Methods of diagnosing and treating a disease using a differentially expressed gene

Abstract

The present disclosure relates to methods for diagnosing and treating patients with neurovascular disease including, but not limited to determining a differentially expressed gene in a subject with a disease and methods for treating a subject with a disease based on a differentially expressed gene. Also disclosed are methods for treating a subject suspected of having a stroke.

Description

[0001] METHODS OF DIAGNOSING AND TREATING A DISEASE USING A DIFFERENTIALLY EXPRESSED GENE

[0002] CROSS-REFERENCE TO RELATED APPLICATIONS

[0003] This application claims priority to, and the benefit of, U.S. Provisional Patent Application No. 63 / 550,283, filed February 6, 2024, and U.S. Provisional Patent Application No. 63 / 559,281, filed February 29, 2024, which are incorporated by reference herein in their entirety.

[0004] FIELD

[0005] The present disclosure relates to methods for determining a differentially expressed gene in a subject with a disease and methods for treating a subject with a disease based on a differentially expressed gene.

[0006] BACKGROUND

[0007] Neurovascular disease, including but not limited to stroke, Moyamoya disease, and brain aneurysm, are a major leading cause of serious disability and death in the United States. Although multiple strategies to prevent these neurovascular diseases have been examined, efficient and precise diagnostic methods and effective targeted treatments and therapies are still lacking. Many of these efforts focus on exploiting agonists and / or antagonists that target cell receptors to protect against neural damage. However, a complete understanding of these cell receptors is still lacking. Exploring and characterizing the expression levels of these cell receptors can be beneficial to preventing, diagnosing, and / or treating neurovascular diseases. Thus, there remains a need to characterize said expression levels of such cell receptors to better direct, and further the development of, treatments and therapies to combat neurovascular diseases. Characterization of altered expression levels can also drive alternative new gene therapies as alternatives to conventional therapies. These methods disclosed herein address these needs and more.

[0008] SUMMARY

[0009] Disclosed herein are methods of diagnosing and treating a subject with a neurovascular disease, such as for example a stroke or a traumatic brain injury (TBI).

[0010] In some aspects, disclosed herein is a method for treating a subject with a disease, comprising obtaining a first biological sample from a first subject having the disease immediately after onset of disease symptoms, obtaining a second biological sample from the first subject having the disease about 12 hours or later after onset of disease symptoms, quantifying an expression level of one or more genes from the first biological sample and the second biological sample, comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the disease, comparing the expression level of the one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the disease, wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample, determining a differentially expressed gene from the subject having the disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample, and administering to the subject a therapeutically effective amount of a specific therapeutic agent for treatment of the disease.

[0011] In some aspects, disclosed herein is a method for determining a differentially expressed gene in a subject with a disease, comprising obtaining a first biological sample from a first subject having the disease immediately after onset of disease symptoms, obtaining a second biological sample from the first subject having the disease about 12 hours or later after onset of disease symptoms, quantifying an expression level of one or more genes from the first biological sample and the second biological sample, comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the disease, comparing the expression level of one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the disease, wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample, determining a differentially expressed gene from the subject having the disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample.

[0012] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms; quantifying an expression level of a differentially expressed gene, wherein the differentially expressed gene is identified according to one of the methods disclosed herein; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of a therapeutic agent for treatment of the stroke.

[0013] In some embodiments, the disease is a stroke. In some embodiments, the disease is a traumatic brain injury (TBI). In some embodiments, the differentially expressed gene is AD0RA3. In some embodiments, the second biological sample is obtained about 24 hours or later after onset of disease symptoms. In some embodiments, the subject without the disease comprises a subject suffering from a mimic disease, such as for example a disease with similar symptoms to the stroke or traumatic brain injury.

[0014] In some embodiments, the method of quantifying expression levels is based on sequencing.

[0015] In some embodiments, determining a differentially expressed gene is based on the expression level of the gene combined with demographic information (for example age, sex and species or race of the subject), technical information (for example, sample amount, method of sequencing, measure of sample degradation), and known function information of the gene or related protein (for example based on existing published biological studies designed to understand the function of a gene).

[0016] In some embodiments, determining a differentially expressed gene is performed by a human after examining the patterns of gene expression in the biological samples.

[0017] In some embodiments, determining a differentially expressed gene is performed by a semiautomated or automated computer machine learning program.

[0018] In some embodiments, the therapeutic agent for treatment of the stroke comprises an antiplatelei agent or an anticoagulant agent.

[0019] In some embodiments, the differentially expressed gene comprises AD0RA3. In some embodiments, the therapeutic agent for treatment of the stroke comprises an agonist or antagonist of AD0RA3. In some embodiments, the agonist is AST-004.

[0020] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Poly merase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

[0021] In some aspects, disclosed herein is a method of treating a subject with a neurovascular disease, comprising obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control, and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the neurovascular disease.

[0022] In some aspects, disclosed herein is a method for diagnosing a subject with a neurovascular disease, comprising obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, and determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control.

[0023] In some aspects, disclosed herein is a method for treating a subject suspected of having a stroke, comprising obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms, quantifying an expression level of a differentially expressed gene, wherein the differentially expressed gene is identified according to the method of any preceding aspect, comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (for example, a disease with symptoms similar to stroke), and administering to the subject a therapeutically effective amount of a therapeutic agent for treatment of the stroke.

[0024] In some embodiments, the biomarker comprises AD0RA3. In some embodiments, the biomarker comprises AD0RA2B. In some embodiments, the biomarker comprises AD0RA2A. In some embodiments, the biomarker comprises ADRB2. In some embodiments, the biological sample comprises a blood sample.

[0025] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Poly merase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

[0026] In some embodiments, the reference control is a sample from the subject taken about 24 hours after onset of neurovascular disease symptoms. In some embodiments, the reference control is a sample from the subject taken greater than 24 hours after onset of neurovascular disease symptoms. In some embodiments, the reference control is a sample from a subject that is not suffering from the neurovascular disease. In some embodiments, the reference control is a sample from the subject that is not experiencing neurovascular disease symptoms.

[0027] In some embodiments, the therapeutic agent is an agonist or antagonist of AD0RA3, ADORA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61. In some embodiments, the agonist is an AD0RA3 agonist. In some embodiments, the agonist is an AD0RA2B agonist. In some embodiments, the agonist is an AD0RA2A agonist. In some embodiments, the agonist is an ADRB2 agonist. In some embodiments, the agonist is selected from an agonist or antagonist listed in Table 1. In some embodiments, the agonist is AST-004. In some embodiments, the therapeutic agent comprises an antiplatelet agent or an anticoagulant agent.

[0028] In some embodiments, the neurovascular disease is stroke. In some embodiments, the neurovascular disease is a traumatic brain injury. In some embodiments, the neurovascular disease is Moyamoya Disease.

[0029] In some embodiments, the differentially expressed gene comprises AD0RA3. In some embodiments, the therapeutic agent for treatment of the stroke comprises an agonist or antagonist of AD0RA3.

[0030] In some embodiments, disclosed herein is a method of treating a stroke in a subject, the method comprising selecting a set of genes expressed in a population of stroke patients, wherein the expression level of the set of genes indicates that the subject has experienced a stroke with approximately 100% certainty relative to two reference standards, wherein the two reference standards are derived from stroke patients and non-stroke patients, wherein a first reference standard defines a set of genes and thresholds met only by stroke patients, wherein a second reference standard defines genes and thresholds where only non-stroke patients meet the threshold, wherein the set of genes have expression levels that meet the first reference standard or the second reference standard, and wherein the subject is administered a therapeutic agent, a gene therapy, a thrombolytic, or undergoes a surgical procedure for treatment of the stroke when the set of genes meet the first reference standard or the second reference standard.

[0031] In some embodiments, the method further comprises a third reference standard that defines stroke and non-stroke thresholds and a third set of genes with less than about 100% certainty.

[0032] In some aspects, disclosed herein is a computer-implemented method of training an algorithm for diagnosing a subject suspected of having a neurovascular disease, comprising obtaining a set of reference biological samples from one or more reference subjects known to have the neurovascular disease, quantifying an expression level of one or more biomarkers in each reference biological sample to create a set of reference gene expression levels, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, creating a first training set comprising the set of reference gene expression levels, and training an algorithm using the first training set, wherein training the algorithm comprises defining an expression level threshold for each of the one or more biomarkers, defining a number of biomarkers which must meet their expression level threshold for the subject to be diagnosed with the neurovascular disease.

[0033] In some embodiments, the biomarker comprises AD0RA3. In some embodiments, the biomarker comprises AD0RA2B. In some embodiments, the biomarker comprises AD0RA2A. In some embodiments, the biomarker comprises ADRB2.

[0034] In some embodiments, the set of reference biological samples comprise blood samples.

[0035] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array. In some embodiments, the set of reference biological samples are taken from the one or more reference subjects about 24 hours after onset of neurovascular disease symptoms. In some embodiments, the set of reference biological samples are taken from the one or more reference subjects greater than 24 hours after onset of neurovascular disease symptoms. In some embodiments, the set of reference biological samples are taken from one or more reference subjects that are not experiencing neurovascular disease symptoms.

[0036] In some embodiments, the one or more reference subjects are human. In some embodiments, the neurovascular disease is a stroke. In some embodiments, the neurovascular disease is a traumatic brain injury.

[0037] In some embodiments, a trained algorithm comprises a machine learning algorithm. In some embodiments, the trained algorithm comprises a neural network. In some embodiments, the computer-implemented method of any preceding aspect further comprises obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, and determining the subject as suffering from the neurovascular disease using the trained algorithm.

[0038] In some embodiments, the subject of any preceding aspect is a human.

[0039] BRIEF DESCRIPTION OF FIGURES

[0040] The accompanying figures, which are incorporated in and constitute a part of this specification, illustrate several aspects described below.

[0041] FIG. 1 shows the level of expression for AD0RA3 in stroke patients is different from the level of expression in patients with other conditions in blood draws obtained within a short time after the onset of symptoms (DI). Expression levels appear to return to baseline when expression levels are compared for the same patients from samples obtained immediately after the onset of symptoms versus samples obtained 24 hours after the onset of symptoms (D3).

[0042] FIG. 2 shows that variations in relative expression rank vary by sex, age and racial subgroups (marked in orange in the Stroke plots) in ways that can explain clinically known disparities in neuronal injury between different groups. Measuring gene expression for each patient can improve treatment decisions by improving precision of diagnosis.

[0043] FIG. 3 shows an example of a threshold line for a single gene of interest (in this example the Ct value represents a measure of expression level that resulted from fast measurement of expression levels for the gene of interest plotted against a control gene that does not change based on the disease condition).

[0044] FIG. 4 shows stroke-specific gene signatures from healthy controls, mimics, stroke, and TIA patients in two different gene panels.

[0045] FIG. 5 shows that the present method of gene-based signatures generates results with high specificity and higher sensitivity relative to single clinical assessment scaling methods.

[0046] FIG. 6 shows that there is a gene expression pattern for target genes of interest.

[0047] FIG. 7 shows examples of achievable levels of sensitivity and specificity for combinations of gene expression and thresholds selected in the manner described. Note that some combinations produce near certain decision for disease patients and some produce near certain non-disease decision.

[0048] FIG. 8 is a flowchart diagram of an example method in accordance with various embodiments of the present disclosure.

[0049] FIG. 9 is an example computing device.

[0050] DETAILED DESCRIPTION Disclosed herein are methods of diagnosing and treating a subject with a neurovascular disease. Further disclosed are methods for determining, predicting, or improving the outcome for a subject suffering from neurovascular disease symptoms. The unexpected findings herein are used for determining and treating subjects with a neurovascular disease, allowing treatment with the appropriate therapeutic regimens or surgical interventions.

[0051] Reference will now be made in detail to the embodiments of the invention, examples of which are illustrated in the drawings and the examples. This invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein.

