FIELD

[0001] The present disclosure relates generally to a method and kit for making a clinical assessment, such as an early diagnostic or prognostic assessment, more particularly to methods and kits for identifying a subject at risk of delayed recovery from mild traumatic brain injury, such as concussion.

BACKGROUND OF THE INVENTION

[0002] Mild traumatic brain injury (mTBI) and concussion are growing public health concerns affecting approximately 4 million children globally each year [2]. Persistent post-concussion symptoms (PPCS), or delayed recovery, is experienced by 30% to 50% of paediatric concussion patients, which represents challenging outcome post-injury [1]. Delayed recovery from concussion is characterised by a constellation of emotional, behavioural, cognitive and physical attributes, which can affect the wellbeing of the child, resulting in significant clinical and community burden [3-8] .

[0003] Early diagnosis and monitoring are crucial to optimise outcomes and to ensure targeted follow-up of individuals at risk of delayed recovery. However, the difficulty with predicting and then treating delayed recovery in a clinical setting is threefold: (1) Clinical assessment tools used to diagnose concussion at an acute point of care are not reliable predictors of delayed recovery [9]; (2) Most children with a concussion will only see a health professional at diagnosis, highlighting the importance of the availability of precise tools at this early timepoint; (3) Without an appropriate prognostic tool, clinicians are unable to provide targeted treatment for children who experience delayed recovery, thereby limiting patient recovery.

[0004] Despite such difficulties, there have been very few advancements in this field. Previous studies have focused on blood markers with potential implications for the underlying pathophysiology of concussion. However, the physiological understanding of concussion is limited and it is difficult to determine the extent to which the neurophysiological mechanisms underlying concussion are heterogeneous, and factors that lead to such variation.

[0005] Hence, there remains an urgent need for determining whether a subject has, or is at risk of developing, delayed recovery post-concussion, in particular in children. SUMMARY OF THE INVENTION

[0006] The present invention is predicated, at least in part, on the inventors’ surprising finding that subjects at risk of delayed recovery from mild traumatic brain injury (mTBI), such as concussion, have a biomarker profile that distinguishes them from individuals who present without delayed recovery from mTBI. In this regard, the present inventors have identified biomarkers that are differentially expressed in subjects who present with delayed recovery from mTBI when compared to subjects that do not. Based on this finding, a subject's biomarker profile can be advantageously used as a tool to predict the subject's risk of delayed recovery from mTBI.

[0007] Accordingly, in one aspect, the present invention provides a method for determining whether a subject is at risk of delayed recovery from mild traumatic brain injury (mTBI), the method comprising: (1) obtaining a sample biomarker profile of a subject, wherein the sample biomarker profile evaluates at least one biomarker selected from the group consisting of alpha- 1 -antichymotrypsin (Alpha- 1 -ACT), IgG3, Hepatitis Growth Factor Like protein (HGFL), Matrix metalloproteinase 9 (MMP-9), Angiotensin-converting enzyme (ACE) and Selenoprotein P (SEPPI); (2) comparing the sample biomarker profile to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and (3) determining whether the subject is at risk of delayed recovery from mTBI based on the comparison of the sample biomarker profile and the reference biomarker profile.

[0008] Also disclosed herein is a method of correlating a biomarker profile with a positive or negative response to a treatment regimen for mTBI, the method comprising: (1) obtaining a sample biomarker profile from a subject at risk of delayed recovery from mTBI following commencement of the treatment regimen, wherein the biomarker profile evaluates at least one biomarker selected from the group consisting of alpha- 1- antichymotrypsin (Alpha- 1 -ACT), IgG3, Hepatitis Growth Factor Like protein (HGFL), Matrix metalloproteinase 9 (MMP-9), Angiotensin-converting enzyme (ACE) and Selenoprotein P (SEPPI); and (2) correlating the sample biomarker profile from the subject with a positive or negative response to the treatment regimen.

[0009] The present disclosure also extends to a method of stratifying a subject determined to be at risk of delayed recovery from mTBI to a treatment regimen, the method comprising: (1) determining whether a subject is at risk of delayed recovery from mTBI according to a method described herein; and (2) where the subject is determined from step (1) to be at risk of delayed recovery from mTBI, stratifying the subject to a treatment regimen for mTBI.

[0010] In yet another aspect disclosed herein, there is provided a method of treating a subject determined to be at risk of delayed recovery from mTBI, the method comprising (1) determining whether a subject is at risk of delayed recovery from mTBI according to a method described herein; and (2) exposing the subject determined from step (1) to be at risk of delayed recovery from mTBI to a treatment regimen for mTBI.

[0011] In another aspect, there is provided a kit comprising one or more reagents and/or devices when used for performing a method described herein.

[0012] The present disclosure also extends to use of vestibular treatment, ocularmotor treatment, cervical treatment, supported and/or graded return to aerobic exercise delivered by a clinician under supervision of a physiotherapist or psychological therapy (such as cognitive behavioural therapy) delivered by a mental health clinician for treating a subject determined to be at risk of delayed recovery from mTBI according to the methods described herein.

[0013] The reference in this specification to any prior publication (or information derived from it), or to any matter which is known, is not, and should not be taken as an acknowledgement or admission or any form of suggestion that the prior publication (or information derived from it) or known matter forms part of the common general knowledge in the field of endeavour to which this specification relates.

BRIEF DESCRIPTION OF THE DRAWINGS

[0014] Figure 1 shows details of the samples analysed in the discovery study and the validation study and the methodology used to identify blood markers. SWATH-MS: Single Window Acquisition of All Theoretical Mass Spectra. MRM: Multiple Reaction Monitoring. ELISA: Enzyme Linked Immunosorbent Assay.

[0015] Figure 2 shows the outcomes from the Discovery and Validation studies. (A) Discovery results for Al -ACT and validation results for Al -ACT using MRM and ELISA. (B) Discovery results for HGFL and validation results for HGFL using MRM. (C) Discovery results for ACE. (D) Discovery results for IgG3 and validation results for IgG3 using MRM. (E) Discovery results for MMP-9. (F) Discovery results for SEPPI. ACE: angiotensin converting enzyme. Alpha-l-ACT: alpha- 1 -antichymotrypsin. HGFL: hepatocyte growth factor-like protein. IgG3: immunoglobulin heavy chain constant gamma. MMP-9: matrix metalloproteinase 9. SEPPI: Selenoprotein P.

[0016] Figure 3 shows the validation study results (MRM) for plasma concentrations for (A) Al-ACT and (B) HGFL and (C) IgG3 across all patient groups. Alpha-l-ACT: alpha- 1 -antichymotrypsin. HGFL: hepatocyte growth factor-like protein. IgG3: Immunoglobulin heavy chain constant gamma.

[0017] Figure 4 shows the validation study results using ELISA for plasma concentrations of alpha- 1 -ACT in all patient groups. Alpha-l-ACT: alpha- 1- antichymotrypsin.

[0018] Figure 5 shows a Distribution Curve showing the accuracy of using alpha- 1- ACT abundance in detecting Delayed Recovery when bootstrapped (n=1000).

[0019] Figure 6 is a confusion matrix showing the sensitivity and specificity of alpha- 1 -ACT abundance in detecting Delayed Recovery from concussion. (A) Training Set (B) Test Set. Legend: 0 = Non-delayed recovery. 1 = Delayed Recovery.

[0020] Figure 7 shows a Distribution Curve showing the accuracy of alpha- 1 -ACT abundance in detecting Delayed Recovery when bootstrapped (n=1000).

[0021] Figure 8 is a confusion matrix showing the sensitivity and specificity of alpha- 1 -ACT concentration in detecting Delayed Recovery from concussion. (A) Training Set. (B) Test Set. Legend: 0=Non-delayed Recovery. 1= Delayed Recovery.

DETAILED DESCRIPTION OF THE INVENTION

[0022] Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by those of ordinary skill in the art to which the invention belongs. Although any methods and materials similar or equivalent to those described herein can be used in the practice or testing of the present invention, preferred methods and materials are described. For the purposes of the present invention, the following terms are defined below.

[0023] The articles "a" and "an" are used herein to refer to one or to more than one (i.e. to at least one) of the grammatical object of the article. By way of example, "a biomarker" means one biomarker or more than one biomarker, unless otherwise indicated. [0024] Throughout this specification, unless the context requires otherwise, the words "comprise", "comprises" and "comprising" will be understood to imply the inclusion of a stated step or element or group of steps or elements but not the exclusion of any other step or element or group of steps or elements. Thus, use of the term "comprising" and the like indicates that the listed elements are required or mandatory, but that other elements are optional and may or may not be present.

[0025] The present invention is predicated, in part, on the inventors' surprising finding that subjects with, or at risk of, delayed recovery from mild traumatic brain injury (mTBI), such as concussion, have a biomarker profile that distinguishes them from individuals who present with non-delayed recovery from TBI. In this regard, the present inventors have found several biomarkers that are differentially expressed in subjects who present with delayed recovery from mTBI as compared to those that do not. Based on this finding, a subject’s biomarker profile can be used as a tool to predict or determine the subject's risk of delayed recovery from mTBI.

[0026] Accordingly, in one aspect, the present invention provides a method for determining whether a subject is at risk of delayed recovery from mild traumatic brain injury (mTBI), the method comprising: (1) obtaining a sample biomarker profile of a subject, wherein the sample biomarker profile evaluates at least one biomarker selected from the group consisting of alpha- 1 -antichymotrypsin (Alpha- 1 -ACT), IgG3, Hepatitis Growth Factor Like protein (HGFL), Matrix metalloproteinase 9 (MMP-9), Angiotensin-converting enzyme (ACE) and Selenoprotein P (SEPPI); (2) comparing the sample biomarker profile to a reference biomarker profile, wherein the reference biomarker profile evaluates, for an individual biomarker in the sample biomarker profile, a corresponding biomarker; and (3) determining whether the subject is at risk of delayed recovery from mTBI based on the comparison of the sample biomarker profile and the reference biomarker profile.

Mild Traumatic brain injury (mTBI) and delayed recovery from mTBI

[0027] As noted by Kehellaf et al. (J Neurol. 2019; 266(11): 2878-2889), traumatic brain injury (TBI) is typically defined as the disruption in brain function, or other evidence of brain pathology, caused by an external physical force. The yearly incidence of TBI is estimated at 50 million cases worldwide, with higher rates of morbidity and mortality typically seen in low- and middle-income countries. TBI is a heterogeneous condition, reflecting several underlying macroscopic modes of injury (e.g., extrinsic compression from mass lesion, contusion, diffuse axonal injury [DAI]), as well as a range of mechanisms by which neuronal injury can be inflicted (e.g., ‘classical’ ischaemia, apoptosis, mitochondrial dysfunction, cortical spreading depression [CSD], and microvascular thrombosis) in differing proportions with resultant varying clinical courses. The clinical severity of TBI is typically classified according to postresuscitation Glasgow Coma Scale scores into mild (GCS 14-15), moderate (9-13), and severe (3-8).

[0028] Mild traumatic brain injury (mTBI) generally encompasses head injuries where there is intracranial pathology present on CT, whereas concussion may present with no intracranial pathology on CT, in particular in children. Concussion is a growing public health concern affecting approximately 4 million children globally, each year. Persistent post-concussion symptoms (PPCS), or delayed recovery, is experienced by 30% to 50% of paediatric concussion patients, which represents challenging outcome post-injury. Delayed recovery from concussion is characterised by two or more symptoms (not present pre-injury) across a range of domains including emotional, behavioural, cognitive and physical, which can affect the wellbeing of the child, resulting in significant clinical and community burden.

[0029] Early diagnosis and monitoring are crucial to optimise outcomes and to ensure targeted follow-up of those at risk for delayed recovery. However, the difficulty with predicting and then treating delayed recovery in a clinical setting is threefold: (1) Clinical assessment tools used to diagnose concussion at an acute point of care are not reliable predictors of delayed recovery; (2) Most children with a concussion will only see a health professional at diagnosis, highlighting the importance of the availability of precise tools at this early timepoint; and (3) Without an appropriate prognostic tool, clinicians are unable to provide targeted treatment for children who experience delayed recovery, thereby limiting patient recovery.

