CN116615702A

CN116615702A - System and method for exposure of clinical application of histology

Info

Publication number: CN116615702A
Application number: CN202180079461.0A
Authority: CN
Inventors: M·阿罗拉; P·柯廷; C·奥斯汀
Original assignee: Icahn School of Medicine at Mount Sinai
Current assignee: Icahn School of Medicine at Mount Sinai
Priority date: 2020-10-06
Filing date: 2021-10-06
Publication date: 2023-08-18

Abstract

The present application provides a computer-implemented exposure set chemistry system comprising an exposure set biochemical characteristics database comprising a corresponding plurality of exposure set biochemical characteristics for each of a plurality of subjects and an intervention result database comprising information regarding intervention result information for: at least one phase of at least one intervention of at least one subject of the plurality of subjects. An association software module determines associations between the corresponding plurality of exposure omics characteristics and the intervention outcome information. A recommendation software module provides an intervention recommendation for the at least one subject based at least in part on the corresponding plurality of exposure histology characteristics, the intervention outcome information, and an association between the corresponding plurality of exposure histology characteristics, clinical phenotype information, and the intervention outcome information for the at least one subject.

Description

System and method for exposure of clinical application of histology

Cross Reference to Related Applications

The present application claims the benefit of U.S. provisional patent application No. 63/088,375, entitled "System and Methods for Screening Temporal Dynamics of Biological Disorders", filed 30/10/2020, U.S. provisional patent application No. 63/121,792, entitled "Systems and Methods for Dynamic Immunohistochemistry Profiling of Biological Diseases and Disorders", filed 4/12/2020, and U.S. provisional patent application No. 63/164,964, entitled "Systems and Methods for Exposomic Clinical Applications", filed 23/2021, each of which is incorporated herein by reference in its entirety.

Background

About 50% of all drug triple interventions (including but not limited to clinical trials and other intervention study designs) will fail due to lack of efficacy or adverse effects resulting from administration of the treatment. One possible explanation for the high failure rate is that human intricate biology may vary greatly from individual to individual. The complexity of complex human biology cannot be captured by only rough screening criteria and intervention qualification of participants.

Accordingly, there is an unmet need for systems and methods for screening patients in more detail to optimize interventions, provide targeted drug interventions, and predict early onset of disease.

Disclosure of Invention

The criteria for selection of subjects for intervention and the resulting criteria for administration of commercial drugs depend on limited range data. Selection criteria such as body weight, sex, history of chronic diseases, family diseases or even blood collection are widely accepted in the medical community as the gold standard, but such metadata are merely unique, changing, transient profiles of complex biology of an individual. Using such rough categorization and classification of individuals by their clinical metadata, there are undesirable side effects and complications of drug administration without clear understanding or inference of variability between subjects with similar clinical metadata. In order to improve the current best methods and mature gold standards, new innovations must be made to the data sets used to screen and classify individuals to make them more abundant and representative.

The present disclosure addresses these needs by systems and methods that enable analysis and classification of subjects by their exposure group student-like characteristics. Exposure group student characterization analysis can analyze in a non-invasive manner over 50,000 biochemical features from a single hair shaft, tooth, and nail sample. Using such an analysis system and method of the present invention, subtle changes in subject biochemistry caused by diet, air pollution, psychological stress, pesticide exposure, industrial chemicals, and the like can be studied and correlated with positive responses and outcomes of targeted drugs.

In addition to the rich data set generated by exposure group student profile analysis, exposure group student profile analysis may also provide insight into the temporal variation of the biochemical features over the time span of a subject's lifetime. Such methods can be used to screen individuals suffering from life-threatening diseases to determine which single exposure group student profile or which combination of exposure group student profiles resulted in the development of the disease. The identified pathways can then be used to train statistical, machine learning, and/or artificial intelligence predictive models that can predict early onset of disease based on exposure set student identity of otherwise healthy subjects at a stage where intervention may provide a substantial impact.

Aspects of the invention disclosed herein provide a computer-implemented exposure histology system, the system comprising: (a) An exposure group biochemical characterization database (EDB) comprising exposure group biochemical characterization (exposomic features) of a plurality of subjects; (b) A Clinical Database (CDB) comprising clinical phenotype information for a plurality of subjects; (c) An Intervention Outcome Database (IODB) comprising information on intervention outcome information for at least one period of at least one intervention; and (d) a computer processor comprising: (i) An association software module communicatively coupled with the EDB, CDB, and IODB, wherein the association software module is programmed to determine an association between exposed histology characteristics, clinical phenotype information, and intervention outcome information of at least one of the plurality of subjects, and (ii) a recommendation software module communicatively coupled with the EDB, CDB, and IODB. The recommendation software module is programmed to provide an intervention recommendation for at least one of the plurality of subjects based at least in part on the exposure histology characteristics, the clinical phenotype information, the intervention outcome information, and the association between the exposure histology characteristics, the clinical phenotype information, and the intervention outcome information of the at least one of the plurality of subjects.

In some embodiments, the exposure-group students features comprise at least 10, at least 100, at least 1,000, or at least 10,000 different exposure-group students features. In some embodiments, the intervention outcome information includes a classification of at least one of a non-responder, an adverse responder, and a positive responder for the intervention. In some embodiments, the intervention result includes one or more inclusion criteria or exclusion criteria for at least one intervention. In some embodiments, the exposure histology is obtained by assaying a biological sample from a plurality of subjects. In some embodiments, the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof. In some embodiments, determining comprises obtaining a mass spectrometry measurement, a laser-induced breakdown spectrometry measurement, a laser ablation-inductively coupled plasma mass spectrometry measurement, a raman spectrometry measurement, a breakdown spectroscopy, an immunohistochemical measurement, or any combination thereof. In some embodiments, the mass spectrometry measurement comprises measurement of one or more chemicals. In some embodiments, the one or more chemicals include chemicals of aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof. In some embodiments, the exposure histology profile comprises a dynamic time biochemical response of the plurality of subjects. In some embodiments, the exposed set of biochemical features comprises a fluorescence image of the biological sample. In some embodiments, the exposed set of biochemical features comprises a spatial map of a raman spectrum of the biological sample. In some embodiments, the exposure group biochemical trait is associated with a disease or disorder. In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof. In some embodiments, the exposure histology features are analyzed using trained statistics, machine learning, and/or artificial intelligence classifiers to determine associations with diseases or conditions. In some embodiments, the classifier is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

Aspects of the invention disclosed herein describe a method for selecting a subject for intervention, the method comprising: (a) Providing a trained predictive model, wherein the trained predictive model is trained in clinical metadata, exposure histology characteristics, and corresponding intervention outcome information of one or more subjects; (b) Detecting a biochemical feature obtained from a biological sample from a subject seeking intervention, thereby producing an exposed histology feature; (c) Predicting predicted intervention outcome information for the subject seeking intervention using the trained predictive model, wherein the exposure histology features and clinical elements of the subject seeking intervention are input information for the trained predictive model; and (d) selecting or excluding the subject from the intervention based at least in part on the predicted intervention outcome information of the subject. In some embodiments, the biochemical features are obtained by assaying a biological sample of the subject. In some embodiments, the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof. In some embodiments, determining comprises collecting data from laser ablation-inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, raman spectroscopy, immunohistochemical measurements, or any combination thereof. In some embodiments, the laser ablation-inductively coupled plasma mass spectrometry measurement includes measurement of one or more chemical species. In some embodiments, the one or more chemicals include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof. In some embodiments, the biochemical features comprise a fluorescence image of the biological sample. In some embodiments, the biochemical features comprise a spatial map of raman spectra of the biological sample. In some embodiments, the biochemical features are associated with a disease or disorder. In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof. In some embodiments, the trained predictive model is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof. In some embodiments, the method further comprises incorporating the subject into the intervention upon selection of the intervention subject. In some embodiments, the method further comprises assessing the subject for another intervention when the subject is excluded from the interventions.

Aspects of the disclosure describe a method of selecting an optimal treatment for a disease or disorder in a subject in need thereof, comprising: (a) Detecting one or more biochemical features obtained from one or more biological samples from one or more subjects not suffering from a disease or disorder, thereby generating one or more reference exposure set of chemical features; (b) Detecting one or more biochemical features obtained from one or more biological samples from a subject suffering from a disease or disorder, thereby producing one or more pre-treatment exposure histology features; (c) administering a treatment to a subject suffering from a disease or disorder; (d) Detecting one or more biochemical features obtained from one or more biological samples from one or more subjects suffering from a disease or disorder after a period of time has elapsed from receiving the treatment, thereby generating one or more post-treatment exposure histology features; (e) determining the difference between: one or more reference exposure histology characteristics of one or more subjects not suffering from a disorder or disease, one or more pre-treatment exposure histology characteristics of one or more subjects suffering from a disease or disorder, and one or more post-treatment exposure histology characteristics of one or more subjects suffering from a disease or disorder; and (f) selecting one or more optimal treatments based at least in part on the determined differences between the one or more reference exposure histology characteristics, the one or more pre-treatment exposure histology characteristics, and the one or more post-treatment exposure histology characteristics, wherein the one or more optimal treatments are selected based on the determined differences meeting predetermined criteria. In some embodiments, the optimal treatment may include a drug, a nutraceutical, or any combination thereof. In some embodiments, the predetermined criteria include one or more pre-treatment exposure histology characteristics and a difference between one or more post-treatment exposure histology characteristics and one or more reference exposure histology characteristics. In some embodiments, the period of time comprises at least about 1 hour, at least about 1 day, at least about 1 week, at least about 1 month, at least about 1 year, or any combination thereof. In some embodiments, the differences comprise a change in the one or more post-treatment exposure histology characteristics to at least 10% of the one or more reference exposure histology characteristics. In some embodiments, the pre-treatment exposure histology, post-treatment exposure histology, or any combination thereof is obtained by assaying a biological sample of the subject. In some embodiments, the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof. In some embodiments, determining comprises obtaining laser ablation-inductively coupled plasma mass spectrometry data, laser induced breakdown spectroscopy, raman spectroscopy, immunohistochemical measurements, or any combination thereof. In some embodiments, the laser ablation-inductively coupled plasma mass spectrometry data includes measurements of one or more chemical species. In some embodiments, the one or more chemicals include chemicals of aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof. In some embodiments, the biochemical features comprise a fluorescence image of the biological sample. In some embodiments, the biochemical features comprise a spatial map of raman spectra of the biological sample. In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof. In some embodiments, the differences between the reference exposure histology features, the pre-treatment exposure histology features, the post-treatment exposure histology features, or any combination thereof are analyzed using trained statistics, machine learning, and/or artificial intelligence classifiers. In some embodiments, the trained classifier is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

In view of the above background, accurate methods and systems are needed to diagnose biological conditions, and in particular, non-invasive diagnosis. Such diagnosis may be based on accurate analysis of biomarkers detectable by non-invasive methods for diagnosing biological conditions. The present disclosure provides improved systems and methods for accurately diagnosing biological conditions based on analysis of dynamic biological response data from a non-invasively obtained biological sample from a subject. Such improved systems and methods for accurately diagnosing biological conditions may be based on a combination of dynamic immunohistochemical analysis of biological samples and artificial intelligence data analysis of such dynamic analysis to assess disease states. The present disclosure addresses these needs, for example, by providing biological sample biomarkers for diagnosing biological conditions. The biological sample includes a human biological sample associated with an incremental expansion. In non-limiting embodiments, the biological sample is a hair shaft, tooth, toenail, fingernail, physiological parameter, or any combination thereof. The non-invasive biomarkers of the present disclosure are useful in the diagnosis of infants, even infants less than one year of age. In some embodiments, the physiological parameters include health metadata as described elsewhere herein. In some embodiments, the physiological parameter includes a parameter measured during a blood test, such as cholesterol, white blood cell count, red blood cell count, hematocrit, the presence or absence of a bacterial infection, and the like.

In one aspect, the present disclosure provides a method for determining the risk of a disease or disorder in a subject, comprising: (a) Staining a dental sample of a subject to produce a stained dental sample; (b) Analyzing the spatial fluorescence intensity of the whole stained tooth sample; and (c) determining the risk of the disease or disorder in the subject based at least in part on the analysis of the fluorescence intensity.

In some embodiments, the analysis determines a temporal dynamics of the underlying biological process. In some embodiments, analyzing includes obtaining a fluorescence image of the stained tooth sample, and analyzing the fluorescence intensity of the fluorescence image. In some embodiments, the fluorescence intensity is spatially varying. In some embodiments, obtaining a fluorescence image of the stained tooth sample includes using an inverted or non-inverted confocal microscope. In some embodiments, staining the tooth sample comprises immunohistochemical staining using C-reactive protein. In some embodiments, the method further comprises slicing the tooth sample. In some embodiments, staining the tooth sample comprises (1) cutting the tooth sample, (2) decalcifying the tooth sample, (3) slicing the decalcified sample, (4) staining the decalcified tooth slice with primary and secondary antibodies, (5) measuring spatial antibody fluorescence with a confocal microscope, and/or (6) extracting a time profile of fluorescence intensity.

In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof. In some embodiments, the subject is a human. In some embodiments, the subject is an adult. In some embodiments, the subject is less than 5 years old. In some embodiments, the subject is less than 4 years old. In some embodiments, the subject is less than 3 years old. In some embodiments, the subject is less than 2 years old. In some embodiments, the subject is less than 1 year old.

Another aspect of the present disclosure provides a system comprising one or more computer processors and computer memory coupled thereto. The computer memory includes machine executable code that when executed by one or more computer processors implements any of the methods described above or elsewhere herein.

Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be realized, the present disclosure is capable of other and different embodiments and its several details are capable of modification in various obvious respects, all without departing from the present disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature and not as restrictive.

Incorporated by reference

All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. If publications and patents or patent applications incorporated by reference contradict the disclosure contained in this specification, this specification is intended to supersede and/or take precedence over any such conflicting material.

Drawings

The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present invention will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as the figures ("Figure" and "fig.)):

FIG. 1 shows an overview of a computer-implemented exposure team system, as seen in some embodiments.

Fig. 2A-B show a data workflow of training an intervention prediction model using clinical database data (clinical metadata) of a subject, exposure set biochemical features, and intervention results, and a data workflow of training an intervention prediction model (fig. 2A) and using a trained prediction model (fig. 2B) to, for example, predict an intervention result for a given subject, as seen in some embodiments.

Figures 3A-B show a data workflow for training a drug and/or nutritional best choice prediction model using clinical database data (clinical metadata) of subjects, exposure histology characteristics, percentage differences between exposure histology characteristics after treatment of subjects and reference exposure histology characteristics. Fig. 3A shows a training workflow for training a predictive model, and fig. 3B shows the use of a training predictive model, as seen in some embodiments.

Fig. 4 illustrates a flow chart for selecting a subject for intervention based on a subject's exposure set biochemical profile, as seen in some embodiments.

Fig. 5 shows a flow chart for selecting an optimal pharmaceutical or nutraceutical treatment based on a comparison of subject exposure histology characteristics versus reference treatment exposure histology characteristics, as seen in some embodiments herein.

Figures 6A-D show exposure group biochemistry spectra of various exposure group characteristics (e.g., tin, lead, calcium, and magnesium) for subjects not receiving intervention (blue) and subjects having been subjected to intervention (orange, gray, and cyan), as seen in some embodiments herein.

Fig. 7A-7B illustrate clustering of one or more exposure group biochemical profiles of one or more subjects (fig. 7A) and how such data clusters correlate with a disease or disorder of one or more subjects (fig. 7B), as seen in some embodiments herein.

Fig. 8 shows subtype typing of subjects using exposure histology characteristics from hair analysis. The exposure set biochemical characterization data is extracted via analytical methods disclosed elsewhere herein. As seen in some embodiments herein, an unsupervised cluster analysis is shown to identify discrete subtypes of patients with autism spectrum disorders.

FIG. 9 illustrates a system and method for geo-temporal dynamics utilizing an exposure group approach to collecting and analyzing annotations through a depth data science framework, as seen in some embodiments herein.

Fig. 10 shows the temporal aspects from 100 data time points obtained from a single biological sample characterizing physiological dynamics of different life stages, as seen in some embodiments herein.

Fig. 11 shows various chemical features and their corresponding groupings measured by the methods and systems, as seen in some embodiments herein.

Fig. 12 shows time and space Immunohistochemical (IHC) fluorescence data captured by the methods and systems described herein. In particular, C-reactive protein IHC fluorescence data demonstrate a dramatic increase in prenatal inflammation associated with the occurrence of autism, as seen in some of the examples herein.

Fig. 13 shows a method of measuring a metal chemical biomarker of a tooth and correlating the spatial distribution of the metal chemical biomarker across the tooth growth line with disease onset, disease prognosis, disease diagnosis, biochemical-physiological changes, and the like, as seen in some embodiments herein.

Fig. 14 shows a machine learning, informatics, and deep learning platform configured to generate a robust and generalized predictive model for diagnosis of a disease (e.g., ASD, ADHD, etc.) prior to onset of the disease.

FIG. 15 shows methods of phenotyping, pathway identification, metabolic phenotyping and clinical subtype of various physiological outcomes by unsupervised pattern recognition of exposure panels.

Figure 16 shows the affected probiotic metabolism and corresponding biochemical pathways measured by the methods and systems, as seen in some embodiments herein.

Figure 17 shows the affected gluten metabolism and corresponding biochemical pathways measured by the methods and systems, as seen in some embodiments herein.

Figure 18 shows pathway importance weights from a study of 500 more participants with autism that uses the described methods and systems to recommend pharmaceutical and nutraceutical compounds to treat autism.

