Systems and methods for dynamic immunohistochemical analysis of biological conditions and feature engineering thereof

Abstract

The present disclosure provides methods and systems for predicting a diagnostic status of a subject with respect to a disease or condition. The method may include staining a tooth, hair, or nail sample of the subject to produce a stained tooth sample, analyzing fluorescence intensity spatially across the stained tooth, hair, or nail sample, and predicting a diagnostic status of the subject with respect to a disease or condition based at least in part on the analysis of the fluorescence intensity.

Description

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to U.S. Provisional Patent Application Serial No. 63 / 350,089, filed on June 8, 2022, which is hereby incorporated by reference in its entirety. Background Art

[0003] Dynamic biological responses may indicate potential biological processes that have structural and functional significance to humans. For example, abnormal or abnormal dynamic biological responses may be associated with many biological conditions, such as diseases and disorders. Examples of such biological conditions may include neurological conditions (e.g., autism spectrum disorders, schizophrenia, or attention deficit / hyperactivity disorder (ADHD)), neurodegenerative diseases (e.g., amyotrophic lateral sclerosis (ALS), Alzheimer's disease, Parkinson's disease, and Huntington's disease) and cancer (e.g., pediatric cancer). Summary of the invention

[0004] In view of the above background, accurate methods and systems for diagnosis of biological conditions and especially non-invasive diagnosis are needed. Such diagnosis can be based on accurate analysis of biomarkers detectable by non-invasive methods for diagnosis of biological conditions. The present disclosure provides improved systems and methods for accurate diagnosis of biological conditions, which are based on analysis of dynamic biological response data of biological samples obtained non-invasively from subjects. Such improved systems and methods for accurate diagnosis of biological conditions can be based on a combination of dynamic immunohistochemical analysis of biological samples and artificial intelligence data analysis of such dynamic curves to assess disease states. The present disclosure addresses these needs, for example, by providing biological sample biomarkers for diagnosis of biological conditions. Biological samples include human biological samples associated with incremental growth. Such biological samples can be hair shafts, teeth and nails. The non-invasive biomarkers of the present disclosure can be used for diagnosis of young children, even infants less than one year old.

[0005] In one aspect, the present disclosure provides a method for predicting a subject's diagnostic status with respect to a disease or condition, the method comprising: (a) staining a dental sample from the subject to produce a stained dental sample; (b) analyzing fluorescence intensity spatially across the stained dental sample; and (c) predicting the subject's diagnostic status with respect to the disease or condition based at least in part on the analysis of the fluorescence intensity.

[0006] In some embodiments, the analysis determines the temporal dynamics of the underlying biological process. In some embodiments, the analysis includes obtaining a fluorescent image of the stained dental sample and analyzing the fluorescence intensity of the fluorescent image. In some embodiments, the fluorescence intensity varies spatially. In some embodiments, obtaining a fluorescent image of the stained dental sample includes using an inverted or non-inverted confocal microscope. In some embodiments, staining the dental sample includes using C-reactive protein immunohistochemical staining. In some embodiments, the method further includes slicing the dental sample. In some embodiments, staining the dental sample includes: (1) cutting the dental sample, (2) decalcifying the dental sample, (3) slicing the decalcified sample, (4) staining the decalcified dental slice with a primary antibody and a secondary antibody, (5) measuring spatial antibody fluorescence using a confocal microscope, and / or (6) extracting a time curve of fluorescence intensity.

[0007] In some embodiments, the disease or condition comprises: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof. In some embodiments, the disease or condition comprises ASD. In some embodiments, the subject is a human. In some embodiments, the subject is an adult. In some embodiments, the age of the subject is between about 12 years old and about 5 years old. In some embodiments, the subject is less than about 12, 11, 10, 9, 8, 7, 5, 4, 3, 2, or 1 years old. In some embodiments, the subject is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 years old.

[0008] In some embodiments, analyzing includes generating an inflammation time curve (eg, one or more traces) based at least in part on the fluorescence intensity, and analyzing the inflammation time curve. In some embodiments, at least a portion of the inflammation time curve corresponds to the prenatal period of the subject.

[0009] In certain embodiments, prediction experimenter's diagnostic state about disease or illness comprises using trained model to process fluorescence intensity.In certain embodiments, processing comprises extracting feature (for example, by recursive quantitative analysis) from fluorescence intensity, and uses trained model to analyze feature.In certain embodiments, trained model is selected from the group consisting of the following: neural network algorithm, support vector machine algorithm, decision tree algorithm, unsupervised clustering algorithm, supervised clustering algorithm, regression algorithm, gradient boosting algorithm (for example, the gradient boosting of machine learning algorithm is realized, such as gradient boosting decision tree) and any combination thereof.In certain embodiments, trained model comprises gradient boosting integrated model.In certain embodiments, trained model is configured to process one or more features selected from the group consisting of the following: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursive trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, quantity of most likely average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof. In some embodiments, one or more features are extracted by applying recursive quantification analysis (RQA) to fluorescence intensity traces derived from analysis of the sample. In some embodiments, the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, and any combination thereof.

[0010] In some embodiments, the trained model is configured to process one or more features of the temporal dynamics of one or more traces. In some embodiments, the temporal dynamics of the one or more traces are determined by a data analysis method. In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to one or more traces: determination of a linear slope, determination of multiple nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0011] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a sensitivity of at least about 70%, 75%, 80%, 85% or 90% in predicting diagnostic status with respect to the disease or condition across a suitable cohort population (e.g., such as provided in the Examples section below).

[0012] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a sensitivity of up to about 70%, 75%, 80%, 85% or 90% in predicting the diagnostic status with respect to the disease or condition across a suitable cohort population.

[0013] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a specificity of at least about 70%, 75%, 80%, 85% or 90% in predicting the diagnostic status with respect to the disease or condition across a suitable cohort population.

[0014] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a specificity of up to about 70%, 75%, 80%, 85% or 90% in predicting the diagnostic status with respect to the disease or condition across a suitable cohort population.

[0015] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a positive predictive value of at least about 70%, 75%, 80%, 85% or 90% when predicting diagnostic status with respect to the disease or condition across a suitable cohort population.

[0016] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a positive predictive value of up to about 70%, 75%, 80%, 85% or 90% when predicting diagnostic status with respect to the disease or condition across a suitable cohort population.

[0017] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a negative predictive value of at least about 70%, 75%, 80%, 85% or 90% when predicting diagnostic status with respect to the disease or condition across a suitable cohort population.

[0018] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to the disease or condition using a model having a negative predictive value of up to about 70%, 75%, 80%, 85% or 90% when predicting diagnostic status with respect to the disease or condition across a suitable cohort population.

[0019] In some embodiments, the method further comprises predicting the subject's diagnostic status with respect to a disease or condition using a model that predicts the diagnostic status with respect to a disease or condition using an area under the receiver operating characteristic (AUROC) of at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.82, at least about 0.84, at least about 0.86, at least about 0.88, or at least about 0.90 for a suitable cohort population.

[0020] In another aspect, the present disclosure provides a device comprising 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 comprising instructions for: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of a subject associated with the subject's c-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with the c-reactive protein; (b) analyzing each fluorescence intensity across the reference line on the biological sample, thereby obtaining a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by a change in the fluorescence intensity of the c-reactive protein; and (d) processing the features using a trained model to determine the likelihood that the subject suffers from a disease or condition associated with the c-reactive protein. In some embodiments, the corresponding second data set is derived by applying a recursive quantization analysis or a related method to the corresponding plurality of fluorescence intensity measurements.

[0021] In some embodiments, a plurality of fluorescence intensity measurements are measured with an inverted or non-inverted confocal microscope. In some embodiments, the biological sample comprises a tooth sample. In some embodiments, the tooth sample is stained using C-reactive protein immunohistochemical staining. In some embodiments, the instruction further comprises slicing the tooth sample. In some embodiments, the instruction further comprises decalcifying the tooth sample. In some embodiments, the disease or condition comprises: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer or any combination thereof. In some embodiments, the disease or condition comprises ASD. In some embodiments, the subject is a human. In some embodiments, the age of the human is between about 12 years old and about 5 years old. In some embodiments, the subject is less than about 12, 11, 10, 9, 8, 7, 5, 4, 3, 2 or 1 years old. In some embodiments, the subject is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 years old. In some embodiments, the analysis includes generating an inflammation time curve (e.g., one or more traces) based at least in part on a plurality of fluorescence intensity measurements, and analyzing the inflammation time curve. In some embodiments, at least a portion of the inflammation time curve corresponds to the prenatal period of the subject. In some embodiments, predicting the diagnostic state of the subject about a disease or condition includes processing a plurality of fluorescence intensity measurements using a trained model. In some embodiments, the trained model is selected from the group consisting of: a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof. In some embodiments, the trained model includes a gradient boosting decision tree. In some embodiments, the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, the number of most likely recursions, average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, and any combination thereof. In some embodiments, the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, the number of most likely recursions, average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, and any combination thereof.In some embodiments, the trained model is configured to process one or more features of the temporal dynamics of one or more traces. In some embodiments, the temporal dynamics of the one or more traces are determined by a data analysis method. In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to one or more traces: determination of a linear slope, determination of multiple nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0022] In another aspect, the present disclosure provides a non-transitory computer-readable storage medium and one or more computer programs embedded therein, the one or more computer programs including instructions that, when executed by a computer system, cause the computer system to perform a method comprising: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of a subject associated with the subject's c-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with the c-reactive protein; (b) analyzing each fluorescence intensity across the reference line on the biological sample, thereby obtaining a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by the sequential variability in the c-reactive protein fluorescence intensity; and (d) processing the features using a trained model to determine the likelihood that the subject suffers from a disease or condition associated with the c-reactive protein. In some embodiments, the corresponding second data set is derived by applying a recursive quantization analysis or a related method to the corresponding plurality of fluorescence intensity measurements.

[0023] In some embodiments, a plurality of fluorescence intensity measurements are measured with an inverted or non-inverted confocal microscope. In some embodiments, the biological sample comprises a tooth sample. In some embodiments, the tooth sample is stained using C-reactive protein immunohistochemical staining. In some embodiments, the method further comprises slicing the tooth sample. In some embodiments, the method further comprises decalcifying the tooth sample. In some embodiments, the disease or condition comprises: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof. In some embodiments, the disease or condition comprises ASD. In some embodiments, the subject is a human. In some embodiments, the subject is less than 5 years old. In some embodiments, the subject is less than 1 year old. In some embodiments, the analysis comprises generating an inflammation time curve (e.g., one or more traces) based at least in part on a plurality of fluorescence intensity measurements, and analyzing the inflammation time curve. In some embodiments, at least a portion of the inflammation time curve corresponds to the prenatal period of the subject. In some embodiments, predicting the diagnostic status of the subject regarding a disease or condition comprises processing a plurality of fluorescence intensity measurements using a trained model. In some embodiments, the trained model is selected from the group consisting of the following: neural network algorithm, support vector machine algorithm, decision tree algorithm, unsupervised clustering algorithm, supervised clustering algorithm, regression algorithm, gradient boosting algorithm and any combination thereof. In some embodiments, the trained model includes a gradient boosting decision tree. In some embodiments, the trained model is configured to process one or more features selected from the group consisting of the following: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, hierarchical, capture time, maximum vertical length, Shannon entropy of vertical length, average recursion time, Shannon entropy of recursion time, the number of most likely recursions, average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof. In some embodiments, the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax, and any combination thereof.

[0024] In some embodiments, the trained model is configured to process one or more features of the temporal dynamics of one or more traces. In some embodiments, the temporal dynamics of the one or more traces are determined by a data analysis method. In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to one or more traces: determination of a linear slope, determination of multiple nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0025] On the other hand, the present disclosure provides a method for training a model, the method comprising: at a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors: (a) for each corresponding training subject in a plurality of training subjects, wherein a first subset of the training subjects in the plurality of training subjects have a first diagnostic state corresponding to a first biological condition associated with C-reactive protein, and a second subset of the training subjects in the plurality of training subjects have a second diagnostic state corresponding to not having the first biological condition associated with C-reactive protein: (i) sampling each corresponding position in a plurality of positions along a reference line on a biological sample of the subject associated with the C-reactive protein of the subject, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and the plurality of Each of the positions represents a different growth period of a biological sample of a subject associated with c-reactive protein; (ii) analyzing each fluorescence intensity across a reference line on the biological sample to obtain a first data set; and (iii) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in a corresponding feature set being determined by a change in the fluorescence intensity of c-reactive protein; and (b) training an untrained or partially untrained model with (i) the corresponding feature set of each corresponding second data set of each training subject in the plurality of training subjects and (ii) the corresponding diagnostic state of each training subject in the plurality of training subjects selected from the first diagnostic state and the second diagnostic state, thereby obtaining a trained model, the trained model providing an indication of whether the test subject has a first biological condition associated with c-reactive protein based on the value of the feature in the feature set obtained from the biological sample associated with c-reactive protein of the test subject. In some embodiments, the corresponding second data set is derived by applying a recursive quantization analysis or a related method to the corresponding plurality of fluorescence intensity measurements.

[0026] In some embodiments, the trained model is a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, a regression model, a gradient boosting algorithm (e.g., a gradient boosting implementation of a machine learning algorithm, such as a gradient boosting decision tree) or any combination thereof. In some embodiments, the trained model includes a gradient boosting integrated model. In some embodiments, the trained model predicts results relative to a multinomial distribution. In some embodiments, the trained model predicts results relative to a binomial distribution. In some embodiments, the first biological condition associated with c-reactive protein is selected from the group consisting of: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, and pediatric cancer.

[0027] In some embodiments, the first biological condition associated with c-reactive protein of the assessment test subject is further included in the presence of the first biological condition associated with c-reactive protein and the absence of the first biological condition associated with c-reactive protein. In some embodiments, the first biological condition associated with c-reactive protein of the assessment test subject is further included in the first biological condition associated with c-reactive protein and the second biological condition associated with c-reactive protein that is different from the first biological condition associated with c-reactive protein. In some embodiments, the first biological condition is autism spectrum disorder, and the second biological condition is neurotypical development; that is, there is no neurodevelopmental disorder. In some embodiments, the first biological condition is autism spectrum disorder, and the second biological condition is attention deficit / hyperactivity disorder. In some embodiments, the test subject is a person. In some embodiments, the age of the person is between about 12 years old and about 5 years old. In some embodiments, the subject is less than about 12, 11, 10, 9, 8, 7, 5, 4, 3, 2 or 1 years old. In some embodiments, the subject is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 years old. In some embodiments, the corresponding biological sample associated with the c-reactive protein of the corresponding training subject is selected from the group consisting of a hair shaft, a tooth, and a nail. In some embodiments, the corresponding biological sample associated with the c-reactive protein of the corresponding training subject is a hair shaft and the reference line corresponds to the longitudinal direction of the hair shaft. In some embodiments, the corresponding biological sample associated with the c-reactive protein of the corresponding training subject is a tooth and the reference line corresponds to the direction across the growth zone, including the new growth line of the tooth. In some embodiments, the corresponding multiple positions are sorted so that the first position along the corresponding multiple positions of the corresponding biological sample associated with the c-reactive protein of the corresponding training subject corresponds to the position closest to the tip of the corresponding biological sample associated with the c-reactive protein of the corresponding training subject. In some embodiments, each trace in the corresponding multiple fluorescence intensity measurements includes multiple data points, each data point being an instance of a corresponding position in the multiple positions. In some embodiments, the corresponding feature set is selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof. In some embodiments, the features are derived from recursive quantitative analysis of the fluorescence trace or related computational analysis.In some embodiments, the corresponding plurality of locations includes at least 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000, 20000 or more than 20000 locations.

[0028] In some embodiments, the corresponding feature set is selected from a set of time-dynamic features of one or more traces. In some embodiments, the time-dynamic features of the one or more traces are determined by a data analysis method. In some embodiments, the data analysis method can apply one or more of the following operations and / or methods to one or more traces: determination of a linear slope, determination of multiple nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0029] Another aspect of the present disclosure provides a non-transitory computer-readable medium comprising machine-executable code that, when executed by one or more computer processors, implements any of the methods described above or elsewhere herein.

[0030] Another aspect of the present disclosure provides a system comprising one or more computer processors and a computer memory coupled thereto. The computer memory includes machine executable code that, when executed by the one or more computer processors, implements any of the methods described above or elsewhere herein.

[0031] For those skilled in the art, additional aspects and advantages of the present disclosure will become apparent from the following detailed description, wherein only illustrative embodiments of the present disclosure are shown and described. As will be appreciated, the present disclosure is capable of other and different embodiments, and its several details can be modified in various obvious aspects, all without departing from the present disclosure. Therefore, the drawings and description will be regarded as illustrative rather than restrictive in nature.