[0030] According to Hearps et al. (Pediatrics. 2017 Feb;139(2):e20162003; the entire contents of which are incorporated herein by reference), delayed recovery following mTBI, or persisting post-concussion symptoms (PPCS), is defined as two or more post-concussion symptoms experienced for at least two-weeks post-injury, with at least one of those symptoms being more severe than pre-injury baseline. In an embodiment, delayed recovery following mTBI (including concussion) is characterised by one or more of the following symptoms persisting beyond 10-14 days: somatic, cognitive, emotional, and fatigue complaints that include headache, dizziness, insomnia, irritability, difficulty concentrating, mood disturbance, and emotional lability and may also include physical signs such as loss of consciousness or amnesia, behavioural changes and sleep disturbance. The one or more symptoms are currently determined based on the Post-Concussion Symptom Inventory (PCSI) (as discussed in Gioia et al. J Int Neuropsychol Soc. 2008;14(suppl l):204 and Gioia et al. Br J Sports Med. 2009; 43 (suppl 1) :il 3- i22; the entire contents of which are incorporated herein by reference) or the Health and Behavior Inventory (as discussed in Yeates et al. J Head Trauma Rehabil. 1999;14(4):337-350; the entire contents of which are incorporated herein by reference).

[0031] In an embodiment, the mTBI is concussion. In an embodiment, the concussion is associated with the absence of intracranial pathology on computer tomography (CT).

Biomarkers

[0032] The term "biomarker" typically refers to a measurable characteristic that reflects the presence or nature (e.g., severity) of a physiological or neurophysiological state, including an indicator of risk of developing a particular physiological or neurophysiological state. For example, a biomarker may be present in a sample obtained from a subject before the onset of a physiological or neurophysiological state, including a symptom thereof. Thus, the presence of the biomarker in a sample obtained from the subject is likely to be indicative of an increased risk that the subject will develop the physiological or neurophysiological state or symptom thereof. Alternatively, or in addition, the biomarker may be normally present in a sample obtained from an individual before the onset of a physiological or neurophysiological state, including a symptom thereof, but its expression may change (i.e., it is increased, upregulated, overexpressed; or decreased, downregulated, under-expressed) following the onset of a physiological or neurophysiological state, including a symptom thereof. Thus, in this context, a change in the level of expression of the biomarker is indicative of an increased risk that the subject will develop the physiological or neurophysiological state or symptom thereof. Alternatively, or in addition, a change in the level of expression of a biomarker may reflect a change in a particular physiological or neurophysiological state, or symptom thereof, in a subject, thereby allowing the nature (e.g., severity) of the physiological or neurophysiological state, or symptom thereof, to be tracked over a period of time. This approach may be useful in, for example, monitoring a treatment regimen for the purpose of assessing its effectiveness (or otherwise) in a subject. As herein described, reference to the level of expression of a biomarker includes the concentration of the biomarker (e.g., pg/mL, mg/mL, etc), the absolute amount (abundance) of the biomarker (e.g., pg, mg, etc), or a gene expression product thereof (e.g., peptide, pro-peptide, metabolite thereof), as will be described in more detail below. Reference to the expression or level of expression of a biomarker may suitably be expressed by the level of activity of the biomarker. For example, where the biomarker is an enzyme, its expression or level of expression may be determined or measured by the level of activity of the enzyme on a suitable substrate.

[0033] In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker. In an embodiment, the sample biomarker profile evaluates at least one biomarker selected from the group consisting of Alpha- 1 -ACT, IgG3, HGFL, MMP-9, ACE and SEPPI. In another embodiment, the sample biomarker profile evaluates at least one biomarker selected from the group consisting of Alpha- 1 -ACT, IgG3, HGFL and MMP-9. In a further embodiment, the sample biomarker profile evaluates at least one biomarker selected from the group consisting of Alpha- 1 -ACT, IgG3 and HGFL.

[0034] As disclosed elsewhere herein, the present inventors have unexpectedly found that the mean relative abundance and the mean absolute abundance of alpha- 1- ACT in plasma are significantly lower in patients with delayed recovery from mTBI when compared to patients without delayed recovery from mTBI. Thus, in an embodiment, the sample biomarker profile evaluates Alpha- 1 -ACT.

[0035] The present inventors have also unexpectedly found that the mean relative abundance of HGFL in plasma is significantly lower in patients with delayed recovery from mTBI when compared to patients without delayed recovery from mTBI. Thus, in embodiment, the sample biomarker profile evaluates HGFL.

[0036] The present inventors have also unexpectedly found that the mean relative abundance and the mean absolute abundance of IgG3 are significantly lower in in patients with delayed recovery from mTBI when compared to patients without delayed recovery from mTBI. Thus, in an embodiment, the sample biomarker profile evaluates IgG3. [0037] The present inventors have also unexpectedly found that the mean relative abundance of MMP-9 is significantly lower in patients with delayed recovery from mTBI compared to those without delayed recovery from mTBI. Thus, in an embodiment, the sample biomarker profile evaluates MMP-9.

[0038] The present inventors have also unexpectedly found that the mean relative abundance of ACE is significantly higher in patients with delayed recovery from mTBI compared to those without delayed recovery from mTBI. Thus, in an embodiment, the sample biomarker profile evaluates ACE.

[0039] The present inventors also have unexpectedly found that the mean relative abundance of SEPPI is significantly higher in patients with delayed recovery from mTBI compared to those without delayed recovery from mTBI. Thus, in an embodiment, the sample biomarker profile evaluates SEPPI.

[0040] In an embodiment, evaluation of the at least one biomarker comprises determining the abundance of the at least one biomarker. In an embodiment, evaluation of the at least one biomarker comprises determining the concentration of the at least one biomarker. In an embodiment, evaluation of the at least one biomarker comprises determining the concentration of at least one biomarker selected from the group consisting of Alpha-l-ACT, IgG3, HGFL, MMP-9, ACE and SEPPI. In an embodiment, evaluation of Alpha-l-ACT comprises determining the abundance of the at least one biomarker. In an embodiment, evaluation of IgG3 comprises determining the abundance of the at least one biomarker. In an embodiment, evaluation of HGFL comprises determining the abundance of the at least one biomarker. In an embodiment, evaluation of MMP-9 comprises determining the abundance of the at least one biomarker. In an embodiment, evaluation of ACE comprises determining the abundance of the at least one biomarker. In an embodiment, evaluation of SEPPI comprises determining the abundance of the at least one biomarker.

[0041] In some embodiments, the sample biomarker profile evaluates at least two, preferably at least three biomarkers selected from the group consisting of Alpha-l-ACT, IgG3, HGFL, MMP-9, ACE and SEPPI. In another embodiment, the sample biomarker profile evaluates at least two, preferably at least three biomarkers selected from the group consisting of Alpha-l-ACT, IgG3, HGFL and MMP-9. In a further embodiment, the sample biomarker profile evaluates at least two, preferably at least three biomarkers selected from the group consisting of Alpha-l-ACT, IgG3 and HGFL. In a still further embodiment, the sample biomarker profile evaluates Alpha- 1 -ACT and IgG3, Alpha- 1- ACT and HGFL or IgG3 and HGFL.

[0042] As noted elsewhere herein, the present inventors have unexpectedly found that subjects with, or at risk of, delayed recovery from mild traumatic brain injury (mTBI), such as concussion, have a biomarker profile that distinguishes them from individuals who present without delayed recovery from mTBI; in particular, they found that the level of Alpha- 1 -ACT, IgG3, HGFL and MMP-9 in blood of patients with delayed recovery from mTBI was significantly lower when compared to the level of Alpha- 1 -ACT, IgG3, HGFL and MMP-9 in blood of patients without delayed recovery from mTBI. The present inventors also found that the level of ACE and SEPPI in blood of patients with delayed recovery from mTBI was significantly higher when compared to the level of ACE and SEPPI in blood of patients without delayed recovery from mTBI. Thus, evaluation of the at least one biomarker may comprise determining the level of the at least one biomarker in a whole blood sample from the subject. However, it would be understood by persons skilled in the art that the level of the at least one biomarker may be suitably determined (i.e., measured) in a component of whole blood, such as a serum or plasma sample from the subject. Thus, in an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in whole blood, serum or plasma. In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker in plasma or whole blood.

[0043] In an embodiment, evaluation of the at least one biomarker comprises determining the level of the at least one biomarker within about 48 hours (e.g., within 30 minutes, preferably within 1 hour, preferably within 2 hours, preferably within 3 hours, preferably within 4 hours, preferably within 5 hours, preferably within 6 hours, preferably within 7 hours, preferably within 8 hours, preferably within 9 hours, preferably within 10 hours, preferably within 11 hours, preferably within 12 hours, preferably within 13 hours, preferably within 14 hours, preferably within 15 hours, preferably within 16 hours, preferably within 17 hours, preferably within 18 hours, preferably within 19 hours, preferably within 20 hours, preferably within 21 hours, preferably within 22 hours, preferably within 23 hours, preferably within 24 hours, preferably within 25 hours, preferably within 26 hours, preferably within 27 hours, preferably within 28 hours, preferably within 29 hours, preferably within 30 hours, preferably within 31 hours, preferably within 32 hours, preferably within 33 hours, preferably within 34 hours, preferably within 35 hours, preferably within 36 hours, preferably within 37 hours, preferably within 38 hours, preferably within 39 hours, preferably within 40 hours, preferably within 41 hours, preferably within 42 hours, preferably within 43 hours, preferably within 44 hours, preferably within 45 hours, preferably within 46 hours, preferably within 47 hours, or more preferably within 48 hours) after the mTBI.

[0044] As noted above, and elsewhere herein, the level of Alpha- 1-ACT, IgG3, HGFL and MMP-9 in blood of patients with delayed recovery from mTBI was unexpectedly found to be significantly lower when compared to the level of Alpha- 1- ACT, IgG3, HGFL and MMP-9, respectively, in blood of patients without delayed recovery from mTBI. Thus, in an embodiment disclosed herein, the subject is determined to be at risk of delayed recovery from mTBI where the at least one biomarker in the sample biomarker profile of the subject is lower as compared to a corresponding biomarker in a subject without delayed recovery from mTBI.

[0045] As noted above, and elsewhere herein, the level of ACE and SEPPI in blood of patients with delayed recovery from mTBI was unexpectedly found to be significantly higher when compared to the level of ACE and SEPPI in blood of patients without delayed recovery from mTBI. Thus, in an embodiment disclosed herein, the subject is determined to be at risk of delayed recovery from mTBI where the at least one biomarker in the sample biomarker profile of the subject is higher as compared to a corresponding biomarker in a subject without delayed recovery from mTBI.

[0046] Following mTBI, there is an immediate alteration in mental state, typically characterised by physical, cognitive, and / or emotional symptoms, known collectively as post-concussion symptoms (PCS). The nature, severity and recovery paths of PCS can vary dramatically across individuals and can be divided into two stages: (1) "nondelayed recovery" (where PCS typically resolves in 0-4 weeks, in particular in children) and (2) "persisting" (where PCS persists beyond 4 weeks post-mTBI). Each stage requires different management. For instance, for "non-delayed recovery from mTBI", the focus is on transient PCS and prevention of secondary symptoms, whereas for "persisting" PSC (or delayed recovery from mTBI), stage symptom-targeted treatments are needed. [0047] In an embodiment, the mTBI is concussion and non-delayed recovery from mTBI is determined on the basis of the absence of a post-concussion symptom (PCS) by about 1 month following the concussion.

[0048] The term "reference biomarker" is used herein to denote a biomarker that has been identified as being associated with, or associated with a risk of, delayed recovery from mTBI; particularly an increased risk of delayed recovery from mTBI. For example, a reference biomarker can have a different abundance for a sample population of reference individuals at risk of delayed recovery from mTBI as compared to individuals who display non-delayed recovery from mTBI healthy controls. A reference biomarker profile provides a compositional analysis (e.g., concentration, number ratio or mole percentage (%) of the biomarker) in which one or more, two or more, three or more, four or more, five or more, six or more, seven or more, eight or more, nine or more, ten or more, twelve or more, fifteen or more, twenty or more, fifty or more, one- hundred or more or a greater number of biomarkers are evaluated.

[0049] In some embodiments, it may be desirable to measure the at least one biomarker at the protein level. As an illustrative example, the at least one biomarker is Alpha- 1 -ACT protein, as measured, for example, in a blood, plasma or serum sample of a subject. However, it will be understood that, in some instances, the biomarker can be a gene expression product, such as a transcript (e.g., mRNA). Methods of measuring expression products such as proteins and transcripts are known to persons skilled in the art, with some illustrative examples described below.

