Classifications

• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
  • G16B25/20—Polymerase chain reaction [PCR]; Primer or probe design; Probe optimisation
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6876—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes
  • C12Q1/6888—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms
  • C12Q1/689—Nucleic acid products used in the analysis of nucleic acids, e.g. primers or probes for detection or identification of organisms for bacteria
• • C—CHEMISTRY; METALLURGY
  • C12—BIOCHEMISTRY; BEER; SPIRITS; WINE; VINEGAR; MICROBIOLOGY; ENZYMOLOGY; MUTATION OR GENETIC ENGINEERING
  • C12Q—MEASURING OR TESTING PROCESSES INVOLVING ENZYMES, NUCLEIC ACIDS OR MICROORGANISMS; COMPOSITIONS OR TEST PAPERS THEREFOR; PROCESSES OF PREPARING SUCH COMPOSITIONS; CONDITION-RESPONSIVE CONTROL IN MICROBIOLOGICAL OR ENZYMOLOGICAL PROCESSES
  • C12Q1/00—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions
  • C12Q1/68—Measuring or testing processes involving enzymes, nucleic acids or microorganisms; Compositions therefor; Processes of preparing such compositions involving nucleic acids
  • C12Q1/6809—Methods for determination or identification of nucleic acids involving differential detection
• • G—PHYSICS
  • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
  • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
  • G16B40/00—ICT specially adapted for biostatistics; ICT specially adapted for bioinformatics-related machine learning or data mining, e.g. knowledge discovery or pattern finding
  • G16B40/20—Supervised data analysis
• • A—HUMAN NECESSITIES
  • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
  • A61K—PREPARATIONS FOR MEDICAL, DENTAL OR TOILETRY PURPOSES
  • A61K35/00—Medicinal preparations containing materials or reaction products thereof with undetermined constitution
  • A61K35/66—Microorganisms or materials therefrom
  • A61K35/74—Bacteria
  • A61K35/741—Probiotics
• • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
  • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
  • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
  • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
  • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

Definitions

• This invention relates generally to the field of microbiology and more specifically to a new and useful method and system for characterizing skin-related conditions in the field of microbiology.
• a microbiome is an ecological community of commensal, symbiotic, and pathogenic microorganisms that are associated with an organism.
• the human microbiome includes over 10 times more microbial cells than human cells, but characterization of the human microbiome is still in nascent stages due to limitations in sample processing techniques, genetic analysis techniques, and resources for processing large amounts of data. Nonetheless, the microbiome is suspected to play at least a partial role in a number of health/disease-related states (e.g., preparation for childbirth, diabetes, autoimmune disorders, gastrointestinal disorders, rheumatoid disorders, neurological disorders, etc.).
• FIGURES lA-iB are flowchart representations of variations of embodiments of a method for microbiome characterization
• FIGURE 2 depicts variations of embodiments of a system and method for microbiome characterization
• FIGURE 3 depicts a schematic representation of a variation of generating and applying a characterization model in an embodiment of a method for microbiome characterization
• FIGURE 4 depicts a variation of applying and updating a characterization model and a therapy model in an embodiment of a method for microbiome characterization
• FIGURE 5 depicts a variation of applying multiple characterization models in an embodiment of a method for microbiome characterization
• FIGURE 6 depicts a variation of promoting therapies in variations of an embodiment of a method for microbiome characterization
• FIGURE 7 depicts variations of mechanisms by which probiotic-based therapies operate in an embodiment of a method for microbiome characterization
• FIGURE 8 depicts a variation of notification provision in an embodiment of a method for microbiome characterization
• FIGURE 9 depicts a variation of an interface for providing skin-related condition information in an embodiment of a method for microbiome characterization
• FIGURE 10 depicts variations of notification provision in an embodiment of a method for microbiome characterization
• FIGURE n depicts a variation of notification provision in an embodiment of a method for microbiome characterization
• FIGURE 12 depicts a variation of sample processing parameter modification in an embodiment of a method for microbiome characterization.
• a system 200 for characterizing (e.g., evaluating) a skin-related condition in relation to a user can include one or more of: a handling network 210 operable to collect containers including material from a set of users (e.g., a population of users), the handling network 210 including a sequencing system operable to determine microorganism sequences from sequencing the material; a microbiome characterization system 220 operable to: determine at least one of microbiome composition data and microbiome functional diversity data based on the microorganism sequences, collect supplementary data associated with the skin-related condition for the set of users, and transform the supplementary data and features extracted from the at least one of the microbiome composition data and the microbiome functional diversity data into a characterization model for the skin-related condition; and a therapy system 230 (e.g., treatment system) operable to promote a treatment to the user for the skin-related condition
• a therapy system 230 e.g., treatment system
• the system 200 and method 100 can function to generate models that can be used to characterize and/ or diagnose users according to at least one of their microbiome composition and functional features (e.g., as a clinical diagnostic, as a companion diagnostic, etc.); provide therapeutic measures (e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small-molecule-based therapeutic measures, clinical measures, etc.) to users based upon microbiome analysis for a population of users; and/or perform any suitable function.
• therapeutic measures e.g., probiotic-based therapeutic measures, phage-based therapeutic measures, small-molecule-based therapeutic measures, clinical measures, etc.
• the system 200 and method 100 can preferably generate and promote characterizations and therapies for skin-related conditions, which can include any one or more of skin-related: symptoms, causes, diseases, disorders, and/or any other suitable aspects associated with skin-related conditions.
• Skin-related conditions can include any one or more of: eczema (e.g., atopic dermatitis, allergic contact dermatitis, irritant contact dermatitis, stasis dermatitis, acrodermatitis, seborrhoeic eczema, xerotic eczema, dyshidrosis, discoid eczema, venous eczema, dermatitis herpetiformis, neurodermatitis, autoeczematization, eczema herpeticum, etc.), dry skin, a scalp-related condition (e.g., dandruff, hair loss, cradle cap, scalp-related, head lice, ringworm, folli
• One or more instances of the method 100 and/or processes described herein can be performed asynchronously (e.g., sequentially), concurrently (e.g., in parallel; concurrently on different threads for parallel computing to improve system processing ability for determining and/or providing characterizations and/or therapies for skin- related conditions; etc.), in temporal relation to a trigger event, and/or in any other suitable order at any suitable time and frequency by and/or using one or more instances of the system 200, elements, and/or entities described herein.
• the method 100 and/or system 200 can be configured in any suitable manner.
• Microbiome analysis can enable accurate and efficient characterization and/or therapy provision for skin-related conditions caused by and/or otherwise associated with microorganisms.
• the technology can overcome several challenges faced by conventional approaches in characterizing and/ or promoting therapies for skin-related conditions.
• conventional approaches can require patients to visit a care provider who performs a physical inspection in relation to skin-related conditions.
• conventional genetic sequencing and analysis technologies for human genome sequencing can be incompatible and/or inefficient when applied to the microbiome (e.g., where the human microbiome can include over 10 times more microbial cells than human cells; where optimal sample processing techniques can differ; where sequence reference databases can differ; where microbiome characterization can include accounting for the different compositions and functional diversity of the microbiome across populations; where the microbiome can vary across different body regions of the user; etc.).
• sequencing technologies e.g., next-generation sequencing
• technological issues e.g., data processing issues, information display issues, microbiome analysis issues, therapy prediction issues, therapy provision issues, etc.
• the technology can confer improvements in computer-related technology (e.g., artificial intelligence, machine learning, biological sample processing and computational analysis network, etc.) by facilitating computer performance of functions not previously performable.
• the technology can computationally generate microbiome characterizations and recommended therapies for skin-related conditions, based on microbiome sequence datasets and microorganism reference sequence databases (e.g., Genome Reference Consortium) that are recently viable due to advances in sample processing techniques and sequencing technology.
• the technology can confer improvements in processing speed, microbiome characterization accuracy, microbiome-related therapy determination and promotion, and/or other suitable aspects in relation to skin-related conditions.
• the technology can generate and apply skin-related feature-selection rules to select an optimized subset of features (e.g., microbiome composition features, microbiome functional diversity features, etc.) out of a vast potential pool of features (e.g., extractable from the plethora of microbiome data) for generating and applying characterization models and/or therapy models.
• microbiomes e.g., human microbiomes, animal microbiomes, etc.
• the potential size of microbiomes can translate into a plethora of data, giving rise to questions of how to process and analyze the vast array of data to generate actionable microbiome insights in relation to skin-related conditions.
• the skin-related feature-selection rules and associated technology can enable shorter training and execution times (e.g., for predictive machine learning models), model simplification facilitating efficient interpretation of results, reduction in overfitting, improvements in data sources (e.g., for collecting and processing microbiome datasets), improvements in identifying and presenting skin-related condition insights in relation to the microbiome, and other suitable improvements to facilitate rapid determination of characterizations and/or therapies.
• the technology can transform entities (e.g., users, biological samples, therapy systems including medical devices, etc.) into different states or things.
• the system 200 and/or method 100 can identify therapies to promote to a patient to modify microbiome composition and/or function to prevent and/or ameliorate skin-related conditions, thereby transforming the microbiome and/or health of the patient.
• the technology can transform biological samples (e.g., through fragmentation, multiplex amplification, sequencing, etc.) received by patients into microbiome datasets, which can subsequently be transformed into features correlated with skin-related conditions, in order to generate characterization models and/ or therapy models.
• the technology can control therapy systems to promote therapies (e.g., by generating control instructions for the therapy system to execute), thereby transforming the therapy system.
