System and method for diagnosis of biological disorder associated with periodic variation in metal metabolism

Abstract

To provide a method for evaluating a subject for a biological condition associated with metal metabolism.SOLUTION: A method includes sampling positions along a biological sample of a subject to obtain a plurality of ion samples. Each ion sample corresponds to a position on the biological sample and each position represents an amount of growth of the biological sample. The method also analyzes the obtained ions with a mass spectrometer to acquire a plurality of traces. Each such trace represents concentration of a corresponding elemental isotope, of a plurality of elemental isotopes, over time. The method further derives a set of features from the traces. Each feature is determined by a variation of a single isotope or a combination of isotopes in the plurality of traces. The method inputs the set of features to a trained classifier to acquire a probability that the subject has a biological condition associated with metal metabolism.SELECTED DRAWING: Figure 2A

Description

[Technical field]

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS This application claims priority to U.S. Provisional Patent Application No. 62 / 858,260, entitled "Systems and Methods for Hair Based Diagnostics for Autism Spectrum Disorders," filed June 6, 2019, which is incorporated herein by reference.

[0002] The present disclosure relates generally to the diagnosis of biological conditions associated with metal metabolism through the analysis of biological samples from subjects being tested for such biological conditions. [Background technology]

[0003] Metal ions play a key role in many biological processes that are structurally and functionally important to humans. Imbalances in the acquisition of certain metal ions due to specific amounts of metals in nutrition or dysmetabolism of certain metals are associated with many biological conditions. The imbalances include either excess acquisition of certain metal ions or lack of certain metal ions. Examples of biological conditions associated with metal metabolism include neurological conditions (e.g., autism spectrum disorder, schizophrenia, or attention-deficit / hyperactivity disorder (ADHD)), neurodegenerative conditions (e.g., amyotrophic lateral sclerosis (ALS), Alzheimer's disease, Parkinson's disease, and Huntington's disease), and some cancers (e.g., childhood cancers).

[0004] Recent studies have shown an association between autism spectrum disorder and metabolic dysfunction, particularly metal dysregulation (see, for example, Cheng et al., “Metabolic Dysfunction Underlying Autism Spectrum Disorder and Potential Treatment Approaches,” Front Mol Neurosci. 10, p. 34, February 2017 and Arora et al., “Fetal and postnatal metal dysregulation in Autism,” Nat. Commun. 8, p. 15493, June 2017). As another example, recent studies have shown a link between neurodegeneration and biological rhythms of metals detectable in a subject's hair and / or teeth (see, e.g., Appenzeller et al., “Stable Isotope Ratios in Hair and Teeth Reflect Biologic Rhythms,” PLoS ONE 2(7):e636. https: / / doi.org / 10.1371 / journal.pone.0000636, April 2017). However,

[0005] In view of the above background, there is a need in the art for improved systems and methods for accurately diagnosing biological conditions associated with metal metabolism. In particular, there is a need for biomarkers that are detectable in a non-invasive manner for the diagnosis of biological conditions associated with metal metabolism. Summary of the Invention

[0006] Thus, there exists a need for accurate methods and systems for the diagnosis of biological conditions associated with metal metabolism, particularly non-invasive diagnosis. The present disclosure addresses these needs, for example, by providing biological sample biomarkers for the diagnosis of biological conditions associated with metal metabolism. The biological sample includes human biological samples that contain deposits of specific metals and are associated with growth. Such biological samples can be hair shafts, teeth, and nails. The non-invasive biomarkers of the present disclosure can be used for the diagnosis of young children, even infants under the age of one.

[0007] According to some embodiments, a method of evaluating a subject for a first biological state associated with metal metabolism includes sampling each of a plurality of locations along a baseline on a biological sample associated with metal metabolism of the subject, thereby obtaining a plurality of ion samples. Each ion sample in the plurality of ion samples corresponds to a different location in the plurality of locations, and each location in the plurality of locations represents a different growth period of the biological sample associated with metal metabolism. The method includes analyzing each ion sample in the plurality of ion samples (e.g., using a mass spectrometer or other spectroscopy method), thereby obtaining a first data set including a plurality of traces. Each trace in the plurality of traces is a concentration over time of a corresponding elemental isotope in a plurality of elemental isotopes collectively determined from the plurality of ion samples. The method includes deriving a second data set from the plurality of traces including a set of features. Each feature in the set of features is determined by the variation of a single isotope or combination of isotopes in the plurality of traces. The method includes inputting the set of features into a trained classifier, thereby obtaining a probability from the trained classifier that the subject has a first biological state associated with metal metabolism.

[0008] In some embodiments, the plurality of elemental isotopes is selected from the elemental isotopes listed in Table 1. In some embodiments, the plurality of elemental isotopes includes at least 22 of the elemental isotopes listed in Table 1.

[0009] According to some embodiments, each feature in the set of features is associated with a single respective trace of the plurality of traces, or with two respective traces of the plurality of traces. In some embodiments, the set of features is selected from the features listed in Table 2, and optionally, the set of features further includes one or more features listed in Table 3. In some embodiments, the set of features includes at least 23 features listed in Table 2.

[0010] In some embodiments, the first biological condition associated with metal metabolism is selected from the group consisting of autism spectrum disorder (ADS), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney transplant rejection, and pediatric cancer.

[0011] In some embodiments, evaluating the subject for a first biological condition associated with metal metabolism further comprises distinguishing between the first biological condition associated with metal metabolism and a second biological condition associated with metal metabolism that is different from the first biological condition associated with metal metabolism. In some embodiments, the first biological condition is an autism spectrum disorder and the second biological condition is attention-deficit / hyperactivity disorder.

[0012] In some embodiments, the subject is a human, hi some embodiments, the subject is less than 1 year old, less than 2 years old, less than 3 years old, less than 4 years old, or less than 5 years old.

[0013] In some embodiments, the biological sample relevant to the subject's metal metabolism is selected from the group consisting of a hair shaft, a tooth, and a nail.

[0014] In some embodiments, the method further comprises pre-treating the hair shaft with a solvent and / or irradiating the hair shaft with a low power laser to remove any debris from the hair shaft prior to sampling the hair shaft of the subject. In some embodiments, the biological sample related to the metal metabolism of the subject is a hair shaft and the reference line corresponds to the longitudinal direction of the hair shaft. In some embodiments, the biological sample related to the metal metabolism of the subject is a tooth and the reference line corresponds to the tooth neonatal line on the tooth enamel surface.

[0015] In some embodiments, the method further comprises pre-treating the biological sample associated with the subject's metal metabolism with a solvent or detergent prior to sampling. In some embodiments, the method further comprises irradiating the biological sample associated with the subject's metal metabolism with a low power laser using a laser to remove any debris from the biological sample associated with the subject's metal metabolism prior to sampling.

[0016] In some embodiments, the sampling includes irradiating the biological sample associated with the subject's metal metabolism with a laser, thereby extracting a plurality of particles from the biological sample associated with the subject's metal metabolism, and ionizing the plurality of particles with an inductively coupled plasma mass spectrometer, thereby obtaining a plurality of ion samples.

[0017] In some embodiments, the plurality of positions is ordered such that a first position in the plurality of positions along the biological sample associated with the subject's metal metabolism corresponds to a position closest to the leading edge of the biological sample associated with the subject's metal metabolism. In some embodiments, the plurality of positions includes at least 100, 150, 200, 250, 300, 350, 400, 450, or 500 positions.

[0018] In some embodiments, each trace in the plurality of traces includes a plurality of data points, each data point being an instance of a respective location in the plurality of locations.

[0019] In some embodiments, deriving the second data set includes removing data points from the plurality of data points that do not satisfy a first criterion, the first criterion including a mean absolute difference between adjacent data points in the plurality of data points being three times the standard deviation of the mean absolute difference between adjacent points.

[0020] In some embodiments, the concentration of the corresponding elemental isotope corresponds to the relative abundance of the corresponding elemental isotope relative to a reference elemental isotope, the reference elemental isotope being included in the plurality of ion samples. In some embodiments, the reference elemental isotope is sulfur.

[0021] In some embodiments, the set of features is selected from average diagonal length, determinism, recursion time, entropy, trapping time, and laminarity.

[0022] In some embodiments, the trained classifier comprises:

number

[0023] In some embodiments, the method further includes considering the subject to have a first biological state associated with metal metabolism according to a determination that p(subject) is above a predetermined threshold.

[0024] In some embodiments, the biological condition associated with metal metabolism is associated with a cyclic dysregulation of the metabolism of multiple metals, where the multiple metals correspond to multiple elemental isotopes.

[0025] According to some embodiments, a device for evaluating a subject for a biological condition associated with metal metabolism includes one or more processors and a memory storing one or more programs for execution by the one or more processors. The one or more programs include instructions for sampling each of a plurality of locations along a baseline on a biological sample associated with the subject's metal metabolism, thereby obtaining a plurality of ion samples. Each ion sample in the plurality of ion samples corresponds to a different location in the plurality of locations. Each location in the plurality of locations represents a different growth period of the biological sample associated with the metal metabolism. The one or more programs include instructions for analyzing each ion sample in the plurality of ion samples with a mass spectrometer, thereby obtaining a first data set including a plurality of traces. Each trace in the plurality of traces is a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the plurality of ion samples. The one or more programs include instructions for deriving a second data set from the plurality of traces including a set of features, each feature in the set of features being determined by the variation of a single isotope or a combination of isotopes in the plurality of traces. The one or more programs include instructions for inputting the set of features into a trained classifier, thereby obtaining from the trained classifier a probability that the subject has a biological condition related to metal metabolism.

[0026] According to some embodiments, a non-transitory computer readable storage medium embeds one or more computer programs for classification. The one or more computer programs include instructions that, when executed by a computer system, cause the computer system to execute a method for evaluating a subject for a biological condition associated with metal metabolism. The method includes sampling each location in a plurality of locations along a baseline on a biological sample associated with the subject's metal metabolism, thereby obtaining a plurality of ion samples. Each ion sample in the plurality of ion samples corresponds to a different location in the plurality of locations, and each location in the plurality of locations represents a different growth period of the biological sample associated with metal metabolism. The method includes analyzing each ion sample in the plurality of ion samples with a mass spectrometer, thereby obtaining a first data set including a plurality of traces. Each trace in the plurality of traces is a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the plurality of ion samples. The method includes deriving a second data set from the plurality of traces including a set of features. Each feature in the set of features is determined by the variation of a single isotope or a combination of isotopes in the plurality of traces. The method includes inputting a set of features into a trained classifier, thereby obtaining from the trained classifier a probability that the subject has a first biological condition associated with metal metabolism.