[0052] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood to one of ordinary skill in the art to which this disclosure belongs. The term “comprising” and variations thereof as used herein is used synonymously with the term “including” and variations thereof and are open, non-limiting terms. Although the terms “comprising” and “including” have been used herein to describe various embodiments, the terms “consisting essentially of” and “consisting of’ can be used in place of “comprising” and “including” to provide for more specific embodiments and are also disclosed. As used in this disclosure and in the appended claims, the singular forms “a”, “an”, “the”, include plural referents unless the context clearly dictates otherwise.

[0053] The following definitions are provided for the full understanding of terms used in this specification.

[0054] Terminology

[0055] In this specification and in the claims which follow, reference will be made to a number of terms which shall be defined to have the following meanings:

[0056] As used in the specification and the appended claims, the singular forms “a,” “an” and “the” include plural referents unless the context clearly dictates otherwise. Thus, for example, reference to “a pharmaceutical carrier” includes mixtures of two or more such carriers, and the like.

[0057] The terms "about" and "approximately" are defined as being “close to” as understood by one of ordinary skill in the art. In one non-limiting embodiment the terms are defined to be within 10%. In another non-limiting embodiment, the terms are defined to be within 5%. In still another non- limiting embodiment, the terms are defined to be within 1%.

[0058] Ranges can be expressed herein as from “about” one particular value, and / or to “about” another particular value. When such a range is expressed, another embodiment includes from the one particular value and / or to the other particular value. Similarly, when values are expressed as approximations, by use of the antecedent “about,” it will be understood that the particular value forms another embodiment. It will be further understood that the endpoints of each of the ranges are significant both in relation to the other endpoint, and independently of the other endpoint. It is also understood that there are a number of values disclosed herein, and that each value is also herein disclosed as “about” that particular value in addition to the value itself. For example, if the value “10” is disclosed, then “about 10” is also disclosed. It is also understood that when a value is disclosed that “less than or equal to” the value, “greater than or equal to the value” and possible ranges between values are also disclosed, as appropriately understood by the skilled artisan. For example, if the value “10” is disclosed the “less than or equal to 10”as well as “greater than or equal to 10” is also disclosed. It is also understood that the throughout the application, data is provided in a number of different formats, and that this data, represents endpoints and starting points, and ranges for any combination of the data points. For example, if a particular data point “10” and a particular data point 15 are disclosed, it is understood that greater than, greater than or equal to, less than, less than or equal to, and equal to 10 and 15 are considered disclosed as well as between 10 and 15. It is also understood that each unit between two particular units are also disclosed. For example, if 10 and 15 are disclosed, then 11, 12, 13, and 14 are also disclosed.

[0059] An "increase" can refer to any change that results in a greater amount of a symptom, disease, composition, condition, or activity. An increase can be any individual, median, or average increase in a condition, symptom, activity, composition in a statistically significant amount. Thus, the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100% or more increase so long as the increase is statistically significant.

[0060] A "decrease" can refer to any change that results in a smaller amount of a symptom, disease, composition, condition, or activity. A substance is also understood to decrease the genetic output of a gene when the genetic output of the gene product with the substance is less relative to the output of the gene product without the substance. Also, for example, a decrease can be a change in the symptoms of a disorder such that the symptoms are less than previously observed. A decrease can be any individual, median, or average decrease in a condition, symptom, activity, composition in a statistically significant amount. Thus, the decrease can be a 1 , 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100%, or more decrease so long as the decrease is statistically significant.

[0061] As used herein, “altered expression” refers to changes in gene expression relative to a control or a reference standard. An “altered expression” of a gene can be increased relative to a control or a reference standard. Thus, the increase can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100% or more increase so long as the increase is statistically significant. An “altered expression” of a gene can also be decreased relative to a control or a reference standard. Thus, the decrease can be a 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75, 80, 85, 90, 95, 100%, or more decrease so long as the decrease is statistically significant.

[0062] As used herein, the terms “may,” “optionally,” and “may optionally” are used interchangeably and are meant to include cases in which the condition occurs as well as cases in which the condition does not occur. Thus, for example, the statement that a formulation “may include an excipient” is meant to include cases in which the formulation includes an excipient as well as cases in which the formulation does not include an excipient.

[0063] As used here, the terms “beneficial agent” and “active agent” are used interchangeably herein to refer to a chemical compound or composition that has a beneficial biological effect. Beneficial biological effects include both therapeutic effects, i.e., treatment of a disorder or other undesirable physiological condition, and prophylactic effects, i.e., prevention of a disorder or other undesirable physiological condition. The terms also encompass pharmaceutically acceptable, pharmacologically active derivatives of beneficial agents specifically mentioned herein, including, but not limited to, salts, esters, amides, prodrugs, active metabolites, isomers, fragments, analogs, and the like. When the terms “beneficial agent” or “active agent” are used, then, or when a particular agent is specifically identified, it is to be understood that the term includes the agent per se as well as pharmaceutically acceptable, pharmacologically active salts, esters, amides, prodrugs, conjugates, active metabolites, isomers, fragments, analogs, etc.

[0064] As used herein, the terms “treating” or “treatment” of a subject includes the administration of a drug to a subject with the purpose of curing, healing, alleviating, relieving, altering, remedying, ameliorating, improving, stabilizing or affecting a disease or disorder, or a symptom of a disease or disorder. The terms “treating” and “treatment” can also refer to reduction in severity and / or frequency of symptoms, elimination of symptoms and / or underlying cause, prevention of the occurrence of symptoms and / or their underlying cause, and improvement or remediation of damage.

[0065] As used herein, the term “preventing” a disorder or unwanted physiological event in a subject refers specifically to the prevention of the occurrence of symptoms and / or their underlying cause, wherein the subject may or may not exhibit heightened susceptibility to the disorder or event.

[0066] By the term “effective amount” of a therapeutic agent is meant a nontoxic but sufficient amount of a beneficial agent to provide the desired effect. The amount of beneficial agent that is “effective” will vary from subject to subject, depending on the age and general condition of the subject, the particular beneficial agent or agents, and the like. Thus, it is not always possible to specify an exact “effective amount.” However, an appropriate “effective” amount in any subject case may be determined by one of ordinary skill in the art using routine experimentation. Also, as used herein, and unless specifically stated otherwise, an “effective amount” of a beneficial can also refer to an amount covering both therapeutically effective amounts and prophylactically effective amounts.

[0067] An “effective amount” of a drug necessary to achieve a therapeutic effect may vary according to factors such as the age, sex, and weight of the subject. Dosage regimens can be adjusted to provide the optimum therapeutic response. For example, several divided doses may be administered daily or the dose may be proportionally reduced as indicated by the exigencies of the therapeutic situation.

[0068] As used herein, a “therapeutically effective amount” of a therapeutic agent refers to an amount that is effective to achieve a desired therapeutic result, and a “prophylactically effective amount” of a therapeutic agent refers to an amount that is effective to prevent an unwanted physiological condition. Therapeutically effective and prophylactically effective amounts of a given therapeutic agent will typically vary with respect to factors such as the type and severity of the disorder or disease being treated and the age, gender, and weight of the subject.

[0069] The term “therapeutically effective amount” can also refer to an amount of a therapeutic agent, or a rate of delivery of a therapeutic agent (e.g., amount over time), effective to facilitate a desired therapeutic effect. The precise desired therapeutic effect will vary according to the condition to be treated, the tolerance of the subject, the drug and / or drug formulation to be administered (e.g., the potency of the therapeutic agent (drug), the concentration of drug in the formulation, and the like), and a variety of other factors that are appreciated by those of ordinary skill in the art.

[0070] As used herein, the term “pharmaceutically acceptable” component can refer to a component that is not biologically or otherwise undesirable, i.e., the component may be incorporated into a pharmaceutical formulation of the invention and administered to a subject as described herein without causing any significant undesirable biological effects or interacting in a deleterious manner with any of the other components of the formulation in which it is contained. When the term “pharmaceutically acceptable” is used to refer to an excipient, it is generally implied that the component has met the required standards of toxicological and manufacturing testing or that it is included on the Inactive Ingredient Guide prepared by the U.S. Food and Drug Administration. Also, as used herein, the term “pharmacologically active” (or simply “active”), as in a “pharmacologically active” derivative or analog, can refer to a derivative or analog (e.g., a salt, ester, amide, conjugate, metabolite, isomer, fragment, etc.) having the same type of pharmacological activity as the parent compound and approximately equivalent in degree.

[0071] The term “subject” or “host” refers to any individual who is the target of administration or treatment. The subject can be a vertebrate, for example, a mammal. Thus, the subject can be a human or veterinary patient. The term “patient” refers to a subject under the treatment of a clinician, e.g., physician. The subject can be either male or female.

[0072] The terms “peptide,” “protein,” and “polypeptide” are used interchangeably to refer to a natural or synthetic molecule comprising two or more amino acids linked by the carboxyl group of one amino acid to the alpha amino group of another.

[0073] The term “nucleic acid” refers to a natural or synthetic molecule comprising a single nucleotide or two or more nucleotides linked by a phosphate group at the 3’ position of one nucleotide to the 5 ’ end of another nucleotide. The nucleic acid is not limited by length, and thus the nucleic acid can include deoxyribonucleic acid (DNA) or ribonucleic acid (RNA).

[0074] “Complementary” or “substantially complementary” refers to the hybridization or base pairing or the formation of a duplex between nucleotides or nucleic acids, such as, for instance, between the two strands of a double stranded DNA molecule or between an oligonucleotide primer and a primer binding site on a single stranded nucleic acid. Complementary nucleotides are, generally, A and T / U, or C and G. Two single-stranded RNA or DNA molecules are said to be substantially complementary when the nucleotides of one strand, optimally aligned and compared and with appropriate nucleotide insertions or deletions, pair with at least about 80% of the nucleotides of the other strand, usually at least about 90% to 95%, and more preferably from about 98 to 100%. Alternatively, substantial complementarity exists when an RNA or DNA strand will hybridize under selective hybridization conditions to its complement. Typically, selective hybridization will occur when there is at least about 65% complementary over a stretch of at least 14 to 25 nucleotides, at least about 75%, or at least about 90% complementary. See Kanehisa (1984) Nucl. Acids Res. 12:203.

[0075] “Hybridization” refers to the process in which two single-stranded oligonucleotides bind non-covalently to form a stable double-stranded oligonucleotide. The term “hybridization” may also refer to triple-stranded hybridization. The resulting (usually) double-stranded oligonucleotide is a “hybrid” or “duplex.” “Hybridization conditions” will typically include salt concentrations of less than about 1 M, more usually less than about 500 mM and even more usually less than about 200 mM. Hybridization temperatures can be as low as 5° C., but are typically greater than 22° C., more typically greater than about 30° C., and often in excess of about 37° C. In certain exemplary embodiments, hybridization takes place at room temperature. The term “stringent hybridization conditions” as used herein is the binding which occurs within a range from about Tm 5° C. (5° C. below the melting temperature Tm of the probe) to about 20° C. to 25° C. below Tm. The term “highly stringent hybridization conditions” as used herein refers to conditions of: at least about 6xSSC and 1% SDS at 65° C., with a first wash for 10 minutes at about 42° C. with about 20% (v / v) formamide in O.lxSSC, and with a subsequent wash with 0.2xSSC and 0.1% SDS at 65° C.

[0076] Methods of Diagnosis and Treatment

[0077] Disclosed herein are methods of diagnosing and treating a subject with a neurovascular disease, such as for example a stroke or a traumatic brain injury (TBI).

[0078] In some aspects, disclosed herein is a method for treating a subject with a disease, comprising obtaining a first biological sample from a first subject having the disease immediately after onset of disease symptoms, obtaining a second biological sample from the first subject having the disease about 12 hours or later after onset of disease symptoms, quantifying an expression level of one or more genes from the first biological sample and the second biological sample, comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the disease, comparing the expression level of the one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the disease, wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample, determining a differentially expressed gene from the subject having the disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample, and administering to the subject a therapeutically effective amount of a specific therapeutic agent, a gene therapy, or a thrombolytic; or performing a surgical procedure for treatment of the disease.