• the improvements in computer-related technology can drive transformations in the biological sample processing approaches, such as selecting a subset of primers compatible with genetic targets identified to correspond to microbiome composition features and/or microbiome functional diversity features correlated (e.g., determined based on skin-related feature selection rules) with skin-related conditions.
• the technology can amount to an inventive distribution of functionality across a network including a sample handling network, microbiome characterization system, and a plurality of users, where the sample handling network can handle simultaneous processing of biological samples (e.g., in a multiplex manner) from the plurality of users, which can be leveraged by the microbiome characterization system in generating user-personalized characterizations and/or therapies (e.g., customized to the user's microbiome, medical history, demographics, behaviors, preferences, etc.) for skin-related conditions.
• the sample handling network can handle simultaneous processing of biological samples (e.g., in a multiplex manner) from the plurality of users, which can be leveraged by the microbiome characterization system in generating user-personalized characterizations and/or therapies (e.g., customized to the user's microbiome, medical history, demographics, behaviors, preferences, etc.) for skin-related conditions.
• the technology can improve the technical fields of at least computational modeling of skin-related conditions in relation to microbiome digital medicine, digital medicine, genetic sequencing, and/or other relevant fields.
• the technology can leverage specialized computing devices (e.g., devices associated with the sample handling network, such as sequencing systems; microbiome characterization systems; treatment systems; etc.) in determining and processing microbiome datasets for characterizing and/ or determining therapies for skin-related conditions.
• the technology can, however, provide any other suitable benefit(s) in the context of using non- generalized computer systems for microbiome characterization and/or modulation. 3. System.
• the handling network 210 of the system 200 can function to receive and process (e.g., fragment, amplify, sequence, etc.) biological samples.
• the handling network 210 can additionally or alternatively function to provide and/or collect sample kits 250 (e.g., including sample containers, instructions for collecting samples, etc.) for a plurality of users (e.g., in response to a purchase order for a sample kit 250), such as through a mail delivery system.
• the sample kits 250 can include materials and associated instructions for a user to collect a skin sample (e.g., through cotton tip swabs; aspiration of skin-related fluids such as fluids from a skin lesion; biopsy; etc.).
• the handling network 210 can additionally or alternatively include a library preparation system operable to automatically prepare biological samples (e.g., fragment and amplify using primers compatible with genetic targets associated with the skin-related condition) in a multiplex manner to be sequenced by a sequencing system; and/or any suitable components.
• a library preparation system operable to automatically prepare biological samples (e.g., fragment and amplify using primers compatible with genetic targets associated with the skin-related condition) in a multiplex manner to be sequenced by a sequencing system; and/or any suitable components.
• the handling network 210 and associated components can be configured in any suitable manner.
• the microbiome characterization system 220 of the system 200 can function to determine and analyze microbiome datasets and/or supplementary datasets for characterizing and/or determining therapies for skin-related conditions.
• the microbiome characterization system 220 can obtain and/or apply computer- implemented rules (e.g., skin-related feature selection rules; model generation rules; user preference rules; microorganism sequence generation rules; sequence alignment rules; and/or any other suitable rules).
• computer- implemented rules e.g., skin-related feature selection rules; model generation rules; user preference rules; microorganism sequence generation rules; sequence alignment rules; and/or any other suitable rules.
• the microbiome characterization system 220 can be configured in any suitable manner.
• the therapy system 230 of the system 200 functions to promote one or more therapies to a user (e.g., subject; care provider administering the therapy; etc.) for treating a skin-related condition (e.g., reducing the risk of a skin-related infection, etc.).
• a user e.g., subject; care provider administering the therapy; etc.
• a skin-related condition e.g., reducing the risk of a skin-related infection, etc.
• the therapy system 230 can include any one or more of: a communications system (e.g., to communicate therapy recommendations; to enable telemedicine; etc.; etc.), an application executable on a user device (e.g., a skin-related condition application for promoting proper skincare therapies operable to modify microbiome composition and/or functional diversity in relation to skin-related conditions, etc.), skin-related therapies (e.g., treatments), supplementary medical devices (e.g., medication dispensers; skin treatment applicators; skin grafting devices; radiofrequency-based skin-related devices; hair-related condition devices such as hair restoration devices, hair removal devices; acne-related medical devices; other diagnostic devices such as cutaneous hydration measurement devices; other treatment devices; etc.), user devices (e.g., including biometric sensors), and/or any other suitable component.
• a communications system e.g., to communicate therapy recommendations; to enable telemedicine; etc.; etc.
• an application executable on a user device e.g., a skin-related condition application for
• One or more therapy systems 230 are preferably controllable by the microbiome characterization system 220.
• the microbiome characterization system 220 can generate control instructions and/ or notifications to transmit to the therapy system 230 for activating and/or otherwise operating the therapy system 230 in promoting the therapy.
• the therapy system 230 can be configured in any other manner.
• the system 200 can additionally or alternatively include an interface 240 that can function to improve presentation of skin-related condition information (e.g., characterizations; therapy recommendations; comparisons to other users; evaluations of therapies on microbiome composition and functional diversity; etc.).
• skin-related condition information e.g., characterizations; therapy recommendations; comparisons to other users; evaluations of therapies on microbiome composition and functional diversity; etc.
• the interface 240 can present skin-related condition information including a microbiome composition (e.g., taxonomic groups), functional diversity (e.g., relative abundance of genes associated with function correlated with skin-related conditions, as shown in FIGURE 11, etc.), and/or risk of infection (e.g., of different skin- related conditions) for the user, such as relative to a user group sharing a demographic characteristic (e.g., patients sharing conditions, smokers, exercisers, users on different dietary regimens, consumers of probiotics, antibiotic users, groups undergoing particular therapies, etc.).
• a microbiome composition e.g., taxonomic groups
• functional diversity e.g., relative abundance of genes associated with function correlated with skin-related conditions, as shown in FIGURE 11, etc.
• risk of infection e.g., of different skin- related conditions
• the interface 240 can be operable to present skin-related condition information including a change in the microbiome composition over time and a change in a microbiome functional diversity over time in relation to the treatment and the skin-related condition.
• the interface's display of skin-related condition information can be improved through selection (e.g., based on components of the characterization satisfying a threshold condition, such as a skin-related condition risk exceeding a threshold, etc.) and presentation of a subset of skin-related condition information (e.g., highlighting and/or otherwise emphasizing a subset of skin-related condition information).
• the interface 240 can display any suitable information and can be configured in any suitable manner.
• the system 200 and/or components of the system 200 can entirely or partially be executed by, hosted on, communicate with, and/or otherwise include: a remote computing system (e.g., a server, at least one networked computing system, stateless, stateful), a local computing system, databases (e.g., user database, microbiome dataset database, skin-related condition database, therapy database, etc.), a user device (e.g., a user smart phone, computer, laptop, supplementary medical device, wearable medical device, care provider device, etc.), and/or any suitable component. While the components of the system 200 are generally described as distinct components, they can be physically and/or logically integrated in any manner.
• a remote computing system e.g., a server, at least one networked computing system, stateless, stateful
• databases e.g., user database, microbiome dataset database, skin-related condition database, therapy database, etc.
• a user device e.g., a user smart phone, computer, laptop, supplementary medical device
• a smartphone application can partially or fully implement the microbiome characterization system 220 (e.g., apply a characterization model to generate a characterization of skin-related conditions in real-time; sequence biological samples; process microorganism sequences; extract features from microbiome datasets; etc.) and the therapy system 230 (e.g., schedule daily events at a calendar application of the smartphone to notify the user to take probiotic therapies in response to generating the characterization).
• the functionality of the system 200 can be distributed in any suitable manner amongst any suitable system components.
• the system 200 and/or method 100 can include any suitable components and/or functions analogous to (e.g., applied in the context of skin-related conditions) those described in U.S. App. No.
• embodiments of a method 100 for characterizing a skin-related condition in relation to a user can include one or more of: generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset based on microorganism sequences derived from biological samples (e.g., microorganism genetic sequences derived from the samples) from a set of users S110; processing a supplementary dataset informative of the skin-related condition for the set of users S120; and performing a characterization process for one or more skin-related conditions, the characterization process derived from the supplementary dataset and features extracted from at least one of the microbiome composition dataset and microbiome functional diversity dataset S130.
• a microbiome composition dataset and a microbiome functional diversity dataset based on microorganism sequences derived from biological samples (e.g., microorganism genetic sequences derived from the samples) from a set of users S110; processing a supplementary dataset informative of the skin-related condition for the set of users S120; and performing a characterization process for
• the method 100 can additionally or alternatively include one or more of: determining a therapy for preventing, ameliorating, and/or otherwise modifying a skin-related condition S140; processing a biological sample from a user (e.g., subject) S150; determining, with the characterization process, a characterization of the user based upon processing a microbiome dataset (e.g., microbiome composition dataset, microbiome functional diversity dataset, etc.) derived from the biological sample of the user S160; promoting a therapy for the skin-related condition to the user (e.g., based upon the characterization) S170; monitoring effectiveness of the therapy for the user, based upon processing biological samples, to assess microbiome composition and/or functional features associated with the therapy for the user over time S180; and/or any other suitable operations.
• a microbiome dataset e.g., microbiome composition dataset, microbiome functional diversity dataset, etc.
• Block Siio recites: generating at least one of a microbiome composition dataset and a microbiome functional diversity dataset based on microorganism sequences derived from biological samples from a set of users.
• Block Siio functions to process each of an aggregate set of biological samples, in order to determine compositional and/ or functional aspects associated with the microbiome of each of a population of users.