[0027] According to some embodiments, the classification method is performed on a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors. The classification method is performed for each training subject in the plurality of training subjects. A first subset of the training subjects in the plurality of training subjects has a first diagnostic status corresponding to having a first biological state associated with metal metabolism, and a second subset of the training subjects in the plurality of training subjects has a second diagnostic status corresponding to not having the first biological state associated with metal metabolism. The classification method includes sampling each location in a corresponding plurality of locations of a corresponding baseline on a corresponding biological sample associated with metal metabolism of each training subject, thereby obtaining a corresponding plurality of ion samples. Each ion sample in the corresponding plurality of ion samples is for a different location in the corresponding plurality of locations. Each location in the corresponding plurality of locations represents a different growth period of the corresponding biological sample associated with metal metabolism. The classification method includes analyzing each ion sample in the corresponding plurality of ion samples using a mass spectrometer, thereby obtaining a respective first data set including a corresponding plurality of traces. Each trace in the corresponding plurality of traces is a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the corresponding plurality of ion samples. The classification method includes deriving a respective second data set from the corresponding plurality of traces including a corresponding set of features. Each feature in the corresponding set of features is determined by the variation of a single isotope or a combination of isotopes in the corresponding plurality of traces. The classification method includes training an untrained or partially untrained classifier with (i) a corresponding set of features of the respective second data set for each training subject of the plurality of training subjects and (ii) a corresponding diagnostic status of each training subject of the plurality of training subjects selected from among the first diagnostic status and the second diagnostic status, thereby obtaining a trained classifier.The classifier provides an indication of whether the test subject has a first biological condition related to metal metabolism based on feature values ​​of the set of features obtained from the biological sample related to metal metabolism of the test subject.

[0028] In some embodiments, the trained classifier is a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, or a regression model.

[0029] In some embodiments, the trained classifier is polynomial or binomial. In some embodiments, the plurality of elemental isotopes is selected from the elemental isotopes listed in Table 1.

[0030] In some embodiments, each feature in the set of features is associated with a single respective trace of the plurality of traces, or with two respective traces of the plurality of traces. In some embodiments, the set of features is selected from the features listed in Table 2, and optionally, the set of features further includes one or more features listed in Table 3.

[0031] In some embodiments, the first biological condition associated with metal metabolism is selected from the group consisting of autism spectrum disorder (ADS), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney transplant rejection, and pediatric cancer.

[0032] In some embodiments, evaluating the subject for a first biological condition associated with metal metabolism further comprises distinguishing between the first biological condition associated with metal metabolism and a second biological condition associated with metal metabolism that is different from the first biological condition associated with metal metabolism. In some embodiments, the first biological condition is an autism spectrum disorder and the second biological condition is attention-deficit / hyperactivity disorder.

[0033] In some embodiments, the subject is a human, hi some embodiments, the subject is less than 1 year old, less than 2 years old, less than 3 years old, less than 4 years old, or less than 5 years old.

[0034] In some embodiments, the biological sample relevant to the subject's metal metabolism is selected from the group consisting of a hair shaft, a tooth, and a nail.

[0035] In some embodiments, the method further comprises pre-treating the hair shaft with a solvent and / or irradiating the hair shaft with a low power laser to remove any debris from the hair shaft prior to sampling the hair shaft of the subject. In some embodiments, the biological sample related to the metal metabolism of the subject is a hair shaft and the reference line corresponds to the longitudinal direction of the hair shaft. In some embodiments, the biological sample related to the metal metabolism of the subject is a tooth and the reference line corresponds to the tooth neonatal line on the tooth enamel surface.

[0036] In some embodiments, the method further comprises pre-treating the biological sample associated with the subject's metal metabolism with a solvent or detergent prior to sampling. In some embodiments, the method further comprises irradiating the biological sample associated with the subject's metal metabolism with a low power laser to remove any debris from the biological sample associated with the subject's metal metabolism prior to sampling.

[0037] In some embodiments, the sampling includes irradiating the biological sample associated with the subject's metal metabolism with a laser, thereby extracting a plurality of particles from the biological sample associated with the subject's metal metabolism, and ionizing the plurality of particles with an inductively coupled plasma mass spectrometer, thereby obtaining a plurality of ion samples.

[0038] In some embodiments, the plurality of positions is ordered such that a first position in the plurality of positions along the biological sample associated with the subject's metal metabolism corresponds to a position closest to the leading edge of the biological sample associated with the subject's metal metabolism. In some embodiments, the plurality of positions includes at least 100, 150, 200, 250, 300, 350, 400, 450, or 500 positions.

[0039] In some embodiments, each trace in the plurality of traces includes a plurality of data points, each data point being an instance of a respective location in the plurality of locations.

[0040] In some embodiments, deriving the second data set includes removing data points from the plurality of data points that do not satisfy a first criterion, the first criterion including a mean absolute difference between adjacent data points in the plurality of data points being three times the standard deviation of the mean absolute difference between adjacent points.

[0041] In some embodiments, the concentration of the corresponding elemental isotope corresponds to the relative abundance of the corresponding elemental isotope relative to a reference elemental isotope, the reference elemental isotope being included in the plurality of ion samples. In some embodiments, the reference elemental isotope is sulfur.

[0042] In some embodiments, the set of features is selected from average diagonal length, determinism, recursion time, entropy, trapping time, and laminarity.

[0043] In some embodiments, the trained classifier comprises:

number

[0044] In some embodiments, the method further includes considering the subject to have a first biological state associated with metal metabolism according to a determination that p(subject) is above a predetermined threshold.

[0045] In some embodiments, the biological condition associated with metal metabolism is associated with a cyclic dysregulation of the metabolism of multiple metals, where the multiple metals correspond to multiple elemental isotopes.

[0046] According to some embodiments, the classification device includes one or more processors and a memory storing one or more programs for execution by the one or more processors. The one or more programs include instructions for performing the classification method. The classification method is performed for each training subject in the plurality of training subjects. A first subset of the training subjects in the plurality of training subjects has a first diagnostic status corresponding to having a first biological state associated with metal metabolism, and a second subset of the training subjects in the plurality of training subjects has a second diagnostic status corresponding to not having the first biological state associated with metal metabolism. The classification method includes sampling each location in a corresponding plurality of locations of a corresponding baseline on a corresponding biological sample associated with metal metabolism of each training subject, thereby obtaining a corresponding plurality of ion samples. Each ion sample in the corresponding plurality of ion samples is for a different location in the corresponding plurality of locations. Each location in the corresponding plurality of locations represents a different growth period of the corresponding biological sample associated with metal metabolism. The classification method includes analyzing each ion sample in the corresponding plurality of ion samples using a mass spectrometer, thereby obtaining a respective first data set including a corresponding plurality of traces. Each trace in the corresponding plurality of traces is a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the corresponding plurality of ion samples. The classification method includes deriving a respective second data set from the corresponding plurality of traces including a corresponding set of features. Each feature in the corresponding set of features is determined by the variation of a single isotope or a combination of isotopes in the corresponding plurality of traces. The classification method includes training an untrained or partially untrained classifier with (i) a corresponding set of features of the respective second data set for each training subject of the plurality of training subjects and (ii) a corresponding diagnostic status of each training subject of the plurality of training subjects selected from among the first diagnostic status and the second diagnostic status, thereby obtaining a trained classifier.The classifier provides an indication of whether the test subject has a first biological condition related to metal metabolism based on feature values ​​of the set of features obtained from the biological sample related to metal metabolism of the test subject.

[0047] According to some embodiments, a non-transitory computer readable storage medium embeds one or more computer programs for classification. The one or more computer programs include instructions that, when executed by a computer system, cause the computer system to perform a classification method. The classification method is performed for each training subject in the plurality of training subjects. A first subset of the training subjects in the plurality of training subjects has a first diagnostic status corresponding to having a first biological state associated with metal metabolism, and a second subset of the training subjects in the plurality of training subjects has a second diagnostic status corresponding to not having the first biological state associated with metal metabolism. The classification method includes sampling each location in a corresponding plurality of locations of a corresponding reference line on a corresponding biological sample associated with metal metabolism of each training subject, thereby obtaining a corresponding plurality of ion samples. Each ion sample in the corresponding plurality of ion samples is for a different location in the corresponding plurality of locations. Each location in the corresponding plurality of locations represents a different growth period of the corresponding biological sample associated with metal metabolism. The classification method includes analyzing each ion sample in the corresponding plurality of ion samples using a mass spectrometer, thereby obtaining a respective first data set including a corresponding plurality of traces. Each trace in the corresponding plurality of traces is a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the corresponding plurality of ion samples. The classification method includes deriving a respective second data set from the corresponding plurality of traces including a corresponding set of features. Each feature in the corresponding set of features is determined by the variation of a single isotope or a combination of isotopes in the corresponding plurality of traces. The classification method includes training an untrained or partially untrained classifier with (i) a corresponding set of features of the respective second data set for each training subject of the plurality of training subjects and (ii) a corresponding diagnostic status of each training subject of the plurality of training subjects selected from among the first diagnostic status and the second diagnostic status, thereby obtaining a trained classifier.The classifier provides an indication of whether the test subject has a first biological condition related to metal metabolism based on feature values ​​of the set of features obtained from the biological sample related to metal metabolism of the test subject.

[0048] As disclosed herein, any embodiment disclosed herein may be applied to any aspect, where applicable.