[0079] In some aspects, disclosed herein is a method for determining a differentially expressed gene in a subject with a disease, comprising obtaining a first biological sample from a first subject having the disease immediately after onset of disease symptoms, obtaining a second biological sample from the first subject having the disease about 12 hours or later after onset of disease symptoms, quantifying an expression level of one or more genes from the first biological sample and the second biological sample, comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the disease, comparing the expression level of one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the disease, wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample, determining a differentially expressed gene from the subject having the disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample.

[0080] In some aspects, disclosed herein are methods for determining, predicting, or improving the outcome for a subject suffering from disease symptoms, comprising obtaining a first biological sample from a first subject having the disease immediately after onset of disease symptoms; obtaining a second biological sample from the first subject having the disease about 12 hours or later after onset of disease symptoms; quantifying an expression level of one or more genes from the first biological sample and the second biological sample; comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the disease; comparing the expression level of the one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the disease, wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample; determining a differentially expressed gene from the subject having the disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample; and administering to the subject a therapeutically effective amount of a specific therapeutic agent for treatment of the disease symptoms.

[0081] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms; quantifying an expression level of a differentially expressed gene, wherein the differentially expressed gene is identified according to one of the methods disclosed herein; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of a therapeutic agent, a gene therapy, or a thrombolytic; or performing a surgical procedure for treatment of the stroke.

[0082] In some embodiments, biological samples are taken from the subject every 6 hours or every 12 hours.

[0083] In some embodiments, the second biological sample is taken from the subject at least 6 hours, at least 12 hours, at least 18 hours, at least 24 hours, at least 36 hours, at least 48 hours, or more after the first biological sample.

[0084] In some embodiments, the fourth biological sample is taken from the subject at least 6 hours, at least 12 hours, at least 18 hours, at least 24 hours, at least 36 hours, at least 48 hours, or more after the third biological sample.

[0085] As used herein, “immediately after” refers to an action, including but not limited to sample collection, being performed in less than or about 24 hours from onset of any disease disclosed herein. In some embodiments, the sample is collected immediately after onset of disease symptoms, for example, within 10 minutes, within 30 minutes, within 1 hour, within 2 hours, within 3 hours, within 4 hours, within 5 hours, within 6 hours, or within 7 hours after onset of disease symptoms. In some embodiments, the sample is collected up to 24 hours after onset of disease. In some embodiments, the sample is collected within 7 hours, within 8 hours, within 9 hours, within 10 hours, within 11 hours, within 12 hours, within 13 hours, within 14 hours, within 15 hours, within 16 hours, within 17 hours, within 18 hours, within 19 hours, within 20 hours, within 21 hours, within 22 hours, within 23 hours, or within 24 hours after onset of disease symptoms.

[0086] In some embodiments, the disease is a stroke. In some embodiments, the disease is a traumatic brain injury (TBI). In some embodiments, the differentially expressed gene is ADORA3. In some embodiments, the second biological sample is obtained about 24 hours or later after onset of disease symptoms. In some embodiments, the second biological sample is obtained 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 80, 81 , 82, 83, 84, 85, 86, 87, 88, 89, 90, 91 , 92, 93, 94, 95, 96, or more hours after onset of disease symptoms. In some embodiments, the subject without the disease comprises a subject suffering from a mimic disease, such as for example a disease with similar symptoms to the stroke or traumatic brain injury. In some embodiments the subject with the disease is a healthy individual.

[0087] In some embodiments, the therapeutic agent for treatment of the stroke comprises an antiplatelet agent or an anticoagulant agent. In some embodiments, the differentially expressed gene comprises ADORA3. In some embodiments, the therapeutic agent for treatment of the stroke comprises an agonist or antagonist of AD0RA3. In some embodiments, the agonist is AST-004.

[0088] AST-004

[0089] Chemical formula:

[0090] C13H17N5O3S

[0091] MW: 323 g / mol

[0092] AST-004

[0093] (1 ?,2 / ?,3S,4 / ?,5S)-4-(6-aniino-2-(methylthio)-9H-purin-9-yl)-l- (hydroxymethyl)bicyclo[3. 1 ,0]hexane-2,3-diol

[0094] See Listen, T. el al. Purinergic Signal. 2020 Dec; 16(4): 543-559.

[0095] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

[0096] In some embodiments, the method of quantifying expression levels is based on sequencing.

[0097] In some embodiments, determining a differentially expressed gene is based on the expression level of the gene combined with demographic information (for example age, sex and species or race of the subject), technical information (for example, sample amount, method of sequencing, measure of sample degradation), and known function information of the gene or related protein (for example based on existing published biological studies designed to understand the function of a gene).

[0098] In some aspects, disclosed herein are at least four gene sets for treating, preventing, diagnosing, determining, predicting, and / or assessing a neurovascular disease, including but not limited to stroke. In some embodiments, the at gene set comprises any combination of genes selected from ADORA3, ADORA2B, ADORA2A, ADRB2, IGFBP3, SERPINF1 , PINT, CTNNB1, MAPK14, CIAPIN1, LEPR0TL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, and / or WDR61.

[0099] In some embodiments, the gene set comprise AD0RA3, ADORA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, CBLB, and / or WDR61.

[0100] In some embodiments, the gene set comprise ADORA3, ADORA2B, ADORA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, and / or WDR61.

[0101] In some embodiments, the gene set comprise AD0RA3, AD0RA2A, IGFBP3, SERPINF1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, LAIR1, B3GALT6, IRAKI, ANKRD17, CBLB, and / or WDR61.

[0102] In some embodiments, the fourth gene set comprises AD0RA3, AD0RA2A, IGFBP3, SERPINF1, CTNNB 1, MAPK14, CIAPIN1, LEPROTL1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, and / or WDR61. In a preferred embodiment, the gene set comprises ADORA3, ADORA2A, IGFBP3, SERPINF1 , CTNNB1 , MAPK14, CIAPIN1 , LEPROTL1 , LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, and / or WDR61. In some embodiments, the gene set of any preceding aspect is selected by identifying sets of thresholds and / or combinations of genes.

[0103] In some aspects, disclosed herein is a method of treating a subject with a neurovascular disease, comprising obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers, wherein the biomarker is selected from a gene set of any preceding aspect, determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control, and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the neurovascular disease.

[0104] In some aspects, disclosed herein is a method of treating a subject with a neurovascular disease, comprising obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, and / or WDR61, determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control, and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the neurovascular disease.

[0105] In some aspects, disclosed herein is a method for diagnosing a subject with a neurovascular disease, comprising obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the one or more biomarkers are selected from a gene set of any preceding aspect, and determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control.

[0106] In some aspects, disclosed herein is a method for diagnosing a subject with a neurovascular disease, comprising obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, and determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control.

[0107] In some aspects, disclosed herein is a method of treating a stroke in a subject, the method comprising selecting a set of genes expressed in a population of stroke patients, wherein the expression level of the set of genes indicates that the subject has experienced a stroke with approximately 100% certainty relative to two reference standards, wherein the two reference standards are derived from stroke patients and non-stroke patients, wherein a first reference standard defines a set of genes and thresholds met only by stroke patients (so all non-stroke and maybe some strokes would not meet the thresholds), wherein a second reference standard defines genes and thresholds where only non-stroke patients meet the threshold, wherein the set of genes have expression that meet the first reference standard or the second reference standard, and wherein the subject is administered a therapeutic agent, a gene therapy, a thrombolytic, or undergoes a surgical procedure for treatment of the stroke when the set of genes have expression that meets the first reference standard or the second reference standard. In some embodiments, the method further comprises utilizing a third reference standard that defines stroke and non-stroke thresholds and sets of genes with less than 100% certainty.

[0108] It should be understood that utilizing a method comprising two reference standards allows for ruling in stroke for a subset of patients who have stroke. It should also be understood that utilizing a method comprising two reference standards also allows for ruling out stroke for a subset of patients who do not have stroke.

[0109] In some aspects, disclosed herein is a method of treating a neurovascular disease in a subject, the method comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease ; quantifying a first set of genes expressed in the biological sample, wherein the expression level of the first set of genes indicates that the subject has experienced the neurovascular disease with about 100% certainty relative based on a first reference standard; quantifying a second set of genes expressed in the biological sample, wherein the expression level of the second set of genes indicates that the subject has not experienced the neurovascular disease with about 100% certainty based on a second reference standard; wherein the first reference standard defines a first set of genes and thresholds where only neurovascular disease patients meet the first thresholds, wherein the second reference standard defines the second set of genes and thresholds where only non-neurovascular disease patients meet the second threshold; and determining the subject as suffering from the neurovascular disease if the expression level of the first set of genes meets the first reference standard; or determining the subject as not suffering from the neurovascular disease if the expression level of the second set of genes meets to the second reference standard; and administering a therapeutic agent, a gene therapy, a thrombolytic, or undergoes a surgical procedure for treatment of the neurovascular disease when the first set of genes have expression that meets the first reference standard.

[0110] In some embodiments, the method comprises further quantifying a third set of genes expressed in the biological sample, wherein the third reference standard defines neurovascular disease and non- neurovascular disease thresholds with less than 100% certainty.

[0111] In some aspects, disclosed herein is a method of treating a stroke in a subject, the method comprising: obtaining a biological sample from a subject suspected of having the stroke; quantifying a first set of genes expressed in the biological sample, wherein the expression level of the first set of genes indicates that the subject has experienced the stroke with about 100% certainty when compared to a first reference standard; quantifying a second set of genes expressed in the biological sample, wherein the expression level of the second set of genes indicates that the subject has not experienced the stroke with about 100% certainty when compared to a second reference standard; wherein the first reference standard defines a first set of genes and thresholds where only stroke patients meet the thresholds, wherein the second reference standard defines a second set of genes and thresholds where only non-stroke patients meet a second threshold; and determining the subject as suffering from the stroke if the expression level of the first set of genes meets the first reference standard; or determining the subject as not suffering from the stroke if the expression level of the second set of genes meets the second reference standard; and administering a therapeutic agent, a gene therapy, a thrombolytic, or undergoes a surgical procedure for treatment of the stroke when the first set of genes meet the first reference standard.

[0112] In some embodiments, the method comprises further quantifying a third set of genes expressed in the biological sample, wherein the third reference standard defines stroke and nonstroke thresholds with less than 100% certainty.

[0113] In some embodiments, the biological sample is obtained immediately after onset of neurovascular disease symptoms. In some embodiments, the biological sample is obtained immediately after onset of stroke symptoms.

[0114] In some embodiments, the second reference control is healthy, is not suffering from a stroke, and / or is not experiencing stroke symptoms. In some embodiments, the set of genes comprises any combination of genes selected from AD0RA3, ADORA2B, ADORA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, and / or WDR61. In some embodiments, the expression level of the set of genes indicates that the subject is experiencing or has experienced a stroke with approximately 50%, 51 %, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61 %, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, 99%, or 100% certainty. In some embodiments, the set of genes further identifies a subset of subjects affected by stroke(s) (i.e., subjects that more than likely experienced or are experiencing a stroke). In some embodiments, the first reference standard comprises a disease reference standard. In some embodiments, the second reference standard comprises a non-disease reference standard.

[0115] In some embodiments, a disease reference standard is established based on gene expression for the selected genes by identifying sets of thresholds and combinations of genes, wherein only diseased patients meet the thresholds.

[0116] In some embodiments, a non-disease reference standard is established based on gene expression for a second set of selected genes, wherein only non-disease patients meet the thresholds.

[0117] In some embodiments, the method of any preceding aspect comprises obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms.

[0118] In some embodiments, the method of any preceding aspect comprises using the reference standard of any preceding aspect to identify a subject who almost certainly has the disease.