Figures 19A-C show various forms of exposure set characterization data representations, as seen in some embodiments herein.

Figures 20A-D show a comparison of the exposed histology characteristics of calcium (figures 20A-B) and copper (figures 20C-D) and their corresponding attractor graphical representations, as seen in some embodiments.

Fig. 21A-B illustrate fetal recurrent networks of neuronormal (fig. 21A) and children with autism spectrum disorders (fig. 21B), as seen in some embodiments.

Fig. 22 illustrates a flow chart of a method of outputting one or more quantitative measures of one or more exposure sets of a subject, as seen in some embodiments.

FIG. 23 illustrates a flow chart of a method for outputting predictions of phenotype data for one or more subjects.

Detailed Description

While various embodiments of the present invention have been shown and described herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Those skilled in the art will now appreciate that many modifications, changes, and substitutions can be made without departing from the invention. It should be understood that various alternatives to the embodiments of the invention described herein may be employed.

About 50% of all drug three-phase interventions failed due to lack of efficacy or adverse effects of treatment experienced by volunteer participants. One possible explanation for the high failure rate is that human intricate biology may vary greatly from individual to individual. The complexity of complex human biology cannot be captured by only rough screening criteria and qualification of intervening participants. Thus, there is an unmet need for systems and methods for screening patients in more detail to optimize interventions, provide targeted drug interventions, and predict early onset of disease.

The present disclosure addresses these needs by a system and method that is capable of analyzing and classifying subjects by their exposure group biochemical profile, as shown in fig. 9. The exposure histology profiling of the present disclosure can include non-invasively profiling over 50,000 biochemical features from hair shaft, teeth, fingernails, toenails, physiological parameters, or any combination thereof (fig. 11). Using such an analysis system and method of the present invention, minute changes in subject biochemistry caused by factors such as diet, air pollution, psychological stress, exposure to pesticides or industrial chemicals, etc. (fig. 10) can be studied and correlated with intervention reaction results and targeted highly effective pharmaceuticals and nutraceuticals.

In addition to the large number of data sets generated by exposure group student profile analysis, exposure group student profile analysis can also provide insight into the temporal variation of the biochemical features over a time span of a subject's lifetime, as shown in fig. 10. Such methods can be used to screen individuals suffering from life-threatening diseases to determine which single exposure group student profile or which combination of exposure group profiles resulted in the development of the disease. The identified pathways can then be used to train statistical, machine learning, and/or artificial intelligence predictive models that can predict early onset of disease based on exposed histology characteristics of otherwise healthy subjects at a stage where intervention may provide a substantial impact, as seen in fig. 13-15.

A computer-implemented exposure histology system.

In one aspect, the present disclosure provides a computer-implemented exposure histology system for collection, storage, cataloging, comparison, analysis, or any combination thereof of exposure set students characteristics for one or more subjects. In some embodiments, exposure group student profile may be used, at least in part, to optimize selection criteria for subjects involved in the intervention. In some embodiments, the exposure group student profile is used, at least in part, to recommend optimal pharmaceutical or nutraceutical treatment for a subject in need thereof. In some embodiments, the intervention may include a clinical trial, a community trial, or any combination thereof.

Turning to fig. 1, the computer-implemented exposure set theory 23 may include one or more of the following: (a) An exposure group biochemical characteristics database (EDB) 1, the EDB may further comprise biochemical characteristics information of a plurality of subjects; (b) A clinical database (CBD) 3, the CBD may further comprise clinical phenotype information for a plurality of subjects; (c) An intervention requirement database (IODB) 5, which may further include information about intervention outcome information for at least one period of at least one intervention; (d) a Treatment Database (TDB) 18; and (e) computer system 11, which may include a processing unit (CPU, also referred to herein as a "processor" and a "computer processor") 21, which may be a single-core or multi-core processor, or multiple processors that process in parallel. The processor 21 may execute a series of machine readable instructions embodied in a program or software, for example, (i) an associated software module located on the storage unit 19, i.e., the memory, which is communicatively coupled with the EDB1 and CDB3, the associated software module being programmable to determine an association between the exposed histology characteristics and clinical phenotype information of at least one of the plurality of subjects, and (ii) a recommended software module located on the memory 19. The software may be loaded from the memory 19 into a Random Access Memory (RAM) 17 or a Read Only Memory (ROM) 17, which provides an intervention recommendation for at least one of the plurality of subjects based at least in part on the exposure histology characteristics, the clinical phenotype information, the intervention outcome information, and an association between the exposure histology characteristics and the clinical phenotype information for at least one of the plurality of subjects.

In some embodiments, exposure group biochemical characteristics database (EDB) 1 may include exposure group chemical characteristics from a plurality of subjects. The exposure set biochemical features may include biochemical features of a perfluoro compound, a paraben, a phthalate, a lipid, an amino acid, a metabolite, a peptide, a metal, a derivative thereof, or any combination thereof, as seen in fig. 11. In some embodiments, the exposure set biochemical features are analyzed or obtained as a function of the subject's lifetime (e.g., as a function of age or as a function of time), in which case the set of exposure set biochemical features, as seen in fig. 6A-6D, may be analyzed to generate one or more exposure set biochemical features. In some embodiments, the period of time represented by the exposure set biochemical signature may comprise at least 1 hour, at least 1 day, at least 1 week, at least 1 month, at least 1 year, or any combination thereof.

In some embodiments, the number of exposed sets of biochemical features can comprise from about 10 features to about 100,000 features. In some embodiments of the present invention, in some embodiments, the number of exposed sets of biochemical features can include from about 10 features to about 100 features, from about 10 features to about 500 features, from about 10 features to about 1,000 features, from about 10 features to about 5,000 features, from about 10 features to about 7,000 features, from about 10 features to about 10,000 features, from about 10 features to about 20,000 features, from about 10 features to about 50,000 features, from about 10 features to about 100,000 features, from about 100 features to about 500 features, from about 100 features to about 1,000 features, from about 100 features to about 5,000 features, from about 100 features to about 7,000 features, from about 100 features to about 10,000 features, from about 100 features to about 20,000 features, from about 100 features to about 50,000 features, from about 100,000 features, from about 500 features to about 1,000 features, from about 500 features to about 5,000 features, from about 7,000 features to about 500 features, from about 7,000 features, from about 500 features, from about 10,000 features, from about 10 features, from about 50,000 features, from about 100 features, from about 10 features, from about 10,000 features, from about 10 features, and from about 10,000 features. About 500 features to about 20,000 features, about 500 features to about 50,000 features, about 500 features to about 100,000 features, about 1,000 features to about 5,000 features, about 1,000 features to about 7,000 features, about 1,000 features to about 10,000 features, about 1,000 features to about 20,000 features, about 1,000 features to about 50,000 features, about 1,000 features to about 100,000 features, about 5,000 features to about 7,000 features, about 5,000 features to about 10,000 features, about 5,000 features to about 20,000 features, about 5,000 features to about 50,000 features, about 5,000 features to about 100,000 features, about 7,000 features to about 10,000 features, about 7,000 features to about 20,000 features, about 7,000 features to about 7,000 features, about 7,000 features to about 10,000 features, about 50,000 features, about 10,000 features, and about 10,000 features, about 10,000 features to about 100,000 features, about 20,000 features to about 50,000 features, about 20,000 features to about 100,000 features, or about 50,000 features to about 100,000 features. In some embodiments, the number of exposed sets of biochemical features can comprise about 10 features, about 100 features, about 500 features, about 1,000 features, about 5,000 features, about 7,000 features, about 10,000 features, about 20,000 features, about 50,000 features, or about 100,000 features. In some embodiments, the number of exposed sets of biochemical features can comprise at least about 10 features, about 100 features, about 500 features, about 1,000 features, about 5,000 features, about 7,000 features, about 10,000 features, about 20,000 features, or about 50,000 features. In some embodiments, the number of exposed sets of biochemical features can comprise up to about 100 features, about 500 features, about 1,000 features, about 5,000 features, about 7,000 features, about 10,000 features, about 20,000 features, about 50,000 features, or about 100,000 features.

In some embodiments, the exposure set biochemical signature may be obtained by assaying a biological sample from a plurality of subjects. In some embodiments, the biological sample may comprise a tooth, nail, or hair shaft sample. In some embodiments, the exposed set of biochemical features may be obtained using laser ablation-inductively coupled plasma mass spectrometry, laser induced breakdown spectrometry, mass spectrometry, raman spectrometry, immunohistochemical measurement, molecular labeling (e.g., with a fluorophore), nuclear magnetic resonance, chromatography, or any combination thereof. In some embodiments, laser ablation-inductively coupled plasma mass spectrometry measurements can measure one or more elemental chemistries. In some embodiments, the one or more elemental chemistries include chemistries of aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof, as described elsewhere herein, e.g., in table 1 and/or table 2. In some embodiments, the exposure group biochemical characteristic information may comprise exposure group time biochemical responses of the plurality of subjects. In some embodiments, the biochemical information may include a fluorescence image of the biological sample. In some embodiments, the exposure set biochemical features may comprise a spatial map of raman spectra of biological samples of a plurality of subjects. In some embodiments, the exposure histology characteristics may be associated with a disease or disorder. In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

In some embodiments, the plurality of chemicals is selected from the chemicals listed in table 1. In some embodiments, the plurality of chemicals comprises at least 50%, 60%, 70%, 80%, or 90% of the isotopes included in table 1.

TABLE 1 list of chemical substances

In some embodiments, the plurality of chemicals is selected from the chemicals listed in table 2. In some embodiments, the plurality of chemicals comprises at least 50%, 60%, 70%, 80%, or 90% of the isotopes included in table 2.

TABLE 2 list of chemicals

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In some embodiments, the one or more exposure-group chemical features are calculated from one or more dynamic exposure-group student features. The exposure histology features from the data analysis may include descriptive statistics or parameters that are used for subsequent statistics, machine learning, or artificial intelligence models. Such exposure set features may include standard descriptive metrics (such as mean, median, mode, and range) and/or associated measurements of error and/or variation (such as standard deviation, variance, confidence interval) and/or associated metrics of one or more dynamic exposure set features. The deriving of the exposure histology features may include application of a computational method to derive a linear slope, a nonlinear parameter describing a curvature of the one or more dynamic exposure histology features, a sudden change in an intensity of the one or more dynamic exposure histology features, a change in a baseline intensity of the one or more dynamic exposure histology features, a change in a frequency domain representation of the one or more dynamic exposure histology features, a change in a power spectral domain representation of the one or more dynamic exposure histology features, a recursive quantitative analysis parameter, a cross-recursive quantitative analysis parameter, a joint recursive quantitative analysis (joint recurrence quantification analysis) parameter, a multidimensional recursive quantitative analysis parameter, an estimate of a lypanuv spectrum or a maximum Lyapunov index, or any combination thereof.

In some embodiments, the one or more exposure histology features include a time dynamic measurement of the one or more dynamic exposure histology features. In some embodiments, the measurement of dynamic exposure histology comprises: linear slope, non-linear parameter describing curvature of one or more dynamic exposure set features, abrupt change in intensity of one or more dynamic exposure set features, change in baseline intensity of one or more dynamic exposure set features, change in frequency domain representation of one or more dynamic exposure set features, change in power spectral domain representation of one or more dynamic exposure set features, recursive quantitative analysis parameter, cross recursive quantitative analysis parameter, joint recursive quantitative analysis parameter, multidimensional recursive quantitative analysis parameter, estimate of lypanuv spectrum, maximum Lyapunov index, or any combination thereof.

In some embodiments, recursive quantitative analysis, cross-recursive quantitative analysis, joint-recursive quantitative analysis, multi-dimensional recursive quantitative analysis of one or more exposure set student characteristics may be used to derive descriptive statistics and/or parameters for training the predictive model described elsewhere herein.

In some cases, the recursion quantitative analysis parameters may include recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, and the number of most likely recursions.

In some cases, the one or more exposure-group chemical features may be derived from one or more attractors (fig. 19B, 20D), wherein the one or more attractors are generated from one or more dynamic exposure-group chemical features (fig. 19A, 20C). In some embodiments, one or more attractors are analyzed by potential energy analysis to create a potential energy data space.

In some embodiments, the dynamic relationship (fig. 19C) is established between the signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, layering, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof, of one or more attractors, which are analyzed and provided as features. In some embodiments, the dynamic relationship is determined by a Cross Convergence Map (CCM).

In some cases, a network may be constructed (fig. 21A-B), with one or more attractors based on similarity of time-exposed omics data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, layering, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in recursion times, most likely number of recursions, or any combination thereof of one or more attractors. In some embodiments, one or more exposed histology features of the network of one or more attractors are analyzed to determine an analysis of network connectivity, efficiency, feature importance, pathway importance, metrics based on correlation graph theory, or any combination thereof.

In some embodiments, the one or more exposure histology characteristics of the one or more dynamic exposure histology characteristics comprise phenotypic exposure histology characteristics. Phenotypic exposure histology characteristics may include: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof.

In some embodiments, the phenotypic exposure histology feature comprises a molecular phenotype. In some cases, the molecular phenotype is determined by an unsupervised analysis, wherein the unsupervised analysis includes clustering, dimension reduction, factor analysis, stacked auto-coding, or any combination thereof.

In some embodiments, CBD3 may comprise clinical phenotype data of a subject. In some embodiments, the clinical phenotype data comprises clinical metadata for a plurality of subjects. In some embodiments, the clinical metadata may include the age, sex, weight, height, blood type, vision, current disease, family disease history, or any combination thereof of the subject. In some embodiments, the subject's clinical metadata and exposure set biochemical features may be considered independent or combined to determine whether the subject is a suitable candidate for intervention.

In some embodiments, IODB5 may include intervention result information. In some embodiments, the intervention result information may include qualification criteria for one or more interventions. In some embodiments, the intervention may include an intervention of stage 1, 2, 3, or any combination thereof. In some embodiments, the intervention outcome information may include information of an intervention outcome classification of one or more subjects, including: positive, negative or no.

In some embodiments, TDB18 may include exposure histology characteristics that are at least partially related to pharmaceutical and nutraceutical treatments. In some embodiments, the exposure histology characteristics of the medicament and the nutritional product may include one or more reference exposure histology characteristics of a subject not suffering from the disease or disorder obtained by assaying one or more biological samples of one or more subjects, one or more pre-treatment exposure histology characteristics of a subject suffering from the disease or disorder, and one or more post-treatment exposure histology characteristics of a subject suffering from the disease or disorder.

In some embodiments, analyzing the one or more pre-treatment exposure histology characteristics and the differences between the one or more post-treatment exposure histology characteristics and the one or more reference dynamic profile biochemistry characteristics can be used to determine a treatment for one or more optimal drugs, nutraceuticals, or any combination thereof for treating a subject having a disease or disorder. In some embodiments, the differences in the one or more post-treatment exposure histology characteristics relative to the one or more reference exposure histology characteristics may provide a basis for recommending one or more drugs or nutrients to a subject suffering from a disease or disorder, and may be used in combination with the one or more subject's exposure histology characteristics to recommend optimal treatment to the one or more subjects to prevent or treat the one or more disease or disorder. In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof. In some embodiments, the criteria for optimal pharmaceutical or nutraceutical treatment may include supplementation of defects in the biochemical characteristics of the exposure group of a given subject of one or more subjects.

In some embodiments, the difference in one or more characteristics of the post-treatment exposure histology characteristics from one or more reference exposure histology characteristics. In some embodiments, the features include features of ensemble averages, variability metrics, movement averages, etc., or any combination thereof, as described elsewhere herein.

In some embodiments, the difference in characteristics comprises a difference of about 10% to about 100%. In some embodiments, the difference in characteristics comprises about 10% to about 20%, about 10% to about 30%, about 10% to about 40%, about 10% to about 50%, about 10% to about 60%, about 10% to about 70%, about 10% to about 80%, about 10% to about 90%, about 10% to about 100%, about 20% to about 30%, about 20% to about 40%, about 20% to about 50%, about 20% to about 60%, about 20% to about 70%, about 20% to about 80%, about 20% to about 90%, about 20% to about 100%, about 30% to about 40%, about 30% to about 50%, about 30% to about 60%, about 30% to about 70%, about 30% to about 80%, about 30% to about 90%, about 40% to about 50%, about 40% to about 60%, about 40% to about 70%, about 40% to about 80%, about 40% to about 90%, about 40% to about 100%, about 50% to about 60%, about 50% to about 70%, about 50% to about 80%, about 60% to about 80%, about 50% to about 80%, about 60% to about 100%, about 60% to about 60%, about 50% to about 80%, about 60% to about 100%, about 60% to about 60%, about 60% to about 100%. In some embodiments, the difference in characteristics may include a difference of about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 100%. In some embodiments, the difference in characteristics may include a difference of at least about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90%. In some embodiments, the difference in characteristics may include a difference of up to about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, about 90%, or about 100%.

The computer system may further include a communication interface 13 (e.g., a network adapter) for communicating with one or more other systems, as well as peripheral devices 15, such as cache memory, other memory, data storage, and/or an electronic display adapter. The memory 17, the storage unit 19, the interface 13, and the peripheral device 15 communicate with the CPU21 through, for example, a communication bus (solid line) such as a motherboard. The storage unit 19 may be a data storage unit (or data repository) for storing data. The computer system 11 may be operatively coupled to a computer network ("network") by means of a communication interface 13. The network may be the internet, an extranet, and/or an intranet in communication with the internet. In some embodiments, the network is a telecommunications and/or data network. The network may include one or more computer servers, which may implement distributed computing, such as cloud computing. In some cases, with the aid of computer system 11, the network may implement a peer-to-peer network, which may enable devices coupled to computer system 11 to act as clients or servers.

