Methods and kits for identifying neurological diseases and conditions

Abstract

A method for identifying a cerebral edema or a cerebral microhemorrhage in a subject, the method including providing a sample obtained from the subject, comprising a level of one or more biomarkers, wherein the one or more biomarkers comprises one or more GLX-related GAGs, one or more GLX-related PGs, one or more GLX-related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof, or any combination of the aforementioned biomarkers; measuring or detecting the level of the one or more biomarkers in the sample; comparing the level of the one or more biomarkers measured or detected to a reference level for the one or more biomarkers; and identifying the subject as having a cerebral edema or a cerebral microhemorrhage if the level of the one or more of the biomarkers measured or detected from the sample are different than the reference level for the one or more biomarkers.

Description

METHODS AND KITS FOR IDENTIFYING NEUROLOGICAL DISEASES AND CONDITIONSCROSS REFERENCE TO RELATED APPLICATIONS

[0001] The present application priority to and the benefit of U.S. App. No. 63 / 834,379, filed June 21, 2024, the entire contents of which are hereby incorporated by reference in their entirety.FIELD

[0002] The technologies described herein relate to methods for identification and assessment of blood-brain barrier (BBB) dysfunction using markers of glycocalyx (GLX) shedding, e.g. GLX disruption. In non-limiting examples, included are methods for identifying altered BBB influx or efflux regulatory function / activity, cerebral edema and / or cerebral microhemorrhage in a subject by assessing the presence or levels of one or more biomarkers of the glycocalyx (GLX) in a sample from the subject. The methods may compare the level of the one or more biomarkers detected to a reference level for the one or more biomarkers; and identifying the subject as having BBB dysfunction if the level of the one or more biomarkers detected are different than the reference level for the one or more biomarkers. It is preferred that the methods comprise the combination of GLX biomarkers and, thus, the assessment of the presence or levels of at least two, i.e. two or more, biomarkers of the GLX in a sample from a subject. The methods can involve in some aspects assigning, under the control of one or more processors, a BBB condition score to each of the one or more biomarkers, or the combination of biomarkers, of the first dataset, wherein the condition score is based on a deviation of the level of each of the one or more biomarkers, or combination of biomarkers, of the first dataset from the reference level of the second dataset and determining an indication status of the BBB condition based on the condition score. In non-limiting examples, the BBB condition score may reflect influx disruption, efflux disruption, the presence or likelihood of a cerebral edema and / or the presence or likelihood of a cerebral microhemorrhage.BACKGROUND

[0003] The glycocalyx, also known as the pericellular matrix, is a layer composed of proteoglycans (PGs), glycosaminoglycans (GAG), glycoproteins (GP), glycoconjugates (GC), and glycolipids (GL) that surrounds the cell membranes of most animal cells. Generally, theglycocalyx is involved in the cell-cell recognition, communication, mechano-sensing, chemosensing, blood vessel size regulation, filtration of blood contents, transcellular signaling, vascular barrier function, and intercellular adhesion. The understanding of the glycocalyx and its impact on human disease is in its infancy.SUMMARY

[0004] The methods disclosed herein each have several aspects, no single one of which is solely responsible for their desirable attributes. Without limiting the scope of the claims, some prominent features will now be discussed briefly. Numerous other embodiments are also contemplated, including embodiments that have fewer, additional, and / or different components, steps, features, objects, benefits, and advantages. The components, aspects, and steps may also be arranged and ordered differently.

[0005] Methods and systems are provided relating to changes in the blood-brain barrier (BBB), in particular the monitoring of such changes to identify or assess the likelihood of alteration in BBB function, i.e. BBB dysfunction. These changes in the blood-brain barrier may be leveraged in a variety of ways to improve the standard of care often reliant on, for example, subjective image analysis. In some embodiments, a method is provided for identifying a change in a blood-brain barrier (BBB) that allows influx of a compound (e.g. large or small molecule) across the BBB from the vascular lumen into the brain parenchyma, or vice versa to allow efflux of one or more compound (e.g. large or small molecule) from the brain parenchyma to the vascular lumen. The methods may be used in the diagnosis and treatment of BBB dysfunction or one or more symptoms of a disease or condition associated with BBB dysfunction. Such methods include the assessment, monitoring and modification of treatments for which such a compound is indicated and / or to assess a patient response, or likely patient response, to treatment. In some embodiments, the identification of changes to the BBB (i.e. identification of a BBB dysfunction) may result in the modification of therapies. The modification of therapies may include the stopping of or decrease in therapeutics believed to be ineffective in view of the identified or suspected changes to the BBB, as well as the starting of or increase in administration of drugs previously avoided due to a determined or suspected decreased passage through the BBB. Exemplary methods include providing a first dataset from a sample obtained from a subject, the first dataset including determination of the presence or a level of one or more biomarkers of GLX- shedding, including one or more GLX-related GAGs (including but not limited tohyaluronic acid (HA), heparan sulfate (HS), Chondroitin Sulfate, Dermatan Sulfate, or Keratan Sulfate, and fragments thereof), one or more GLX-related PGs (including but not limited to CD44, a syndecan, perlecan, small leucine-rich proteoglycans, betaglycans, or glypicans, and fragments theoreof), one or more GLX-related GPs (including but not limited to podocalyxin or mucins, and fragments thereof), one or more GLX-related GLs (including but not limited to glycosphngolipids, gangliosides and fragments thereof), one or more GLX-related GCs, and / or any combination of the aforementioned biomarkers. In particular embodiments, the methods comprise the determination of the presence or levels of the one or more GLX-biomarkers is the combination of the presence or level values of the one or more markers. That is, in such embodiments, the methods comprise the combination of GLX-biomarker indicators to combine information (e.g. indicating or relating to the presence or level) of at least two (i.e. two or more) of the GLX-biomarkers as described herein. The providing of the first dataset as used herein does not imply that the dataset or one or more component thereof must be generated de novo, but includes the receiving of such dataset or one or more components thereof, i.e. obtaining the dataset or one or more component thereof whether independently and / or newly determined or not. Thus, providing the first dataset or one or more component thereof includes datasets generated by third party sources both under explicit instruction and absent such instruction. The subject may be at risk to changes in the BBB due to, for example, disease, aging, and / or drug treatment.The exemplary methods also include associating (e.g., classifying) the one or more biomarkers (e.g., based on concentration and / or presence of the one or more biomarkers) with the state of the BBB. In various implementations, the concentration or presence of the one or more biomarker may be based on direct detection or may be indirect detection. Indirect detection is detection based on any method known in the art or described herein that is not reliant on the presence of the biomarker in the sample, and includes, in non-limiting examples, the detection of the presence and / or levels of autoantibodies specific for the one or more biomarkers (or a fragment thereof), as well as the detection of the presence and / or levels of GLX-cutting enzymes. The associating (e.g. classifying) of each biomarker from the one or more biomarkers, or the combination of biomarkers (e.g. two or more biomarkers) to the condition of the BBB may be to an influx-condition, and effluxcondition, the presence or likelihood of a cerebral edema, or the presence or likelihood of a cerebral microhemorrhage.The exemplary method further includes: providing (including receiving as described herein) a second dataset comprising at least the same information relating to the same one or more biomarkers analyzed in the first dataset. The second dataset includes reference level(s) of the one or more biomarkers, which reference level may be (a) biomarker information as known in the art or determined in a population of subjects similarly situated to that of the first dataset but which second population is known or believed to not have BBB dysfunction; (b) biomarker information as known in the art or determined in a population of subjects similarly situated to that of the first dataset and which second population is known or believed to have BBB dysfunction; or (c) biomarker information determined in the subject of the first dataset but at a different point in time, i.e. before or after the information of the first dataset.The exemplary method may further comprise comparing the biomarker information, e.g. relating to the respective presence or level, of the one or more biomarkers from the first dataset to the same information of the second dataset; and assigning, under control of one or more processors, an BBB condition score to each of the one or more biomarkers, or combination of biomarkers, of the first dataset.

[0006] The BBB condition score is based on a deviation of the level of the one or more biomarkers of the first dataset, or a deviation of the combination of biomarkers (i.e. combination of at least two biomarkers) from the respective level or levels of the one or more biomarkers, or combination of biomarkers, of the second dataset; and determining an indication status of the BBB based on the condition score. For example, where the state or condition is a BBB influx condition, the likely influx of a composition can be modeled, estimated or determined based on the BBB influx-condition score. As described herein, the second dataset comprises the levels of at least the same one or more biomarkers analyzed or classified in the first dataset and may be reference levels of the one or more biomarkers as known in the art or determined in healthy subjects but otherwise similarly situated to the subject of the first dataset,or may be reference levels from the same subject but determined at an different time point, e.g. before or after the administration of a compound. Accordingly, in some embodiments, the compound is administered between acquisition of the first and second datasets. The methods may monitor the prognosis or progression of a disease and / or treatment protocol. In some embodiments, the methods may be used to identify that a person is likely to respond to a drug or unlikely to respond to a drug. The methods may involve alteration or administration of a therapeutic based on the BBB condition score, including the starting, stopping or modification of the dosage of a therapeutic.

[0007] The levels of the biomarkers may be determined directly or indirectly. In some embodiments, the levels of the one or more biomarkers include concentrations of the one or more biomarkers. In some embodiments, the concentrations of the one or more biomarkers are obtained from a biochemical analysis of the sample. In some embodiments, the concentrations of the one or more biomarkers are determined from pixel intensities of the biomarkers obtained from an image analysis. In some embodiments, the levels of the one or more biomarkers include pixel intensities of the biomarkers obtained from an image analysis.

[0008] In some embodiments, the levels or presence of the one or more biomarkers are determined indirectly by detecting the level or presence of autoantibodies specific for the respective one or more biomarkers or the level or presence of splicing enzymes that act on GLX components, i.e. GLX-cutting enzymes. In some embodiments, the levels of the one or more biomarkers are determined by determining the concentrations of the one or more respective autoantibodies or GLX-cutting enzymes in a sample. In some embodiments, the concentrations of the one or more biomarkers are determined from signal intensities of respective autoantibodies or GLX-cutting enzymes obtained from a biochemical analysis.

[0009] In some embodiments, the method further includes generating training data based on correlations between the reference levels (of the one or more biomarkers, or the combination of biomarkers) and indicators for the BBB condition; and training a machine learning model on the training data to output the BBB condition score. In some embodiments, the BBB condition score indicates whether the subject has or is likely to have BBB dysfunction. In a non-limiting example, the BBB dysfunction is an influx condition, an efflux condition, a cerebral edema, or a cerebral microhemorrhage and the condition score is an influx-condition score, an efflux-condition score, a score reflecting the presence or likelihood of cerebral edema, or a score reflecting the presence or likelihood of cerebral microhemorrhage. In someembodiments, the assigning of the BBB condition score to the one or more biomarkers, or the combination of biomarkers, includes transforming the levels of the one or more biomarkers, or the combination of biomarkers, to the BBB condition score via the machine learning model. In some embodiments, the machine learning model includes a multivariate statistical test.

[0010] The methods disclosed herein assess or determine the presence of levels of biomarkers of GLX shedding in samples from a subject, in particular, a blood sample (which may be whole blood, plasma or serum). Thus, the methods include the assessment of the presence of level of one or more GLX component in a blood sample, including but not limited a PG, GAG, GP, GL and GC, or fragment thereof. The methods may, in particular, include the determination of combination information, e.g. the assessment of the presence or level of any two or more of the aforementioned biomarkers in combination. In some embodiments, the glycocalyx is the endothelial glycocalyx. In some embodiments, the one or more PGs include CD44, a syndecan, perlecan, small leucine-rich proteoglycans, betaglycans, glypicans, or a fragment thereof. In some embodiments, the one or more GAGs include hyaluronic acid (HA), heparan sulfate, Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate or a fragment thereof. In some embodiments, the one or more GPs include podocalyxin (Podo), a mucin, or a fragment thereof. In some embodiments, the one or more GLs include a glycosphngolipid, a ganglioside or a fragment thereof. The one or more biomarkers may also include any combination of the aforementioned biomarkers.

[0011] Provided are also methods of treatment or diagnosis of BBB dysfunction and diseases or conditions associated with BBB dysfunction. For example provided are methods of treatment of a condition in a subject for which a composition is indicated, further including reducing a dosage of the composition, increasing a dosage interval of the composition, or discontinuing administrating the composition if the indication status of the influx of the composition across the BBB indicates the BBB dysfunction, or proceeding with the administrating of the composition if the indication status of the influx does not indicate the BBB dysfunction. In some embodiments, the method of treatment may comprise administering a therapeutic composition identified as being able to cross the BBB based on the BBB characteristics determined from the one or more datasets as described herein. The method of treating a disease, disorder, or condition with a therapeutic composition that crosses the BBB in a subject in need thereof may comprise:

[0012] providing (e.g., receiving) a dataset from a sample obtained by a subject, the dataset comprising information reflecting the presence or level of one or more biomarkers associated with glycocalyx (GLX) shedding (e.g., glycoproteins (GPs) or fragments thereof; glycoconjugates (GCs) or fragments thereof; glycosaminoglycans (GAGs) or fragments thereof; glycolipids (GLs) or fragments thereof; proteoglycans (PG) or fragments thereof; or galectins or fragments thereof) or information relating to the combination thereof, and correlating the information with BBB condition (e.g. permeability) of the subject. In a nonlimiting example, the method may further comprise; determining the permeability of the BBB to the therapeutic composition; and administering the therapeutic composition to the patient based on the BBB condition with respect to the composition (e.g. the permeability of the BBB to the composition. The method may include beginning administration of the drug, stopping administration of the drug, reducing administration frequency and / or dosage of the drug, increasing administration frequency and / or dosage of the drug.

[0013] In some embodiments, the method further includes monitoring a change in a state of the subject (e.g., during a clinical trial) with respect to: a change in a disease extent of the subject; a change in a side effect of the composition; or a change in an efficacy of the composition. In some embodiments, the composition includes a small or large molecule therapeutic as known in the art or described herein, including but not limited to an antibody or a molecule comprising one or more antibody antigen-binding domains (including single domain binding molecules such as dAbs and VHH).

[0014] In some embodiments, the method further includes assigning or advising the subject to receive one or more magnetic resonance imaging (MRI) sessions if the subject is indicated for the BBB dysfunction (e.g., via the biomarker measurement described herein). In some embodiments, the method further includes imaging a brain of the subject using the MRI to obtain MRI images, wherein the MRI images indicate signal abnormalities related to the BBB dysfunction, wherein the signal abnormalities may relate to an Amyloid-Related Imaging Abnormality (ARIA). In some embodiments, the method does not comprise one or more MRI sessions.