[0050] Thus, a biomarker can be a gene expression product, including a polynucleotide or polypeptide. The term "gene" as used herein refers to any and all discrete coding regions of the cell’s genome, as well as associated non-coding and regulatory regions. The term "gene" is also intended to mean the open reading frame encoding specific polypeptides, introns, and adjacent 5' and 3' non-coding nucleotide sequences involved in the regulation of expression. In this regard, the gene may further comprise control signals such as promoters, enhancers, termination and/or polyadenylation signals that are naturally associated with a given gene, or heterologous control signals. The DNA sequences may be cDNA or genomic DNA or a fragment thereof. The gene may be introduced into an appropriate vector for extrachromosomal maintenance or for integration into the host. [0051] The term "nucleic acid" or "polynucleotide" as used herein designates mRNA, RNA, cRNA, cDNA or DNA. The term typically refers to a polymeric form of nucleotides of at least 10 bases in length, either ribonucleotides or deoxynucleotides or a modified form of either type of nucleotide. The term includes single and double stranded forms of DNA or RNA. "Protein," "polypeptide" and "peptide" are also used interchangeably herein to refer to a polymer of amino acid residues and to variants and synthetic analogues of the same.

[0052] In some embodiments, evaluation of the biomarker comprises determining the level of the at least one biomarker. As used herein the terms "level" and "amount" are used interchangeably herein to refer to a quantitative amount e.g., weight or moles or number), a semi-quantitative amount, a relative amount (e.g., weight % or mole % within class or a ratio), a concentration, and the like. Thus, these terms encompasses absolute or relative amounts or concentrations of biomarkers in a sample, including ratios of levels of biomarkers, and odds ratios of levels or ratios of odds ratios. Biomarker levels in cohorts of subjects may be represented as mean levels and standard deviations as shown in some of the Tables and Figures herein.

[0053] Biomarkers may be quantified or detected using any suitable technique, including, but not limited to, nucleic acid- and protein-based assays. In illustrative nucleic acid-based assays, nucleic acid is isolated from cells contained in a biological sample according to standard methodologies (Sambrook, et al., 1989, supra-, and Ausubel et al., 1994, supra). The nucleic acid is typically fractionated (e.g., poly A ⁺ RNA) or whole cell RNA. Where RNA is used as the subject of detection, it may be desired to convert the RNA to a complementary DNA. In some embodiments, the nucleic acid is amplified by a template-dependent nucleic acid amplification technique. A number of template dependent processes are available to amplify the biomarker sequences present in a given template sample. An exemplary nucleic acid amplification technique is the polymerase chain reaction (referred to as PCR), which is described in detail in U.S. Pat. Nos. 4,683,195, 4,683,202 and 4,800,159, Ausubel et al. (supra), and in Innis et al., ("PCR Protocols", Academic Press, Inc., San Diego Calif., 1990). Briefly, in PCR, two primer sequences are prepared that are complementary to regions on opposite complementary strands of the biomarker sequence. An excess of deoxynucleotide triphosphates are added to a reaction mixture along with a DNA polymerase, e.g., Taq polymerase. If a cognate biomarker sequence is present in a sample, the primers will bind to the biomarker and the polymerase will cause the primers to be extended along the biomarker sequence by adding on nucleotides. By raising and lowering the temperature of the reaction mixture, the extended primers will dissociate from the biomarker to form reaction products, excess primers will bind to the biomarker and to the reaction products and the process is repeated. A reverse transcriptase PCR amplification procedure may be performed in order to quantify the amount of mRNA amplified. Methods of reverse transcribing RNA into cDNA are well known and described in Sambrook et al., 1989, supra. Alternative methods for reverse transcription utilize thermostable, RNA-dependent DNA polymerases. These methods are described in WO 90/07641. Polymerase chain reaction methodologies are well known in the art.

[0054] In certain embodiments, the template-dependent amplification involves quantification of transcripts in real-time. For example, RNA or DNA may be quantified using the Real-Time PCR technique (Higuchi, 1992, et al., Biotechnology 10: 413-417). By determining the concentration of the amplified products of the target DNA in PCR reactions that have completed the same number of cycles and are in their linear ranges, it is possible to determine the relative concentrations of the specific target sequence in the original DNA mixture. If the DNA mixtures are cDNAs synthesized from RNAs isolated from different tissues or cells, the relative abundance of the specific mRNA from which the target sequence was derived can be determined for the respective tissues or cells. This direct proportionality between the concentration of the PCR products and the relative mRNA abundance is only true in the linear range of the PCR reaction. The final concentration of the target DNA in the plateau portion of the curve is determined by the availability of reagents in the reaction mix and is independent of the original concentration of target DNA. In specific embodiments, multiplexed, tandem PCR (MT- PCR) is employed, which uses a two-step process for gene expression profiling from small quantities of RNA or DNA, as described for example in US Pat. Appl. Pub. No. 20070190540. In the first step, RNA is converted into cDNA and amplified using multiplexed gene specific primers. In the second step each individual gene is quantitated by real time PCR.

[0055] In certain embodiments, target nucleic acids are quantified using blotting techniques, which are well known to those of skill in the art. Southern blotting involves the use of DNA as a target, whereas Northern blotting involves the use of RNA as a target. Each provides different types of information, although cDNA blotting is analogous, in many aspects, to blotting of RNA species. Briefly, a probe is used to target a DNA or RNA species that has been immobilized on a suitable matrix, often a filter of nitrocellulose. The different species should be spatially separated to facilitate analysis. This often is accomplished by gel electrophoresis of nucleic acid species followed by "blotting" on to the filter. Subsequently, the blotted target is incubated with a probe (usually labelled) under conditions that promote denaturation and rehybridisation. Because the probe is designed to base pair with the target, the probe will bind a portion of the target sequence under renaturing conditions. Unbound probe is then removed, and detection is accomplished as described above. Following detection/quantification, one may compare the results seen in a given subject with a control reaction or a statistically significant reference group or population of control subjects as defined herein. In this way, it is possible to correlate the amount of a biomarker nucleic acid detected with the likelihood that a subject is at risk of delayed recovery from mTBI.

[0056] Also contemplated are biochip-based technologies such as those described by Hacia et al. (1996, Nature Genetics 14: 441-447) and Shoemaker et al. (1996, Nature Genetics 14: 450-456). Briefly, these techniques involve quantitative methods for analysing large numbers of genes rapidly and accurately. By tagging genes with oligonucleotides or using fixed probe arrays, one can employ biochip technology to segregate target molecules as high-density arrays and screen these molecules on the basis of hybridization. See also Pease et al. (1994, Proc. Natl. Acad. Sci. U.S.A. 91: 5022-5026); Fodor et al. (1991, Science 251: 767-773). Briefly, nucleic acid probes to biomarker polynucleotides are made and attached to biochips to be used in screening and diagnostic methods, as outlined herein. The nucleic acid probes attached to the biochip are designed to be substantially complementary to specific expressed biomarker nucleic acids, i.e., the target sequence (either the target sequence of the sample or to other probe sequences, for example in sandwich assays), such that hybridization of the target sequence and the probes of the present invention occur. This complementarity need not be perfect; there may be any number of base pair mismatches, which will interfere with hybridization between the target sequence and the nucleic acid probes of the present invention. However, if the number of mismatches is so great that no hybridization can occur under even the least stringent of hybridization conditions, the sequence is not a complementary target sequence. In certain embodiments, more than one probe per sequence is used, with either overlapping probes or probes to different sections of the target being used. That is, two, three, four or more probes, with three being desirable, are used to build in a redundancy for a particular target. The probes can be overlapping (i.e. have some sequence in common), or separate.

[0057] In an illustrative biochip analysis, oligonucleotide probes on the biochip are exposed to or contacted with a nucleic acid sample suspected of containing one or more biomarker polynucleotides under conditions favouring specific hybridization. Sample extracts of DNA or RNA, either single or double-stranded, may be prepared from fluid suspensions of biological materials, or by grinding biological materials, or following a cell lysis step which includes, but is not limited to, lysis effected by treatment with SDS (or other detergents), osmotic shock, guanidinium isothiocyanate and lysozyme. Suitable DNA, which may be used in the method of the invention, includes cDNA. Such DNA may be prepared by any one of a number of commonly used protocols as for example described in Ausubel, et al., 1994, supra, and Sambrook, et al., et al., 1989, supra.

[0058] Suitable RNA, which may be used in the method of the invention, includes messenger RNA, complementary RNA transcribed from DNA (cRNA) or genomic or subgenomic RNA. Such RNA may be prepared using standard protocols as for example described in the relevant sections of Ausubel, et al. 1994, supra and Sambrook, et al. 1989, supra).

[0059] cDNA may be fragmented, for example, by sonication or by treatment with restriction endonucleases. Suitably, cDNA is fragmented such that resultant DNA fragments are of a length greater than the length of the immobilized oligonucleotide probe(s) but small enough to allow rapid access thereto under suitable hybridization conditions. Alternatively, fragments of cDNA may be selected and amplified using a suitable nucleotide amplification technique, as described for example above, involving appropriate random or specific primers.

[0060] Usually, the target biomarker polynucleotides are detectably labelled so that their hybridization to individual probes can be determined. The target polynucleotides are typically detectably labelled with a reporter molecule, illustrative examples of which include chromogens, catalysts, enzymes, fluorochromes, chemiluminescent molecules, bioluminescent molecules, lanthanide ions (e.g., Eu ³⁴ ), a radioisotope and a direct visual label. In the case of a direct visual label, use may be made of a colloidal metallic or non- metallic particle, a dye particle, an enzyme or a substrate, an organic polymer, a latex particle, a liposome, or other vesicle containing a signal producing substance and the like. Illustrative labels of this type include large colloids, for example, metal colloids such as those from gold, selenium, silver, tin and titanium oxide. In some embodiments, in which an enzyme is used as a direct visual label, biotinylated bases are incorporated into a target polynucleotide.

[0061] The hybrid-forming step can be performed under suitable conditions for hybridizing oligonucleotide probes to test nucleic acid including DNA or RNA. In this regard, reference may be made, for example, to NUCLEIC ACID HYBRIDIZATION, A PRACTICAL APPROACH (Homes and Higgins, eds.) (IRL press, Washington D.C., 1985). In general, whether hybridization takes place is influenced by the length of the oligonucleotide probe and the polynucleotide sequence under test, the pH, the temperature, the concentration of mono- and divalent cations, the proportion of G and C nucleotides in the hybrid-forming region, the viscosity of the medium and the possible presence of denaturants. Such variables also influence the time required for hybridization. The preferred conditions will therefore depend upon the particular application. Such empirical conditions, however, can be routinely determined without undue experimentation.

[0062] After the hybrid-forming step, the probes are washed to remove any unbound nucleic acid with a hybridization buffer. This washing step leaves only bound target polynucleotides. The probes are then examined to identify which probes have hybridized to a target polynucleotide.

[0063] The hybridization reactions are then detected to determine which of the probes has hybridized to a corresponding target sequence. Depending on the nature of the reporter molecule associated with a target polynucleotide, a signal may be instrumentally detected by irradiating a fluorescent label with light and detecting fluorescence in a fluorimeter; by providing for an enzyme system to produce a dye which could be detected using a spectrophotometer; or detection of a dye particle or a coloured colloidal metallic or non-metallic particle using a reflectometer; in the case of using a radioactive label or chemiluminescent molecule employing a radiation counter or autoradiography. Accordingly, a detection means may be adapted to detect or scan light associated with the label which light may include fluorescent, luminescent, focused beam or laser light. In such a case, a charge couple device (CCD) or a photocell can be used to scan for emission of light from a probe:target polynucleotide hybrid from each location in the micro-array and record the data directly in a digital computer. In some cases, electronic detection of the signal may not be necessary. For example, with enzymatically generated colour spots associated with nucleic acid array format, visual examination of the array will allow interpretation of the pattern on the array. In the case of a nucleic acid array, the detection means is suitably interfaced with pattern recognition software to convert the pattern of signals from the array into a plain language genetic profile. In certain embodiments, oligonucleotide probes specific for different biomarker polynucleotides are in the form of a nucleic acid array and detection of a signal generated from a reporter molecule on the array is performed using a ‘chip reader’ . A detection system that can be used by a ‘chip reader’ is described for example by Pirrung et al (U.S. Patent No. 5,143,854). The chip reader will typically also incorporate some signal processing to determine whether the signal at a particular array position or feature is a true positive or maybe a spurious signal. Exemplary chip readers are described for example by Fodor et al (U.S. Patent No., 5,925,525). Alternatively, when the array is made using a mixture of individually addressable kinds of labelled microbeads, the reaction may be detected using flow cytometry.