[0049] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in the art from the following detailed description. Only exemplary embodiments of the present disclosure are shown and described herein. As will be understood, 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 should be regarded as illustrative in nature, and not as restrictive. [Brief description of the drawings]

[0050] [Figure 1A] 1 illustrates a block diagram of an exemplary computing device in accordance with some embodiments of the present disclosure. [Figure 2A] 1 provides a flowchart of a method for evaluating a subject for a biological condition according to some embodiments of the present disclosure. [Figure 2B] 1 provides exemplary diagrams of subject hair, tooth, and nail samples, according to some embodiments of the present disclosure. [Figure 2C] 1 provides an exemplary schematic diagram of a laser sampling a hair shaft of a subject, according to some embodiments of the present disclosure. [Figure 2D] 1 provides an exemplary diagram of a trace illustrating the concentration of an elemental isotope over time, according to some embodiments of the present disclosure. [Figure 2E] 1 provides an exemplary diagram of features corresponding to a single isotope variation from a trace, according to some embodiments of the present disclosure. [Figure 2F]According to some embodiments of the present disclosure, a diagram of experimental data for distinguishing autism spectrum disorder from other neurodevelopmental disorders is provided. In Fig. 2F, autism spectrum disorder (ASD label) cases are contrasted with attention deficit / hyperactivity disorder (ADHD label) cases, subjects diagnosed with comorbid ASD and ADHD diagnoses (CM label), and neurotypical subjects without a neurodevelopmental disorder diagnosis (NT label). [Figure 3A-3E] 1 collectively provide a flowchart of processes and functions for evaluating a subject for a biological condition, according to some embodiments of the present disclosure, where optional blocks are indicated with dashed boxes. [Figure 4] FIG. 1 provides a flowchart of processes and functions for training a classifier for assessing a subject for a biological condition according to some embodiments of the present disclosure, where optional blocks are indicated with dashed boxes. [Figure 5A-5D] 1 shows an empirical Receiver Operating Characteristic (ROC) curve for assessing autism spectrum disorder, according to some embodiments. [Figure 6] 1 shows a ROC curve for assessing the accuracy of the disclosed methods for assessing amyotrophic lateral sclerosis, according to some embodiments. [Figure 7] 1 shows a ROC curve for assessing the accuracy of the disclosed methods for assessing schizophrenia, according to some embodiments. [Figure 8] 1 shows a ROC curve for assessing the accuracy of the disclosed methods for assessing irritable bowel disorder, according to some embodiments. [Figure 9] 1 shows a ROC curve for assessing the accuracy of the disclosed method for assessing kidney transplant rejection, according to some embodiments. [Figure 10] 1 shows a ROC curve for assessing the accuracy of the disclosed methods for assessing childhood cancer, according to some embodiments.

[0051] Like reference numbers refer to corresponding parts throughout the several views of the drawings. The drawings are not drawn to scale. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0052] The present disclosure provides a system and method for evaluating a subject for biological conditions related to metal metabolism from a biological sample related to the subject's metal metabolism.In particular, the disclosed method provides a biological sample biomarker that can be obtained non-invasively from a subject.The method can be applied to evaluate subjects of any age, and is particularly useful for diagnosing children, even infants under 1 year old, to enable early treatment and intervention.

[0053] Definition. The terms used in this disclosure are merely for the purpose of describing certain embodiments and are not intended to limit the invention. When used in the description of the invention and the appended claims, the singular forms "a", "an" and "the" are intended to include the plural forms unless the context clearly indicates otherwise. As used herein, the term "and / or" will also be understood to refer to and include any and all possible combinations of one or more of the associated listed items. It will be further understood that the terms "comprises" and / or "comprising" as used herein specify the presence of the stated features, integers, steps, operations, elements, and / or components, but do not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and / or groups thereof.

[0054] As used herein, the term "if" may be interpreted to mean "when" or "upon" or "upon determining" or "upon detecting," depending on the context. Similarly, the phrase "when it is determined" or "when [a described condition or event] is detected" may be interpreted to mean "when it is determined" or "upon determining" or "upon detecting [a described condition or event]" or "in response to [a described condition or event]," depending on the context.

[0055] As used herein, biological conditions related to metal metabolism (also referred to as metal metabolism disorders) herein refer to biological conditions related to or caused by the periodic dysregulation of the metabolism of a particular metal. Periodic dysregulation can be manifested as a periodic decrease (e.g., deficiency) in the uptake of one or more metals, a periodic increase in the uptake of one or more metals, or a combination of a periodic decrease and a periodic increase in the uptake of one or more metals. Non-limiting examples of biological conditions related to metal metabolism include autism spectrum disorder (ADS), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, kidney transplant rejection, some types of cancer, Alzheimer's disease, Parkinson's disease, Huntington's disease, metabolic disorders (obesity and irritable bowel disease (IBD)), and / or any condition or disorder related to metal metabolism.

[0056] As used herein, a biological sample related to metal metabolism refers herein to a human biological sample that contains deposits of a particular metal and is related to growth (e.g., hair, nails, and teeth). The biological sample related to metal metabolism of the present disclosure has the requirement of expressing growth along a reference line such that the abundance of a deposit of a certain metal is detectable with respect to time. These biological samples related to metal metabolism thereby facilitate the detection of periodic variations in the abundance of a particular metal. In some embodiments, the biological sample related to metal metabolism comprises a hair shaft, whose reference line corresponds to a line along the length of the hair shaft. In some embodiments, the biological sample related to metal metabolism comprises a tooth, whose reference line corresponds to a neonatal line of the tooth on the enamel surface of the tooth. In some embodiments, the biological sample related to metal metabolism comprises a nail, whose reference line corresponds to a line in the direction of nail growth. For example, the reference line extends from the base of the nail to the tip of the nail.

[0057] As used herein, the term "trained classifier" refers to a model (e.g., a machine learning algorithm such as logistic regression, neural networks, regression, support vector machines, clustering algorithms, decision trees, etc.) with specific parameters (weights) and thresholds, ready to be applied to previously unseen samples.

[0058] As used herein, the term "untrained or partially trained classifier" refers to a model (e.g., a machine learning algorithm such as logistic regression, neural networks, regression, support vector machines, clustering algorithms, decision trees, etc.) that has at least some unfixed parameters (weights) and thresholds, and is ready to be trained on a training set to optimize and fix the parameters and thresholds.

[0059] It will also be understood that terms such as first, second, etc. may be used herein to describe various elements, but these elements should not be limited by these terms. These terms are used only to distinguish one element from another. For example, a first subject may be referred to as a second subject, and similarly, a second subject may be referred to as a first subject, without departing from the scope of the present disclosure. A first subject and a second subject are both subjects, but are not the same subject. Furthermore, the terms "subject", "user", and "patient" are used interchangeably herein.

[0060] As used herein, the term "subject" refers to a human (e.g., a male human, a female human, a fetus, a pregnant woman, a child, etc.). In some embodiments, a subject is a male or female (e.g., a man, a woman, or a child) at any stage of life.

[0061] As used herein, the term "autism spectrum disorder" refers to a group of neurodevelopmental conditions associated with impaired social interaction, developmental language and communication skills, and repetitive behaviors. For example, the standardized criteria for the diagnosis of autism spectrum disorder by the Centers for Disease Control and Prevention (CDC) include: 1) persistent deficits in social communication and social interaction, and 2) restricted, repetitive patterns of behavior, interests, or activities. Autism spectrum disorders include, for example, autistic disorder (aka "classic autism"), Asperger syndrome, and generalized developmental disorder (aka "atypical" autism).

[0062] As used herein, the term "recurrence quantitative analysis" ("RQA") refers to nonlinear data analysis that quantifies the number and duration of recurrences in a dynamical system. RQA is used to characterize the behavior of dynamical systems in phase space.

[0063] As used herein, the term "recurrence plot" refers to the graphical visualization of time-dependent periodic structures in experimental data.

[0064] As used herein, the term "trace" refers to the time-dependent abundance (or concentration) of an elemental isotope. A trace includes multiple data points, where each data point is associated with a time scale and an abundance scale.

[0065] As used herein, the term "feature" refers to a dynamic periodic feature extracted from a time-dependent abundance trace of an elemental isotope, or a combination of two or more time-dependent abundance traces of elemental isotopes, e.g., by using RQA.

[0066] As used herein, the term "mean diagonal length" ("MDL") refers to an important measure derived from RQA that reflects a direct measurement of the average length of the diagonals present in a two-dimensional recurrence plot. This measure can be considered an absolute indication of the duration of periodic components in a given signal.

[0067] As used herein, the term "determinism" in relation to the average diagonal length refers to the relative proportion of periodic and non-periodic components in a recursive analysis. Determinism indicates the overall periodic content of a given signal.

[0068] As used herein, the term "recurrence time" ("RT2") refers to the average time interval between diagonal elements, i.e., the interval between periodicities.

[0069] As used herein, the term "entropy" refers to the variability in the distribution of the mean diagonal lengths, with low entropy signals exhibiting little complexity in the distribution of periodic components and high entropy signals exhibiting diversity in short-term and long-term periods.

[0070] As used herein, the term "trapping time" ("TT") refers to the average length of a layered (vertical or horizontal) structure in a two-dimensional recurrence plot that exhibits a stable state, similar to how the average diagonal length captures the duration of a periodic process.

[0071] As used herein, the term "laminarity" refers to an overall measure of signal stability. Laminarity quantifies the ratio of recursive points that belong to a layered structure relative to the total frequency of recursive points.

[0072] The terms used herein are merely for the purpose of describing a particular case and are not intended to be limiting. As used herein, the singular forms "a," "an," and "the" are intended to include the plural forms unless the context clearly indicates otherwise. Furthermore, to the extent that "including," "includes," "having," "has," "with," or variations thereof are used in either the detailed description and / or claims, such terms are intended to be inclusive in a manner similar to the term "comprising."

[0073] A number of aspects are described below with reference to exemplary applications for illustration. It should be understood that numerous specific details, relationships, and methods are described to provide a complete understanding of the features described herein. However, those skilled in the art will readily recognize that the features described herein can be implemented without one or more specific details, or with other methods. The features described herein are not limited by the order of the illustrated acts or events, since some acts may occur in different orders and / or simultaneously with other acts or events. Furthermore, not all illustrated acts or events are required to implement the methodology in accordance with the features described herein.

[0074] Reference will now be made in detail to the embodiments, examples of which are illustrated in the accompanying figures. In the following detailed description, numerous specific details are set forth in order to provide a thorough understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details. In other instances, well-known methods, procedures, components, circuits, and networks have not been described in detail so as not to unnecessarily obscure aspects of the embodiments.