In some embodiments, EDB1, CDB3, IODB5, and TDB18 may be located on a network and accessed remotely by computer system 11. In some embodiments, EDB1, CDB3, IODB5, and TDB18 may reside on secure encrypted network servers that protect personal health information. In some embodiments, the EDB1, CDB3, IODB5, and TDB18 may be accessed remotely by one or more computer systems 11, either internal or external to the hospital network. In some embodiments, EDB1, CDB3, IODB5, and TDB18 may be accessed by one or more subjects through a secure network protocol to view recommendations and their personalized data.

The CPU21 may execute a series of machine readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as random access memory 17. The instructions may be directed to the CPU21, which may then program or otherwise configure the CPU21 to implement the methods of the present disclosure. Examples of operations performed by the CPU21 may include fetch, decode, execute, and write back.

The CPU21 may be part of a circuit, such as an integrated circuit. One or more other components of system 11 may be included in the circuit. In some embodiments, the circuit is an Application Specific Integrated Circuit (ASIC).

The storage unit 19 may store files such as drivers, libraries, and saved programs. The storage unit 19 may store user data such as user preferences and user programs. In some cases, computer system 11 may include one or more additional data storage units external to computer system 11, such as on a remote server in communication with computer system 11 via an intranet or the Internet.

The computer system 11 may communicate with one or more remote computer systems over a network. For example, computer system 11 may communicate with a remote computer system of a user (e.g., healthcare provider, subject, etc.). Examples of remote computer systems include personal computers (e.g., pocket PCs), tablet or tablet computers (e.g. iPad、Galaxy Tab), phone, smart phone (e.g. +.>iPhone, android device, < >>) Or a personal digital assistant. The user may access the computer system 11 via a network.

The methods described herein may be implemented by machine (e.g., a computer processor) executable code stored on an electronic storage location of computer system 11, such as, for example, memory 17 or electronic storage unit 19. The machine executable code or machine readable code may be provided in the form of software. During use, code may be executed by processor 21. In some embodiments, the code is retrieved from the memory unit 19 and stored on the random access memory 17 for ready access by the processor 21. In some cases, the storage unit 19 may be omitted and the machine executable instructions stored in the random access memory 17.

The code may be precompiled and configured for use with a machine having a processor adapted to execute the code, or may be compiled at runtime. The code may be provided in a programming language that is selectable to enable execution of the code in a precompiled or similarly compiled manner.

Aspects of the systems and methods provided herein, such as computer system 11, may be embodied in programming. Aspects of the technology may be considered to be "articles of manufacture" or "articles of manufacture," typically in the form of machine (or processor) executable code and/or associated data, which are carried or embodied in a machine-readable medium. The machine executable code may be stored on an electronic storage unit such as a memory (e.g., read only memory, random access memory, flash memory) or a hard disk. A "storage" type of medium may include any or all of the tangible memory of a computer, processor, etc., or associated modules thereof, such as various semiconductor memories, tape drives, disk drives, etc., which may provide non-transitory storage for software programming at any time. All or part of the software may sometimes be transmitted over the internet or various other telecommunication networks. Such communication may, for example, enable loading of software from one computer or processor into another computer or processor, such as from a management server or host computer into a computer platform of an application server. Thus, another type of medium that can carry software elements includes light waves, electric waves, and electromagnetic waves, such as those used across physical interfaces between local devices, through wired and optical landline networks, and over various airlink networks. Physical elements carrying such waves, such as wired or wireless links, optical links, etc., may also be considered as media carrying software. As used herein, unless limited to a non-transitory, tangible "storage" medium, terms such as computer or machine "readable medium" refer to any medium that participates in providing instructions to a processor for execution.

Thus, a machine-readable medium, such as computer-executable code, may take many forms, including but not limited to, tangible storage media, carrier wave media, or physical transmission media. Nonvolatile storage media includes, for example, optical or magnetic disks, such as any storage devices in any computer, etc., may be used to implement the databases shown in the figures, etc. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire; and optical fibers, including wires, that comprise a bus within a computer system. Carrier wave transmission media can take the form of electrical or electromagnetic signals, and also acoustic or light waves, such as those generated during Radio Frequency (RF) and Infrared (IR) data communications. Thus, common forms of computer-readable media include, for example: a floppy disk (floppy disk), a hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD, or DVD-ROM, any other optical medium, punch paper tape, any other physical storage medium with patterns of holes, RAM, ROM, PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, a cable or link transporting such a carrier wave, or any other medium from which a computer can read program code and/or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

The computer system 11 may include or be in communication with an electronic display 7 that includes a User Interface (UI) 9 for providing, for example, fluoroscopic image data, fluoroscopic intensity data, temporal distribution of inflammation, and machine learning categorization. Examples of UIs include, but are not limited to, graphical User Interfaces (GUIs) and web-based user interfaces.

The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithm may be implemented by means of software when executed by the central processing unit 21, as described elsewhere herein.

And (5) a prediction model.

Aspects disclosed herein may include a trained predictive model implemented on a computer-implemented exposure histology system 23. In some embodiments, the trained predictive model may be configured to provide a retrospective or prospective prediction of the likelihood of success of a subject undergoing intervention, pharmaceutical or nutraceutical treatment, or any combination thereof, to a subject in need thereof. In some embodiments, the trained predictive model may include statistics, machine learning, artificial intelligence classifiers, sets of classifiers, or any combination thereof.

The classifier may include one or more statistical, machine learning, or artificial intelligence algorithms. Examples of algorithms used may include Support Vector Machines (SVMs), simple bell-type classification, random forests, neural networks such as Deep Neural Networks (DNNs), recurrent Neural Networks (RNNs), deep RNNs, long term memory (LSTM) Recurrent Neural Networks (RNNs), or gate-controlled recursive units (GRUs), or other supervised or unsupervised machine learning, statistics, deep learning algorithms, shallow learning algorithms for classification and regression. The classifier may also involve the estimation of an integrated model consisting of multiple predictive models and utilize techniques such as gradient boosting in the construction of a gradient boosting decision tree. The classifier may be trained using one or more training data sets corresponding to patient data. In some embodiments, the one or more training data sets may include exposure set biochemical features, dynamic exposure set biochemical features, clinical metadata, clinical trial information, exposure set chemical features for pharmaceutical and nutraceutical treatments, or any combination thereof.

In some embodiments, the training data features may include dynamic exposure set student feature data of the subject generated from the biological sample. For each biological sample of a given subject, multiple locations of a reference line on the biological sample of the training subject may be sampled to generate measurements therefrom to obtain multiple exposure set students features. Each exposure set biochemical feature of the corresponding plurality of exposure set biochemical features corresponds to a different location of the corresponding plurality of locations, and each location of the corresponding plurality of locations represents a different growth period for the corresponding biological sample. Next, each respective location of the biological sample is analyzed (e.g., using a laser ablation-inductively coupled plasma mass spectrometer (LA-ICP-MS), a fluorescence image sensor, or a raman spectrometer) to obtain a plurality of traces. Each trace of the corresponding plurality of traces corresponds to an abundance measurement of the respective species, which is determined collectively over time from the corresponding plurality of dynamic exposure set biochemical features.

In some embodiments, the tag may include an intervention result, such as a positive response, a negative response (i.e., an adverse reaction), or a non-responder. The intervention outcome may include a temporal feature associated with a classification of positive, negative, or non-responder events, the response occurring during the intervention for a duration following administration of the therapy provided. Such a period of time may be, for example, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 10 days, about 2 weeks, about 3 weeks, about 4 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 6 months, about 8 months, about 10 months, about 1 year, or more than about 1 year.

The input features for training the classifier may be constructed by aggregating the data into the receiver or alternatively using one-hot encoding. The inputs may also include eigenvalues or vectors derived from the aforementioned inputs, such as calculated cross-correlations between exposure histology features or other measurements alone over a fixed period of time, and discrete derivatives or finite differences between successive measurements, as described elsewhere herein. Such a period of time may be, for example, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 10 days, about 2 weeks, about 3 weeks, about 4 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 6 months, about 8 months, about 10 months, about 1 year, or more than about 1 year.

Training records may be constructed from the observation sequence. Such sequences may include a fixed length to facilitate data processing. For example, the sequence may be zero-padded or selected as a separate subset of individual patient records

To train the classifier model (e.g., by determining the weights and correlations of the model) to generate real-time classification or prediction, a data set training model may be used. Such data sets may be large enough to generate statistically significant classifications or predictions. For example, the data set may include: a database of de-identified data that includes dynamic spectral biometric data and other clinical metadata measurements from a hospital or other clinical setting.

The data sets may be divided into subsets (e.g., discrete or overlapping) such as training data sets, development data sets, and test data sets. For example, the data set may be divided into a training data set comprising 80% of the data set and a test data set comprising 20% of the data set. The training data set may comprise about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the data set. The development dataset may comprise about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the dataset. The test data set may comprise about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80%, or about 90% of the data set. The training set (e.g., training data set) may be selected by randomly sampling a set of data corresponding to one or more subject cohorts to ensure independence of the sampling. Alternatively, a training set (e.g., training data set) may be selected by scaling the sampling for a set of data corresponding to one or more subject cohorts to ensure independence of the sampling.

To improve the accuracy of model predictions and reduce over-fitting of models, the dataset may be expanded to increase the number of samples in the training set. For example, data augmentation may include rearranging the order of observations in the training records. To accommodate the missing observed value data sets, missing value filling methods such as forward filling, backward filling, linear interpolation, and a multitasking gaussian process may be used. The data set may be filtered to remove confounding factors. For example, in the database, a portion of the subjects may be excluded.

In some embodiments, data science techniques, such as dropouts or regularization, may be used during training of the classifier to prevent overfitting. The neural network may include a plurality of sub-networks, each of which is configured to generate a classification or prediction of different types of output information (e.g., they may be combined to form an overall output of the neural network). The classifier may alternatively utilize statistical or correlation algorithms, including random forests, classification and regression trees, support vector machines, discriminant analysis, regression techniques, and sets and gradient-lifting variants thereof.

In some embodiments, the systems and methods of the present disclosure are implemented in a hospital setting for patients who are actively receiving treatment for their disease or disorder. When the classifier generates a classification or prediction of the best drug or nutritional, a notification (e.g., a prompt or alert) may be generated and transmitted to a healthcare provider, such as a doctor, nurse, or other member of an in-hospital patient treatment team. The notification may be transmitted via an automated telephone call, a Short Message Service (SMS) or Multimedia Message Service (MMS) message, an email, an in-dashboard prompt, or any combination thereof. The notification may include output information such as a prediction of the outcome of the intervention or the best medication or nutritional product.

To verify the performance of the classifier model, different performance metrics may be generated. For example, the area under the receiver operating curve (AUROC) may be used to determine the predictive power of the classifier. For example, the classifier may use an adjustable classification threshold such that the specificity and sensitivity are adjustable, and may use a Receiver Operating Curve (ROC) to identify different operating points for different values of specificity and sensitivity.

In some embodiments, the performance of the predictive method is evaluated by constructing a table to provide the frequency and overlap of predicted positive and actual positive cases, predicted positive and actual negative cases, predicted negative and actual negative cases, and/or predicted negative and actual positive cases. In some cases, the constructed table may be an confusion matrix. In some cases, cross-tabulation of confusion matrices may provide sensitivity, specificity, accuracy, and related performance metrics associated with the systems and methods described elsewhere herein at a given prediction threshold.

In some embodiments, such as when the data set is not large enough, cross-validation may be performed to evaluate the robustness of the classifier model across different training and testing data sets.

For calculating performance metrics such as sensitivity, specificity, accuracy, positive Predictive Value (PPV), negative Predictive Value (NPV), AUPRC, AUROC, or the like, the following definitions may be used. "false positives" may refer to results that erroneously yield a positive result or outcome (e.g., subjects are classified as positive responders to an intervention, but they have a negative or adverse effect on participation in the intervention). "true positive" may refer to a result that positively produces a positive result or outcome (e.g., a subject is classified as a positive responder to an intervention, and they have a positive response). "false negative" may refer to a result in which a negative result or outcome is produced (e.g., a subject is classified as a negative responder, where the subject is a non-responder or a positive responder after participation in a intervention). "true negative" may refer to a result in which a negative result or outcome is produced (e.g., a subject is classified as a negative responder and the subject has an adverse effect on the drug treatment of the intervention after participation in the intervention).

In some embodiments, the classifier may be trained until certain predetermined conditions of accuracy or performance are met, such as having a minimum expected value corresponding to a prediction accuracy measure. For example, the prediction accuracy measure may correspond to a correct prediction of the outcome of the intervention or the best drug or nutraceutical recommendation and/or selection. Examples of diagnostic accuracy measurements may include sensitivity, specificity, positive Predictive Value (PPV), negative Predictive Value (NPV), accuracy, precision and recall area under the curve (AUPRC) and area under the curve (AUC) of the Recipient Operating Characteristic (ROC) curve corresponding to features that detect or predict diagnostic accuracy of a disease or disorder.

For example, such predetermined conditions may be that the sensitivity of the predicted outcome of the intervention or the optimal pharmaceutical or nutraceutical recommendation and/or selection comprises a value of, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

As another example, such predetermined conditions may be that the predicted intervention outcome or optimal pharmaceutical or nutraceutical recommended and/or selected specificity comprises a value of, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

As another example, such predetermined conditions may be that the predicted intervention outcome or optimal pharmaceutical or nutraceutical recommended and/or selected Positive Predictive Value (PPV) comprises a value of, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

As another example, such predetermined conditions may be that the Negative Predictive Value (NPV) of an intervention outcome or optimal pharmaceutical or nutraceutical recommendation and/or selection comprises a value such as at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

As another example, such predetermined condition may be the area under the curve (AUC) (AUROC) of a Recipient Operating Characteristic (ROC) curve that predicts an outcome of an intervention or optimal pharmaceutical or nutraceutical recommendation and/or selection comprising values of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

As another example, such predetermined conditions may be that the accuracy and area under the recall curve (AUPRC) of the predicted outcome of the intervention or the optimal pharmaceutical or nutraceutical recommendation and/or selection comprises a value of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

In some embodiments, the trained classifier can be trained or configured to predict an intervention outcome or sensitivity of optimal pharmaceutical or nutraceutical recommendation and/or selection is at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

In some embodiments, the trained classifier can be trained or configured to predict the specificity of intervention outcome or optimal pharmaceutical or nutraceutical recommendation and/or selection to be at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

In some embodiments, the trained classifier can be trained or configured to predict an intervention outcome or optimal pharmaceutical or nutraceutical recommended and/or selected Positive Predictive Value (PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

In some embodiments, the Negative Predictive Value (NPV) that a trained classifier can be trained or configured to predict an intervention outcome or optimal pharmaceutical or nutraceutical recommendation and/or selection is at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%.

In some embodiments, the trained classifier can be trained or configured to predict an intervention outcome or an Area Under Curve (AUC) (AUROC) of a best pharmaceutical or nutraceutical recommended and/or selected Recipient Operating Characteristic (ROC) curve of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

In some embodiments, the trained classifier can be trained or configured to predict the accuracy rate and recall ratio area under the curve (AUPRC) of an intervention outcome or optimal pharmaceutical or nutraceutical recommendation and/or selection of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99.

In some embodiments, the classifier is a neural network or convolutional neural network. See Vincent et al 2010, "stacked noise reduction auto encoder: learning useful representations (Stacked denoising autoencoders: learning useful representations in a deep network with a local denoising criterion) "in deep networks with local denoising criteria (J Mach Learn Res) 11, pages 3371-3408; larochelle et al 2009, "exploration strategy for deep neural network training (Exploring strategies for training deep neural networks)", "machine learning study journal (J Mach Learn Res) 10", pages 1-40; and Hassoun,1995, basic principles of artificial neural networks (Fundamentals of Artificial Neural Networks), institute of technology of hemp and technology (Massachusetts Institute of Technology), each of which is hereby incorporated by reference.

SVM is described in Cristianii and Shawe-Taylor,2000, "support vector machine Profile (An Introduction to Support Vector Machines)" Cambridge university Press (Cambridge University Press), cambridge; boser et al, 1992, "training algorithm for best edge classifier (A training algorithm for optimal margin classifiers)", 5th ACM computer theory annual meeting discussion (Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory), american computer Association Press (ACM Press), pittsburgh, pa, pages 142-152; vapnik,1998, statistical learning theory (Statistical Learning Theory), wiley publishing company (Wiley), new York; mount,2001, & bioinformatics: sequence and genome analysis (Bioinformatics: sequence and genome analysis), cold spring harbor laboratory Press (Cold Spring Harbor Laboratory Press), cold spring harbor, N.Y.; duda, pattern Classification (Pattern Classification), second edition, 2001, john Wiley father-son company (John Wiley & Sons, inc.), pages 259, 262-265; and hasie, 2001, elements of statistical learning, schpringer publishing company (Springer), new york; and Furey et al, 2000, bioinformatics (Bioinformatics) 16, 906-914, each of which is hereby incorporated by reference in its entirety. When used for classification, the SVM separates a given binary labeled dataset from a hyperplane that is furthest from the labeled data. For the case where there may be no linear separation, the SVM may operate in conjunction with a "kernel" technique that automatically implements nonlinear mapping of feature space. The hyperplane found in the feature space by the SVM corresponds to a nonlinear decision boundary in the input space.