[0015] Additional non-limiting embodiments of the present disclosure are provided in the following numbered alternatives:

[0016] 1. A method (e.g., for treatment, for diagnosis, for identifying cerebral edema, for identifying cerebral microhemorrhage, an influx condition or an efflux condition), the method comprising: providing a first dataset obtained from a subject (e.g. from a sample from a subject), the first dataset comprising information indicating the presence or a level of one or more biomarkers, or the combination of biomarkers (e.g. two or more biomarkers) wherein the one or more, or combination of, biomarkers comprise a biomarker of GLX- shedding including one or more of GLX-related proteoglycans (PGs), one or more glycocalyx (GLX)-related glycosaminoglycans (GAGs), one or more GLX-related glycoproteins (GPs), one or more GLX-related glycolipids (GLs), one or more GLX- related glycoconjugates (GCs), or one or more fragments thereof,; classifying each of the one or more biomarkers to BBB dysfunction, the classifying comprising: providing a second dataset comprising a reference level of the same one or more biomarkers, or combination of biomarkers, analyzed in the first dataset; comparing the level of the one or more biomarkers, or the combination of biomarkers, from the first dataset to the reference level of the second dataset; and optionally assigning, under control of one or more processors, a BBB-condition score to each the one or more biomarkers of the first dataset, or to a combination of biomarkers of the first dataset, wherein the condition score is based on a deviation of the level of each of the one or more biomarkers, or the level of a combination of biomarkers, of the first dataset from the reference level of the second dataset; and determining an indication status of the condition based on the cerebral edema-condition score.

[0017] 2. The method of alternative 1, wherein the BBB dysfunction is an influx condition, an efflux condition, a cerebral edema, or a cerebral microhemorrhage, and the condition score is an influx-condition score, an efflux-condition score, a score indicating the presence or likelihood of a cerebral edema, or a score indicating the presence or likelihood of a cerebral microhemorrhage, respectively.

[0018] 3. The method of alternative 2, wherein the information relating to the presence or level of the one or more biomarkers of the first dataset is information relating to the level of the one or more biomarkers that is preferably concentration of the one or more biomarkers obtained from a biochemical analysis of the sample.

[0019] 4. The method of alternative 3, wherein the concentration of the one or more biomarkers are determined from signal intensities of the biomarkers obtained from a biochemical analysis, or the level of a respective autoantibody or GLX-cutting enzyme.

[0020] 5. The method of alternative 1, wherein the levels of the one or more biomarkers comprise pixel intensities of the biomarkers obtained from an image analysis, or are determined from a mass spectroscopy readout comprising one or more of signal intensity, ion count, peak area, or spectral data corresponding to the biomarkers.

[0021] 6. The method of any one of alternatives 1-5, further comprising: generating training data based on correlations between the reference levels and indicators for an influx condition, an efflux condition, cerebral edema, or cerebral hemorrhage; and training a machine learning model on the training data to output the respective condition score.

[0022] 7. The method of alternative 6, wherein the influx-condition score, effluxcondition score, cerebral edema-condition score, or cerebral hemorrhage-condition score indicates whether the subject is has or is at risk for an influx condition, an efflux condition, cerebral edema, or cerebral hemorrhage, respectively.

[0023] 8. The method of alternative 6 or 7, wherein the assigning of the condition score to each of the biomarkers, or the combination of the biomarkers, comprises transforming the level of the one or more biomarkers to the condition score via the machine learning model.

[0024] 9. The method of any one of alternatives 6-8, wherein the machine learning model comprises a multivariate statistical test.

[0025] 10. The method of alternative 9, wherein the multivariate statistical test comprises a dimensionality reduction partial least squares discriminant analysis.

[0026] 11. The method of alternative 9, wherein the multivariate statistical test comprises decision tree algorithms.

[0027] 12. The method of alternative 9, wherein the multivariate statistical test comprises a support vector machine (SVM).

[0028] 13. The method of alternative 12, wherein the SVM comprises a linear SVM.

[0029] 14. The method of alternative 12, wherein the SVM comprises a non-linear SVM.

[0030] 15. The method of alternative 14, wherein the non-linear SVM comprises a polynomial kernel.

[0031] 16. The method of alternative 14, wherein the non-linear SVM comprises a radial basis function (RBF) kernel.

[0032] 17. The method of alternative 14, wherein the non-linear SVM comprises a sigmoid kernel.

[0033] 18. The method of alternative 12, wherein the SVM comprises a support vector regression (SVR).

[0034] 19. The method of any one of alternatives 1-18, wherein the glycocalyx is the endothelial glycocalyx.

[0035] 20. The method of any one of alternatives 1-19, wherein the one or more PGs comprise CD44, a syndecan, a perlecan, a small leucine-rich proteoglycan, a betaglycan, a glypican, or a fragment thereof.

[0036] 21. The method of any one of alternatives 1-20, wherein the one or more GAGs comprise hyaluronic acid (HA), heparan sulfate (HS), chondroitin sulfate, dermatan sulfate, keratan sulfate, or a fragment thereof.

[0037] 22. The method of any one of alternatives 1-21, wherein the one or more GPs comprise podocalyxine (Podo), a mucin, or a fragment thereof.

[0038] 23. The method of any one of alternatives 1-22, wherein the method comprises the comparison of information of a combination of biomarkers, which combination is thecombination of information from at least two, at least three, at least four, or at least five biomarkers..

[0039] 24. The method of any one of alternatives 1-23, wherein the first and second dataset comprise information indicating the presence or a level of a wherein the one or more PGs comprise CD44 or a fragment thereof, wherein the one or more GAGs comprise HA, CS, or a fragment thereof or any combination of the aforementioned biomarkers, and wherein the one or more PGs comprise syndecan, perlecan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0040] 25. The method of any one of alternatives 1 -24, wherein the one or more biomarkers comprises an glycoprotein (GP) or a fragment thereof.

[0041] 26. The method of alternative 25, wherein the GP comprises a mucin or a fragment thereof.

[0042] 27. The method of alternative 26, wherein the mucin comprises Mucin 1, Mucin 16, Mucin 20, Mucin 5b, Mucin 6, Muc 18, Muc 14, or a fragment thereof or any combination of the aforementioned biomarkers.

[0043] 28. The method of any one of alternatives 25-27, wherein the GP comprises a siaolmucin, or a fragment thereof.

[0044] 29. The method of alternative 28, wherein the siaolmucin comprises podocalyxin, endoglycan, podoplanin, or a fragment thereof or any combination of the aforementioned biomarkers.

[0045] 30. The method of any one of alternatives 25-29, wherein the GP is an immunoglobulin super family (IgSF) comprises a siglec, or a fragment thereof.

[0046] 31. The method of alternative 30, wherein the siglec comprises Siglec-5, Siglec-9, Siglec- 10, Siglec- 15, or a fragment thereof or any combination of the aforementioned biomarkers.

[0047] 32. The method of any one of alternatives 1-31, wherein the one or more biomarkers comprises a galectin, or a fragment thereof.

[0048] 33. The method of alternative 32, wherein the galectin comprises Galectin-1, Galectin-2, Galectin-3, Galectin-4, Galectin-8, Galectin-9, Galectin-12, or a fragment thereof or any combination of the aforementioned biomarkers.

[0049] 34. The method of any one of alternatives 1-33, wherein the one or more biomarkers comprises a sialic acid or a fragment thereof.

[0050] 35. The method of alternative 34, wherein the sialic acid comprises a polysialic acid or a fragment thereof.

[0051] 36. The method of any one of alternatives 1-35, wherein the one or more biomarkers comprises a sphingolipid or a fragment thereof.

[0052] 37. The method of any one of alternatives 1-36, wherein the one or more GLs comprises a glycosphingolipid, a ganglioside, or a combination thereof.

[0053] 38. The method of alternative 37, wherein the glycosphingolipid comprises sulfatide or a fragment thereof.

[0054] 39. The method of alternative 36 or 37, wherein the ganglioside comprises GM1, GDlb, GT lb, or a combination thereof.

[0055] 40. The method of any one of alternatives 1-39, wherein the glycoprotein comprises a selectin or a fragment thereof.

[0056] 41. The method of alternative 40, wherein the selectin comprises P-selectin, E- selectin, L-selectin, or a fragment thereof or any combination of the aforementioned biomarkers.

[0057] 42. The method of any one of alternatives 1-41, wherein the one or more PGs comprise a small leucine-rich proteoglycan (SLRP) or a fragment thereof.

[0058] 43. The method of alternative 42, wherein the SLRP comprises Fibromodulin, Asporin, Proline / arginine-rich end leucine-rich repeat protein (PRELP), Chondroadherin,Podocan, Opticin, Tsukushi, Testican (SPOCK), Epiphycan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0059] 44. The method of alternative 42, wherein the SLRP comprises Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0060] 45. The method of alternative 42, wherein the SLRP comprises Fibromodulin, Asporin, PRELP, Chondroadherin, Podocan, Opticin, Tsukushi, SPOCK, Epiphycan, Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0061] 46. The method of any one of alternatives 1-45, wherein the one or more PGs comprise a betaglycan, or a fragment thereof.

[0062] 47. The method of any one of alternatives 1-46, wherein the one or more PGs comprise a Glypican or a fragment thereof.

[0063] 48. The method of alternative 47, wherein the Glypican comprises Glypican-1, Glypican-2, Glypican-3, Glypican-4, Glypican-5, Glypican-6, or a fragment thereof or any combination of the aforementioned biomarkers.

[0064] 49. The method of any one of alternatives 1-48, wherein the one or more PGs comprises a hyalectan or a fragment thereof.

[0065] 50. The method of alternative 49, wherein the hyalectan comprises aggrecan, brevican, neurocan, versican, or a fragment thereof or any combination of the aforementioned biomarkers.

[0066] 51. The method of any one of alternatives 1-50, wherein the one or more GAGs comprise Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, or a fragment thereof or any combination of the aforementioned biomarkers.

[0067] 52. The method of any one of alternatives 1-51, wherein the first dataset comprises one or more autoantibodies against one or more of the one or more biomarkers.

[0068] 53. The method of any one of alternatives 1-52, further comprising:reducing a dosage of a therapy, increasing a dosage interval of the therapy, or discontinuing the therapy if the indication status of the condition indicates a presence or likelihood of the presence of the condition for the subject, or proceeding with the therapy if the indication status of the condition does not indicate the presence of the condition for the subject.

[0069] 54. The method of any one of alternatives 1-52, further comprising: monitoring a change in a state of the subject (e.g., during a clinical trial) with respect to: a change in a disease extent of the subject; a change in a side effect of a therapy; or a change in an efficacy of the therapy.

[0070] 55. The method of alternative 53, further comprising: monitoring a change in a state of the subject (e.g., during a clinical trial) with respect to: a change in a disease extent of the subject; a change in a side effect of the therapy; or a change in an efficacy of the therapy.

[0071] 56. The method of any one of alternatives 53-55, wherein the therapy comprises administering a therapeutically effective amount of a composition to the subject.

[0072] 57. The method of alternative 56, wherein the composition comprises an antibody or a molecule comprising one or more antibody antigen-binding domains (including single domain binding molecules such as dAbs and VHH).

[0073] 58. The method of alternative 57, wherein the antibody comprises a monoclonal antibody or comprises an antibody-BBB shuttling drug conjugate.

[0074] 59. The method of alternative 57 or 58, wherein the antibody is directed against an Ap peptide.

[0075] 60. The method of alternative 59, wherein the antibody comprises lecanemab, donanemab or trontinemab

[0076] 61. The method of alternative 57 or 58, wherein the antibody is directed against hyperphosphorylated tau.

[0077] 62. The method of any one of alternatives 59-61, further comprising assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the cerebral edema.

[0078] 63. The method of alternative 62, further comprising imaging a brain of the subject using the MRI to obtain MRI images, wherein the MRI images indicate signal abnormalities related to the cerebral edema known as Amyloid-Related Imaging Abnormality (ARIA).

[0079] 64. The method of any one of alternatives 1-63, wherein the subject has been diagnosed with a neurological disease or disorder, or sequela of the neurological disease or disorder.

[0080] 65. The method of any one of alternatives 1-64, wherein the sample comprises a blood sample, plasma sample, serum sample, a cerebrospinal fluid sample, a saliva sample, a urine sample, a tear sample, or any combination thereof.BRIEF DESCRIPTION OF THE DRAWINGS

[0081] In order to describe the manner in which the above-recited and other desirous properties and features of the technologies described herein can be obtained, a more particular description will be rendered by reference to specific embodiments thereof which are illustrated in the appended drawings. Understanding that these drawings depict only exemplary embodiments and are not therefore to be considered to be limiting of its scope, some examples will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

[0082] FIG. 1 is a schematic illustration of a method for identifying a disease or condition based on a cerebral-edema condition score according to some embodiments.

[0083] FIG. 2 is a schematic illustration of a method for identifying a disease or condition based on a cerebral-microhemorrhage-condition score according to some embodiments.

[0084] FIG. 3 is a schematic illustration of a method for identifying a disease or condition based on a blood-brain barrier influx-condition score according to some embodiments.

[0085] FIG. 4 is a schematic illustration of a method for identifying a disease or condition based on a blood-brain barrier efflux-condition score according to some embodiments.

[0086] FIGS. 5A-C are diagrams that illustrate various in vitro BBB models representing organ-on-a-chip microfluidic devices for assessing BBB permeability according to some embodiments. FIG. 5 A illustrates the BBB model under control conditions. FIG. 5B illustrates the BBB model under BBB Breakdown conditions. FIG. 5C illustrates the BBB model under BBB Protection conditions.

[0087] FIGS. 6A-F are measurements of various changes in GLX marker levels across different conditions of the in vitro model of the BBB. FIGS. 6 A, 6B, and 6C show the degree of GLX markers shedding from the blood vessel chamber of the in vitro model of BBB under various conditions. FIGS. 6D, 6E, and 6F show the degree of GLX shedding from the brain chamber of the in vitro model of BBB under various conditions.

[0088] FIGS. 7A-7E illustrates how combining GLX-related markers from FIG. 5 in an in vitro model of the BBB with differing levels of BBB leakiness, as detected by fluorescent tracer permeability, shows an increased ability to classify which group has a BBB breakdown.

