Lactobacillus (order), Lactobacillus (family, phylobacter), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (order), Lactobacillus-11 (order), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus (family, phylobacter (order), Lactobacillus (order), Lactobacillus (family), Lactobacillus (order), Lactobacillus-Microphylobacter), Lactobacillus (order), Lactobacillus-Microphylobacter (order), Lactobacillus.

26. The method of claim 25, wherein determining the user microbiome characteristics comprises determining, based on the microbial data set, their functional activity in neurodegenerative diseases, signal molecules and interactions, biodegradation and metabolism of exogenous substances, ascorbic acid and uronic acid metabolism, Huntington's disease, phosphoinositide metabolism, propionate metabolism, starch and sucrose metabolism, caprolactam degradation, cell motility and secretion, valine, leucine and isoleucine degradation, tryptophan metabolism, type I diabetes, phenylalanine metabolism, selenium metabolism, lysine degradation, polycyclic aromatic hydrocarbon degradation, polysaccharide biosynthesis and metabolism, renal cell carcinoma, butyrate metabolism, carbon fixation pathways in prokaryotes, citric acid cycle (TCA cycle), lipopolysaccharide biosynthesis, RNA transport, thiamine metabolism, 1,1, 1-trichloro-2, 2-bis (4-chlorophenyl) ethane (DDT) degradation, electron transfer vectors, Amyotrophic Lateral Sclerosis (ALS), prion diseases, tolune degradation, α -metabolism modification [ KEV ] translation mechanisms, KEO ] 2-bis (4-chlorophenyl) ethane (DDA) degradation, GG, folate metabolism, and other metabolic activity-related protein synthesis and protein synthesis pathways in the metabolic pathways of genes of microorganisms, and other metabolic pathways characterized by the metabolic pathways of genes of the genes, the genes of the genes, the metabolic pathways of the genes, the genes of which are involved in which characterize the metabolic pathways of the genes, the genes of the genes, the genes of the metabolic pathways of the genes, the genes of the genes, the genes of which are involved in the genes of the synthesis of the genes, the genes of the genes, the genes of the synthesis of the genes, the genes of the metabolic pathways of the genes, the genes of.

27. The method of claim 21, wherein determining the user microbiome characteristic comprises: based on the microbial dataset, the skin site is determined from pseudomonas (genus), neisseriaceae (family), parabacteroides diesei (species), prevotella (genus), clostridium prasukii (species), streptococcus paracasei (species), dermatophyte acnes (species), veillonellaceae (family), cilium (genus), corallobacter (genus), flavobacteriaceae (family), daltephrix (genus), flavobacterium (class), prevotella (family), lachnospiraceae (family), peptostridiaceae (family), pediococcaceae (family), dorferi (genus), flavobacterium (order), neisseriaceae (order), parabacteroides (genus), streptococcus oral taxon G63 (species), aminoacidococcaceae (family), virginia CM60 (species), staphylococcus C9I2 (species), ciliiaceae (family), glycophilus, and streptococcus (species), At least one associated site-specific compositional feature of Fusicatenibacter (genus), Staphylococcus 3348O2 (species), Bacteroides faecalis (species), Coprinus aerogenes (species), Coleobacterium (class), Coleobacterium (order), Peptophilus 1-14 (species), anaerobic Bacillus (genus), Propionibacterium KPL1844 (species), Methylobacterium longitubum (species), and Staphylococcus C5I16 (species); wherein determining the appendix-related trait comprises: based on the site-specific compositional features, a relevant characterization of the appendix-related condition is determined for the user.

28. The method of claim 21, wherein determining the user microbiome characteristic comprises: based on the microbial data set, the reproductive site was determined from twinning coccus (genus), veillonella typica (species), parvulus pneumophila (species), lactobacillus curvatus (species), phyllobacteriaceae (family), corynebacterium (genus), anaerobe (genus), geminiform anaerobe (species), ochrobactrum (genus), campylobacter krusei (species), actinomyces neugesii (species), anoxycoccus lactis (species), lactobacillus johnsonii (species), order verruciformes (order), microsomycota wartae (phylum), class verrucomicroberiidae (family), succinobacterium succinicides (species), mirabilis F0209 (species), corynebacterium feldianum (species), lactobacillus Akhmro1 (species), anaerobococcus 9401487 (species), mesorhizobium (genus), lactobacillus reuteri (species), macrococcus UPII 199-6 (species), lactobacillus C30An8 (species), lactobacillus C30a, lactobacillus (species), Site-specific compositional features associated with at least one of Peptococcus species S9Pr-12, and Streptococcus marinus species; wherein determining the appendix-related trait comprises: based on the site-specific compositional features, a relevant characterization of the appendix-related condition is determined for the user.

29. The method of claim 21, wherein determining the user microbiome characteristic comprises: determining site-specific compositional features of an oral site associated with at least one of moraxellaceae (family), moraxella (genus), akenlium (genus), arkinson (species), roamaway (genus), phyllobacterium (genus), veillonella dispar (species), gordonia (species), lazy johnson (species), bacteroides acidiferus (species), cilium hertzeri (species), cilium sarnyi (species), cabochytrophagia AHN9756 (species), bergeria AF14 (species), eriodictyoides F0004 (species), bacteroides D22 (species), phyllobacterium T50 (species), actinomycetium ICM47 (species), clostridium AS2 (species), and ciliaceae (family) based on the microbial data set; wherein determining the appendix-related trait comprises: based on the site-specific compositional features, a relevant characterization of the appendix-related condition is determined for the user.

30. The method of claim 21, wherein determining the user microbiome characteristic comprises: determining nasal location and comamonas (genus), peptostreptococcus (genus), actinomyces viscosus (species), actinomyces carinii (species), bifidobacterium (genus), bifidobacterium (family), rhodospiraceae (family), order bifidobacterium (order), ralstonia enterobacter (species), gyrobacterium (genus), bifidobacterium longum (species), aggregatibacter (genus), streptococcus (genus)₁₁aTha₁(species), Saxatilidae (family), Flavobacterium (genus), Xanthium (genus), Enterobacter sakazakii (species), Anoectococcus vaginalis (species), Coleobacterium (class), Brucella (family), Coleobacterium (order), Ackermansia (genus), Peptophilus gpaco18A (species), Citrobacter BW4 (species), Cronobacter (genus), Corynebacterium jw37 (species), Staphylococcus aureus (species), Brevundimonas (genus), Aureobacterium (family), Aureobacterium (order), Achnsoniaceae (species), Alcaligenes diazotrophomonas (species), anaerobic Bacillus (genus), and Acinetobacter WB22-23 (species); wherein determining the appendix-related trait comprises: based on the site-specific compositional features, a relevant characterization of the appendix-related condition is determined for the user.