[0032] Incorporated by Reference

[0033] All publications, patents, and patent applications mentioned in this specification are incorporated herein by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of the invention will be obtained by reference to the following detailed description which sets forth illustrative embodiments in which the principles of the invention are utilized, and in the accompanying drawings (also referred to herein as "Figures" and "FIG."):

[0035] Figure 1 An example of a block diagram of a computing device 100 of the present disclosure is shown.

[0036] FIG. 2A to FIG. 2C The hair samples of the subjects are shown ( Figure 2A )、Tooth samples( Figure 2B ) and nail samples ( Figure 2C ) icon.

[0037] Figure 3 A flow chart of a method 300 for assessing a biological condition in a subject is shown.

[0038] Figure 4 A computer system is shown that is programmed or otherwise configured to implement the methods provided herein.

[0039] Figure 5 An example of a daily C-reactive protein profile over time for a subject is shown, with the y-axis indicating CRP intensity and the x-axis indicating developmental age.

[0040] FIG. 6A to FIG. 6B Receiver operating characteristic (ROC) curves are shown to characterize the sensitivity and specificity of the method for diagnosing autism at different prediction thresholds using features derived from recursive quantitative analysis of C-reactive protein profiles sampled prenatally and in early childhood (e.g., under 1 year old) by trained models. Device performance is measured by calculating the area under the curve (AUC) of the ROC plot, which provides a measure of performance at different classification thresholds; here, the AUC is 0.86, indicating very accurate predictive performance. DETAILED DESCRIPTION

[0041] Although various embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided as examples only. Without departing from the present invention, those skilled in the art may appreciate that many variations, changes and substitutions may be made. It should be understood that various alternatives to the embodiments of the present invention described herein may be employed.

[0042] Dynamic biological responses may indicate potential biological processes that have structural and functional significance to humans. For example, abnormal or abnormal dynamic biological responses may be associated with many biological conditions, such as diseases and disorders. Examples of such biological conditions may include neurological conditions (e.g., autism spectrum disorders, schizophrenia, or attention deficit / hyperactivity disorder (ADHD)), neurodegenerative diseases (e.g., amyotrophic lateral sclerosis (ALS), Alzheimer's disease, Parkinson's disease, and Huntington's disease) and cancer (e.g., pediatric cancer).

[0043] In view of the above background, accurate methods and systems for diagnosis of biological conditions and especially non-invasive diagnosis are needed. Such diagnosis can be based on accurate analysis of biomarkers that can be detected by non-invasive methods for diagnosis of biological conditions. The present disclosure provides improved systems and methods for accurate diagnosis of biological conditions, which are based on analysis of dynamic biological response data of biological samples obtained non-invasively from subjects. Such improved systems and methods for accurate diagnosis of biological conditions can be based on a combination of dynamic immunohistochemical analysis of biological samples and artificial intelligence data analysis of such dynamic curves to assess disease states. The present disclosure addresses these needs, for example, by providing biological sample biomarkers for diagnosis of biological conditions. Biological samples include human biological samples associated with incremental growth. Such biological samples can be hair shafts, teeth and nails. The non-invasive biomarkers of the present disclosure can be used for diagnosis of young children, even infants less than one year old. In some cases, the age of the child is between about 12 years old and about 5 years old. In some embodiments, the child is less than about 12, 11, 10, 9, 8, 7, 5, 4, 3, 2 or 1 years old. In some embodiments, the child is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 years old.

[0044] In one aspect, the present disclosure provides a method for predicting a subject's diagnostic status with respect to a disease or condition, the method comprising: (a) staining a dental sample from the subject to produce a stained dental sample; (b) analyzing fluorescence intensity spatially across the stained dental sample; and (c) predicting the subject's diagnostic status with respect to the disease or condition based at least in part on the analysis of the fluorescence intensity.

[0045] In some embodiments, the analysis includes obtaining a fluorescent image of the stained tooth sample and analyzing the fluorescence intensity of the fluorescent image. In some embodiments, obtaining a fluorescent image of the stained tooth sample includes using an inverted or non-inverted confocal microscope. In some embodiments, staining the tooth sample includes using C-reactive protein immunohistochemical staining. In some embodiments, the method further includes slicing the tooth sample. In some embodiments, staining the tooth sample includes decalcifying the tooth sample.

[0046] In some embodiments, the systems and methods disclosed herein can use a single C-reactive protein fluorescent immunohistochemical stain, or use it in combination with other techniques. Such techniques may include laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), Raman spectroscopy, or any combination thereof. In some embodiments, the combined technique can improve the diagnostic accuracy or precision of a given technique alone. In some embodiments, the addition of LA-ICP-MS provides a plurality of non-invasive metal metabolism biomarkers of a given biological sample, which can supplement the diagnostic capabilities of C-reactive protein fluorescent immunohistochemical data. In some embodiments, metal metabolism biomarkers include zinc, tin, magnesium, copper, iodide, lithium, aluminum, phosphorus, sulfur, calcium, chromium, manganese, iron, cobalt, nickel, arsenic, strontium, cadmium, tin, iodine, barium, mercury, lead, bismuth, molybdenum, or any combination thereof. In some embodiments, the addition of Raman spectroscopy provides a plurality of spectra indicating physiological changes induced by disease or external stressors to supplement the diagnostic capabilities of C-reactive protein fluorescent immunohistochemical data. In some embodiments, a plurality of metal metabolism biomarkers include at least 2, at least 5, or at least 10 metal metabolism biomarkers. In some embodiments, a plurality of metal metabolism biomarkers include no more than 20, no more than 10 or no more than 5 metal metabolism biomarkers. In some embodiments, a plurality of metal metabolism biomarkers are composed of 2 to 5, 3 to 10 or 8 to 20 metal metabolism biomarkers. In some embodiments, a plurality of metal metabolism biomarkers fall into another range starting from no less than 2 metal metabolism biomarkers to no more than 20 metal metabolism biomarkers. In some embodiments, a plurality of spectra include at least 2, at least 5 or at least 10 spectra. In some embodiments, a plurality of spectra include no more than 20, no more than 10 or no more than 5 spectra. In some embodiments, a plurality of spectra are composed of 2 to 5, 3 to 10 or 8 to 20 spectra. In some embodiments, a plurality of spectra fall into another range starting from no less than 2 spectra to no more than 20 spectra.

[0047] In some embodiments, the disease or condition comprises: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof. In some embodiments, the disease or condition comprises ASD. In some embodiments, the subject is a human. In some embodiments, the subject is an adult. In some embodiments, the age of the subject is between about 12 years old and about 5 years old. In some embodiments, the subject is less than about 12, 11, 10, 9, 8, 7, 5, 4, 3, 2, or 1 years old. In some embodiments, the subject is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, or 12 years old.

[0048] In some embodiments, analyzing comprises generating an inflammation time curve based at least in part on the fluorescence intensity, and analyzing the inflammation time curve. In some embodiments, at least a portion of the inflammation time curve corresponds to a prenatal period of the subject.

[0049] In some embodiments, predicting the diagnostic status of a subject about a disease or condition includes processing fluorescence intensity using a trained model. In some embodiments, the trained model includes multiple parameters, wherein the term "parameter" refers to any coefficient in the model or similarly, any value of an internal or external element (e.g., weight and / or hyperparameter) (e.g., where the model is a regressor or classifier), which can affect (e.g., modify, customize and / or adjust) one or more inputs, outputs and / or functions in the model. For example, in some embodiments, the parameters of the model refer to any coefficient, weight and / or hyperparameter that can be used to control, modify, customize and / or adjust the behavior, learning and / or performance of the model. In some cases, the parameter is used to increase or decrease the impact of the input (e.g., feature) on the model. As a non-limiting example, in some embodiments, the parameter is used to increase or decrease the impact of a node (e.g., neural network), wherein the node includes one or more activation functions. The specific input, output and / or function assigned to the parameter to the model is not limited to any one paradigm of a given model, but can be used for any suitable model to achieve the desired performance. In some embodiments, the parameter has a fixed value. In some embodiments, the value of the parameter can be adjusted manually and / or automatically. In some embodiments, the values ​​of the parameters are modified through a validation and / or training process of the model (eg, through error minimization and / or back-propagation methods). In some embodiments, the models of the present disclosure include multiple parameters. In some embodiments, the plurality of parameters associated with a model (e.g., an untrained, partially trained, or fully trained model) is n parameters, where: n≥2; n≥5; n≥10; n≥25; n≥40; n≥50; n≥75; n≥100; n≥125; n≥150; n≥200; n≥225; n≥250; n≥350; n≥500; n≥600; n≥750; n≥1,000; n≥2,000; n≥4,000; n≥5,000; n≥7,500; n≥10,000; n≥20,000; n≥40,000; n≥75,000; n≥100,000; n≥200,000; n≥500,000, n≥1×10 6 , n≥5×10 6 or n ≥ 1 × 10 7 In some embodiments, n is between 10,000 and 1×10 7 Between 100,000 and 5×10 6 or between 500,000 and 1×10 6 between.

[0050] In some embodiments, the plurality of parameters comprises at least 10, at least 100, at least 1000, at least 10,000, at least 100,000, at least 1×10 6At least 5×10 6 At least 1×10 7 At least 5×10 7 or at least 1×10 8 In some embodiments, the plurality of parameters include 1×10 9 No more than 1×10 8 No more than 1×10 7 No more than 1×10 6 In some embodiments, the plurality of parameters ranges from 10 to 10,000, 100 to 100,000, 1000 to 1×10 6 100,000 to 1×10 7 pcs, 1×10 6 Up to 1×10 8 or 1×10 7 Up to 1×10 9 In some embodiments, the plurality of parameters falls within a range from no less than 10 parameters to no more than 1×10 9 The next parameter ends in another scope.

[0051] In some embodiments, the trained model is used to process the fluorescence intensity (e.g., one or more traces of the fluorescence intensity described elsewhere herein) including extracting features from the fluorescence intensity (e.g., by recursive quantitative analysis), and analyzing features using the trained model. In some embodiments, the trained model is selected from the group consisting of the following: a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm (e.g., a gradient boosting implementation of a machine learning algorithm, such as a gradient boosting decision tree) and any combination thereof. In some embodiments, the trained model includes a gradient boosting integrated model. In some embodiments, the trained model is configured to process one or more features selected from the group consisting of the following: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursive trend, hierarchical, capture time, maximum vertical length, Shannon entropy of vertical length, average recursion time, Shannon entropy of recursion time, the number of most likely recursions, average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof. In some embodiments, one or more features are extracted by applying recursive quantification analysis (RQA) to fluorescence intensity traces derived from analysis of the sample. In some embodiments, the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, average diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, and any combination thereof.

[0052] In some embodiments, the trained model is configured to process one or more features of the temporal dynamics of one or more traces. In some embodiments, the temporal dynamics of the one or more traces are determined by a data analysis method. In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to one or more traces: determination of a linear slope, determination of multiple nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0053] In some embodiments, the method further includes predicting the diagnostic status of the subject about a disease or condition with a sensitivity of at least about 80%. In some embodiments, the method further includes predicting the diagnostic status of the subject about a disease or condition with a specificity of at least about 80%. In some embodiments, the method further includes predicting the diagnostic status of the subject about a disease or condition with a positive predictive value of at least about 80%. In some embodiments, the method further includes predicting the diagnostic status of the subject about a disease or condition with a negative predictive value of at least about 80%. In some embodiments, the method further includes predicting the diagnostic status of the subject about a disease or condition with a receiver operating characteristic area under (AUROC) of at least about 0.80.

[0054] In another aspect, the present disclosure provides a device comprising 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 comprising instructions for: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of a subject associated with the subject's c-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with the c-reactive protein; (b) analyzing each fluorescence intensity across the reference line on the biological sample, thereby obtaining a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by the sequential variability in the c-reactive protein fluorescence intensity; and (d) processing the features using a trained model to determine the likelihood that the subject suffers from a disease or condition associated with the c-reactive protein. In some embodiments, the corresponding second data set is derived by applying a recursive quantization analysis or a related method to the corresponding plurality of fluorescence intensity measurements.

[0055] In another aspect, the present disclosure provides a non-transitory computer-readable storage medium and one or more computer programs embedded therein, the one or more computer programs including instructions that, when executed by a computer system, cause the computer system to perform a method comprising: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of a subject associated with the subject's c-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with the c-reactive protein; (b) analyzing each fluorescence intensity across the reference line on the biological sample, thereby obtaining a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by the sequential variability in the c-reactive protein fluorescence intensity; and (d) processing the features using a trained model to determine the likelihood that the subject suffers from a disease or condition associated with the c-reactive protein. In some embodiments, the corresponding second data set is derived by applying a recursive quantization analysis or a related method to the corresponding plurality of fluorescence intensity measurements.

[0056] On the other hand, the present disclosure provides a method for training a model, the method comprising: at a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors: (a) for each corresponding training subject in a plurality of training subjects, wherein a first subset of the training subjects in the plurality of training subjects have a first diagnostic state corresponding to a first biological condition associated with C-reactive protein, and a second subset of the training subjects in the plurality of training subjects have a second diagnostic state corresponding to not having the first biological condition associated with C-reactive protein: (i) sampling each corresponding position in a plurality of positions along a reference line on a biological sample of the subject associated with the C-reactive protein of the subject, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and the plurality of Each of the positions represents a different growth period of a biological sample of a subject associated with c-reactive protein; (ii) analyzing each fluorescence intensity across a reference line on the biological sample to obtain a first data set; and (iii) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in a corresponding feature set being determined by a change in the fluorescence intensity of c-reactive protein; and (b) training an untrained or partially untrained model with (i) the corresponding feature set of each corresponding second data set of each training subject in the plurality of training subjects and (ii) the corresponding diagnostic state of each training subject in the plurality of training subjects selected from the first diagnostic state and the second diagnostic state, thereby obtaining a trained model, the trained model providing an indication of whether the test subject has a first biological condition associated with c-reactive protein based on the value of the feature in the feature set obtained from the biological sample associated with c-reactive protein of the test subject. In some embodiments, the corresponding second data set is derived by applying a recursive quantization analysis or a related method to the corresponding plurality of fluorescence intensity measurements.

[0057] In certain embodiments, corresponding subject (for example, test subject) is selected from a plurality of subjects. In certain embodiments, the plurality of subjects include at least 2, at least 5, at least 10, at least 20, at least 50, at least 100 or at least 500 subjects. In certain embodiments, the plurality of subjects include no more than 1000, no more than 500, no more than 100, no more than 50, no more than 20 or no more than 10 subjects. In certain embodiments, the plurality of subjects are composed of 2 to 10, 5 to 20, 10 to 100 or 100 to 1000 subjects. In certain embodiments, the plurality of subjects fall into another range starting from no less than 2 subjects to no more than 1000 subjects.

[0058] In certain embodiments, the plurality of training subjects include at least 2, at least 5, at least 10, at least 20, at least 50, at least 100, at least 500, at least 1000, at least 5000 or at least 100,000 training subjects. In certain embodiments, the plurality of training subjects include no more than 1,000,000, no more than 100,000, no more than 10,000, no more than 100, no more than 500, no more than 100, no more than 20 or no more than 10 training subjects. In certain embodiments, the plurality of training subjects are composed of 2 to 1000, 500 to 10,000, 10,000 to 100,000 or 100,000 to 1,000,000 training subjects. In certain embodiments, the plurality of training subjects fall into another range starting from no less than 2 training subjects to no more than 1,000,000 training subjects.

[0059] In some embodiments, the corresponding subset of the training subjects in the multiple training subjects (e.g., the first subset and / or the second subset) includes at least 2, at least 5, at least 10, at least 20, at least 50, at least 100, at least 500, at least 1000, at least 5000 or at least 10,000 training subjects. In some embodiments, the corresponding subset of the training subjects includes no more than 500,000, no more than 10,000, no more than 1000, no more than 500, no more than 100, no more than 50, no more than 20 or no more than 10 training subjects. In some embodiments, the corresponding subset of the training subjects is composed of 2 to 100, 50 to 2000, 1000 to 10,000 or 10,000 to 500,000 training subjects. In some embodiments, the corresponding subset of the training subjects falls into another range starting from no less than 2 training subjects to no more than 500,000 training subjects.

[0060] In some embodiments, a feature set (e.g., collected from a biological sample associated with a subject's c-reactive protein) includes at least 1, at least 2, at least 3, at least 5, at least 8, at least 10, at least 15, or at least 20 features. In some embodiments, the feature set includes no more than 50, no more than 20, no more than 10, no more than 5, or no more than 3 features. In some embodiments, the feature set consists of 1 to 10, 4 to 15, 8 to 20, or 15 to 50 features. In some embodiments, the feature set falls within another range starting from no less than 1 feature to no more than 50 features.