[0089] FIG 8 provides several glycocalyx-associated biomarker concentrations in the endo compartment (B) and the brain compartment (C) for the natural control. The concentrations of each biomarker are compared when the BBB is Closed (black) and Open (gray; 20% increased permeability; A). The concentrations of each biomarker are compared when the BBB is Closed (black) and Open (gray).

[0090] FIG 9 provides several glycocalyx-associated biomarker concentrations in the endo compartment (B) and the brain compartment (C) following use of the GLX Cocktail Application. The concentrations of each biomarker are compared when the BBB is Closed(black) and Open (gray; 20% increased permeability; A). The concentrations of each biomarker are compared when the BBB is Closed (black) and Open (gray).

[0091] FIG. 10 is the biomarker levels of the Endo and Brain combined for the natural (FIG 8) and the GLX-enzymes (FIG 9).

[0092] FIG 11 provides Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) visualizations of biomarker expression across blood-brain barrier (BBB) states for the Endo chamber with the following groups BBB closed (N=l l) and BBB open (N=6). Top left: LDA projection showing clear separation between BBB closed and open states. Top right: PCA projection demonstrating variance across principal components. Bottom left: PCA scree plot indicating the cumulative variance explained by each component. Bottom right: PCA loadings plot identifying key glycocalyx-associated biomarkers contributing to BBB state differentiation.

[0093] FIG 12 provides PCA and LDA visualizations of biomarker expression across bloodbrain barrier (BBB) states for the Brain chamber with the following groups BBB closed (N=l 1) and BBB open (N=6). Top left: LDA projection showing clear separation between BBB closed and open states. Top right: PCA projection demonstrating variance across principal components. Bottom left: PCA scree plot indicating the cumulative variance explained by each component. Bottom right: PCA loadings plot identifying key glycocalyx-associated biomarkers contributing to BBB state differentiation.

[0094] FIG 13 provides PCA and LDA visualizations of biomarker expression across bloodbrain barrier (BBB) states for combination of the Endo and the Brain chamber with the following groups BBB closed (N=l 1) and BBB open (N=6). Top left: LDA projection showing clear separation between BBB closed and open states. Top right: PCA projection demonstrating variance across principal components. Bottom left: PCA scree plot indicating the cumulative variance explained by each component. Bottom right: PCA loadings plot identifying key glycocalyx-associated biomarkers contributing to BBB state differentiation.

[0095] FIG 14 provides PCA and LDA visualizations of biomarker expression across bloodbrain barrier (BBB) states for the Endo chamber using the model generated in the first dataset on the second dataset with GLX-enzymes (BBB-Open, N=7, BBB-Closed N=2). Left: LDA projection showing strong separation between BBB closed and open states in the second dataset. Right: PCA projection demonstrating variance across principal components.

[0096] FIG. 15 provides PCA and LDA visualizations of biomarker expression across bloodbrain barrier (BBB) states for the Endo chamber using the model generated in the first dataset on the second unseen dataset where the BBB is treated with GLX-enzymes (BBB -Open, N=7, BBB-Closed N=2). Left: LDA projection showing strong separation between BBB closed and open states states in the second dataset. Right: PCA projection demonstrating variance across principal components.

[0097] FIG. 16 provides PCA and LDA visualizations of biomarker expression across bloodbrain barrier (BBB) states for the combination of the Endo and the Brain chamber using the model generated in the first dataset on the second dataset with GLX-enzymes (BBB-Open, N=7, BBB-Closed N=2). Left: LDA projection showing strong separation between BBB closed and open states in the second dataset. Right: PCA projection demonstrating variance across principal components.DETAILED DESCRIPTION

[0098] Described herein are various examples of techniques for evaluating a state of the bloodbrain barrier of a subject (e.g., a clinical trial subject, a research subject, a patient, or other subject, who may be human or a non-human animal, such as a mammal), which may include evaluating a health of the blood-brain barrier, using a sample from the subject, e.g. a blood sample. Such a blood sample may be obtained from a portion of the body other than the brain, such as a peripheral blood sample obtained from, e.g., a blood vessel in a limb or other part of the body of a subject. In accordance with some techniques described herein, the subject’s blood sample may be evaluated for signs of shedding of the glycocalyx, such as by identifying levels in the blood sample of components derived from the glycocalyx due to the splicing or degradation of one or more components of the glycocalyx. However, as will be appreciated from the discussion below, as the glycocalyx is present throughout the body, mere presence or absence in a blood sample, particularly a peripheral blood samples, of glycocalyx-derived components may not be indicative of a state of the subject’s blood-brain barrier. Accordingly, described herein are computational techniques for evaluating levels of glycocalyx-derived components in the blood sample and determining, based on the computational evaluation, a condition of the blood-brain barrier of the subject.

[0099] In some embodiments, evaluating the health of the blood-brain barrier may include determining whether the blood-brain barrier is healthy or non-pathogenic, or whether the blood-brain barrier deviates from a healthy or reference condition or is pathogenic. Such areference condition may, in some cases, be a reference state for a healthy population, a reference state for a particular demographic population, a reference state for a particular disease population, a reference state for the subject (e.g. at a different time point from which the health of the blood-brain barrier is to be evaluated), or other reference (e.g., the models described herein). Evaluating the blood-brain barrier may include evaluating or measuring a functioning of the blood-brain barrier, including by evaluating or measuring a permeability of the bloodbrain barrier. For example, an objective measure may be obtained in some embodiments of whether the blood-brain barrier is or is not permeable to one or more large or small molecules to a greater extent than a reference or normal amount or to a greater extent than reference or normal rate from a vasculature into a perivascular space or otherwise into tissues adjacent to vasculature. The reference condition or state, or a collection of reference condition or states from healthy individuals may be incorporated into the training data sets in the machine learning algorithms in order to provide a module capable of correlating measured biomarker levels of a subject with those of the reference states to identify subject biomarker changes that may correlate with alterations to the blood-brain barrier.

[0100] In some cases, an evaluation of the blood-brain barrier may be performed in connection with a therapeutic or a further diagnostic test. For example, evaluation of the blood-brain barrier may be done to determine whether the subject is a candidate for a drug or a category of drugs or to identify or exclude one of a set of drugs or categories of drugs. As another example, evaluation of the blood-brain barrier may be done to determine or adjust a dose of a drug for the subject. As a further example, evaluation of the blood-brain barrier may be done to determine whether to obtain medical imaging (e.g., magnetic resonance imaging (MRI)) of the subject’s brain. Further details regarding such evaluation of the blood-brain barrier in connection with a therapeutic or a further diagnostic test are provided below.

[0101] Conventionally, many neurodegenerative diseases and other medical conditions of the central nervous system are diagnosed or characterized using medical imaging. Such medical imaging may include MRI, and in some cases the imaging quality may be enhanced through the use of contrast dyes such as gadolinium. A radiologist may review such medical images to determine structural variations between a subject’s brain and a reference brain, which may be indicative of a disease state.

[0102] Among the structural variations that a radiologist may review a medical image to identify is Amyloid-Related Imaging Abnormalities (ARIA). ARIA refers to abnormalitiesdetected by MRI, often in individuals with Alzheimer's Disease (AD), and particularly those undergoing treatment with anti-amyloid therapies targeting amyloid-beta (A ) plaques. There are two main types of ARIA: ARIA-E (Edema), characterized in part by areas of increased signal intensity on MRI scans, indicating the accumulation of fluid in the brain's white matter; and ARIA-H (Hemorrhage / Hemosiderin), characterized in part by areas of hypointensity on MRI scans, indicating microhemorrhages or small areas of bleeding within the brain parenchyma. ARIA detected on MRI scans is an important consideration in the management of AD patients undergoing anti-amyloid therapies, and it may necessitate treatment adjustments and / or discontinuation. It is contemplated that ARIA is predicted, diagnosed, prognosticated, monitored for emergence and resolution, selected for treatment, selected for inclusion or exclusion from clinical trials and / or monitored for treatment response with ARIA-reducing interventions.

[0103] ARIA (as the name, “imaging abnormalities,” suggests) is a phenomenon uniquely associated with medical imaging and medical imaging is always used to detect ARIA. ARIA, and its related medical imaging, have long been used as the standard of care for patients that are candidates for or are undergoing anti-amyloid therapies, including for identifying whether a patient should start or stop such a therapy or whether an adjustment to such therapy is warranted.

[0104] Despite that medical imaging for identification of ARIA has long been and still is the standard of care, clinicians and subjects are all well aware of many significant drawbacks of this approach. For example, medical imaging can be difficult to obtain, due to scarcity of imaging equipment and of time slots for patients to be imaged, and of radiologists to review imaging to draw conclusions. As such, even obtaining an appointment can be difficult for patients, which presents challenges to obtaining the images necessary for identifying presence of ARIA in a patient and then taking the actions (e.g., starting, stopping, or adjusting a therapy) that presence of ARIA can gate.

[0105] Nevertheless, clinicians are well aware that even when imaging can be obtained, ARIA is difficult to detect with certainty. Cerebral edema and / or microhemorrhaging that is the basis for ARIA might have been developing for weeks before it is detected in an analysis of an MRI scan. Additionally, a patient whose medical image shows no signs of ARIA may still be a patient who has cerebral edema and / or microhemorrhage but in an early stage. However, without a positive ARIA reading from a medical image, clinical decisions that hinge on anARIA determination cannot be made. This means that patients that should otherwise adjust dosage or discontinue use may continue to receive treatment, exacerbating the damage.

[0106] This inability to reliably detect ARIA also means that frequent and repeated medical imaging may be needed to try to detect ARIA. Given the lack of imaging resources noted above, this presents an additional complication to ARIA detection, as having one patient repeatedly imaged takes resources away from other patients who may need to be imaged to try to detect ARIA. Additionally, frequent imaging may not be available for some patients, leaving many patients with a degenerative illness without access to crucial therapies. Medical imaging with a contrast dye such as gadolinium can present health risks for patients, and some clinicians may be cautious about repeated contrast administration and repeated imaging for some patients. Some patients may even develop adverse side effects from contrast administration and may no longer be able to receive contrast agent, after which they may not be able to receive sufficient medical imaging. Some payors (e.g., insurance companies, government programs) may also not provide coverage for repeated imaging in an attempt to detect ARIA, even if a patient is able to receive a first medical imaging scan or who might otherwise have access to medical imaging. For these reasons, even patients who had access to medical imaging at one time may lose access to imaging and may not be able to receive the safety monitoring necessary to receive therapies.

[0107] Further, radiologists and other clinicians acknowledge that ARIA is inherently a subjective reading in a medical image. Two radiologists may read the same medical image for the same patient and come to opposite conclusions about whether the image shows signs of ARIA, as there is no objective and universally adopted definition for ARIA in a medical image. As such, even if a patient were to obtain a medical image at a time that the patient has detectable ARIA, and even if one radiologist might conclude the resulting imaging demonstrates ARIA, it is not guaranteed that the reviewing radiologist would conclude ARIA is shown and would provide the diagnosis necessary for therapies to be made available or proposed to a patient, xxx

[0108] These challenges with ARIA have long been well-known. Despite the foregoing, medical imaging and ARIA remains the standard of care in certain fields, including gating administration or gating regulation of administration of anti-amyloid therapies. It remains a widespread problem that patients do not have access to crucial, life-saving therapies for neurodegenerative illnesses.

[0109] The inventors have recognized and appreciated that ARIA, when present, is related to dysfunction of the blood-brain barrier (BBB) and vascular pathology. The inventors further recognized and appreciated that if other techniques were available for diagnosis of BBB dysfunction the information regarding the prognosis of disease states would be greatly enhanced and provide more treatment options providing clinically better outcomes. More particularly, the inventors recognized and appreciated that it would be advantageous if an objective test were available for BBB dysfunction, that could be perhaps indicate appropriate timing for a medical image that may demonstrate ARIA or could even take the place of (and obviate the need for) medical imaging to identify ARIA from an image. Such an objective test has not been available to date.

[0110] In blood vessels, the glycocalyx is located on the apical surface facing the lumen. When vessels are stained with cationic dyes, transmission electron microscopy shows a large, irregularly shaped layer extending approximately 50-1000 nm into the lumen of a blood vessel. This has also been validated with orthogonal labelling and imaging methods. The endothelial glycocalyx is present throughout a diverse range of microvascular beds (capillaries) and macrovessels (arteries and veins), through the body of humans and other mammals, and is particularly dense at the blood-brain barrier (BBB). The glycocalyx also includes a wide range of proteins, glycans and enzymes that maintain its structure, provide chemical signals and regulate leukocyte and thrombocyte adherence. The primary role for the glycocalyx in the vasculature is to maintain plasma and vessel-wall homeostasis and endothelial health. Another main function of the glycocalyx within the vascular endothelium is that it shields the vascular walls from direct exposure to blood flow and blood cell binding as well as serving as a vascular barrier to permeability. Another protective function of the glycocalyx throughout the cardiovascular system is its ability to affect the filtration of interstitial fluid from capillaries into the interstitial space and to regulate the cellular behavior of the endothelial cell and cells that interact with the basal side of the endothelial cell.[OHl] The protective functions of the glycocalyx are universal throughout the vascular system. Without being bound by theory, because the glycocalyx is so prominent throughout the vascular system, disruption to this structure has detrimental effects that can cause disease and signal distress. Certain stimuli may lead to enhanced sensitivity of vasculature.

[0112] Shedding of the glycocalyx can be triggered by inflammatory stimuli and chemical imbalance at the vasculature and can lead to an increase in vascular permeability. Vascularwalls being permeable is disadvantageous, since it enables passage of some macromolecules, other harmful antigens and circulating immune cells as well as a change the hydraulic and osmotic pressure of the system, leading to vascular heterogeneity.

[0113] Neurodegenerative diseases, such as Alzheimer’s Disease (AD), can be characterized by vascular pathology, including BBB insufficiency leading to leaking of both liquid and blood cells through the BBB, resulting in edema and hemosiderin deposits. The inventors recognized and appreciated that shedding of the glycocalyx in vessels of the brain is associated with dysfunction of the BBB.

[0114] The inventors recognized and appreciated that, when glycocalyx shedding is present, components of the glycocalyx, fragments thereof, and other markers may be present in the blood. If these glycocalyx markers were indicative of glycocalyx shedding in the brain, this could be an indication of BBB dysfunction. Evaluation of glycocalyx fragments could therefore be performed to evaluate status of the blood-brain barrier, including evaluating functionality (such as permeability) of the blood-brain barrier. Leveraging the analyses of glycocalyx associated biomarkers described herein, the methods and systems of the present disclosure may afford an objective characterization of ARIA previously unavailable. This objective characterization, in addition to increasing the standard of care, may be leveraged to augment treatment protocols where BBB leakage may afford crossing of certain drugs across the barrier previously unavailable or the identification of changes to treatment protocols that could further promote their efficacy or decrease side effects.