• compositional and functional aspects can include compositional aspects at the microorganism level, including parameters related to distribution of microorganisms across different groups of kingdoms, phyla, classes, orders, families, genera, species, subspecies, strains, and/or any other suitable infraspecies taxon (e.g., as measured in total abundance of each group, relative abundance of each group, total number of groups represented, etc.).
• Compositional and functional aspects can also be represented in terms of operational taxonomic units (OTUs).
• compositional and functional aspects can additionally or alternatively include compositional aspects at the genetic level (e.g., regions determined by multilocus sequence typing, i6S sequences, i8S sequences, ITS sequences, other genetic markers, other phylogenetic markers, etc.).
• compositional and functional aspects can include the presence or absence or the quantity of genes associated with specific functions (e.g. enzyme activities, transport functions, immune activities, etc.).
• Outputs of Block Siio can thus be used to provide features of interest for the characterization process of Block S130 and/or therapy process of Block S140, where the features can be microorganism-based (e.g., presence of a genus of bacteria), genetic-based (e.g., based upon representation of specific genetic regions and/or sequences) and/or functional -based (e.g., presence of a specific catalytic activity), and/or otherwise configured.
• microorganism-based e.g., presence of a genus of bacteria
• genetic-based e.g., based upon representation of specific genetic regions and/or sequences
• functional -based e.g., presence of a specific catalytic activity
• sample processing in Block S110 can include any one or more of: lysing a biological sample, disrupting membranes in cells of a biological sample, separation of undesired elements (e.g., RNA, proteins) from the biological sample, purification of nucleic acids (e.g., DNA) in a biological sample, amplification (e.g., with a library preparation system) of nucleic acids from the biological sample, further purification of amplified nucleic acids of the biological sample, and sequencing of amplified nucleic acids of the biological sample, and/or any other suitable sample processing operations, such as those described in relation to U.S. App. No. 15/374,890 filed 09-DEC-2016, which is incorporated in its entirety by this reference.
• Block S110 amplification of purified nucleic acids preferably includes 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.
• PCR polymerase chain reaction
• HDA helicase-dependent amplification
• LAMP loop mediated isothermal amplification
• NASBA nucleic acid sequence based amplification
• SDA strand displacement amplification
• RCA rolling circle amplification
• LCR ligase chain reaction
• the primers used are preferably selected to prevent or minimize amplification bias, as well as configured to amplify nucleic acid regions/sequences (e.g., of the 16S region, the 18S region, the ITS region, etc.) that are informative taxonomically, phylogenetically, for diagnostics, for formulations (e.g., for probiotic formulations), and/or for any other suitable purpose.
• amplification bias e.g., a F27-R338 primer set for 16S RNA, a F515-R806 primer set for 16S RNA, etc.
• Primers used in variations of Block S110 can additionally or alternatively include incorporated barcode sequences specific to each biological sample, which can facilitate identification of biological samples post-amplification.
• Selected primers can additionally or alternatively be associated with a skin-related condition and/or microbiome composition features (e.g., identified primers compatible with a genetic target corresponding to microbiome composition features associated with a group of taxa correlated with eczema, etc.), functional diversity features, supplementary features, and/or other features associated with the skin-related conditions.
• the primers can be complementary to genetic targets associated with the features (e.g., genetic sequences from which relative abundance features are derived; genes associated with different skin-related conditions; etc.).
• Primers used in variations of Block Siio can additionally or alternatively include adaptor regions configured to cooperate with sequencing techniques involving complementary adaptors (e.g., Illumina Sequencing). Additionally or alternatively, Block Siio can implement any other step configured to facilitate processing (e.g., using a Nextera kit).
• sequencing of purified nucleic acids can include methods involving targeted amplicon 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 an Oxford Nanopore technique).
• sequencing-by-synthesis techniques e.g., Illumina sequencing
• capillary sequencing techniques e.g., Sanger sequencing
• pyrosequencing techniques e.g., using an Oxford Nanopore technique
• amplification and sequencing of nucleic acids from biological samples of the set of biological samples includes: solid-phase PCR involving bridge amplification of DNA fragments of the biological samples on a substrate with oligo adapters, where amplification involves primers having a forward index sequence (e.g., corresponding to an Illumina forward index for MiSeq/NextSeq/HiSeq platforms), a forward barcode sequence, a transposase sequence (e.g., corresponding to a transposase binding site for MiSeq/NextSeq/HiSeq platforms), a linker (e.g., a zero, one, or two-base fragment configured to reduce homogeneity and improve sequence results), an additional random base, a sequence for targeting a specific target region (e.g., i6S region, i8S region, ITS region), a reverse index sequence (e.g., corresponding to an Illumina reverse index for MiSeq/
• a forward index sequence e.g.,
• sample processing in Block Siio can include further purification of amplified nucleic acids (e.g., PCR products) prior to sequencing, which functions to remove excess amplification elements (e.g., primers, dNTPs, enzymes, salts, etc.).
• additional purification can be facilitated using any one or more of: purification kits, buffers, alcohols, pH indicators, chaotropic salts, nucleic acid binding filters, centrifugation, and any other suitable purification technique.
• computational processing in Block Siio can include any one or more of: identification of microbiome-derived sequences (e.g., as opposed to subject sequences and contaminants), alignment and mapping of microbiome-derived sequences (e.g., alignment of fragmented sequences using one or more of single-ended alignment, ungapped alignment, gapped alignment, pairing), and generating features derived from compositional and/or functional aspects of the microbiome associated with a biological sample.
• identification of microbiome-derived sequences can include mapping of sequence data from sample processing to a subject reference genome (e.g., provided by the Genome Reference Consortium), in order to remove subject genome- derived sequences.
• Block Siio can include determining alignments between microorganism nucleic acid sequences and reference sequences associated with the skin-related condition (e.g., microbiome biomarkers associated with the skin-related conditions, such as biomarkers indicative of a presence and/or abundance of genetic sequences representative of groups of taxa associated with skin-related conditions; microbiome markers identified through processing microbiome datasets collected by the method 100; etc.) where generating the microbiome composition dataset and the microbiome functional diversity dataset is based on the alignments.
• mapping sequence data can be performed in any suitable manner, such as analogous to U.S. App. No. 15/374,890 filed 09-DEC-2016, which is incorporated in its entirety by this reference.
• generating features derived from compositional and functional aspects of the microbiome associated with a biological sample can be performed.
• generating features can include generating features based upon multilocus sequence typing (MLST), in order to identify markers useful for characterization in subsequent blocks of the method 100.
• generated features can include generating features that describe the presence or absence of certain taxonomic groups of microorganisms, and/or ratios between exhibited taxonomic groups of microorganisms.
• generating features can include generating features describing one or more of: quantities of represented taxonomic groups, networks of represented taxonomic groups, correlations in representation of different taxonomic groups, interactions between different taxonomic groups, products produced by different taxonomic groups, interactions between products produced by different taxonomic groups, ratios between dead and alive microorganisms (e.g., for different represented taxonomic groups, based upon analysis of RNAs), phylogenetic distance (e.g., in terms of Kantorovich- Rubinstein distances, Wasserstein distances etc.), any other suitable taxonomic group-related feature(s), any other suitable genetic or functional feature(s).
• generating features can include generating features describing relative abundance of different microorganism groups, for instance, using a sparCC approach, using Genome Relative Abundance and Average size (GAAS) approach and/or using a Genome Relative Abundance using Mixture Model theory (GRAMMy) approach that uses sequence-similarity data to perform a maximum likelihood estimation of the relative abundance of one or more groups of microorganisms.
• generating features can include generating statistical measures of taxonomic variation, as derived from abundance metrics.
• generating features can include generating features derived from relative abundance factors (e.g., in relation to changes in abundance of a taxon, which affects abundance of other taxa).
• generating features can include generation of qualitative features describing presence of one or more taxonomic groups, in isolation and/or in combination. Additionally or alternatively, generating features can include generation of features related to genetic markers (e.g., representative 16S, 18S, and/ or ITS sequences) characterizing microorganisms of the microbiome associated with a biological sample. Additionally or alternatively, generating features can include generation of features related to functional associations of specific genes and/or organisms having the specific genes. Additionally or alternatively, generating features can include generation of features related to pathogenicity of a taxon and/or products attributed to a taxon. Block S120 can, however, include generation of any other suitable feature(s) derived from sequencing and mapping of nucleic acids of a biological sample.
• genetic markers e.g., representative 16S, 18S, and/ or ITS sequences
• the feature(s) can be combinatory (e.g. involving pairs, triplets), correlative (e.g., related to correlations between different features), and/or related to changes in features (e.g., temporal changes, changes across sample sites, etc., spatial changes, etc.).
• Block S110 can be performed in any suitable manner.
• Block S120 recites: receiving a supplementary dataset, associated with at least a subset of the population of users, where the supplementary dataset facilitates characterization of users.
• Block S120 functions to acquire additional data associated with one or more users of the set of users, which can be used to train and/or validate the characterization process generated in Block S130.
• the supplementary dataset preferably includes survey-derived data, but can additionally or alternatively include any one or more of: contextual data derived from sensors, medical data (e.g., current and historical medical data), and any other suitable type of data.
• the survey-derived data preferably provides physiological, demographic, and behavioral information in association with a user.
• Block S130 recites: performing a characterization process for one or more skin- related conditions, the characterization process derived from the supplementary dataset and features extracted from at least one of the microbiome composition dataset and microbiome functional diversity dataset S130.
• Block S130 functions to identify features and/or feature combinations that can be used to characterize users or groups based upon their microbiome composition and/or functional features.