[0064] In other embodiments, biomarker protein levels are assayed using proteinbased assays known in the art. For example, when a biomarker protein is an enzyme, the protein can be quantified based upon its catalytic activity or based upon the number of molecules of the protein contained in a sample.

[0065] Antibody-based techniques may also be employed to determine the level of a biomarker in a sample, non-limiting examples of which include immunoassays, such as the enzyme-linked immunosorbent assay (EUISA) and the radioimmunoassay (RIA).

[0066] In specific embodiments, protein-capture arrays that permit simultaneous detection and/or quantification of a large number of proteins are employed. For example, low-density protein arrays on filter membranes, such as the universal protein array system (Ge, 2000 Nucleic Acids Res. 28(2):e3) allow imaging of arrayed antigens using standard EEISA techniques and a scanning charge-coupled device (CCD) detector. Immuno-sensor arrays have also been developed that enable the simultaneous detection of clinical analytes. It is now possible using protein arrays to profile protein expression in bodily fluids, such as in sera of healthy subjects or patients, as well as in subjects pre- and post- treatment. [0067] Exemplary protein capture arrays include arrays comprising spatially addressed antigen-binding molecules, commonly referred to as antibody arrays, which can facilitate extensive parallel analysis of numerous proteins defining a proteome or subproteome. Antibody arrays have been shown to have the required properties of specificity and acceptable background, and some are available commercially (e.g., BD Biosciences, Clontech, BioRad and Sigma). Other illustrative examples of suitable arrays, including the OLink and SOMAscan platforms, as described by Raffield et al. (Proteomics, 2020; 20(12)el900278), the entire contents of which is incorporated herein by reference. Various methods for the preparation of antibody arrays have been reported (see, e.g., Lopez et al., 2003 J. Chromatogr. B 787:19-27; Cahill, 2000 Trends in Biotechnology 7:47-51; U.S. Pat. App. Pub. 2002/0055186; U.S. Pat. App. Pub. 2003/0003599; PCT publication WO 03/062444; PCT publication WO 03/077851; PCT publication WO 02/59601; PCT publication WO 02/39120; PCT publication WO 01/79849; PCT publication WO 99/39210). The antigen-binding molecules of such arrays may recognise at least a subset of proteins expressed by a cell or population of cells, illustrative examples of which include growth factor receptors, hormone receptors, neurotransmitter receptors, catecholamine receptors, amino acid derivative receptors, cytokine receptors, extracellular matrix receptors, antibodies, lectins, cytokines, serpins, proteases, kinases, phosphatases, ras-like GTPases, hydrolases, steroid hormone receptors, transcription factors, heat-shock transcription factors, DNA-binding proteins, zinc-finger proteins, leucine-zipper proteins, homeodomain proteins, intracellular signal transduction modulators and effectors, apoptosis-related factors, DNA synthesis factors, DNA repair factors, DNA recombination factors and cell-surface antigens.

[0068] Individual spatially distinct protein-capture agents are typically attached to a support surface, which is generally planar or contoured. Common physical supports include glass slides, silicon, microwells, nitrocellulose or PVDF membranes, and magnetic and other microbeads.

[0069] Particles in suspension can also be used as the basis of arrays, providing they are coded for identification; systems include colour coding for microbeads (e.g., available from Quanterix e.g., Simoa Bead-based assay), Luminex, Bio-Rad and Nanomics Biosystems) and semiconductor nanocrystals (e.g., QDots™, available from Quantum Dots), and barcoding for beads (UltraPlex™, available from Smartbeads) and multimetal microrods (Nanobarcodes™ particles, available from Surromed). Beads can also be assembled into planar arrays on semiconductor chips (e.g., available from LEAPS technology and BioArray Solutions). Where particles are used, individual protein-capture agents are typically attached to an individual particle to provide the spatial definition or separation of the array. The particles may then be assayed separately, but in parallel, in a compartmentalized way, for example in the wells of a microtitre plate or in separate test tubes.

[0070] In an illustrative example, a protein sample, which is optionally fragmented to form peptide fragments (see, e.g., U.S. Pat. App. Pub. 2002/0055186) is delivered to a protein-capture array under conditions suitable for protein or peptide binding, and the array is washed to remove unbound or non-specifically bound components of the sample from the array. Next, the presence or amount of protein or peptide bound to each feature of the array is detected using a suitable detection system. The amount of protein bound to a feature of the array may be determined relative to the amount of a second protein bound to a second feature of the array. In certain embodiments, the amount of the second protein in the sample is already known or known to be invariant.

[0071] In another illustrative example of a protein-capture array is Luminex-based multiplex assay, which is a bead-based multiplexing assay, where beads are internally dyed with fluorescent dyes to produce a specific spectral address. Biomolecules (such as an oligo or antibody) can be conjugated to the surface of beads to capture analytes of interest. Flow cytometric or other suitable imaging technologies known to persons skilled in the art can then be used for characterization of the beads, as well as for detection of analyte presence. The Luminex technology enables are large number of proteins, genes or other gene expression products (e.g., 100 or more, 200 or more, 300 or more, 400 or more) to be detected using very small sample volume e.g., in a 96 or 384-well plate). In some embodiments, the protein-capture array is Bio-Plex Luminex- 100 Station (Bio-Rad) as described previously.

[0072] Other methods of determining the level of a biomarker protein in a sample include mass spectrometry (MS). Suitable MS-based methods will be familiar to persons skilled in the art, illustrative examples of which include multiple reactionmonitoring (MRM) mass spectrometry (as described by Lieber and Zimmerman, 2013, Biochem. 52(22):3797-3806 and by Cohen Freue and Zimmerman, 2012, Circ. Cardiovasc. Genet., 5:738), the entire contents of which are incorporated herein by reference), liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS; as described by Aebersold and Mann (2016, Nature, 537: 347-355; the entire contents of which is incorporated herein by reference), data-independent acquisition mass spectrometry (DIA-MS), trapped ion mobility time of flight mass spectrometry (timsTOF proMS), diaPASEF and sequential window acquisition of all theoretical mass spectra (SWATH-MS; as described by Ludwig et al. (Mol. Syst. Biol, 2018, 14(8)e8126) and Gillet et al. (2012; Mol. Cell Proteomics 11: 0111.016717), the entire contents of which are incorporated herein by reference).

[0073] In some embodiments, the level of a biomarker is normalized against a housekeeping biomarker. The term "housekeeping biomarker" refers to a biomarker or group of biomarkers (e.g., polynucleotides and/or polypeptides), which are typically found at a constant level in the cell type(s) being analysed and across the conditions being assessed. In some embodiments, the housekeeping biomarker is a "housekeeping gene. " A "housekeeping gene" refers herein to a gene or group of genes which encode proteins whose activities are essential for the maintenance of cell function and which are typically found at a constant level in the cell type(s) being analysed and across the conditions being assessed.

Biomarker Profiles

[0074] As used herein, the terms "profile" and "biomarker profile" are used interchangeably herein to denote any set of data that represents the distinctive features or characteristics associated with a condition of interest, such as with a particular prediction, diagnosis and/or prognosis of a specified condition as taught herein. The term generally encompasses quantification of one or more biomarkers, inter alia, nucleic acid profiles, such as, for example, gene expression profiles (e.g., sets of gene expression data that represents mRNA levels of one or more genes associated with a condition of interest), as well as protein, polypeptide or peptide profiles, such as, for example, protein expression profiles e.g., sets of protein expression data that represents the levels of one or more proteins associated with a condition of interest), and any combination thereof. In one embodiment, the terms "profile" or "biomarker profile" are used herein to denote any set of data that represents the distinctive features or characteristics associated with mTBI.

[0075] The term "reference biomarker profile" is used herein to denote a pattern of expression of at least one biomarker for a sample population of reference individuals who may be individuals at risk of delayed recovery from mTBI or individuals having non-delayed recovery. In some embodiments, the "reference biomarker profile" is used herein to denote a pattern of expression of at least one biomarker for a sample population of reference individuals at risk of delayed recovery from mTBI. In some embodiments, the "reference biomarker profile" is used herein to denote a pattern of expression of at least one biomarker for a sample population of reference individuals not at risk of delayed recovery from mTBI. A reference biomarker profile may be identified based on reference data measured for individuals in the sample population. Reference data typically include the measurement of at least one biomarker. The measurement may include information regarding the activity, such as its concentration and / or abundance, of any expression product or measurable molecule (e.g., protein), as will be described in more detail herein. The reference data may also include other additional relevant information, such as clinical data, including, but not limited to, age, the presence, absence, degree, severity or progression of a symptom associated with the mTBI, amino acid or nucleotide related genomics information associated with delayed recovery from the mTBI, and the like and this is not intended to be limiting, as will be apparent from the description herein.

[0076] The reference data may be acquired in any appropriate manner, such as obtaining gene expression product data from a plurality of subjects, selected to include individuals with delayed recovery from mTBI. The terms "expression" or "gene expression" refer to production of RNA message or translation of RNA message into proteins or polypeptides, or both. Detection of either types of gene expression in use of any of the methods described herein is encompassed by the present invention. Gene expression product data are collected, for example, by obtaining a biological sample, such as a blood sample from the subject, and performing a quantification, semiquantification or qualification process, such as sequence- specific nucleic acid amplification, including PCR (Polymerase Chain Reaction) or the like, in order to assess the expression, and in particular, the level or abundance of one or more reference biomarker. Quantified values indicative of the relative activity can then be stored as part of the reference data.

[0077] Biomarker profiles may be created in a number of ways and may be the combination of measurable biomarkers or aspects of biomarkers using methods such as ratios, or other more complex association methods or algorithms (e.g., rule-based methods), as discussed for example in more detail herein. A biomarker profile evaluates at least one biomarker. However, in other embodiments, the biomarker profile evaluates at least 2 biomarkers (e.g., 2 or more, 3 or more, 4 or more, 5 or more, 6 or more, 7 or more, 8 or more, 9 or more, 10 or more, 11 or more, 12 or more, 13 or more, 14 or more, 15 or more, 16 or more, 17 or more, 18 or more, 19 or more, 20 or more, 25 or more, 30 or more, 40 or more, 50 or more, 60 or more, 70 or more, 80 or more, 90 or more, 100 or more, 200 or more). Where the biomarker profile comprises two or more measurements, the measurements can correspond to the same or different biomarkers. For example, distinct reference profiles may represent the degree of risk (e.g., an abnormally elevated risk) of delayed recovery from mTBI, as compared to no or normal risk of delayed recovery from mTBI. In another example, distinct reference profiles may represent predictions of differing degrees of risk of delayed recovery from mTBI.

[0078] A reference biomarker profile or a sample biomarker profile can be quantitative, semi-quantitative and/or qualitative. For example, the biomarker profile can evaluate the presence or absence of at least one biomarker, can evaluate the presence of at least one biomarker above or below a particular threshold, and/or can evaluate the relative or absolute amount of at least one biomarker.

[0079] In some embodiments, the subject’s risk of delayed recovery from mTBI is determined by comparing the biomarker profile in a sample obtained from the subject (i.e., the sample biomarker profile) with a reference biomarker profile in a healthy population of subjects or in a population of subjects who present without delayed recovery from mTBI. Alternatively, the subject’s risk of delayed recovery from mTBI is determined by comparing the biomarker profile in a sample obtained from the subject (i.e., the sample biomarker profile) with a reference biomarker profile from a population of subjects who present with delayed recovery from mTBI. For example, a subject’s risk of delayed recovery from mTBI can be determined by comparing the level of expression of a biomarker in a sample obtained from the subject with a level that is representative of a mean or median level of the expression in a population of individuals who present with delayed recovery from mTBI.