[0115] The inventors have additionally recognized and appreciated, however, that because the glycocalyx is present in vessels throughout the body, and shedding of the glycocalyx in one part of the body is not necessarily correlated with shedding of the glycocalyx in another part of the body, indications in the blood of glycocalyx shedding is not necessarily a sign of glycocalyx shedding in the brain and thus not necessarily indicative of a status of the BBB. It therefore may not be enough in some cases to identify a particular glycocalyx fragment or level of a particular fragment and from that draw a conclusion regarding the BBB. This might be particularly the case with a blood sample obtained from outside the brain, such as a peripheral blood sample.

[0116] The inventors have recognized and appreciated, however, that if glycocalyx-related signals could be obtained, such as levels in the blood of different glycocalyx-derivedcomponents, levels of enzyme components known to be associated with GLX-shedding (e.g. GLX-cutting enzymes) or autoimmune response to the presence of these components (e.g., autoantibodies to different fragments of glycocalyx structures), the signals could be evaluated using computational techniques to draw an indirect inference regarding a state of the bloodbrain barrier. IN particular different combinations of glycocalyx-derived compounds or different levels of such compounds may be indicative of glycocalyx shedding in the brain and be a sign of potential BBB dysfunction. Such an indication may be determined using computational techniques as a likelihood of BBB dysfunction or a likelihood of the health of the BBB or of permeability or other function of the BBB. Such a likelihood determined using computational techniques may be useable to determine whether cerebral edema and / or hemorrhage that is associated medical imaging abnormalities such as ARIA may be present, or may be usable as an indication of BBB health to supplant the need for medical imaging to determine BBB status and / or ARIA. Such a test may therefore in some embodiments be used to determine what therapies to offer to a subject, or to start, adjust, or start a treatment regimen (e.g., a drug or category of drug) for a patient.

[0117] The disclosure provides a method for predicting and monitoring cerebrovascular injury, including the pathological processes underlying ARIA, for example, in individuals undergoing anti-amyloid therapy. The method may involve quantitative measurement of glycocalyx- associated biomarkers from blood samples collected before and during treatment. Moreover, the method may comprise assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the BBB dysfunction. In addition to providing a more qualitative assessment of ARIA, the methods of the present disclosure will decrease reliance on MRI as solely the standard of care for ARIA, decreasing its demand since the present disclosure may screen out those patients who do not need to seek medical imaging while increasing the ability to reliably detect life-threatening side effects of anti-amyloid therapies.

[0118] In some embodiments, the method of identifying a change in the BBB, may comprise: providing a first dataset obtained from a subject (e.g. from a sample from a subject), the first dataset comprising information indicating the presence or a level of one or more biomarkers, or the combination of biomarkers (e.g. two or more biomarkers) wherein the one or more, or combination of, biomarkers comprise a biomarker of GLX- shedding including one or more of GLX-related proteoglycans (PGs), one or moreglycocalyx (GLX)-related glycosaminoglycans (GAGs), one or more GLX-related glycoproteins (GPs), one or more GLX-related glycolipids (GLs), one or more GLX- related glycoconjugates (GCs), or one or more fragments thereof, galectins or fragments thereof, or detecting the presence or levels of such biomarkers by detecting the presence or levels of autoantibodies of GPs, GCs, GAGs, GLs, PGs, or fragments thereof); and correlating the levels of the one or more biomarkers associated with the glycocalyx with the BBB permeability of the subject to verify, detect or assess potential BBB dysfunction in the subject.

[0119] Multiple datasets from the subject may be used in the assessment. These datasets may help understand the course of any disease or treatment regimen on the BBB. For example, the method for identifying a change in BBB that allows influx or efflux of a composition (or active) across the BBB from the vascular lumen into the brain parenchyma or vice versa may comprise: providing a first dataset from a sample obtained by a subject, the first dataset comprising information indicating the presence or a level of one or more biomarkers, or the combination of biomarkers (e.g. two or more biomarkers) wherein the one or more, or combination of, biomarkers comprise a biomarker of GLX-shedding including one or more of GLX-related GAGs, one or more GLX-related PGs, one or more GLX- related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof,; classifying each biomarker from the one or more biomarkers to BBB condition, which may be a BBB dysfunction such as an influx-condition, an efflux condition, a cerebral edema or a cerebral hemorrhage, the classifying comprising: providing a second dataset comprising a reference level of the same one or more, or combination of, biomarkers analyzed in the first dataset; comparing the levels of the one or more biomarkers from the first dataset to the reference level of the second dataset; and assigning, optionally under control of one or more processors, an BBB condition score to each of the biomarkers of the one or more biomarkers of the first dataset, or the combination of biomarkers of the first dataset, wherein the BBB condition score isbased on a deviation of the level of each of the biomarkers of the one or more biomarkers, or the combination of the biomarkers, of the first dataset from the reference level of the second dataset; and determining an indication status of the condition based on the BBB condition score.

[0120] Typically, the levels of the one or more biomarkers comprise concentrations of the one or more biomarkers, or combinations of such information, which may be obtained from a biochemical analysis of the sample. Where the method comprises the combination of information regarding the presence or concentration of biomarkers, information regarding two or more, three or more, four or more, or five or more biomarkers can be combined. The concentrations of the one or more biomarkers may be determined from signal intensities of the biomarkers from biochemical reporters, pixel intensities of the biomarkers obtained from an image analysis (e.g., pixel intensities of the biomarkers obtained from an image analysis). The concentrations of the one or more biomarkers may also be determined indirectly, e.g. assessing or determining the presence or levels of autoantibodies to the one or more biomarkers, or assessing or determining the presence or levels or biological activities of GLX-cutting enzymes.

[0121] These associations may be determined from a machine learning algorithm analyzing levels in a training dataset of glycocalyx associated biomarkers associated with various conditions of the BBB (e.g., open vs. closed). Correlating the levels and / or assigning of the BBB condition score (e.g. an influx- or efflux-condition score) may be performed using a correlation module identified by a machine learning model (e.g., a model comprising a multivariate statistical test) with training data based on the reference levels of biomarkers and indicators for the influx of the composition across the BBB; wherein the machine learning model used the training data to determine how to output the BBB influx-condition score. In various implementations, the methods comprise training the machine learning module with the training dataset to create a correlation module. The BBB condition score may indicate whether the subject is indicative for a BBB dysfunction, and a symptom of the BBB dysfunction comprises the particular condition trained, e.g. the influx condition score correlates with the influx or likelihood of influx of the composition across a BBB. In various embodiments, assigning of the BBB condition score to each of the biomarkers, or a combination of the biomarkers, comprises transforming the levels of the one or more biomarkers to the BBB condition score via the machine learning model. In particular embodiments, the glycocalyx isthe endothelial glycocalyx. The set of biomarkers sampled may be, for example, one or more PGs GAGs, GPs, GLs and / or a mucin, or any fragment thereof, or any combination of the biomarkers aforementioned or otherwise disclosed herein.

[0122] These analyses may be used to treat a subject in need thereof. For example, the method may comprise reducing a dosage of a composition (such as a pharmaceutical composition comprising an active such as antibody), increasing a dosage interval of the composition, or discontinuing administrating the composition if the indication status of the condition of the BBB indicates BBB dysfunction. Additionally, the method may comprise: monitoring a change in a state of the subject (e.g., during a clinical trial) with respect to: a change in a disease extent of the subject; a change in a side effect of the composition; or a change in an efficacy of the composition.

[0123] In certain implementations, the method of treating a disease, disorder, or condition with a drug that crosses the BBB in a subject in need thereof, the method may comprise: providing (e.g., receiving) a dataset from a sample obtained by a subject, the dataset comprising information indicating the presence or a level of one or more biomarkers, or the combination of biomarkers (e.g. two or more biomarkers) wherein the one or more, or combination of, biomarkers comprise a biomarker of GLX-shedding including one or more of glycoproteins (GPs) or fragments thereof, glycoconjugates (GCs) or fragments thereof, glycosaminoglycans (GAGs) or fragments thereof, glycolipids (GLs) or fragments thereof, proteoglycans (PG) or fragments thereof, galectins or fragments thereof, autoantibodies of GPs, GCs, GAGs, GLs, PGs, or fragments thereof); correlating the levels of the one or more biomarkers associated with the glycocalyx with BBB permeability of the subject; determining the permeability of the BBB to the composition; andadministering the drug to the patient based on the permeability of the BBB to the drug (e.g., beginning administration of the drug, stopping administration of the drug, reducing administration frequency and / or dosage of the drug, increasing administration frequency and / or dosage of the drug).Definitions

[0124] Terms used herein will be understood to take on their ordinary meaning in the relevant art unless specified otherwise. Several terms used herein, and their meanings are set forth below.

[0125] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0126] As used herein, term “and / or” shall be taken to provide explicit support for both meanings or for either meaning.

[0127] As used herein, the word “comprise,” or variations such as “comprises” or “comprising,” will be understood to imply the inclusion of a stated element, integer or step, or group of elements, integers or steps, but not the exclusion of any other element, integer or step, or group of elements, integers or steps.

[0128] The following explanations of terms and methods are provided to better describe the present disclosure and to guide those of ordinary skill in the art in the practice of the present disclosure. The singular forms “a,” “an,” and “the” refer to one or more than one, unless the context clearly dictates otherwise. For example, the term “comprising a molecule” includes single or plural molecules and is considered equivalent to the phrase “comprising at least one molecule.” The term “or” refers to a single element of stated alternative elements or a combination of two or more elements, unless the context clearly indicates otherwise. As used herein, “comprises” means “includes.” Thus, “comprising A or B,” means “including A, B, or A and B,” without excluding additional elements. Unless otherwise specified, the definitions provided herein control when the present definitions may be different from other possible definitions.

[0129] As used herein, the term “exogenous” refers to something that originates outside of the organism or system that it affects. Exogenous substances are introduced into the system from an outside source, as opposed to endogenous elements which are inherently produced or arisefrom within the organism itself. In the context of therapeutics, exogenous therapeutics refers to a wide range of medically relevant substances, such as drugs, biologies, and other therapeutic compounds that are not naturally produced within the body. Such agents are synthesized or derived from external sources and are administered to a subject to treat, diagnose, or prevent diseases or the conditions thereof. Exogenous therapeutics are distinctly different from endogenous compounds, which are naturally occurring substances produced within the body itself.

[0130] As used herein, "GLX" or "glycocalyx,” refers to the carbohydrate-rich outer part of the cell surface of the majority of cells in the body, including the luminal endothelium. This layer is the first interaction between the blood and the vessel wall, both throughout the body and at the blood-brain barrier (BBB) junction. As described herein, shedding of the GLX is contemplated to be a predictor of BBB dysfunction, including but not limited to cerebral edema, cerebral microhemorrhages, an influx condition, or an efflux condition. Non-limiting examples of molecules found in the GLX include Proteoglycans (PGs), Glycosaminoglycans (GAGs), Glycoproteins (GPs), Glycolipids (GLs), and Glycoconjugates (GCs). These may be brain derived. Thus, the term "GLX-related" is to be understood as molecules associated with (or has been associated with) the GLX structure, or the fragments of such molecules. That is, the "GLX- related" may be understood as molecules (or molecular fragments) originating from the GLX structure. The combinations of these GLX molecules or fragments, as detected from biological samples of various types will be able to screen, diagnose, prognose, and monitor for cerebral edema, cerebral microhemorrhages, influx of a compound across the BBB, efflux of the a compound across the BBB, ARIA, and treatment response in ARIA-directed treatment, and support other standard of care (SOC) techniques, such as MRI other imaging modalities, or experimental imaging modalities such as side-stream darkfield imaging and experimental BBB opening techniques such as focused ultrasound-mediated BBB disruption.

[0131] As used herein, “blood-brain barrier” (BBB) refers to the highly selective semipermeable border that separates the circulating blood from the brain within the central nervous system (CNS). Comprising endothelial cells that line the vascular lumen along with other components like astrocytes and pericytes, the BBB is crucial not only for safeguarding the physical cells of the brain parenchyma — comprising neurons and glial cells — but also for regulating the composition of the extracellular fluid. This regulation is essential as it maintains the chemical environment required for critical physiological and biochemical processes in thebrain. By controlling what substances can pass from the blood into the brain, the BBB protects neural function and ensures stability in the brain's internal environment, protecting it from potentially harmful substances circulating in the blood. This barrier is crucial for maintaining the brain's stable environment, which is essential for proper neurological function. It is formed by brain endothelial cells lining the cerebral capillaries, which are tightly joined together by tight junctions, preventing most substances in the blood from non-selectively crossing into the brain. These endothelial cells, along with astrocytes and pericytes, regulate the passage of molecules and ions into and out of the brain, while blocking the entry of potentially harmful substances, such as pathogens and toxins. Certain substances that are lipophilic (fat-soluble) or those that have specific transport mechanisms can cross the BBB efficiently. This selective permeability protects the brain from fluctuations in plasma composition and from substances that could impair brain function.

[0132] As used herein, “Glycosaminoglycan” (GAGs) or "mucopolysaccharides" are long unbranched polysaccharides including of a repeating disaccharide unit. The repeating unit (except for keratan) includes an amino sugar (N-acetylglucosamine or N-acetylgalactosamine) along with a uronic sugar (glucuronic acid or iduronic acid) or galactose. As used herein, “proteoglycans” (PGs) are proteins that are heavily glycosylated. The basic proteoglycan unit includes a "core protein" with one or more GAG chain(s).

[0133] As used herein, “glycoproteins” (GPs) are proteins that have carbohydrate groups covalently bonded to the polypeptide chain. These carbohydrate groups, or glycans, are typically attached to the protein via N-glycosidic or O-glycosidic bonds, which link the carbohydrate to the nitrogen atom of asparagine or the oxygen atom of serine or threonine residues, respectively. GPs play a diverse range of roles in biological systems, including cell recognition, adhesion, and signaling processes. They are key components of the cell membrane and the extracellular matrix, contributing to the formation of the glycocalyx, which protects cells and facilitates interactions with other cells and molecules.