[0119] In some embodiments, the method of any preceding aspect comprises using the reference standard of any preceding aspect to identify a subject who almost certainly does not have the disease.

[0120] In some aspects, disclosed herein is a method for treating a subject suspected of having a stroke, comprising obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms, quantifying an expression level of a differentially expressed gene, wherein the differentially expressed gene is identified according to the method of any preceding aspect, comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke), and administering to the subject a therapeutically effective amount of a therapeutic agent, a gene therapy, or a thrombolytic; or performing a surgical procedure for treatment of the stroke.

[0121] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms, quantifying an expression level of a differentially expressed gene, wherein the differentially expressed gene is selected from AD0RA3, AD0RA2B, AD0RA2A, or ADRB2; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of a therapeutic agent, a gene therapy, or a thrombolytic; or performing a surgical procedure for treatment of the stroke.

[0122] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms, quantifying an expression level of a differentially expressed gene, wherein the differentially expressed gene is selected from AD0RA3, AD0RA2B, AD0RA2A, or ADRB2; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of an agonist or antagonist of AD0RA3, AD0RA2B, AD0RA2A, or ADRB2.

[0123] In some embodiments, the biomarker comprises AD0RA3. In some embodiments, the biomarker comprises AD0RA2B. In some embodiments, the biomarker comprises AD0RA2A. In some embodiments, the biomarker comprises ADRB2. In some embodiments, the biomarker comprises IGFBP3. In some embodiments, the biomarker comprises SERPINF1. In some embodiments, the biomarker comprises PIN1. In some embodiments, the biomarker comprises CTNNB1. In some embodiments, the biomarker comprises MAPK14. In some embodiments, the biomarker comprises CIAPIN1. In some embodiments, the biomarker comprises LEPROTL1. In some embodiments, the biomarker comprises FDFT1. In some embodiments, the biomarker comprises LAIR1. In some embodiments, the biomarker comprises B3GALT6. In some embodiments, the biomarker comprises IRAKI. In some embodiments, the biomarker comprises ANKRD17. In some embodiments, the biomarker comprises SLC2A8. In some embodiments, the biomarker comprises CBLB. In some embodiments, the biomarker comprises WDR61.

[0124] Gene information and protein information can be found at NCBI (ncbi.nlm.nih.gov; for example, AD0RA2B (NCBI Gene: 136 Ensembl:ENSG00000170425), AD0RA2A (NCBI Gene: 135 Ensembl:ENSG00000128271), AD0RA3 (NCBI Gene: 140 Ensembl:ENSG00000282608), ADRB2 (NCBI Gene: 154 En sembl ENS G00000169252), TGFBP3 (NCBI Gene: 3486; Ensembl: ENSG00000146674), SERPINF1 (NCBI Gene: 5176; Ensembl: ENSG00000132386), PIN1 (NCBI Gene: 5300; Ensembl: ENSG00000127445), CTNNB1 (NCBI Gene: 1499; Ensembl: ENSG00000168036), MAPK14 (NCBI Gene: 1432; Ensembl: ENSG00000112062), CIAPIN1 (NCBI Gene: 57019; Ensembl: ENSG00000005194), LEPROTL1 (NCBI Gene: 23484; Ensembl: ENSG00000104660), FDFT1 (NCBI Gene: 2222; Ensembl: ENSG00000079459), LAIR1 (NCBI Gene: 3903; Ensembl: ENSG00000167613), B3GALT6 (NCBI Gene: 126792; Ensembl: ENS GOOOOO 176022), IRAKI (NCBI Gene: 3654; Ensembl: ENSG00000184216), ANKRD17 (NCBI Gene: 26057; Ensembl: ENS GOOOOO 132466), SLC2A8 (NCB Gene: 29988; Ensembl: ENSG00000136856), CBLB (NCBI Gene: 868; Ensembl: ENSG00000114423), and WDR61 (NCBI Gene: 415359; Ensembl: ENSGALG0009024240)).

[0125] In some embodiments, the biological sample comprises a blood sample.

[0126] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array. In some embodiments, the quantifying is carried out to detect gene methylation and gene copy number.

[0127] In some embodiments, the reference control is a sample from the subject taken about 24 hours after onset of neurovascular disease symptoms. In some embodiments, the reference control is a sample from the subject taken greater than 24 hours after onset of neurovascular disease symptoms. In some embodiments, the reference control is a sample from a subject that is not suffering from the neurovascular disease. In some embodiments, the reference control is a sample from the subject that is not experiencing neurovascular disease symptoms. In some embodiments, the reference control is a sample that provides a baseline for quantifying the expression level of the one or more biomarkers of any preceding aspect. In some embodiments, the sample is from a different subject, a second subject, or from a population of subjects. In some embodiments, the sample is from a different subject (or population of subjects) that is healthy, that is not suffering from the neurovascular disease, and / or that is not experiencing neurovascular disease symptoms.

[0128] In some embodiments, the sample is collected immediately after onset of neurovascular disease symptoms, for example, within 10 minutes, within 30 minutes, within 1 hour, within 2 hours, within 3 hours, within 4 hours, within 5 hours, within 6 hours, or within 7 hours after onset of neurovascular disease symptoms. In some embodiments, the sample is collected up to 24 hours after onset of neurovascular disease. In some embodiments, the sample is collected within 7 hours, within 8 hours, within 9 hours, within 10 hours, within 11 hours, within 12 hours, within 13 hours, within 14 hours, within 15 hours, within 16 hours, within 17 hours, within 18 hours, within 19 hours, within 20 hours, within 21 hours, within 22 hours, within 23 hours, or within 24 hours after onset of neurovascular disease.

[0129] In some embodiments, the therapeutic agent is an agonist or antagonist of ADORA3, ADORA2B, ADORA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61. In some embodiments, the agonist is an ADORA3 agonist. In some embodiments, the agonist is an AD0RA2B agonist. In some embodiments, the agonist is an AD0RA2A agonist. In some embodiments, the agonist is an ADRB2 agonist. In some embodiments, the agonist is an IGFBP3 agonist. In some embodiments, the agonist is a SERPINF1 agonist. In some embodiments, the agonist is a PIN1 agonist. In some embodiments, the agonist is a CTNNB1 agonist. In some embodiments, the agonist is MAPK14. In some embodiments, the agonist is a CIAPIN1 agonist. In some embodiments, the agonist is a LEPROTL1 agonist. In some embodiments, the agonist is a LAIR1 agonist. In some embodiments, the agonist is B3GALT6 agonist. In some embodiments, the agonist is an IRAKI agonist. In some embodiments, the agonist is an ANKRD17 agonist. In some embodiments, the agonist is an SLC2A8 agonist. In some embodiments, the agonist is CBLB agonist. In some embodiments, the agonist is a WDR61 agonist.

[0130] In some embodiments, the antagonist is an AD0RA3 antagonist. In some embodiments, the antagonist is an AD0RA2B antagonist. In some embodiments, the antagonist is an AD0RA2A antagonist. In some embodiments, the antagonist is an ADRB2 antagonist. In some embodiments, the antagonist is an IGFBP3 antagonist. In some embodiments, the antagonist is a SERPINF1 antagonist. In some embodiments, the antagonist is a PIN 1 antagonist. In some embodiments, the antagonist is a CTNNB1 antagonist. In some embodiments, the antagonist is MAPK14 antagonist. In some embodiments, the antagonist is a CIAPIN1 antagonist. In some embodiments, the antagonist is a LEPROTL1 antagonist. In some embodiments, the antagonist is a LAIR1 antagonist. In some embodiments, the antagonist is B3GALT6 antagonist. In some embodiments, the antagonist is an IRAKI antagonist. In some embodiments, the antagonist is an ANKRD17 antagonist. In some embodiments, the antagonist is an SLC2A8 antagonist. In some embodiments, the antagonist is CBLB antagonist. In some embodiments, the antagonist is a WDR61 antagonist.

[0131] In some embodiments, the agonist is selected from an agonist or antagonist listed in Table 1. In some embodiments, the agonist is AST-004. In some embodiments, the therapeutic agent comprises an antiplatelet agent or an anticoagulant agent.

[0132] In some embodiments, the agonist or antagonist of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1 , B3GALT6, IRAKI , ANKRD17, SLC2A8, CBLB, or WDR61 can be a nucleic acid, for example, an siRNA, shRNA, or an antisense oligonucleotide.

[0133] In some embodiments, the neurovascular disease is stroke. In some embodiments, the neurovascular disease is a traumatic brain injury. In some embodiments, the neurovascular disease is Moyamoya Disease.

[0134] In some embodiments, the differentially expressed gene comprises AD0RA3. In some embodiments, the therapeutic agent for treatment of the stroke comprises an antiplaielet. agent or an anticoagulant agent. In some embodiments, the therapeutic agent for treatment of the stroke comprises an agonist or antagonist of AD0RA3.

[0135] In some aspects, disclosed herein are methods for determining, predicting, or improving the outcome for a subject suffering from neurovascular disease symptoms, comprising obtaining a biological sample from a subject; quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of an adenosine receptor; determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control; and administering to the subject a therapeutically effective amount of an agonist or antagonist of an adenosine receptor.

[0136] Gene expression measured in blood can act as a surrogate for the level of expression of various receptors on cell surfaces that may respond to treatments designed to trigger protective mechanisms or stop damaging mechanisms within the cell. To this end, measuring the level of expression before, during or after such treatment can be useful to help diagnose the patient with respect to requiring the treatment, determining the level of treatment necessary and monitoring the effect of the treatment. In some embodiments, the level of expression of the biomarker is useful in maximizing dose for maximizing drug effectiveness or for reducing side effects.

[0137] In some embodiments, the method further comprises administering a therapeutic agent for treating a stroke. In some embodiments, the therapeutic agent comprises an antiplatelet agent or an anticoagulant agent. In some embodiments, the therapeutic agent comprises aspirin. In some embodiments, the therapeutic agent comprises a blood thinner. In some embodiments, the therapeutic agent comprises warfarin.

[0138] In some embodiments, the method further comprises administering a surgical treatment to the subject. In some embodiments, the surgical treatment comprises a revascularization surgery.

[0139] In some aspects, disclosed herein is a computer-implemented method of training an algorithm for diagnosing a subject suspected of having a neurovascular disease, comprising obtaining a set of reference biological samples from one or more reference subjects known to have the neurovascular disease, quantifying an expression level of one or more biomarkers in each reference biological sample to create a set of reference gene expression levels, wherein the biomarker is selected from a gene set of any preceding aspect, creating a first training set comprising the set of reference gene expression levels, and training an algorithm using the first training set, wherein training the algorithm comprises defining an expression level threshold for each of the one or more biomarkers, defining a number of biomarkers which must meet their expression level threshold for the subject to be diagnosed with the neurovascular disease. In some aspects, disclosed herein is a computer-implemented method of training an algorithm for diagnosing a subject suspected of having a neurovascular disease, comprising obtaining a set of reference biological samples from one or more reference subjects known to have the neurovascular disease, quantifying an expression level of one or more biomarkers in each reference biological sample to create a set of reference gene expression levels, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, creating a first training set comprising the set of reference gene expression levels, and training an algorithm using the first training set, wherein training the algorithm comprises defining an expression level threshold for each of the one or more biomarkers, defining a number of biomarkers which must meet their expression level threshold for the subject to be diagnosed with the neurovascular disease.

[0140] In some embodiments, the biomarker comprises AD0RA3. hi some embodiments, the biomarker comprises AD0RA2B. In some embodiments, the biomarker comprises AD0RA2A. In some embodiments, the biomarker comprises ADRB2.

[0141] In some embodiments, the set of reference biological samples comprise blood samples.