Decision trees are generally described in the following: duda,2001, pattern Classification (Pattern Classification), john Wiley father and son company (John Wiley & Sons, inc.), new York, pages 395-396, which is hereby incorporated by reference. The tree-based approach divides the feature space into a set of rectangles and then fits a model (e.g., a constant) in each rectangle. In some embodiments, the decision tree is a random forest regression. One specific algorithm that may be used is classification and regression trees (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and random forest. CART, ID3 and C4.5 are described in the following documents: duda,2001, pattern Classification (Pattern Classification), john Wiley father and son company (John Wiley & Sons, inc.), new York, pages 396-408 and 411-412, which are hereby incorporated by reference. CART, MART and C4.5 are described in the following documents: hastie et al, 2001, statistical learning foundation (The Elements of Statistical Learning), springer-Verlag, N.Y., chapter 9, which is hereby incorporated by reference in its entirety. Random forests are described in the following documents: breiman,1999, technical Report 567, university of california berkeley statistical series (Statistics Department, u.c. berkeley), month 9 1999, which is hereby incorporated by reference in its entirety.

Clustering (e.g., unsupervised and supervised clustering model algorithms) is described in Duda and Hart, pattern classification and scene analysis (Pattern Classification and Scene Analysis), 1973, john Wiley international publication (John Wiley & Sons, inc.), pages 211-256 of new york (hereinafter "Duda 1973"), which is hereby incorporated by reference in its entirety. As described in Duda 1973, section 6.7, the clustering problem is described as finding one of the natural groupings in the dataset. To identify natural groupings, two problems are solved. First, the manner in which the similarity (or dissimilarity) between two samples is measured is determined. This metric (similarity measure) is used to ensure that samples in one cluster are more similar than samples in other clusters. Second, a mechanism is determined to partition data into clusters using similarity measures. Similarity measures are discussed in Duda 1973, section 6.7, where one way to declare the start of a clustering study is to define a distance function and calculate a distance matrix between all pairs of samples in the training set. If the distance is a good measure of similarity, the distance between reference entities in the same cluster will be significantly smaller than the distance between reference entities in different clusters. However, clustering does not require the use of distance measures, as described by Duda 1973 at page 215. For example, a non-metric similarity function s (x, x ') may be used to compare the two vectors x and x'. In general, s (x, x ') is a symmetric function, with a larger value when x and x' are "similar" in some way. An example of a non-metric similarity function s (x, x') is provided on page 218 of Duda 1973. Once the method for measuring "similarity" or "dissimilarity" between points in a dataset is selected, clustering requires a standard function of the cluster quality of any partition of the measured data. The data is clustered using partitions of the data set that extremum the standard function. See Duda 1973, page 217. Section 6.8 of Duda 1973 discusses standard functions. Recently Duda et al, pattern Classification (Pattern Classification), 2 nd edition, john Wiley father-son company (John Wiley & Sons, inc.), new York have been published. Pages 537 to 563 describe clustering in detail. More information about clustering techniques can be found in groups in data, kaufman and rousseuw, 1990: cluster analysis profile (An Introduction to Cluster Analysis), wili publishing company, new York, n.y.); everitt,1993, cluster analysis (3 rd edition), weili Press, N.Y.; and Backer,1995, computer-aided reasoning in cluster analysis (Computer-Assisted Reasoning in Cluster Analysis), proteus Hall, inc. (Prentice Hall, upper Saddle River, new Jersey), of saddle river, N.J., each of which is hereby incorporated by reference. Specific exemplary clustering techniques that may be used in the present disclosure include, but are not limited to, hierarchical clustering (clustered using nearest neighbor algorithms, furthest neighbor algorithms, average connected algorithms, centroid algorithms, or sum of squares algorithms), k-means clustering, fuzzy k-means clustering algorithms, and Patrick gian-trick (Jarvis-Patrick) clustering. In some embodiments, the clustering includes unsupervised clustering, where no prior idea of what clusters should be formed when the training set is clustered is imposed.

Regression models, such as the multi-category logic model, are described in Agresti, categorical profile analysis brief (An Introduction to Categorical Data Analysis), 1996, john wili father company (John Wiley & Sons, inc.), new york, chapter 8, which is hereby incorporated by reference in its entirety. In some embodiments, the classifier utilizes a regression model disclosed in Hastie et al, 2001, statistical learning basis (The Elements of Statistical Learning), springer-Verlag, new York, which is hereby incorporated by reference in its entirety. In some embodiments, the gradient lifting model is used for a classification algorithm such as described herein; these gradient lifting models are described in Boehmke, bradley; greenwell, brandon (2019), "gradient lifting (Gradient Boosting)", machine learning together with R.Chapman and Hall (handles-On Machine Learning with R.Chapman & Hall), pages 221-245 ISBN 978-1-138-49568-5, which is hereby incorporated by reference in its entirety. In some embodiments, integrated modeling techniques are used for classification algorithms such as those described herein; these integrated modeling techniques are described in the implementation of the classification model herein, in the Zhou Zhihua (2012) integration method: basic and algorithms (Ensemble Methods: foundations and Algorithms) Chapman and Hall/CRC.ISBN 978-1-439-83003-1, which documents are hereby incorporated by reference in their entirety.

In some embodiments, the machine learning analysis is performed by executing one or more programs (fig. 1), including instructions for performing data analysis, e.g., one or more programs stored in non-persistent memory (i.e., RAM or ROM) 17 or storage unit 19 (i.e., hard disk). In some embodiments, the data analysis is performed by a system including at least one processor (e.g., processing core 21) and memory (e.g., one or more programs stored in non-persistent memory 17 or storage unit 19), the system including instructions to perform the data analysis.

Intervention hierarchical prediction model

In some embodiments, predictive model 26 may include one or more classifiers that may be trained to predict the probability of an intervention outcome for one or more subjects based on their exposure histology characteristics, as seen in fig. 2A. In some embodiments, the inputs for training the classifier may include clinical metadata 20 for the subject, exposure set student characterization data 22 for the subject, and corresponding intervention results 24 for the subject. In some embodiments, the intervention outcome for a given subject may include non-responders, adverse responders, or positive responders. In some embodiments, the predictive model may be initially trained on a dataset of one or more subjects who have received intervention therapy. In some embodiments, the training data set for training the classifier may be generated from, for example, clinical metadata of the subject and an exposure set student profile of the corresponding subject or features derived from the exposure set student profile (e.g., via RQA, described elsewhere herein) and intervention results.

In some embodiments, the clinical metadata features may include demographic information of the subject derived from Electronic Medical Records (EMR), physiological measurements, and intervention results. Furthermore, the training features may include clinical features such as, for example, dynamic exposure set biochemical feature data of certain ranges or categories. For example, a set of features collected from a given patient at a given point in time may collectively be the features of CBD3 and EDB1, which may be indicative of the health or status of the subject.

In some embodiments, the trained predictive model 32 may include a trained classifier, as described elsewhere herein, configured to provide predictions regarding the outcome of an intervention of a subject interested in participating in the intervention, as seen in fig. 2B. In some embodiments, clinical metadata 28 and corresponding exposure histology features 30 of one or more subjects may be fed as inputs to a trained predictive model 32. The trained predictive model 32 may then output probabilities 34 of predicting test results for the subject. In some embodiments, predicting the output probability of a test result for a subject may include classification of positive responders, negative or adverse responders, or non-responders. In some embodiments, the clinical metadata of the subject may include clinical metadata, such as the subject's age, gender, weight, height, blood type, vision, current disease, family disease history, or any combination thereof. Various machine learning techniques may be cascaded such that the output of the machine learning technique may also be used as an input feature for a subsequent layer or subsection of the classifier.

Optimal drug or nutraceutical selection predictive model

In some embodiments, predictive model 42 may include one or more classifiers, as described elsewhere herein, that may be trained to produce a trained predictive model 48 configured to predict optimal drugs or nutraceuticals to be administered for their given disease or condition based on features derived from the corresponding one or more exposure histology features of one or more subjects, as seen in fig. 3A, elsewhere herein. In some embodiments, the inputs for training the statistical, machine learning, and/or artificial intelligence classifier may include (a) a disease or disorder 36 of the subject; (b) Pre-treatment characteristics 38 of the subject derived from the one or more exposure histology characteristics; (c) an administered pharmaceutical or nutraceutical treatment 40; and (d) a percentage difference between the post-treatment profile of the one or more exposed omic profiles of the subject compared to one or more reference profiles derived from the exposed omic profile of the one or more profiles derived from the biochemical profile of the subject not suffering from the disease or disorder.

In some embodiments, the trained predictive model 48 may include a trained classifier, as described elsewhere herein, configured to provide a prediction 50 of a percentage difference between one or more exposure set chemical characteristics and one or more reference exposure set chemical characteristics after treatment of the subject, as seen in fig. 3B. In some embodiments, the trained predictive model may take as input the following: (a) clinical data 44 of the subject; (b) a pharmaceutical or nutraceutical treatment under consideration 46; and (c) one or more exposure histology characteristics of the subject prior to treatment. In some embodiments, one or more pharmaceutical or nutraceutical treatments may be considered.

Exposure histology cluster analysis

In some embodiments, one or more exposure set profiles of one or more subjects may be analyzed by a clustering method to explicitly classify or group subjects based on disease or disorder, as seen in fig. 7A-7B. One or more subjects are represented by an exposure set dataset cluster 97, 100 of one or more exposure set of chemical features 104. One or more exposure omics characteristics of the subject may be compared to the mean value 102 in the cohort for analysis or classification. Alternatively, exposure histology features may be used to subtype subjects before, during, or after clinical intervention to see which subjects may respond positively, negatively, or non-responsive to a given intervention.

The method.

Aspects disclosed herein may include methods of intervention optimization and recommendations for optimal pharmaceutical and/or nutraceutical recommendations for subjects suffering from a disease or disorder. In some embodiments, the methods described herein may be performed on the systems of the present disclosure described elsewhere herein.

Intervention optimization

In some embodiments, the methods of the present disclosure may include a method 60 of optimizing the outcome of an intervention by a subject, as seen in fig. 4. In some embodiments, the intervention may include an intervention of a phase I, phase II, phase III clinical trial study, or any combination thereof. In some embodiments, the method comprises the steps of: (a) Providing a trained predictive model, wherein the trained predictive model is trained 61 in clinical metadata, exposure histology characteristics, and corresponding intervention outcome information of one or more subjects; (b) detecting a feature derived from: biochemical features obtained from a biological sample from a subject seeking intervention, thereby producing retrospective exposure set biochemical features 62; (c) Predicting predicted intervention outcome information for the subject seeking intervention using the trained predictive model, wherein the retrospective exposure set biochemistry profile and clinical element of the subject seeking intervention are input information 64 of the trained predictive model; and (d) selecting the subject for intervention or excluding the subject from the intervention based at least in part on the predicted intervention outcome information of the subject 66. Alternatively, the intervention may comprise a community trial, which may or may not be performed in a clinical setting.

In some cases, one or more exposure histology characteristics of a subject may be used to determine the effectiveness or efficacy of a given intervention. For example, as seen in fig. 6A-D, performing an exposure histology analysis on a biological sample of a subject to generate exposure histology features may provide insight into the effectiveness of lead poisoning intervention. Fig. 6A shows an exposure group biochemical signature 81 with days as x-axis and exposure group signature intensity as y-axis. Fig. 6A shows the exposure set characteristics of tin, where the start point 77 and end point 79 of the intervention are indicated. Figures 6B through D show the exposure histology 85, 89, and 93 of lead, calcium, and magnesium, respectively, for one or more subjects without intervention compared to the comparison exposure histology of 83, 87, and 91 for subjects with intervention. For this particular example, a decrease in the lead exposure group biochemical profile can be observed as in fig. 6C, indicating that intervention can prove effective. However, it is also observed that other exposure set features (such as magnesium) increase may cause unwanted effects. In some embodiments, such exposed histology features of the biochemical features are used to observe the effectiveness of the intervention, or supplemental indications of unwanted increases or decreases in one or more exposed histology features for non-intervention targets recommend supplemental intervention. In some embodiments, such methods of intervention efficacy or effectiveness analysis are used to re-use the intervention for unintended applications or to help alleviate symptoms of a disease or disorder for which the intervention was not intended initially.

In some embodiments, a plurality of analysis modules are used to further analyze exposure histology features obtained from a subject receiving the intervention. In some embodiments, the first analysis module (module 1) focuses on the impact of clinical intervention on elemental signal intensity, where the time course of the intervention is established relative to the time of exposure to the set of biochemical characteristic signal intensities. The time-varying signal intensity of the exposed set of biochemical features can be traced back to the time of the clinical intervention, allowing the exposed set of biochemical feature signal intensities to be depicted as occurring before, concurrently with, or after the intervention. The exposure set biochemical signature signal intensities for these periods can be aggregated at the level of the subject via summary statistics, such as the average or median exposure set biochemical signature signal intensities detected during the period. The effects of intervention across subjects participating in the study can then be assessed by applying a traditional generic linear model, wherein the exposure set biochemical signature signal intensities before and after intervention across all subjects are compared to identify statistically significant differences in exposure set biochemical signature signal intensities corresponding to the effects of intervention. In this case, the statistical significance is assessed by a standard probabilistic hypothesis test.

In some embodiments, the second analysis module (module 2) may include a simultaneous impact that focuses on intervention on multiple exposure sets of chemical features (i.e., biochemical feature pathways). As with module 1, this module can be applied when establishing a time course of intervention relative to the time of exposure to the group biochemical signature signal intensities, allowing pre-intervention, simultaneous intervention and post-intervention intensities to be aggregated with descriptive statistics. The aggregated measurements taken at the individual level across the participants of the clinical trial are then pooled and used to construct a multivariate model. These may take the form of an unsupervised analysis such as principal component analysis, factor analysis or correlation methods whereby dimension reduction techniques are applied to derive metrics (principal components; factor scores) that summarize the exposed set of biochemical signature signal intensities of the multiple exposed set of biochemical signature pathways, which may then be used in a subsequent generic linear model to examine assumptions about clinical intervention, as shown in block 1. Alternatively, supervised dimension reduction techniques, such as partial least squares, partial least squares discriminant analysis, linear discriminant analysis, weighted quantiles and regression or bayesian kernel machine regression, may be used to directly relate the effect of intervention to changes in the exposed set of biochemical characteristic signal intensity indicators across multiple exposed sets of biochemical characteristics.

In some embodiments, the third analysis module (module 3) is used in cases where the exact time of the intervention is unknown, or in embodiments where the intervention effect is expected to have a time-lag effect; for example, if the change in the biochemical characteristic signal intensity of the exposed group does not manifest itself for a period of time following treatment, or if the time at which the treatment induces the change varies from person to person. In these cases, modeling strategies derived from economics of metering may be used; in particular, the implementation of a distributed hysteresis model and related nonlinear methods. These methods can be extended as shown in block 2 to include simultaneous assessment of the effects of intervention in multiple exposure sets of biochemical features, for example by implementing hysteresis weight quantiles and regression. Alternative analysis strategies may include switching point detection methods and related methodologies via moving average methods, self-excited threshold autoregressive (SETAR) models, autoregressive moving average models (ARMA), bayesian change point detection, and in particular methods related to longitudinal modeling and change point detection.

In some embodiments, the fourth analysis module (module 4), unlike the previous models, focuses on the analysis of signal dynamics derived from the analysis of longitudinal biochemical signature signals. Signal dynamics in this context refers to parameters derived from biochemical profile signal strength analysis, which may include estimates of parameters describing the underlying process, such as periodicity, entropy and stationarity. One method of achieving this goal may include applying Recursive Quantitative Analysis (RQA) to individual biochemical signature signals measured in each subject test participant. For each longitudinal measurement of a given biochemical signature spectral pathway, application of RQA may yield a plurality of quantitative measurements or features including any combination of recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, and the most likely number of recursions. These features can be extracted from multiple biochemical feature pathways and from interaction analysis between pathways via cross-recursive quantitative analysis (CRQA) for subsequent analysis, where features derived from each subject are combined and used to test the overall study range for effects related to intervention. This approach may be particularly useful in case-control study design where some subjects received placebo treatment, with the objective of distinguishing the difference in element signaling kinetics between intervention and placebo experimental conditions. In this case, the parameters derived from RQA/CRQA may be tested in a conventional analysis framework, for example via a generic linear model, to assess differences in signal parameters between treatment conditions. This approach may also be applicable to cases similar to those described in modules 1 and 2, where the time course of a given treatment may be known, and the goal is to distinguish between pre-treatment, simultaneous treatment, and post-treatment conditions. In this case, RQA/CRQA may be applied to a subset of complete elemental traces, corresponding to pre-treatment, simultaneous treatment, and post-treatment conditions; or variants applied to RQA/CRQA, using window binning techniques, may be used to derive longitudinal measurements of RQA/CRQA parameters, which may then be analyzed using the methods described in modules 1, 2, or 3. In any combination of these conditions, features derived from dynamic signal analysis via RQA and related methods can also be used in both supervised and unsupervised dimension reduction techniques, as described in block 2, for subtype typing of subjects based on biochemical profile. These methods may be used prior to intervention to achieve the goal of identifying patient/participant subtypes, or may be used after treatment in order to correlate the effects of clinical intervention with associated metabolic pathways. As can be seen in fig. 8, a hair sample provided by a subject suffering from Autism Spectrum Disorder (ASD) was analyzed via this method. The resulting projections illustrate the derivation of three ASD subtypes from biochemical profile analysis-in this case, by applying k-means clustering to RQA of the elemental spectra. The description of the subject type may then be used for subsequent clinical analysis and decision making.