[0134] As used herein, “glycolipids” (GLs) are molecules featuring a lipid linked to one or more sugar residues. Found primarily in cell membranes, GLs play pivotal roles in cell recognition, signaling, and adhesion. The lipid portion of GLs typically anchors them into the lipid bilayer, while the carbohydrate moiety extends outwards from the cell surface, interacting with the extracellular environment. This structure allows GLs to mediate interactions between cells and their surroundings, facilitating processes, such as immune response, tissuedevelopment, and pathogen recognition. Glycosphingolipids, a major subclass, are especially significant in the nervous system, where they participate in signal transduction and cell-to-cell communication.

[0135] As used herein, “glycoconjugates” (GCs) are biomolecules where carbohydrates are covalently bonded to proteins, lipids, or other organic molecules. Under this classification, GCs include GPs, GLs, and PGs. The carbohydrate portions of GCs contribute to a diverse array of biological functions, such as mediating cellular recognition, signaling, and adhesion. In the GLX, GCs play a critical role in maintaining vascular homeostasis and mediating interactions between the endothelium and circulating blood components. Furthermore, GCs contribute significantly to the structure and function of the glycocalyx, providing a dynamic and protective interface between the bloodstream and the endothelial cells. They help regulate vascular permeability, prevent leukocyte and platelet adhesion, and contribute to shear stress sensing, thereby influencing blood flow and pressure. Additionally, these GCs play a role in modulating the inflammatory response and act as a barrier to pathogens, thereby maintaining the overall health and functionality of the vascular system. In the immune system, GCs play a crucial role in distinguishing self from non-self, as well as in pathogen recognition.

[0136] Without being bound by theory, it is contemplated that damage to and insufficiency of the BBB can be induced by signaling from the inside the brain, within the endothelial cell itself, or from the blood, and / or a cellular defect, lack of available biomolecule resources to sustain BBB protection, exogenous agents, and / or disease processes that hinder proper BBB formation and / or function, such as beta-amyloid plaques. These processes can lead to ineffective production, trafficking, positioning and function of the GLX on the BBB resulting in a reduced GLX content in the blood and / or release of the GLX induced by the BBB, and / or removal of the GLX from signals originating in the blood leading to an increase in GLX content in the blood. Indeed, GLX content in the blood may change in a number of diseases, such as multiple sclerosis and stroke, the present application is partially premised on the discovery that GLX content may be related to the BBB and how changes in the GLX content can be used to modify treatment protocols, increase standards of care, and treat patients. It is in particular contemplated that a composite biomarker for GLX status, e.g. the combination of information relating to one or more GLX-related biomarkers, would improve screening, diagnosis, prognosis, and monitoring of BBB condition, e.g. as a replacement for ARIA detection, based on precision medicine principles and improve the design and execution of new experimentaltrials. Thus, the identification of the entire GLX-phenotype in the blood, as well as identifying groupings of biomarkers that may provide increased statistical success, and the integration of these markers into a composite (i.e. combination) biomarker allows for the detection of a variety of diseases, disorders, and conditions including ARIA, susceptibility to ARIA, severity of ARIA, treatment response to ARIA-prevention, and the resolution of ARIA, returning to BBB sufficiency. The composite (i.e. combination) biomarker may include information regarding the presence or level of two or more, three or more, four or more, or five or more GLX-related biomarkers as indicated herein.

[0137] As used herein, “antibodies” and like terms refer to immunoglobulin molecules and immunologically active portions of immunoglobulin (Ig) molecules, i.e., molecules that contain an antigen binding site that specifically binds (immunologically reacts with) an antigen. These include, but are not limited to, polyclonal, monoclonal, chimeric, single chain, Fc, Fab, Fab’, and Fab? fragments, and a Fab expression library. Antibody molecules relate to any of the classes IgG, IgM, IgA, IgE, IgD, which differ from one another by the nature of heavy chain present in the molecule. These include subclasses as well, such as IgGl, IgG2, and others. The light chain may be a kappa chain or a lambda chain. Reference herein to antibodies includes a reference to all classes, subclasses, and types. Also included are chimeric antibodies, for example, monoclonal antibodies or fragments thereof that are specific to more than one source, e.g., a mouse or human sequence. Also included are bispecific antibodies that are designed to bind to two distinct antigens or epitopes. The unique structure of bispecific antibodies combines different variable regions from two monoclonal antibodies into a single molecule, including, but not limited to, tandem scFVs (single-chain variable fragments), dualvariable domain antibodies, a two-in-one antibody design (e.g., each Fab arm can bind to two different antigens), or single domain VHH chains.

[0138] As used herein, “autoantibodies” refer to antibodies that are directed to self-antigens. The self-antigens of relevance to the invention are the GLX-related biomarkers and fragments thereof as discussed herein.

[0139] As used herein, “shuttling molecule” refers to a compound designed to facilitate the transport of other molecules across the BBB by exploiting specific transport mechanisms or receptors on the surface of endothelial cells that make up the BBB. The shuttling molecule can be engineered biomolecules, peptides, antibodies, or small molecules that employ differentmechanisms to allow the molecule associated with the shuttling molecule to be transported across the BBB into the brain.Reference Level

[0140] As used herein, “reference level” refers to a standard in relation to a quantity, to which other values or characteristics can be compared. In some embodiments, it is possible to determine a reference level by investigating the abundance of one or more of the biomarkers from samples of healthy subjects or subjects that do not have or are not suspected of having a condition but otherwise similarly situated to the subject of interest. In some embodiments, the reference level is that determined in the subject of interest but at a different time point, e.g. before or after, when the BBB condition is to be assessed or determined. The different time point can include time points before of after one or more therapeutic or investigative compositions have been administered to the subject. In some embodiments, the condition includes cerebral edema. In some embodiments, the condition includes cerebral hemorrhage. In some embodiments, the condition includes an influx of a composition across the BBB from the vascular lumen into the brain parenchyma. In some embodiments, the condition includes an efflux of a composition across the BBB from the brain parenchyma into the vascular lumen. In some embodiments, the condition is a neurological disease or disorder. In some embodiments, the condition is a sequela of the neurological disease or disorder. In some embodiments, the condition is a side effect of administration of a therapeutic treatment to treat a disease or disorder. In some embodiments, the condition is a side effect of administration of a therapeutic treatment to treat a sequela of a disease or disorder. In some embodiments, the disease or disorder is a neurological disease or disorder. By applying different statistical means, such as multivariate analysis, one or more reference levels can be calculated. The reference level can be the same subject prior to developing the condition. Based on these results, a cutoff may be obtained that shows the relationship between the level(s) detected and patients at risk. The cut-off can be used to determine the levels of one or more biomarkers that correspond to, for instance, an increased risk of a condition.

[0141] To determine whether a subject has an increased risk of developing the condition, the results presented in the examples show that the described biomarkers (typically in combination) may be used for determining whether the subject has an increased risk of developing the condition. The cut-off may be obtained by the laboratory, the physician, or on a case-by-case basis for each patient. The cut-off level can be established by using a number of methods, whichinclude, but are not limited to, multivariate statistical tests. In some embodiments, the multivariate statistical tests include machine learning. In some embodiments, the machine learning includes partial least squares discriminant analysis (PLS-DA), random forest, support vector machine, and the like. In some embodiments, the multivariate statistical tests include, percentiles, mean plus or minus standard deviation(s); median value; fold changes. In some embodiments, the machine learning includes an artificial neural network (ANN), a Bayesian network, a boosting ensemble, decision tree algorithms, elastic-net, gaussian process classifier, gradient boosting classifier, k-nearest neighbor (KNN), least absolute shrinkage and selection operator (LASSO), learning vector quantization, linear discriminant analysis, linear regression, logistic regression, polynomial regression, logistical model tree, multinomial logistic regression, random forest, quadratic classifier, or support vector machine, or the like, or any combination thereof. In some embodiments, the any combination thereof is a stacked classifier. For example, the predictive model can be the stacked classifier that includes both a linear regression and a decision tree algorithm. In some embodiments, the decision tree includes random forest analysis, classification and regression trees (CART), iterative dichotomiser 3 (ID3), C4.5, C5.0, chi-squared automatic interaction detection (CHAID), multivariate regression splines (MARS) or any combination thereof. In some embodiments, the any combination of the decisional trees can be a stacked classifier. Changing the risk cut-off level may change the results of the discriminant analysis for each subject. Statistical tests may be employed to evaluate the significance of each level, such tests include, but are not limited to, t-test, f-test, or even more advanced statistical tests and comparative methods to determine whether the biomarker levels biomarker levels of two of more groups are significantly different.

[0142] In some embodiments, the cut-off level is established by an output from training one or more predictive models. In some embodiments, the one or more predictive models is trained on training data. In some embodiments, the training data includes the level of the one or more biomarkers. In some embodiments, the training data includes reference levels of biomarkers from healthy subjects. In some embodiments, the reference level of the biomarkers are obtained from a third party. In some embodiments, the reference level of the biomarkers includes the level of the same one or more biomarkers from an earlier sample obtained from the same subject, such as a sample provided prior to onset of the disease, disorder, or condition; or prior to the manifestations from sequela of the disease, disorder, or condition or prior to receiving one or more therapies the subject had received.

[0143] In some embodiments, the training data further includes one or more reference indicators that indicate a disease, disorder, or condition activity. In some embodiments, the reference indicators include clinical data for the presence or absence of the disease, disorder, or condition. In some embodiments, the reference indicators include clinical data of a relapse, re-emergence, or flair-up of the disease, disorder, or condition. In some embodiments, the reference indicators include clinical data of a rate of the relapse, re-emergence, or flair-up of the disease, disorder, or condition. In some embodiments, the reference indicators include a state of the disease, disorder, or condition.

[0144] In some embodiments, the predictive model includes a machine learning model. In some embodiments, the machine learning model includes an artificial neural network (ANN), a Bayesian network, a boosting ensemble, decision tree algorithms, elastic-net, gaussian process classifier, gradient boosting classifier, k-nearest neighbor (KNN), least absolute shrinkage and selection operator (LASSO), learning vector quantization, linear discriminant analysis, linear regression, logistic regression, polynomial regression, logistical model tree, multinomial logistic regression, random forest, quadratic classifier, or support vector machine, or the like, or any combination thereof. In some embodiments, the any combination thereof is a stacked classifier or ensemble method. For example, the predictive model can be the stacked classifier that includes both a linear regression and a decision tree algorithm. In some embodiments, the decision tree includes random forest analysis, classification and regression trees (CART), iterative dichotomiser 3 (ID3), C4.5, C5.0, chi-squared automatic interaction detection (CHAID), multivariate regression splines (MARS) or any combination thereof. In some embodiments, the any combination of the decisional trees can be a stacked classifier.

[0145] The chosen reference level may be changed depending on the subject for which the test is applied. Preferably, the subject is a human subject, such as a subject considered at risk of having or developing cerebral microhemorrhage or cerebral edema, or an individual who is about to receive an exogenous agent known to or considered to have a risk of causing disruption in the BBB, such as Alzheimer’s patients, especially ones receiving anti-A0 antibody therapies, which can be confirmed to disrupt the BBB, e.g., as shown through the inducement of ARIA revealed by MRI.

[0146] The chosen reference level may be changed if desired to give a different performance metric, such as sensitivity, specificity, positive-predictive value, negative-predictive value, accuracy, or precision, as known in the art. Sensitivity, specificity, positive-predictive value,negative-predictive value, accuracy, or precision are statistics used to describe and quantify how predictive and reliable a biomarker or a diagnostic test is. Sensitivity evaluates how good a biomarker or test is at correctly identifying a subject as being positive, while specificity is the probability that a test returns a negative, e.g. that a given subject is healthy. Several terms are used along with the description of sensitivity and specificity; true positives (TP), true negatives (TN), false negatives (FN) and false positives (FP). If a disease is proven to be present in a sick patient, and the diagnostic test confirms the presence of disease, the result of the diagnostic test is considered to be TP. If a disease is not present in an individual (i.e. control, patient without disease), and the diagnostic test confirms the absence of disease, the test result is TN. If the diagnostic test indicates the presence of disease in an individual with no such disease, the test result is FP. Finally, if the diagnostic test indicates no presence of disease in a patient with disease, the test result is FN.

[0147] Sensitivity = TP / (TP + FN) = number of true positive assessments / number of all samples from patients with disease. As used herein, the sensitivity refers to the measures of the proportion of actual positives, which are correctly identified as such - in analogy with a diagnostic test, e.g., the percentage of people having BBB dysfunction normal who are identified as having BBB dysfunction.

[0148] Specificity = TN / (TN+ FP) = number of true negative assessments / number of all samples from controls. As used herein, the specificity refers to measures of the proportion of negatives, which are correctly identified. The relationship between both sensitivity and specificity can be assessed by the ROC curve. This graphical representation helps to decide the optimal model through determining the best threshold or cut-off for a diagnostic test or a biomarker candidate.

[0149] Positive Predictive Value (PPV) = TP / (TP + FP) refers to the proportion of positive test results that are true positives. It reflects the probability that a subject with a positive test result truly has the condition. Higher PPV indicates a lower false-positive rate and better reliability in identifying true cases among the positives.

[0150] Negative Predictive Value (NPV) = TN / (TN + FN) refers to the proportion of negative test results that are true negatives. It reflects the probability that a subject with a negative test result is indeed disease-free. A higher NPV implies a lower false-negative rate, which is critical for ruling out disease accurately.

[0151] The analysis may occur using one or more datasets obtained from a subject. With reference to FIG. 1, in some embodiments, a method 100 for identifying cerebral edema is provided. The method 100 includes providing 102 a first dataset from a sample obtained by a subject, the first dataset including a level of one or more biomarkers, wherein the one or more biomarkers include one or more glycocalyx (GLX)-related glycosaminoglycans (GAGs), one or more GLX-related proteoglycans (PGs), one or more GLX-related glycoproteins (GPs), one or more GLX-related glycolipids (GLs), one or more GLX-related glycoconjugates (GCs), or one or more fragments thereof, or any combination of the aforementioned biomarkers; classifying 110 each biomarker from the one or more biomarkers to a cerebral edema condition, the classifying 110 including providing 112 a second dataset including a reference level of the same one or more biomarkers analyzed in the first dataset; comparing the level of the one or more biomarkers from the first dataset to the reference level of the second dataset; and assigning 114, under control of one or more processors, a cerebral edema-condition score to each of the one or more biomarkers of the first dataset, wherein the cerebral edema-condition score is based on a deviation of the level of each of the one or more biomarkers of the first dataset from the reference level of the second dataset; and determining 120 an indication status of the cerebral edema based on the cerebral edema-condition score.