[0080] In some embodiments, the expression of the at least one biomarker in a sample population of reference individuals, as broadly defined herein, is used to generate a biomarker profile; namely, of subjects at risk of delayed recovery from mTBI (the reference group) and of healthy controls or subjects without delayed recovery from mTBI (the control group). For instance, a particular biomarker may be less abundant in the reference group as compared to the control group. The data may be represented as an overall signature score or the profile may be represented as a barcode, heat-map, z- score, receiver-operator characteristics (ROC) curve or other graphical representation known to persons skilled in the art to facilitate the determination of a test subject's risk of delayed recovery from mTBI. The abundance of the corresponding biomarker in a test subject may be represented in the same way, thereby providing a sample biomarker profile, such that a comparison of the sample profile with the reference profile may be undertaken to determine the test subject's risk of delayed recovery from mTBI.

[0081] It will be appreciated that once collected, the reference data can be stored in a database allowing them to be subsequently retrieved, for example, by a processing system for subsequent use in accordance with the methods described herein. The processing system may also store an indication of the identity of each of the reference biomarkers as a reference biomarker collection or panel where there are two or more reference biomarkers.

[0082] In illustrative examples, the biomarker profile evaluates at least one biomarker (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 35, 40, 45, 50, 60 or more).

[0083] In an embodiment, the level of a biomarker in the reference biomarker profile is at least 101%, 102%, 103%, 104%, 105%, 106%, 107% 108%, 109%, 110%, 120%, 130%, 140%, 150%, 160%, 170%, 180%, 190%, 200%, 300%, 400%, 500%, 600%, 700%, 800%, 900% or 1000% (i.e. an increased or higher level) of the level of a corresponding biomarker in the control group.

[0084] In other embodiments, the level of a biomarker in the reference biomarker profile is at least 99%, 98%, 97%, 96%, 95%, 94%, 93% 92%, 91%, 90%, 80%, 70%, 60%, 50%, 40%, 30%, 20% or at least 10%, (i.e. a decreased or lower level) of the level of a corresponding biomarker in the control group.

Corresponding biomarker

[0085] By "corresponding biomarker" or "corresponding biomarker" is meant a biomarker that is structurally and/or functionally similar or identical to a reference biomarker. Representative corresponding biomarkers include expression products of allelic variants (same locus), homologs (different locus), and orthologs (different organism) of reference biomarker genes. Nucleic acid variants of reference biomarker genes and encoded biomarker polynucleotide expression products can contain nucleotide substitutions, deletions, inversions and/or insertions. Variation can occur in either or both the coding and non-coding regions. The variations can produce both conservative and non-conservative amino acid substitutions (as compared in the encoded product). For nucleotide sequences, conservative variants include those sequences that, because of the degeneracy of the genetic code, encode the amino acid sequence of a reference biomarker.

[0086] Variants of a particular biomarker gene or polynucleotide may have at least about 40%, 45%, 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 more sequence identity to that particular nucleotide sequence as determined by sequence alignment programs known in the art using default parameters.

[0087] Corresponding biomarkers also include amino acid sequences that display substantial sequence similarity or identity to the amino acid sequence of a reference biomarker polypeptide. An amino acid sequence that corresponds to a reference amino acid sequence may display at least about 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, 97, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99% or even up to 100% sequence similarity or identity to a reference amino acid as determined by sequence alignment programs known in the art using default parameters.

[0088] In some embodiments, calculations of sequence similarity or sequence identity between sequences can be performed. For example, to determine the percent identity of two amino acid sequences, or of two nucleic acid sequences, the sequences are aligned for optimal comparison purposes (e.g., gaps can be introduced in one or both of a first and a second amino acid or nucleic acid sequence for optimal alignment and non-homologous sequences can be disregarded for comparison purposes). In some embodiments, the length of a reference sequence aligned for comparison purposes is at least 30%, usually at least 40%, more usually at least 50%, 60%, and even more usually at least 70%, 80%, 90%, 100% of the length of the reference sequence. The amino acid residues or nucleotides at corresponding amino acid positions or nucleotide positions are then compared. When a position in the first sequence is occupied by the same amino acid residue or nucleotide at the corresponding position in the second sequence, then the molecules are identical at that position. For amino acid sequence comparison, when a position in the first sequence is occupied by the same or similar amino acid residue (i.e., conservative substitution) at the corresponding position in the second sequence, then the molecules are similar at that position.

[0089] The percent identity between the two sequences is a function of the number of identical amino acid residues or nucleotides shared by the sequences at individual positions, considering the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences. By contrast, the percent similarity between two amino acid sequences is a function of the number of identical and similar amino acid residues shared by the sequences at individual positions, considering the number of gaps, and the length of each gap, which need to be introduced for optimal alignment of the two sequences.

[0090] The comparison of sequences and determination of percent identity or percent similarity between sequences can be accomplished using a mathematical algorithm. In certain embodiments, the percent identity or similarity between amino acid sequences is determined using the Needleman and Wiinsch, (1970, J. Mol. Biol. 48: 444-453) algorithm which has been incorporated into the GAP program in the GCG software package (available at http://www.gcg.com), using either a Blossum 62 matrix or a PAM250 matrix, and a gap weight of 16, 14, 12, 10, 8, 6, or 4 and a length weight of 1, 2, 3, 4, 5, or 6. In specific embodiments, the percent identity between nucleotide sequences is determined using the GAP program in the GCG software package (available at http://www.gcg.com), using a NWSgapdna.CMP matrix and a gap weight of 40, 50, 60, 70, or 80 and a length weight of 1, 2, 3, 4, 5, or 6. An non-limiting set of parameters (and the one that should be used unless otherwise specified) includes a Blossum 62 scoring matrix with a gap penalty of 12, a gap extend penalty of 4, and a frameshift gap penalty of 5.

[0091] In some embodiments, the percent identity or similarity between amino acid or nucleotide sequences can be determined using the algorithm of E. Meyers and W. Miller (1989, Cabios, 4: 11-17) which has been incorporated into the ALIGN program (version 2.0), using a PAM120 weight residue table, a gap length penalty of 12 and a gap penalty of 4. [0092] The nucleic acid and protein sequences described herein can be used as a "query sequence" to perform a search against public databases to, for example, identify other family members or related sequences. Such searches can be performed using the NBLAST and XBLAST programs (version 2.0) of Altschul, et al., (1990, J. Mol. Biol, 215: 403-10). BLAST nucleotide searches can be performed with the NBLAST program, score = 100, wordlength = 12 to obtain nucleotide sequences homologous to 53010 nucleic acid molecules of the invention. BLAST protein searches can be performed with the XBLAST program, score = 50, wordlength = 3 to obtain amino acid sequences homologous to 53010 protein molecules of the invention. To obtain gapped alignments for comparison purposes, Gapped BLAST can be utilized as described in Altschul et al., (1997, Nucleic Acids Res, 25: 3389-3402). When utilizing BLAST and Gapped BLAST programs, the default parameters of the respective programs (e.g., XBLAST and NBLAST) can be used.

[0093] Corresponding biomarker polynucleotides also include nucleic acid sequences that hybridize to reference biomarker polynucleotides, or to their complements, under stringency conditions described below. As used herein, the term "hybridizes under low stringency, medium stringency, high stringency, or very high stringency conditions" describes conditions for hybridization and optionally washing. "Hybridization" is used herein to denote the pairing of complementary nucleotide sequences to produce a DNA-DNA hybrid or a DNA-RNA hybrid. Complementary base sequences are those sequences that are related by the base-pairing rules. In DNA, A pairs with T and C pairs with G. In RNA, U pairs with A and C pairs with G. In this regard, the terms "match" and "mismatch" as used herein refer to the hybridization potential of paired nucleotides in complementary nucleic acid strands. Matched nucleotides hybridize efficiently, such as the classical A-T and G-C base pair mentioned above. Mismatches are other combinations of nucleotides that do not hybridize efficiently.

[0094] Guidance for performing hybridization reactions can be found in Ausubel et al., (1998, supra), Sections 6.3.1-6.3.6. Aqueous and non-aqueous methods are described in that reference and either can be used. Reference herein to low stringency conditions include and encompass from at least about 1% v/v to at least about 15% v/v formamide and from at least about 1 M to at least about 2 M salt for hybridization at 42° C, and at least about 1 M to at least about 2 M salt for washing at 42° C. Low stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPC>4 (pH 7.2), 7% SDS for hybridization at 65° C, and (i) 2 X SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPCU (pH 7.2), 5% SDS for washing at room temperature. One embodiment of low stringency conditions includes hybridization in 6 X sodium chloride/sodium citrate (SSC) at about 45° C, followed by two washes in 0.2 X SSC, 0.1% SDS at least at 50° C (the temperature of the washes can be increased to 55° C for low stringency conditions). Medium stringency conditions include and encompass from at least about 16% v/v to at least about 30% v/v formamide and from at least about 0.5 M to at least about 0.9 M salt for hybridization at 42° C, and at least about 0.1 M to at least about 0.2 M salt for washing at 55° C. Medium stringency conditions also may include 1% Bovine Serum Albumin (BSA), 1 mM EDTA, 0.5 M NaHPC>4 (pH 7.2), 7% SDS for hybridization at 65° C, and (i) 2 X SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPO ₄ (pH 7.2), 5% SDS for washing at 60-65° C. One embodiment of medium stringency conditions includes hybridizing in 6 X SSC at about 45°C, followed by one or more washes in 0.2 X SSC, 0.1% SDS at 60° C. High stringency conditions include and encompass from at least about 31% v/v to at least about 50% v/v formamide and from about 0.01 M to about 0.15 M salt for hybridization at 42° C, and about 0.01 M to about 0.02 M salt for washing at 55° C. High stringency conditions also may include 1% BSA, 1 mM EDTA, 0.5 M NaHPCC (pH 7.2), 7% SDS for hybridization at 65° C, and (i) 0.2 X SSC, 0.1% SDS; or (ii) 0.5% BSA, 1 mM EDTA, 40 mM NaHPC>4 (pH 7.2), 1% SDS for washing at a temperature in excess of 65° C. One embodiment of high stringency conditions includes hybridizing in 6 X SSC at about 45°C, followed by one or more washes in 0.2 X SSC, 0.1% SDS at 65° C.

[0095] In certain embodiments, a corresponding biomarker polynucleotide is one that hybridizes to a disclosed nucleotide sequence under very high stringency conditions. One embodiment of very high stringency conditions includes hybridizing 0.5 M sodium phosphate, 7% SDS at 65° C, followed by one or more washes at 0.2 X SSC, 1% SDS at 65° C.

[0096] Other stringency conditions are well known in the art and a skilled addressee will recognize that various factors can be manipulated to optimize the specificity of the hybridization. Optimization of the stringency of the final washes can serve to ensure a high degree of hybridization. For detailed examples, see Ausubel et al. , supra at pages 2.10.1 to 2.10.16 and Sambrook et al. (1989, supra) at sections 1.101 to 1.104.

Risk or likelihood of delayed recovery from mTBI

[0097] The term "risk" is used to denote a subject's likelihood, based on the sample biomarker profile as determined for that subject, of delayed recovery from mTBI (or not) on the basis of the reference biomarker profile, as herein described. Accordingly, the terms "risk" and "likelihood" are used interchangeably herein, unless otherwise stated.

[0098] It would be apparent to persons skilled in the art that the risk that a subject will present with delayed recovery from mTBI will vary, for example, from being at low or decreased risk of delayed recovery from mTBI to being at high or increased risk of delayed recovery from mTBI. By "low or decreased risk" is meant that the subject is less likely to have delayed recovery from mTBI as compared to a subject determined to be a "high or increased risk" subject. Conversely, a "high or increased risk" subject is a subject who is more likely to have delayed recovery from mTBI as compared to a subject who is not at risk or a "low risk" subject.

[0099] Likelihood is suitably based on mathematical modeling. An increased likelihood, for example, may be relative or absolute and may be expressed qualitatively or quantitatively. For instance, an increased risk may be expressed as simply determining the subject's level of a given biomarker and placing the test subject in an "increased risk" category, based upon the corresponding reference biomarker profile as determined, for example, from previous population studies. Alternatively, a numerical expression of the test subject's increased risk may be determined based upon biomarker level analysis.

[0100] As used herein, the term "probability" refers to the probability of class membership for a sample as determined by a given mathematical model and is construed to be equivalent likelihood in this context.