[0075] 1 is an exemplary system embodiment. An overview of some aspects of the disclosure will now be provided and details of an example system will be described in conjunction with Figure 1. Figure 1A illustrates a block diagram of an example computing device 100 according to some embodiments of the disclosure. In some implementations, the device 100 includes one or more processing units CPU 102 (also referred to as a processor), one or more network interfaces 104, a user interface 106, a non-persistent memory 111, a persistent memory 112, and one or more communication buses 114 for interconnecting these components. The one or more communication buses 114 optionally include circuitry (sometimes referred to as a chipset) that interconnects and controls communications between the system components. Non-persistent memory 111 typically includes high-speed random access memory such as DRAM, SRAM, DDR RAM, ROM, EEPROM, flash memory, while persistent memory 112 typically includes CD-ROM, digital versatile disk (DVD) or other optical storage device, magnetic cassette, magnetic tape, magnetic disk storage device or other magnetic storage device, magnetic disk storage device, optical disk storage device, flash memory device, or other non-volatile solid-state storage device. Persistent memory 112 optionally includes one or more storage devices located remotely from CPU 102. Persistent memory 112 and non-volatile memory devices within non-persistent memory 112 include non-transitory computer-readable storage media. In some implementations, non-persistent memory 111 or alternatively non-transitory computer-readable storage media store the following programs, modules, and data structures, or a subset thereof, possibly in combination with persistent memory 112: Any operating system 116 that includes procedures for handling various basic system services and for performing hardware-dependent tasks • optional network communication module (or instructions) 118 for connecting the system 100 with other devices and / or the communication network 104; An optional classifier training module 120 for training a classifier for assessing a subject for a biological condition related to metal metabolism. an optional data store for a dataset of biological samples from training subjects 122, including feature data for one or more training subjects 124, the feature data including parameters associated with each of the features 126, and a diagnostic status 128 (e.g., an indication that each training subject has been diagnosed with a biological condition associated with metal metabolism or has not been diagnosed with a biological condition associated with metal metabolism); an optional classifier validation module 130 for validating classifiers that distinguish biological states related to metal metabolism; An arbitrary data store for a dataset of biological samples from 132 validation subjects An optional patient classification module 134 for classifying subjects having a biological condition related to metal metabolism, e.g., as trained using the classifier training module 120

[0076] In various implementations, one or more of the above-identified elements are stored in one or more of the aforementioned memory devices and correspond to sets of instructions for performing the functions described above. The above-identified modules, data, or programs (e.g., sets of instructions) need not be implemented as separate software programs, procedures, data sets, or modules, and thus various subsets of these modules and data may be combined or otherwise rearranged in various implementations. In some implementations, the non-persistent memory 111 optionally stores a subset of the above-identified modules and data structures. Furthermore, in some embodiments, the memory stores additional modules and data structures not described above. In some embodiments, one or more of the above-identified elements are stored in a computer system other than that of the visualization system 100 and are addressable by the visualization system 100. As a result, the visualization system 100 can retrieve all or a portion of such data when needed.

[0077] In some embodiments, the system 100 is connected to or includes one or more analytical devices for performing chemical analysis. For example, an optional network communication module (or instructions) 118 is configured to connect the system 100 with one or more analytical devices, for example, via a communication network 104. In some embodiments, the one or more analytical devices include a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS).

[0078] While FIG. 1 illustrates a "system 100," the diagram is intended as a functional description of various features that may be present in a computer system, rather than as a structural schematic of the implementations described herein. In practice, items shown separately may be combined and some items may be separated, as will be recognized by those skilled in the art. Additionally, while FIG. 1 depicts certain data and modules in non-persistent memory 111, some or all of these data and modules may be in persistent memory 112.

[0079] Classification method. Although the system according to the present disclosure is disclosed with reference to FIG. 1, detailed processes and functions of a method 200 for evaluating a subject for a biological condition associated with metal metabolism from a biological sample according to the present disclosure are provided in conjunction with FIGS. 2A-2F.

[0080] As defined above, biological samples related to metal metabolism (also referred to herein as "biological samples") include human biological samples associated with the deposition of certain metals and associated with growth (e.g., hair, nails, and teeth). The biological samples related to metal metabolism of the present disclosure have the requirement of expressing growth along a reference line such that the abundance of a certain metal deposit is detectable over time. In some embodiments, the biological samples related to metal metabolism include a hair shaft, where the reference line corresponds to a line along the length of the hair shaft. In some embodiments, the biological samples related to metal metabolism include a tooth, where the reference line corresponds to a neonatal line of the tooth on the tooth enamel surface. In some embodiments, the biological samples related to metal metabolism include a nail, where the reference line corresponds to a line along the direction of nail growth. For example, the reference line extends from the base of the nail to the tip of the nail.

[0081] In some embodiments, the method 200 includes obtaining (202) a biological sample (e.g., a strand of hair including a hair shaft). The subject is a human. In some embodiments, the subject is a child aged 5 years or less (e.g., the child is aged 5 years or less, 4 years, 3 years, 2 years, 1 year, 9 months, 6 months, 3 months, or 1 month). In some embodiments, the subject is an adult. Section I of FIG. 2B provides an exemplary image of a hair sample of a subject including a hair shaft, according to some embodiments of the present disclosure. The hair sample may simply be cut from the subject (e.g., with the aid of scissors). Thus, the method of obtaining the hair sample is non-invasive. The obtained hair sample has a minimum length of 1 cm (e.g., the hair sample is 1 cm, 2 cm, 3 cm, 4 cm, or 5 cm long). The hair sample may include any portion of the hair (e.g., the tip or the portion between the tip and the hair follicle). Notably, there is no particular requirement that the hair sample include a hair follicle. Section II of Figure 2B provides an exemplary image of a subject's tooth sample, according to some embodiments of the present disclosure. Section III of Figure 2B provides an exemplary image of a subject's nail sample, according to some embodiments of the present disclosure. In the example of teeth or hair, obtaining a biological sample refers to positioning the subject so that the tooth or nail may be sampled.

[0082] In some embodiments, the acquired biological sample is pretreated (204) by washing the biological sample with one or more solvents and / or surfactants and drying. In an example where the biological sample is hair, the hair sample is washed with TRITON X-100® and ultra-pure metal-free water (e.g., MILLI-Q® water) and dried overnight in an oven (e.g., 60° C.). The pretreatment further includes preparing the hair shaft for measurement by placing the hair shaft on a glass slide (e.g., a microscope slide) with an adhesive film (e.g., double-sided tape). The hair shaft is positioned so that the hair shaft is substantially straight. The slide with the hair shaft is then placed in a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS) to perform the analysis (206). If the biological sample is a tooth or nail, the surface of the biological sample is washed (e.g., with surfactants, water, or one or more solvents). The subject is positioned near the LA-ICP-MS to perform the analysis.

[0083] In some embodiments, the LA-ICP-MS analysis includes pre-ablation of the biological sample to remove surface debris and / or impurities from the biological sample. Pre-ablation is performed using very low laser energy to release particles only on the surface of the biological sample, but not from below the surface of the biological sample. For example, pre-ablation can be performed using a laser wavelength of 193 nm and 0.4 J / cm. 2 Laser energy of less than 0.4 J / cm 2 , 0.3J / cm 2 , 0.2J / cm 2 , or 0.1 J / cm 2 In some embodiments, the laser energy is 0.2 J / cm 2 ~0.4J / cm 2 The range is.

[0084] After pre-ablation, the method 200 includes laser sampling the biological sample to obtain ion samples from respective locations along the reference line of the biological sample (208). As described above, in the hair shaft example, the reference line corresponds to a line along the length of the hair shaft. For example, section A of FIG. 2B shows a hair shaft with a reference line 201 along the length of the hair shaft. In the tooth example, the reference line corresponds to the tooth's neonatal line on the tooth's enamel surface. For example, section II of FIG. 2B shows a tooth 220 including a portion of enamel 226 and primary dentin 224. The reference line 222 corresponds to the neonatal line of the tooth 220. Neonatal line in this specification refers to a particular band of growth line on the enamel portion of the tooth. In the nail example, the reference line corresponds to a line in the direction of nail growth. For example, section II of FIG. 2B shows a nail 230 with a reference line 232 extending from the base of the nail to the tip of the nail. Sampling involves irradiating a biological sample with a laser beam (e.g., a laser ablating the hair shaft) and ionizing a plurality of particles with an inductively coupled plasma mass spectrometer. For example, regions 200A and 200B in section I of FIG. 2B correspond to exemplary locations along the hair shaft irradiated with a laser during laser ablation. The mass spectrometer analyzes ion samples obtained from each location (210). FIG. 2C provides an exemplary schematic diagram of a laser sampling a subject's hair shaft according to some embodiments of the present disclosure. The laser 202 in FIG. 2C irradiates region 200C on the hair shaft, thereby releasing particles 204. The particles 204 are ionized by an inductively coupled plasma (ICP) and further analyzed by a mass spectrometer (MS).

[0085] In some embodiments, the laser radiation has a wavelength of 193 nm and a wavelength of 0.6 to 1.5 J / cm 2 Laser energies in the range of 0.6 J / cm 2 , 0.7J / cm 2 , 0.8J / cm 2 , 0.9J / cm 2 , 1.0J / cm 2 , 1.1J / cm 2 , 1.2J / cm 2 , 1.3J / cm2 , 1.4J / cm 2 , or 1.5 J / cm 2 In some embodiments, the laser energy is 0.9-1.3 J / cm. 2 In some embodiments, the laser has a beam diameter in the range of 25 micrometers to 35 micrometers (e.g., 25, 27.5, 30, 32.5, or 35 micrometers). In some embodiments, the laser has a beam diameter of 30 micrometers. In the example of sampling a hair shaft, the size of the laser beam, wavelength and / or laser energy are adjusted such that the laser sampling ablates a majority of the hair shaft without releasing particles from the adhesive film and / or glass slide holding the hair shaft.

[0086] The laser irradiation is repeated and elemental isotope data is collected sequentially at a plurality of locations along the biological sample (e.g., regions 200A and 200B of the hair shaft in section I of FIG. 2B). In some embodiments, the plurality of locations along the reference line of the biological sample includes at least 100 locations (e.g., 100, 150, 200, 250, 300, 350, 400, 450, or 500 locations). In some embodiments, each location (e.g., regions 200A and 200B in section I of FIG. 2B) is adjacent to one another. By this method, each region (e.g., regions 200A and 200B) corresponding to a different location on the biological sample is thereby associated with an abundance of an elemental isotope (e.g., metal isotopes Zn, Fe, Pb, and Mn shown in FIG. 2C). In some embodiments, each location is separated by a predetermined distance. In some embodiments, sampling is performed along the baseline of the biological sample starting from the respective position closest to the tip of the hair (e.g., the position corresponding to the subject's youngest age). Generally, sampling can be performed starting from the respective position closest to the tip or root, as long as the sampling direction is known and an appropriate trained classifier is used for the analysis.