[0152] In some embodiments, the second dataset includes a level of the same one or more biomarkers analyzed in the first data, wherein the level of the same one or more biomarkers is from a sample obtained from the subject at a different time. In some embodiments, if the subject does not have or is not suspected of having cerebral edema, then the reference level includes the level of the same one or more biomarkers from a sample obtained from the subject at a different time. In some embodiments, the different time includes a time prior to the sample obtained from the subject for the first dataset. In some embodiments, the different time includes a time after the sample obtained from the subject for the first dataset.

[0153] In some embodiments, the one or more biomarkers include 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1. In some embodiments, the level of the one or more biomarkers include levels of twoor 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1.Table 1 : Various GLX-related biomarkersFor example, the one or more biomarkers may include: perlecan, hyaluronic acid (HA), syndecan-2 (syn2), podocalyxyin (podo), and biglycan; perlecan, HA, glypican-1 (GPC1), syndecan-1 (synl), syndecan-4 (syn4), syn2, podo, chondroitin sulfate (CS), and biglycan;CD44, podo, CS, HA, GPC1, perlecan, syn4, biglycan, and syn2; perlecan, syn4, keratan sulfate (ks), GPC1, HA, biglycan, syn2, CS, podo, CD44, and synl; or synl, syn2, syn4, perlecan, gpcl, cd44, podo, biglycan, cs, ha, and ks.In various implementations, the biomarkers are measured from the subject’s blood. In certain embodiments, the biomarkers are measured from the subject’s cerebrospinal fluid (CSF). In various implementations, samples are taken from both the blood and CSF, and different groups of biomarkers are measured for each sample type.

[0154] In some embodiments, the identification of cerebral edema is determined by detecting the level, signal intensity or concentration of the one or more biomarkers. In some embodiments, the level, signal intensity or concentration of the one or more biomarkers is obtained from a biochemical analysis of the sample. The level of the one or more biomarkers may be determined in the sample by different methods, or a combination of different methods. In some embodiments, the level for each of the one or more biomarkers are determined by an in vitro method. In some embodiments, the level for each of the one or more biomarkers aredetermined by a method selected from the group consisting of: binding agents, fluorescence detection, immunoassays, antibody recognition, aptamers (e.g. SOMAmer), engineered scaffold binders (e.g. affibodies, DARPins), immunostrips, lateral flow assays, multiplexing, dot blotting, beads, microspheres, immunoturbidity, immunoturbidity with latex beads, electrochemical sensors, fluorescence polarization, fluorescent linking, immnuoturbidimetry, turbidity, immunoturbidity magnetic beads, immunonephelometry, agglutination PCR, nucleic acid coupled assays, proximity extension assays (e.g., oligonucleotide-labeled binding agents), hybridization-based assays, barcoded detection systems, sequencing-based quantification, lanthanides, lectins, GAG-binding molecules, Alcian Blue, Toluidine blue, dimethylmethylene blue, single-molecule array technology, mass spectrometry (MS), GC-MS, Liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ES-MS / MS), LC- MS, MALDI-MS, HPLC-MS, HPLC, Raman spectroscopy, NIR spectroscopy, or NMR spectroscopy. Multiple analytes may be measured simultaneously from a single sample and in a single experiment or analytes may be measured individually. In a non-limiting example, magnetic microspheres are coated with antibodies that are specific to the biomarkers to be quantified. The microspheres are mixed with the sample, washed, and another antibody mixture is added linked to a reporter agent that releases a quantitative and / or qualitative signal. In a non-limiting example, the binding agent, including but not limited to one or more antibodies, binds to a specific epitope of the biomarker. The binding agent itself may be labelled with a detectable reporter, and / or secondary binding agents can be used to signal the biomarkerbinding agent complex, e.g. via chemiluminescence detection system based on HRP enzymatic activity or via fluorescence labelling. Any number of methods, individually or in combination, described herein can be used.

[0155] In some embodiments, the level of one or more biomarkers are detected in one sample (e.g. multiplexing). In some embodiments, the level of one or more biomarkers are detected by a device. In some embodiments, the device is configured for multiplexing. In some embodiments, the device includes microfluidics. In some embodiments, the device includes magnetic beads. In some embodiments, the device includes latex beads. In some embodiments, the device involves binding agent-coupling, DNA-coupling, separation on a surface, e.g., chromatography, different wavelengths, fluorescence polarization, or a combination thereof. In some embodiments, the level of a one or more biomarkers are captured by a binding agent and then tested for post-translational modification (PTMs), such as a glycosylation pattern in a manner described as “profiling” or “glycoprofiling.” The sample is ideally concentrated for thedetection of the one or more biomarkers, preferably from Table 1, and thereafter probed for PTMs.

[0156] In some embodiments, the concentration or levels of the one or more biomarkers are determined from pixel intensities of the one or more biomarkers obtained from an image analysis with or without a labeled binding agent or detection agent. In some embodiments, the levels of the one or more biomarkers are determined by evaluating the pixel intensities of the biomarkers obtained from an image analysis, and optionally these detected biomarker levels are compared to a trained database of pixel intensities for the biomarkers and disease states associated therewith (e.g., cerebral edema or cerebral microhemorrhage) and a classification score is generated outputting a predilection or prediction of cerebral edema or cerebral microhemorrhage. In some embodiments, the image analysis includes measuring the pixel intensities by a program executed by one or more processors. In some embodiments, the program includes Imaged.

[0157] In some embodiments, the one or more biomarkers include 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1. In some embodiments, the level of the one or more biomarkers include levels of 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1.

[0158] As used herein, “BBBx disease” refers to any group of conditions or diseases disrupting the blood-brain barrier (BBB). The conditions or diseases affecting the BBB include, but are not limited to, neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), multiple system atrophy (MSA), frontotemporal dementia (FTD), vascular dementia, Lewy body dementia (LBD), chronic traumatic encephalopathy (CTE), and rare or pediatric-onset neurodegenerative diseases including Niemann-Pick disease, Gaucher disease, Tay-Sachs disease, Rett syndrome, Batten disease (neuronal ceroid lipofuscinosis), Krabbe disease, andmetachromatic leukodystrophy (MLD); autoimmune and immune-mediated diseases such as systemic lupus erythematosus with CNS involvement, neuromyelitis optica spectrum disorder, neurosarcoidosis, primary angiitis of the CNS, and CNS vasculitis; psychiatric and neurodevel opmental disorders including schizophrenia, major depressive disorder, bipolar disorder, and post-traumatic stress disorder (PTSD); oncological conditions such as brain tumors, glioblastoma multiforme, primary CNS lymphoma, leptomeningeal carcinomatosis, and brain metastases; cerebrovascular diseases including stroke, small vessel disease, subarachnoid hemorrhage, and intracerebral hemorrhage; traumatic and structural injuries such as traumatic brain injury, spinal cord injury (SCI), and acute brain trauma; metabolic and genetic disorders including lysosomal storage diseases and mitochondrial encephalopathies; neurological conditions such as epilepsy, particularly in drug-resistant forms where BBB disruption may play a role, normal pressure hydrocephalus (NPH), and acute confusional states such as hospital-induced and postoperative delirium; treatment-induced or iatrogenic conditions including chemotherapy-induced cognitive impairment (“chemo brain”) and radiation-induced encephalopathy; and infectious and inflammatory conditions including encephalitis (e.g., herpes or HIV), meningitis (including tuberculous forms), septic encephalopathy, cerebral malaria, viral hemorrhagic fevers, COVID-19-associated encephalopathy, progressive multifocal leukoencephalopathy, and brain abscess, as well as any other neurological, psychiatric, autoimmune, oncological, traumatic, metabolic, or infectious conditions in which BBB dysfunction or restricted CNS drug delivery is observed or suspected.

[0159] In some embodiments, the method includes generating training data based on correlations between the reference level and indicators for cerebral edema; and training a machine learning model on the training data to output the condition score. In some embodiments, the condition score indicates whether the subject is indicative for the specifically trained condition. In some embodiments, the assigning of the condition score to each of the biomarkers includes transforming the level of the one or more biomarkers to the condition score via the machine learning model. In some embodiments, the machine learning model includes an ensemble of classifiers. In some embodiments, the machine learning model includes a multivariate statistical test. In some embodiments, multivariate statistical test is a classifier. In some embodiments, the multivariate statistical test includes partial least squares discriminant analysis. In some embodiments, the multivariate statistical test includes random decision forests.

[0160] In some embodiments, the multivariate statistical test includes a support vector machine (SVM). In some embodiments, the SVM includes a linear SVM. In some embodiments, the SVM includes a non-linear SVM. In some embodiments, the non-linear SVM includes a polynomial kernel. In some embodiments, the non-linear SVM includes a radial basis function (RBF) kernel. In some embodiments, the non-linear SVM includes a sigmoid kernel. In some embodiments, the SVM includes a support vector regression (SVR).

[0161] In some embodiments, the glycocalyx is the endothelial glycocalyx.

[0162] In some embodiments, the one or more GAGs include hyaluronic acid (HA), or a fragment thereof. HA, also referred to as hyaluronan, is an anionic, nonsulfated GAG distributed widely throughout connective, epithelial, and neuronal tissues. HA is unique among GAGs in that HA is non-sulfated, forms in the plasma membrane instead of the Golgi apparatus, and can exhibit a large variance in molecular weight (MW), with high MW HA reaching several million Da. In some embodiments, the MW of HA is 1 kDa, 10 kDa, 20 kDa, 30 kDa, 40 kDa, 50 kDa, 60 kDa, 70 kDa, 80 kDa, 90 kDa, 100 kDa, 120 kDa, 140 kDa, 160 kDa, 180 kDa, 200 kDa, 220 kDa, 240 kDa, 260 kDa, 280 kDa, 300 kDa, 320 kDa, 340 kDa, 360 kDa, 380 kDa, 400 kDa, 420 kDa, 440 kDa, 460 kDa, 480 kDa, 500 kDa, 520 kDa, 540 kDa, 560 kDa, 580 kDa, 600 kDa, 620 kDa, 640 kDa, 660 kDa, 680 kDa, 700 kDa, 720 kDa, 740 kDa, 760 kDa, 780 kDa, 800 kDa, 820 kDa, 840 kDa, 860 kDa, 880 kDa, 900 kDa, 920 kDa, 940 kDa, 960 kDa, 980 kDa, 1000 kDa, 1200 kDa, 1400 kDa, 1600 kDa, 1800 kDa, 2000 kDa, 2200 kDa, 2400 kDa, 2600 kDa, 2800 kDa, 3000 kDa, 3200 kDa, 3400 kDa, 3600 kDa,3800 kDa, 4000 kDa, 4200 kDa, 4400 kDa, 4600 kDa, 4800 kDa, 5000 kDa, 5200 kDa, 5400 kDa, 5600 kDa, 5800 kDa, 6000 kDa, 6200 kDa, 6400 kDa, 6600 kDa, 6800 kDa, 7000 kDa,7200 kDa, 7400 kDa, 7600 kDa, 7800 kDa, 8000 kDa, 8200 kDa, 8400 kDa, 8600 kDa, 8800 kDa, 9000 kDa, 9200 kDa, 9400 kDa, 9600 kDa, 9800 kDa, or 10000 kDa, or an amount within a range defined by any two of the aforementioned values. In some embodiments, the one or more GAGs include heparan sulfate (HS), or a fragment thereof. HS is a linear polysaccharide found in all animal tissues. A form of HS may exist as a PG (HSPG) in which two or three HS chains are attached in close proximity to cell surface or extracellular matrix proteins.

[0163] In some embodiments, the one or more GAGs include Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, or a fragment thereof or any combination of the aforementioned biomarkers. Chondroitin sulfate (CS) is a sulfated GAG composed of a chain of alternating sugars (N-acetylgalactosamine and glucuronic acid). CS is usually found attached to proteinsas part of a proteoglycan. Dermatan sulfate (DS) is a GAG (formerly called a mucopolysaccharide) found mostly in skin, but also in blood vessels, heart valves, tendons, and lungs. It is also referred to as chondroitin sulfate B, although it is no longer classified as a form of chondroitin sulfate by most sources. The formula for DS is C14H21NO15S. Keratan sulfate (KS), also called keratosulfate, is any of several sulfated GAGs (structural carbohydrates) that have been found especially in the cornea, cartilage, and bone. It is also synthesized in the central nervous system where it participates both in development and in the glial scar formation following an injury and on the blood vessel wall, e,g., of the BBB.

[0164] In some embodiments, the one or more PGs include CD44, or a fragment thereof. As used herein, “CD44” is a proteoglycan that plays a crucial role in the GLX by mediating interactions between cells and the extracellular matrix. CD44 exists in various isoforms, with its MW ranging from 85 to 200 kDa due to alternative splicing and glycosylation. Structurally, CD44 is a transmembrane PG composed of a hyaluronan-binding domain, a variable extracellular region, a transmembrane domain, and a cytoplasmic tail. This modular structure allows CD44 to bind hyaluronic acid and other glycosaminoglycans, which are significant components of the endothelial GLX. Functionally, CD44 is involved in numerous cellular processes, including cell adhesion, migration, and signal transduction. It acts as a cell surface receptor for hyaluronic acid, facilitating cell-cell and cell-matrix interactions. This function is particularly relevant in tissue remodeling, wound healing, and inflammation, where CD44 plays a pivotal role in recruiting immune cells and promoting cell migration. Located predominantly on the surface of various cell types, including endothelial cells and immune cells, CD44's versatile functions and structural variability make it a key component of the GLX, contributing to the regulation of vascular permeability, inflammation, and tissue repair.

[0165] In some embodiments, the one or more PGs include CD44 or a fragment thereof, wherein the one or more GAGs include HA, CS, or a fragment thereof or any combination of the aforementioned biomarkers, and wherein the one or more PGs include syndecan, perlecan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0166] In some embodiments, the one or more PGs include a syndecan, or a fragment thereof. Syndecans are a family of transmembrane PGs that are composed of a core protein adorned with varying numbers of GAG side chains. These GAG side chains can include HS and CS. Syndecans are involved in a variety of cellular processes by interacting with a wide range of extracellular matrix components and growth factors. These cellular processes include, but arenot limited to, cellular growth and differentiation, cell spreading, cell adhesion, cell migration, cytoskeletal organization, infiltration, and angiogenesis. There are four syndecan members (syndecan-1, -2, -3, and -4), each with distinct tissue distributions and functions. Syndecan-1 and syndecan-3 carry both HS and CS chains, whereas syndecan-2 and syndecan-4 carry HS chains.