[0101] In some embodiments, likelihood is assessed by comparing the level or abundance of at least one biomarker to one or more preselected level, also referred to herein as a threshold or reference level. Thresholds may be selected that provide an acceptable ability to predict risk, treatment success, etc. In illustrative examples, receiver operating characteristic (ROC) curves are calculated by plotting the value of a variable versus its relative frequency in two populations in which a first population is considered at risk of delayed recovery from mTBI and a second population that is not considered to be at risk, or have a low risk, of delayed recovery from mTBI (called, arbitrarily, for example, "control subjects").

[0102] In some embodiments, the subject is considered at risk of delayed recovery from mTBI where the at least one biomarker in the sample biomarker profile for the subject is downregulated as compared to the level of the corresponding biomarker in a control subject.

[0103] In some embodiments, the subject is considered at risk of delayed recovery from mTBI where the level of the at least one biomarker in the sample biomarker profile for the subject is higher as compared to the level of the corresponding biomarker in a control subject.

[0104] In an embodiment, the threshold level of alpha- 1 -ACT, by absolute abundance, is 25107 ± 6557 / ng. In an embodiment, the threshold level of alpha- 1- ACT, by concentration, is 83900 ± 27006 ng/mL. In an embodiment, the threshold level of IgG3, by absolute abundance, is about 9100 ± 4369 / ng. In an embodiment, the threshold level of HGFL, by absolute abundance, is about 80.45+37.14 / ng.

[0105] For any particular biomarker, a distribution of biomarker levels for subjects who are at risk or not at risk of delayed recovery from mTBI may overlap. Under such conditions, a test may not absolutely distinguish a subject who is at risk of delayed recovery from mTBI from a subject who is not at risk of delayed recovery from mTBI with absolute (i.e., 100%) accuracy, and the area of overlap indicates where the test cannot distinguish the two subjects. A threshold can be selected, above which (or below which, depending on how a biomarker changes with risk) the test is considered to be "positive" and below which the test is considered to be "negative." The area under the ROC curve (AUC) provides the C-statistic, which is a measure of the probability that the perceived measurement will allow correct identification of risk (see, e.g., Hanley et al., Radiology 143: 29-36 (1982)).

[0106] In some embodiments, a positive likelihood ratio, negative likelihood ratio, odds ratio, and/or AUC or receiver operating characteristic (ROC) values are used as a measure of a method’s ability to predict risk of delayed recovery from mTBI. As used herein, the term "likelihood ratio" is the probability that a given test result would be observed in a subject with a likelihood of such risk, divided by the probability that that same result would be observed in a subject without a likelihood of such risk. Thus, a positive likelihood ratio is the probability of a positive result observed in subjects with the specified risk divided by the probability of a positive results in subjects without the specified risk. A negative likelihood ratio is the probability of a negative result in subjects without the specified risk divided by the probability of a negative result in subjects with specified risk. The term "odds ratio," as used herein, refers to the ratio of the odds of an event occurring in one group (e.g., a control group) to the odds of it occurring in another group (e.g., the delayed recovery from mTBI group), or to a data- based estimate of that ratio. The term "area under the curve" or "AUC" refers to the area under the curve of a receiver operating characteristic (ROC) curve, both of which are well known in the art. AUC measures are useful for comparing the accuracy of a classifier across the complete data range. Classifiers with a greater AUC have a greater capacity to classify unknowns correctly between two groups of interest (e.g., a control group and a delayed recovery from mTBI group). ROC curves are useful for plotting the performance of a particular feature (e.g., any of the biomarkers described herein and/or any item of additional biomedical information) in distinguishing or discriminating between two populations (e.g., cases having a condition and controls without the condition). Typically, the feature data across the entire population (e.g., the cases and controls) are sorted in ascending order based on the value of a single feature. Then, for each value for that feature, the true positive and false positive rates for the data are calculated. The sensitivity is determined by counting the number of cases above the value for that feature and then dividing by the total number of cases. The specificity is determined by counting the number of controls below the value for that feature and then dividing by the total number of controls. Although this definition refers to scenarios in which a feature is elevated in cases compared to controls, this definition also applies to scenarios in which a feature is lower in cases compared to the controls (in such a scenario, samples below the value for that feature would be counted). ROC curves can be generated for a single feature as well as for other single outputs, for example, a combination of two or more features can be mathematically combined (e.g., added, subtracted, multiplied, etc.) to produce a single value, and this single value can be plotted in a ROC curve. Additionally, any combination of multiple features, in which the combination derives a single output value, can be plotted in a ROC curve. These combinations of features may comprise a test. The ROC curve is the plot of the sensitivity of a test against the specificity of the test, where sensitivity is traditionally presented on the vertical axis and specificity is traditionally presented on the horizontal axis. Thus, "AUC ROC values" are equal to the probability that a classifier will rank a randomly chosen positive instance higher than a randomly chosen negative one. An AUC ROC value may be an alternative to the Mann-Whitney U test, which tests for the median difference between scores obtained in the two groups considered if the groups are of continuous data, or to the Wilcoxon test of ranks.

[0107] In some embodiments, at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, or more) biomarker or a panel of biomarkers is selected to discriminate between subjects with or without risk of delayed recovery from mTBI with at least about 50%, 55% 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95% accuracy or having a C-statistic of at least about 0.50, 0.55, 0.60, 0.65, 0.70, 0.75, 0.80, 0.85, 0.90, 0.95.

[0108] In the case of a positive likelihood ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the " delayed recovery from mTBI risk" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the delayed recovery from mTBI group; and a value less than 1 indicates that a positive result is more likely in the control group. In this context, "delayed recovery from mTBI risk group" is meant to refer to a population of reference individuals considered to be at risk of delayed recovery from mTBI and a "control group" is meant to refer to a group of subjects considered not to be at risk of delayed recovery from mTBI. In the case of a negative likelihood ratio, a value of 1 indicates that a negative result is equally likely among subjects in both the "delayed recovery from mTBI risk" and "control" groups; a value greater than 1 indicates that a negative result is more likely in the "delayed recovery from mTBI risk" group; and a value less than 1 indicates that a negative result is more likely in the "control" group. In the case of an odds ratio, a value of 1 indicates that a positive result is equally likely among subjects in both the "delayed recovery from mTBI risk" and "control" groups; a value greater than 1 indicates that a positive result is more likely in the "delayed recovery from mTBI risk" group; and a value less than 1 indicates that a positive result is more likely in the "control" group. In the case of an AUC ROC value, this is computed by numerical integration of the ROC curve. The range of this value can be 0.5 to 1.0. A value of 0.5 indicates that a classifier (e.g., a biomarker profile) is no better than a 50% chance to classify unknowns correctly between two groups of interest, while 1.0 indicates the relatively best prognostic accuracy. In certain embodiments, biomarkers and/or biomarker panels are selected to exhibit a positive or negative likelihood ratio of at least about 1.5 or more or about 0.67 or less, at least about 2 or more or about 0.5 or less, at least about 5 or more or about 0.2 or less, at least about 10 or more or about 0. 1 or less, or at least about 20 or more or about 0.05 or less.

[0109] In certain embodiments, the at least one biomarker is selected to exhibit an odds ratio of at least about 2 or more or about 0.5 or less, at least about 3 or more or about 0.33 or less, at least about 4 or more or about 0.25 or less, at least about 5 or more or about 0.2 or less, or at least about 10 or more or about 0.1 or less.

[0110] In certain embodiments, the at least one biomarker is selected to exhibit an AUC ROC value of greater than 0.5, preferably at least 0.6, more preferably 0.7, still more preferably at least 0.8, even more preferably at least 0.9, and most preferably at least 0.95.

[0111] In some cases, multiple thresholds may be determined in so-called "tertile," "quartile," or "quintile" analyses. In these methods, the "delayed recovery from mTBI risk" and "control" groups are considered together as a single population, and are divided into 3, 4, or 5 (or more) "bins" having equal numbers of individuals. The boundary between two of these "bins" may be considered "thresholds." The degree of risk can then be assigned based on which "bin" a test subject falls into.

[0112] In other embodiments, particular thresholds for the reference biomarker(s) measured are not relied upon to determine if the biomarker level(s) obtained from a subject are correlated to risk of delayed recovery from mTBI. For example, a temporal change in the biomarker(s) can be used to rule in or out such risk. Alternatively, biomarker(s) are correlated to such risk by the presence or absence of one or more biomarkers in a particular assay format. In the case of biomarker profiles, the present invention may utilize an evaluation of the entire profile of biomarkers to provide a single result value (e.g., a "panel response" value expressed either as a numeric score or as a percentage risk). In such embodiments, an increase, decrease, or other change (e.g., slope over time) in a certain subset of biomarkers may be sufficient to indicate risk of delayed recovery from mTBI in a subject, while an increase, decrease, or other change in a different subset of biomarkers may be sufficient to indicate the same risk in another subject. [0113] In certain embodiments, a panel of biomarkers is selected to assist in distinguishing between "delayed recovery from mTBI risk" and "control" groups with at least about 70%, 80%, 85%, 90% or 95% sensitivity, suitably in combination with at least about 70% 80%, 85%, 90% or 95% specificity. In some embodiments, both the sensitivity and specificity are at least about 75%, 80%, 85%, 90% or 95%.

[0114] The phrases "assessing the likelihood" and "determining the likelihood," as used herein, refer to methods by which the skilled artisan can predict a subject's risk of delayed recovery from mTBI. The skilled artisan will understand that this phrase includes within its scope an increased probability that the subject will have delayed recovery from mTBI; that is, such risk is more likely to be present or absent in a subject. For example, the probability that an individual identified as being at risk of delayed recovery from mTBI may be expressed as a "positive predictive value" or "PPV." Positive predictive value can be calculated as the number of true positives divided by the sum of the true positives and false positives. PPV is determined by the characteristics of the predictive methods of the present invention as well as the prevalence of the condition in the population analysed. The statistical algorithms can be selected such that the positive predictive value in a population considered to be at risk of delayed recovery from mTBI is in the range of 70% to 99% and can be, for example, at least 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0115] In other examples, the probability that a subject is identified as not being at risk of delayed recovery from mTBI may be expressed as a "negative predictive value" or "NPV." Negative predictive value can be calculated as the number of true negatives divided by the sum of the true negatives and false negatives. Negative predictive value is determined by the characteristics of the diagnostic or prognostic method, system, or code as well as the prevalence of risk in the population analysed. The statistical methods and models can be selected such that the negative predictive value in a population considered at risk of delayed recovery from mTBI is in the range of about 70% to about 99% and can be, for example, at least about 70%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. [0116] In some embodiments, a subject is determined as being at significant risk of delayed recovery from mTBI. By "significant risk" is meant that the subject has a reasonable probability (e.g., 0.6, 0.7, 0.8, 0.9 or more) of delayed recovery from mTBI.

[0117] The methods of the present invention, as broadly described herein, also permit the generation of high-density data sets that can be evaluated using informatics approaches. High data density informatics analytical methods are known and software is available to those in the art, e.g., cluster analysis (Pirouette, Informetrix), class prediction (SIMCA-P, Ume tries), principal components analysis of a computationally modeled dataset (SIMCA-P, Umetrics), 2D cluster analysis (GeneLinker Platinum, Improved Outcomes Software), and metabolic pathway analysis (biotech.icmb.utexas.edu). The choice of software packages offers specific tools for questions of interest (Kennedy et al., Solving Data Mining Problems Through Pattern Recognition. Indianapolis: Prentice Hall PTR, 1997; Golub et al., (2999) Science 286:531-7; Eriksson et al., Multi and Megavariate Analysis Principles and Applications: Umetrics, Umea, 2001). In general, any suitable mathematic analyses can be used to evaluate at least one (e.g., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, et.) biomarker in a biomarker profile with respect to determining the likelihood that a subject is at risk of delayed recovery from mTBI. For example, methods such as multivariate analysis of variance, multivariate regression, and/or multiple regression can be used to determine relationships between dependent variables e.g., clinical measures) and independent variables (e.g., levels of biomarkers). Clustering, including both hierarchical and non- hierarchical methods, as well as nonmetric Dimensional Scaling can be used to determine associations or relationships among variables and among changes in those variables.

[0118] In some embodiments, a biomarker profile is used to assign a risk score which describes a mathematical equation for evaluation or prediction of risk.

[0119] In addition, principal component analysis is a common way of reducing the dimension of studies, and can be used to interpret the variance-covariance structure of a data set. Principal components may be used in such applications as multiple regression and cluster analysis. Factor analysis is used to describe the covariance by constructing "hidden" variables from the observed variables. Factor analysis may be considered an extension of principal component analysis, where principal component analysis is used as parameter estimation along with the maximum likelihood method. Furthermore, simple hypothesis such as equality of two vectors of means can be tested using Hotelling’s T squared statistic.