[0142] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array. In some embodiments, the set of reference biological samples are taken from the one or more reference subjects about 24 hours after onset of neurovascular disease symptoms. In some embodiments, the set of reference biological samples are taken from the one or more reference subjects greater than 24 hours after onset of neurovascular disease symptoms. In some embodiments, the set of reference biological samples are taken from one or more reference subjects that are not experiencing neurovascular disease symptoms.

[0143] In some embodiments, the one or more reference subjects are human. In some embodiments, the neurovascular disease is a stroke. In some embodiments, the neurovascular disease is a traumatic brain injury.

[0144] In some embodiments, a trained algorithm comprises a machine learning algorithm. In some embodiments, the trained algorithm comprises a neural network. In some embodiments, the computer-implemented method of any preceding aspect further comprises obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker is selected from a gene set of any preceding aspect, and determining the subject as suffering from the neurovascular disease using the trained algorithm.

[0145] In some embodiments, the computer-implemented method of any preceding aspect further comprises obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms, quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61, and determining the subject as suffering from the neurovascular disease using the trained algorithm.

[0146] In some embodiments, the subject of any preceding aspect is a human.

[0147] In some aspects, disclosed herein is a method of treating a subject with a neurovascular disease, comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms; quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, CBLB, or WDR61; determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control; and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the neurovascular disease and / or performing an interventional procedure to the correct the cause or relieve symptoms of the neurovascular disease.

[0148] In some embodiments, the biomarker comprises AD0RA3. In some embodiments, the biomarker comprises AD0RA2B. In some embodiments, the biomarker comprises AD0RA2A. In some embodiments, the biomarker comprises ADRB2.

[0149] In some embodiments, the biological sample comprises a blood sample.

[0150] In some embodiments, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase- Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array. In some embodiments, the reference control is a sample from the subject taken about 24 hours after onset of neurovascular disease symptoms. In some embodiments, the reference control is a sample from the subject taken greater than 24 hours after onset of neurovascular disease symptoms. In some embodiments, the reference control is a sample from a subject that is not suffering from the neurovascular disease. In some embodiments, the reference control is a sample from the subject that is not experiencing neurovascular disease symptoms.

[0151] In some embodiments, the specific therapeutic agent is an antiplatelet agent or an anticoagulant agent.

[0152] In some embodiments, the specific therapeutic agent is an agonist or antagonist of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, CBLB, or WDR61.

[0153] In some embodiments, the agonist is an AD0RA3 agonist. In some embodiments, the agonist is an AD0RA2B agonist. In some embodiments, the agonist is an AD0RA2A agonist. In some embodiments, the agonist is an ADRB2 agonist.

[0154] In some embodiments, the agonist or antagonist is selected from an agonist or antagonist listed in Table 3 herein.

[0155] In some embodiments, the AD0RA3 agonist is AST-004.

[0156] In some embodiments, the subject is a human.

[0157] In some embodiments, the neurovascular disease is a stroke. In some embodiments, the neurovascular disease is a traumatic brain injury.

[0158] In some aspects, disclosed herein is a method for diagnosing a subject with a neurovascular disease, comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms; quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1 , FDFT1 , LAIR1 , B3GALT6, IRAKI , ANKRD17, CBLB, or WDR61 ; and determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control.

[0159] In some embodiments, the method further comprises administering an antiplatelet agent or an anticoagulant agent. In some embodiments, the method further comprises administering an agonist or antagonist of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNBI, MAPK14, CIAPIN1, LEPROTLI, FDFT1, LAIRI, B3GALT6, IRAKI, ANKRD17, CBLB, or WDR61.

[0160] In some embodiments, the method further comprises administering an agonist or antagonist of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNBI, MAPK14, CIAPIN1, LEPROTLI, FDFT1, LAIRI, B3GALT6, IRAKI, ANKRD17, CBLB, or WDR61.

[0161] In some embodiments, the method further comprises administering an antisense therapeutic or a nucleic acid therapeutic (for example, siRNA, shRNA, miRNA, etc.) targeting AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNBI, MAPK14, CIAPIN1, LEPROTLI, FDFT1, LAIRI, B3GALT6, IRAKI, ANKRD17, CBLB, or WDR61

[0162] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising: obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms; quantifying an expression level of a differentially expressed gene; wherein the differentially expressed gene is selected from AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNBI, MAPK14, CIAPIN1, LEPROTLI, FDFT1, LAIRI, B3GALT6, IRAKI, ANKRD17, CBLB, WDR61 or a combination thereof; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of a therapeutic agent for treatment of the stroke.

[0163] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising: obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms; quantifying an expression level of a differentially expressed gene; wherein the differentially expressed gene is selected from AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNBI, MAPK14, CIAPIN1, LEPROTLI, FDFT1, LAIRI, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the stroke and / or performing an interventional procedure to the correct the cause or relieve symptoms of the stroke.

[0164] In one aspect, disclosed herein is a method for treating a subject suspected of having a stroke, comprising: obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms; quantifying an expression level of a differentially expressed gene; wherein the differentially expressed gene is selected from AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1 , LATR1 , B3GALT6, IRAKI , ANKRD17, CBLB, or WDR61 ; comparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease (a disease with symptoms similar to stroke); and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the stroke and / or performing an interventional procedure to the correct the cause or relieve symptoms of the stroke.

[0165] In some aspects, disclosed herein is a method of treating a subject with a traumatic brain injury, comprising: obtaining a biological sample from a subject suspected of having the traumatic brain injury immediately after onset of traumatic brain injury symptoms; quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1 , CTNNB1, MAPK14, CIAPIN1, LEPR0TL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; determining the subject as suffering from the traumatic brain injury if the expression level of the one or more biomarkers is changed in the subject immediately after onset of traumatic brain injury symptoms in comparison to a reference control (or a standard); and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the traumatic brain injury.

[0166] In some aspects, disclosed herein is a method of treating a subject with Moyamoya Disease, comprising: obtaining a biological sample from a subject suspected of having Moyamoya Disease; quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; determining the subject as suffering from Moyamoya Disease if the expression level of the one or more biomarkers is changed in the subject in comparison to a reference control (or a standard); and administering to the subject a therapeutically effective amount of a specific therapeutic agent for the treatment of the Moyamoya Disease.

[0167] In some aspects, disclosed herein is a method for diagnosing a subject with a traumatic brain injury, comprising: obtaining a biological sample from a subject suspected of having the traumatic brain injury immediately after onset of traumatic brain injury symptoms; quantifying an expression level of one or more biomarkers immediately after onset of traumatic brain injury symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; and determining the subject as suffering from the traumatic brain injury if the expression level of the one or more biomarkers is changed in the subject immediately after onset of traumatic brain injury symptoms in comparison to a reference control.

[0168] In some aspects, disclosed herein is a method for diagnosing a subject with Moyamoya Disease, comprising: obtaining a biological sample from a subject suspected of having Moyamoya Disease; quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; and determining the subject as suffering from Moyamoya Disease if the expression level of the one or more biomarkers is changed in the subject in comparison to a reference control.

[0169] EXAMPLES

[0170] The following examples are set forth below to illustrate the methods and results according to the disclosed subject matter. These examples are not intended to be inclusive of all aspects of the subject matter disclosed herein, but rather to illustrate representative methods and results. These examples are not intended to exclude equivalents and variations of the present invention which are apparent to one skilled in the art.

[0171] Example 1. Identifying biomarkers using differential gene expression.

[0172] Identifying biomarkers detectable in blood or other easily obtainable samples is key to diagnosing and understanding disease and disease states. Advances in methods for measuring genomic information in human samples allow for systematic identification of biomarkers for complex diseases. Gene expression studies measure the abundance of RNA transcripts in samples and find differences in expression between two or more groups of samples in order to identify candidate genes with disrupted expression values in one group relative to one or more other groups. The candidate genes are typically further investigated and validated in subsequent studies or used to develop diagnostic tests.

[0173] Gene expression studies use technologies such as sequencing or microarrays to measure the abundance of thousands of RNA transcripts for each individual sample. The data generated requires sophisticated statistical methods to find truly differentially expressed genes between two groups (typically a disease state group of interest versus a control group, for example, confirmed stroke patients versus patients with conditions that mimic stroke). The measurements performed in a single experiment can lead to identifying a large number of genes with a statistically significant difference in the level of expression between groups. Selecting a concise set of genes that can consistently identify the group of interest in subsequent validation studies with high sensitivity and specificity can be challenging.

[0174] Disclosed herein is a method for substantially improving the specificity of selected genes of interest in gene expression studies. The method exploits the dynamic nature of gene expression and utilizes repeat sequencing of select samples to identify genes that are specific to a group of interest, and that differ across different clinical groups, to isolate a disease specific effect. Also disclosed herein is a method for identifying genes of interest in gene expression studies based on measuring abundance of transcripts in patients exhibiting clinical symptoms of a suspected disease or condition where:

[0175] At least two samples are obtained from each patient at two different time points for a group of patients with the disease condition of interest;

[0176] At least two samples at two different time points are obtained from each patient in a control group or second disease state;

[0177] Measuring the difference in expression for all genes between the two different time points for the group of patients with the condition of interest;

[0178] Measuring the level of differential expression for all genes between the two different time points for the group of patients in the control group;

[0179] Selecting genes of interest for which there is a statistically significant difference in expression between the two time points in the group of patients with the condition of interest and there is no difference in expression in the control group between the two time points;

[0180] Using a computer algorithm to select a combination of genes and levels of gene expression that can classify the group of interest with high sensitivity and specificity.;

[0181] Considering the level of difference in gene expression within and across groups, patient demographics and known gene function in selecting the genes of interest; and

[0182] The first time point may be obtained as soon as possible after the first symptoms appear for the condition of interest.

[0183] Sequencing studies require batch processing of multiple samples in each sequencing run. Further, sequencing runs require multiple hours of processing. Batch processing is not suitable for fast measurement of expression level in a single sample as may be required for real time assessment of patients in a clinical setting.

[0184] Also disclosed herein is a method for diagnosing a disease based on gene expression measured in real time for a SMALL number of disease specific genes of interest (DS-GOI) each identified as described above. The number of genes must be limited such that the expression level for all selected genes can be measured rapidly using a manual, semiautomated, or fully automated fast measurement of expression level from a single sample at one time (for example using a manual benchtop standard mRNA extraction technique utilizing any mRNA extraction technique, then measuring expression level for a small number of genes from a standard blood sample using a standard quantitative polymerase chain reaction (qPCR) technique with standard or custom designed primers and probes).

[0185] Also disclosed herein is an algorithm for combining the expression levels measured for the small number of genes to provide a diagnostic measure or measures that indicate the likelihood that the patient is experiencing a specific condition or disease of interest. The combination of expression levels for multiple genes improves the sensitivity for detecting the disease or condition across a range of patients and disease presentations. For example, one gene of interest may be most or least highly expressed immediately after a stroke event, while another gene of interest may be most or least expressed after a certain period within a diagnostic time window. Another example may be genes that are most or least highly expressed in male patients experiencing a stroke while another gene may be most or least highly expressed in female patients experiencing a stroke. In a third example, two genes of interest may be redundant and may be most or least expressed in the same patient population or same disease conditions. Designing an algorithm by combining the expression levels for the genes improves the chances of detecting the condition within the full diagnostic time window and for female and male patients, for example. Further, combining redundant genes improves the robustness of the algorithm. Since the proposed method uses genes of interest that are highly specific to the disease or condition, combining the genes improves the sensitivity of detection without compromising specificity.

[0186] Identifying a Biomarker

[0187] Conditions such as stroke cause injury to the central nervous system which can trigger various damaging as well as protective mechanisms within the human body [1]. Research has shown that agonists that target certain cell receptors can protect against neuronal damage. Some of these agonists specifically bind to the Adenosine A3 Receptor (associated with the gene identified with GeneCards Symbol: AD0RA3) [2,2,4]. The level of expression of these receptors can vary in the human body based on physiological conditions and may have baseline levels of expression that differ in different individuals or groups of individuals.