• the characterization process can be used as a diagnostic tool that can characterize a user (e.g., in terms of behavioral traits, in terms of medical conditions, in terms of demographic traits, etc.) based upon their microbiome composition and/or functional features, in relation to one or more of their health condition states, behavioral traits, medical conditions, demographic traits, and any other suitable traits.
• Such characterization can then be used to suggest or provide personalized therapies by way of the therapy model of Block S140.
• Block S130 can use computational methods (e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.) to characterize a user as exhibiting features characteristic of a group of users with a health condition.
• computational methods e.g., statistical methods, machine learning methods, artificial intelligence methods, bioinformatics methods, etc.
• the characterization process can be performed in any suitable manner.
• performing a characterization process can include generating one or more characterizations of one or more skin-related conditions.
• the characterization process of Block S130 can facilitate identification of which microorganism population(s) (e.g., taxonomic groups, microbiome composition features, etc.) are upregulated or downregulated in relation to skin-related conditions, and/ or which microbiome functional aspects (e.g., in relation to Clusters of Orthologous Groups / Kyoto Encyclopedia of Genes and Genomes pathways, microbiome functional diversity features, etc.) are upregulated or downregulated in relation to skin-related conditions.
• microorganism population(s) e.g., taxonomic groups, microbiome composition features, etc.
• microbiome functional aspects e.g., in relation to Clusters of Orthologous Groups / Kyoto Encyclopedia of Genes and Genomes pathways, microbiome functional diversity features, etc.
• Characterizing upregulation and/or downregulation can be at any suitable taxonomic level (e.g., kingdom, phylum, class, order, family, genus, species, strain, etc.), any suitable granularity of functional diversity, and/or at any suitable granularity.
• taxonomic level e.g., kingdom, phylum, class, order, family, genus, species, strain, etc.
• any suitable granularity of functional diversity e.g., any suitable granularity of functional diversity, and/or at any suitable granularity.
• characterizing a skin-related condition in Block S130 can include generating a diagnostic analysis (e.g., estimating a risk of being inflicted by the skin-related condition; calculating the change in risk conferred by an identified therapy; diagnosing the presence of the skin-related condition; diagnosing the severity of the skin-related condition over time in relation to microbiome composition and/or functional diversity; etc.) and/ or associated complications.
• a diagnostic analysis e.g., estimating a risk of being inflicted by the skin-related condition; calculating the change in risk conferred by an identified therapy; diagnosing the presence of the skin-related condition; diagnosing the severity of the skin-related condition over time in relation to microbiome composition and/or functional diversity; etc.
• characterizing a skin-related condition can be based on one or more supplementary datasets.
• the set of feature-selection rules can correlate one or more skin-related conditions to one or more biometric features derived from biometric sensor data informative of a skin-related condition (e.g., optical data of the skin such as skin lesions, acne, etc.; skin-related parameters, such as cutaneous hydration, associated with skin-related conditions; blood data; temperature data; user behavior data; temperature data; cardiovascular data; stool data; etc.).
• a skin-related condition e.g., optical data of the skin such as skin lesions, acne, etc.
• skin-related parameters such as cutaneous hydration
• performing a characterization process can include determining a series of characterizations over time based on therapies promoted over time (e.g., based on therapy data including antibiotic regimen data, probiotic regimen data, and/or other suitable therapy data collected over time and associated with a population of users), where the effect of different therapies over time can aid in illuminating insights associated with microbiome compositions and/or functional diversity correlated with skin-related conditions.
• therapies promoted over time e.g., based on therapy data including antibiotic regimen data, probiotic regimen data, and/or other suitable therapy data collected over time and associated with a population of users
• performing a characterization process in relation to a skin-related condition can be performed in any suitable manner.
• Block S130 can additionally or alternatively include Block S132: generating features, which can function to generate one or more features for the characterization process (e.g., for use in training a characterization model).
• Features can include any one or more of: microbiome composition features (e.g., absolute and/or relative abundance of taxonomic groups in a user's microbiome), microbiome functional diversity features, and/ or other suitable features.
• Microbiome functional diversity features can include any one or more of: Kyoto Encyclopedia of Genes and Genomes (KEGG) functional features (e.g., KEGG features associated with flagellum biosynthesis, etc.), Clusters of Orthologous Groups (COG) of proteins features, L2, L3, L4 derived features, genomic functional features, functional features associated with and/or specific to a taxonomic group, chemical functional features (e.g., cysteine metabolism, etc.), systemic functional features (e.g., systemic immune function; functions associated with systemic diseases; etc.), and/or any suitable functional features.
• KEGG Kyoto Encyclopedia of Genes and Genomes
• COG Clusters of Orthologous Groups
• Microbiome features can additionally or alternatively be derived from and/or associated with at least one of: relative abundance monotonic transformations, non-monotonic transformations, normalizations, feature vectors such as derived from at least one of linear latent variable analysis and nonlinear latent variable analysis, linear regression, nonlinear regression, kernel methods, feature embedding methods, machine learning, statistical inference methods and/ or any other suitable approaches.
• determining features is preferably based on processing microbiome composition data and/or microbiome functional diversity data according to one or more computer-implemented rules (e.g., a feature-selection rule, a user preference rule, etc.), but features can be determined based on any suitable information.
• a feature-selection rule e.g., a user preference rule, etc.
• the method 100 can include obtaining a set of skin-related feature- selection rules correlating the skin-related condition to a subset of microbiome composition features and/ or a subset of microbiome functional diversity features (e.g., from a pool of potential microbiome composition and/or functional diversity features); and generating features based on evaluating the microbiome composition data and the microbiome functional diversity data against the set of skin-related feature-selection rules, where the set of skin-related feature selection rules are operable to improve the microbiome characterization system (e.g., by facilitating decreased processing time such as for transforming supplementary data and features into a characterization model; by improving speed of model retrieval, and/or execution; by improving characterization and/or therapy provision accuracy; etc.).
• a set of skin-related feature- selection rules correlating the skin-related condition to a subset of microbiome composition features and/ or a subset of microbiome functional diversity features (e.g., from a pool of potential microbiome composition and/or functional diversity
• Block S132 and/or other portions of the method 100 preferably include applying computer-implemented rules to process population-level data, but can additionally or alternatively include applying computer- implemented rules to process microbiome-related data on a demographic-specific basis (e.g., subgroups sharing a demographic feature such as skin hygiene regimens, ethnicity, age, gender, etc.), condition-specific basis (e.g., subgroups exhibiting a particular skin- related condition), a sample type-specific basis (e.g., applying different computer- implemented rules to process microbiome data derived from skin samples versus fecal matter samples), and/or any other suitable basis.
• a demographic-specific basis e.g., subgroups sharing a demographic feature such as skin hygiene regimens, ethnicity, age, gender, etc.
• condition-specific basis e.g., subgroups exhibiting a particular skin- related condition
• a sample type-specific basis e.g., applying different computer- implemented rules to process microbiome data derived from skin samples versus
• Block S132 can include assigning users from the population of users to one or more subgroups; and applying different computer-implemented rules for determining features (e.g., the set of feature types used; the types of characterization models generated from the features; etc.) for the different subgroups.
• features e.g., the set of feature types used; the types of characterization models generated from the features; etc.
• Block S132 can include applying feature-selection rules (e.g., feature selection algorithms such as exhaustive, best first, simulated annealing, greedy forward, greedy backward, and/or other suitable feature selection algorithms) to filter, rank, and/or otherwise select features for use in generating one or more characterization models (e.g., using skin-related feature-selection rules correlating one or more skin-related conditions to microbiome composition features and/or microbiome functional diversity features, etc.), therapy models (e.g., using rules correlating one or more therapies to one or more microbiome composition features, microbiome functional diversity features, and/or features derived from characterizations generated in Block S160, etc.), and/or other suitable models.
• feature-selection rules e.g., feature selection algorithms such as exhaustive, best first, simulated annealing, greedy forward, greedy backward, and/or other suitable feature selection algorithms
• the method 100 can include: applying a set of skin- related feature selection rules to identify features (e.g., microbiome composition features indicating particular taxonomic groups; microbiome functional diversity features indicating particular microorganism functions) correlated (e.g., most correlated; etc.) with the skin-related conditions (e.g., presence, risk, therapies, etc.); and selecting primers (e.g., for use in amplification and sequencing to generate microbiome datasets; etc.) compatible with genetic targets associated with the identified features (e.g., using primers associated with genetic targets corresponding to the microbiome functional diversity features correlated with the skin-related condition; etc.).
• features e.g., microbiome composition features indicating particular taxonomic groups; microbiome functional diversity features indicating particular microorganism functions
• the skin-related conditions e.g., presence, risk, therapies, etc.
• primers e.g., for use in amplification and sequencing to generate microbiome datasets; etc.
• the feature-selection rules and/or other computer-implemented rules can additionally or alternatively function to determine sample processing parameters (e.g., described in relation to Blocks S110-S120, S150, etc.). Additionally or alternatively, applying feature-selection rules can be performed in a manner analogous to U.S. App. No. 15/452,529 filed 07-MAR-2017, which is herein incorporated in its entirety by this reference, and/or can be performed in any suitable manner. However, any suitable number and/ or type of feature-selection rules can be applied in any manner to define one or more feature sets.
• feature-selection rules can include application of a statistical analysis (e.g., an analysis of probability distributions) of similarities and/or differences between a first group of users exhibiting a target state (e.g., a health condition state) and a second group of users not exhibiting the target state (e.g., a "normal" state).
• a statistical analysis e.g., an analysis of probability distributions
• KS Kolmogorov-Smirnov
• permutation test e.g., a permutation test
• Cramer-von Mises test e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.