[0167] In some embodiments, the one or more PGs include a small leucine-rich proteoglycan (SLRP) or a fragment thereof. SLRPs typically include a core protein, ranging from about 25 to about 50 kDa, wherein the core protein is characterized by approximately 12 to 22 leucine- rich repeats (LRRs), each LRR approximately 20-29 amino acids long, which form a horseshoe-like shape that facilitates protein-protein interactions. The LRR domains are flanked by cysteine-rich regions that stabilize the protein structure. The core protein of an SLRP is often linked to GAG chains, such as CS or KS, which can extend the MW to between 50 and 200 kDa. SLRPs contribute significantly to the structural integrity and functionality of the GLX. Their GAG chains enable them to interact closely with other GLX components, thereby providing structural support and regulating the permeability of the endothelial barrier. By binding to collagen and other matrix components, SLRPs help organize and maintain the extracellular matrix's fibrillar structure, thus influencing the physical properties of the GLX. Functionally, SLRPs participate in modulating cellular processes, such as cell adhesion, proliferation, and differentiation, largely through their ability to bind to growth factors and modulate signaling pathways. This interaction helps regulate inflammatory responses, wound healing, and tissue repair by influencing the bioavailability of growth factors and cytokines. By acting as signaling hubs within the GLX, SLRPs help maintain the delicate balance between tissue integrity and repair, playing a crucial role in various physiological processes and potentially in the pathogenesis of diseases affecting the glycocalyx.

[0168] In some embodiments, the SLRP includes Fibromodulin, Asporin, Proline / arginine-rich end leucine-rich repeat protein (PRELP), Chondroadherin, Podocan, Opticin, Tsukushi, Testican (SPOCK), Epiphycan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Fibromodulin, Asporin, PRELP, Chondroadherin, Podocan, Opticin, Tsukushi, SPOCK, Epiphycan, Biglycan, Mimecan,Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0169] In some embodiments, the one or more PGs include a betaglycan, or a fragment thereof. Betaglycan is a proteoglycan that plays a significant role in modulating the signaling of TGF- P, a key growth factor involved in cell growth, differentiation, and repair. Structurally, betaglycan features a large extracellular domain, a single transmembrane region, and a short cytoplasmic tail. The MW of betaglycan can vary, but its core protein typically ranges from 100 to 150 kDa. Betaglycan can be further modified with GAG chains, such as HS and CS, which can extend its total molecular weight beyond 250 kDa. Within the GLX, betaglycan serves as a co-receptor that binds TGF- and presents it to type I and type II TGF- receptors, thereby modulating its signaling pathways. This feature is particularly important in regulating cellular responses to TGF- , influencing cellular growth, migration, and differentiation. By anchoring itself within the GLX on the surface of various cells, betaglycan helps modulate the availability and activity of TGF-P, thereby contributing to the regulation of tissue remodeling, immune responses, and the maintenance of tissue homeostasis. Additionally, betaglycan's interaction with its associated GAG chains in the GLX further enhances its role in signaling, adhesion, and cellular communication.

[0170] In some embodiments, the one or more PGs include a Glypican or a fragment thereof. Glypican are one of two major families of HS PGs (the other being syndecans) that are anchored to the cell surface through a glycosylphosphatidylinositol (GPI) anchor. Within the GLX, glypicans play a significant role in cellular signaling and providing structural integrity to the GLX. In cellular signaling, glypicans act as co-receptors for a variety of growth factors, such as Wnts, Hedgehog, and fibroblast growth factors. For providing structural integrity to the GLX, glypicans provide a scaffold that maintains the protective and regulatory functions of the GLX. Structurally, glypicans feature a core protein of about 60 to 70 kDa, with a total MW ranging from 100 to 200 kDa after the addition of HS GAG chains. This core protein is attached to the cell membrane through the GPI anchor, which is embedded into the lipid bilayer. The GAG chains are attached near the N-terminus and typically include HS, which extends out into the extracellular space. In some embodiments, the Glypican includes Glypican- 1, Glypican-2, Glypican-3, Glypican-4, Glypican-5, Glypican-6, or a fragment thereof or any combination of the aforementioned biomarkers.

[0171] In some embodiments, the one or more PGs includes a hyalectan or a fragment thereof. Hyalectans, also known as lecticans, are a family of large proteoglycans. In some embodiments, the hyalectan includes aggrecan, brevican, neurocan, versican, or a fragment thereof or any combination of the aforementioned biomarkers. These proteoglycans are characterized by a core protein that ranges from approximately 100 to 400 kDa. Their MW significantly increases due to the addition of glycosaminoglycan (GAG) chains, primarily chondroitin sulfate, with the total size often exceeding 1,000 kDa in some forms. Typically, hyalectans feature several domains, including an N-terminal link domain, GAG binding regions, and C-terminal lectin- like domain. Within the GLX, hyalectans play a significant role in maintaining the structural integrity and function of the extracellular matrix. They bind to HA via their N-terminal domain, forming large proteoglycan aggregates that contribute to the gel-like consistency of the extracellular matrix. These aggregates provide the GLX with its resilience and ability to resist compression, essential for various tissues, such as cartilage. They also facilitate cellular signaling by interacting with growth factors, cytokines, and other matrix proteins, influencing processes like cell adhesion, proliferation, and migration.

[0172] In some embodiments, the one or more GPs include a selectin or a fragment thereof. Selectins are glycoproteins involved in cell adhesion, particularly in the immune system’s interaction with the endothelium. Structurally, selectins feature several domains: a C-type lectin-like domain at the N-terminus, an epidermal growth factor (EGF)-like domain, multiple short consensus repeats (SCRs), a transmembrane domain, and a cytoplasmic tail. Depending on the type, selectins generally range between 90 to 120 kDa. Within the GLX, selectins facilitate the initial steps of leukocyte recruitment to sites of inflammation. The lectin-like domain of selectins specifically binds to carbohydrate structures on GPs or GLs on leukocytes, leading to the rolling adhesion of leukocytes along the endothelium. This rolling adhesion is the first step in the extravasation process, where leukocytes move from the bloodstream to the site of inflammation in the tissue. The endothelial GLX includes these selectins, allowing them to present their lectin domains effectively to circulating leukocytes. This interaction is essential for orchestrating the immune response by guiding leukocytes to sites of infection or injury, underscoring the importance of selectins in the endothelial GLX function. In some embodiments, the selectin includes P-selectin, E-selectin, L-selectin, or a fragment thereof or any combination of the aforementioned biomarkers.

[0173] In some embodiments, the one or more GPs includes a mucin or a fragment thereof. Mucins feature a protein backbone rich in serine, threonine, and proline residues, which allows for extensive O-linked glycosylation. Typically, this glycosylation leads to tandem repeat regions that form highly glycosylated rod-like structures. These structures contribute to a MW ranging from approximately 200 kDa to over 1,000 kDa. These tandem repeat regions also impart a high density of sugar residues to mucins, giving them their hydrophilic and gelforming properties. In the endothelial GLX, mucins contribute to forming a barrier that protects endothelial cells from mechanical stress and pathogens. Their heavily glycosylated nature allows them to trap and retain water, maintaining a hydrated environment that is crucial for endothelial cell function and vascular permeability. Additionally, mucins play a role in cellcell communication and modulate interactions between the endothelium and circulating cells, which is essential for processes like inflammation, immune response, and vascular permeability regulation. In some embodiments, the mucin includes Mucin 1, Mucin 16, Mucin 20, Mucin 5b, Mucin 6, Muc 18, Muc 14, or a fragment thereof or any combination of the aforementioned biomarkers.

[0174] In some embodiments, the one or more GPs include a sialomucin, or a fragment thereof. In particular embodiments, the sialomucin includes podocalyxin, a heavily sialylated glycoprotein that plays a critical role in the structure and function of the GLX. Podocalyxin is a member of the CD34 family of sialomucins and is characterized by an extended extracellular domain rich in serine and threonine residues, which are extensively modified by O-linked glycans terminating in sialic acid. This dense negative charge contributes significantly to the anti-adhesive and barrier functions of the GLX. Structurally, podocalyxin comprises a mucinlike extracellular domain, a transmembrane region, and a short cytoplasmic tail that interacts with cytoskeletal and signaling proteins such as ezrin and NHERF. The molecular weight of podocalyxin ranges from 120 to 160 kDa, depending on the degree of glycosylation. Functionally, podocalyxin is involved in maintaining vascular permeability, preventing unwanted cell-cell adhesion, and supporting leukocyte trafficking through interactions with selectins and integrins. In endothelial cells, podocalyxin helps preserve vascular lumen integrity and participates in dynamic remodeling processes during inflammation, angiogenesis, and injury repair. Its expression and glycosylation state are known to change in response to inflammatory cytokines and vascular stress, making it a relevant biomarker and potential therapeutic target in diseases involving GLX disruption. In some embodiments, the sialomucinincludes podocalyxin, endoglycan, podoplanin, or a fragment thereof, or any combination of the aforementioned biomarkers.

[0175] In some embodiments, the GP includes a IgSF, which includes a siglec, or a fragment thereof. Siglecs (sialic acid-binding immunoglobulin-like lectins) feature an extracellular region with one or more immunoglobulin-like domains that specifically bind to sialic acid residues, a transmembrane region, and an intracellular signaling domain that usually contains inhibitory motifs. The MW of siglecs vary on their respective types due to variation in the number of Ig-like domains and glycosylation patterns. Siglecs play an important role in mediating cell-cell interactions, particularly in immune responses. They help regulate leukocyte adhesion, modulate immune cell activation, and maintain immune homeostasis within the endothelial GLX by recognizing sialylated glycans on the surface of various cells, including leukocytes. Siglecs are involved in the regulation of inflammatory responses and in preventing excessive immune activation by delivering inhibitory signals when they bind to their sialylated ligands. Their functions in the GLX contribute to immune regulation and the prevention of unnecessary tissue damage due to excessive inflammation. In some embodiments, the siglec includes Siglec-5, Siglec-9, Siglec- 10, Siglec- 15, or a fragment thereof or any combination of the aforementioned biomarkers.

[0176] In some embodiments, the one or more biomarkers includes a galectin, or a fragment thereof. Galectins are a family of lectin proteins characterized by their ability to bind specifically to beta-galactoside sugars. Structurally, galectins feature one or two carbohydrate recognition domains (CRDs), each capable of binding to beta-galactosides. These proteins typically have a molecular weight ranging from 14 to 50 kDa, depending on the specific galectin type. Galectins can form lattices with GPs and GLs, which help maintain the structure of the GLX and mediate interactions with circulating cells. Galectins also influence leukocyte adhesion and migration by binding to glycosylated receptors on the endothelial surface, thereby modulating immune responses and inflammation. Additionally, they participate in signaling pathways that influence cell survival, apoptosis, and angiogenesis, contributing to the regulation of vascular homeostasis and the body's immune defenses. In some embodiments, the galectin includes Galectin- 1, Galectin-2, Galectin-3, Galectin-4, Galectin-8, Galectin-9, Galectin- 12, or a fragment thereof or any combination of the aforementioned biomarkers.

[0177] In some embodiments, the one or more biomarkers includes a sialic acid or a fragment thereof. Sialic acid is an acylated derivative of the nine-carbon sugar neuraminic acid, andtypically is a terminal sugar of glycan chains attached to GLs and GPs. Sialic acid has several roles in the GLX. The high negative charge of sialic acids contributes to the negative charge of the endothelial surface, which helps repel circulating blood cells and regulate vascular permeability. Sialic acids act as binding sites for various lectins, such as selectins and siglecs, mediating cell-cell interactions important in immune responses, inflammation, and leukocyte trafficking. Sialic acids protect the underlying GLs and GPs from degradation by shielding them from enzymatic cleavage, thus preserving the integrity of the endothelial GLX. In some embodiments, the sialic acid includes a polysialic acid or a fragment thereof.

[0178] In some embodiments, the one or more biomarkers includes a sphingolipid or a fragment thereof. Sphingolipids are a class of lipids that include a sphingoid base backbone, such as sphingosine, and are essential components of cell membranes, including the GLX. They feature a long-chain amino alcohol (the sphingoid base) attached to a fatty acid via an amide bond, forming a ceramide. Additional functional groups attached to the ceramide, such as phosphocholine or sugars, give rise to different subclasses of sphingolipids like sphingomyelins and glycosphingolipids. As part of the GLX, sphingolipids have several important functions, which include being crucial components of the lipid bilayer of cell membranes, contributing to the structural integrity and stability of the endothelial cell membrane and GLX. In some embodiments, the one or more GLs includes a glycosphingolipid, a ganglioside, or a combination thereof. In some embodiments, the glycosphingolipid includes sulfatide or a fragment thereof. In some embodiments, the ganglioside includes GM1, GDlb, GT lb, or a combination thereof.

[0179] In some embodiments, the first and / or second dataset includes information indicating the presence or level of one or more autoantibodies against the one or more biomarkers, and thus, indirect information regarding the presence or level of the one or more respective biomarkers.

[0180] In some embodiments, a composite biomarker for the GLX improves personalized approaches to the methods disclosed herein, e.g. disease monitoring and design therapies based on a precision medicine principle. In some embodiments, the method includes identifying information relating to the presence or levels of at least two or more biomarkers of GLX shedding (e.g. GLX-components or fragments) in the blood, and the integration of these biomarkers into a composite (combination) biomarker, a GLX profile or fingerprint or signature that allows for the detection of disease, severity and a treatment response. In someembodiments, the method includes a comparison between the levels of several biomarkers to their corresponding reference level to obtain a “profile” for the analyzed sample. Such a profile can include more than one dimension, such as elevated and decreased levels of several biomarkers and / or increased or decreased relationships between the markers.

[0181] In some embodiments, the method includes reducing a dosage of a therapy, increasing a dosage interval of the therapy, or discontinuing the therapy if the indication status of the BB dysfunction or condition indicates a presence of the condition for the subject, or proceeding with the therapy if the indication status of the condition does not indicate the presence of the condition for the subject. In some embodiments, the method includes monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of a therapy; or a change in an efficacy of the therapy.