Pharmaceutical and nutraceutical recommendations

In some embodiments, the methods of the present disclosure may include a method 68 of recommending optimal drugs and/or nutraceuticals for a subject suffering from a disease or condition, as seen in fig. 5. In some embodiments, the method may include the steps of: (a) detecting a feature derived from: one or more biochemical features obtained from one or more biological samples from one or more subjects not suffering from the disease or disorder, thereby producing one or more reference exposure set chemical features 70; (b) detecting a feature derived from: one or more biochemical features obtained from one or more biological samples from a subject suffering from a disease or disorder, thereby producing one or more pre-treatment exposure histology features 71; (c) Administering a treatment 72 to a subject suffering from the disease or disorder; (d) Detecting one or more exposure histology characteristics obtained from one or more biological samples from one or more subjects having a disease or disorder that recurs after a period of time after receiving the treatment, thereby producing one or more post-treatment exposure histology characteristics 73; (e) Determining a difference 74 between the one or more reference exposure histology characteristics of the one or more subjects not suffering from the disorder or disease, the one or more pre-treatment exposure histology characteristics of the one or more subjects suffering from the disorder or disease, and the one or more post-treatment exposure histology characteristics of the one or more subjects suffering from the disorder or disease; and (f) selecting one or more optimal treatments based at least in part on the determined differences between the one or more reference exposure set mathematical characteristics, one or more pre-treatment exposure set mathematical characteristics, and one or more post-treatment exposure set mathematical characteristics, wherein the one or more optimal treatments are selected based on the determined differences meeting predetermined criteria 75.

In some embodiments, the differences between one or more reference exposure set characteristics for one or more subjects not suffering from a disorder or disease, one or more pre-treatment exposure set characteristics for one or more subjects suffering from a disease or disorder, and one or more post-treatment exposure set characteristics for one or more subjects suffering from a disease or disorder may be achieved by way of predictive models as described elsewhere herein.

In some embodiments, the method of recommending optimal drugs and/or nutraceuticals for a subject is used to analyze the impact and/or importance of exposure group biochemical trait pathways impacted by an intervention (e.g., drugs and/or nutraceuticals), as seen in fig. 16-18. In some cases, the characteristics of the exposed group biochemical characteristic pathways of the intervention group and the control group may be compared. In some embodiments, the intervention includes the use of probiotics (fig. 16), gluten-free diet (fig. 17), cannabidiol, zinc (fig. 18), or any combination thereof. In some cases, the intervention may be delivered as an infant formula (fig. 18). The characterization results of the exposure set biochemical characterization pathway analysis may include recommended intervention, with the exposure set biochemical characterization pathway being complementary to or affected in a similar manner to the disease or disorder. In some embodiments, the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder. In some embodiments, the disease or disorder includes cancer, e.g., pediatric cancer, lung cancer, and the like. In some embodiments, the disease or disorder includes, for example, autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

Determining health results

In some embodiments, the present disclosure describes a method 2301 for outputting one or more quantitative measures of one or more dynamic exposure histology characteristics of a subject, as seen in fig. 22. The method may include the operations of: (a) receiving a biological sample 2300 from a subject; (b) Determining one or more dynamic exposure team chemical characteristics 2302 from a biological sample of a subject; (c) Computing a first one or more features of the one or more dynamic exposure sets of chemical features, wherein each feature of the one or more features comprises one or more quantitative metrics 2304; and (d) outputting one or more quantitative metrics 2306 for one or more characteristics of the subject.

In some embodiments, the method further comprises outputting a health result for the subject based at least in part on an association of the normalized score of the one or more features of the subject with the normalized score of the one or more quantitative measures of the one or more features of the second group of subjects. In some cases, the one or more quantitative measures of the one or more characteristics of the second group of subjects may be stored in a database, wherein the database is a local server or a cloud-based server. In some cases, the health outcome may include diagnosis of a disease state, a disease subtype, a clinical subtype, a non-clinical subset associated with physiology, a anthropometric index, a behavioral index, a socioeconomic index, a body mass index, a wisdom quotient, a socioeconomic status, or any combination thereof. In some embodiments, the one or more features include a time dynamic measurement of one or more dynamic exposure histology features.

In some cases, the measurement of dynamic exposure histology may include: linear slope, non-linear parameter describing curvature of one or more dynamic exposure histology features, abrupt change in intensity of one or more dynamic exposure histology features, change in baseline intensity of one or more dynamic exposure histology features, change in frequency domain representation of one or more dynamic exposure histology features, change in power spectral domain representation of one or more dynamic exposure histology features, recursive quantitative analysis parameters, cross-recursive quantitative analysis parameters, joint recursive quantitative analysis parameters, multi-dimensional recursive quantitative analysis parameters, estimate of lypanuv spectrum, maximum Lyapunov index, or any combination thereof. In some embodiments, the disease state comprises Autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

In some cases, the one or more characteristics of the one or more dynamic exposure histology characteristics may include a phenotypic characteristic, wherein the phenotypic characteristic includes: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof. In some embodiments, the one or more features are derived from one or more attractors. In some cases, one or more dynamic exposure histology characteristics may be measured by mass spectrometry, laser ablation-inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, raman spectroscopy, immunohistochemical fluorescence, or any combination thereof. In some cases, the biological sample may include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof. In some cases, the phenotypic characteristic may include a molecular phenotype. In some cases, the molecular phenotype may be determined by an unsupervised analysis, where the unsupervised analysis may include clustering, dimension reduction, factor analysis, stacked auto-coding, or any combination thereof.

In some embodiments, the recursive quantitative analysis parameters include a rate of recursion, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal length, a recursive trend, a layering, a capture time, a maximum vertical length, shannon entropy in vertical length, an average recursive time, shannon entropy in number of recursions, and a number of most likely recursions. In some embodiments, the method further comprises analyzing the one or more attractors by potential energy analysis to create a potential energy data space. In some cases, the one or more dynamic exposure set of chemical characteristics of the subject may include dynamic exposure set data retrospectively, prospective, or any combination thereof. In some cases, the method may further include analyzing a dynamic relationship between a signal recursion rate, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy of diagonal lengths, a recursion trend, layering, a capture time, a maximum vertical line length, shannon entropy of vertical line lengths, an average recursion time, shannon entropy in a number of recursions, a number of most likely recursions, or any combination thereof, of the one or more attractors. In some embodiments, the dynamic relationship is determined by a Cross Convergence Map (CCM). The method of claim 1, further comprising reducing the one or more dynamic exposure histology features to reduced one or more exposure histology dynamic features. In some cases, the method may further comprise constructing a network of one or more attractors based on the similarity of: the time exposure of the one or more attractors includes a data signal recursion rate, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in the diagonal length, a recursion trend, a stratification, a capture time, a maximum vertical line length, shannon entropy in the vertical line length, an average recursion time, shannon entropy in the number of recursions, a number of most likely recursions, or any combination thereof. In some cases, the method may further include analyzing one or more characteristics of the network of one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, metrics based on correlation graph theory, or any combination thereof.

Phenotype data is predicted.

In some embodiments, the present disclosure describes a method 2401 for outputting a prediction of phenotype data of one or more subjects, as seen in fig. 23. The method may include the operations of: (a) Receiving one or more biological samples and phenotypic data 2400 from a first group of subjects; (b) Determining a first set of exposure set of biological features 2402 from one or more biological samples of a first set of subjects; (c) Computing a first set of features 2404 of the first set of exposed set of features; (d) Training a predictive model 2406 with a first set of characteristics and phenotypic data for a first set of subjects; (e) Receiving one or more biological samples 2408 from a second group of subjects different from the first group of subjects; (f) Determining a second set of exposure set biological characteristics 2410 from one or more biological samples of a second set of subjects; (g) Calculating a second set of features 2412 from the second set of exposure sets of features; and (h) outputting predictions 2414 of the second set of subject phenotype data determined by inputting the second set of features into the trained predictive model.

In some embodiments, the first and second sets of features include one or more quantitative measures. In some cases, the one or more quantitative measures may include time-dynamic measurements of the first and second sets of exposure set characteristics. In some cases, the measurement of time dynamics may include: linear slope, non-linear parameters describing curvature of the first and second sets of exposed set of chemical features, abrupt changes in intensity of the first and second sets of exposed set of chemical features, changes in baseline intensity of the first and second sets of dynamic exposed set of chemical features, changes in frequency domain representations of the first and second sets of dynamic exposed set of chemical features, changes in power spectral domain representations of the first and second sets of exposed set of chemical features, recursive quantitative analysis parameters, cross-recursive quantitative analysis parameters, joint recursive quantitative analysis parameters, multidimensional recursive quantitative analysis parameters, lypanuv spectra, or estimates of maximum Lyapunov indices, or any combination thereof.

In some embodiments, the first and second sets of features comprise phenotypic features, wherein the phenotypic features may comprise a disease state or a health state, wherein the disease state comprises: autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof. In some cases, the phenotypic characteristics may further include: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof.

In some cases, the first and second sets of features may be represented as or derived from one or more attractors. In some embodiments, the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof. In some embodiments, the first and second sets of exposure set of chemical characteristics are measured by mass spectrometry, laser ablation-inductively coupled plasma mass spectrometry, raman spectroscopy, immunohistochemical fluorescence, or any combination thereof. In some cases, the one or more biological samples of the first and second subjects can include hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

In some embodiments, the phenotypic characteristic comprises a molecular phenotype. In some cases, the molecular phenotype may be determined by an unsupervised analysis, wherein the unsupervised analysis includes clustering, dimension reduction, factor analysis, stacked auto-coding, or any combination thereof. In some cases, the recursive quantitative analysis parameters may include a rate of recursion, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal lengths, a recursive trend, a layering, a capture time, a maximum vertical length, shannon entropy in vertical lengths, an average recursive time, shannon entropy in number of recursions, and a number of most likely recursions, or any combination thereof.

In some cases, the method may further comprise analyzing the one or more attractors by potential energy analysis, thereby creating a potential energy data space. In some embodiments, the first and second sets of exposure set mathematical characteristics include exposure set mathematical data retrospectively, prospectively, or any combination thereof.

In some embodiments, the method further analyzes the dynamic relationship between the recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof, of the one or more attractors. In some embodiments, the dynamic relationship is determined by a Cross Convergence Map (CCM).

In some cases, the method may further include constructing a network of one or more attractors based on similarity of time-exposure histology data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, layering, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in recursion number, and most likely number of recursions of the one or more attractors, or any combination thereof. In some embodiments, the method further comprises analyzing one or more characteristics of the network of one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, metric feature importance based on a correlation graph theory, pathway importance, or any combination thereof.

Although the above steps illustrate each method or set of operations, one of ordinary skill in the art will recognize many variations based on the teachings described herein. The steps may be completed in a different order. Steps may be added or omitted. Some steps may include sub-steps. Many steps can be repeated as much as possible.

One or more steps of each method or set of operations may be performed by circuitry described herein, for example, one or more processors or logic circuits, such as programmable array logic for a field programmable gate array. The circuitry may be programmed to provide one or more steps of each method or set of operations described elsewhere herein, and the program may include programming steps of program instructions or logic circuitry (such as programmable array logic or field programmable gate array) stored, for example, on a computer readable memory.

Examples

Example 1: the exposure set student profile is obtained from a biological sample.

Time-exposed group biochemical features were obtained from biological samples using laser ablation-inductively coupled-plasma mass spectrometry (LA-ICP-MS). For this particular example, a hair shaft sample was used and chemical exposure set data was obtained. Hair shafts are collected from subjects and pre-treated by washing the sample with one or more solvents and/or surfactants and then drying. Specifically, the hair shaft sample is washed in TRITON X-100 and ultra-pure metal-free water (e.g., MILLI-Q water) and dried in an oven overnight (e.g., at 60 ℃). The pretreatment further includes preparing the hair shaft for measurement by placing the hair shaft on a slide (e.g., a microscope slide) with an adhesive film (e.g., double-sided tape). The hair shafts are secured such that the hair shafts are substantially straight. The slides with the hair shafts were then placed into a LA-ICP-MS system for analysis.

The analysis begins with the LA-ICP-MS system, completing the pre-ablation step, wherein the hair shaft sample is ablated to remove surface debris and/or impurities from the hair shaft. Pre-ablation is performed with low laser energy, about 10% of which only releases particles at the surface of the hair shaft sample, but not below the surface. Using a laser wavelength of 193nm and 0.6J/cm ² The following laser energy is used for pre-ablation (for example, the laser energy is 0.6J/cm2, 0.5J/cm2, 0.4J/cm) ² 、0.3J/cm ² 、0.2J/cm ² Or 0.1J/cm ² )。

After pre-ablation, the LA-ICP-MS system irradiates the hair shaft sample with a laser energy of 1.8J/cm2 or less, a laser spot size of 10 μm to 30 μm, and obtains a chemical sample along a corresponding location of the hair shaft sample reference line. Each location along the hair shaft corresponds to approximately 2.2 hours or 130 minutes of subject life. The spatially irradiated laser irradiates the entire length of the hair shaft, producing particles at discrete locations of the hair shaft, and then ionizing the particles with an inductively coupled plasma. The obtained chemical samples are then analyzed by mass spectrometry to provide corresponding chemical data, and to read what chemicals are present in what amounts at a given spatial location. The process is repeated and chemical data is collected sequentially at a plurality of locations along the hair shaft from the root of the hair shaft to the tip of the hair shaft furthest from the root. The position data, the indicated times and the corresponding isotopes present at each position of the hair shaft are correlated for further analysis.

The output data is further analyzed and processed by peak removal to remove extreme peaks in the exposed set of biochemical features and to smooth the data. Outliers were identified by calculating the average absolute difference between each adjacent measurement along the hair shaft. Values indicating that the average absolute difference from the previous point exceeds the average value by three standard differences are marked as abnormal values. These outliers were then replaced by a moving median filter that calculated the running median of the original exposure set biochemical features in the receivers of 10 adjacent data points.

The processed data can then be used in various bioinformatic analysis methods to identify the impact of clinical intervention on elemental signal intensity and signal dynamics.

Example 2: dynamic molecular profiling in dental samples for determining disease risk.

Using the methods and systems of the present disclosure, molecular spectra in dental samples are generated and then analyzed to determine a subject's risk of disease. Typically, the time dynamics of a biological response (e.g., inflammation) is found imprinted in a sample (e.g., a tooth sample) and can be analyzed to determine the disease risk of a subject. Dynamic molecular profiles of C-reactive protein (CRP), a marker of inflammation, were generated. Dynamic time series profiles of CRP and inflammation were generated in two groups of children including fetal (prenatal) development and childhood using dental biomarkers-the first group had autism spectrum disorders (37 cases) and the second group did not have autism spectrum disorders (77 controls). The time-series CRP spectra were analyzed to reveal novel features of CRP signal dynamics to accurately distinguish autism cases from control cases. For example, the spectrum of inflammation that exists before 1 year old varies greatly between cases and controls. In contrast, clinical diagnosis of autism is generally determined around 3 to 4 years of age.

One deciduous tooth sample was obtained from each child subject. Tooth samples were cut, decalcified, and immunohistochemical staining (e.g., dentin) was applied to the tooth samples. Immunohistochemical staining effectively mapped C-reactive proteins (molecular markers of inflammation) along the growth cycle of tooth samples to create a time profile of inflammation during fetal and postnatal periods. The time spectrum is analyzed using the machine learning algorithm of the present disclosure to train a highly accurate classifier to determine disease risk (e.g., autism).

Fig. 12 shows an example of a daily C-reactive protein profile of a subject over time, with the y-axis indicating CRP intensity and the x-axis indicating developmental age. The developmental age of a pediatric subject includes a period of time from mid-gestation (e.g., from 140 days before birth, when the subject is in fetal stage) to about 6 months of gestational age. As shown in fig. 12, the spectrum of inflammation (as shown by CRP intensity) in autistic children with high fetal CRP intensity.

Examples

Embodiment 1. A computer-implemented exposure histology system, the system comprising: (a) An exposure group biochemical features database (EDB) comprising exposure group chemical features of a plurality of subjects; and (b) an intervention result database (IODB) comprising information about intervention result information for at least one period of at least one intervention; (c) a computer processor comprising: (i) An association software module communicatively coupled with the EDB and the IODB, wherein the association software module is programmed to determine an association between the exposure histology characteristic, the clinical phenotype information, and the intervention outcome information for at least one of the plurality of subjects, and (ii) a recommendation software module communicatively coupled with the EDB and the IODB, the recommendation software module being programmed to provide an intervention recommendation provision for at least one of the plurality of subjects based at least in part on the exposure histology characteristic, the clinical phenotype information, the intervention outcome information, and the association between the exposure histology characteristic, the clinical phenotype information, and the intervention outcome information for at least one of the plurality of subjects.

Embodiment 2. The system of embodiment 1, further comprising a Clinical Database (CDB) comprising clinical phenotype information for the plurality of subjects.

Embodiment 3. The system of embodiment 1, wherein the exposure set biochemical features comprise at least 100, at least 1,000, or at least 10,000 different exposure set biochemical features.

Embodiment 4. The system of embodiment 1, wherein the intervention outcome information comprises a classification of non-responders, adverse responders, and positive responders to the at least one intervention.

Embodiment 5. The system of embodiment 1 wherein the intervention result comprises one or more inclusion criteria or exclusion criteria for the at least one intervention.

Embodiment 6. The system of embodiment 1, wherein the exposure set biochemical signature is obtained by assaying a biological sample of the plurality of subjects.

Embodiment 7. The system of embodiment 6, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, a physiological parameter, or any combination thereof.

Embodiment 8. The system of embodiment 6, wherein the determining comprises obtaining a mass spectrometry measurement, a laser ablation-inductively coupled plasma mass spectrometry measurement, a laser induced breakdown spectroscopy measurement, a raman spectroscopy measurement, an immunohistochemical measurement, or any combination thereof.