• any other statistical test e.g., t-test, z-test, chi-squared test, test associated with distributions, etc.
• one or more such statistical hypothesis tests can be used to assess a set of features having varying degrees of abundance in a first group of users exhibiting a target state (e.g., a sick state) and a second group of users not exhibiting the target state (e.g., having a normal state).
• the set of features assessed can be constrained based upon percent abundance and/or any other suitable parameter pertaining to diversity in association with the first group of users and the second group of users, in order to increase or decrease confidence in the characterization.
• a feature can be derived from a taxon of bacteria that is abundant in a certain percentage of users of the first group and users of the second group, where a relative abundance of the taxon between the first group of users and the second group of users can be determined from the KS test, with an indication of significance (e.g., in terms of p-value).
• an output of Block S132 can include a normalized relative abundance value (e.g., 25% greater abundance of a taxon in sick users vs. healthy users) with an indication of significance (e.g., a p-value of 0.0013).
• Variations of feature generation can additionally or alternatively implement or be derived from functional features or metadata features (e.g., non-bacterial markers)
• Block S132 can additionally or alternatively transform input data from at least one of the microbiome composition diversity dataset and microbiome functional diversity dataset into feature vectors that can be tested for efficacy in predicting characterizations of the population of users.
• Data from the supplementary dataset can be used to provide indication of one or more characterizations of a set of characterizations, where the characterization process is trained with a training dataset of candidate features and candidate classifications to identify features and/or feature combinations that have high degrees (or low degrees) of predictive power in accurately predicting a classification.
• refinement of the characterization process with the training dataset identifies feature sets (e.g., of user features, of combinations of features) having high correlation with specific classifications of users.
• feature vectors effective in predicting classifications of the characterization process can include features related to one or more of: microbiome diversity metrics (e.g., in relation to distribution across taxonomic groups, in relation to distribution across archaeal, bacterial, viral, and/or eukaryotic groups), presence of taxonomic groups in one's microbiome, representation of specific genetic sequences (e.g., 16S sequences) in one's microbiome, relative abundance of taxonomic groups in one's microbiome, microbiome resilience metrics (e.g., in response to a perturbation determined from the supplementary dataset), abundance of genes that encode proteins or RNAs with given functions (enzymes, transporters, proteins from the immune system, hormones, interference RNAs, etc.) and/or any other suitable features derived from the microbiome diversity dataset and/or the supplementary dataset.
• microbiome diversity metrics e.g., in relation to distribution across taxonomic groups, in relation to distribution across archaeal, bacterial, viral, and
• Block S132 can include generating a set of microbiome feature vectors (e.g., a feature vector for each user of subgroup or population of users) based on microbiome composition features (e.g., a subset selected based on feature-selection rules), microbiome functional diversity features (e.g., a subset selected based on feature- selection rules), and supplementary features (e.g., biometric features derived from the supplementary biometric sensor data, etc.), where the set of microbiome feature vectors can be used in training the characterizations model and/or other suitable models. Additionally or alternatively, combinations of features can be used in a feature vector, where features can be grouped and/ or weighted in providing a combined feature as part of a feature set.
• microbiome composition features e.g., a subset selected based on feature-selection rules
• microbiome functional diversity features e.g., a subset selected based on feature- selection rules
• supplementary features e.g., bio
• one feature or feature set can include a weighted composite of the number of represented classes of bacteria in one's microbiome, presence of a specific genus of bacteria in one's microbiome, representation of a specific i6S sequence in one's microbiome, and relative abundance of a first phylum over a second phylum of bacteria.
• the feature vectors and features can additionally or alternatively be determined in any other suitable manner.
• Block S130 can additionally or alternatively include Block S134: generating a characterization model.
• Block S134 functions to generate one or more characterization models for skin-related conditions based on applying one or more features, microbiome datasets, supplementary data, and/or any other suitable data.
• Characterization models can include any one or more of: probabilistic properties, heuristic properties, deterministic properties, and/ or any other suitable properties.
• Block S134 and/or any other suitable portions of the method 100 e.g., generating a therapy model S140
• Block S134 and/or any other suitable portions of the method 100 can employ one or more algorithms analogous to those described in U.S. App. No. 15/452,529 filed 07-MAR-2017 and/or U.S. App. No. 15/374,890 filed 09-DEC-2016, which are incorporated in their entirety by this reference, but any suitable algorithms can be employed.
• a characterization model can be generated and trained according to a random forest predictor (RFP) algorithm that combines bagging (e.g., bootstrap aggregation) and selection of random sets of features from a training dataset to construct a set of decision trees, T, associated with the random sets of features.
• RFP random forest predictor
• N cases from the set of decision trees are sampled at random with replacement to create a subset of decision trees, and for each node, m prediction features are selected from all of the prediction features for assessment.
• the prediction feature that provides the best split at the node is used to perform the split (e.g., as a bifurcation at the node, as a trifurcation at the node).
• measures to prevent bias e.g., sampling bias
• account for an amount of bias can be included during processing to increase robustness of the model.
• characterization of the user(s) can additionally or alternatively implement use of a high false positive test and/or a high false negative test to further analyze sensitivity of the characterization process in supporting analyses generated according to embodiments of the method 100.
• preventing bias and/or improving sensitivity can be performed in any suitable manner.
• different characterization models can be generated for different demographic groups (e.g., a first characterization model characterizing a skin-related condition for users who bathe on a daily basis, a second characterization model for users who bathe on a non-daily basis, etc.), skin-associated conditions (e.g., different characterization models for eczema, dry skin, pimple-related conditions, scalp-related conditions, photosensitivity, dandruff, etc., as shown in FIGURE 5), individual users, supplementary data (e.g., models incorporating features derived from biometric sensor data and/or survey response data vs. models independent of supplementary data, etc.), and/or other suitable criteria.
• supplementary data e.g., models incorporating features derived from biometric sensor data and/or survey response data vs. models independent of supplementary data, etc.
• the method 100 can include generating a first characterization model (e.g., eczema- related characterization model for eczema) based on a first feature set derived from at least one of the microbiome composition dataset and microbiome functional diversity dataset; and generating one or more additional characterization models (e.g., scalp- related characterization model for scalp-related conditions; dry-skin related characterization model for dry skin-related conditions; etc.) based on additional feature sets (e.g., sharing feature types with the first feature set or other feature sets; including a set of feature types distinct from those of the first feature set or other feature sets; etc.) derived from the at least one of the microbiome composition dataset and the microbiome functional diversity dataset, and/or other suitable data (e.g., other microbiome composition datasets and/or functional diversity datasets, etc.).
• a first characterization model e.g., eczema- related characterization model for eczema
• additional characterization models e
• characterizations outputted from different characterization models can be used in determining and/ or promoting a therapy, such as by inputting features derived from a different characterizations (e.g., outputs by an eczema-related characterization model, a scalp-related characterization model, dry skin-related characterization model, etc.) into a therapy model (e.g., to generate a single therapy or a plurality of therapies tailored to treating the different skin-related conditions, etc.).
• a therapy model e.g., to generate a single therapy or a plurality of therapies tailored to treating the different skin-related conditions, etc.
• Block S134 can include generating a characterization model for a demographic group of users sharing a skin hygiene characteristic (e.g., using moisturizer, bathing, sunscreen, at a particular frequency, etc.); associating the characterization model with user accounts (e.g., at a database of the microbiome characterization system) for the users who indicate the skin hygiene characteristic (e.g., at a digital survey presented by the interface; based on user device sensor data such as location data); and retrieving the characterization model (e.g., from the database) for characterizing the users.
• a skin hygiene characteristic e.g., using moisturizer, bathing, sunscreen, at a particular frequency, etc.
• user accounts e.g., at a database of the microbiome characterization system
• the users who indicate the skin hygiene characteristic e.g., at a digital survey presented by the interface; based on user device sensor data such as location data
• retrieving the characterization model e.g., from the database for characterizing the users.
• Generating a plurality of characterization models suited to different contexts can confer improvements to the microbiome characterization system by improving characterization accuracy (e.g., by tailoring analysis to a particular user's demographic and/or situation, etc.), retrieval speed for the appropriate characterization model from a database (e.g., by associating customized characterization models with particular user accounts and/or other identifiers), training and/or execution of characterization models (e.g., where the customized models are associated with a subset of a pool of potential features correlated with skin-related conditions, and where the remaining unselected features are less correlated with the skin-related conditions), and/ or other suitable aspects of the microbiome characterization system.
• characterization accuracy e.g., by tailoring analysis to a particular user's demographic and/or situation, etc.
• retrieval speed for the appropriate characterization model from a database e.g., by associating customized characterization models with particular user accounts and/or other identifiers
• generating feature sets for different characterization models can be based on different feature selection rules (e.g., applying eczema-associated feature-selection rules to generate a feature set specific to eczema in generating an eczema-related characterization model, etc.).
• overlapping or the same set of feature selection rules can be used for generating different characterization models (e.g., using the same functional diversity feature in generating two different characterization models for two different skin-related conditions, etc.).
• generating any number of characterization models can be performed in any suitable manner.
• performing a characterization process can be based upon statistical analyses that identify the sets of features that have the highest correlations with one or more skin-related conditions for which one or more therapies would have a positive effect, based upon a Kolmogorov-Smirnov statistical test that compares a dataset derived from a subset of the population of users that present the skin-related condition, and a dataset derived from a subset of the population of users that do not present the skin-related condition.
• performing characterization processes can be performed in any suitable manner.
• performing a characterization process can be for one or more photosensitivity-associated conditions.