[0061] In some embodiments, the trained model is a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, a regression model, a gradient boosting algorithm (e.g., a gradient boosting implementation of a machine learning algorithm, such as a gradient boosting decision tree) or any combination thereof. In some embodiments, the trained machine learning model includes a gradient boosting ensemble model. In some embodiments, the trained model predicts results relative to a multinomial distribution. In some embodiments, the trained model predicts results relative to a binomial distribution. In some embodiments, the first biological condition associated with c-reactive protein is selected from the group consisting of: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, and pediatric cancer.

[0062] In some embodiments, the first biological condition and / or the second biological condition are selected from a plurality of biological conditions. In some embodiments, the plurality of biological conditions include at least 2, at least 5 or at least 10 biological conditions. In some embodiments, a plurality of biological conditions include no more than 20, no more than 10 or no more than 5 biological conditions. In some embodiments, the plurality of biological conditions consist of 2 to 5, 3 to 10 or 8 to 20 biological conditions. In some embodiments, the plurality of biological conditions fall within another range starting from no less than 2 biological conditions to no more than 20 biological conditions. In some embodiments, the first diagnostic state and / or the second diagnostic state are selected from a plurality of diagnostic states. In some embodiments, the plurality of diagnostic states include at least 2, at least 5 or at least 10 diagnostic states. In some embodiments, the plurality of diagnostic states include no more than 20, no more than 10 or no more than 5 diagnostic states. In some embodiments, the plurality of diagnostic states consist of 2 to 5, 3 to 10 or 8 to 20 diagnostic states. In some embodiments, the plurality of diagnostic states fall within another range starting from no less than 2 diagnostic states to no more than 20 diagnostic states.

[0063] In some embodiments, the first biological condition associated with c-reactive protein of the assessment test subject is further included in the presence of the first biological condition associated with c-reactive protein and the absence of the first biological condition associated with c-reactive protein. In some embodiments, the first biological condition associated with c-reactive protein of the assessment test subject is further included in the first biological condition associated with c-reactive protein and the second biological condition associated with c-reactive protein that is different from the first biological condition associated with c-reactive protein. In some embodiments, the first biological condition is autism spectrum disorder, and the second biological condition is neurotypical development; that is, there is no neurodevelopmental disorder. In some embodiments, the first biological condition is autism spectrum disorder, and the second biological condition is attention deficit / hyperactivity disorder. In some embodiments, the test subject is a person. In some embodiments, the age of the person is between about 12 years old and about 5 years old. In some embodiments, the person is less than about 12, 11, 10, 9, 8, 7, 5, 4, 3, 2 or 1 years old. In some embodiments, the person is at least about 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11 or 12 years old. In some embodiments, the human is at least 13 years old, at least 14 years old, at least 15 years old, at least 18 years old, or at least 30 years old. In some embodiments, the human is no more than 40 years old, no more than 30 years old, no more than 18 years old, or no more than 15 years old. In some embodiments, the age of the human is between about 1 year old and about 6 years old, between about 5 years old and about 10 years old, or between about 10 years old and about 40 years old. In some embodiments, the human falls within another age range starting from no less than about 1 year old and ending no more than about 40 years old. In some embodiments, the corresponding biological sample associated with the c-reactive protein of the corresponding subject (e.g., the test subject and / or the training subject) is selected from the group consisting of hair shaft, teeth, and nails. In some embodiments, the corresponding biological sample associated with the c-reactive protein of the corresponding subject (e.g., the test subject and / or the training subject) is a hair shaft and the reference line corresponds to the longitudinal direction of the hair shaft. In some embodiments, the corresponding biological sample associated with the c-reactive protein of the corresponding subject (e.g., the test subject and / or the training subject) is a tooth and the reference line corresponds to the direction across the growth zone, including the new growth line of the tooth. In some embodiments, the corresponding plurality of positions are ordered such that a first position in the corresponding plurality of positions along the corresponding biological sample associated with the c-reactive protein of the corresponding subject (e.g., a test subject and / or a training subject) corresponds to a position closest to the tip of the corresponding biological sample associated with the c-reactive protein of the corresponding subject. In some embodiments, each trace in the corresponding plurality of fluorescence intensity measurements includes a plurality of data points, each data point being an instance of a corresponding position in the plurality of positions.In some embodiments, the corresponding feature set is selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof. In some embodiments, the features are derived from recursive quantitative analysis of the fluorescence trace or related computational analysis. In some embodiments, the corresponding plurality of locations includes at least 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000, 20000 or more than 20000 locations.

[0064] In some embodiments, the plurality of locations along the reference line on the biological sample comprises at least 50, at least 100, at least 500, at least 1000, at least 2000, at least 5000, at least 10,000, at least 50,000, at least 100,000, at least 500,000, or at least 1×10 6 In some embodiments, the plurality of locations includes no more than 1×10 7 No more than 1×10 6 , no more than 100,000, no more than 10,000, no more than 1000, or no more than 100 positions. In some embodiments, the plurality of positions ranges from 50 to 1000, 500 to 50,000, 10,000 to 1×10 6 or 1×10 6 Up to 1×10 7 In some embodiments, the plurality of positions falls within a range from no less than 50 positions to no more than 1×10 7 In some embodiments, each corresponding growth period of the biological sample is selected from a plurality of growth periods. In some embodiments, the plurality of growth periods includes at least 50, at least 100, at least 500, at least 1000, at least 2000, at least 5000, at least 10,000, at least 50,000, at least 100,000, at least 500,000, or at least 1×10 6 In some embodiments, the plurality of growth periods includes no more than 1×10 7 No more than 1×10 6In some embodiments, the plurality of growth periods ranges from 50 to 1000, 500 to 50,000, 10,000 to 1×10 6 or 1×10 6 to 1×10 7 In some embodiments, the plurality of growth periods falls within a range from no less than 50 growth periods to no more than 1×10 7 Another range at the end of a growth period.

[0065] In some embodiments, the plurality of fluorescence intensity measurements comprises at least 50, at least 100, at least 500, at least 1000, at least 2000, at least 5000, at least 10,000, at least 50,000, at least 100,000, at least 500,000, or at least 1×10 6 In some embodiments, the plurality of fluorescence intensity measurements includes no more than 1×10 7 No more than 1×10 6 In some embodiments, the plurality of fluorescence intensity measurements ranges from 50 to 1000, 500 to 50,000, 10,000 to 1×10 6 or 1×10 6 Up to 1×10 7 In some embodiments, the plurality of fluorescence intensity measurements falls within a range from no less than 50 fluorescence intensity measurements to no more than 1×10 7 The fluorescence intensity measurements ended up in another range.

[0066] In some embodiments, the corresponding fluorescence intensity measurement results include one or more traces of fluorescence intensity. In some embodiments, the corresponding fluorescence intensity measurement results include at least 1, at least 2, at least 3, at least 4, at least 5, or at least 10 traces. In some embodiments, the corresponding fluorescence intensity measurement results include no more than 50, no more than 10, no more than 5, or no more than 3 traces. In some embodiments, the corresponding fluorescence intensity measurement results consist of 1 to 5, 2 to 10, or 10 to 20 traces. In some embodiments, the corresponding fluorescence intensity measurement results include another range of traces starting from no less than 1 trace to no more than 20 traces.

[0067] In some embodiments, for a corresponding trace in a plurality of fluorescence intensity measurements, a plurality of data points includes at least 2, at least 5, at least 10, at least 20, at least 50, at least 100, at least 500, at least 1000, at least 5000, or at least 10,000 data points. In some embodiments, the plurality of data points includes no more than 500,000, no more than 10,000, no more than 1000, no more than 500, no more than 100, no more than 50, no more than 20, or no more than 10 data points. In some embodiments, the plurality of data points consists of 2 to 100, 50 to 2000, 1000 to 10,000, or 10,000 to 500,000 data points. In some embodiments, the plurality of data points falls within another range starting from no less than 2 data points to no more than 500,000 data points.

[0068] In some embodiments, the temporal dynamics of one or more traces are analyzed by a data analysis method. In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to the one or more traces: determination of a linear slope, determination of a plurality of nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0069] Combination Figure 1Describing the details of an exemplary system, the figure shows an example of a block diagram of a computing device 100 of the present disclosure. In some embodiments, the device 100 includes one or more processing units CPU 102 (also referred to as processors), one or more network interfaces 104, a user interface 106, non-persistent memory 111, persistent memory 112, and one or more communication buses 114 for interconnecting these components. One or more communication buses 114 optionally include circuits (sometimes referred to as chipsets) that interconnect and control communication between 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, tape cassettes, tapes, magnetic disk storage or other magnetic storage devices, magnetic disk storage devices, optical disk storage devices, flash memory devices or other non-volatile solid-state storage devices. Persistent memory 112 optionally includes one or more storage devices remote from CPU 102. Persistent memory 112 and non-volatile storage devices within non-persistent memory 112 contain non-transitory computer-readable storage media. In some embodiments, non-persistent memory 111 or alternatively non-transitory computer-readable storage media stores the following programs, modules, and data structures, or subsets thereof, sometimes in combination with persistent memory 112: an optional operating system 116, which includes processes for handling various basic system services and performing hardware-related tasks; an optional network communication module (or instructions) 118 for connecting system 100 to other devices and / or communication networks 104; an optional classifier training module 120 for training models to assess biological conditions in subjects; and an optional data store 122 for biological samples from training subjects. a dataset comprising feature data for one or more training subjects 124, wherein the feature data comprises parameters associated with each feature 126, and a diagnostic status 128 (e.g., indicating whether the corresponding training subject has been diagnosed with a biological condition or has not been diagnosed with the biological condition); an optional classifier validation module 130 for validating a model for distinguishing biological conditions; an optional data store 132 for a dataset of biological samples from the validation subjects; and an optional patient classification module 134 for classifying a subject as having a biological condition, e.g., as trained using the classifier training module 120.

[0070] In various embodiments, one or more of the above elements are stored in one or more previously mentioned memory devices, and correspond to an instruction set for performing the above functions. The modules, data or programs (e.g., a set of instructions) identified above do not need to be implemented as separate software programs, processes, data sets or modules, and therefore various subsets of these modules and data can be combined or otherwise rearranged in various embodiments. In some embodiments, non-persistent memory 111 optionally stores a subset of the modules and data structures identified above. In addition, in some embodiments, 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 visualization system 100, which can be addressed by visualization system 100 so that visualization system 100 can retrieve all or part of these data when needed.

[0071] In some embodiments, system 100 is connected to or includes one or more analytical devices for performing chemical analysis. For example, optional network communication module (or instructions) 118 is configured to connect system 100 to one or more analytical devices, for example, via communication network 104. In some embodiments, one or more analytical devices include a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS), a fluorescence image sensor, or a Raman spectrometer.

[0072] although Figure 1 A "system 100" is depicted, but the diagram is intended more as a functional description of various features that may be present in a computer system than as a schematic diagram of the architecture of the implementations described herein. In practice, and as recognized by one of ordinary skill in the art, items shown separately may be combined and some items may be separated. Furthermore, although Figure 1 Certain data and modules are described in non-persistent storage 111 , but some or all of these data and modules may be in persistent storage 112 .

[0073] In some embodiments, the methods of the present disclosure include obtaining a biological sample (e.g., a strand of hair including a hair shaft). In some embodiments, the subject is a human. In some embodiments, the subject is a child aged 5 years or less (e.g., a child 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. Figure 2AAn example of a hair sample of a subject including a hair shaft is shown. In some embodiments, the hair sample is cut from the subject (e.g., with the aid of scissors). In some embodiments, the method of obtaining the hair sample is non-invasive. The hair sample obtained can have a length of at least 1 cm (e.g., the hair sample is 1 cm, 2 cm, 3 cm, 4 cm, or 5 cm long). The hair sample can include any portion of the hair (e.g., the tip or the portion between the tip and the hair follicle). In particular, there is no special requirement for including hair follicles in the hair sample. Figure 2B Examples of dental samples from subjects are shown. Figure 2C An example of a nail sample of a subject is shown. In the case of a nail or hair, obtaining a biological sample can refer to positioning a subject so that the nail or hair is sampled. The nail sample can include a complete nail or nail clippings.

[0074] In some embodiments, the obtained biological sample is pretreated, such as by washing the biological sample with one or more solvents and / or surfactants and drying. In the case where the biological sample is hair, the hair sample is subjected to a pretreatment in a TRITON and ultrapure metal-free water (e.g. The hair shafts are washed in a solution of (e.g., water) and dried in an oven overnight (e.g., at 60 degrees Celsius). The pretreatment may further include preparing the hair shafts for measurement by placing the hair shafts on a slide (e.g., a microscope slide) with an adhesive film (e.g., double-sided tape). The hair shafts may be positioned so that the hair shafts are substantially straight. The slide with the hair shafts may be placed in or near a measurement system (e.g., a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS), a fluorescence image sensor, or a Raman spectrometer) for performing the analysis. In the case where the biological sample is a tooth or nail, the surface of the biological sample may be cleaned (e.g., by a surfactant, water, or one or more solvents). In some cases, the sample is decalcified before, after, immediately before, or in any combination about the time frame for performing the analysis, as described elsewhere herein. In some cases, decalcifying the sample may include the following steps: (a) soaking the tooth in a solution of ethylenediaminetetraacetic acid (EDTA), wherein the EDTA may include a pH of about 7.0 to about 7.4, for a period of up to about 5 weeks; (b) weighing the tooth once a week; (c) removing the sample when the weight change of the tooth stabilizes. The sample may be placed in or near a measurement system (e.g., a laser ablation-inductively coupled plasma-mass spectrometer (LA-ICP-MS), a fluorescence image sensor, or a Raman spectrometer) for performing analysis.

[0075] Figure 3A flow chart of a method 300 for assessing a biological condition of a subject, such as a method for predicting a diagnostic status of a subject with respect to a disease or condition, is shown. The method 300 may include staining a dental sample of the subject to produce a stained dental sample (as in operation 302). Next, the method 300 may include analyzing fluorescence intensity across the stained dental sample spatially (as in operation 304). Next, the method 300 may include predicting a diagnostic status of the subject with respect to a disease or condition based at least in part on the analysis of the fluorescence intensity (as in operation 306).

[0076] In some embodiments, the analysis includes obtaining a fluorescent image of the stained tooth sample and analyzing the fluorescence intensity of the fluorescent image. In some embodiments, obtaining a fluorescent image of the stained tooth sample includes using an inverted or non-inverted confocal microscope. In some embodiments, staining the tooth sample includes using C-reactive protein immunohistochemical staining. In some embodiments, the method further includes slicing the tooth sample. In some embodiments, staining the tooth sample includes decalcifying the tooth sample.

[0077] In some embodiments, analyzing comprises generating an inflammation time curve based at least in part on the fluorescence intensity, and analyzing the inflammation time curve. In some embodiments, at least a portion of the inflammation time curve corresponds to a prenatal period of the subject.

[0078] In some embodiments, measurement data is collected sequentially from a biological sample at multiple locations along the biological sample. In some embodiments, the multiple locations along the reference line of the biological sample include at least 100 locations (e.g., 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500, 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000, 20000 or more than 20000 locations). In some embodiments, the corresponding locations are adjacent to each other. By this method, each region corresponding to a different location on the biological sample can therefore be associated with a dynamic (e.g., time-varying) abundance measurement. In some embodiments, the corresponding locations are separated by a predetermined distance. In some embodiments, sampling is performed along the reference line of the biological sample starting from the corresponding location closest to the tip of the biological sample such as a hair sample (e.g., at a location corresponding to the youngest age of the subject). In general, sampling can be performed starting from the corresponding position closest to the tip or root, as long as the sampling direction is known and analyzed using an appropriate trained model.

[0079] Sampling can generate a set of data points. Each data point node can correspond to a measurement result (e.g., abundance or concentration) of a substance that indicates a dynamic biological response measured at multiple locations along the biological sample. Each location on the reference line of the biological sample can correspond to a specific growth time of the biological sample.

[0080] In some embodiments, in the case of a biological sample of a tooth, the reference line may include 240 to 510 days of growth (e.g., the period of crown formation depends on the tooth type). In some embodiments, each position along the reference line may correspond to about 1 to about 0.5 microns. Alternatively or in addition, the biological sample may include a hair shaft, wherein the position along the reference line corresponds to about 5 minutes of growth (e.g., the hair growth period calculated using a 1 micron resolution and an average hair growth rate of 1 cm per month). A first data set including a plurality of traces is obtained by associating a plurality of positions along the reference line of the biological sample with corresponding growth time periods. Each trace includes a time-dependent abundance measurement result (e.g., abundance or concentration) of a substance, indicating a dynamic biological response measured from the biological sample. For example, the distance between the positions may correspond to an estimated growth (e.g., biological time) of the biological sample. For example, the abundance of a tooth sample may be measured along a distance of up to about 8 millimeters (mm), which corresponds to a biological time of about 240 to 510 days. Alternatively, or in addition, the abundance of a hair sample may be measured along a distance of 1.2 cm, which corresponds to a biological time of about 35 days. Biological time can be estimated by using the average rate of hair growth (eg, 1 cm per month).