[0087] The laser sampling thereby generates a set of data points. Each set of data points corresponds to the abundance (e.g., concentration) of each elemental isotope measured at multiple positions along the biological sample. Each position on the baseline of the biological sample corresponds to a specific growth time of the biological sample. In some embodiments, in the example of a hair shaft, each position corresponds to about a 130-minute period of hair growth (e.g., the period of hair growth calculated using a laser beam size of 30 micrometers and an average speed of hair growth of 1 cm per month). By correlating the multiple positions along the baseline of the biological sample to the corresponding period of growth, a first data set is obtained that includes multiple traces. Each trace includes the time-dependent abundance of each elemental isotope measured from the biological sample.

[0088] FIG. 2D provides an example diagram of a trace 208, according to some embodiments of the present disclosure. Each data point in FIG. 2D corresponds to the abundance of a particular elemental isotope (i.e., count ratio on the y-axis) measured at multiple positions along the biological sample (i.e., laser distance on the bottom x-axis). The distance traveled by the laser along the biological sample corresponds to the estimated growth (i.e., biological time) of the biological sample, as shown on the top x-axis. For example, FIG. 2D shows the abundance of a particular elemental isotope measured for a hair along a distance of 1.2 cm (12,000 micrometers). Such a distance corresponds to a biological time of approximately 35 days. The biological time is estimated using the average rate of hair growth (e.g., 1 cm per month).

[0089] In some embodiments, the plurality of elemental isotopes is selected from the elemental isotopes listed in Table 1. In some embodiments, the plurality of elemental isotopes includes at least 50%, 60%, 70%, 80% or 90% of the isotopes included in Table 1. [Table 1]

[0090] In some embodiments, the method 200 includes analyzing a first data set including a plurality of acquired traces (212), each trace corresponding to a time-dependent abundance (e.g., a time-dependent concentration) of a respective elemental isotope. In some embodiments, analyzing the data includes performing a customized operation to clean the data (214). In some embodiments, cleaning the data includes smoothing the data over a period of time and / or removing data points above or below a predefined threshold. In some embodiments, the data analysis includes removing data points from the traces where the mean absolute difference between adjacent data points is three times the standard deviation of the mean absolute difference between adjacent points. FIG. 2D illustrates the operation of removing data points above a predefined threshold. Peak 210 corresponds to a data point where the mean absolute difference between adjacent data points is three times or more the standard deviation of the mean absolute difference between adjacent points. Thus, peak 210 is removed from trace 208.

[0091] In some embodiments, analyzing the data set further comprises normalizing each trace to an internal standard. In some embodiments, in an example where the sample is a hair shaft, the internal standard is sulfur, which is the most abundant elemental isotope in hair, and therefore can be used as a measure of hair density and / or hardness. However, in practice, any element detected in the sample that is evenly incorporated during development / growth of a biological sample that does not vary with environmental exposure (e.g., diet) can serve as an internal standard, including any of the elements disclosed in the table of the present disclosure. For example, if the sample is a tooth, bismuth-209 can be used as the internal standard.

[0092] The method 200 includes performing recurrence quantification analysis (RQA) on a first data set including time-dependent traces of elemental isotopes to obtain a set of features describing the dynamic periodic properties of the traces. RQA measures the variation of the time-dependent traces of elemental isotopes. RQA involves the estimation of features describing the periodic nature of a given waveform, including determinism, average diagonal length, and entropy. RQA methods and features are described, for example, in Webber et al., “Simpler Methods Do It Better: Success of Recurrence Quantification Analysis as a General Purpose Data Analysis Tool,” Physics Letters A 373, 3753-3756 (2009), and Marwan et al., “Recurrence Plots for the Analysis of Complex Systems,” Physics Reports 438, 237-239 (2007), the contents of each of which are incorporated herein by reference in their entirety. In some embodiments, the time-dependent traces of elemental isotopes are analyzed using other analytical methods known in the art, such as Fourier transform, wavelet analysis, and cosine analysis. Such methods may be applied to derive similar metrics, including spectral analysis of frequency components and their associated power. These metrics and related derivative metrics may be used in place of RQA-derived features to analyze the time-dependent traces of elemental isotopes obtained from biological samples for predictive classification purposes.

[0093] RQA includes constructing a recursion plot (216) to visualize and analyze the dynamic time structure in each acquired trace. FIG. 2E provides an exemplary illustration of the variation in abundance of a single isotope from each trace, according to some embodiments of the present disclosure. Section I of FIG. 2E shows a trace corresponding to the time-dependent abundance (or concentration) of copper (Cu) measured from a hair shaft of a subject. The y-axis shows the measured abundance of copper, and the x-axis shows successive measurements along the hair shaft reflecting longitudinal increments of time. Section II of FIG. 2E is a phase portrait derived from the trace of section I. From the one-dimensional trace measured from the hair shaft, an additional dimension has been computationally derived to embed the trace into a higher dimensional space called a phase portrait, where t refers to the value of the original trace, and the dimensions (t+τ) and (t+2τ) are derived from delaying the original time series by intervals τ. Subsequent analysis is then performed on the embedded phase portrait to construct a recursion plot and a recursion quantitative analysis. Section III shows the recursion quantification plots for copper isotopes derived from the phase portraits shown in Section II. The RQA method examines the intervals of delay between states in a given system, with the black dots reflecting the time intervals when the system revisits the same state. Periodic processes in which a system continuously repeats a given pattern of states appear in the recursion plot as diagonal black lines, stable periods appear as square structures, spurious repetitions appear as black dots, and unique events appear as white spaces.

[0094] In some embodiments, the recurrence plot is constructed for a single elemental isotope trace (e.g., for an elemental isotope selected from Table 1), or for a combination of two elemental isotopes. For example, FIG. 2E shows a recurrence plot for copper isotopes. Alternatively, a recurrence plot is constructed to visualize the interacting periodic patterns of two elemental isotopes. In some embodiments, a recurrence plot is constructed for a combination of three or more elemental isotopes.

[0095] The method 200 further includes analyzing the recurrence plot to obtain a set of features associated with the recurrence plot (218). These features, which may be interchangeably referred to as "rhythmic features" or "dynamic features," provide a quantitative measure that describes the periodicity present in the multiple traces. The features are selected from mean diagonal length (MDL), determinism (or predictability), recurrence time (RT), entropy, trapping time (TT), and laminarity. Definitions of each of these feature types are provided in the definitions section above.

[0096] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 2.

[0097] In some embodiments, the set of features includes all of the features listed in Table 2.

[0098] In some embodiments, the set of features includes at least 50%, 60%, 70%, 80% or 90% of the features listed in Table 2. In some embodiments, the features thus derived from Table 2 are considered to be "core" features for assessing a subject for a first biological condition (such as, for example, an autism spectrum disorder) according to the present disclosure. In some embodiments, the set of features further includes (in addition to the core features) one or more features listed in Table 3. [Table 2] [Table 3-1] [Table 3-2] [Table 3-3] [Table 3-4] [Table 3-5]

[0099] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of three elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 2. In some embodiments, the set of features includes all of the features listed in Table 3. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 3.

[0100] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Tables 2 and 3. In some embodiments, the set of features includes all of the features listed in Tables 2 and 3. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Tables 2 and 3.

[0101] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 4. In some embodiments, the set of features includes all of the features listed in Table 4. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 4.

[0102] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 5. In some embodiments, the set of features includes all of the features listed in Table 5. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 5.

[0103] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 6. In some embodiments, the set of features includes all of the features listed in Table 6. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 6.

[0104] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 7. In some embodiments, the set of features includes all of the features listed in Table 7. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 7.

[0105] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 8. In some embodiments, the set of features includes all of the features listed in Table 8. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 8.

[0106] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 9. In some embodiments, the set of features includes all of the features listed in Table 9. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 9.

[0107] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in Table 10. In some embodiments, the set of features includes all of the features listed in Table 10. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80%, or 90% of the features listed in Table 10.

[0108] In some embodiments, the set of features, each feature associated with a respective elemental isotope or combination of elemental isotopes (e.g., a combination of two elemental isotopes, or a combination of more than two elemental isotopes), is selected from the features listed in any combination of Tables 2, 3, 4, 5, 6, 7, 8, 9, and 10. In some embodiments, the set of features includes all of the features listed in Tables 2, 3, 4, 5, 6, 7, 8, 9, and 10. In some embodiments, the set of features includes at least 5%, 10%, 15%, 20%, or 25% of the features listed in Tables 2, 3, 4, 5, 6, 7, 8, 9, and 10.

[0109] The method 200 further includes inputting the obtained set of features into a trained classifier (220). In some embodiments, the trained classifier includes a predictive computation algorithm for obtaining a probability of the subject having a biological condition associated with metal metabolism (222). In some embodiments, the predictive computation algorithm calculates Equation 1:

number

[0110] The features 1 through k are the features listed in Table 2, and optionally selected from Table 3. The weight parameter β 1,…,k is defined based on training of the classifier. The probability p(subject) is provided as a number ranging from 0 to 1, with 1 corresponding to a 100% probability that the subject has a biological condition related to metal metabolism.

[0111] In some embodiments, the method 200 also includes applying (224) a predefined threshold to the obtained probability p(subject). If the obtained probability p(subject) exceeds the predefined threshold, the subject is evaluated as having a biological condition associated with metal metabolism. If the obtained probability is below the predefined threshold, the subject is evaluated as not having a biological condition associated with metal metabolism. In some embodiments, the predefined threshold is between 0.3 and 0.6 (e.g., the predefined threshold is 0.3, 0.35, 0.4, 0.45, 0.5, 0.55, or 0.6). In some embodiments, the predefined threshold is 0.45. In some embodiments, the obtained probability is expressed in terms of associated odds (e.g., an odds ratio (OR) that may be derived from the probability such that OR=p / (1-p)). For example, the evaluation includes evaluating the odds that the subject has a biological condition associated with metal metabolism.