[0188] The current example characterizes variations in gene expression for AD0RA3 measured in blood samples collected from patients who enter the emergency room with symptoms that raise suspicion for stroke. These symptoms typically trigger an alert to mobilize neurology personnel to properly assess the patient and confirm if the patient is experiencing a stroke event. Ischemic stroke specifically is caused by disruption of the blood supply to the brain because of some blockage that affects the vessels that carry blood to the brain tissue. Lacking oxygen and nutrients, brain cells begin to experience stress and are susceptible to damage that can lead to cell death.

[0189] Gene expression measured in blood can act as a surrogate for the level of expression of various receptors on cell surfaces that may respond to treatments designed to trigger protective mechanisms or stop damaging mechanisms within the cell. To this end, measuring the level of expression before, during or after such treatment can be useful to help diagnose the patient with respect to requiring the treatment, determining the level of treatment necessary and monitoring the effect of the treatment.

[0190] The gene expression level for AD0RA3 was specifically found to be different for patients who were confirmed to have a stroke versus patients with other conditions that have similar symptoms when measured immediately after a code stroke alert (Figure 1). Further, the level of gene expression changed significantly for patients who experienced a stroke in a subsequent blood draw obtained 24 hours after the onset of their symptoms. This change was not observed in patients later confirmed to have other conditions. This supports the fact that the expression of AD0RA3 changed in the hyperacute stage in patients with stroke (Figure 1 and Table 2).

[0191] The AD0RA3 expression patterns were also found to vary by age for male versus female patients and for different racial groups (Figure 2) showing that changes in expression of the receptor explain some of the known differences in the risk for stroke as well as the outcome for the disease among racial and demographic groups. Understanding these differences and providing means to measure the expression level of AD0RA3 for each patient can lead to more informed and more precise treatment for each patient.

[0192] Additional Genes of Interest

[0193] The Biomarker identified above is one example of genes found to have an expression level that differs in stroke patients, but not in non-stroke patients. Other examples identified are shown in Table 3.

[0194] Example Computer-Implemented Method

[0195] Figure 8 is a flowchart diagram of an example computer-implemented method 800 for training an algorithm for diagnosing a subject suspected of having a neurovascular disease that can lead to determining a disease state, prognosis, and / or treatment. In some implementations, the method 800 can be performed by a processing circuitry (for example, but not limited to, an application-specific integrated circuit (ASIC), or a central processing unit (CPU)). In some examples, the processing circuitry is electrically coupled to and / or in electronic communication with other circuitries of an example computing device, such as, but not limited to, the example computing device 900 described in connection with Figure 9. In some examples, embodiments may take the form of a computer program product on a non-transitory computer-readable storage medium storing computer-readable program instruction (e.g., computer software). Any suitable computer-readable storage medium may be utilized, including non-transitory hard disks, CD- ROMs, flash memory, optical storage devices, or magnetic storage devices. This disclosure contemplates that the example operations can be performed using one or more computing devices (e.g., at least the basic configuration illustrated in Figure 9 by box 902).

[0196] Referring to Figure 8, at step / operation 802, the method 800 includes obtaining a set of reference biological samples (e.g., one or more data objects describing a set of reference biological samples) from one or more reference subjects (e.g., human subjects) known to have the neurovascular disease. The set of reference biological samples can comprise blood samples. In some implementations, the set of reference biological samples are taken from the one or more reference subjects about 24 hours or more than 24 hours after onset of neurovascular disease symptoms. Additionally, and / or alternatively, the set of reference biological samples are taken from one or more reference subjects that are not experiencing neurovascular disease symptoms.

[0197] At step / operation 804, the method 800 includes quantifying an expression level of one or more biomarkers in each reference biological sample to create a set of reference gene expression levels. In some implementations, the biomarker comprises one or more of ADORA3, ADORA2B, ADORA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1 , LAIR1 , B3GALT6, IRAKI , ANKRD17, SLC2A8, CBLB, or WDR61. In some implementations, the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase-Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

[0198] At step / operation 806, the method 800 includes creating a first training set comprising the set of reference gene expression levels. This disclosure contemplates that multiple training data sets may be generated.

[0199] At step / operation 810, the method 800 includes training an algorithm (e.g., neural network model, a supervised, unsupervised, or semi-supervised machine learning model) using the first training set. Step / operation 810 can include defining an expression level threshold for each of the one or more biomarkers and defining a number of biomarkers which must meet their expression level threshold (e.g., a target expression level threshold) for the subject to be diagnosed with the neurovascular disease.

[0200] At step / operation 812, the method 800 includes determining a disease state (e.g., a presence, non-presence, progression of neurovascular disease, or determining that the subject is suffering from the neurovascular disease) and / or treatment for the subject using the trained algorithm. An example neurovascular disease can be stroke or traumatic brain injury.

[0201] Optionally, in some implementations, the method 800 further includes generating a report describing the disease state, diagnosis, prognosis, and / or treatment for the subject. Optionally, the report is integrated into the subject’s electronic health record (EHR). Alternatively or additionally, the method optionally further includes generating display data for the report. Alternatively or additionally, the method optionally further includes transmitting the report over a network. This disclosure contemplates that operations related to generation of the report can be performed using one or more computing devices (e.g., at least the basic configuration illustrated in Figure 9 by box 902). Alternatively or additionally, the method 800 optionally further includes recommending a treatment for the subject. Treatment approaches can vary depending on the disease state and relevant patient factors. This disclosure contemplates that the operations related to providing diagnosis, prognosis, and / or treatment options can be performed using one or more computing devices (e.g., at least the basic configuration illustrated in Figure 9 by box 902).

[0202] Computing Devices and Methods of Use

[0203] It should be appreciated that the logical operations described herein with respect to the various figures may be implemented (1) as a sequence of computer-implemented acts or program modules (i.e., software) running on a computing device (e.g., the computing device described in Figure 9), (2) as interconnected machine logic circuits or circuit modules (i.e., hardware) within the computing device and / or (3) a combination of software and hardware of the computing device. Thus, the logical operations discussed herein are not limited to any specific combination of hardware and software. The implementation is a matter of choice dependent on the performance and other requirements of the computing device. Accordingly, the logical operations described herein are referred to variously as operations, structural devices, acts, or modules. These operations, structural devices, acts and modules may be implemented in software, in firmware, in special-purpose digital logic, and any combination thereof. It should also be appreciated that more or fewer operations may be performed than shown in the figures and as described herein. These operations may also be performed in a different order than those described herein.

[0204] Referring to Figure 9, an example computing device 900 upon which embodiments of the present disclosure may be implemented is illustrated. It should be understood that the example computing device 900 is only one example of a suitable computing environment upon which embodiments of the present disclosure may be implemented. Optionally, the computing device 900 can be a well-known computing system including, but not limited to, personal computers, servers, handheld or laptop devices, multiprocessor systems, microprocessor-based systems, personal network computers (PCs), mini-computers, mainframe computers, embedded systems, and / or distributed computing environments including a plurality of any of the above systems or devices. Distributed computing environments enable remote computing devices, which are connected to a communication network or other data transmission medium, to perform various tasks. In the distributed computing environment, the program modules, applications, and other data may be stored on local and / or remote computer storage media.

[0205] In its most basic configuration, the computing device 900 typically includes at least one processing unit 906 and system memory 904. Depending on the exact configuration and type of computing device, system memory 904 may be volatile (such as random-access memory (RAM)), non-volatile (such as read-only memory (ROM), flash memory, etc.), or some combination of the two. This most basic configuration is illustrated in Figure 9 by the dashed line 902. The processing unit 906 may be a standard programmable processor that performs arithmetic and logic operations necessary for the operation of the computing device 900. The computing device 900 may also include a bus or other communication mechanism for communicating information among various components of the computing device 900.

[0206] Computing device 900 may have additional features / functionality. For example, the computing device 900 may include additional storage such as removable storage 908 and nonremovable storage 910 including, but not limited to, magnetic or optical disks or tapes. Computing device 900 may also contain network connection(s) 916 that allow the device to communicate with other devices. Computing device 900 may also have input device(s) 914 such as a keyboard, mouse, touch screen, etc. Output device(s) 912, such as a display, speakers, printer, etc., may also be included. The additional devices may be connected to the bus in order to facilitate communication of data among the components of the computing device 900. All these devices are well-known in the art and need not be discussed at length here.

[0207] The processing unit 906 may be configured to execute program code encoded in tangible, computer-readable media. Tangible, computer-readable media refers to any media that is capable of providing data that causes the computing device 900 (i.e., a machine) to operate in a particular fashion. Various computer-readable media may be utilized to provide instructions to the processing unit 906 for execution. Example of tangible, computer-readable media may include but is not limited to, volatile media, non-volatile media, removable media and non-removable media implemented in any method or technology for storage of information such as computer-readable instructions, data structures, program modules or other data. System memory 904, removable storage 908, and non-removable storage 910 are all examples of tangible computer storage media. Examples of tangible, computer-readable recording media include but are not limited to, an integrated circuit (e.g., field-programmable gate array or application-specific IC), a hard disk, an optical disk, a magneto-optical disk, a floppy disk, a magnetic tape, a holographic storage medium, a solid-state device, RAM, ROM, electrically erasable program read-only memory (EEPROM), flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices.

[0208] In an example implementation, the processing unit 906 may execute program code stored in the system memory 904. For example, the bus may carry data to the system memory 904, from which the processing unit 906 receives and executes instructions. The data received by the system memory 904 may optionally be stored on the removable storage 908 or the non-removable storage 910 before or after execution by the processing unit 906.

[0209] It should be understood that the various techniques described herein may be implemented in connection with hardware or software or, where appropriate, with a combination thereof. Thus, the methods and apparatuses of the presently disclosed subject matter, or certain aspects or portions thereof, may take the form of program code (i.e., instructions) embodied in tangible media, such as floppy diskettes, CD-ROMs, hard drives, or any other machine-readable storage medium wherein, when the program code is loaded into and executed by a machine, such as a computing device, the machine becomes an apparatus for practicing the presently disclosed subject matter. In the case of program code execution on programmable computers, the computing device generally includes a processor, a storage medium readable by the processor (including volatile and nonvolatile memory and / or storage elements), at least one input device, and at least one output device. One or more programs may implement or utilize the processes described in connection with the presently disclosed subject matter, for example, through the use of an application programming interface (API), reusable controls, or the like. Such programs may be implemented in a high-level procedural or object-oriented programming language to communicate with a computer system. However, the program(s) can be implemented in assembly or machine language if desired. In any case, the language may be a compiled or interpreted language, and it may be combined with hardware implementations.

[0210] In one embodiment, disclosed herein is a non-transitory computer-readable storage medium comprising instructions that, when executed, cause at least one processor to perform the method of any preceding embodiments.

[0211] Although the subject matter has been described in language specific to structural features and / or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims. Artificial Intelligence and Machine Learning

[0212] The term “artificial intelligence” is defined herein to include any technique that enables one or more computing devices or comping systems (i.e., a machine) to mimic human intelligence. Artificial intelligence (Al) includes, but is not limited to, knowledge bases, machine learning, representation learning, and deep learning. The term “machine learning” is defined herein to be a subset of Al that enables a machine to acquire knowledge by extracting patterns from raw data. Machine learning techniques include, but are not limited to, logistic regression, support vector machines (SVMs), decision trees, Naive Bayes classifiers, and artificial neural networks. The term “representation learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, or classification from raw data. Representation learning techniques include, but are not limited to, autoencoders. The term “deep learning” is defined herein to be a subset of machine learning that enables a machine to automatically discover representations needed for feature detection, prediction, classification, etc. using layers of processing. Deep learning techniques include, but are not limited to, artificial neural network or multilayer perceptron (MLP).