[0120] In some embodiments, the data sets corresponding to biomarker profiles are used to create a diagnostic or predictive rule or model based on the application of a statistical and machine learning algorithm. Such an algorithm uses relationships between a biomarker profile and risk of delayed recovery from mTBI observed in control subjects or typically cohorts of control subjects (sometimes referred to as training data), which provides combined control or reference biomarker profiles for comparison with biomarker profiles of a subject. The data are used to infer relationships that are then used to predict the status of a subject and the presence or absence of risk of delayed recovery from mTBI.

[0121] Persons skilled in the art of data analysis will recognize that many different forms of inferring relationships in the training data may be used without materially changing the present invention.

Subject

[0122] The terms "subject," "individual" and "patient" are used interchangeably herein to refer to any subject, particularly a vertebrate subject, and even more particularly a mammalian subject. Suitable vertebrate animals that fall within the scope of the invention include, but are not restricted to, any member of the subphylum Chordata including primates, rodents (e.g., mice rats, guinea pigs), lagomorphs (e.g., rabbits, hares), bovines (e.g., cattle), ovines e.g., sheep), caprines (e.g., goats), porcines (e.g., pigs), equines (e.g., horses), canines (e.g., dogs), felines (e.g., cats), avians (e.g., chickens, turkeys, ducks, geese, companion birds such as canaries, budgerigars etc), marine mammals (e.g., dolphins, whales), reptiles (snakes, frogs, lizards, etc.), and fish. A preferred subject is a primate (e.g., a human, ape, monkey, chimpanzee). In an embodiment, the subject is a human subject. In an embodiment, the subject is a child. In an embodiment, the child is from birth to about 18 years of age. In an embodiment, the child is from about 1 day to about 18 years of age. In an embodiment, the child is from about 5 days to about 18 years of age

Sample

[0123] The one or more biomarkers disclosed herein are detected or measured in a biological sample. A biological sample may include a sample that may be extracted, untreated, treated, diluted or concentrated from a subject. In some embodiments, the biological sample has not been extracted from the subject, such as when the sample biomarker profile can be obtained by evaluating the level of the at least one biomarker in situ. In preferred embodiments, the biological sample is a sample obtained from the subject that is reasonably expected to comprise the at least one biomarker of interest. Non-limiting examples of biological samples include, but are not limited to, tissue, bodily fluid (for example, blood, serum, plasma, saliva, urine, tears, peritoneal fluid, ascitic fluid, vaginal secretion, breast fluid, breast milk, lymph fluid, cerebrospinal fluid or mucosa secretion), umbilical cord blood, chorionic villi, amniotic fluid, an embryo, embryonic tissues, lymph fluid, cerebrospinal fluid, mucosa secretion, or other body exudate, fecal matter, an individual cell or extract of the such sources that contain the protein or nucleic acid of the same, and subcellular structures such as mitochondria, obtained using protocols well established within the art. In certain embodiments, the biological sample contains blood, especially peripheral blood, or a fraction or extract thereof, such as serum or plasma.

[0124] In some embodiments disclosed herein, the biological sample is a whole blood sample. In some embodiments, the biological sample is a serum sample. In some embodiments, the biological sample is a plasma sample.

[0125] In some embodiments, the sample is a venous blood sample obtained by venous phlebotomy or indwelling cannulation.

[0126] In some embodiments, the sample is a blood sample obtained by microsampling. Microsampling is a term often used to refer to techniques that enable the collection of smaller amounts of blood (typically 50 pL or less) from, for example, a skin prick rather, than by venous phlebotomy or indwelling cannulation. Suitable methods of microsampling of blood will be familiar to persons skilled in the art, illustrative examples of which are described in Guerra Valero et al. (Pediatr. Res. 2012:1-5) and include drawing capillary blood from a lancet finger prick using, for example, a Mitra device. Microsampling may present a simple and convenient solution to the challenges of intravenous blood sample collection. For example, collecting venous blood samples intravenously in children can be difficult, often causing pain and distress to the subject, and the volume of blood that can be collected is much smaller compared to their adult counterparts. Microsampling techniques, in particular those that involve collecting blood from the fingertip using a lancet, at least partly alleviate such difficulties and can minimise the need for patients or study participants to visit a clinic to facilitate sample collection. In summary, microsampling collects a smaller amount of blood, involves a less painful procedure, and is less burdensome and resource intensive. In some embodiments, the sample is a blood sample obtained using microfluidic capillary sampling or capillary microsampling.

[0127] The biological sample may be processed and analyzed for the purpose of determining the sample biomarker profile, in accordance with the present invention, almost immediately following collection (i.e., as a fresh sample), or it may be stored for subsequent analysis. If storage of the biological sample is desired or required, it would be understood by persons skilled in the art that it should ideally be stored under conditions that preserve the integrity of the biomarker of interest within the sample (e.g., at -80°C). In some embodiments, the biological sample is stored at room temperature.

[0128] By " obtained" is meant to come into possession. Biological or reference samples so obtained include, for example, nucleic acid extracts or polypeptide extracts isolated or derived from a particular source. For instance, the extract may be isolated directly from a biological fluid or tissue of a subject.

Stratifying a subject to a treatment regimen

[0129] As noted elsewhere herein, the present disclosure also extends to a method of stratifying a subject determined to be at risk of delayed recovery from mTBI to a treatment regimen, the method comprising: (1) determining whether a subject is at risk of delayed recovery from mTBI according to a method described herein; and (2) where the subject is determined from step (1) to be at risk of delayed recovery from mTBI, stratifying the subject to a treatment regimen for mTBI.

[0130] In yet another aspect disclosed herein, there is provided a method of treating a subject determined to be at risk of delayed recovery from mTBI, the method comprising (1) determining whether a subject is at risk of delayed recovery from mTBI according to a method described herein; and (2) exposing the subject determined from step (1) to be at risk of delayed recovery from mTBI to a treatment regimen for mTBI.

[0131] The present disclosure also extends to the management of risk of delayed recovery from mTBI in a subject. The management of said risk can include use of therapeutic agents or treatment regimens for treating the mTBI, or a symptom thereof. Suitable treatment regimens will be familiar to persons skilled in the art, illustrative examples of which may include limited rest and / or early introduction of graded exercise soon after injury.

[0132] The term "treating" as used herein, unless otherwise indicated, means alleviating, inhibiting the progress of, or preventing, either partially or completely, the pathophysiology of mTBI, or a symptom thereof. The term "treatment" as used herein, unless otherwise indicated, refers to the act of treating.

[0133] Following diagnosis, the treatment regimen to be adopted or prescribed may depend on several factors, including the age, weight and general health of the subject. Another determinative factor may be the degree of risk of delayed recovery from mTBI determined by the sample biomarker profile in accordance with the present invention, as herein described. For instance, where the subject is determined to be at high risk of delayed recovery from mTBI, a more aggressive treatment regimen may be prescribed as compared to a subject who is determined to be at low risk of delayed recovery from mTBI. The treatment regimen may also depend on existing clinical parameters relevant to the recovery from mTBI, such as the age and / or sex of the subject.

[0134] Thus, the present disclosure contemplates exposing the subject to a treatment regimen if the subject is determined to be at risk of delayed recovery from mTBI in accordance with the methods of the present invention. Non-limiting examples of such treatment regimens include vestibular treatment, ocular-motor treatment, cervical treatment, supported and/or graded return to aerobic exercise delivered by a clinician under supervision of a physiotherapist and psychological therapy (such as cognitive behavioural therapy) delivered by a mental health clinician.

[0135] In some embodiments, the subject is exposed to a combination of two or more additional treatment regimens (e.g., 2, 3 or more, 4 or more, 5 or more, 6 or more).

Kits

[0136] In another aspect there is provided a kit comprising one or more reagents and/or devices for use in performing any one of the methods of the present invention as broadly described above and elsewhere herein. Hence, in another aspect, disclosed herein is a kit comprising one or more reagents and/or devices when use for performing a method described herein.

[0137] The kits may suitably contain reagents for obtaining a sample biomarker profile in accordance with the methods as herein described. Kits for carrying out the methods of the present invention may include, in suitable container means, (i) a reagent for detecting the at least one biomarker, (ii) a probe that comprises an antibody or nucleic acid sequence that specifically binds to the at least one biomarker, (iii) a label for detecting the presence of the probe and (iv) instructions for how to measure the level of expression of the at least one biomarker. The container means of the kits will generally include at least one vial, test tube, flask, bottle, syringe and/or other container into which a first antibody specific for the at least one biomarker or a first nucleic acid specific for the at least one biomarker may be placed and/or suitably aliquoted. Where a second and/or third and/or additional component is provided, the kit will also generally contain a second, third and/or other additional container into which this component may be placed. Alternatively, a container may contain a mixture of more than one reagent, each reagent specifically binding a different biomarker in accordance with the present invention, when required. The kits of the present invention will also typically include means for containing the reagents (e.g., nucleic acids, polypeptides etc.) in close confinement for commercial sale. Such containers may include injection and/or blow- moulded plastic containers into which the desired vials are retained.

[0138] The kits may further comprise positive and negative controls, including a reference biomarker profile, as well as instructions for the use of kit components contained therein, in accordance with the methods of the present invention.

[0139] All the essential materials and reagents required for detecting and quantifying biomarker expression products may be assembled together in a kit, which is encompassed by the present invention. The kits may also optionally include appropriate reagents for detection of labels, positive and negative controls, washing solutions, blotting membranes, microtiter plate dilution buffers and the like. For example, a nucleic acid-based detection kit may include (i) a biomarker polynucleotide (which may be used as a positive control), (ii) a primer or probe that specifically hybridizes to a biomarker polynucleotide. Also included may be enzymes suitable for amplifying nucleic acids including various polymerases (Reverse Transcriptase, Taq, Sequenase™ DNA ligase etc. depending on the nucleic acid amplification technique employed), deoxynucleotides and buffers to provide the necessary reaction mixture for amplification. Such kits also generally will comprise, in suitable means, distinct containers for each individual reagent and enzyme as well as for each primer or probe. Alternatively, a protein-based detection kit may include (i) a biomarker polypeptide (which may be used as a positive control), (ii) an antibody that binds specifically to a biomarker polypeptide. The kit can also feature various devices (e.g., one or more) and reagents (e.g., one or more) for performing one of the assays described herein; and/or printed instructions for using the kit to quantify the expression of a biomarker gene.

[0140] It will be appreciated that the above-described terms and associated definitions are used for the purpose of explanation only and are not intended to be limiting.

[0141] In order that the invention may be readily understood and put into practical effect, particular preferred embodiments will now be described by way of the following non-limiting examples.

EXAMPLES

Materials and Methods

[0142] This study was a component of the Take Concussion Assessment and Recovery Research (Take CARe) Biomarkers study that was undertaken at the Murdoch Children’s Research Institute (MCRI; Melbourne, Australia).

A. Concussion Participant Recruitment and Data Collection

[0143] The Take CARe Biomarkers study is a single -centre, prospective cohort study evaluating children with concussion and upper body orthopaedic injuries, which took place at the Royal Children’s Hospital (RCH), Melbourne (RCH HREC, Study ID 33122) [1]. Blood samples were collected from patients aged 5 to 18 years old presenting to the emergency department (ED) less than 48 hours post-injury with a concussion or upper body orthopaedic injury.

B. Participant Assessment

[0144] For the purpose of this study, a concussion was defined as a direct or indirect force to the head resulting in one or more somatic, cognitive or emotional symptoms, physical signs (e.g. loss of consciousness or amnesia), behavioural changes, or sleep disturbance, a definition based on the Berlin and Zurich Concussion in Sport Group Consensus Statements [10,11].

[0145] Initial clinical assessment was conducted in the ED by treating doctors, with follow-up assessments including the Post-Concussion Symptoms Inventory (PCSI) and SCAT3 undertaken by a researcher. A second Clinical Report Form detailing mechanism of injury and related symptoms was completed by the treating clinician after recruitment of the patient at admission.

[0146] 1.4mL of venous blood was collected by a qualified clinician in S-Monovette tubes (Sarstedt, Australia), containing 1:9 volumes of citrate to blood. Samples were centrifuged at 3800g for lOmin at room temperature within 1 hour of collection and stored at -70°C until testing.