• photosensitivity can be a skin condition characterized by an abnormal reaction of the skin to a component of the electromagnetic spectrum of sunlight. It is typically diagnosed by skin examination, phototests and photopatch tests.
• photosensitivity-associated conditions can be associated with specific microbiota diversity and/or health conditions related to relative abundance of gut microorganisms, and/ or microbiome functional diversity.
• a set of features useful for characterizations of photosensitivity-associated conditions and/or other skin-associated conditions can include features derived from one or more of the following taxa: Marvinbryantia (genus), Erysipelotrichales (order), Erysipelotrichia (class), Bacteroidetes (phylum), and/or any other suitable taxa.
• characterization of the subject comprises characterization of the subject as someone with photosensitivity based upon detection of one or more of the above features, in a manner that is an alternative or supplemental to typical methods of diagnosis.
• the set of features can, however, include any other suitable features useful for diagnostics.
• the set of features can include functional features (e.g., diversity features) associated with photosensitivity-associated conditions (e.g., associated with photosensitivity diagnostics using skin samples) and/or other skin-associated conditions, including one or more of: COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional features.
• functional features e.g., diversity features
• photosensitivity-associated conditions e.g., associated with photosensitivity diagnostics using skin samples
• other skin-associated conditions including one or more of: COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional features.
• COG derived features e.g., COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional features.
• features can include: an infectious diseases KEGG L2 derived feature.
• performing the characterization process for photosensitivity-associated conditions can be performed in any suitable manner using any suitable features.
• performing a characterization process can be for one or more dry skin-associated conditions.
• dry skin can be characterized by rough skin, itching, flaking, scaling or peeling, fine lines or cracks, gray skin in people with dark skin, redness, deep cracks that may bleed and which can lead to infections.
• dry skin-associated conditions can be associated with specific microbiota diversity and/ or health conditions related to relative abundance of gut microorganisms, and/ or microbiome functional diversity.
• a set of features useful for characterizations of dry skin-associated conditions and/or other skin-associated conditions can include features derived from one or more of the following taxa: Staphylococcus (genus), Staphylococcaceae (family), Bacillales (order), Actinobacteria (class), Firmicutes (phylum), Actinobacteria (phylum), Propionibacterium (genus), and/or any other suitable taxa, where sampling of subjects can involve sampling of the skin and/or other body region.
• characterization of the subject comprises characterization of the subject as someone with dry skin based upon detection of one or more of the above features, in a manner that is an alternative or supplemental to typical methods of diagnosis.
• the set of features can, however, include any other suitable features useful for diagnostics.
• the set of features can include functional diversity features associated with dry skin-associated conditions (e.g., associated with dry skin diagnostics using skin samples) and/or other skin-associated conditions, including one or more of: COG derived features, KEGG L2, L3, L4 derived features and/or any other suitable combination of features.
• performing the characterization process for dry skin-associated conditions can be performed in any suitable manner using any suitable features.
• performing a characterization process can be for one or more scalp-related conditions.
• dandruff can be a chronic scalp condition characterized by flaking, itching and scaling of the skin in the scalp, that can be caused by dry skin, irritated oily skin, sensitivity to hair care products, that finally results in an imbalance of the scalp microbiome.
• scalp-related conditions can be associated with specific microbiota diversity and/or health conditions related to relative abundance of gut microorganisms, and/ or microbiome functional diversity.
• a set of features useful for characterizations of dandruff conditions and/or other scalp-associated conditions can include features derived from one or more of the following taxa: Propionibacterium sp. MSP09A (species), Bacteroides vulgatus (species, Streptococcus sp. BS35a (species), Staphylococcus sp. C9I2 (species), Phascolarctobacterium sp. 377 (species), Faecalibacterium prausnitzii (species), Alistipes putredinis (species), Alistipes sp. EBA6-25CI2 (species), Alistipes sp.
• RMA 9912 (species) Anaerostipes sp. 5_i_63FA (species), Bacteroides acidifaciens (species), Bacteroides caccae (species), Bacteroides fragilis (species), Bacteroides plebeius (species), Bacteroides sp. AR20 (species), Bacteroides sp. AR29 (species), Bacteroides sp. D22 (species), Bacteroides sp. DJF (species), Bacteroides sp. SLCi-38 (species), Bacteroides sp.
• XB12B Bacteroides vulgatus (species), Blautia faecis (species), Blautia luti (species), Blautia sp.
• YHC-4 species
• Blautia stercoris species
• Blautia wexlerae species
• Collinsella aerofaciens species
• MSP09A (species), Roseburia intestinalis (species), Roseburia inulinivorans (species), Roseburia sp. 11SE39 (species), Staphylococcus sp. C9I2 (species), Staphylococcus sp. WB18-16 (species), Streptococcus sp. BS35a (species), Streptococcus sp.
• characterization of the subject comprises characterization of the subject as someone with dandruff based upon detection of one or more of the above features, in a manner that is an alternative or supplemental to typical methods of diagnosis.
• the set of features can, however, include any other suitable features useful for diagnostics.
• the set of features can include functional diversity features associated with scalp-related conditions (e.g., associated with dandruff diagnostics using skin samples) and/or other skin-associated conditions, including one or more of: COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional diversity features.
• scalp-related conditions e.g., associated with dandruff diagnostics using skin samples
• other skin-associated conditions including one or more of: COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional diversity features.
• such features can include one or more of: a glycan biosynthesis and metabolism KEGG L2 derived feature, an environmental adaptation KEGG L2 derived feature, a cancers KEGG L2 derived feature, an immune system diseases KEGG L2 derived feature, a transcription KEGG L2 derived feature, a signaling molecules and interaction KEGG L2 derived feature, a membrane transport KEGG L2 derived feature, a cell motility KEGG L2 derived feature, a cellular processes and signaling KEGG L2 derived feature, a metabolism of cofactors and vitamins KEGG L2 derived feature, a metabolism KEGG L2 derived feature, a neurodegenerative diseases KEGG L2 derived feature, a metabolic diseases KEGG L2 derived feature, an enzyme families KEGG L2 derived feature, a cell growth and death KEGG L2 derived feature, a carbohydrate metabolism KEGG L2 derived feature, a transport and catabolism KEGG L2
• performing the characterization process for in particular dandruff condition and/or any other scalp-related conditions can be performed in any suitable manner using any suitable features.
• performing a characterization process can be for one or more pimple-related conditions.
• pimple-related conditions can include any suitable acne-related conditions.
• a set of features for characterizations of pimple-related and/or other skin-related conditions can include features including and/or otherwise derived from one or more of the following taxa: Lactobacillales, and/ or any other suitable taxa.
• the set of features associated can include functional diversity features including and/or otherwise derived from one or more of: COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional diversity features, and/ or any other suitable features.
• performing a characterization process can be for one or more eczema conditions.
• eczema can include a skin condition characterized by skin inflammation.
• eczema can be characterized by non- microbiome-based tests.
• a set of features for characterizations of eczema and/or other skin- related conditions can include features including and/or otherwise derived from one or more of the following taxa: Streptococcaceae, Streptococcus, Lactobacillales, Veillonella, and/ or any other suitable taxa.
• the set of features associated can include functional diversity features including and/or otherwise derived from one or more of: COG derived features, KEGG L2, L3, L4 derived features, and any other suitable functional diversity features, and/ or any other suitable features.
• characterization of the subject can include characterization of the subject as someone with one or more skin-related conditions based upon detection of one or more of the above features, in a manner that is an alternative or supplemental to typical methods of diagnosis.
• the set of features can, however, include any other suitable features useful for diagnostics.
• the method 100 can additionally or alternatively include Block S140, which recites: determining a therapy for preventing, ameliorating, and/or otherwise modifying a skin-related condition.
• Block S140 functions to identify and/or predict therapies (e.g., probiotic-based therapies, phage-based therapies, small molecule-based therapies, etc.) that can shift a user's microbiome composition and/or functional diversity features toward a desired equilibrium state in promotion of the user's health.
• Block S140 can additionally or alternatively include generating and/or applying a therapy model for determining the therapy.
• the therapies can be selected from therapies including one or more of: probiotic therapies, prebiotic therapies, antibiotic therapies, antifungal therapies, phage-based therapies, small molecule-based therapies, cognitive/behavioral therapies, physical therapies (e.g., physical rehabilitation, bathing hygiene, daily hygiene, etc.), clinical therapies, medication-based therapies, topical application-based therapies (e.g., salicylic acid, benzoyl peroxide, vitamin A creams, moisturizer, steroid creams, antihistamines, shampoo, lotions, oils, creams, etc.), alternative medicine-based therapies, environmental-based therapies (e.g., light -based therapies, temperature-based therapies, etc.), diet-related therapies, and/or any other suitable therapy designed to operate in any other suitable manner in promoting a user's health.
• probiotic therapies e.g., prebiotic therapies, antibiotic therapies, antifungal therapies, phage-based therapies, small molecule-based therapies, cognitive/behavioral therapies
• physical therapies e.g., physical rehabilitation
• bacteriophage-based therapy In a specific example of a bacteriophage-based therapy, one or more populations (e.g., in terms of colony forming units) of bacteriophages specific to a certain bacteria (or other microorganism) represented in the user can be used to down-regulate or otherwise eliminate populations of the certain bacteria.
• bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the user.
• bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
• Blocks S140 and/or S170 can include automatically initiating a signal that controls a treatment system to promote the therapy (e.g., based on a characterization, a therapy model output, etc.), where initiating the signal can include one or more of: generating and transmitting control instructions to a treatment system (e.g., controlling a probiotic dispensing system to provide a probiotic to a user, etc.), initiating notification provision (e.g., to inform a user regarding one or more characterizations and/or therapies, etc.), and/or any other suitable operation in controlling treatment systems to promote therapies.