Embodiment 9. The system of embodiment 8, wherein the mass spectrometry measurement comprises a measurement of one or more chemicals.

Embodiment 10. The system of embodiment 9, wherein the one or more chemicals comprises aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

Embodiment 11. The system of embodiment 1, wherein the exposure set chemical profile comprises a profile of a dynamic time biochemical reaction of the plurality of subjects.

Embodiment 12. The system of embodiment 1, wherein the exposure set biochemical features comprise a fluorescence image of the biological sample.

Embodiment 13. The system of embodiment 1, wherein the exposed set of biochemical features comprises a spatial map of a raman spectrum of the biological sample.

Embodiment 14. The system of embodiment 1, wherein the exposure set biochemical trait is associated with a disease or disorder.

Embodiment 15. The system of embodiment 14, wherein the disease or disorder comprises Autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

Embodiment 16. The system of embodiment 1 wherein the exposure histology characteristics are analyzed using a trained classifier to determine an association with the disease or condition.

Embodiment 17. The system of embodiment 16 wherein the trained classifier is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

Embodiment 18. A method for selecting a subject for intervention, the method comprising: (a) Providing a trained predictive model, wherein the trained predictive model is trained in clinical metadata, exposure set of features, and corresponding intervention outcome information for one or more subjects; (b) Detecting a biochemical signature obtained from a biological sample from a subject seeking the intervention, thereby producing a prospective exposure histology signature; (c) Predicting the predicted intervention outcome information of a subject seeking the intervention using the trained predictive model, wherein exposure histology features and clinical elements of the subject seeking the intervention are input information of the trained predictive model; and (d) selecting or excluding the subject from the intervention based at least in part on the predicted intervention outcome information for the subject.

Embodiment 19. The method of embodiment 18, wherein the biochemical features are obtained by assaying a biological sample of the subject.

Embodiment 20. The method of embodiment 19, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

Embodiment 21. The method of embodiment 19, wherein the determining comprises collecting data from a laser ablation-inductively coupled plasma mass spectrometry measurement, a laser induced breakdown spectroscopy measurement, a raman spectroscopy measurement, an immunohistochemical measurement, or any combination thereof.

Embodiment 22. The method of embodiment 21, wherein the laser ablation-inductively coupled plasma mass spectrometry measurement comprises a measurement of one or more elemental chemicals.

Embodiment 23. The method of embodiment 22, wherein the one or more elemental chemistries include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

Embodiment 24. The method of embodiment 18, wherein the biochemical features comprise a fluorescence image of the biological sample.

Embodiment 25. The method of embodiment 18, wherein the biochemical features comprise a spatial map of a raman spectrum of the biological sample.

Embodiment 26. The method of embodiment 18, wherein the biochemical trait is associated with a disease or disorder.

Embodiment 27. The method of embodiment 26, wherein the disease or disorder comprises a disease or disorder of psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof.

Embodiment 28. The method of embodiment 18 wherein the trained predictive model is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

Embodiment 29 the method of embodiment 18, further comprising incorporating the subject into the intervention when the subject is selected to perform the intervention.

Embodiment 30 the method of embodiment 18, further comprising assessing the subject for another intervention when the subject is excluded from the interventions.

Example 31 a method of selecting an optimal treatment for a disease or condition in a subject in need thereof, the method comprising: (a) Detecting one or more biochemical features obtained from one or more biological samples from one or more subjects not suffering from the disease or disorder, thereby generating one or more reference exposure set of chemical features; (b) Detecting a characteristic of one or more biochemical features obtained from one or more biological samples from the subject suffering from the disease or disorder, thereby producing one or more pre-treatment exposure histology features; (c) Administering a treatment to the subject suffering from the disease or disorder; (d) Detecting a characteristic of one or more biochemical characteristics obtained from one or more biological samples from the one or more subjects suffering from the disease or disorder after a period of time has elapsed from receiving the treatment, thereby generating one or more post-treatment exposure histology characteristics; (e) determining the difference between: the one or more reference exposure histology characteristics of the one or more subjects not suffering from the disorder or disease, the one or more pre-treatment exposure histology characteristics of the one or more subjects suffering from the disease or disorder, and the one or more post-treatment exposure histology characteristics of the one or more subjects suffering from the disease or disorder; and (f) selecting one or more optimal treatments based at least in part on the one or more reference exposure histology characteristics, the determined differences between the one or more pre-treatment exposure histology characteristics and the one or more post-treatment exposure histology characteristics, wherein the one or more optimal treatments are selected based on the determined differences meeting predetermined criteria.

Embodiment 32. The method of embodiment 31 wherein the optimal treatment may comprise a drug, a nutraceutical, or any combination thereof.

Embodiment 33. The method of embodiment 31 wherein the predetermined criteria comprises differences between the one or more pre-treatment exposure histology characteristics and the one or more post-treatment exposure histology characteristics and the one or more reference exposure histology characteristics.

Embodiment 34. The method of embodiment 33, wherein the period of time comprises at least about 1 hour, at least about 1 day, at least about 1 week, at least about 1 month, at least about 1 year, or any combination thereof.

Embodiment 35. The method of embodiment 33, wherein the difference comprises a change in the one or more post-treatment exposure histology characteristics to at least 10% of the one or more reference exposure histology characteristics.

Embodiment 36. The method of embodiment 31, wherein the pre-treatment exposure histology, the post-treatment exposure histology, or any combination thereof is obtained by assaying a biological sample of the subject.

Embodiment 37. The method of embodiment 36, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, a physiological parameter, or any combination thereof.

Embodiment 38. The method of embodiment 36, wherein the determining comprises obtaining a laser ablation-inductively coupled plasma mass spectrometry measurement, a laser induced breakdown spectrometry measurement, a raman spectrometry measurement, an immunohistochemical measurement, or any combination thereof.

Embodiment 39. The method of embodiment 38, wherein the laser ablation-inductively coupled plasma mass spectrometry comprises measurement of one or more elemental chemicals.

Embodiment 40. The method of embodiment 39, wherein the one or more elemental chemistries include aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

Embodiment 41. The method of embodiment 31, wherein the biochemical features comprise a fluorescence image of the biological sample.

Embodiment 42. The method of embodiment 31, wherein the biochemical features comprise a spatial map of a raman spectrum of the biological sample.

Embodiment 43. The method of embodiment 31, wherein the disease or disorder comprises a psychological, cardiac, gastroenterology, pulmonary, neurological, circulatory, nephrology, or any combination thereof disease or disorder.

Embodiment 44. The method of embodiment 31, wherein the differences between the reference exposure histology features, pre-treatment exposure histology features, post-treatment exposure histology features, or any combination thereof are analyzed using a trained classifier.

Embodiment 45. The method of embodiment 44 wherein the trained classifier is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

Embodiment 46. A method for assessing the effect of an intervention in a plurality of subjects, the method comprising: for each respective subject of the plurality of subjects, sampling at each respective location of a plurality of locations along a reference line on a corresponding biological sample associated with a chemical kinetics of the respective subject, thereby obtaining a corresponding plurality of chemical samples of the respective subject, each chemical sample of the plurality of chemical samples corresponding to a different location of the plurality of locations, and each location of the plurality of locations representing a different growth period of the corresponding biological sample associated with a chemical kinetics, wherein the plurality of locations comprises: one or more positions representing a growth period prior to the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each respective subject of the plurality of subjects, analyzing the respective plurality of chemical samples of the respective subject with a mass spectrometer to obtain a respective first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration over time of a respective chemical of a plurality of chemicals, the concentration being determined jointly from the plurality of chemical samples, generating, for each of one or more chemicals of the plurality of chemicals, a respective isotope dataset comprising: a set of pre-intervention features corresponding to a period of time during which the biological sample prior to the intervention grows, the set of pre-intervention features including, for each respective subject of the plurality of subjects, one or more features derived from a concentration of the respective chemical substance measured from the one or more locations representative of a period of time of growth prior to the intervention, a set of intervention features corresponding to a period of time during which the biological sample grows, the set of intervention features including, for each respective subject of the plurality of subjects, one or more features derived from a concentration of the respective chemical substance measured from the one or more locations representative of a period of time of growth during the intervention, and a set of post-intervention features corresponding to a period of time during which the biological sample after the intervention grows, the set of post-intervention features including, for each respective subject of the plurality of subjects, one or more features derived from a concentration of the respective chemical substance measured from the one or more locations representative of a period of time of growth after the intervention; and for each of the one or more chemicals, using the corresponding isotope data set to evaluate a change in chemical dynamics in response to the intervention.

Embodiment 47. The method of embodiment 46, wherein the evaluating comprises probability hypothesis testing each of the one or more chemicals using (i) the set of pre-intervention features and (ii) one or both of the set of intervention features and the set of post-intervention features.

Embodiment 48. A method for assessing the effect of an intervention in a plurality of subjects, the method comprising: for each respective subject of the plurality of subjects, sampling at each respective location of a plurality of locations along a reference line on a corresponding biological sample associated with a chemical kinetics of the respective subject, thereby obtaining a corresponding plurality of chemical samples of the respective subject, each chemical sample of the plurality of chemical samples corresponding to a different location of the plurality of locations, and each location of the plurality of locations representing a different growth period of the corresponding biological sample associated with a chemical kinetics, wherein the plurality of locations comprises: one or more positions representing a growth period prior to the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each respective subject of the plurality of subjects, analyzing the respective plurality of chemical samples of the respective subject with a mass spectrometer to obtain a respective first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration over time of a respective chemical of a plurality of chemicals, the concentration being determined jointly from the plurality of chemical samples, generating respective aggregated isotope datasets for groups of two or more chemicals of a plurality of chemicals, comprising: a set of pre-intervention features corresponding to biological sample growth prior to the intervention, the set of pre-intervention features comprising values of one or more dimension-reducing components, the values of the one or more dimension-reducing components forming, for each respective subject of the plurality of subjects, features derived from concentrations of each of the two or more chemicals measured from the one or more locations representative of a period of growth prior to the intervention, and a set of post-intervention features corresponding to biological sample growth during the intervention, the set of pre-intervention features comprising values of one or more dimension-reducing components, the values of the one or more dimension-reducing components forming, for each respective subject of the plurality of subjects, features derived from concentrations of each of the two or more chemicals measured from the one or more locations representative of a period of growth during the intervention, and the set of post-intervention features corresponding to biological sample growth after the intervention, the set of pre-intervention features comprising values of one or more dimension-reducing components, the values of the one or more dimension-reducing components forming, for each respective subject of the plurality of concentrations derived from the one or more locations; and using the polymeric isotope data set to evaluate a change in chemical dynamics in response to the intervention.

Embodiment 49 the method of embodiment 48 wherein the evaluating comprises performing a probabilistic hypothesis test using (i) the set of pre-intervention features and (ii) one or both of the set of intervention features and the set of post-intervention features.

Embodiment 50. The method of embodiment 48 or 49, wherein, for each of the set of pre-intervention features, the set of intervention features, and the set of post-intervention features, the value of the dimension-reducing component is each determined by a feature derived from the feature of each of the two or more chemicals, the feature measured from a single respective subject of the plurality of subjects.

Embodiment 51. The method of embodiment 48 or 49, wherein, for each of the set of pre-intervention features, the set of intervention features, and the set of post-intervention features, the values of the dimension-reducing component are each determined by an aggregation of the features derived from each of the two or more chemicals, the features measured from a plurality of respective ones of the plurality of subjects.

Embodiment 52 the method of any of embodiments 46-51, wherein the one or more characteristics derived from the concentrations of the respective chemicals measured from the one or more locations representing a growth period are concentrations, normalized concentrations thereof, or related descriptive statistics, or parameters derived thereof.

Embodiment 53. The method of any of embodiments 46-51, wherein the one or more characteristics derived from the concentration of the respective chemical species are selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, layering, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, and number of most likely recursions; these measurements are derived by applying recursive quantitative analysis parameters, cross-recursive quantitative analysis parameters, joint recursive quantitative analysis parameters, and/or multidimensional recursive quantitative analysis.

Embodiment 54. A method for assessing the effect of an intervention in a plurality of subjects, the method comprising: for each respective subject of the plurality of subjects, sampling at each respective location of a plurality of locations along a reference line on a corresponding biological sample associated with a chemical kinetics of the respective subject, thereby obtaining a corresponding plurality of chemical samples of the respective subject, each chemical sample of the plurality of chemical samples corresponding to a different location of the plurality of locations, and each location of the plurality of locations representing a different growth period of the corresponding biological sample associated with a chemical kinetics, wherein the plurality of locations comprises: one or more positions representing a growth period prior to the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each respective subject of the plurality of subjects, analyzing the respective plurality of chemical samples of the respective subject with a mass spectrometer to obtain a respective first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration of a respective chemical of a plurality of chemicals over time, the concentration being determined collectively from the plurality of chemical samples, for each respective subject of the plurality of subjects and for each of one or more chemicals of the plurality of chemicals, applying a distribution hysteresis model or similar nonlinear distribution model to the concentration of the respective chemical measured from the plurality of locations as a function of time relative to the intervention, or a feature derived from the concentration, to generate a respective contribution dataset representative of a function of a change in contribution of the intervention to the concentration of the respective chemical of the respective subject over time; and for each of the one or more chemicals from each respective subject of the plurality of subjects, assessing a change in chemical dynamics in response to the intervention using the corresponding contribution data set.

Embodiment 55. A method for assessing the effect of an intervention in a plurality of subjects, the method comprising: for each respective subject of the plurality of subjects, sampling at each respective location of a plurality of locations along a reference line on a corresponding biological sample associated with a chemical kinetics of the respective subject, thereby obtaining a corresponding plurality of chemical samples of the respective subject, each chemical sample of the plurality of chemical samples corresponding to a different location of the plurality of locations, and each location of the plurality of locations representing a different growth period of the corresponding biological sample associated with a chemical kinetics, wherein the plurality of locations comprises: one or more positions representing a growth period prior to the intervention, one or more positions representing a growth period during the intervention, and one or more positions representing a growth period after the intervention; for each respective subject of the plurality of subjects, analyzing the respective plurality of chemical samples of the respective subject with a mass spectrometer, thereby obtaining a respective first dataset comprising a plurality of traces, each trace of the plurality of traces being a concentration over time of a respective chemical of a plurality of chemicals, the concentration being determined collectively from the plurality of chemical samples, generating, for each respective subject of the plurality of subjects and for a set of two or more chemicals of the plurality of chemicals, a respective aggregated isotope dataset comprising a set of features comprising a set of one or more dimension-reducing components formed from features derived from the concentration of each of the two or more chemicals measured across different growth periods of the respective subject; for each respective subject of the plurality of subjects, applying a distribution hysteresis model or a similar non-linear distribution model to the respective aggregated isotope data set as a function of time relative to the intervention to generate a respective contribution data set representative of a function of the intervention's contribution to the set of two or more chemicals in the respective subject over time; and assessing a change in chemodynamics in response to the intervention using the corresponding contribution data for each respective subject of the plurality of subjects.

Embodiment 56. The method of any of embodiments 46-55, wherein the intervention is ingestion of a nutraceutical composition.

Embodiment 57 the method of embodiment 56, further comprising altering the composition of the nutraceutical composition to adjust the effect of the intake of the nutraceutical composition in response to assessing a change in chemodynamics.

Embodiment 58 the method of embodiment 56, further comprising supplementing the intake of the nutritional composition with the intake of one or more dietary supplements in response to assessing a change in chemodynamics.

Embodiment 59. The method of any one of embodiments 46-58, further comprising assessing a metabolic change in one or more additional metabolites in response to the intervention.

Embodiment 60. The method of embodiment 59, wherein the one or more additional metabolites is selected from the group consisting of: perfluorinated compounds, parabens, phthalates, lipids, amino acids, amino acid derivatives and peptides.

Example 61 a method for outputting one or more quantitative measures of one or more exposure set of a subject, comprising: (a) receiving a biological sample from a subject; (b) Determining one or more exposure histology characteristics from the biological sample of the subject; (c) Calculating a first one or more features of the one or more exposure sets of chemical features, wherein each feature of the one or more features comprises one or more quantitative measures; and (d) outputting the one or more quantitative measures of the one or more characteristics of the subject.

Embodiment 62 the method of embodiment 60, further comprising outputting a health result for the subject based at least in part on an association of a normalized score of one or more features of the subject with a normalized score of one or more quantitative metrics of one or more features of a second group of subjects.

Embodiment 63. The method of embodiment 61, wherein the one or more quantitative measures of one or more characteristics of the second set of subjects are stored in a database, wherein the database is a local server or a cloud-based server.

Embodiment 64. The method of embodiment 61, wherein the health outcome comprises a diagnosis of a disease state, a subtype of a disease, a clinical subtype, a non-clinical subset associated with physiology, a anthropometric index, a behavioral index, a socioeconomic index, a body mass index, a intelligence quotient, a socioeconomic status, or any combination thereof.

Embodiment 65. The method of embodiment 60, wherein the one or more features comprise a time-dynamic measurement of the one or more exposure-team features.

Embodiment 66. The method of embodiment 64 wherein the measuring of the temporal dynamics comprises: linear slope, a nonlinear parameter describing curvature of the one or more exposure histology features, a sudden change in intensity of the one or more exposure histology features, a change in baseline intensity of the one or more exposure histology features, a change in frequency domain representation of the one or more exposure histology features, a change in power spectral domain representation of the one or more exposure histology features, a recursive quantitative analysis parameter, a cross-recursive quantitative analysis parameter, a joint recursive quantitative analysis parameter, a multidimensional recursive quantitative analysis parameter, an estimate of the lypanuv spectrum, a maximum Lyapunov index, or any combination thereof.