[0112] In some embodiments, the method 200 further includes distinguishing an alternative condition, for example, a first biological condition associated with metal metabolism from a second biological condition associated with metal metabolism. In some embodiments, the alternative condition is associated with the absence of a known condition (e.g., a neurotypical condition (NT)). In some embodiments, the first biological condition associated with metal metabolism is associated with autism spectrum disorder (ASD) and the alternative condition is associated with attention-deficit / hyperactivity disorder (ADHD). In some embodiments, the alternative condition is any other neurodevelopmental condition, or a comorbid diagnosis of two neurodevelopmental conditions. FIG. 2F provides a diagram of experimental data illustrating the distinction between autism spectrum disorder (ASD) and other neurodevelopmental disorders according to some embodiments of the present disclosure. It is noted that based on the experimental data shown in FIG. 2F, the method 200 of the present disclosure can distinguish between autism spectrum disorder and ADHD. As shown, the present disclosure can also distinguish autism spectrum disorder from comorbidity (CM) cases diagnosed with both autism spectrum disorder and ADHD.

[0113] Process and functional details of method 200 for assessing a subject for a biological condition related to metal metabolism from a biological sample are now disclosed with reference to Figure 2, and Figures 3A-3E collectively provide a basic process and functional flow chart of method 3000 for assessing a subject for a biological condition related to metal metabolism from a biological sample, according to some embodiments of the present disclosure, with optional blocks indicated by dashed boxes. In some embodiments, method 3000 corresponds to method 200.

[0114] Block 3100 of Figure 3A. Method 3000 includes sampling, e.g., with a laser (e.g., LA-ICP-MS), each location in a plurality of locations along a baseline on a biological sample associated with a metal metabolism of a subject, thereby obtaining a plurality of ion samples (e.g., hair shaft regions 200A and 200B of section I of Figure 2B). Each ion sample in the plurality of ion samples corresponds to a different location in the plurality of locations, and each location in the plurality of locations represents a different growth period of the biological sample associated with metal metabolism.

[0115] Block 3200 of FIG. 3A. The method 3000 includes analyzing each ion sample in the plurality of ion samples with a mass spectrometer, thereby obtaining a first data set. The first data set includes a plurality of traces (e.g., trace 208 of FIG. 2D). Each trace in the plurality of traces is a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the plurality of ion samples.

[0116] Block 3300 of FIG. 3A. Method 3000 includes deriving a second data set from a plurality of traces including a set of features (e.g., a set of features selected from the features listed in Table 2). Each feature in the set of features is determined by the variation of a single isotope or combination of isotopes in the plurality of traces. For example, section III of FIG. 2E shows a recurrence plot of copper isotopes derived from the traces of section II of FIG. 2E. The variation in copper isotope abundance is observed as a diagonal pattern in the recurrence plot.

[0117] Block 3400 of Figure 3A. In some embodiments, the method 3000 also includes inputting the set of features into a trained classifier, thereby obtaining from the trained classifier a probability that the subject has a first biological condition related to metal metabolism. In some embodiments, the trained classifier is a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, or a regression model.

[0118] Block 3110 of Figure 3B. In some embodiments, sampling includes irradiating the biological sample associated with the metal metabolism of the subject with a laser, thereby extracting a plurality of particles from the biological sample associated with the metal metabolism of the subject, and ionizing the plurality of particles with an inductively coupled plasma mass spectrometer, thereby obtaining a plurality of ion samples (e.g., Figure 2C).

[0119] Block 3120 of Figure 3B. In some embodiments, the multiple locations along the hair shaft (e.g., regions 200A and 200B of the hair shaft in section I of Figure 2B) are ordered such that a first location in the multiple locations along the biological sample associated with the subject's metal metabolism corresponds to a location closest to a tip of the biological sample associated with the subject's metal metabolism.

[0120] Block 3130 of FIG. 3B. Method 3000 also includes a biological sample that is associated with the subject's metal metabolism and a solvent or detergent prior to sampling the subject's hair shaft. For example, the hair shaft is washed with TRITON X-100® and ultra-pure metal-free water (e.g., MILLI-Q® water) and dried overnight in an oven (e.g., 60° C.).

[0121] Block 3140 of FIG. 3B. Method 3000 also includes irradiating the biological sample associated with the subject's metal metabolism with a low power laser prior to sampling the hair shaft of the subject to remove any debris from the biological sample associated with the subject's metal metabolism (e.g., pre-removing hair shafts, teeth, or nails). For example, pre-ablation may be performed using a laser wavelength of 193 nm and 0.4 J / cm. 2 Laser energy of less than 0.4 J / cm 2 , 0.3J / cm 2 , 0.2J / cm 2 , or 0.1 J / cm 2 In some embodiments, the laser energy is 0.2 J / cm 2 ~0.4J / cm 2 The range is.

[0122] Block 3141 of Figure 3B. The biological sample relevant to the metal metabolism of the subject is selected from the group consisting of hair shafts, teeth, and nails (e.g., the hair shafts, teeth, and nails illustrated in sections I, II, and III of Figure 2B, respectively).

[0123] Block 3141-1 of Figure 3B. The biological sample relevant to the metal metabolism of the subject is a hair shaft, and the reference line corresponds to the longitudinal direction of the hair shaft (eg, reference line 201 of section I of Figure 2B).

[0124] Block 3141-1 of Figure 3B. The biological sample relevant to the metal metabolism of the subject is a tooth and the reference line corresponds to the tooth's neonatal line on the tooth's enamel surface (e.g., reference line 222 along the neonatal line of tooth 220 in section II of Figure 2B). In some embodiments, the biological sample relevant to the metal metabolism of the subject is a nail and the reference line corresponds to a line extending from the base of the nail to the tip of the nail (e.g., reference line 232 of nail 230 in section III of Figure 2B).

[0125] Block 3210 of Figure 3C. The plurality of elemental isotopes is selected from the elemental isotopes listed in Table 1. In some embodiments, the plurality of elemental isotopes includes at least 50%, 60%, 70%, 80% or 90% of the isotopes included in Table 1.

[0126] Block 3220 of FIG. 3C. Each trace in the plurality of traces includes a plurality of data points. Each data point is an instance of a respective location in the plurality of locations. In some embodiments, each trace includes at least 100 locations (e.g., 100, 150, 200, 250, 300, 350, 400, 450, or 500 locations). In some embodiments, each data point corresponds to an approximately 130 minute period of hair growth (e.g., the period of hair growth is calculated using a laser beam size of 30 micrometers and an average rate of hair growth of 1 cm per month).

[0127] Block 3230 of FIG. 3C. The concentration of the corresponding elemental isotope corresponds to the relative abundance of the corresponding elemental isotope relative to the reference elemental isotope. The reference elemental isotope is included in the plurality of ion samples. In some embodiments, the reference elemental isotope is sulfur.

[0128] Block 3310 of FIG. 3D. The set of features is selected from the features listed in Table 2. In some embodiments, the set of features includes the features listed in Table 2. In some embodiments, the set of features includes at least 50%, 60%, 70%, 80% or 90% of the features listed in Table 2. Each feature in the set of features is associated with a single respective trace of the plurality of traces or two respective traces of the plurality of traces.

[0129] Block 3320 of FIG. 3D. The set of features includes, in addition to the features selected from the features listed in Table 2, one or more features listed in Table 3.

[0130] Block 3330 of Figure 3D. Derivation of the second data set includes removing data points from the plurality of data points that do not meet a first criterion. In some embodiments, the first criterion includes a mean absolute difference between adjacent data points in the plurality of data points being three times the standard deviation of the mean absolute difference between adjacent points (e.g., peak 210 is removed from trace 208 of Figure 2D).

[0131] Block 3340 of Figure 3D. The set of features is selected from average diagonal length, determinism, recursion time, entropy, trapping time, and laminarity.

[0132] Block 3410 of FIG. 3E. In some embodiments, the trained classifier

number

[0133] Block 3420 of Figure 3E. In accordance with determining that p(subject) exceeds a predetermined threshold, the subject is determined to have a biological condition associated with metal metabolism.

[0134] Block 3500 of Figure 3E. In some embodiments, evaluating the subject for a biological state associated with metal metabolism further includes distinguishing between a first biological state associated with metal metabolism and a second biological state associated with metal metabolism that is different from the first biological state associated with metal metabolism.

[0135] Block 3510 of Figure 3E. In some embodiments, the first biological condition is an autism spectrum disorder and the second biological condition is attention-deficit / hyperactivity disorder.

[0136] Block 3510 of Figure 3E. In some embodiments, the first biological condition associated with metal metabolism is selected from the group consisting of autism spectrum disorder (ADS), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney transplant rejection, and pediatric cancer.

[0137] 3A-3E is performed by a device executing one or more programs (e.g., one or more programs stored in non-persistent memory 111 or persistent memory 112 of FIG. 1) that include instructions to perform method 3000. In some embodiments, method 3000 is performed by a system that includes at least one processor (e.g., processing core 102) and memory (e.g., one or more programs stored in non-persistent memory 111 or persistent memory 112) that include instructions to perform method 3000.

[0138] Training the classifier. Methods and features of method 3000 are now disclosed with reference to Figures 3A-3E, and Figure 4 provides a process and functional flow chart of a method 4000 for training a classifier for assessing a subject for a biological condition associated with metal metabolism according to some embodiments of the present disclosure, with optional blocks indicated by dashed boxes. The method of training a classifier includes collecting biological samples associated with metal metabolism for each training subject from a plurality of training subjects, and training a classifier using the collected biological samples. The training subjects are humans. Each training subject has a diagnostic status indicating that it has been diagnosed with a biological condition associated with metal metabolism or has not been diagnosed with a biological condition associated with metal metabolism. In some embodiments, the training subjects are children aged 5 years or less (e.g., 5 years, 4 years, 3 years, 2 years, 1 year, 9 months, 6 months, 3 months, or 1 month or less). The steps of method 4000 described below with reference to blocks 4100-4300 are performed for each training subject in the plurality of training subjects.