[0081] In some embodiments, data analysis is performed on one or more traces corresponding to time-dependent abundance (e.g., time-dependent concentration) of a substance indicative of a dynamic biological response measured from a biological sample. This can include customized operations to clean the data (e.g., smoothing the data over a time span, and / or removing data points above or below a predetermined threshold). In some embodiments, the data analysis includes removing data points from the traces where the mean absolute difference between adjacent data points is at least one, two, or three times the standard deviation of the mean absolute difference between adjacent points.

[0082] In some embodiments, the temporal dynamics of one or more traces are analyzed by a data analysis method. In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to the one or more traces: determination of a linear slope, determination of a plurality of nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0083] In some embodiments, data analysis further comprises normalizing each trace according to an internal standard.For example, a measured substance detected in a sample that is uniformly spiked during development / growth of a biological sample that does not fluctuate with environmental exposure (e.g., diet) can be used as an internal standard.

[0084] In some embodiments, the data analysis further includes performing recursive quantification analysis (RQA) on the time-dependent traces to obtain a feature set describing the dynamic periodic characteristics of the traces. RQA measures variability in the time-dependent traces. RQA involves the estimation of features describing the periodic characteristics in a given waveform, including certainty, entropy, mean diagonal length (MDL), stratification, entropy, capture time (TT), recursion time (RT), Vmax and Lmax, each of which captures different aspects of the signal dynamics, such as the determination of the linear slope described in the accompanying references, the determination of multiple nonlinear parameters describing the curvature of the one or more traces, the determination of the sudden change in the intensity of the one or more traces, the determination of one or more changes in the baseline intensity of the one or more traces, the determination of the change in the frequency domain representation of the one or more traces, the determination of the change in the power spectrum domain representation of the one or more traces, the determination of one or more recursive quantitative analysis parameters, the determination of one or more cross-recursive quantitative analysis parameters, the determination of one or more joint recursive quantitative analysis parameters, the determination of one or more multidimensional recursive quantitative analysis parameters, the estimation of the Lyapunov spectrum or the determination of the maximum Lyapunov exponent. The methods and features of RQA are described, for example, by Webber et al. in "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 by Marwan et al. in "Recurrence Plots for the Analysis of Complex Systems," Physics Reports 438, 237-239 (2007), the contents of each of which are hereby incorporated by reference in their entirety. In some embodiments, time-dependent traces are analyzed using other analysis methods such as Fourier transforms, wavelet analysis, and cosine analysis. Such techniques can be applied to derive similar indicators, including spectral analysis of frequency components and their associated powers. These indicators and related derived metrics can be used in place of features derived from RQA to analyze time-dependent traces obtained from biological samples for the purpose of predicting classification.

[0085] RQA includes the construction of recurrence diagrams that visualize and analyze the dynamic temporal structure in the corresponding obtained traces. Such recurrence diagrams can illustrate the stage process in sequential measurements by plotting a diagram derived from the time lag of a given sequence relative to that sequence. From the one-dimensional trace measured from the hair shaft, an additional dimension is calculated to embed the trace in a higher dimensional space, which is called a phase diagram, where t refers to the value of the original trace, and the dimensions (t+τ) and (t+2τ) are derived from lagging the original time series by an interval τ. The embedded phase diagram is then subsequently analyzed to construct the recurrence diagram and perform recursive quantitative analysis. The recurrence diagram can be derived from the phase diagram by applying a threshold function to each point in the phase diagram; on the corresponding recurrence diagram, consisting of a square binary matrix, usually represented as a white or black space, a given point is assigned a value of 1 at each time interval, where another point in the phase diagram shares the spatial limit of the assigned threshold boundary. The RQA method is applied to the recurrence diagram to examine the interval of delays between states in a given system, and the black points reflect the time interval when the system revisits the same state. Periodic processes where the system continuously repeats a given state pattern will appear as black diagonal lines in the recurrence diagram, while stable periods will appear as square structures, spurious repetitions as black dots, and unique events as blanks.

[0086] In some embodiments, a recurrence plot is constructed for the traces of a single substance or a combination of two substances (e.g., to visualize the interactive periodic pattern of two substances; this may be referred to as cross-recurrence quantitative analysis, or joint recurrence quantitative analysis). In some embodiments, a recurrence plot is constructed for a combination of three or more substances.

[0087] In some embodiments, data analysis includes analyzing the recurrence graph to obtain a feature set associated with the recurrence graph. These features, which may be interchangeably referred to as "rhythmic features" or "dynamic features", provide quantitative measures describing the periodicity, predictability, and transitivity present in multiple traces. These features are selected from a set including: recurrence rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recurrence trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recurrence time, Shannon entropy of recurrence time, the number of most likely recurrences, average diagonal length (MDL), hierarchical, entropy, capture time (TT), recurrence time (RT), Vmax, Lmax, and any combination thereof.

[0088] In some embodiments, the data analysis method may apply one or more of the following operations and / or methods to one or more traces: determination of a linear slope, determination of multiple nonlinear parameters describing the curvature of the one or more traces, determination of a sudden change in the intensity of the one or more traces, determination of one or more changes in the baseline intensity of the one or more traces, determination of a change in the frequency domain representation of the one or more traces, determination of a change in the power spectrum domain representation of the one or more traces, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, or determination of a maximum Lyapunov exponent.

[0089] In some embodiments, the data analysis further comprises inputting the obtained feature set into a trained model. In some embodiments, the trained model comprises a predictive computational algorithm to obtain the probability of a subject having a biological condition. In some embodiments, the predictive computational algorithm performs the following calculations:

[0090]

[0091] where p(subject) is the probability that the subject has the first biological condition, e is the Euler number, and α is β1x1+…+β k x k The calculated parameters, x1,…,x, are related to the probability that the subject has a biological condition when they are equal to zero. k corresponds to the value derived for each feature in the feature set, which includes features from 1 to k, and β1,…,β k corresponds to a weight parameter associated with each feature in a feature set including features from 1 to k.

[0092] Weight parameters β1,…,β k It may be defined based on model training.The probability p(subject) may be provided as a number in the range of 0 to 1, where 1 corresponds to a 100% probability that the subject has the biological condition.

[0093] In some embodiments, data analysis includes applying a threshold to the probability p (subject) obtained. If the probability p (subject) obtained is higher than the threshold, the subject is assessed to have a biological condition. If the probability obtained is lower than the threshold, the subject is assessed to not have a biological condition. In some embodiments, the threshold is between about 0.3 and 0.6 (for example, a predetermined threshold is about 0.3, 0.35, 0.4, 0.45, 0.5, 0.55 or 0.6). In some embodiments, the threshold is at least 0.1, at least 0.2, at least 0.3, at least 0.4, at least 0.5, at least 0.6 or at least 0.7. In some embodiments, the threshold is no more than 0.9, no more than 0.8, no more than 0.7, no more than 0.6, no more than 0.5 or no more than 0.4. In some embodiments, the threshold falls within another range from not less than 0.1 to not more than 0.9. The value assigned to the probability threshold can be predetermined, or estimated by using a receiver operating characteristic (ROC) chart during model training, wherein the optimal threshold used corresponds to the value that produces the maximum area under the curve (ROC-AUC). In some embodiments, the obtained probability is expressed as an associated odds (eg, an odds ratio (OR), which can be derived from the probability such that OR=p / (1-p)). For example, assessing includes assessing the odds that the subject has a biological condition.

[0094] In certain embodiments, data analysis includes distinguishing the first biological condition and alternative condition, for example, the second biological condition.In certain embodiments, alternative condition is related to unknown condition (for example, neurotypical condition (NT)).In certain embodiments, the first biological condition is related to autism spectrum disorder (ASD), and alternative condition is related to attention deficit / hyperactivity disorder (ADHD).In certain embodiments, alternative condition is any other neurodevelopmental condition, or the comorbidity diagnosis of two neurodevelopmental conditions.Therefore, data analysis may be able to distinguish between two neurodevelopmental conditions (for example, between autism spectrum disorder and ADHD, or between ASD and comorbidity (CM) cases diagnosed as both ASD and ADHD).

[0095] Healthcare providers, such as physicians and the patient's treatment team, may have access to patient data (e.g., dynamic biological response data or other health data) and / or have predictions or assessments generated from such data. Based on the data analysis results, the healthcare provider may determine a clinical decision or outcome.

[0096] For example, based at least in part on the predicted disease or condition of the subject, the doctor may instruct the patient to undergo one or more clinical tests at a hospital or other clinical location. These instructions may be provided when a predetermined criterion (e.g., a minimum threshold of the likelihood of a disease or condition) is met.

[0097] In some embodiments, such minimum threshold is at least about 5% probability, at least about 10% probability, at least about 20% probability, at least about 25% probability, at least about 30% probability, at least about 35% probability, at least about 40% probability, at least about 45% probability, at least about 50% probability, at least about 55% probability, at least about 60% probability, at least about 65% probability, at least about 70% probability, at least about 75% probability, at least about 80% probability, at least about 85% probability, at least about 90% probability, at least about 95% probability, at least about 96% probability, at least about 97% probability, at least about 98% probability, or at least about 99% probability. In some embodiments, the minimum threshold is no more than 99%, no more than 90%, no more than 80%, no more than 70%, no more than 60%, no more than 50%, or no more than 40%. In some embodiments, the minimum threshold is 5% to 20%, 10% to 50%, 30% to 70%, or 60% to 99%. In some embodiments, the minimum threshold falls within another range starting from no less than 5% and ending no more than 99%.

[0098] As another example, a physician may prescribe a therapeutically effective dose of a treatment (e.g., a drug) based at least in part on the predicted disease or condition of the subject, a clinical course, or further clinical testing to administer to the patient. For example, a physician may prescribe an anti-inflammatory treatment drug in response to an indication of inflammation in a patient.

[0099] Model

[0100] The methods and systems disclosed herein can utilize or acquire external capabilities of artificial intelligence technology to develop features for various diseases or conditions. These features can be used to accurately predict diseases or conditions (e.g., months or years ahead of the standard of clinical care). Using such predictive capabilities, healthcare providers (e.g., doctors) can be able to make wise, accurate risk-based decisions, thereby improving the quality of care and monitoring provided to patients.

[0101] The methods and systems of the present disclosure can analyze the acquired dynamic biological response data from the subject (patient) to generate the possibility that the subject has a disease or condition. For example, the system can apply a trained (e.g., prediction) algorithm to the acquired dynamic biological response data to generate the possibility that the subject has a disease or condition. The training algorithm may include an artificial intelligence-based model, such as a classifier or regressor, which is configured to process the acquired dynamic biological response data to generate the possibility that the subject has a disease or condition. The model can be trained using a clinical data set from one or more patient cohorts, for example, using the patient's clinical health data and / or dynamic biological response data as input to the model, and using the patient's known clinical health results (e.g., disease or condition) as output.

[0102] The model may include one or more machine learning algorithms. Examples of machine learning algorithms may include support vector machines (SVM), naive Bayes classification, random forests, neural networks (e.g., deep neural networks (DNNs), recurrent neural networks (RNNs), deep RNNs, long short-term memory (LSTM) recurrent neural networks (RNNs), or gated recurrent units (GRUs), or other supervised learning algorithms or unsupervised machine learning, statistical or deep learning algorithms for classification and regression. The model may also involve the estimation of an integrated model consisting of multiple predictive models, and utilize techniques such as gradient boosting, such as in the construction of gradient boosted decision trees. The model may be trained using one or more training data sets corresponding to patient data.

[0103] The training data set can be generated from one or more patient cohorts, for example, having common clinical characteristics (features) and clinical outcomes (labels). The training data set may include a feature set and a label corresponding to the feature. The feature may correspond to an algorithm input that includes dynamic biological response data, patient demographic information derived from an electronic medical record (EMR), and medical observations. The feature may include clinical characteristics, such as, for example, certain ranges or categories of dynamic biological response data. The feature may include patient information, such as patient age, patient medical history, other medical conditions, current or past medications, and time since the last observation. For example, a feature set collected from a given patient at a given point in time may be used together as a feature that may indicate the health status or state of the patient at a given point in time.

[0104] For example, ranges of dynamic bio-response data and other health measurements can be represented as multiple disjoint continuous ranges of continuous measurements, and categories of dynamic bio-response data and other health measurements can be represented as multiple disjoint sets of measurements (e.g., {"high", "low"}, {"high", "normal"}, {"low", "normal"}, {"high", "high to near critical value", "normal", "low"}, etc.). Clinical characteristics can also include clinical labels indicating a patient's health history, such as a diagnosis of a disease or condition, previous administration of clinical treatments (e.g., medications, surgical treatments, chemotherapy, radiotherapy, immunotherapy, etc.), behavioral factors, or other health conditions (e.g., hypertension or high blood pressure, hyperglycemia or high blood sugar, hypercholesterolemia or high blood cholesterol, a history of allergic reactions or other adverse reactions, etc.).

[0105] Label can include clinical results, such as the presence, absence, diagnosis or prognosis of a disease or illness in a subject (for example, a patient).Clinical results can include the time characteristics related to the presence, absence, diagnosis or prognosis of a disease or illness with a patient.For example, the time characteristics can indicate that a patient has the occurrence of a disease or illness in a certain time period after a previous clinical result (for example, being discharged from hospital, receiving such as drug therapy, experiencing clinical procedures such as surgery, etc.).Such time period can be, for example, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 10 days, about 2 weeks, about 3 weeks, about 4 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 6 months, about 8 months, about 10 months, about 1 year or more than about 1 year. In some embodiments, the time period is no more than 5 years, no more than 1 year, no more than 6 months, no more than 3 months, no more than 1 month, no more than 2 weeks, no more than 1 week, no more than 1 day, or no more than 12 hours. In some embodiments, the time period is 1 hour to 12 hours, 12 hours to 24 hours, 1 day to 7 days, 1 week to 4 weeks, 1 month to 12 months, or 1 year to 5 years. In some embodiments, the time period falls within another range starting from no less than 1 hour and ending no more than 5 years.

[0106] Input features can be constructed by integrating data into bins or alternatively using one-hot encoding. Input can also include eigenvalues ​​or vectors derived from the aforementioned inputs, such as cross-correlations calculated between separate dynamic bioreaction data or other measurements within a fixed time period, and discrete derivatives or finite differences between continuous measurements. Such time periods can be, for example, about 1 hour, about 2 hours, about 3 hours, about 4 hours, about 6 hours, about 8 hours, about 10 hours, about 12 hours, about 14 hours, about 16 hours, about 18 hours, about 20 hours, about 22 hours, about 24 hours, about 2 days, about 3 days, about 4 days, about 5 days, about 6 days, about 7 days, about 10 days, about 2 weeks, about 3 weeks, about 4 weeks, about 1 month, about 2 months, about 3 months, about 4 months, about 6 months, about 8 months, about 10 months, about 1 year or more than about 1 year. In some embodiments, the time period is no more than 5 years, no more than 1 year, no more than 6 months, no more than 3 months, no more than 1 month, no more than 2 weeks, no more than 1 week, no more than 1 day, or no more than 12 hours. In some embodiments, the time period is 1 hour to 12 hours, 12 hours to 24 hours, 1 day to 7 days, 1 week to 4 weeks, 1 month to 12 months, or 1 year to 5 years. In some embodiments, the time period falls within another range starting from no less than 1 hour and ending no more than 5 years.

[0107] Training records can be constructed from observation sequences. Such sequences can include fixed lengths to facilitate data processing. For example, the sequences can be zero-padded or selected as independent subsets of a single patient's records.

[0108] The model can process input features to generate output values ​​including one or more classifications, one or more predictions, or a combination thereof. For example, such classifications or predictions can include: binary classification of healthy / normal health state (e.g., absence of disease or condition) or poor health state (e.g., presence of disease or condition), classification between groups of classification labels (e.g., 'no disease or condition', 'obvious disease or condition', and 'possible disease or condition'), the likelihood of developing a particular disease or condition (e.g., relative likelihood or probability), a score indicating the presence of a disease or condition, a score indicating the level of systemic inflammation experienced by the patient, a 'risk factor' for the likelihood of death of the patient, a prediction of the time when the patient is expected to have developed the disease or condition, and confidence intervals for any numerical predictions. Various machine learning techniques can be cascaded so that the output of the machine learning technique can also be used as an input feature for subsequent layers or sub-parts of the model.