Embodiment 67. The method of embodiment 63, wherein the disease state comprises: autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

Embodiment 68. The method of embodiment 64, wherein the one or more characteristics of the one or more exposure histology characteristics comprise a phenotypic characteristic, wherein the phenotypic characteristic comprises: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof.

Embodiment 69. The method of embodiment 60, wherein the one or more characteristics are derived from one or more attractors.

Embodiment 70. The method of embodiment 60, wherein the one or more exposure histology characteristics are measured by mass spectrometry, laser ablation-inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, raman spectroscopy, immunohistochemical fluorescence, or any combination thereof.

Embodiment 71. The method of embodiment 60, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

Embodiment 72. The method of embodiment 67, wherein the phenotypic characteristic comprises a molecular phenotype.

Embodiment 73. The method of embodiment 71, wherein the molecular phenotype is determined by an unsupervised analysis, wherein the unsupervised analysis comprises clustering, dimension reduction, factor analysis, stacked auto-coding, or any combination thereof.

Embodiment 74. The method of embodiment 65 wherein the recursion quantitative analysis parameters include a recursion rate, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal lengths, a recursion trend, layering, a capture time, a maximum vertical line length, shannon entropy in vertical line lengths, an average recursion time, shannon entropy in recursion times, and a number of most likely recursions.

Embodiment 75. The method of embodiment 68, further comprising analyzing the one or more attractors by potential energy analysis to create a potential energy data space.

Embodiment 76. The method of embodiment 60, wherein the one or more exposure histology characteristics of the subject comprises retrospective exposure histology data.

Embodiment 77. The method of embodiment 68, further comprising analyzing a dynamic relationship between: the one or more attractors may include, for example, a signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof.

Embodiment 78. The method of embodiment 76 wherein the dynamic relationship is determined by Cross Convergence Mapping (CCM).

Embodiment 79 the method of embodiment 60, further comprising reducing the one or more exposure histology features to a reduced one or more exposure histology features.

Embodiment 80. The method of embodiment 68, further comprising constructing a network of the one or more attractors based on similarity: the time exposure group of the one or more attractors may include a data signal recursion rate, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal length, a recursion trend, a stratification, a capture time, a maximum vertical line length, shannon entropy in vertical line length, an average recursion time, shannon entropy in number of recursions, a number of most likely recursions, or any combination thereof.

Embodiment 81 the method of embodiment 79 further comprising analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, metrics based on correlation graph theory, or any combination thereof.

Embodiment 82. A method for outputting a prediction of phenotype data for one or more subjects, comprising: (a) Receiving one or more biological samples and phenotype data from a first group of subjects; (b) Determining a first set of exposure set of biological features from one or more biological samples of the first set of subjects; (c) Computing a first set of features of the first set of exposure sets of mathematical features; (d) Training a predictive model with the first set of characteristics and the phenotypic data of the first set of subjects; (e) Receiving one or more biological samples from a second group of subjects different from the first group of subjects; (f) Determining a second set of exposure set of biological features from the one or more biological samples of the second set of subjects; (g) Computing a second set of features from the second set of exposure set of features; and (h) outputting a prediction of the phenotype data of the second set of subjects determined by inputting the second set of features into a trained predictive model.

Embodiment 83. The method of embodiment 81 wherein the first set of features and the second set of features comprise one or more quantitative measures.

Embodiment 84. The method of embodiment 82 wherein the one or more quantitative metrics comprise a time-dynamic measurement of the one or more exposure histology characteristics.

Embodiment 85 the method of embodiment 83 wherein said measuring of said temporal dynamics comprises: linear slope, a nonlinear parameter describing curvature of the first and second sets of exposure histology features, abrupt changes in intensity of the first and second sets of exposure histology features, changes in baseline intensity of the first and second sets of dynamic exposure histology features, changes in frequency domain representations of the first and second sets of dynamic exposure histology features, changes in power spectral domain representations of the first and second sets of exposure histology features, recursive quantitative analysis parameters, cross-recursive quantitative analysis parameters, joint recursive quantitative analysis parameters, multidimensional recursive quantitative analysis parameters, lypanuv spectra, or estimates of maximum Lyapunov indexes, or any combination thereof.

Embodiment 86 the method of embodiment 82, wherein the first set of features and the second set of features comprise phenotypic features, wherein the phenotypic features comprise a disease state or a health state, wherein the disease state comprises: autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

Embodiment 87. The method of embodiment 85, wherein the phenotypic characteristics further comprise: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof.

Embodiment 88. The method of embodiment 81 wherein the first set of features and the second set of features are represented as or derived from one or more attractors.

Embodiment 89 the method of embodiment 87, wherein the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof.

Embodiment 90. The method of embodiment 81, wherein the first set of exposed histology features and the second set of exposed histology features are measured by mass spectrometry, laser ablation-inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, raman spectroscopy, immunohistochemical fluorescence, or any combination thereof.

Embodiment 91. The method of embodiment 81, wherein the one or more biological samples of the first subject and the second subject comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

Embodiment 92. The method of embodiment 85 wherein the phenotypic characteristic comprises a molecular phenotype.

Embodiment 93. The method of embodiment 91, wherein the molecular phenotype is determined by an unsupervised analysis, wherein the unsupervised analysis comprises clustering, dimension reduction, factor analysis, stacked auto-coding, or any combination thereof.

Embodiment 94. The method of embodiment 84 wherein the recursion quantitative analysis parameters include a recursion rate, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal lengths, a recursion trend, a layering, a capture time, a maximum vertical line length, shannon entropy in vertical line lengths, an average recursion time, shannon entropy in recursion times, and a number of most likely recursions, or any combination thereof.

Embodiment 95 the method of embodiment 87, further comprising analyzing the one or more attractors by potential energy analysis to create a potential energy data space.

Embodiment 96. The method of embodiment 81 wherein the first set of exposure omic features and the second set of exposure omic features comprise dynamic exposure omic data retrospectively, prospective, or any combination thereof.

Embodiment 97 the method of embodiment 87, further comprising analyzing a dynamic relationship between: the one or more attractors may include, for example, a rate of recursion, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal length, a recursion trend, a stratification, a capture time, a maximum vertical length, shannon entropy in vertical length, an average recursion time, shannon entropy in number of recursions, and a number of most likely recursions, or any combination.

Embodiment 98. The method of embodiment 96 wherein the dynamic relationship is determined by Cross Convergence Mapping (CCM).

Embodiment 99. The method of embodiment 87, further comprising constructing the network of one or more attractors based on similarity: the time exposure group of one or more attractors may include a data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, and the number of most likely recursions, or any combination thereof.

Embodiment 100. The method of embodiment 98 further comprising analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, metric feature importance based on a correlation graph theory, pathway importance, or any combination thereof.

Claims

1. A computer-implemented exposure histology system, the system comprising:

one or more processors; and

a memory addressable by the one or more processors, the memory storing:

(a) An exposure group biochemical features database (EDB) comprising, in electronic form, a corresponding plurality of exposure group chemical features for each of a plurality of subjects; and

(b) An intervention result database (IODB) comprising information in electronic form regarding intervention result information for: at least one phase of at least one intervention of at least one subject of the plurality of subjects;

the memory further stores at least one program for execution by the one or more processors, the at least one program comprising:

(i) An association software module communicatively coupled with the EDB and the IODB, wherein the association software module is programmed to determine an association between the corresponding plurality of exposure omic features and the intervention outcome information for at least one period of the at least one subject, and

(ii) A recommendation software module communicatively coupled with the EDB and the IODB, the recommendation software module programmed to provide an intervention recommendation for the at least one subject based at least in part on the corresponding plurality of exposure set characteristics for the at least one subject, the intervention result information for the at least one subject, and the association between the corresponding plurality of exposure set characteristics, clinical phenotype information, and the intervention result information for the at least one subject.

2. The computer-implemented exposure histology system of claim 1, wherein the memory further stores a Clinical Database (CDB) comprising clinical phenotype information for the plurality of subjects, and wherein

Programming the association software module to determine an association between the corresponding plurality of exposure histology features, the clinical phenotype information, and the intervention outcome information for at least one subject; and is also provided with

The recommendation software module is communicatively programmed to provide the intervention recommendation for the at least one subject based at least in part on the corresponding plurality of exposure histology characteristics of the at least one subject, the clinical phenotype information of the at least one subject, the intervention outcome information of the at least one subject, and the association between the corresponding plurality of exposure histology characteristics of the at least one subject, the clinical phenotype information, and the intervention outcome information.

3. The computer-implemented exposure histology system of claim 1, wherein the EDB comprises a different corresponding plurality of exposure histology features for each of at least 100, at least 1,000, or at least 10,000 subjects.

4. The computer-implemented exposure histology system of claim 1, wherein the information about intervention outcome information for comprises a classification of non-responders, adverse responders, or positive responders: at least one phase of at least one intervention of at least one subject of the plurality of subjects.

5. The computer-implemented exposure histology system of claim 1, wherein the information regarding intervention outcome information for one or more of inclusion criteria or exclusion criteria: at least one phase of at least one intervention of at least one subject of the plurality of subjects.

6. The computer-implemented exposure histology system of claim 1, wherein the corresponding plurality of exposure histology characteristics of a subject of the plurality of subjects is obtained by assaying a biological sample of the subject.

7. The computer-implemented exposure histology system of claim 6, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

8. The computer-implemented exposure histology system of claim 6, wherein the determining comprises obtaining a mass spectrometry measurement, a laser ablation-inductively coupled plasma mass spectrometry measurement, a laser induced breakdown spectrometry measurement, a raman spectrometry measurement, an immunohistochemical measurement, a physiological parameter, or any combination thereof.

9. The computer-implemented exposure histology system of claim 6, wherein the determining comprises obtaining one or more mass spectrometry measurements, wherein the one or more mass spectrometry measurements comprise measurements of one or more chemicals.

10. The computer-implemented exposure histology system of claim 9, wherein the one or more chemicals comprise aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

11. The computer-implemented exposure histology system of claim 1, wherein the corresponding plurality of exposure histology features of a corresponding subject of the plurality of subjects comprises one or more features of one or more dynamic temporal biochemical reactions of the corresponding subject.

12. The computer-implemented exposure histology system of claim 1, wherein the corresponding plurality of exposure histology features of a corresponding subject of the plurality of subjects comprises one or more fluorescence images of one or more biological samples of the corresponding subject.

13. The computer-implemented exposure histology system of claim 1, wherein the corresponding plurality of exposure histology features of a corresponding subject of the plurality of subjects comprises one or more spatial maps of one or more raman spectra of a biological sample of the corresponding subject.

14. The computer-implemented exposure histology system of claim 1, wherein each respective plurality of exposure histology characteristics of each respective subject of the plurality of subjects is associated with an absence of a disease or disorder, a presence of a disease or disorder, or a degree of affliction with a disease or disorder in the respective subject.

15. The computer-implemented exposure histology system of claim 14, wherein the disease or condition comprises a disease or condition of psychology, heart, gastroenterology, lung, nerves, circulation, nephrology, or any combination thereof.

16. The computer-implemented exposure histology system of claim 14, wherein the at least one program further comprises instructions for analyzing the corresponding plurality of exposure histology features using a trained model to determine an association with a disease or disorder.

17. The computer-implemented exposure histology system of claim 16, wherein the trained model is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

18. A method for selecting a subject for a first intervention, the method comprising:

(a) Providing a trained predictive model, wherein the trained predictive model is trained according to one or more of clinical metadata, exposure histology characteristics, and corresponding intervention outcome information of a training queue;

(b) Detecting biochemical features using a biological sample obtained from the subject, thereby generating a plurality of prospective exposure histology features;

(c) Inputting the plurality of prospective exposure omics features and clinical metadata of the subject into the trained predictive model, thereby obtaining predictive intervention outcome information for the subject; and

(d) Selecting the subject for the first intervention or excluding the subject from the first intervention based at least in part on the predicted intervention outcome information for the subject.

19. The method of claim 18, wherein the biochemical characteristic is obtained by assaying a biological sample of the subject.

20. The method of claim 19, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

21. The method of claim 19, wherein the determining comprises collecting data from a laser ablation-inductively coupled plasma mass spectrometry measurement, a laser induced breakdown spectrometry measurement, a raman spectrometry measurement, an immunohistochemical measurement, or any combination thereof.

22. The method of claim 19, wherein the determining comprises collecting data from a laser ablation-inductively coupled plasma mass spectrometry measurement, and wherein the laser ablation-inductively coupled plasma mass spectrometry measurement comprises a measurement of one or more elemental chemicals.

23. The method of claim 22, wherein the one or more elemental chemicals comprise aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

24. The method of claim 18, wherein the biochemical features comprise a spatial map of a raman spectrum of the biological sample.

25. The method of claim 18, wherein the biochemical trait is associated with a disease or disorder.

26. The method of claim 26, wherein the disease or disorder comprises a disease or disorder of psychology, heart, gastroenterology, lung, nerves, circulation, nephrology, or any combination thereof.

27. The method of claim 18, wherein the trained predictive model is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

28. The method of claim 18, further comprising incorporating the subject into the first intervention when the subject is selected to perform the first intervention.

29. The method of claim 18, further comprising assessing the subject for a second intervention when the subject is excluded from the first intervention.

30. A method of selecting an optimal treatment for a disease or disorder in a subject in need thereof, the method comprising:

(a) Detecting one or more biochemical features obtained from one or more biological samples from one or more subjects not suffering from the disease or disorder, thereby generating one or more reference exposure set of chemical features;

(b) Detecting a characteristic of one or more biochemical features obtained from one or more biological samples from one or more subjects suffering from the disease or disorder, thereby producing one or more pre-treatment exposure histology features;

(c) Administering a treatment to the one or more subjects suffering from the disease or disorder;

(d) Detecting a characteristic of one or more biochemical features obtained from one or more biological samples from the one or more subjects suffering from the disease or disorder after a period of time has elapsed after receiving the treatment, thereby generating one or more post-treatment exposure histology features;

(e) The difference between: the one or more reference exposure histology characteristics of the one or more subjects not suffering from the disorder or disease, the one or more pre-treatment exposure histology characteristics of the one or more subjects suffering from the disease or disorder, and the one or more post-treatment exposure histology characteristics of the one or more subjects suffering from the disease or disorder; and

(f) One or more optimal treatments are selected based at least in part on the one or more reference exposure histology characteristics, the determined differences between the one or more pre-treatment exposure histology characteristics and the one or more post-treatment exposure histology characteristics, wherein the one or more optimal treatments are selected based on the determined differences meeting predetermined criteria.

31. The method of claim 30, wherein the optimal treatment of the one or more optimal treatments comprises a drug, a nutraceutical, or any combination thereof.

32. The method of claim 30, wherein the predetermined criteria comprises differences between the one or more pre-treatment exposure histology characteristics and the one or more post-treatment exposure histology characteristics and the one or more reference exposure histology characteristics.

33. The method of claim 30, wherein the period of time comprises at least about 1 hour, at least about 1 day, at least about 1 week, at least about 1 month, at least about 1 year, or any combination thereof.

34. The method of claim 30, wherein the difference comprises a change in the one or more post-treatment exposure histology characteristics to at least 10% of the one or more reference exposure histology characteristics.

35. The method of claim 30, wherein the one or more pre-treatment exposure histology features, the one or more post-treatment exposure histology features, or any combination thereof, are obtained by assaying a biological sample of the corresponding subject.

36. The method of claim 35, wherein the biological sample comprises a tooth sample, a nail sample, a hair sample, or any combination thereof.

37. The method of claim 35, wherein the determining comprises obtaining a laser ablation-inductively coupled plasma mass spectrum, a laser induced breakdown spectroscopy measurement, a raman spectroscopy measurement, an immunohistochemical measurement, or any combination thereof.

38. The method of claim 35, wherein the determining comprises obtaining a laser ablation-inductively coupled plasma mass spectrum, and wherein the laser ablation-inductively coupled plasma mass spectrum comprises a measurement of one or more elemental chemicals.

39. The method of claim 38, wherein the one or more elemental chemicals comprise aluminum, arsenic, barium, bismuth, calcium, copper, iodide, lead, lithium, magnesium, manganese, phosphorus, sulfur, tin, strontium, zinc, or any combination thereof.

40. The method of claim 31, wherein a biochemical feature of the one or more biochemical features obtained from a biological sample of the one or more biological samples from a subject of the one or more subjects suffering from the disease or disorder comprises a fluorescence image of the biological sample.

41. The method of claim 31, wherein a biochemical feature of the one or more biochemical features obtained from a biological sample of the one or more biological samples from a subject of the one or more subjects suffering from the disease or disorder comprises a spatial map of a raman spectrum of the biological sample.

42. The method of claim 31, wherein the disease or disorder comprises a disease or disorder of psychology, heart, gastroenterology, lung, nerves, circulation, nephrology, or any combination thereof.

43. The method of claim 31, wherein the trained model is used to analyze differences between: the one or more reference exposure histology characteristics of the one or more subjects not suffering from the disorder or disease, the one or more pre-treatment exposure histology characteristics of the one or more subjects suffering from the disease or disorder, and the one or more post-treatment exposure histology characteristics of the one or more subjects suffering from the disease or disorder.

44. The method of claim 43, wherein the trained model is selected from the group consisting of: neural network algorithms, support vector machine algorithms, decision tree algorithms, unsupervised clustering algorithms, supervised clustering algorithms, regression algorithms, gradient boosting algorithms, and any combination thereof.