• a treatment system e.g., controlling a probiotic dispensing system to provide a probiotic to a user, etc.
• initiating notification provision e.g., to inform a user regarding one or more characterizations and/or therapies, etc.
• Block S140 can include facilitating an interaction between a user and a care provider (e.g., scheduling an appointment with a care provider; initiating a telemedicine conference over a wireless communication channel, as shown in FIGURE 6; etc.), such as in response to and/or concurrently with a trigger condition (e.g., characterizing a skin-related condition risk exceeding a threshold; manual request by a user or care provider; identifying an effectiveness score below a threshold based on analysis of post-therapy biological samples; etc.).
• a trigger condition e.g., characterizing a skin-related condition risk exceeding a threshold; manual request by a user or care provider; identifying an effectiveness score below a threshold based on analysis of post-therapy biological samples; etc.
• candidate therapies of the therapy model can perform one or more of: blocking pathogen entry into an epithelial cell by providing a physical barrier (e.g., by way of colonization resistance), inducing formation of a mucous barrier by stimulation of goblet cells, enhance integrity of apical tight junctions between epithelial cells of a user (e.g., by stimulating up regulation of zona-occludens 1, by preventing tight junction protein redistribution), producing antimicrobial factors, stimulating production of anti-inflammatory cytokines (e.g., by signaling of dendritic cells and induction of regulatory T-cells), triggering an immune response, and performing any other suitable function that adjusts a user's microbiome away from a state of dysbiosis.
• a physical barrier e.g., by way of colonization resistance
• inducing formation of a mucous barrier by stimulation of goblet cells e.g., by stimulating up regulation of zona-occludens 1, by preventing tight junction protein redistribution
• Block S140 can include generating a therapy model based upon data from a large population of users, which can include the population of users from which the microbiome datasets are derived (e.g., in Block S110), where microbiome composition and/ or functional features or states of health, prior exposure to and post exposure to a variety of therapeutic measures, are well characterized. Such data can be used to train and validate the therapy provision model, in identifying therapeutic measures that provide desired outcomes for users based upon different microbiome characterizations. Additionally or alternatively, generating (and/ or applying) a therapy model can be based on characterizations outputted from one or more characterization models.
• therapy models, characterization models, and/or other suitable models can leverage machine learning approaches analogous to those described in U.S. App. No. 15/374,890 filed 09-DEC-2016, which is herein incorporated in its entirety by this reference.
• generating and/or applying a therapy model can be based on one or more causes for a skin-related condition (e.g., a cause of a skin-related condition risk).
• the method 100 can include: generating a characterization including a skin-related condition risk (e.g., for any suitable skin-related condition); determining a cause for the skin-related condition risk based on at least one microbiome dataset (e.g., a user microbiome composition feature and a user microbiome functional diversity feature extracted from microbiome datasets); and determining the therapy based on the cause, where the therapy can be operable to reduce the skin-related condition risk.
• therapy models and/or other suitable models can include any one or more probabilistic properties, heuristic properties, deterministic properties, and/or any other suitable properties, and/or can be configured in any suitable manner.
• processing of therapy models can be analogous to processing of characterization models (e.g., described for Block S130), where any number and/or types of treatment models can be generated for different purposes.
• the therapy model can be derived in relation to identification of a "normal" or baseline microbiome composition and/or functional features, as assessed from users of a population of users who are identified to be in good health.
• therapies that modulate microbiome compositions and/or functional features toward those of users in good health can be generated in Block S140.
• Block S140 can thus include identification of one or more baseline microbiome compositions and/or functional features (e.g., one baseline microbiome for each of a set of demographics), and potential therapy formulations and therapy regimens that can shift microbiomes of users who are in a state of dysbiosis toward one of the identified baseline microbiome compositions and/or functional features.
• the therapy model can, however, be generated and/or refined in any other suitable manner.
• microorganism compositions associated with probiotic therapies associated with the therapy model preferably include microorganisms that are culturable (e.g., able to be expanded to provide a scalable therapy) and non-lethal (e.g., non-lethal in their desired therapeutic dosages).
• microorganism compositions can include a single type of microorganism that has an acute or moderated effect upon a user's microbiome.
• microorganism compositions can include balanced combinations of multiple types of microorganisms that are configured to cooperate with each other in driving a user's microbiome toward a desired state.
• a combination of multiple types of bacteria in a probiotic therapy can include a first bacteria type that generates products that are used by a second bacteria type that has a strong effect in positively affecting a user's microbiome.
• a combination of multiple types of bacteria in a probiotic therapy can include several bacteria types that produce proteins with the same functions that positively affect a user's microbiome.
• probiotic compositions can be naturally or synthetically derived.
• a probiotic composition can be naturally derived from fecal matter or other biological matter (e.g., of one or more users having a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model).
• probiotic compositions can be synthetically derived (e.g., derived using a benchtop method) based upon a baseline microbiome composition and/or functional features, as identified using the characterization process and the therapy model.
• microorganism agents that can be used in probiotic therapies can include one or more of: yeast (e.g., Saccharomyces boulardii), gram-negative bacteria (e.g., E. coli Nissle, Akkermansia muciniphila, Prevotella hryantii, etc.), gram-positive bacteria (e.g., Bifidobacterium animalis (including subspecies lactis), Bifidobacterium longum (including subspecies infantis), Bifidobacterium bifidum, Bifidobacterium pseudolongum, Bifidobacterium thermophilum, Bifidobacterium breve, Lactobacillus rhamnosus, Lactobacillus acidophilus, Lactobacillus casei, Lactobacillus helveticus, Lactobacillus plantarum, Lactobacillus fei'mentum, Lactobacillus salivarius, Lactobacillus del
• Bacillus polyfermenticus Bacillus clausii, Bacillus licheniformis, Bacillus coagulans, Bacillus pumilus, Faecalibacterium prausnitzii, Streptococcus thermophiles, Brevibacillus brevis, Lactococcus lactis, Leuconostoc mesenteroides, Enterococcus faecium, Enterococcus faecalis, Enterococcus durans, Clostridium butyricum, Sporolactobacillus inulinus, Sporolactobacillus vineae, Pediococcus acidilactic, Pediococcus pentosaceus, etc.), and/ or any other suitable type of microorganism agent.
• a probiotic therapy can include a combination of one or more of: Bacteroidetes (phylum), Propionibacterium (genus), Staphylococcus (genus), Corynebacterium (genus), Streptococcus (genus) provided at dosages of 1 million to 10 billion CFUs and/or other suitable CFUs, such as determined from a therapy model that predicts positive adjustment of a patient's microbiome in response to the therapy.
• a probiotic therapy can include a combination of one or more of: Streptococcaceae, Streptococcus, Lactobacillales, Veillonella provided at dosages of 1 million to 10 billion CFUs, and/or other suitable CFUs, such as determined from a therapy model that predicts positive adjustment of a patient's microbiome in response to the therapy.
• a probiotic therapy can include a combination of one or more microorganisms from: Actinobacteria (class), Actinobacteria (phylum), Propionibacterium (genus) provided at dosages of 1 million to 10 billion CFUs and/ or other suitable CFUs, such as determined from a therapy model that predicts positive adjustment of a patient's microbiome in response to the therapy.
• a probiotic therapy can include a combination of one or more of: Roseburia (genus), Blautia (genus), Bacteroides (genus), Pseudobutyrivibrio (genus), Alistipes (genus), Faecalibacterium (genus), Collinsella (genus), Clostridium (genus), Anaerostipes (genus), Dorea (genus), Subdoligranulum (genus), Sarcina (genus), Lachnospira (genus), Anaerotruncus (genus), Parabacteroides (genus), Flavonifractor (genus), Intestinibacter (genus), Erysipelatoclostridium (genus), Phascolarctobacterium (genus), Streptococcus (genus), Odoribacter (genus), Sutterella (genus), Corynebacterium (genus), Bilophila (genus), Roseburia (genus), Blautia (genus), Bacteroides (
• a probiotic therapy can include a combination of one or more of: Lactobacillales provided at dosages of 1 million to 10 billion CFUs and/ or other suitable CFUs, such as determined from a therapy model that predicts positive adjustment of a patient's microbiome in response to the therapy.
• Block S140 a user can be instructed to ingest capsules including the probiotic formulation according to a regimen tailored to one or more of his/her: physiology (e.g., body mass index, weight, height), demographics (e.g., gender, age), severity of dysbiosis, sensitivity to medications, and any other suitable factor.
• physiology e.g., body mass index, weight, height
• demographics e.g., gender, age
• therapies promoted by the therapy model of Block S140 can include one or more of: consumables (e.g., food items, beverage items, nutritional supplements), suggested activities (e.g., exercise regimens, adjustments to alcohol consumption, adjustments to cigarette usage, adjustments to drug usage), topical therapies (e.g., lotions, ointments, antiseptics, etc.), adjustments to hygienic product usage (e.g., use of shampoo products, use of conditioner products, use of soaps, use of makeup products, etc.), adjustments to diet (e.g., sugar consumption, fat consumption, salt consumption, acid consumption, etc.), adjustments to sleep behavior, living arrangement adjustments (e.g., adjustments to living with pets, adjustments to living with plants in one's home environment, adjustments to light and temperature in one's home environment, etc.), nutritional supplements (e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, probiotics, etc.), medications, antibiotics, and any other suitable therapeutic measure
• consumables e.g., food items
• DHNA 1,4- dihydroxy-2-naphthoic acid
• Inulin trans-Galactooligosaccharides
• MOS Mannan oligosaccharides
• FOS Neoagaro- oligosaccharides
• NAOS N-linked oligosaccharides
• XOS Xylo-oligosaccharides
• IMOS Isomalto- oligosaccharides
• SBOS Soybean oligosaccharide
• SBOS Soybean oligosaccharide
• LS lactitol
• LS lactosucrose
• Isomaltulose including Palatinose
• Arabinoxylooligosaccharides AXOS
• Raffmose oligosaccharides Raffmose oligosaccharides
• AX Polyphenols or any other compound capable of changing the microbiota
• Blocks S140 and/or S170 can include deriving a therapeutic composition associated with at least one of a microbiome composition and/or functional diversity dataset (e.g., extracted features).