[0109] In order to train the model (e.g., by determining the weights and relevance of the model) to generate real-time classifications or predictions, the model can be trained using a data set. Such a data set may be large enough to generate statistically significant classifications or predictions. For example, the data set may include: a database of de-identified data including dynamic biological response data and other measurements, and dynamic biological response data and other measurements from a hospital or other clinical setting.

[0110] The data set can be divided into subsets (e.g., discrete or overlapping), such as a training data set, a development data set, and a test data set. For example, the data set can be divided into a training data set including 80% of the data set and a test data set including 20% ​​of the data set. The training data set can include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80% or about 90% of the data set. The development data set can include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80% or about 90% of the data set. The test data set can include about 10%, about 20%, about 30%, about 40%, about 50%, about 60%, about 70%, about 80% or about 90% of the data set. The training set (e.g., training data set) can be selected by randomly sampling the data set corresponding to one or more patient cohorts to ensure the independence of sampling. Alternatively, a training set (eg, a training data set) can be selected by sampling proportionally from data sets corresponding to one or more patient cohorts to ensure independence of the sampling.

[0111] To improve the accuracy of model predictions and reduce overfitting of the model, the data set can be augmented to increase the number of samples in the training set. For example, data augmentation can include rearranging the order of observations in the training records. To accommodate data sets with missing observations, methods for interpolating missing values, such as forward filling, backward filling, linear interpolation, and multi-task Gaussian processes, can be used. Data sets can be filtered to remove confounding factors. For example, within a database, a subset of patients can be excluded.

[0112] The model may include one or more neural networks, such as a neural network, a convolutional neural network (CNN), a deep neural network (DNN), a recurrent neural network (RNN), or a deep RNN. The recurrent neural network may include a unit that may be a long short-term memory (LSTM) unit or a gated recurrent unit (GRU). For example, the model may include an algorithmic architecture that includes a neural network with a set of input features (such as vital signs and other measurements, patient medical history, and / or patient demographics). Neural network techniques, such as discarding or regularization, may be used during training the model to prevent overfitting. The neural network may include multiple subnetworks, each of which is configured to generate a classification or prediction of a different type of output information (e.g., which may be combined to form the overall output of the neural network). The model may alternatively utilize statistical or related algorithms, including random forests, classification and regression trees, support vector machines, discriminant analysis, regression techniques, and their ensemble and gradient boosting variants.

[0113] When the model generates a classification or prediction of a disease or condition, a notification (e.g., an alert or alarm) can be generated and transmitted to a healthcare provider, such as a doctor, nurse, or other member of the patient's treatment team within a hospital. The notification can be transmitted via an automated phone call, a short message service (SMS) or multimedia messaging service (MMS) message, an email, or an alert within a dashboard. The notification can include output information such as a prediction of a disease or condition, a predicted likelihood of a disease or condition, a time until the expected onset of a disease or condition, a confidence interval for the likelihood or time, or a recommended treatment regimen for the disease or condition.

[0114] To validate the performance of the model, different performance metrics can be generated. For example, the area under the receiver operating curve (AUROC) can be used to determine the diagnostic ability of the model. For example, the model can use an adjustable classification threshold so that specificity and sensitivity are tunable, and a receiver operating curve (ROC) can be used to identify different operating points corresponding to different values ​​of specificity and sensitivity.

[0115] In some cases, such as when the dataset is not large enough, cross-validation is performed to assess the robustness of the model across different training and testing datasets.

[0116] In order to calculate performance indicators, such as sensitivity, specificity, accuracy, positive predictive value (PPV), negative predictive value (NPV), AUPRC, AUROC or similar indicators, the following definitions can be used. "False positive" can refer to the result of generating a positive result or consequence by mistake or prematurely (for example, before the actual onset of a disease or condition or without any onset of a disease or condition). "True positive" can refer to the result of correctly generating a positive result or consequence when a patient suffers from a disease or condition (for example, the patient shows the symptom of a disease or condition, or the patient's record shows a disease or condition). "False negative" can refer to the result of generating a negative result or consequence, but the patient suffers from the result of a disease or condition (for example, the patient shows the symptom of a disease or condition, or the patient's record shows a disease or condition). "True negative" can refer to the result of generating a negative result or consequence (for example, before the actual onset of a disease or condition or without any onset of a disease or condition).

[0117] Models can be trained until certain predetermined conditions of accuracy or performance are met, such as having a minimum expected value corresponding to a diagnostic accuracy measurement. For example, a diagnostic accuracy measurement can correspond to a prediction of the likelihood of a disease or condition occurring in a subject. As another example, a diagnostic accuracy measurement can correspond to a prediction of the likelihood of a disease or condition that a subject has previously been treated for deteriorating or recurring. Examples of diagnostic accuracy metrics can include sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), accuracy, area under the precision-recall curve (AUPRC), and the area under the curve (AUC) (AUROC) of a receiver operating characteristic (ROC) curve corresponding to the diagnostic accuracy of detecting or predicting a disease or condition.

[0118] For example, in some embodiments, such predetermined conditions can be that the sensitivity of predicting a disease or condition includes, for example, a value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the predetermined condition is that the sensitivity includes a value of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the predetermined condition is that the sensitivity includes a value of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the predetermined condition is that the sensitivity includes a value falling within another range starting from no less than 50% and ending at no more than 100%.

[0119] As another example, in some embodiments, such predetermined conditions are that the specificity of predicting a disease or condition includes, for example, a value of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the predetermined condition is that the specificity includes a value of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the predetermined condition is that the specificity includes a value of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the predetermined condition is that the specificity includes a value falling within another range starting from no less than 50% and ending no more than 100%.

[0120] As another example, in some embodiments, such a predetermined condition is that the positive predictive value (PPV) for predicting a disease or condition includes, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the predetermined condition is that the PPV includes a value of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the predetermined condition is that the PPV includes a value of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the predetermined condition is that the PPV includes a value falling within another range starting from no less than 50% to no more than 100%.

[0121] As another example, in some embodiments, such a predetermined condition is that the negative predictive value (NPV) for predicting a disease or condition includes, for example, at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the predetermined condition is that the NPV includes a value of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the predetermined condition is that the NPV includes a value of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the predetermined condition is that the NPV includes a value falling within another range starting from no less than 50% to no more than 100%.

[0122] As another example, in some embodiments, such a predetermined condition is that the area under the curve (AUC) of the receiver operating characteristic (ROC) curve predicting the disease or condition (AUROC) comprises a value of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99. In some embodiments, the predetermined condition is that the AUROC comprises a value of no more than 1, no more than 0.99, no more than 0.90, no more than 0.80, no more than 0.70, or no more than 0.60. In some embodiments, the predetermined condition is that the AUROC comprises a value of 0.50 to 0.70, 0.60 to 0.80, 0.70 to 0.90, or 0.90 to 1. In some embodiments, the predetermined condition is that the AUROC includes a value falling within another range starting from no less than 0.50 and ending no greater than 1.

[0123] As another example, in some embodiments, such a predetermined condition is that the area under the precision-recall curve (AUPRC) for predicting a disease or condition comprises a value of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99. In some embodiments, the predetermined condition is that the AUPRC comprises a value of no more than 1, no more than 0.99, no more than 0.90, no more than 0.80, no more than 0.70, no more than 0.60, or no more than 0.50. In some embodiments, the predetermined condition is that AUPRC includes values ​​of 0.10 to 0.40, 0.30 to 0.70, 0.60 to 0.90, or 0.80 to 1. In some embodiments, the predetermined condition is that AUPRC includes values ​​falling within another range starting from no less than 0.10 and ending no more than 1.

[0124] In some embodiments, the model is trained or configured to predict a disease or condition with a sensitivity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the model is trained or configured to predict a disease or condition with a sensitivity of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the model is trained or configured to predict a disease or condition with a sensitivity of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the model is trained or configured to predict a disease or condition with a sensitivity that falls within another range starting from no less than 50% and ending at no more than 100%.

[0125] In some embodiments, the model is trained or configured to predict a disease or condition with a specificity of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the model is trained or configured to predict a disease or condition with a specificity of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the model is trained or configured to predict a disease or condition with a specificity of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the model is trained or configured to predict a disease or condition with a specificity falling within another range starting from no less than 50% and ending at no more than 100%.

[0126] In some embodiments, the model is trained or configured to predict a disease or condition with a positive predictive value (PPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the model is trained or configured to predict a disease or condition with a PPV of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the model is trained or configured to predict a disease or condition with a PPV of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the model is trained or configured to predict a disease or condition with a PPV falling within another range starting from no less than 50% to no more than 100%.

[0127] In some embodiments, the model is trained or configured to predict a disease or condition with a negative predictive value (NPV) of at least about 50%, at least about 55%, at least about 60%, at least about 65%, at least about 70%, at least about 75%, at least about 80%, at least about 85%, at least about 90%, at least about 95%, at least about 96%, at least about 97%, at least about 98%, or at least about 99%. In some embodiments, the model is trained or configured to predict a disease or condition with an NPV of no more than 100%, no more than 99%, no more than 90%, no more than 80%, no more than 70%, or no more than 60%. In some embodiments, the model is trained or configured to predict a disease or condition with an NPV of 50% to 70%, 60% to 80%, 70% to 90%, or 90% to 100%. In some embodiments, the model is trained or configured to predict a disease or condition with an NPV falling within another range starting from no less than 50% to no more than 100%.

[0128] In some embodiments, the trained model is trained or configured to predict a disease or condition with an area under the curve (AUC) (AUROC) of a receiver operating characteristic (ROC) curve of at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99. In some embodiments, the trained model is trained or configured to predict a disease or condition with an AUROC of no more than 1, no more than 0.99, no more than 0.90, no more than 0.80, no more than 0.70, or no more than 0.60. In some embodiments, the trained model is trained or configured to predict a disease or condition with an AUROC of 0.50 to 0.70, 0.60 to 0.80, 0.70 to 0.90, or 0.90 to 1. In some embodiments, the trained model is trained or configured to predict a disease or condition with an AUROC that falls within another range starting from no less than 0.50 and ending no greater than 1.

[0129] In some embodiments, the model is trained or configured to predict a disease or condition with an area under the precision-recall curve (AUPRC) of at least about 0.10, at least about 0.15, at least about 0.20, at least about 0.25, at least about 0.30, at least about 0.35, at least about 0.40, at least about 0.45, at least about 0.50, at least about 0.55, at least about 0.60, at least about 0.65, at least about 0.70, at least about 0.75, at least about 0.80, at least about 0.85, at least about 0.90, at least about 0.95, at least about 0.96, at least about 0.97, at least about 0.98, or at least about 0.99. In some embodiments, the model is trained or configured to predict a disease or condition with an AUPRC of no more than 1, no more than 0.99, no more than 0.90, no more than 0.80, no more than 0.70, no more than 0.60, or no more than 0.50. In some embodiments, the model is trained or configured to predict a disease or condition with an AUPRC of 0.10 to 0.40, 0.30 to 0.70, 0.60 to 0.90, or 0.80 to 1. In some embodiments, the model is trained or configured to predict a disease or condition with an AUPRC that falls within another range starting from no less than 0.10 and ending no greater than 1.

[0130] The training data set can be collected from training subjects (e.g., people). Each training has a diagnosis status indicating that they have either been diagnosed with a biological condition or have not been diagnosed with a biological condition. In some embodiments, the training subject is a child whose age is equal to or less than 5 years old (e.g., equal to or less than 5 years old, 4 years old, 3 years old, 2 years old, 1 year old, 9 months, 6 months, 3 months, or 1 month). The following training process can be performed for each training subject in a plurality of training subjects. In some embodiments, the training subject is no more than 18 years old, no more than 15 years old, no more than 12 years old, no more than 11 years old, no more than 10 years old, no more than 9 years old, no more than 8 years old, no more than 7 years old, no more than 5 years old, no more than 4 years old, no more than 3 years old, no more than 2 years old, or no more than 1 year old.

[0131] In some embodiments, training data (e.g., dynamic IHC data) is generated from a biological sample of a training subject. For each biological sample, multiple positions of a reference line on the biological sample of the training subject can be sampled to generate measurement results therefrom, thereby obtaining multiple dynamic biological reaction samples. Each of the corresponding multiple dynamic biological reaction samples corresponds to a different position in the corresponding multiple positions, and each position in the corresponding multiple positions represents a different growth period of the corresponding biological sample. Next, each corresponding position of the biological sample is analyzed (e.g., using a laser ablation inductively coupled plasma mass spectrometer (LA-ICP-MS), a fluorescent image sensor, or a Raman spectrometer) to obtain multiple traces. Each of the corresponding multiple traces corresponds to an abundance measurement result of a corresponding substance, which is jointly determined over time from the corresponding multiple dynamic biological reaction samples.

[0132] Next, a corresponding second data set can be obtained by applying recursive quantification analysis (RQA) or a related method to the corresponding multiple traces to measure a corresponding feature set, each corresponding feature in the corresponding feature set is determined by the change in the abundance of one or more substances in the corresponding multiple traces.

[0133] Next, an untrained or partially untrained model can be generated with (i) a corresponding feature set for each corresponding second data set of each training subject in a plurality of training subjects and (ii) a corresponding diagnostic state selected from a first diagnostic state and a second diagnostic state for each training subject in a plurality of training subjects, thereby obtaining a trained model. The trained model provides an indication of whether the test subject has a first biological condition based on the value of the feature in the feature set collected from the biological sample of the test subject. In some embodiments, the trained model 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, a regression model, or any combination or variant thereof, particularly including a gradient boosting implementation of the described algorithm, such as a gradient boosting decision tree. In some embodiments, the trained model predicts results relative to a multinomial or binomial distribution. In some embodiments, the trained model is used to make a binary prediction about whether the sample is derived from a subject with a first biological condition; alternatively, it can be a multinomial, distinguishing subjects without a diagnosis from subjects with a first biological condition or a second biological condition, wherein the second biological condition is different from the first biological condition.

[0134] In some embodiments, the model 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 hereby incorporated by reference.

[0135] SVMs are described in 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, 2nd ed., 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 hereby incorporated by reference in its entirety. When used for classification, SVM separates a given binary labeled data set with a hyperplane that is maximally away from the labeled data. For cases where there is no linear separation, the SVM can be run in conjunction with kernel techniques that automatically implement 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.

[0136] Decision trees are generally described in Duda, 2001, Pattern Classification, John Wiley & Sons, Inc., New York, pp. 395-396, which is hereby incorporated by reference. Tree-based methods divide the feature space into a set of rectangles and then fit a model (such as a constant) in each rectangle. In some embodiments, the decision tree is a random forest regression. One specific algorithm that can be used is classification and regression tree (CART). Other specific decision tree algorithms include, but are not limited to, ID3, C4.5, MART, and random forests. 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 hereby incorporated 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 hereby incorporated 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 hereby incorporated by reference in its entirety.

[0137] Clustering (e.g., unsupervised clustering model algorithms and supervised clustering model algorithms) is described in Duda and Hart, Pattern Classification and Scene Analysis, 1973, John Wiley & Sons, Inc., New York, pages 211-256 (hereinafter referred to as "Duda 1973"), which is hereby incorporated by reference in its entirety. As described in Section 6.7 of Duda 1973, the clustering problem is described as one of finding natural groupings in a data set. In order to identify natural groupings, two problems are solved. First, determine a way to measure the similarity (or dissimilarity) between two samples. This metric (similarity measure) is used to ensure that the samples in one cluster are more similar than the samples in other clusters. Second, determine a mechanism for partitioning data into clusters using a similarity measure. Similarity measures are discussed in Section 6.7 of Duda 1973, where it is stated that one way to start a clustering study is to define a distance function and calculate a distance matrix between all pairs of samples in a training set. If distance is a good measure of similarity, the distance between reference entities in the same cluster will be significantly smaller than the distance between reference entities in different clusters. However, as described on page 215 of Duda 1973, clustering does not need to use a distance metric. For example, a non-metric similarity function s(x, x') can be used to compare two vectors x and x'. Typically, s(x, x') is a symmetric function that has a larger value when x and x' are "similar" in some way. An example of a non-metric similarity function s(x, x') is provided on page 218 of Duda 1973. Once a method for measuring the "similarity" or "dissimilarity" between points in a data set is selected, clustering requires a standard function that measures the clustering quality of any partition of the data. The data is clustered using a partition of the data set that maximizes the standard function. See page 217 of Duda 1973. Duda 1973, section 6.8, discusses standard functions. Recently, Duda et al., Pattern Classification, 2nd edition, John Wiley & Sons, Inc. New York, has been published. Clustering is described in detail on pages 537-563.More information about clustering techniques can be found in the following references: Kaufman and Rousseeuw, 1990, Finding Groups in Data: An Introduction to Cluster Analysis, Wiley, New York, NY; Everitt, 1993, Cluster analysis (3rd edition), Wiley, New York, NY; and Backer, 1995, Computer-Assisted Reasoning in Cluster Analysis, Prentice Hall, Upper Saddle River, New Jersey, each of which is hereby incorporated by reference. Specific exemplary clustering techniques that can be used in the present disclosure include, but are not limited to, hierarchical clustering (agglomerative clustering using the nearest neighbor algorithm, the farthest neighbor algorithm, the average connection algorithm, the centroid algorithm, or the sum of squares algorithm), k-means clustering, fuzzy k-means clustering algorithm, and Jarvis-Patrick clustering. In some embodiments, clustering includes unsupervised clustering, in which no preconceived notion of what clusters should be formed when the training set is clustered is imposed.