45. A method for assessing the impact of an intervention in a plurality of subjects, the method comprising:

for each respective subject of the plurality of subjects, sampling each respective location of a plurality of locations along a reference line on a respective biological sample associated with a chemical kinetics of the respective subject, thereby obtaining a respective plurality of chemical samples of the respective subject, each chemical sample of the plurality of chemical samples corresponding to a different location of the plurality of locations, and each location of the plurality of locations representing a different growth period of the respective biological sample associated with a chemical kinetics, wherein the plurality of locations comprises:

Representing one or more positions of the growth phase prior to said intervention,

one or more positions representing the growth period during said intervention, and

one or more positions representing a growth period following the intervention;

analyzing, with a mass spectrometer, the corresponding plurality of chemical samples of each of the plurality of subjects, thereby obtaining a corresponding first data set comprising a plurality of traces, each trace of the plurality of traces being a concentration over time of a corresponding chemical of a plurality of chemicals, the concentration being determined jointly from the plurality of chemical samples,

for each of the one or more chemicals of the plurality of chemicals, generating a corresponding isotope data set comprising:

a set of pre-intervention features corresponding to a period of time for which the pre-intervention biological sample is grown, the set of pre-intervention features comprising, for each respective subject of the plurality of subjects, one or more features derived from a concentration of the respective chemical measured from the one or more locations representing a period of growth prior to the intervention,

A set of intervention features corresponding to a period of biological sample growth during the intervention, the set of intervention features comprising, for each respective subject of the plurality of subjects, one or more features derived from the concentration of the respective chemical measured from the one or more locations representative of a period of growth during the intervention, and a set of post-intervention features corresponding to a period of biological sample growth after the intervention, the set of post-intervention features comprising, for each respective subject of the plurality of subjects, one or more features derived from the concentration of the respective chemical measured from the one or more locations representative of a period of growth after the intervention; and

the corresponding isotope data set is used for each of the one or more chemicals to evaluate a change in chemical dynamics in response to the intervention.

46. The method of claim 45, wherein the evaluating comprises probability hypothesis testing for each of the plurality of chemicals using one or both of (i) the set of pre-intervention features and (ii) the set of intervention features and the set of post-intervention features.

47. A method for assessing the impact of an intervention in a plurality of subjects, the method comprising:

one or more positions representing a growth period following the intervention;

For a set of two or more of the plurality of chemicals, generating a respective polymeric isotope dataset comprising:

a set of pre-intervention features corresponding to growth of the pre-intervention biological sample, the set of pre-intervention features comprising values of one or more dimension-reducing components that form, for each respective subject of the plurality of subjects, a feature derived from a concentration of each of the two or more chemicals measured from the one or more locations representative of a growth period prior to the intervention,

a set of intervention features corresponding to biological sample growth during the intervention, the set of intervention features comprising values of one or more dimension-reducing components forming, for each respective subject of the plurality of subjects, features derived from concentrations of each of the two or more chemicals measured from the one or more locations representative of growth periods during the intervention, and

a set of post-intervention features corresponding to biological sample growth after the intervention, the set of post-intervention features comprising values of one or more dimension-reducing components that form, for each respective subject of the plurality of subjects, a feature derived from a concentration of each of the two or more chemicals measured from the one or more locations representative of a growth period after the intervention; and

The polymeric isotope data set is used to evaluate a change in chemical dynamics in response to the intervention.

48. The method of claim 47, wherein the evaluating comprises performing a probabilistic hypothesis test using one or both of (i) the set of pre-intervention features and (ii) the set of intervention features and the set of post-intervention features.

49. The method of claim 47 or 48, wherein, for each of the set of pre-intervention features, the set of intervention features, and the set of post-intervention features, the value of the dimension-reducing component is determined by a feature derived from the feature of each of the two or more chemicals, the feature measured from a single respective subject of the plurality of subjects.

50. The method of claim 48 or 49, wherein, for each of the set of pre-intervention features, the set of intervention features, and the set of post-intervention features, the value of the dimension-reducing component is determined by an aggregation of the features derived from each of the set of two or more chemicals, the features measured from a plurality of respective ones of the plurality of subjects.

51. The method of any one of claims 47-50, wherein the one or more characteristics derived from the concentrations of the respective chemicals measured from the one or more locations representing a growth period are concentrations, normalized concentrations thereof or related descriptive statistics or parameters derived thereof.

52. The method of any one of claims 46-51, wherein the one or more characteristics derived from the concentration of the respective chemical species are selected from the group consisting of measured from the one or more locations representing growth periods: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, layering, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, and number of most likely recursions; these measurements are derived by applying recursive quantitative analysis parameters, cross-recursive quantitative analysis parameters, joint recursive quantitative analysis parameters, and/or multidimensional recursive quantitative analysis.

53. A method for assessing the impact of an intervention in a plurality of subjects, the method comprising:

one or more positions representing a growth period following the intervention;

For each respective subject of the plurality of subjects and for each of the one or more chemicals of the plurality of chemicals, applying a distribution hysteresis model or a similar non-linear distribution model to the concentrations of the respective chemical measured from the plurality of locations as a function of time relative to the intervention, or a characteristic derived from the concentrations, to generate a corresponding contribution dataset representative of a function of the contribution of the intervention to the concentration of the respective chemical in the respective subject over time; and

for each of the one or more chemicals, assessing a change in chemodynamics in response to the intervention using the corresponding contribution data set from each respective subject of the plurality of subjects.

54. A method for assessing the impact of an intervention in a plurality of subjects, the method comprising:

one or more positions representing a growth period following the intervention;

for each respective subject of the plurality of subjects and for a group of two or more chemicals of the plurality of chemicals, generating a corresponding aggregated isotope dataset comprising a set of features including values of one or more dimension-reducing components formed from features derived from concentrations of each of the two or more chemicals measured across different growth periods of the respective subject;

For each respective subject of the plurality of subjects, applying a distribution hysteresis model or a similar non-linear distribution model to the respective aggregated isotope data set as a function of time relative to the intervention to generate a respective contribution data set representative of a function of the intervention's contribution to the set of two or more chemicals in the respective subject over time; and

for each respective subject of the plurality of subjects, assessing a change in chemical dynamics in response to the intervention using the corresponding contribution data.

55. The method of any one of claims 47-54, wherein the intervention is ingestion of a nutraceutical composition.

56. The method of claim 55, further comprising altering a composition of the nutraceutical composition to adjust an effect of the ingestion of the nutraceutical composition in response to assessing a change in chemodynamics.

57. The method of claim 55, further comprising supplementing the intake of the nutritional composition by the intake of one or more dietary supplements in response to assessing a change in chemodynamics.

58. The method of any one of claims 46-57, further comprising assessing a metabolic change in one or more additional metabolites in response to the intervention.

59. The method of claim 58, wherein the one or more additional metabolites is selected from the group consisting of: perfluorinated compounds, parabens, phthalates, lipids, amino acids, amino acid derivatives and peptides.

60. A method for outputting one or more quantitative measures of one or more exposure set of chemical characteristics of a first subject, comprising:

(a) Receiving a biological sample from the first subject;

(b) Determining one or more exposure histology characteristics from the biological sample of the first subject;

(c) Calculating a first one or more features of the one or more exposure sets of chemical features, wherein each feature of the first one or more features comprises one or more quantitative metrics; and

(d) The one or more quantitative measures of the first one or more characteristics of the first subject are output.

61. The method of claim 60, further comprising outputting a health result for the first subject based at least in part on an association of a normalized score for the first one or more features of the first subject with a normalized score for a second set of the first one or more features of a second subject.

62. The method of claim 61, wherein the second set of the first one or more features are stored in a database, wherein the database is hosted on a local server, a cloud-based server, or a virtual machine.

63. The method of claim 61, wherein the health outcome comprises diagnosis of a disease state, a disease subtype, a clinical subtype, a non-clinical subset of physiological relevance, a anthropometric index, a behavioral index, a socioeconomic index, a body mass index, intelligence, a socioeconomic status, or any combination thereof.

64. The method of claim 60, wherein the first one or more features comprise a time-dynamic measurement of the one or more exposure-team features.

65. The method of claim 64, wherein the measuring of the temporal dynamics comprises: the method may include determining a linear slope, determining a plurality of non-linear parameters describing a curvature of the one or more exposure histology features, determining a sudden change in an intensity of the one or more exposure histology features, determining one or more changes in a baseline intensity of the one or more exposure histology features, determining a change in a frequency domain representation of the one or more exposure histology features, determining a change in a power spectral domain representation of the one or more exposure histology features, determining one or more recursive quantitative analysis parameters, determining one or more cross-recursive quantitative analysis parameters, determining one or more joint recursive quantitative analysis parameters, determining one or more multi-dimensional recursive quantitative analysis parameters, estimating a lypanuv spectrum, determining a maximum Lyapunov index, or any combination thereof.

66. The method of claim 61, wherein the health outcome comprises a diagnosis of a disease state and wherein the disease state comprises: autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

67. The method of claim 60, wherein the first one or more characteristics of the one or more exposure-histology characteristics comprise a phenotypic characteristic, wherein the phenotypic characteristic comprises: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof.

68. The method of claim 60, wherein the first one or more characteristics are derived from one or more attractors.

69. The method of claim 60, wherein the one or more exposure histology characteristics are measured by mass spectrometry, laser ablation-inductively coupled plasma mass spectrometry, laser-induced breakdown spectroscopy, raman spectroscopy, immunohistochemical fluorescence, or any combination thereof.

70. The method of claim 60, wherein the biological sample comprises hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

71. The method of claim 67, wherein the phenotypic characteristic comprises a molecular phenotype.

72. The method of claim 71, wherein the molecular phenotype is determined by an unsupervised analysis, wherein an unsupervised analysis comprises clustering, dimension reduction, factor analysis, stacked autoencoding, or any combination thereof.

73. The method of claim 64, wherein said measuring of said temporal dynamics comprises a determination of one or more of said recursive quantitative analysis parameters, wherein said one or more of said recursive quantitative analysis parameters comprises a rate of recursion, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal lengths, a recursion trend, a stratification, a capture time, a maximum vertical length, shannon entropy in vertical length, an average recursion time, shannon entropy in a number of recursions, or a number of most likely recursions.

74. The method of claim 68, further comprising analyzing the one or more attractors by potential energy analysis, thereby creating a potential energy data space.

75. The method of claim 60, wherein the one or more exposure histology characteristics of the first subject comprises retrospective, prospective, or any combination thereof exposure histology data.

76. The method of claim 68, further comprising analyzing a dynamic relationship between: the one or more attractors may include, for example, a signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, the number of most likely recursions, or any combination thereof.

77. The method of claim 76, wherein the dynamic relationship is determined by Cross Convergence Mapping (CCM).

78. The method of claim 60, further comprising reducing the one or more exposure histology features to a reduced one or more exposure histology features.

79. The method of claim 68, further comprising constructing a network of the one or more attractors based on similarity of: the time exposure group of the one or more attractors may include a data signal recursion rate, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal length, a recursion trend, a stratification, a capture time, a maximum vertical line length, shannon entropy in vertical line length, an average recursion time, shannon entropy in number of recursions, a number of most likely recursions, or any combination thereof.

80. The method of claim 79, further comprising analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, correlation graph theory-based metrics, or any combination thereof.

81. A method for outputting a prediction of phenotype data for one or more subjects, comprising:

(a) Receiving one or more biological samples and phenotype data from a first group of subjects;

(b) Determining a first set of exposure set of biological features from one or more biological samples of the first set of subjects;

(c) Computing a first set of features of the first set of exposure sets of mathematical features;

(d) Training a predictive model with the first set of characteristics and the phenotypic data of the first set of subjects;

(e) Receiving one or more biological samples from a second group of subjects different from the first group of subjects;

(f) Determining a second set of exposure set of biological features from the one or more biological samples of the second set of subjects;

(g) Computing a second set of features from the second set of exposure set of features; and

(h) Outputting the predictions of the phenotype data for the second set of subjects determined by inputting the second set of features into a trained predictive model.

82. The method of claim 81, wherein the first set of features and the second set of features comprise one or more quantitative measures.

83. The method of claim 82, wherein the one or more quantitative metrics comprise a time-dynamic measurement of the one or more exposure histology characteristics.

84. The method of claim 83, wherein the measuring of the temporal dynamics comprises: the method further comprises determining a linear slope, determining one or more non-linear parameters describing a curvature of the first and second sets of exposure histology features, determining one or more abrupt changes in intensity of the first and second sets of exposure histology features, determining one or more changes in baseline intensity of the first and second sets of exposure histology features, determining one or more changes in frequency domain representations of the first and second sets of exposure histology features, determining one or more changes in power spectrum domain representations of the first and second sets of exposure histology features, determining one or more recursive quantitative analysis parameters, determining one or more cross-recursive quantitative analysis parameters, determining one or more joint recursive quantitative analysis parameters, determining one or more multi-dimensional recursive quantitative analysis parameters, determining a pannuv or maximum Lyapunov spectrum, or any combination thereof.

85. The method of claim 82, wherein the first set of features and the second set of features comprise phenotypic features, wherein the phenotypic features comprise a disease state or a health state, wherein the disease state comprises: autism Spectrum Disorder (ASD), attention Deficit Hyperactivity Disorder (ADHD), amyotrophic Lateral Sclerosis (ALS), schizophrenia, irritable Bowel Disease (IBD), pediatric kidney disease, renal transplant rejection, cancer, or any combination thereof.

86. The method of claim 85, wherein the phenotypic characteristic further comprises: data of an Electrocardiogram (ECG), an electroencephalogram, a Magnetic Resonance Imaging (MRI), a functional magnetic resonance imaging (fMRI), a Positron Emission Tomography (PET), a genome, an epigenomic, a transcriptome, a proteome, a metabolome, or any combination thereof.

87. The method of claim 81, wherein the first set of features and the second set of features are represented as or derived from one or more attractors.

88. The method of claim 87, wherein the one or more attractors are limit cycle attractors, bistable attractors, or any combination thereof.

89. The method of claim 81, wherein the first and second sets of exposed histology features are measured by mass spectrometry, laser ablation-inductively coupled plasma mass spectrometry, laser induced breakdown spectroscopy, raman spectroscopy, immunohistochemical fluorescence, or any combination thereof.

90. The method of claim 81, wherein the one or more biological samples of the first subject and the second subject comprise hair, teeth, toenails, fingernails, physiological parameters, or any combination thereof.

91. The method of claim 85, wherein the first set of phenotypic features and the second set of phenotypic features each comprise a plurality of molecular phenotypes.

92. The method of claim 91, wherein the molecular phenotype is determined by an unsupervised analysis, wherein an unsupervised analysis comprises clustering, dimension reduction, factor analysis, stacked autoencoding, or any combination thereof.

93. The method of claim 83, wherein the measurement of the temporal dynamics comprises a determination of one or more recursive quantitative analysis parameters, wherein the one or more recursive quantitative analysis parameters comprise one or more of a rate of recursion, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal length, a recursive trend, a layering, a capture time, a maximum vertical line length, shannon entropy in vertical line length, an average recursive time, shannon entropy in number of recursions, a number of most likely recursions, or any combination thereof.

94. The method of claim 87, further comprising analyzing the one or more attractors by potential energy analysis, thereby creating a potential energy data space.

95. The method of claim 81, wherein the first set of exposure omics features and the second set of exposure omics features comprise dynamic exposure omic data retrospectively, prospective, or any combination thereof.

96. The method of claim 87, further comprising analyzing a dynamic relationship between: the one or more attractors may include, for example, a rate of recursion, a certainty, an average diagonal length, a maximum diagonal length, a divergence, shannon entropy in diagonal length, a recursion trend, a stratification, a capture time, a maximum vertical length, shannon entropy in vertical length, an average recursion time, shannon entropy in number of recursions, and a number of most likely recursions, or any combination.

97. The method of claim 96, wherein the dynamic relationship is determined by a Cross Convergence Map (CCM).

98. The method of claim 87, further comprising constructing a network of the one or more attractors based on similarity of: the time exposure group of the one or more attractors is based on a data signal recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, shannon entropy in diagonal length, recursion trend, stratification, capture time, maximum vertical line length, shannon entropy in vertical line length, average recursion time, shannon entropy in number of recursions, and the number of most likely recursions, or any combination thereof.

99. The method of claim 98, further comprising analyzing one or more characteristics of the network of the one or more attractors to determine network connectivity, efficiency, feature importance, pathway importance, metric feature importance based on correlation graph theory, pathway importance, or any combination thereof.

100. The method of claim 18, wherein the biochemical features comprise a fluorescence image of the biological sample.

101. The computer-implemented exposure histology system of claim 16, wherein the trained model is a regressor or classifier.

102. The computer-implemented exposure omics system of claim 16, wherein the trained model comprises one or more regression tasks, one or more classification tasks, or a combination of both one or more regression tasks and one or more classification tasks.

103. The computer-implemented exposure histology system of claim 16, wherein the disease or condition comprises a disease or condition of psychology, heart, gastroenterology, lung, nerves, circulation, nephrology, or any combination thereof.

104. The method of claim 18, wherein the trained predictive model is a regressor or classifier.

105. The method of claim 18, wherein the trained predictive model includes one or more regression tasks, one or more classification tasks, or a combination of both one or more regression tasks and one or more classification tasks.

106. The method of claim 44, wherein the trained model is a regressor or classifier.

107. The method of claim 44, wherein the trained model comprises one or more regression tasks, one or more classification tasks, or a combination of both one or more regression tasks and one or more classification tasks.