C. Determination of Recovery Status Post-Concussion

[0147] All concussion patients were required to complete the PCSI at one-month post-injury, where they were determined to have delayed or non-delayed recovery from concussion.

[0148] For this project, delayed recovery, or persisting post-concussion symptoms (PPCS), was defined as two or more post-concussion symptoms experienced for at least two-weeks post-injury, with at least one of those symptoms being more severe than preinjury baseline [11].

D. Healthy Participant Recruitment and Data Collection

[0149] Blood samples were collected as part of the Harmonising Age Pathology Parameters in Kids (HAPPI Kids, RCH HREC, Study ID 34183) study from children undergoing minor day surgical procedures at the Royal Children's Hospital (Melbourne, Australia). For this study, these samples acted as age and sex matched healthy controls. An outline of the study protocol and full details have been published in Hoq et al. BMJ Open 2019:9:e025897.

E. Blood Protein Analysis

[0150] The process taken to identify clinically relevant blood markers of delayed recovery from concussion in this study is shown in Figure 1.

F. Discovery Phase

[0151] Untargeted testing of all proteins detectable in plasma was conducted at the Australian Proteome Analysis Facility (APAF) using Single Window Acquisition of All Theoretical Mass Spectra (SWATH-MS), an approach used previously in the paediatric setting (Bjelosevic, S. et al. Mol. Cell Proteomics (2017): 16: 924-35). [0152] Differential expression of proteins in patients with and without delayed recovery from concussion was determined using analysis of variance, with proteins indicated to be differentially expressed included as candidates for validation.

G. Validation Phase

[0153] Proteins identified as being differentially expressed between groups with and without delayed recovery were selected for validation.

Gl. Multiple reaction monitoring (MRM)

[0154] The absolute abundance of proteins in plasma were further validated using high resolution multiple reaction monitoring (MRM-HR) technology.

[0155] Stable isotope labelled (SIL) peptides were used as spiked internal standards. These peptides are outlined in Table 1, below.

Table 1: Isotope Labelled Peptides Purchased from Synpeptide Co., Ltd.

[0156] Assay Method Development - Six healthy control samples and 48 concussion samples were combined to create a healthy plasma and concussion plasma pool respectively. Pooled samples were digested using a previously established protocol [25]. SIL peptides were pooled and spiked into the plasma pools prior to MRM analysis. Digestion efficiency, sample loading and precursor ion selection was optimised for peptide measurement.

[0157] Sample Preparation - Each sample was diluted with ammonium bicarbonate (ABC) solution before reducing with dithiothreitol and alkylating with iodoacetamide. The sample was then further diluted with 50mM ABC solution to give Img/mL concentration of plasma. From this, 50pg (50pL) of sample was taken for trypsin digestion and finally acidified with formic acid. [0158] Post digest sample pools were prepared by taking 5pL of each sample. Each sample was spiked with SIL peptides prior to MRM-HR analysis, with samples being acquired in random and post digest samples being acquired after every five samples. Spiked amounts of each SIL peptides for each injection is outlined in Table 2.

Table 2: Spiked Amount of Each SIL Peptide

[0159] Data Acquisition - lOpL of each sample was injected onto a reverse-phase peptide trap using the Triple TOF 6600 (Sciex) mass spectrometer for pre-concentration and desalting. The peptide trap was then switched into line with the analytical column, and peptides were eluted. A time-of-flight mass-spectrometry survey scan was acquired in MRM-HR mode (m/z 100-1800, 0.25 seconds) followed by the product ion scans of 12 target QI precursor ions, which are outlined in Table 3.

Table 3: MRM-HR Target Peptides and their Corresponding Selected Precursor Ion Masses [0160] Data Processing - MRM-HR data was processed in MultiQuant (Version 2.1.1, Sciex). Individual peptide and absolute protein abundances were calculated for each of the quantified proteins. Error tolerant searches were conducted on the healthy control plasma pool to identify evidence of post-translational and chemical modifications, as well as amino acid substitutions listed on the UniMod database (unimod.org).

G2. Enzyme Linked Immunosorbent Assay (ELISA)

[0161] The concentration of alpha- 1 -Antichymotrypsin in plasma was assessed by Enzyme Linked Immunosorbent Assay (ELISA). The ELISA test kit was acquired from ABCAM (bttps:^y^.3feam.cpm/; # AB 157706), and testing was conducted according to the manufacturer's specifications, as detailed in the test kit insert.

[0162] ELISA testing was performed in a blinded fashion to limit investigator bias.

H. Statistical Methods

[0163] For the Discovery study, protein peak areas were normalized to the total peak area of each run, and the difference between groups was assessed by analysis of variance in R. Differential expression of proteins were row median normalised. Proteins were considered differentially expressed with a p < 0.05 adjusted for mixed effects, with proteins indicated to be differentially expressed included as candidates for the Validation Phase.

[0164] For the Validation study, scatter plots for each protein’s concentration and abundance were developed in GraphPad Prism (Version 9.3.0) and were used to visually check results for outliers and distributions across groups. Mann- Whitney Tests were conducted to assess the difference between participant groups for all validated proteins, where statistical significance was determined to be p<0.05.

[0165] Individual univariate logistic regression models were created in Stata Data Analysis Software (Version 16) to assess the predictive power of each validated protein. The effectiveness of the protein in predicting delayed recovery was assessed by the statistical significance of the model.

[0166] Support Vector Machines (SVM), a type of machine learning model, were then designed to emulate the power of the predictive blood protein signature. Separate SVMs were developed and optimized using absolute protein abundance and protein concentration. The SVM was designed using Anaconda, a free open-source distribution of Python.

[0167] Proteins were iteratively input into the SVM, to assess the sensitivity and specificity of individual proteins and combinations of proteins in predicting delayed recovery outcomes in concussion patients.

Example 1: Development of Reference Ranges

1. Patient Demographics

[0168] Eighty patients were recruited for this study: concussed (n = 40), healthy controls (n = 33) and children with orthopedic injury controls (n = 7). Participant demographics for all participants involved in the study are reported below in Table 4.

Table 4: Baseline Participant Characteristics. 2. Discovery Study

[0169] To identify proteins of interest in samples collected as part of the Take CARe Biomarkers study, untargeted testing of all proteins detectable in plasma was conducted. Untargeted analysis allows for discovery of potentially clinically relevant proteins that have not yet been investigated in the field of concussion and more specifically delayed recovery from concussion. This process was conducted using SWATH-MS, which is able to determine the relative expression of proteins in plasma. Eighteen plasma samples from patients underwent SWATH-MS testing, with 9 samples from concussion patients who experienced delayed recovery and 9 from concussion patients who did not (Parkin GM et al., J Neurotrauma 2019;36:1768-75).

[0170] SWATH-MS results were analysed to determine the difference in relative protein expression between patients with and without delayed recovery from concussion. Differential expression between groups was determined using analysis of variance on both row median normalised and internal reference scaling normalised values.

[0171] The proteins identified as being differentially expressed in children experiencing normal and delayed recovery from concussion were ranked in order of their fold change (mean protein expression in delayed recovery group/mean protein expression in non-delayed recovery group). These results are presented in Table 5, below where the mean values are presented as relative protein abundances, as SWATH- MS is not a suitable technique for absolute protein quantification.

Table 5: Discovery Study results showing Proteins Differentially Expressed in Children with and without Delayed Recovery from Concussion.

NR: Non-Delayed Recovery or without Delayed Recovery. DR: Delayed Recovery. ANOVA: Analysis of Variance. All mean values demonstrated are row median normalised.

[0172] Proteins that were identified as being differentially expressed in plasma samples from children with and without delayed recovery from concussion were further investigated (see Figure 1). The six proteins that were selected for validation are: alpha- 1 -antichymotrypsin (Alpha- 1 -ACT), IgG3, Hepatitis Growth Factor Like protein (HGFL), Matrix metalloproteinase 9 (MMP-9), Selenoprotein P (SEPPI) and angiotensin converting enzyme (ACE).

Example 2: Validating Selected Proteins for the Development of a Predictive Blood Protein Signature

[0173] Alpha- 1 -ACT was validated using both MRM and ELISA to investigate absolute protein abundance and protein concentration respectively (Figure 2).

[0174] Absolute abundance of alpha- 1 -ACT, as determined by MRM, was significantly lower in delayed compared to non-delayed recovery groups (18887 ± 3273 /ng vs. 25107 ± 6557 /ng, p=0.003), as was protein concentration as determined by ELISA (44682 ± 12971 ng/mL vs. 83900 ± 27006 ng/mL, p<0.001) as shown in Figure 2A. This confirms findings from the Discovery study, where the row median normalised mean relative expression of alpha- 1 -ACT was lower in patients with delayed recovery compared to those without (22.55 ± 0.16 vs. 22.76 ± 0.11, p=0.004).

[0175] Absolute abundance of IgG3 as determined by MRM was significantly lower in delayed compared to non-delayed recovery groups (4558 ± 1500 /ng, 9100 ± 4369 /ng, p<0.0001) as shown in Figure 2D, which confirms findings from the discovery study, which showed a significantly lower row median normalised mean relative expression of IgG3 in participants with delayed recovery (22.21+ 0.34 vs. 22.74 + 0.38, p = 0.011).

[0176] HGFL was validated using MRM only (Figure 2B). Absolute abundance of HGFL was lower in delayed compared to non-delayed recovery groups (64.10 + 44.81 /ng, 80.45 + 37.14 /ng, p=0.283), which confirms findings from our Discovery study, which showed the row median normalised mean relative expression of HGFL to be significantly lower in delayed compared to non-delayed recovery groups (15.98 + 0.65, 16.53 ± 0.26, p=0.0.046). 1. Absolute Protein Abundance

[0177] The absolute abundance of alpha- 1 -ACT, HGFL and IgG3 in participants with and without delayed recovery from concussion, as well as healthy and orthopaedic controls is shown in Figure 3.

2. Protein Concentration

[0178] The concentration of alpha- 1 -ACT in participants with and without delayed recovery from concussion, as well as healthy and orthopaedic controls, is shown in Figure 4.

Example 3: Predicting Delayed Recovery Outcomes using Protein Abundance

[0179] The absolute abundance of alpha- 1 -ACT in 40 concussion samples was input into an SVM, and randomly divided up into a training and testing set, which consisted of 26 and 14 patients, respectively.

[0180] In a single model, alpha- 1 -ACT abundance correctly predicted recovery outcomes for 67% and 100% of patients with and without delayed recovery included in the testing set respectively (Figure 5). The model was then bootstrapped, and repeated 1000 times, which demonstrated a median accuracy of 0.73 with 95% confidence intervals of 0.58 and 0.88 (Figure 5).

Example 4: Predicting Delayed Recovery Outcomes using Protein Concentration

[0181] The concentration of alpha- 1 -ACT in 40 concussion samples was input into an SVM, and randomly divided up into a training and testing set, which consisted of 26 and 14 patients respectively.

[0182] In a single model, alpha- 1 -ACT correctly predicted recovery outcomes for 94% and 80% of patients with and without delayed recovery in the training and testing set respectively (Figure 7). The model was then bootstrapped, and repeated 1000 times, which demonstrated a mean accuracy of 0.81 with 95% confidence intervals of 0.65 and 0.96 (Figure 7).

Conclusion

[0183] The present study surprisingly found that subjects with, or at risk of, delayed recovery from mild traumatic brain injury (mTBI), such as concussion, have a biomarker profile that distinguishes them from individuals who present without delayed recovery from mTBI; in particular, a biomarker profile that evaluates at least one biomarker selected from the group consisting of alpha- 1 -antichymotrypsin (alpha- 1- ACT), IgG3, Hepatitis Growth Factor Like protein (HGFL), Matrix metalloproteinase 9 (MMP-9), Angiotensin-converting enzyme (ACE) and Selenoprotein P (SEPPI). This study also found that alpha- 1 -ACT concentration can differentiate between children with and without delayed recovery from concussion with a mean accuracy of 81%. The identified biomarkers, in particular alpha- 1 -ACT, have the capacity to transform the way that mTBI, in particular concussion, is assessed and treated in a clinical setting, by allowing clinicians to advantageously provide targeted and personalised treatment to subjects most likely to experience delayed recovery.

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