[0138] Regression models, such as multi-class logit models, are described in Agresti, An Introduction to Categorical Data Analysis, 1996, John Wiley & Sons, Inc., New York, Chapter 8, which is hereby incorporated by reference in its entirety. In some embodiments, the model utilizes the regressors disclosed in Hastie et al., 2001, The Elements of Statistical Learning, Springer-Verlag, New York, which is hereby incorporated by reference in its entirety. In some embodiments, gradient boosting models are used for classification algorithms such as those described herein; these gradient boosting models are described in the following references: Boehmke, Bradley; Greenwell, Brandon (2019). "Gradient Boosting". Hands-On Machine Learning with R. Chapman & Hall. Pages 221–245. ISBN 978-1-138-49568-5., which is hereby incorporated by reference in its entirety. In some embodiments, integrated modeling techniques are used, for example, in the classification algorithms described herein; these integrated modeling techniques are described in the implementation of the classification model herein, which is described in Zhou Zhihua (2012). Ensemble Methods: Foundations and Algorithms. Chapman and Hall / CRC. ISBN 978-1-439-83003-1, which is hereby incorporated by reference in its entirety.

[0139] In some embodiments, the model is generated by executing one or more programs (e.g., stored in a computer program) including instructions for performing data analysis. Figure 1 In some embodiments, the data analysis is performed by a system including at least one processor (e.g., processor core 102) and a memory containing instructions for performing the data analysis (e.g., one or more programs stored in non-persistent memory 111 or persistent memory 112).

[0140] Computer Systems

[0141] The present disclosure provides a computer system programmed to implement the method of the present disclosure. Figure 4A computer system 401 is shown that is programmed or otherwise configured to, for example: stain a tooth sample, obtain a fluorescence image of the stained tooth sample, analyze the fluorescence intensity across the stained tooth sample spatially, generate an inflammation time curve, process the data using a trained model, and determine the risk of a disease or condition for a subject. The computer system 401 can regulate various aspects of the sensor data analysis of the present disclosure, such as, for example: stain a tooth sample, obtain a fluorescence image of the stained tooth sample, analyze the fluorescence intensity across the stained tooth sample spatially, generate an inflammation time curve, measure the dynamics of the time curve, process the data using a trained model, and predict the diagnostic status of the subject with respect to a disease or condition. The computer system 401 can be an electronic device of a user or a computer system remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0142] The computer system 401 includes a central processing unit (CPU, also referred to herein as a "processor" and "computer processor") 405, which may be a single-core or multi-core processor, or may be multiple processors for parallel processing. The computer system 401 also includes a memory or storage location 410 (e.g., random access memory, read-only memory, flash memory), an electronic storage unit 415 (e.g., a hard disk), a communication interface 420 for communicating with one or more other systems (e.g., a network adapter), and peripheral devices 425 (such as cache, other memory, data storage, and / or an electronic display adapter). The memory 410, the storage unit 415, the interface 420, and the peripheral devices 425 communicate with the CPU 405 via a communication bus (solid lines), such as a motherboard. The storage unit 415 may be a data storage unit (or data repository) for storing data. The computer system 401 may be operably coupled to a computer network ("network") 430 with the help of the communication interface 420. The network 430 may be the Internet, an internetwork and / or an extranet, or an intranet and / or an extranet in communication with the Internet. Network 430 is a telecommunications and / or data network in some cases. Network 430 may include one or more computer servers that may implement distributed computing, such as cloud computing. In some cases, with the help of computer system 401, network 430 may implement a peer-to-peer network that may enable devices coupled to computer system 401 to act as clients or servers.

[0143] The CPU 405 may execute a series of machine-readable instructions, which may be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 410. The instructions may be directed to the CPU 405, which may then program or otherwise configure the CPU 405 to implement the methods of the present disclosure. Examples of operations performed by the CPU 405 may include fetching, decoding, executing, and writing back.

[0144] CPU 405 may be part of a circuit, such as an integrated circuit. One or more other components of system 401 may be included in the circuit. In some cases, the circuit is an application specific integrated circuit (ASIC).

[0145] Storage unit 415 can store files, such as drivers, libraries, and saved programs. Storage unit 415 can store user data, such as user preferences and user programs. In some cases, computer system 401 can include one or more additional data storage units external to computer system 401, such as located on a remote server that communicates with computer system 401 via an intranet or the Internet.

[0146] Computer system 401 can communicate with one or more remote computer systems via network 430. For example, computer system 401 can communicate with a remote computer system of a user (e.g., a healthcare provider). Examples of remote computer systems include a personal computer (e.g., a portable PC), a notebook computer, or a tablet computer (e.g., iPad, Galaxy Tab), phones, smartphones (e.g. iPhone, Android-supported devices, ) or a personal digital assistant. A user can access the computer system 401 via the network 430.

[0147] The methods described herein may be implemented by machine (e.g., computer processor) executable code stored in an electronic storage location of computer system 401, such as, for example, memory 410 or electronic storage unit 415. Machine executable or machine readable code may be provided in the form of software. During use, the code may be executed by processor 405. In some cases, the code may be retrieved from storage unit 415 and stored on memory 410 for access by processor 405 at any time. In some cases, electronic storage unit 415 may be excluded, and machine executable instructions may be stored on memory 410.

[0148] The code may be precompiled and configured for use with a machine having a processor suitable for executing the code, or may be compiled during runtime. The code may be provided in a programming language, which may be selected so that the code can be executed in a precompiled or compiled manner.

[0149] Aspects of the systems and methods provided herein, such as computer system 401, can be embodied in programming. Various aspects of the technology can be considered as "products" or "articles" generally in the form of machine (or processor) executable code and / or related data, which are carried or embodied in a type of machine-readable medium. The machine executable code can be stored on an electronic storage unit, such as a memory (e.g., read-only memory, random access memory, flash memory) or a hard disk. "Storage" type media can include any or all tangible memories of a computer, processor, etc. or its related modules, such as various semiconductor memories, tape drives, disk drives, etc., which can provide non-transitory storage for software programming at any time. All or part of the software can sometimes communicate through the Internet or various other telecommunications networks. For example, such communications can enable software to be loaded from one computer or processor to another computer or processor, such as from a management server or host computer to a computer platform of an application server. Therefore, another type of medium that can carry software elements includes light waves, radio waves, and electromagnetic waves, such as through wired and optical landline networks and through various air links on the physical interface between local devices. Physical elements that carry such waves, such as wired or wireless links, optical links, etc., can also be considered to be the medium that carries the software. As used herein, unless limited to non-transitory, tangible "storage" media, terms such as computer or machine "readable media" refer to any medium that participates in providing instructions to a processor for execution.

[0150] Therefore, machine-readable media such as computer executable code can take a variety of forms, including but not limited to tangible storage media, carrier media, or physical transmission media. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer, etc., such as can be used to implement the database shown in the accompanying drawings, etc. Volatile storage media include dynamic memory, such as the main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wires and optical fibers, including wires that constitute the bus within a computer system. Carrier transmission media can take the form of electrical or electromagnetic signals or sound or light waves, such as those generated during radio frequency (RF) and infrared (IR) data communications. Therefore, common forms of computer-readable media include, for example: floppy drives, floppy disks, hard disks, magnetic tapes, any other magnetic media, CD-ROMs, DVDs or DVD-ROMs, any other optical media, punched card tapes, any other physical storage media with hole patterns, RAMs, ROMs, PROMs and EPROMs, FLASH-EPROMs, any other storage chips or boxes, carriers that transmit data or instructions, cables or links that transmit such carriers, or any other media from which a computer can read program code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0151] The computer system 401 may include or communicate with an electronic display 435, which includes a user interface (UI) 440 for providing, for example, fluorescence image data, fluorescence intensity data, inflammation time curves, and machine learning classifications. Examples of UIs include, but are not limited to, graphical user interfaces (GUIs) and web-based user interfaces.

[0152] The methods and systems of the present disclosure may be implemented by one or more algorithms. The algorithms may be implemented by software when executed by the central processing unit 405. The algorithms may, for example: stain a tooth sample, obtain a fluorescent image of the stained tooth sample, analyze the fluorescence intensity across the stained tooth sample spatially, generate an inflammation time curve, process the data using a trained model, and determine the risk of a disease or condition in a subject.

[0153] Although the methods described elsewhere herein illustrate sets of steps or operations according to embodiments, those of ordinary skill in the art will recognize many variations based on the teachings described herein. The steps may be performed in a different order. Steps may be added or omitted. Some steps may include sub-steps. Many steps may be repeated multiple times as long as it is beneficial.

[0154] One or more steps of each of the methods or sets of operations may be performed using circuit systems described herein, for example, one or more processors or logic circuit systems, such as programmable array logic or field programmable gate arrays. The circuits may be programmed to provide one or more steps of each method or set of operations, and the program may include program instructions stored on a computer readable memory or programming steps of logic circuits, such as programmable array logic or, for example, field programmable gate arrays.

[0155] Examples

[0156] Example 1: Dynamic molecular profiling in dental samples to determine disease risk

[0157] Using the method and system disclosed herein, the molecular spectrum in the tooth sample is generated and then analyzed to determine the disease risk of the subject. In general, the temporal dynamics of biological reactions (e.g., inflammation) are found to be printed in samples (e.g., tooth samples), and can be analyzed to determine the disease risk of the subject. Dynamic molecular spectrum is generated for C-reactive protein (CRP), which is a marker of inflammation. Using tooth biomarkers, dynamic time series curves of CRP and inflammation during the time period including fetal (prenatal) development and early childhood of two groups of children were generated-the first group suffers from autism spectrum disorder (37 cases) and the second group does not suffer from autism spectrum disorder (77 controls). The CRP curve of the time series is analyzed to reveal the dynamic new features of the CRP signal, which accurately distinguishes autism cases and controls. For example, the inflammatory curve that exists before the age of 1 has a great difference between cases and controls. In contrast, the clinical diagnosis of autism is usually determined at around 3 to 4 years old.

[0158] From each child subject, a deciduous tooth sample is obtained. The tooth sample is cut, decalcified, and an immunohistochemical stain (e.g., dentin) is applied to the tooth sample. The immunohistochemical stain effectively maps C-reactive protein (a molecular marker of inflammation) along the growth rings of the tooth sample to form an inflammation time curve for the prenatal and postnatal periods. The time curve is analyzed using a machine learning algorithm of the present disclosure to train a highly accurate model to determine disease risk (e.g., autism).

[0159] Figure 5 An example of a daily C-reactive protein profile over time for a subject is shown, where the y-axis indicates CRP intensity and the x-axis indicates developmental age. The developmental age of a child subject includes the period from mid-pregnancy (e.g., beginning 140 days before birth, when the subject is in the prenatal stage) to about 6 months of age. Figure 5 As shown, the inflammation profile (as indicated by CRP intensity) was observed to be higher prenatally in children with autism.

[0160] FIG. 6A to FIG. 6B Receiver operating characteristic (ROC) curves are shown to characterize the sensitivity and specificity of the method for diagnosing autism at different prediction thresholds using features derived from recursive quantitative analysis of C-reactive protein profiles sampled prenatally and in early childhood (e.g., under 1 year of age). Fig. 6A An experimental receiver operating characteristic (ROC) curve for evaluating the accuracy of the disclosed method for evaluating autism spectrum disorder in a subject is shown. ROC curves can be used to evaluate the performance of a binary classifier. ROC curves are plotted as sensitivity (also known as true positive rate) versus specificity (also known as true negative rate). A perfect classifier can have 100% sensitivity and 100% specificity and an area under the curve (AUC) of 1.0. Fig. 6AAs shown, a classifier configured to determine the presence of autism in a subject based on a dynamic C-reactive protein profile has a receiver operating characteristic (ROC) area under the curve (AUC) of 0.86, with a 95% confidence interval (CI) of 0.72 to 1.00. The receiver operating characteristic (ROC) shows how the sensitivity and specificity values ​​of a classifier change as higher or lower thresholds are applied to the predicted probability of case status; for example, a lower threshold will produce a more sensitive classification, but will correspondingly be less specific. As Figure 6B As shown, the primary dynamic features that contribute to the classifier performance are arranged in descending order of feature importance (e.g., as indicated by the numerical feature weights), including hierarchical, entropy, TT, MDL, RT1, RT2, Vmax, certainty, and Lmax. For example, hierarchical is determined to have a higher feature importance than the others.

[0161] Thus, analysis of features derived from analysis of C-reactive protein profiles using the methods and systems of the present disclosure successfully determined disease risk for autism with an AUC of 0.86, using only features derived from analysis of C-reactive protein features measured on non-invasively obtained biological samples (e.g., dental samples) from child subjects. These results suggest that the dynamics of inflammatory responses early in life are associated with later disease, which can be accurately detected and analyzed using the methods and systems of the present disclosure.

[0162] Although the preferred examples of the present invention have been shown and described herein, it should be clear to those skilled in the art that such embodiments are provided only as examples. The present invention is not intended to be limited by the specific examples provided in the specification. Although the present invention has been described with reference to the above description, the description and illustration of the embodiments herein are not intended to be understood in a limiting sense. Without departing from the present invention, those skilled in the art will now be aware of many variations, changes and substitutions. In addition, it should be understood that all aspects of the present invention are not limited to the specific depictions, configurations or relative proportions depending on various conditions and variables set forth herein. It should be understood that each alternative of the embodiments of the present invention described herein can be used to practice the present invention. Therefore, it is contemplated that the present invention should also cover any such substitutions, modifications, variations or equivalents. The following claims are intended to define the scope of the present invention, and accordingly cover methods and structures within the scope of these claims and their equivalents.

[0163] Example

[0164] Embodiment 1. A method for predicting a subject's diagnostic status with respect to a disease or condition, the method comprising: (a) staining a dental sample from the subject to produce a stained dental sample; (b) analyzing fluorescence intensity spatially across the stained dental sample; and (c) predicting the subject's diagnostic status with respect to the disease or condition based at least in part on the analysis of the fluorescence intensity.

[0165] Embodiment 2. The method according to embodiment 1, wherein the analyzing comprises obtaining a fluorescence image of the stained tooth sample, and analyzing the fluorescence intensity of the fluorescence image.

[0166] Embodiment 3. The method of embodiment 2, wherein obtaining a fluorescent image of the stained dental sample comprises using an inverted or non-inverted confocal microscope.

[0167] Embodiment 4. The method according to any one of embodiments 1 to 3, wherein staining the tooth sample comprises using C-reactive protein immunohistochemical staining.

[0168] Embodiment 5. The method according to any one of embodiments 1 to 4, further comprising slicing the tooth sample.

[0169] Embodiment 6. The method of any one of embodiments 1 to 5, wherein staining the dental sample comprises decalcifying the dental sample.

[0170] Embodiment 7. The method of any one of embodiments 1 to 6, wherein the disease or condition comprises: autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof.

[0171] Embodiment 8. The method of any one of embodiments 1 to 6, wherein the disease or condition comprises an autism spectrum disorder.

[0172] Embodiment 9. The method according to any one of embodiments 1 to 8, wherein the subject is a human.

[0173] Embodiment 10. The method according to embodiment 9, wherein the subject is less than 12 years old.

[0174] Embodiment 11. The method according to embodiment 9, wherein the subject is less than 1 year old.

[0175] Embodiment 12. The method of embodiment 1, wherein analyzing comprises: generating an inflammation time curve based at least in part on the fluorescence intensity, and analyzing the inflammation time curve.

[0176] Embodiment 13. The method of embodiment 12, wherein at least a portion of the inflammation time profile corresponds to the prenatal period of the subject.

[0177] Embodiment 14. The method of embodiment 1, wherein predicting the subject's diagnostic status with respect to a disease or condition comprises processing the fluorescence intensity using a trained model.

[0178] Embodiment 15. The method according to embodiment 14, wherein the trained model is selected from the group consisting of: a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm and any combination thereof.

[0179] Embodiment 16. The method according to embodiment 14, wherein the trained model comprises a gradient boosting decision tree.