[0139] Block 4100 of Figure 4. Method 4000 includes sampling, with a laser, each location in the corresponding plurality of locations of the corresponding baseline on the corresponding biological sample associated with metal metabolism of each training subject, thereby obtaining a corresponding plurality of ion samples, each ion sample in the corresponding plurality of ion samples for a different location in the corresponding plurality of locations, and each location in the corresponding plurality of locations representing a different growth period of the corresponding biological sample associated with metal metabolism.

[0140] Block 4200 of Figure 4. The method 4000 includes acquiring a respective first data set including a respective ion sample in the respective plurality of ion samples using a mass spectrometer, the respective first data set including a respective plurality of traces, each trace in the respective plurality of traces being a concentration over time of a corresponding elemental isotope in the plurality of elemental isotopes collectively determined from the respective plurality of ion samples.

[0141] Block 4300 of FIG. 4. Method 4000 includes deriving respective second data sets from the corresponding plurality of traces including corresponding sets of features, each feature in the corresponding set of features determined by variation of a single isotope or combination of isotopes in the corresponding plurality of traces.

[0142] Block 4400 of FIG. 4. Method 4000 further includes training an untrained classifier or a partially untrained classifier with (i) a corresponding set of features of the second data set of each of the training subjects of the plurality of training subjects, and (ii) a corresponding diagnostic status of each of the training subjects of the plurality of training subjects selected from among the first diagnostic status and the second diagnostic status, thereby obtaining a trained classifier. The trained classifier provides an indication of whether the test subject has a first biological condition related to metal metabolism based on the value of the features of the set of features obtained from the biological sample related to metal metabolism of the test subject. In some embodiments (block 4410), the trained classifier is a neural network algorithm, a convolutional neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, or a regression model. In some embodiments (block 4420), the trained classifier is a multinomial or binomial type. In some embodiments, the trained classifier can be used to make a binary prediction as to whether the sample was from a subject having a first biological state related to metal metabolism, or it may be polynomial, where subjects without a diagnosis are distinguished from subjects having a first biological state related to metal metabolism or a second biological state related to metal metabolism, where the second biological state is different from the first biological state.

[0143] In some embodiments, the classifier is a neural network or a convolutional neural network. See Vincent et al., 2010, "Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion," J Mach Learn Res 11, pp. 3371-3408; Larochelle et al., 2009, "Exploring strategies for training deep neural networks," J Mach Learn Res 10, pp. 1-40; and Hassoun, 1995, Fundamentals of Artificial Neural Networks, Massachusetts Institute of Technology, each of which is incorporated herein by reference.

[0144] SVM is a method used by Cristianini and Shawe-Taylor, 2000, “An Introduction to Support Vector Machines,” Cambridge University Press, Cambridge, Boser et al., 1992, “A training algorithm for optimal margin classifiers,” in Proceedings of the 5 thAnnual ACM Workshop on Computational Learning Theory, ACM Press, Pittsburgh, Pa., pp. 142-152, Vapnik, 1998, Statistical Learning Theory, Wiley, New York, Mount, 2001, Bioinformatics: sequence and genome analysis, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NY, Duda, Pattern Classification, Second Edition, 2001, John Wiley & Sons, Inc., pp. 259, 262-265, and Hastie, 2001, The Elements of Statistical Learning, Springer, New York, and Furey et al., 2000, Bioinformatics 16, 906-914, each of which is incorporated herein by reference in its entirety. When used for classification, SVM separates a given set of binary labeled data using a hyperplane that is maximally far from the labeled data. When linear separation is not possible, SVMs can be combined with the technique of "kernels" that automatically achieve a nonlinear mapping to the feature space: the hyperplane found by the SVM in the feature space corresponds to a nonlinear decision boundary in the input space.

[0145] Decision trees are generally described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is incorporated herein by reference. Tree-based methods divide the feature space into a set of rectangles and then fit a model (such as a constant) to each rectangle. In some embodiments, the decision tree is a random forest regression. One specific algorithm that can be used is a classification and regression tree (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 Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York. pp. 396-408 and pp. 411-412, which are incorporated herein by reference. CART, MART, and C4.5 are described in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, Chapter 9, which is incorporated herein by reference in its entirety. Random forests are described in Breiman, 1999, "Random Forests--Random Features," Technical Report 567, Statistics Department, UC Berkeley, September 1999, which is incorporated herein by reference in its entirety.

[0146] Clustering (e.g., unsupervised and supervised clustering model algorithms) are described in Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, (hereinafter "Duda 1973"), pages 211-256, which is incorporated herein by reference in its entirety. As described in section 6.7 of Duda 1973, the problem of clustering is described as one of finding natural groupings in a data set. To identify natural groupings, two problems are addressed. First, a method for measuring the similarity (or dissimilarity) between two samples is determined. This metric (similarity measure) is used to ensure that samples in one cluster are more similar to each other than samples in other clusters. Second, the similarity measure is used to determine a mechanism for partitioning the data into clusters. Similarity measures are discussed in section 6.7 of Duda 1973, where it is stated that one way to begin a clustering study is to define a distance function and compute a matrix of distances between all pairs of samples in the training set. If distance is a good measure of similarity, then the distance between reference entities in the same cluster will be significantly shorter than the distance between reference entities in different clusters. However, as stated on page 215 of Duda 1973, clustering does not require the use of a distance metric. For example, a non-metric similarity function s(x,x') may be used to compare two vectors x and x'. Conventionally, s(x,x') is a symmetric function whose value is large if x and x' are "similar" in some way. An example of a non-metric similarity function (x,x') is provided on page 218 of Duda 1973. Once a method for measuring "similarity" or "dissimilarity" between points in a data set has been chosen, clustering requires a criterion function that measures the clustering quality of any partition of the data. Partitions of the data set that drive the criterion function to its extremes are used to cluster the data, see Duda 1973, p. 217.Criterion functions are discussed in Section 6.8 of Duda 1973. More recently, Duda et al., Pattern Classification,2. nd edition, John Wiley & Sons, Inc. New York. Pages 537-563 provide details on clustering. Details on clustering techniques are provided in Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, NY; Everitt, 1993, Cluster analysis (3d ed.), Wiley, New York, NY; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, New Jersey, each of which is incorporated herein by reference. Specific exemplary clustering techniques that may be used in the present disclosure include, but are not limited to, hierarchical clustering (agglomerative clustering using nearest neighbor, farthest neighbor, average linkage, centroid, or sum of squares algorithms), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. In some embodiments, the clustering comprises unsupervised clustering, where no preconceptions are imposed as to which clusters should be formed when the training set is clustered.

[0147] Regression models such as the multicategory logit model are described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, which is incorporated by reference in its entirety. In some embodiments, the classifier uses the regression model disclosed in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York.

[0148] 4 is performed by a device executing one or more programs (e.g., one or more programs stored in non-persistent memory 111 or persistent memory 112 of FIG. 1) that include instructions to perform method 4000. In some embodiments, method 4000 is performed by a system that includes at least one processor (e.g., processing core 102) and memory (e.g., one or more programs stored in non-persistent memory 111 or persistent memory 112) that include instructions to perform method 4000. EXAMPLES

[0149] Working Example Example 1 - Assessment of Subjects with Autism Spectrum Disorder Two subjects (subject 1 and subject 2) were evaluated for autism spectrum disorder using the method 200 described with respect to Figures 2A-2F. Table 4 shows the results including the features of Table 2 (e.g., column "Feature") associated with the respective parameter estimates β obtained from the training set and the empirical results (e.g., x values) for subject 1 and subject 2. The β values ​​are obtained by estimating each feature of the training data set describing the change in log odds of autism spectrum disorder status associated with a one unit change on the respective feature. The estimated parameters β and x values ​​for each subject are input into an algorithm that calculates p(subject) for each subject (see Equation 1 above) given the calculated α parameter of 36.31. For subject 1, the estimated parameters β and experimental outcome x yielded an estimated probability p(subject 1) of 2.28% that subject 1 has autism spectrum disorder. For subject 2, the estimated parameters β and experimental outcome x yielded an estimated probability p(subject 2) of 96.9% that subject 2 has autism spectrum disorder. Thus, at a given threshold of 50%, subject 1 is assessed as not having an autism spectrum disorder and subject 2 is assessed as having an autism spectrum disorder. Furthermore, the odds of subject 1 having an autism spectrum disorder are equal to 0.023 and the odds of subject 2 having an autism spectrum disorder are equal to 31.2. The odds are calculated from the probabilities using Equation 2:

number

[0150] Example 2 - Receiver Operating Characteristic (ROC) curve. FIG. 5A shows an experimental receiver operating characteristic (ROC) curve for evaluating the accuracy of the disclosed method for evaluating a subject for autism spectrum disorder, according to some embodiments. In the experiment described with respect to FIG. 5A, the evaluation is performed by measuring the hair shaft of the subject. The ROC curve can be used to evaluate the performance of a binary classifier. The ROC curve is plotted as sensitivity (also referred to as true positive rate) against specificity (also referred to as true negative rate). A perfect classifier would have 100% sensitivity and 100% specificity, and an area under the curve (AUC) corresponding to 1. As shown in FIG. 5A, the ROC curve derived from the experimental data for evaluating the performance of the disclosed classification method has an AUC corresponding to 0.947, indicating that the disclosed method has an accuracy of more than 90% for evaluating that a subject has autism spectrum disorder.

[0151] Example 3 - Evaluation of a subject for autism spectrum disorder from one or two parental hair samples To develop a classifier that can determine whether a subject has autism spectrum disorder or not, hair was collected from the parents (biological mother and father) of twins in a Sweden-based study (Roots of Autism and ADHD Study in Sweden-RATSS; Marwan et al., 2007, “Recurrence plots for the analysis of complex systems,” Phys. Rep. 438, 237-329.). The aim of the study was to predict a child's diagnosis of autism spectrum disorder (ASD) from the parents' hair alone. The children underwent clinical testing for autism. No data about the children other than the diagnosis was used in the analysis. Three classifiers were developed: a) a classifier using only maternal hair to predict autism in children (n=29; 14 ASD cases, 15 controls), b) a classifier using only paternal hair (n=23; 9 ASD cases, 14 controls), and c) a classifier using both maternal and paternal hair (n=52; 23 ASD cases, 29 controls).

[0152] Table 5 shows the features and their β values ​​used for the maternal hair cohort, the paternal hair cohort, and the maternal and paternal hair cohorts combined. The β values ​​are obtained by estimating each feature for each cohort describing the change in the log odds of autism spectrum disorder status associated with a one-unit change on the respective feature.