• the method 100 can include determining a modulator of a biomolecule associated with the skin-related condition (e.g., a modulator of a biomolecule derived from a set of taxa associated with the skin-related condition); deriving a therapeutic composition for the skin-related condition based on the modulator; and promoting the therapeutic composition.
• the method 100 can additionally or alternatively include Block S150, which recites: receiving a biological sample from a user.
• Block S150 functions to facilitate generation of a microbiome dataset for the user that can be used to derive inputs for the characterization process.
• receiving, processing, and analyzing the biological sample preferably facilitates generation of a microbiome dataset for the user, which can be used to provide inputs for a characterization process.
• the biological sample is preferably generated from the user and/or an environment of the user in a non-invasive manner (e.g., using a provided sample kit, etc.), but can additionally or alternatively be received in a semi-invasive manner, invasive manner, and/or in any suitable manner.
• Block S150 processing and analyzing the biological sample from the user is preferably performed in a manner similar to that of one of the embodiments, variations, and/or examples of sample processing described in relation to Block S110 above, and/or in U.S. App. No. 15/452,529 filed 07-MAR-2017, which is incorporated in its entirety by this reference.
• biological sample reception and processing in Block S150 can alternatively be performed in any other suitable manner.
• Block S160 can additionally or alternatively include Block S160, which recites: determining, with the characterization process, a characterization of the user based upon processing a microbiome dataset derived from a biological sample of the user.
• Block S160 can function to extract features from microbiome-derived data of the user (e.g., based on evaluating the microbiome datasets against computer-implemented rules), and use the features as inputs into an embodiment, variation, or example of the characterization process (e.g., a characterization model) described in Block S130 above.
• Identifying the characterization in Block S160 thus preferably includes identifying features and/or combinations of features associated with the microbiome composition and/or functional features of the user, inputting the features into the characterization process, and receiving an output that characterizes the user as belonging to one or more of: a behavioral group, a gender group, a dietary group, a disease-state group, and/or any other suitable group capable of being identified by the characterization process.
• Block S160 can further include generation of and/or output of a confidence metric associated with the characterization of the user.
• a confidence metric can be derived from the number of features used to generate the characterization, relative weights or rankings of features used to generate the characterization, measures of bias in the characterization process, and/or any other suitable parameter associated with aspects of the characterization process.
• features extracted from the microbiome dataset of the user can be supplemented with survey-derived and/ or medical history-derived features from the user, which can be used to further refine the characterization process of Block S130.
• the microbiome dataset of the user can additionally or alternatively be used in any other suitable manner to enhance the models of the method 100, and Block S160 can be performed in any suitable manner.
• Block S170 which recites: promoting a therapy for the skin-related condition to the user (e.g., based upon the characterization, a therapy model, etc.).
• Block S170 functions to determine, recommend, and/or provide a personalized therapy to the user, in order to shift the microbiome composition and/or functional features of the user toward a desired equilibrium state.
• Block S170 can include provision of a customized therapy to the user according to their microbiome composition and functional features, as shown in FIGURE 8, where the customized therapy is a formulation of microorganisms configured to correct dysbiosis characteristic of users having the identified characterization.
• outputs of Block S140 can be used to directly promote a customized therapy formulation and regimen (e.g., dosage, usage instructions) to the user based upon a trained therapy model.
• therapy provision can include recommendation of available therapeutic measures configured to shift microbiome composition and/or functional features toward a desired state.
• available therapeutic measures can include one or more of: consumables (e.g., food items, beverage items, etc.), topical therapies (e.g., lotions, ointments, antiseptics, etc.), nutritional supplements (e.g., vitamins, minerals, fiber, fatty acids, amino acids, prebiotics, etc.), medications, antibiotics, bacteriophages, and any other suitable therapeutic measure.
• a combination of commercially available probiotic supplements can include a suitable probiotic therapy for the user according to an output of the therapy model.
• the therapy of Block S170 can include a bacteriophage-based therapy.
• bacteriophage-based therapies can be used to reduce the size(s) of the undesired population(s) of bacteria represented in the user.
• bacteriophage-based therapies can be used to increase the relative abundances of bacterial populations not targeted by the bacteriophage(s) used.
• Therapy provision in Block S170 can include provision of notifications to a user regarding the recommended therapy and/or other forms of therapy.
• Types of notifications and manners of providing notifications can be analogous to that described in U.S. App. No. 15/374,890 filed 09-DEC-2016, which is incorporated in its entirety by this reference.
• Block S180 which recites: monitoring effectiveness of the therapy for the user, based upon processing biological samples, to assess microbiome composition and/or functional diversity for the user over time.
• Block S180 functions to gather additional data regarding positive effects, negative effects, and/or lack of effectiveness of a probiotic therapy suggested by the therapy model for users of a given characterization, where the additional data can be used, for example, to generate, update, and/or execute one or more characterization models, therapy models, and/ or other suitable models.
• the method 100 can include updating a model (e.g., characterization model, therapy model, etc.) based one or more of a user microbiome composition dataset (e.g., features extracted from the dataset, etc.), a user microbiome functional diversity dataset (e.g., features extracted from the dataset, etc.), and/or other suitable dataset (e.g., updating a model based on modulation of the skin-related condition, determined based on comparisons of pre-therapy and post-therapy microbiome datasets); and in response to updating the model, determining an update (e.g., to a characterization, to a therapy, etc.) for a second user in relation to the skin-related condition, based on the updated model.
• a model e.g., characterization model, therapy model, etc.
• the method 100 can include: receiving a post-therapy biological sample from the user (e.g., after promoting the therapy); generating a post- therapy characterization of the user in relation to the skin-related condition based on the post-therapy biological sample (e.g., post -therapy microbiome composition and/ or functional diversity features extracted from the post -therapy biological sample using skin-related feature- selection rules, where the features can be used with a characterization model, etc.); characterizing modulation of the skin-related condition in relation to the user based on the post-therapy characterization (e.g., and one or more pre-therapy characterizations for the user, for a second user, etc.).
• the post-therapy biological sample e.g., after promoting the therapy
• generating a post- therapy characterization of the user in relation to the skin-related condition based on the post-therapy biological sample e.g., post -therapy microbiome composition and/ or functional diversity features extracted from the post -therapy biological sample using skin-related feature- selection rules, where
• any suitable portion of the method 100 and/ or any suitable operation can be performed in response to updating of models.
• Monitoring of a user during the course of a therapy promoted by the therapy model e.g., by receiving and analyzing biological samples from the user throughout therapy, by receiving survey-derived data from the user throughout therapy
• Block S180 the user can be prompted to provide additional biological samples at one or more key time points of a therapy regimen that incorporates the therapy, and the additional biological sample(s) can be processed and analyzed (e.g., in a manner similar to that described in relation to Block S120) to generate metrics characterizing modulation of the user's microbiome composition and/or functional features.
• metrics related to one or more of: a change in relative abundance of one or more taxonomic groups represented in the user's microbiome at an earlier time point, a change in representation of a specific taxonomic group of the user's microbiome, a ratio between abundance of a first taxonomic group of bacteria and abundance of a second taxonomic group of bacteria of the user's microbiome, a change in relative abundance of one or more functional families in a user's microbiome, and any other suitable metrics can be used to assess therapy effectiveness from changes in microbiome composition and/or functional features.
• survey-derived data from the user can be used to determine effectiveness of the therapy in Block S180.
• monitoring effectiveness of one or more therapies can be performed in any suitable manner.
• the method 100 can, however, include any other suitable blocks or steps configured to facilitate reception of biological samples from users, processing of biological samples from users, analyzing data derived from biological samples, and generating models that can be used to provide customized diagnostics and/or probiotic- based therapeutics according to specific microbiome compositions and/or functional features of users.
• the method 100 and/or system of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
• the instructions can be executed by computer-executable components integrated with the application, applet, host, server, network, website, communication service, communication interface, hardware/firmware/software elements of a patient computer or mobile device, or any suitable combination thereof.
• Other systems and methods of the embodiments can be embodied and/or implemented at least in part as a machine configured to receive a computer-readable medium storing computer-readable instructions.
• the instructions can be executed by computer-executable components integrated by computer- executable components integrated with apparatuses and networks of the type described above.
• the computer-readable medium can be stored on any suitable computer readable media such as RAMs, ROMs, flash memory, EEPROMs, optical devices (CD or DVD), hard drives, floppy drives, or any suitable device.
• the computer-executable component can be a processor, though any suitable dedicated hardware device can (alternatively or additionally) execute the instructions.
• each block in the flowchart or block diagrams may represent a module, segment, step, or portion of code, which includes one or more executable instructions for implementing the specified logical function(s).
• the functions noted in the block can occur out of the order noted in the FIGURES. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved.
• the embodiments include every combination and permutation of the various system components and the various method processes, including any variations, examples, and specific examples.

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