[0180] Embodiment 17. A method according to embodiment 14, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof.

[0181] Embodiment 18. The method according to embodiment 17, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof.

[0182] Embodiment 19. The method of embodiment 18, wherein the trained model predicts the diagnostic status regarding the disease or disorder with a sensitivity of at least about 80%.

[0183] Embodiment 20. The method of embodiment 18, wherein the trained model predicts a diagnostic status regarding a disease or condition with a specificity of at least about 80%.

[0184] Embodiment 21. The method of embodiment 18, wherein the trained model predicts a diagnostic status regarding a disease or condition with a positive predictive value of at least about 80%.

[0185] Embodiment 22. The method of embodiment 18, wherein the trained model predicts the diagnostic status regarding the disease or condition with a negative predictive value of at least about 80%.

[0186] Embodiment 23. The method of embodiment 18, wherein the trained model predicts the diagnostic status regarding the disease or condition with an area under the receiver operating characteristic (AUROC) of at least about 0.80.

[0187] Embodiment 24. A device comprising 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 comprising instructions for: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of a subject associated with the subject's C-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position of the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with the C-reactive protein; (b) analyzing each fluorescence intensity across the reference line on the biological sample, thereby obtaining a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by sequential variability in the fluorescence intensity of the C-reactive protein; and (d) processing the features using a trained model to predict the diagnostic status of the subject with respect to a disease or condition associated with the C-reactive protein.

[0188] Embodiment 25. The device of embodiment 24, wherein the plurality of fluorescence intensity measurements are measured using an inverted or non-inverted confocal microscope.

[0189] Embodiment 26. A device according to embodiment 24 or 25, wherein the biological sample comprises a dental sample.

[0190] Embodiment 27. The device of any one of embodiments 24 to 26, wherein the dental sample is stained using C-reactive protein immunohistochemical staining.

[0191] Embodiment 28. The apparatus of embodiment 26, wherein the instructions further comprise slicing the tooth sample.

[0192] Embodiment 29. The device of Embodiment 26, wherein the instructions further comprise decalcifying the tooth sample.

[0193] Embodiment 30. A device according to any one of embodiments 24 to 29, wherein the disease or condition comprises: autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof.

[0194] Embodiment 31. The device of any one of Embodiments 24 to 29, wherein the disease or condition comprises autism spectrum disorder (ASD).

[0195] Embodiment 32. A device according to any one of embodiments 24 to 31, wherein the subject is a human.

[0196] Embodiment 33. The device of any one of embodiments 24 to 32, wherein the subject is less than 12 years old.

[0197] Embodiment 34. The device of any one of Embodiments 24 to 32, wherein the subject is less than 1 year old.

[0198] Embodiment 35. The device of any one of Embodiments 24 to 34, wherein analyzing comprises generating an inflammation time curve based at least in part on a plurality of fluorescence intensity measurements, and analyzing the inflammation time curve.

[0199] Embodiment 36. The device of embodiment 35, wherein at least a portion of the inflammation time profile corresponds to the prenatal period of the subject.

[0200] Embodiment 37. The device of any one of Embodiments 24 to 36, wherein predicting the subject's diagnostic status with respect to a disease or condition comprises processing a plurality of fluorescence intensity measurements using a trained model.

[0201] Embodiment 38. A device according to embodiment 37, wherein the trained model is selected from the group consisting of: a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm and any combination thereof.

[0202] Embodiment 39. A device according to embodiment 37, wherein the trained model includes a gradient boosting decision tree.

[0203] Embodiment 40. An apparatus according to embodiment 12, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, hierarchical, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of linear slope of time curve, description time Determination of multiple nonlinear parameters of the curvature of a time curve, determination of a sudden change in the intensity of a time curve, determination of one or more changes in the baseline intensity of a time curve, determination of a change in the frequency domain representation of a time curve, determination of a change in the power spectrum domain representation of a time curve, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

[0204] Embodiment 41. The apparatus of embodiment 12, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the time curve, Determination of multiple nonlinear parameters describing the curvature of a time curve, determination of a sudden change in the intensity of a time curve, determination of one or more changes in the baseline intensity of a time curve, determination of a change in the frequency domain representation of a time curve, determination of a change in the power spectrum domain representation of a time curve, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

[0205] Embodiment 42. The device of any one of Embodiments 24 to 41, wherein the trained model predicts a diagnostic status regarding a disease or condition with a sensitivity of at least about 80%.

[0206] Embodiment 43. The device of any one of Embodiments 24 to 41, wherein the trained model predicts a diagnostic status regarding a disease or condition with a specificity of at least about 80%.

[0207] Embodiment 44. The device of any one of Embodiments 24 to 41, wherein the trained model predicts a diagnostic status regarding a disease or condition with a positive predictive value of at least about 80%.

[0208] Embodiment 45. The device of any one of Embodiments 24 to 41, wherein the trained model predicts a diagnostic status regarding a disease or condition with a negative predictive value of at least about 80%.

[0209] Embodiment 46. The device of any one of Embodiments 24 to 41, wherein the trained model predicts a diagnostic status regarding a disease or condition with an area under the receiver operating characteristic (AUROC) of at least about 0.80.

[0210] Embodiment 47. A non-transitory computer-readable storage medium and one or more computer programs embedded therein, the one or more computer programs comprising instructions that, when executed by a computer system, cause the computer system to perform a method comprising: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of a subject associated with the subject's C-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position of the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with the C-reactive protein; (b) analyzing each fluorescence intensity across the reference line on the biological sample, thereby obtaining a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by a change in the fluorescence intensity of the C-reactive protein; and (d) processing the features using a trained model to predict the subject's diagnostic status with respect to a disease or condition associated with the C-reactive protein.

[0211] Embodiment 48. The non-transitory computer readable storage medium of Embodiment 47, wherein the plurality of fluorescence intensity measurements are measured using an inverted or non-inverted confocal microscope.

[0212] Embodiment 49. The non-transitory computer-readable storage medium of claim 47 or 48, wherein the biological sample comprises a dental sample.

[0213] Embodiment 50. The non-transitory computer readable storage medium of claim 49, wherein the dental sample is stained using C-reactive protein immunohistochemical staining.

[0214] Embodiment 51. The non-transitory computer-readable storage medium of claim 49, wherein the method further comprises slicing the tooth sample.

[0215] Embodiment 52. The non-transitory computer-readable storage medium of any one of Embodiments 47 to 51, wherein the method further comprises decalcifying the tooth sample.

[0216] Embodiment 53. A non-transitory computer-readable storage medium according to any one of embodiments 47 to 52, wherein the disease or condition comprises: autism spectrum disorder (ASD), attention deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof.

[0217] Embodiment 54. The non-transitory computer-readable storage medium of any one of Embodiments 47 to 52, wherein the disease or condition comprises autism spectrum disorder (ASD).

[0218] Embodiment 55. A non-transitory computer-readable storage medium according to any one of Embodiments 47 to 54, wherein the subject is a human.

[0219] Embodiment 56. A non-transitory computer-readable storage medium according to any one of embodiments 47 to 55, wherein the subject is less than 12 years old.

[0220] Embodiment 57. A non-transitory computer-readable storage medium according to any one of embodiments 47 to 55, wherein the subject is less than 1 year old.

[0221] Embodiment 58. The non-transitory computer-readable storage medium of any one of Embodiments 47 to 57, wherein analyzing comprises generating an inflammation time curve based at least in part on a plurality of fluorescence intensity measurements, and analyzing the inflammation time curve.

[0222] Embodiment 59. The non-transitory computer-readable storage medium of Embodiment 58, wherein at least a portion of the inflammation time profile corresponds to a prenatal period of the subject.

[0223] Embodiment 60. The non-transitory computer-readable storage medium of any one of Embodiments 47 to 59, wherein predicting the subject's diagnostic status with respect to a disease or condition comprises processing a plurality of fluorescence intensity measurements using a trained model.

[0224] Embodiment 61. A non-transitory computer-readable storage medium according to embodiment 60, wherein the trained model is selected from the group consisting of: a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

[0225] Embodiment 62. A non-transitory computer-readable storage medium according to embodiment 60, wherein the trained model comprises a gradient boosting decision tree.

[0226] Embodiment 63. A non-transitory computer-readable storage medium according to embodiment 60, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of linear slope of fluorescence intensity across reference lines, describing the linear slope of fluorescence intensity across reference lines Determination of multiple nonlinear parameters of the curvature of the fluorescence intensity, determination of the sudden change in the intensity of the fluorescence intensity across a reference line, determination of one or more changes in the baseline intensity of the fluorescence intensity across a reference line, determination of the change in the frequency domain representation of the fluorescence intensity across a reference line, determination of the change in the power spectrum domain representation of the fluorescence intensity across a reference line, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

[0227] Embodiment 64. A non-transitory computer-readable storage medium according to embodiment 60, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of linear slope of fluorescence intensity across reference lines, description of linear slope across reference lines Determination of multiple nonlinear parameters of the curvature of the fluorescence intensity of a line, determination of a sudden change in the intensity of the fluorescence intensity across a reference line, determination of one or more changes in the baseline intensity of the fluorescence intensity across a reference line, determination of a change in the frequency domain representation of the fluorescence intensity across a reference line, determination of a change in the power spectrum domain representation of the fluorescence intensity across a reference line, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

[0228] Embodiment 65. A non-transitory computer-readable storage medium according to any one of Embodiments 47 to 64, wherein the trained model predicts a diagnostic status regarding a disease or condition with a sensitivity of at least about 80%.

[0229] Embodiment 66. A non-transitory computer-readable storage medium according to any one of Embodiments 47 to 64, wherein the trained model predicts a diagnostic status regarding a disease or condition with a specificity of at least about 80%.

[0230] Embodiment 67. The non-transitory computer-readable storage medium of Embodiment 47, wherein the trained model predicts a diagnostic status regarding a disease or condition with a positive predictive value of at least about 80%.

[0231] Embodiment 68. The non-transitory computer-readable storage medium of Embodiment 47, wherein the trained model predicts a diagnostic status regarding a disease or condition with a negative predictive value of at least about 80%.

[0232] Embodiment 69. A non-transitory computer-readable storage medium according to Embodiment 47, wherein the trained model predicts a diagnostic status regarding a disease or condition with an area under the receiver operating characteristic (AUROC) of at least about 0.80.

[0233] Embodiment 70. A method for training a model, the method comprising: at a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors: (a) for each corresponding training subject in a plurality of training subjects, wherein a first subset of the training subjects in the plurality of training subjects have a first diagnostic state, the first diagnostic state corresponding to having a first biological condition associated with c-reactive protein, and a second subset of the training subjects in the plurality of training subjects have a second diagnostic state, the second diagnostic state corresponding to not having the first biological condition associated with c-reactive protein: (i) sampling each corresponding position in a plurality of positions along a reference line on a biological sample of the subject associated with the subject's c-reactive protein, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position in the plurality of positions, and each of the plurality of positions The positions represent different growth periods of a biological sample of a subject associated with c-reactive protein; (ii) analyzing each fluorescence intensity across the reference line on the biological sample to obtain a first data set; and (iii) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in a corresponding feature set being determined by the sequential variability in the c-reactive protein fluorescence intensity; and (b) training an untrained or partially untrained model with (i) a corresponding feature set for each corresponding second data set of each training subject in the plurality of training subjects and (ii) a corresponding diagnostic state selected from a first diagnostic state and a second diagnostic state for each training subject in the plurality of training subjects to obtain a trained model that provides an indication of whether the test subject has a first biological condition associated with c-reactive protein based on values ​​of features in the feature set obtained from a biological sample associated with c-reactive protein of the test subject.

[0234] Embodiment 71. The method according to embodiment 70, wherein the trained model is a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, a regression model, a gradient boosting algorithm or any combination thereof.

[0235] Embodiment 72. A method according to Embodiment 70, wherein the trained model is a polynomial classifier.

[0236] Embodiment 73. The method of embodiment 70, wherein the trained model is a binomial classifier.

[0237] Embodiment 74. A method according to any one of embodiments 70 to 73, wherein the first biological condition associated with C-reactive protein is selected from the group consisting of: autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection and pediatric cancer.

[0238] Embodiment 75. A method according to embodiment 70, wherein the method further comprises: evaluating the test subject for the first biological condition associated with C-reactive protein by distinguishing the first biological condition associated with C-reactive protein from a second biological condition associated with C-reactive protein that is different from the first biological condition associated with metal metabolism.

[0239] Embodiment 76. The method of embodiment 75, wherein the first biological condition is autism spectrum disorder and the second biological condition is attention-deficit / hyperactivity disorder.

[0240] Embodiment 77. The method of any one of Embodiments 70 to 76, wherein the test subject is a human.

[0241] Embodiment 78. The method of Embodiment 77, wherein the human is less than 12 years old.

[0242] Embodiment 79. The method of Embodiment 78, wherein the human is less than 1 year old.

[0243] Embodiment 80. The method according to any one of embodiments 70 to 79, wherein the corresponding biological sample associated with C-reactive protein of the respective training subject is selected from the group consisting of hair shaft, teeth and nails.

[0244] Embodiment 81. The method of embodiment 80, wherein the corresponding biological sample of the corresponding training subject associated with c-reactive protein is a hair shaft and the reference line corresponds to the longitudinal direction of the hair shaft.

[0245] Embodiment 82. A method according to any one of embodiments 70 to 79, wherein the corresponding biological sample of the corresponding training subject associated with C-reactive protein is a tooth and the reference line corresponds to a direction across the growth zone, including the new growth line of the tooth.

[0246] Embodiment 83. A method according to any one of Embodiments 70 to 82, wherein the corresponding multiple positions are ordered so that the first position along the corresponding multiple positions of the corresponding biological sample associated with the C-reactive protein of the corresponding training subject corresponds to the position closest to the tip of the corresponding biological sample associated with the C-reactive protein of the corresponding training subject.

[0247] Embodiment 84. A method according to any one of Embodiments 70 to 79, wherein each trace in the corresponding plurality of fluorescence intensity measurements comprises a plurality of data points, each data point being an instance of a corresponding position in the plurality of positions.

[0248] Embodiment 85. A method according to any one of embodiments 70 to 84, wherein the corresponding feature set is selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof.

[0249] Embodiment 86. A method according to any one of embodiments 70 to 85, wherein the corresponding multiple positions include at least 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500 or 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000, 20000 or more than 20000 positions.

[0250] Embodiment 87. A method according to any one of embodiments 70 to 86, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the multiple fluorescence intensity measurements, description of the multiple fluorescence intensity measurements Determination of multiple nonlinear parameters of curvature, determination of sudden changes in the intensity of the multiple fluorescence intensity measurements, determination of one or more changes in the baseline intensity of the multiple fluorescence intensity measurements, determination of changes in the frequency domain representation of the multiple fluorescence intensity measurements, determination of changes in the power spectrum domain representation of the multiple fluorescence intensity measurements, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

[0251] Embodiment 88. A method according to any one of embodiments 70 to 86, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the multiple fluorescence intensity measurements, description of the multiple fluorescence intensity measurements Determination of multiple nonlinear parameters of curvature, determination of sudden changes in the intensity of the multiple fluorescence intensity measurements, determination of one or more changes in the baseline intensity of the multiple fluorescence intensity measurements, determination of changes in the frequency domain representation of the multiple fluorescence intensity measurements, determination of changes in the power spectrum domain representation of the multiple fluorescence intensity measurements, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

Claims

1. A method for predicting a subject's diagnostic status with respect to a disease or condition, the method comprising: (a) staining a tooth sample from the subject to produce a stained tooth sample; (b) analyzing the fluorescence intensity spatially across the stained tooth sample; as well as (c) predicting a diagnostic status of the subject with respect to the disease or condition based at least in part on the analysis of the fluorescence intensity. 2 . The method of claim 1 , wherein the analyzing comprises obtaining a fluorescence image of the stained tooth sample, and analyzing the fluorescence intensity of the fluorescence image.

3. The method of claim 2, wherein obtaining the fluorescent image of the stained dental sample comprises using an inverted or non-inverted confocal microscope.

4. The method of any one of claims 1 to 3, wherein staining the dental sample comprises using C-reactive protein immunohistochemical staining.

5. The method according to any one of claims 1 to 4, further comprising sectioning the tooth sample.

6. The method of any one of claims 1 to 5, wherein staining the dental sample comprises decalcifying the dental sample.

7. The method according to any one of claims 1 to 6, wherein the disease or condition comprises: Autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof.

8. The method of any one of claims 1 to 6, wherein the disease or condition comprises an autism spectrum disorder.

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

10. The method of claim 9, wherein the subject is less than 12 years old.

11. The method of claim 9, wherein the subject is less than 1 year old.

12. The method according to any one of claims 1 to 11, wherein the analysis comprises: An inflammation time profile is generated based at least in part on the fluorescence intensity, and the inflammation time profile is analyzed.