[0153] 5B, 5C, and 5D show experimental ROC curves for evaluating the accuracy of trained classifiers for autism spectrum disorder based on mother's hair, father's hair, and a combination of mother's hair and father's hair, respectively, according to some embodiments. As shown in FIG. 5B, the ROC curve derived from experimental data for evaluating the performance of the disclosed classification method has an AUC corresponding to 0.886, indicating that the disclosed method has an accuracy of more than 85% for assessing that a subject has autism spectrum disorder based on a hair sample of the subject's mother. As shown in FIG. 5C, the ROC curve derived from experimental data for evaluating the performance of the disclosed classification method has an AUC corresponding to 0.800, indicating that the disclosed method has an accuracy of more than 80% for assessing that a subject has autism spectrum disorder based on a hair sample of the subject's father. As shown in FIG. 5D, the ROC curve derived from the experimental data for evaluating the performance of the disclosed classification method has an AUC corresponding to 0.859, indicating that the disclosed method has greater than 85% accuracy for assessing that a subject has an autism spectrum disorder based on a combination of a hair sample from the subject's mother and a hair sample from the subject's father. [Table 5-1] [Table 5-2] [Table 5-3] [Table 5-4]

[0154] Example 4 - Amyotrophic Lateral Sclerosis (ALS) ALS participants fulfilling the revised EI Escorial Word Federation of Neurology criteria (N = 36) were recruited at the ALS clinic. Clinical and family history data were obtained. Age- and sex-matched control participants were recruited at the oral surgery clinic. Control subjects (N = 31) were excluded if they or a first- or second-degree family member had a neurodegenerative disease. Participants or next of kin provided informed consent.

[0155] ALS was assessed from tooth samples. Table 6 shows the features used and their corresponding β values. β values ​​are obtained by estimating each feature in each cohort, describing the change in log odds of ALS status associated with a one-unit change in the respective feature. Figure 6 shows an experimental ROC curve for evaluating the accuracy of the disclosed method for assessing ALS across cohorts. As shown in Figure 6, the ROC curve derived from the experimental data for evaluating the performance of the disclosed classification method has an AUC corresponding to 0.869, indicating that the disclosed method has an accuracy of 85% across cohorts for the assessment of ALS based on tooth samples. [Table 6]

[0156] Example 5 - Schizophrenia Participants with a DSM-IV diagnosis of schizophrenia were selected from the Genetic Risk and Outcome of Psychosis (GROUP) study (n = 20), and unaffected siblings served as controls (n = 7). Severity of positive symptoms, negative symptoms, and general psychopathology was assessed by the Positive and Negative Symptom Scale (PANSS). In addition, participants with a DSM-IV diagnosis of schizophrenia (n = 25) and controls (n = 24) were selected from the Avon Longitudinal Study of Parents and Children (ALSPAC), a prospective longitudinal cohort study based in the UK. The presence of DSM-IV schizophrenia in ALSPAC was determined at ages 18 and 24 using semi-structured interviews based on the Schedule for Clinical Assessment of Neuropsychiatric Disorders (Scan version 2.0).

[0157] Schizophrenia was assessed from dental samples. Table 7 shows the features used and their corresponding β values. The β values ​​are obtained by estimating each feature in each cohort, describing the change in log odds of schizophrenia status associated with a one-unit change in the respective feature. Figure 7 shows the empirical ROC curve for assessing schizophrenia across the cohort. As shown in Figure 7, the ROC curve has an AUC corresponding to 1.000, indicating that the disclosed method has 100% accuracy in determining schizophrenia based on dental samples across the cohort. [Table 7-1] [Table 7-2]

[0158] Example 6 - Irritable Bowel Disease (IBD) Subjects were recruited from a study based in Portugal. Dental samples were obtained from 11 patients diagnosed with IBD (Crohn's disease = 6, ulcerative colitis / indeterminate colitis = 5) and 16 unaffected controls. All participants were born and raised in the same Portuguese province. Each subject was assessed for IDB using a similar method described above for Examples 2 and 3. IDB was assessed from dental samples. Table 8 shows the features used and their corresponding β values. β values ​​are obtained by estimating each feature in each cohort, describing the change in the log odds of IBD status associated with a one-unit change for the respective feature.

[0159] Figure 8 shows an experimental ROC curve for evaluating the accuracy of the disclosed method for assessing subjects with schizophrenia. As shown in Figure 8, the ROC curve derived from experimental data for evaluating the performance of the disclosed classification method has an AUC corresponding to 0.915, indicating that the disclosed method has an accuracy of over 90% for IBD measurement based on dental samples. [Table 8-1] [Table 8-2] [Table 8-3] [Table 8-4] [Table 8-5] [Table 8-6] [Table 8-7] [Table 8-8] [Table 8-9] [Table 8-10] [Table 8-11] [Table 8-12]

[0160] Example 7 - Prediction of kidney transplant rejection Hair samples were collected from kidney transplant recipients at the time of biopsy-proven acute rejection (n=6) and from age- and sex-matched control kidney transplant recipients without acute rejection on concurrent post-transplant surveillance biopsy (n=5). All participants were recruited from Mount Sinai Hospital. Table 9 shows the characteristics used and their corresponding β values. β values ​​are obtained by estimating each characteristic in each cohort, describing the change in log odds of kidney graft status associated with a one-unit change for each characteristic.

[0161] Figure 9 shows an ROC curve for assessing the accuracy of the disclosed method for assessing subjects for kidney transplant rejection. As shown in Figure 9, the ROC curve derived from experimental data for assessing the performance of the disclosed classification method has an AUC corresponding to 0.900, indicating that the disclosed method has 90% accuracy for assessing kidney transplant rejection based on hair samples. [Table 9-1] [Table 9-2]

[0162] Example 8 - Childhood Cancer Subjects were evaluated for childhood cancer using similar methods described above for Examples 2 and 3. A total of 28 children were recruited from the cancer center. There were 22 childhood cancer cases and 6 controls. Diagnosis was made using standard clinical protocols - blood tests and histopathology, and confirmed by an oncologist. Table 10 shows the features used and their corresponding β values. β values ​​are obtained by estimating each feature in each cohort, describing the change in log odds of childhood cancer status associated with a one-unit change in each feature.

[0163] Figure 10 shows an ROC curve for assessing the accuracy of the disclosed method for assessing subjects with pediatric cancer. As shown in Figure 10, the ROC curve derived from experimental data for assessing the performance of the disclosed classification method has an AUC corresponding to 0.962, indicating that the disclosed method has an accuracy of over 95% across the entire cohort of 28 children with pediatric cancer based on dental sampling. [Table 10-1] [Table 10-2] [Table 10-3] [Table 10-4] [Table 10-5] [Table 10-6] [Table 10-7] [Table 10-8] [Table 10-9]

[0164] References and Alternative Embodiments All references cited in this specification are incorporated by reference in their entirety for all purposes as if each individual publication or patent or patent application was specifically and individually indicated to be incorporated by reference in its entirety for all purposes.

[0165] Many modifications and variations of the present invention can be made without departing from its spirit and scope, as will be apparent to those skilled in the art. The specific embodiments described herein are provided by way of example only. The embodiments have been selected and described in order to best explain the principles of the invention and its practical applications, so that those skilled in the art can optimally utilize the invention and its various embodiments with various modifications suited to the particular applications contemplated. The present invention is limited only by the terms of the appended claims, together with the full scope of equivalents to which such claims are entitled.

Claims

1. 1. A method for assessing a subject for a biological condition, comprising: providing a biological sample from the subject; Sampling the biological sample along a plurality of locations of the biological sample to collect a plurality of ion samples; and analyzing the plurality of ion samples with a mass spectrometer to obtain a probability that the subject has a biological state.

2. 10. The method of claim 1, further comprising pre-treating the biological sample with a solvent or detergent prior to said sampling.

3. The method of claim 1 or 2, wherein the biological sample comprises a tooth, a hair shaft, or a nail.

4. The method of any one of claims 1 to 3, wherein the multiple ion samples are collected along a reference line on the biological sample.

5. The method of claim 4 , wherein the reference line corresponds to the neonatal line of a tooth on the enamel surface of the tooth.

6. The method of claim 4 , wherein the reference line corresponds to the longitudinal direction of the hair shaft.

7. The method of claim 4 , wherein the reference line corresponds to a line of nail growth direction.

8. The method according to any one of claims 1 to 7, wherein the subject is a human.

9. The method of claim 8, wherein the human is under 5 years of age.

10. 10. The method of claim 9, wherein the human is less than 1 year old.

11. The method of any one of claims 1 to 10, wherein the sampling comprises sampling the biological sample using a laser.

12. The method of any one of claims 1 to 11, wherein the mass spectrometer comprises an inductively coupled plasma mass spectrometer.

13. The method of any one of claims 1 to 12, wherein the analysis comprises identifying one or more elemental isotopes from a plurality of ion samples.

14. The method of claim 13 , wherein the analyzing comprises determining a concentration of one or more elemental isotopes of a plurality of ion samples.

15. The method of claim 14 , wherein the analyzing further comprises comparing the concentration of one or more elemental isotopes to the concentration of a control elemental isotope.

16. 16. The method of claim 15, wherein the control element isotope is sulfur.

17. 17. The method of any one of claims 1 to 16, wherein the analysis comprises use of a trained classifier to which a plurality of ion samples are provided as input, the trained classifier outputting a probability that the subject has a biological condition associated with elemental isotope variation.

18. 18. The method of any one of claims 1 to 17, wherein the biological condition associated with elemental isotope variation is selected from the group consisting of a neurological condition, autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), neurodegenerative disease, amyotrophic lateral sclerosis (ALS), schizophrenia, Parkinson's disease, Alzheimer's disease, Huntington's disease, irritable bowel disease (IBD), pediatric kidney transplant rejection, and pediatric cancer.

19. The method of any one of claims 1 to 18, wherein the analyzing comprises determining a set of dynamic signatures of the plurality of ion samples.

20. The method of any one of claims 1 to 19, wherein the analysis comprises determining a set of dynamic periodic characteristics of the plurality of ion samples.

21. The method of any one of claims 1 to 20, wherein said analysis comprises performing a recursive quantification analysis on said plurality of ion samples.

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