[0213] Machine learning models include supervised, semi-supervised, and unsupervised learning models. In a supervised learning model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or targets) during training with a labeled data set (or dataset). In an unsupervised learning model, the model learns patterns (e.g., structure, distribution, etc.) within an unlabeled data set. In a semi- supervised model, the model learns a function that maps an input (also known as feature or features) to an output (also known as target or target) during training with both labeled and unlabeled data.

[0214] Artificial Neural Networks: An artificial neural network (ANN) is a computing system including a plurality of interconnected neurons (e.g., also referred to as “nodes”). This disclosure contemplates that the nodes can be implemented using a computing device (e.g., a processing unit and memory as described herein). The nodes can be arranged in a plurality of layers such as input layer, output layer, and optionally one or more hidden layers. An ANN having hidden layers can be referred to as deep neural network or multilayer perceptron (MLP). Each node is connected to one or more other nodes in the ANN. For example, each layer is made of a plurality of nodes, where each node is connected to all nodes in the previous layer. The nodes in a given layer are not interconnected with one another, i.e., the nodes in a given layer function independently of one another. As used herein, nodes in the input layer receive data from outside of the ANN, nodes in the hidden layer(s) modify the data between the input and output layers, and nodes in the output layer provide the results. Each node is configured to receive an input, implement an activation function (e.g., bin ar}' step, linear, sigmoid, tanH, or rectified linear unit (ReLU) function), and provide an output in accordance with the activation function. Additionally, each node is associated with a respective weight. ANNs are trained with a dataset to maximize or minimize an objective function. In some implementations, the objective function is a cost function, which is a measure of the ANN’S performance (e.g., error such as LI or L2 loss) during training, and the training algorithm tunes the node weights and / or bias to minimize the cost function. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used for training the ANN. Training algorithms for ANNs include, but are not limited to, backpropagation. It should be understood that an artificial neural network is provided only as an example machine learning model. This disclosure contemplates that the machine learning model can be any supervised learning model, semi-supervised learning model, or unsupervised learning model. Optionally, the machine learning model is a deep learning model. Machine learning models are known in the art and are therefore not described in further detail herein.

[0215] A convolutional neural network (CNN) is a type of deep neural network that has been applied, for example, to image analysis applications. Unlike a traditional neural networks, each layer in a CNN has a plurality of nodes arranged in three dimensions (width, height, depth). CNNs can include different types of layers, e.g., convolutional, pooling, and fully-connected (also referred to herein as “dense”) layers. A convolutional layer includes a set of filters and performs the bulk of the computations. A pooling layer is optionally inserted between convolutional layers to reduce the computational power and / or control overfitting (e.g., by downsampling). A fully- connected layer includes neurons, where each neuron is connected to all of the neurons in the previous layer. The layers are stacked similar to traditional neural networks.

[0216] Logistic Regression: A logistic regression (LR) classifier is a supervised classification model that uses the logistic function to predict the probability of a target, which can be used for classification. LR classifiers are trained with a data set (also referred to herein as a “dataset”) to maximize or minimize an objective function, for example a measure of the LR classifier’s performance (e.g., error such as LI or L2 loss), during training. This disclosure contemplates that any algorithm that finds the maximum or minimum of the objective function can be used. LR classifiers are known in the art and are therefore not described in further detail herein.

[0217] Naive Bayes: An Naive Bayes’ (NB) classifier is a supervised classification model that is based on Bayes’ Theorem, which assumes independence among features (i.e., presence of one feature in a class is unrelated to presence of any other features). NB classifiers are trained with a data set by computing the conditional probability distribution of each feature given label and applying Bayes’ Theorem to compute conditional probability distribution of a label given an observation. NB classifiers are known in the art and are therefore not described in further detail herein.

[0218] KNN: A k-NN classifier is a supervised classification model that classifies new data points based on similarity measures (e.g., distance functions). k-NN classifier is a non-parametric algorithm, i.e., it does not make strong assumptions about the function mapping input to output and therefore has flexibility to find a function that best fits the data. k-NN classifiers are trained with a data set (also referred to herein as a “dataset”) by learning associations between all samples and classification labels in the training dataset. k-NN classifiers are known in the art and are therefore not described in further detail herein.

[0219] Clustering model: A clustering model is a type of machine learning model that is used for unsupervised learning tasks. Clustering is the process of grouping similar data points together based on certain features or characteristics, without any predefined labels. The goal is to identify inherent patterns or structures in the data. In a clustering model, the algorithm aims to partition a dataset into groups or clusters, where data points within the same cluster are more similar to each other than they are to points in other clusters. Clustering is often used for tasks such as segmentation and anomaly detection. Examples of clustering algorithms include the K-means algorithm, hierarchical clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), Mean Shift, Spectral Clustering, and Agglomerative Clustering.

[0220] K-means algorithm: The K-means algorithm is an unsupervised machine learning model that is used for iterative clustering. The K-means algorithm minimizes the sum of squared distances between data points and their respective cluster centroids. It can be used to partition a dataset into k non-overlapping, distinct subsets or clusters, where each data point belongs to a cluster with the nearest mean or centroid.

[0221] Trained Algorithm

[0222] Gene expression is measured for the disease condition specific genes of interest by obtaining samples from patients suspected of having the disease and subsequently confirmed to either have or not have the disease condition with high confidence. Training of the algorithm consists of identifying a range of expression levels, measured using qPCR or any fast method intended for use to measure gene expression level. The range is expression level is defined by one or more thresholds above or below which the gene expression level occurs only, or mostly, in patients with the disease condition. The threshold may be a single threshold, or it may be s varying threshold based on the level of expression measured for a control gene measured in the sample. Figure 3 shows an example of a threshold line for a single gene of interest (in this example the Ct value represents a measure of expression level that resulted from fast measurement of expression levels for the gene of interest plotted against a control gene that does not change based on the disease condition).

[0223] Training the algorithm consists of defining thresholds for each gene of interest and defining the number of genes for which the threshold must be met in a sample for the patient to be considered as having the condition. In some cases, the threshold must be met for one or more genes. In some cases, the genes may carry different weights and the number of genes that must meet the threshold may differ based on the mix of genes that meet the threshold.

[0224] Example 2. ADORA3 Agonists and Adenosine Receptor Agonists and Antagonists

[0225] Specific AD0RA3 agonists include:

[0226] AST-004, which binding data indicated was an adenosine A3 receptor (A3R) agonist [6]. Adenosine A3 receptor (A3R) agonist AST-004 was synthesized, in part, at the National Institute of Health (Bethesda, MD), as previously reported [7] and in part, was a gift of Astrocyte Pharmaceuticals, Inc. Unbound brain concentrations of AST-004 were determined following dosing using a specific LC / MS / MS assay [7]. Maximum unbound brain concentrations were measured at 5 ng / gm, demonstrating that AST-004 was distributed in the brain and available to interact with A3R.

[0227] Additional compounds with a measure of their affinity to various adenosine receptors are listed in Table 1 (Table adapted from Reference 4 (below)).

[0228] Example 3. Gene Expression Analysis in Stroke.

[0229] There is a long history of searching for biomarkers in stroke, such as the protein marker: SIOOB, GFAP, NSE, MMP-9, NR2A / 2B aAbs, NR2, Apo C-III, Apo C-I, Apo B, Apo A-I, Apo B / Apo A-I, PARK 7, NDKA, Glycogen phosphorylase isoenzyme BB, c-Fn, PAI-1, and TAFI, Glutamate, GABA, GST-71, NIL, and any combinations thereof; and gene expression markers comprising a panel of 22 genes, a panel of 18 genes, a panel of 23 genes, a panel of 40 genes, a panel of 34 genes, a panel of 26 genes, and a panel of 41 genes. (Donnan, et al. “Acute Stroke Biomarkers: Are We There Yet?”, Front Neurol. 2021.)

[0230] The present challenges with identifying stroke biomarkers include, but are not limited to disease complexities and population complexities, biomarkers being dependent on time from onset of symptoms, identification protein markers being sensitive, but not specific, wide range of sample timing occurring at a later time, most biomarkers often compared to healthy controls, instead of mimic controls, often leading to inconsistent results or lack of specificity for stroke in clinical test conditions. Said challenges and limitations of searching for biomarkers in stroke has led to the identification needs, such as for example: 1) understanding ischemic stroke (IS) vs. intra-cerebral hemorrhage (ICH); 2) stroke and mimic, stroke / healthy control, stroke / mimic / control; 3) hemorrhagic transformation and risk of malignant edema; 4) outcomes after recombinant tissue plasminogen activator (rtPA) treatment; 5) risk of early neurological deterioration; 6) stroke etiologies (i.e., cardio-embolic / large vessel strokes); 7) transient ischemic attack (TIA) / cardiovascular disease (CVD), ischemic stroke (IS) / TIA / control; 8) lacunar stroke vs. non- lacunar stroke; and 9) early stroke vs. late stroke, that need to be addressed.

[0231] An effective method for performing a systematic search of biomarkers includes whole genome gene expression, wherein such experimental designs; including, but not limited to comparison groups, time since the stroke event, type and source of RNA sample, and technology used to measure expression, are critical for identifying meaningful biomarkers. Herein, the present disclosure uses rich data to identify stroke signatures. Three blood draws were taken within 24 hours of stroke code alerts presenting within 12 hours of symptom onset. Initial blood draw was done on arrival. Extensive clinical information (such as diagnostic, demographic, and outcome data) was taken from patients. Then, mRNA was extracted from all the samples and sequenced to create a database of gene expression values.

[0232] Results

[0233] RNA-seq Identifies Gene Signatures

[0234] The present disclosure identified stroke-specific genes from two sequencing studies with overlapping samples spanning three time points for each patient, including an early pre-treatment time point; comparisons to mimics and healthy controls; analysis by relevant clinical and technical variables; and investigation of known gene functions to determine stroke versus mimic classification. Panels of candidate gene were combined in an algorithm, and differentiation between stroke and stroke mimics was observed with high accuracy (Table 4, and Figures 4 & 5). Thus, the present disclosure presents a significant advantage over single clinical scale studies. For example, the gene-based signatures, herein, are more informative, and have higher sensitivity and specificity than a standard clinical assessment scale. The present disclosure also demonstrates a gene expression pattern for target genes of interest (Figure 6). Lastly, using the methods disclosed herein, the present disclosure presents that an AD0RA3 receptor has been identified, and is targeted in therapeutic applications.

[0235] References 1- Shehjar, Faheem et al. “Stroke: Molecular mechanisms and therapies: Update on recent developments.” Neurochemistry international vol. 162 (2023)

[0236] 2- Zheng J, Wang R, Zambraski E, Wu D, Jacobson KA, Liang BT. Protective roles of adenosine Al, A2A, and A3 receptors in skeletal muscle ischemia and reperfusion injury. Am J Physiol Heart Circ Physiol. 2007 Dec;293(6)

[0237] 3- Effendi WI, Nagano T, Kobayashi K, Nishimura Y. Focusing on Adenosine Receptors as a Potential Targeted Therapy in Human Diseases. Cells. 2020 Mar 24;9(3)

[0238] 4- Jacobson KA, Tosh DK, Jain S and Gao Z-G Historical and Current Adenosine Receptor Agonists in Preclinical and Clinical Development. Front. Cell. Neurosci. 13:124, (2019)

[0239] 5- Monik C. Jimenez, et al., Racial Variation in Stroke Risk Among Women by Stroke Risk Factors, Stroke 2019-04-01 50(4): 797-804.

[0240] 6- Bozdemir E, Vigil FA, Chun SH, Espinoza L, Bugay V, Khoury SM, Holstein DM, Stoja A, Lozano D, Tunca C, Sprague SM, Cavazos JE, Brenner R, Liston TE, Shapiro MS, Lechleiter JD. Neuroprotective Roles of the Adenosine As Receptor Agonist AST-004 in Mouse Model of Traumatic Brain Injury. Neurotherapeutics. 2021 Oct;18(4):27()7-2721

[0241] 7- Theodore E. Liston, PhD, Aldric Hama, PhD, Johannes Boitze, MD, PhD, Russell B. Poe, PhD, Takahiro Natsume, PhD, Ikuo Hayashi, PhD, Hiroyuki Takamatsu, PhD, William S. Korinek, PhD, James D. Lechleiter PhD, Adenosine A1R / A3R (Adenosine Al and A3 Receptor) Agonist AST-004 Reduces Brain Infarction in a Nonhuman Primate Model of Stroke, Stroke 2022-01-01 53(1): 238-248

[0242] Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of skill in the art to which the disclosed invention belongs. Publications cited herein and the materials for which they are cited are specifically incorporated by reference.