13. The method of claim 12, wherein at least a portion of the inflammation time profile corresponds to the prenatal period of the subject.

14. The method of any one of claims 1 to 13, wherein predicting the subject's diagnostic status with respect to the disease or condition comprises processing the fluorescence intensity using a trained model.

15. The method of claim 14, wherein the trained model is selected from the group consisting of a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

16. The method of claim 14, wherein the trained model comprises a gradient boosted decision tree.

17. The method of claim 14, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the time curve, describing the time curve The invention relates to determination of multiple nonlinear parameters of curvature, determination of sudden changes in the intensity of the time curve, determination of one or more changes in the baseline intensity of the time curve, determination of changes in the frequency domain representation of the time curve, determination of changes in the power spectrum domain representation of the time curve, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

18. The method of claim 17, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the time curve, description of the time The invention relates to determination of multiple nonlinear parameters of the curvature of the time curve, determination of a sudden change in the intensity of the time curve, determination of one or more changes in the baseline intensity of the time curve, determination of a change in the frequency domain representation of the time curve, determination of a change in the power spectrum domain representation of the time curve, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

19. The method of claim 18, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a sensitivity of at least about 80%.

20. The method of claim 18, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a specificity of at least about 80%.

21. The method of claim 18, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a positive predictive value of at least about 80%.

22. The method of claim 18, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a negative predictive value of at least about 80%.

23. The method of claim 18, wherein the trained model predicts a diagnostic status for the disease or condition with an area under the receiver operating characteristic (AUROC) of at least about 0.

80.

24. An apparatus comprising 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 comprising instructions for: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of the subject associated with C-reactive protein of the subject, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position of the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with C-reactive protein; (b) analyzing each fluorescence intensity across a reference line on the biological sample to obtain a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by the sequential variability in the fluorescence intensity of the C-reactive protein; and (d) processing the features using the trained model to predict the subject's diagnostic status with respect to a disease or condition associated with C-reactive protein.

25. The apparatus of claim 24, wherein the plurality of fluorescence intensity measurements are measured using an inverted or non-inverted confocal microscope.

26. The device of claim 24 or 25, wherein the biological sample comprises a dental sample.

27. The device according to any one of claims 24 to 26, wherein the tooth sample is stained using C-reactive protein immunohistochemical staining.

28. The apparatus of claim 26, wherein the instructions further comprise slicing the tooth sample.

29. The apparatus of claim 26, wherein the instructions further comprise decalcifying the tooth sample.

30. The device according to any one of claims 24 to 29, wherein the disease or condition comprises: Autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof.

31. The apparatus of any one of claims 24 to 29, wherein the disease or condition comprises autism spectrum disorder (ASD).

32. The device of any one of claims 24 to 31, wherein the subject is a human.

33. The device according to any one of claims 24 to 32, wherein the subject is less than 12 years old.

34. The device according to any one of claims 24 to 32, wherein the subject is less than 1 year old.

35. The apparatus of any one of claims 24 to 34, wherein the analysis comprises: An inflammation time curve is generated based at least in part on the plurality of fluorescence intensity measurements, and the inflammation time curve is analyzed.

36. The device of claim 35, wherein at least a portion of the inflammation time profile corresponds to a prenatal period of the subject.

37. The apparatus of any one of claims 24 to 36, wherein predicting the subject's diagnostic status with respect to the disease or condition comprises processing the plurality of fluorescence intensity measurements using the trained model.

38. The apparatus of claim 37, wherein the trained model is selected from the group consisting of a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

39. The apparatus of claim 37, wherein the trained model comprises a gradient boosted decision tree.

40. The apparatus of claim 37, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, mean diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, mean recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of a linear slope of the fluorescence intensity across the reference line, a plurality of non-linear functions describing the curvature of the fluorescence intensity across the reference line, Determination of linear parameters, determination of a sudden change in the intensity of the fluorescence intensity across the reference line, determination of one or more changes in the baseline intensity of the fluorescence intensity across the reference line, determination of a change in the frequency domain representation of the fluorescence intensity across the reference line, determination of a change in the power spectrum domain representation of the fluorescence intensity across the reference line, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

41. An apparatus according to claim 37, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the fluorescence intensity across the reference line, description of the curvature of the fluorescence intensity across the reference line Determination of multiple nonlinear parameters, determination of sudden changes in the intensity of the fluorescence intensity across the reference line, determination of one or more changes in the baseline intensity of the fluorescence intensity across the reference line, determination of changes in the frequency domain representation of the fluorescence intensity across the reference line, determination of changes in the power spectrum domain representation of the fluorescence intensity across the reference line, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

42. The device of any one of claims 24 to 41, wherein the trained model predicts a diagnostic status regarding the disease or condition with a sensitivity of at least about 80%.

43. The device of any one of claims 24 to 41, wherein the trained model predicts a diagnostic status regarding the disease or condition with a specificity of at least about 80%.

44. The apparatus of any one of claims 24 to 41, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a positive predictive value of at least about 80%.

45. The apparatus of any one of claims 24 to 41, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a negative predictive value of at least about 80%.

46. ​​An apparatus according to any one of claims 24 to 41, wherein the trained model predicts a diagnostic status regarding the disease or condition with an area under the receiver operating characteristic (AUROC) of at least about 0.

80.

47. A non-transitory computer-readable storage medium and one or more computer programs embedded therein, the one or more computer programs comprising instructions that, when executed by a computer system, cause the computer system to perform a method comprising: (a) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of the subject associated with C-reactive protein of the subject, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position of the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with C-reactive protein; (b) analyzing each fluorescence intensity across a reference line on the biological sample to obtain a first data set; (c) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by a change in the fluorescence intensity of C-reactive protein; and (d) processing the features using the trained model to predict the subject's diagnostic status with respect to a disease or condition associated with C-reactive protein.

48. The non-transitory computer readable storage medium of claim 47, wherein the plurality of fluorescence intensity measurements are measured using an inverted or non-inverted confocal microscope.

49. The non-transitory computer-readable storage medium of claim 47 or 48, wherein the biological sample comprises a dental sample.

50. The non-transitory computer readable storage medium of claim 49, wherein the dental sample is stained using C-reactive protein immunohistochemical staining.

51. The non-transitory computer readable storage medium of claim 49, wherein the method further comprises sectioning the tooth sample.

52. The non-transitory computer readable storage medium of any one of claims 47 to 51, wherein the method further comprises decalcifying the tooth sample.

53. The non-transitory computer-readable storage medium of any one of claims 47 to 52, wherein the disease or condition comprises: Autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, pediatric cancer, or any combination thereof.

54. The non-transitory computer-readable storage medium of any one of claims 47 to 52, wherein the disease or condition comprises autism spectrum disorder (ASD).

55. The non-transitory computer-readable storage medium of any one of claims 47 to 54, wherein the subject is a human.

56. The non-transitory computer-readable storage medium of any one of claims 47 to 55, wherein the subject is less than 12 years old.

57. The non-transitory computer-readable storage medium of any one of claims 47 to 55, wherein the subject is less than 1 year old.

58. The non-transitory computer-readable storage medium of any one of claims 47 to 57, wherein analyzing comprises: An inflammation time curve is generated based at least in part on the plurality of fluorescence intensity measurements, and the inflammation time curve is analyzed.

59. The non-transitory computer-readable storage medium of claim 58, wherein at least a portion of the inflammation time profile corresponds to a prenatal period of the subject.

60. The non-transitory computer-readable storage medium of any one of claims 47 to 59, wherein predicting the subject's diagnostic status with respect to the disease or condition comprises processing the plurality of fluorescence intensity measurements using the trained model.

61. The non-transitory computer-readable storage medium of claim 60, wherein the trained model is selected from the group consisting of a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering algorithm, a supervised clustering algorithm, a regression algorithm, a gradient boosting algorithm, and any combination thereof.

62. The non-transitory computer-readable storage medium of claim 60, wherein the trained model comprises a gradient boosted decision tree.

63. The non-transitory computer-readable storage medium of claim 60, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the fluorescence intensity across the reference line, describing the linear slope of the fluorescence intensity across the reference line Determination of multiple nonlinear parameters of curvature, determination of a sudden change in the intensity of the fluorescence intensity across the reference line, determination of one or more changes in the baseline intensity of the fluorescence intensity across the reference line, determination of a change in the frequency domain representation of the fluorescence intensity across the reference line, determination of a change in the power spectrum domain representation of the fluorescence intensity across the reference line, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of the Lyapunov spectrum, determination of the maximum Lyapunov exponent, and any combination thereof.

64. The non-transitory computer-readable storage medium of claim 60, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time (TT), maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the fluorescence intensity across the reference line, a function describing the linear slope of the fluorescence intensity across the reference line, The invention also provides the invention claims a method for determining a plurality of nonlinear parameters of curvature of a fluorescence intensity spectrum, a method for determining a sudden change in the intensity of the fluorescence intensity across the reference line, a method for determining one or more changes in the baseline intensity of the fluorescence intensity across the reference line, a method for determining a change in the frequency domain representation of the fluorescence intensity across the reference line, a method for determining a change in the power spectrum domain representation of the fluorescence intensity across the reference line, a method for determining one or more recursive quantitative analysis parameters, a method for determining one or more cross-recursive quantitative analysis parameters, a method for determining one or more joint recursive quantitative analysis parameters, a method for determining one or more multidimensional recursive quantitative analysis parameters, an estimation of a Lyapunov spectrum, a determination of a maximum Lyapunov exponent, and any combination thereof.

65. The non-transitory computer-readable storage medium of any one of claims 47 to 64, wherein the trained model predicts a diagnostic status regarding the disease or condition with a sensitivity of at least about 80%.

66. The non-transitory computer-readable storage medium of any one of claims 47 to 64, wherein the trained model predicts a diagnostic status regarding the disease or condition with a specificity of at least about 80%.

67. The non-transitory computer-readable storage medium of claim 47, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a positive predictive value of at least about 80%.

68. The non-transitory computer-readable storage medium of claim 47, wherein the trained model predicts a diagnostic status with respect to the disease or condition with a negative predictive value of at least about 80%.

69. The non-transitory computer-readable storage medium of claim 47, wherein the trained model predicts a diagnostic status regarding the disease or condition with an area under a receiver operating characteristic (AUROC) of at least about 0.

80.

70. A method for training a model, the method comprising: At a computer system having one or more processors and a memory storing one or more programs for execution by the one or more processors: (a) for each respective training subject in a plurality of training subjects, wherein a first subset of the training subjects in the plurality of training subjects have a first diagnostic status corresponding to having a first biological condition associated with C-reactive protein, and a second subset of the training subjects in the plurality of training subjects have a second diagnostic status corresponding to not having the first biological condition associated with C-reactive protein: (i) sampling each corresponding position of a plurality of positions along a reference line on a biological sample of the subject associated with C-reactive protein of the subject, thereby obtaining a plurality of fluorescence intensity measurements, each of the plurality of fluorescence intensity measurements corresponding to a different position of the plurality of positions, and each of the plurality of positions representing a different growth period of the biological sample of the subject associated with C-reactive protein; (ii) analyzing each fluorescence intensity across the reference line on the biological sample to obtain a first data set; as well as (iii) deriving a corresponding second data set from the corresponding plurality of fluorescence intensity measurements, each corresponding feature in the corresponding feature set being determined by sequential variability in C-reactive protein fluorescence intensity; as well as (b) training an untrained or partially untrained model with (i) the corresponding feature set of each corresponding second data set of each training subject in the plurality of training subjects and (ii) the corresponding diagnostic state of each training subject in the plurality of training subjects selected from the first diagnostic state and the second diagnostic state, thereby obtaining a trained model, wherein the trained model provides an indication of whether the test subject has the first biological condition associated with C-reactive protein based on values ​​of features in the feature set obtained from a biological sample associated with C-reactive protein of the test subject.

71. The method of claim 70, wherein the trained model is a neural network algorithm, a support vector machine algorithm, a decision tree algorithm, an unsupervised clustering model algorithm, a supervised clustering model algorithm, a regression model, a gradient boosting algorithm, or any combination thereof.

72. The method of claim 70, wherein the trained model is a polynomial classifier.

73. The method of claim 70, wherein the trained model is a binomial classifier.

74. The method of any one of claims 70 to 73, wherein the first biological condition associated with c-reactive protein is selected from the group consisting of autism spectrum disorder (ASD), attention-deficit / hyperactivity disorder (ADHD), amyotrophic lateral sclerosis (ALS), schizophrenia, irritable bowel disease (IBD), pediatric kidney disease, renal transplant rejection, and pediatric cancer.

75. The method of claim 70, wherein the method further comprises: The test subject is assessed for the first biological condition associated with c-reactive protein by distinguishing the first biological condition associated with c-reactive protein from a second biological condition associated with c-reactive protein that is different from the first biological condition associated with metal metabolism.

76. The method of claim 75, wherein the first biological condition is autism spectrum disorder and the second biological condition is attention-deficit / hyperactivity disorder.

77. The method of any one of claims 70 to 76, wherein the test subject is a human.

78. The method of claim 77, wherein the human is less than 12 years old.

79. The method of claim 78, wherein the human is less than 1 year old.

80. The method of any one of claims 70 to 79, wherein the corresponding biological sample associated with the c-reactive protein of the respective training subject is selected from the group consisting of hair shafts, teeth and nails.

81. The method of claim 80, wherein the corresponding biological sample associated with the corresponding training subject's c-reactive protein is the hair shaft and the reference line corresponds to a longitudinal direction of the hair shaft.

82. The method of any one of claims 70 to 79, wherein the corresponding biological sample associated with the respective training subject's c-reactive protein is a tooth and the reference line corresponds to a direction across growth zones, including a new growth line of the tooth.

83. A method according to any one of claims 70 to 82, wherein the corresponding multiple positions are ordered so that the first position of the corresponding multiple positions along the corresponding biological sample associated with the c-reactive protein of the corresponding training subject corresponds to the position closest to the tip of the corresponding biological sample associated with the c-reactive protein of the corresponding training subject.

84. The method of any one of claims 70 to 79, wherein each trace in the corresponding plurality of fluorescence intensity measurements comprises a plurality of data points, each data point being an instance of the respective position in the plurality of positions.

85. A method according to any one of claims 70 to 84, wherein the corresponding feature set is selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal line length (MDL), recursion time (RT), Vmax, certainty, Lmax and any combination thereof.

86. A method according to any one of claims 70 to 85, wherein the corresponding plurality of locations includes at least 1000, 1500, 2000, 2500, 3000, 3500, 4000, 4500 or 5000, 6000, 7000, 8000, 9000, 10000, 12000, 14000, 16000, 18000, 20000 or more than 20000 locations.

87. A method according to any one of claims 70 to 86, wherein the trained model is configured to process one or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the multiple fluorescence intensity measurements, curve describing the multiple fluorescence intensity measurements The invention relates to determination of multiple nonlinear parameters of a rate, determination of a sudden change in the intensity of the multiple fluorescence intensity measurement results, determination of one or more changes in the baseline intensity of the multiple fluorescence intensity measurement results, determination of a change in the frequency domain representation of the multiple fluorescence intensity measurement results, determination of a change in the power spectrum domain representation of the multiple fluorescence intensity measurement results, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, determination of a maximum Lyapunov exponent, and any combination thereof.

88. A method according to any one of claims 70 to 86, wherein the trained model is configured to process two or more features selected from the group consisting of: recursion rate, certainty, average diagonal length, maximum diagonal length, divergence, Shannon entropy of diagonal length, recursion trend, stratification, capture time, maximum vertical line length, Shannon entropy of vertical line length, average recursion time, Shannon entropy of recursion time, number of most likely recursions, mean diagonal length (MDL), recursion time (RT), Vmax, certainty, Lmax, determination of the linear slope of the multiple fluorescence intensity measurements, curve describing the multiple fluorescence intensity measurements The invention relates to determination of multiple nonlinear parameters of a rate, determination of a sudden change in the intensity of the multiple fluorescence intensity measurement results, determination of one or more changes in the baseline intensity of the multiple fluorescence intensity measurement results, determination of a change in the frequency domain representation of the multiple fluorescence intensity measurement results, determination of a change in the power spectrum domain representation of the multiple fluorescence intensity measurement results, determination of one or more recursive quantitative analysis parameters, determination of one or more cross-recursive quantitative analysis parameters, determination of one or more joint recursive quantitative analysis parameters, determination of one or more multidimensional recursive quantitative analysis parameters, estimation of a Lyapunov spectrum, determination of a maximum Lyapunov exponent, and any combination thereof.

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