[0243] Those skilled in the art will appreciate that numerous changes and modifications can be made to the preferred embodiments of the invention and that such changes and modifications can be made without departing from the spirit of the invention. It is, therefore, intended that the appended claims cover all such equivalent variations as fall within the true spirit and scope of the invention.

[0244] TABLES

[0245] Table 1. Compounds with a measure of their affinity to various adenosine receptors

[0246] Table 2. Differential expression analysis shows highly significant difference in gene expression between the first and third draws from the same patients in the group of interest but almost no change in expression between the first and third draw for the mimic group.

[0247] Table 3. Additional genes of interest.

[0248] Table 4.

[0249] Acc = Accuracy; AUC = Area Under the ROC Curve

[0250] Table 4 (Continued)

[0251] Table 5. Gene Expression Pattern for Target of Interest

Claims

CLAIMSWe claim:

1. A method for treating a subject with a neurovascular disease, comprising: obtaining a first biological sample from a first subject having the neurovascular disease immediately after onset of disease symptoms; obtaining a second biological sample from the first subject having the neurovascular disease about 12 hours or later after onset of disease symptoms; quantifying an expression level of one or more genes from the first biological sample and the second biological sample; comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the neurovascular disease; comparing the expression level of the one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the neurovascular disease; wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample; determining a differentially expressed gene from the subject having the neurovascular disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the neurovascular disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample; and administering to the subject a therapeutically effective amount of a specific therapeutic agent for treatment of the neurovascular disease.

2. A method for determining a differentially expressed gene in a subject with a neurovascular disease, comprising: obtaining a first biological sample from a first subject having the neurovascular disease immediately after onset of disease symptoms; obtaining a second biological sample from the first subject having the neurovascular disease about 12 hours or later after onset of disease symptoms; quantifying an expression level of one or more genes from the first biological sample and the second biological sample;comparing the expression level of the one or more genes from the first biological sample to the expression level of the one or more genes from a third biological sample from a subject without the neurovascular disease; comparing the expression level of one or more genes from the second biological sample to the expression level of the one or more genes from a fourth biological sample from the subject without the neurovascular disease; wherein the fourth biological sample is obtained about 12 hours or later after the third biological sample; determining a differentially expressed gene from the subject having the neurovascular disease wherein the expression level of the one or more genes from the first biological sample or the second biological sample is changed in the subject having the neurovascular disease in comparison to the expression level of the one or more genes in the third biological sample and the fourth biological sample.

3. The method of claim 1 or 2, wherein the neurovascular disease is a stroke.

4. The method of claim 1 or 2, wherein the neurovascular disease is a traumatic brain injury (TBI).

5. The method of any one of claims 1-4, wherein the differentially expressed gene is AD0RA3.

6. The method of any one of claims 1-5, wherein the second biological sample is obtained about 24 hours or later after onset of disease symptoms.

7. The method of any one of claims 1-6, wherein the subject without the neurovascular disease comprises a subject suffering from a mimic disease.

8. A method for treating a subject suspected of having a stroke, comprising: obtaining a biological sample from the subject suspected of having a stroke immediately after onset of disease symptoms; quantifying an expression level of a differentially expressed gene; wherein the differentially expressed gene is identified according to the method of any one of claimscomparing the expression level of the differentially expressed gene in the biological sample to the expression level of the differentially expressed gene in a control sample from a subject not having the disease or a control sample from a subject suffering from a mimic disease; and administering to the subject a therapeutically effective amount of a therapeutic agent for treatment of the stroke.

9. The method of claim 8, wherein the therapeutic agent for treatment of the stroke comprises an anliplatelei agent or an anticoagulant agent.

10. The method of claim 8 or 9, wherein the differentially expressed gene comprises ADORA3.

11. The method of any one of claims 8-10, wherein the therapeutic agent for treatment of the stroke comprises an agonist or antagonist of ADORA3.

12. The method of claim 1 1 , wherein the agonist is AST-004.

13. The method of any one of claims 1-12, wherein the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase-Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

14. The method of any one of claims 1-13, wherein the therapeutic agent for treatment is a gene therapy compound.

15. A method of treating a subject with a neurovascular disease, comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms; quantifying an expression level of one or more biomarkers, wherein the biomarker comprises one or more of ADORA3, ADORA2B, ADORA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control; andadministering to the subject a therapeutically effective amount of a therapeutic agent for the treatment of the neurovascular disease.

16. A method for diagnosing a subject with a neurovascular disease, comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms; quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; and determining the subject as suffering from the neurovascular disease if the expression level of the one or more biomarkers is changed in the subject immediately after onset of neurovascular disease symptoms in comparison to a reference control.

17. The method of claim 15 or 16, wherein the biomarker comprises AD0RA3.

18. The method of claim 15 or 16, wherein the biomarker comprises AD0RA2B.

19. The method of claim 15 or 16, wherein the biomarker comprises AD0RA2A.

20. The method of claim 15 or 16, wherein the biomarker comprises ADRB2.

21. The method of any one of claims 15-20, wherein the biological sample comprises a blood sample.

22. The method of any one of claims 15-21, wherein the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-Polymerase Chain Reaction, Real Time Reverse Transcriptase-Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

23. The method of any one of claims 15-22, wherein the reference control is a sample from the subject taken about 24 hours after onset of neurovascular disease symptoms.

24. The method of any one of claims 15-23, wherein the reference control is a sample from the subject taken greater than 24 hours after onset of neurovascular disease symptoms.

25. The method of any one of claims 15-24, wherein the reference control is a sample from a subject that is not suffering from the neurovascular disease.

26. The method of any one of claims 15-25, wherein the reference control is a sample from the subject that is not experiencing neurovascular disease symptoms.

27. The method of any one of claims 15-26, wherein the therapeutic agent is an agonist or antagonist of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPR0TL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61.

28. The method of any one of claims 15-27, wherein the agonist is an AD0RA3 agonist.

29. The method of any one of claims 15-27, wherein the agonist is an AD0RA2B agonist.

30. The method of any one of claims 15-27, wherein the agonist is an ADORA2A agonist.

31. The method of any one of claims 15-27, wherein the agonist is an ADRB2 agonist.

32. The method of any one of claims 15-27, wherein the agonist is selected from an agonist or antagonist listed in Table 1.

33. The method of any one of claims 15-27, wherein the agonist is AST-004.

34. The method of any one of claims 15-33, wherein the therapeutic agent comprises an antiplatelet agent or an anticoagulant agent.

35. The method of any one of claims 15-34, wherein the neurovascular disease is stroke.

36. The method of any one of claims 15-35, wherein the neurovascular disease is a traumatic brain injury (TBI).

37. The method of any one of claims 15-36, wherein the neurovascular disease is Moyamoya Disease.

38. The method of any one of claims 15-37, wherein the differentially expressed gene is AD0RA3.

39. The method of any one of claims 15-38, wherein the subject without the disease comprises a subject suffering from a mimic disease.

40. A method of treating a neurovascular disease in a subject, the method comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease ; quantifying a first set of genes expressed in the biological sample, wherein the expression level of the first set of genes indicates that the subject has experienced the neurovascular disease with about 100% certainty when compared to a first reference standard; quantifying a second set of genes expressed in the biological sample, wherein the expression level of the second set of genes indicates that the subject has not experienced the neurovascular disease with about 100% certainty when compared to a second reference standard; wherein the first reference standard defines a first set of genes and thresholds where only neurovascular disease patients meet the first thresholds, wherein the second reference standard defines a second set of genes and thresholds where only non-neurovascular disease patients meet the second thresholds; and determining the subject as suffering from the neurovascular disease if the expression level of the first set of genes meets the first reference standard; or determining the subject as not suffering from the neurovascular disease if the expression level of the second set of genes meets the second reference standard; and administering a therapeutic agent, a gene therapy, a thrombolytic, or undergoes a surgical procedure for treatment of the neurovascular disease when the first set of genes have expression that meets the first reference standard.

41. The method of claim 40, further quantifying a third set of genes expressed in the biological sample, wherein the third reference standard defines neurovascular disease and non- neurovascular disease thresholds with less than 100% certainty.

42. The method of any one of claims 1-41, wherein the subject is a human.

43. A computer- implemented method of training an algorithm for diagnosing a subject suspected of having a neurovascular disease, comprising: obtaining a set of reference biological samples from one or more reference subjects known to have the neurovascular disease; quantifying an expression level of one or more biomarkers in each reference biological sample to create a set of reference gene expression levels, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; creating a first training set comprising the set of reference gene expression levels; and training an algorithm using the first training set, wherein training the algorithm comprises: defining an expression level threshold for each of the one or more biomarkers; and defining a number of biomarkers which must meet their expression level threshold for the subject to be diagnosed with the neurovascular disease.The computer-implemented method of claim 43, wherein the biomarker comprisesAD0RA3.

45. The computer-implemented method of claim 43, wherein the biomarker comprisesAD0RA2B.

46. The computer-implemented method of claim 43, wherein the biomarker comprises AD0RA2A.

47. The computer-implemented method of claim 43, wherein the biomarker comprises ADRB2.

48. The computer-implemented method of any one of claims 43-47, wherein the set of reference biological samples comprise blood samples.

49. The computer-implemented method of any one of claims 43-48, wherein the quantifying is carried out by one or a combination of Polymerase Chain Reaction, Real Time-PolymeraseChain Reaction, Real Time Reverse Transcriptase-Polymerase Chain Reaction, Real-time quantitative RT-PCR, Northern blot analysis, in situ hybridization, or probe array.

50. The computer-implemented method of any one of claims 43-49, wherein the set of reference biological samples are taken from the one or more reference subjects about 24 hours after onset of neurovascular disease symptoms.

51. The computer-implemented method of any one of claims 43-50, wherein the set of reference biological samples are taken from the one or more reference subjects greater than 24 hours after onset of neurovascular disease symptoms.

52. The computer-implemented method of any one of claims 43-51, wherein the set of reference biological samples are taken from one or more reference subjects that are not experiencing neurovascular disease symptoms.

53. The computer-implemented method of any one of claims 43-52, wherein the one or more reference subjects are human.

54. The computer-implemented method of any one of claims 43-53 , wherein the neurovascular disease is a stroke.

55. The computer- implemented method of any one of claims 43-54, wherein the neurovascular disease is a traumatic brain injury.

56. The computer-implemented method of any one of claims 43-55, wherein a trained algorithm comprises a machine learning algorithm.

57. The computer-implemented method of claim 56, wherein the trained algorithm comprises a neural network.

58. The computer-implemented method of any one of claims 43-57, further comprising: obtaining a biological sample from a subject suspected of having the neurovascular disease immediately after onset of neurovascular disease symptoms;quantifying an expression level of one or more biomarkers immediately after onset of neurovascular disease symptoms, wherein the biomarker comprises one or more of AD0RA3, AD0RA2B, AD0RA2A, ADRB2, IGFBP3, SERPINF1, PIN1, CTNNB1, MAPK14, CIAPIN1, LEPROTL1, FDFT1, LAIR1, B3GALT6, IRAKI, ANKRD17, SLC2A8, CBLB, or WDR61; and determining the subject as suffering from the neurovascular disease using the trained algorithm.

59. A system or non-transitory computer-readable medium comprising a memory having instructions stored thereon to perform any of the computer- implemented methods of claims 42-58.

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