[0182] In some embodiments, the therapy includes administering a therapeutically effective amount of a composition (e.g., a pharmaceutical composition comprising an active agent and one or more pharmaceutically acceptable excipients) to the subject. The composition may be designed to remove A0 from the brain. In some embodiments, the composition includes a small molecule. In some embodiments, the composition includes a peptide. In some embodiments, the peptide includes a GLP-1 -receptor acting agent. In some embodiments, the composition includes an antibody or antibody-drug conjugate. In some embodiments, the antibody includes a monoclonal antibody. In some embodiments, the antibody is directed against an A0 peptide. In some embodiments, the antibody includes lecanemab. In some embodiments, the antibody includes donanemab. In some embodiments, the antibody includes gantenerumab. In some embodiments, the antibody includes remtemetug. In some embodiments, the antibody includes solanezumab. In some embodiments, the antibody includes trontinemab. In some embodiments, the antibody includes crenezumab. In some embodiments, the antibody includes bapineuzumab. In some embodiments, the antibody includes ponezumab. In some embodiments, the antibody includes cevipabulin. In some embodiments, the antibody includes AAB-003. In some embodiments, the antibody includes AAB-004. In some embodiments, the antibody includes BAN2410. In some embodiments, the antibody includes BAN2402. In some embodiments, the antibody includes BAN2403. In some embodiments, the antibody includes SAR231893. In some embodiments, the antibody includes A4B7. In some embodiments, the antibody includes CB1. In some embodiments, the antibody includes MEDI1814. In some embodiments, the antibody includes ELND005. In some embodiments, the antibody includesaducanumab, lecanemab, donanemab, gantenerumab, solanezumab, crenezumab, bapineuzumab, ponezumab, cevipabulin, AAB-003, AAB-004, BAN2410, BAN2402, BAN2403, SAR231893, A4B7, CB1, MEDI1814, or ELND005, or any combination thereof. In some embodiments, the antibody includes a BBB shuttle molecule.

[0183] In some embodiments, the antibody is directed against tubulin associated unit (“tau”). In some embodiments, the antibody is directed against hyperphosphorylated tau. In Alzheimer's disease tau undergoes abnormal hyperphosphorylation, which reduces its affinity for microtubules and causes it to detach. These hyperphosphorylated tau proteins then aggregate into insoluble fibrils, forming neurofibrillary tangles (NFT) inside neurons. These tangles disrupt cellular processes, impair intracellular transport, and contribute to neuronal dysfunction and death, which are characteristic features of Alzheimer's disease pathology.In some embodiments, the BBB is disrupted or permeabilized intentionally using external or internal modulation techniques to enhance the delivery or detection of biomarkers, therapeutics, or imaging agents. Such techniques include, but are not limited to, focused ultrasound in combination with microbubbles (FUS+MB), which transiently and reversibly opens the BBB by inducing localized acoustic cavitation; osmotic disruption (e.g., mannitol infusion); pharmacological agents (e.g., bradykinin analogs, VEGF); radiation-induced opening; nanoparticle-mediated delivery systems; and electrical or electromagnetic stimulation. In some embodiments, these techniques are employed in a controlled clinical or research setting to facilitate access of GLX-targeting agents, antibodies, or tracers to the CNS compartment.

[0184] In some embodiments, the therapeutic agent is a cell-based therapy, such as a chimeric antigen receptor T cell (CAR-T) therapy, designed to target CNS conditions, including malignancies, inflammatory diseases, or neurodegenerative disorders. CAR-T cells may be engineered to recognize antigens expressed within the CNS compartment, and may enter or act upon regions where the BBB is compromised due to disease-associated processes.

[0185] The present methods may provide more efficacious treatment regimens that avoid a variety of safety issues associated with certain drugs. For example, some drugs have central nervous system safety issues due to their BBB disruption or off-target cytotoxicity. Table 2 identifies several exemplary drug classes, indications, and exemplary drugs within those classes that have these safety issues.Table 2: Drugs with CNS Safety IssuesThe present disclosure includes monitoring and treating individuals with drugs having CNS Safety Issues (e.g., those identified in Table 2). For example, the methods may comprise sample collection before, during, and / or after administration of any of these drugs. Following identification of a BBB-score indicating the presence of BBB leakage, administration of these drugs may be stopped or reduced. In certain embodiments, the methods comprise administering the drugs in the new identified treatment regime.

[0186] Additionally, the BBB analysis provided herein may afford access to certain treatment modalities that were previously thought unable to cross the BBB or have variable crossingmechanics across the BBB. These drugs often require specific treatment protocols, such as high dosing or intrathecal injection, to overcome their limited BBB crossing ability. Table 3 provides several of these exemplary drug classes and drugs.Table 3: Drugs with possibly low BBB permeabilityThe present disclosure includes identifying, monitoring, and treating individuals with drugs that have difficulty crossing the BBB normally (e.g. but not limited to those identified in Table 3). For example, the methods may comprise the identification of a BBB state in a subject where influx of the drugs becomes possible. Following identification of a BBB-score indicating the presence of drug influx from the vascular lumen into the brain parenchyma, administration of these drugs via the blood (e.g., intravenous administration) may begin. In certain embodiments, the methods comprise administering low BBB permeability drugs (e.g., intravenously) to patients identified as having BBB characteristics that would allow their influx through the BBB to the brain parenchyma.

[0187] In some embodiments, the method includes assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the BBB dysfunction or condition. In some embodiments, the method includes imaging a brain of the subject using the MRI to obtain MRI images, wherein the MRI images indicate signal abnormalities related to the cerebral edema known as Amyloid-Related Imaging Abnormality (ARIA). Risk assessment, clinical trial stratification, detection and monitoring of ARIA-E and ARIA-H are the top targets for the precision medicine tool, and treatments that are targets plaques and tangles would be a primary use for monitoring safety both in singular and in tandem with MRI. In some embodiments, the precision medical tool can be used in tandem with a genetic risk factor for assessing a risk of developing ARIA if one or more treatments have started, the genetic risk factor including, but not limited to, polymorphisms of the APOE gene that encodes Apolipoprotein E (Apo-E). In some embodiments, the genetic risk factor includes APOE-e4.

[0188] In some embodiments, the method determines a risk of developing ARIA prior to the administrating step of the therapy. In some embodiments, the condition score determines the risk of developing ARIA. In some embodiments, the sample for the first dataset is obtained from the subject prior to the administrating step of the therapy. In some embodiments, the sample for the first dataset is obtained from the subject after the administrating step of the therapy to determine whether or not the subject has ARIA. In some embodiments, the samplefor the first dataset is obtained from the subject after the subject is indicated or suspected of having a risk of developing ARIA to determine whether the subject is ARIA-negative or continues to be ARIA-positive.

[0189] In some embodiments, the subject has been diagnosed with a neurological disease or disorder, or sequela of the neurological disease or disorder.

[0190] In some embodiments, the sample includes a biological fluid, such as a blood sample, plasma sample, serum sample, a cerebrospinal fluid sample, a saliva sample, a urine sample, a tear sample, or any combination thereof.

[0191] In some embodiments, the method achieves a high sensitivity, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a sensitivity within a range defined by any two of the aforementioned values. In some embodiments, the method achieves a high specificity, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a specificity within a range defined by any two of the aforementioned values. In some embodiments, the method achieves a high positive predictive value, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a specificity within a range defined by any two of the aforementioned values. In some embodiments, the method achieves a high negative predictive value, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a specificity within a range defined by any two of the aforementioned values.

[0192] With reference to FIG. 2, in some embodiments, a method 200 for identifying a cerebral microhemorrhage is provided. In some embodiments, the method 200 includes providing 202 a first dataset 202 from a sample obtained by a subject, the first dataset including a level of one or more biomarkers, wherein the one or more biomarkers includes one or more GLX-related GAGs, one or more GLX-related PGs, one or more GLX-related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof, or any combination of the aforementioned biomarkers; classifying 210 each biomarker from the one or more biomarkers to a cerebral microhemorrhage condition, the classifying including providing 212 a second dataset including a reference level of the same one or more biomarkers analyzed in the first dataset; comparing the level of the one or more biomarkers from the first dataset to the reference level of the from the second dataset; and assigning 214, under control of one or more processors, a cerebral microhemorrhage-condition score to each of the one or more biomarkers of the first dataset, wherein the cerebral microhemorrhage-condition score is based on adeviation of the levels of each of the one or more biomarkers of the first dataset from the reference level of the one or more biomarkers of the second dataset; and determining 220 an indication status of the cerebral microhemorrhage based on the cerebral microhemorrhagecondition score.

[0193] In some embodiments, the second dataset includes a level of the same one or more biomarkers analyzed in the first dataset, wherein the level of the same one or more biomarkers is from a sample obtained from the subject at a different time. In some embodiments, if the subject does not have or is not suspected of having cerebral microhemorrhage, then the reference level includes the level of the same one or more biomarkers from a sample obtained from the subject at a different time. In some embodiments, the different time includes a time prior to the sample obtained from the subject for the first dataset. In some embodiments, the different time includes a time after the sample obtained from the subject for the first dataset.

[0194] In some embodiments, the level or concentration of the one or more biomarkers for identifying cerebral microhemorrhage is obtained from a biochemical analysis of the sample. The level of the one or more biomarkers may be determined in the sample by different methods, or a combination of different methods. In some embodiments, the level for each of the one or more biomarkers are determined by an in vitro method. In some embodiments, the level for each of the one or more biomarkers are determined by a method selected from the group consisting of: binding agents, fluorescence detection, immunoassays, antibody recognition, immunostrips, lateral flow assays, multiplexing, dot blotting, beads, microspheres, immunoturbidity magnetic beads, electrochemical sensors, fluorescence polarization, fluorescent linking, turbidity, immunoturbidity magnetic beads, agglutination PCR, nucleic acid coupled assays, proximity extension assays (e.g., oligonucleotide-labeled binding agents), hybridization-based assays, barcoded detection systems, sequencing-based quantification, lanthanides, lectins, GAG-binding molecules, Alcian Blue, Toluidine blue, dimethylmethylene blue, single-molecule array technology, mass spectrometry (MS), GC-MS, Liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ES-MS / MS), LC- MS, MALDLMS, HPLC-MS)), HPLC, Raman spectroscopy, NIR spectroscopy, or NMR spectroscopy. By using a multiplex immunoassay, multiple analytes may be measured simultaneously from a single sample and in a single experiment. In a non-limiting example, microspheres of designated colors are coated with antibodies that are specific to the biomarkers to be quantified. The beads are quantifiable and qualitatively distinguishable by flow cytometryaccording to their signal intensity and fluorescent signature. The number of biomarkers measured determines the number of bead colors required. In a non-limiting example, the binding agent, including but not limited to one or more antibodies, binds to a specific epitope of the biomarker. The binding agent itself may be labelled with a detectable reporter, and / or secondary binding agents can be used to signal the biomarker-binding agent complex, e.g. via chemiluminescence detection system based on HRP enzymatic activity or via fluorescence labelling. Any number of methods, individually or in combination, described herein can be used.

[0195] In some embodiments, the level of one or more biomarkers are detected in one sample (e.g., multiplexing). In some embodiments, the level of one or more biomarkers are detected by a device. In some embodiments, the device is configured for multiplexing. In some embodiments, the device includes microfluidics. In some embodiments, the device includes magnetic beads. In some embodiments, the device involves binding agent-coupling, DNA- coupling, separation on a surface, e.g., chromatography, different wavelengths, fluorescence polarization, or a combination thereof. In some embodiments, the level of a one or more biomarkers are captured by a binding agent and then tested for post-translational modification (PTMs), such as a glycosylation pattern in a manner described as “profiling” or “gly coprofiling.” The sample is ideally concentrated for the detection of the one or more biomarkers, preferably from Table 1, and thereafter probed for PTMs.

[0196] In some embodiments, the concentration or levels of the one or more biomarkers are determined from pixel intensities of the one or more biomarkers obtained from an image analysis with or without a labeled binding agent or detection agent. In some embodiments, the levels of the one or more biomarkers are determined by evaluating the pixel intensities of the biomarkers obtained from an image analysis, and optionally these detected biomarker levels are compared to a trained database of pixel intensities for the biomarkers and disease states associated therewith (e.g., cerebral edema or cerebral microhemorrhage) and a classification score is generated outputting a predilection or prediction of cerebral edema or cerebral microhemorrhage. In some embodiments, the image analysis includes measuring the pixel intensities by a program executed by one or more processors. In some embodiments, the program includes Imaged.

[0197] In some embodiments, the one or more biomarkers include 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 ormore, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers selected from Table 1. In some embodiments, the level of the one or more biomarkers include levels of 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1.

[0198] For example, the one or more biomarkers may include:CD44, hyaluronic acid (HA), perlecan, syndecan-2 (syn2), podocalyxyin (podo), and biglycan; perlecan, HA, glypican-1 (GPC1), syndecan-1 (synl), syndecan-4 (syn4), syn2, podo, chondroitin sulfate (CS), and biglycan;CD44, podo, CS, HA, GPC1, perlecan, syn4, biglycan, and syn2; perlecan, syn4, keratan sulfate (ks), GPC1, HA, biglycan, syn2, CS, podo, CD44, and synl; or synl, syn2, syn4, perlecan, gpcl, cd44, podo, biglycan, cs, ha, and ks.

[0199] In various implementations, the biomarkers are measured from the subject’s blood. In certain embodiments, the biomarkers are measured from the subject’s cerebrospinal fluid (CSF). In various implementations, samples are taken from both the blood and CSF, and different groups of biomarkers are measured for each sample type.

[0200] As used herein, “BBBx disease” refers to any group of conditions or diseases disrupting the blood-brain barrier (BBB). The conditions or diseases affecting the BBB include, but are not limited to, neurodegenerative disorders such as Alzheimer’s disease, Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis (ALS), multiple sclerosis (MS), multiple system atrophy (MSA), frontotemporal dementia (FTD), vascular dementia, Lewy body dementia (LBD), chronic traumatic encephalopathy (CTE), and rare or pediatric-onset neurodegenerative diseases including Niemann-Pick disease, Gaucher disease, Tay-Sachs disease, Rett syndrome, Batten disease (neuronal ceroid lipofuscinosis), Krabbe disease, and metachromatic leukodystrophy (MLD); autoimmune and immune-mediated diseases such as systemic lupus erythematosus with CNS involvement, neuromyelitis optica spectrum disorder, neurosarcoidosis, primary angiitis of the CNS, and CNS vasculitis; psychiatric andneurodevel opmental disorders including schizophrenia, major depressive disorder, bipolar disorder, and post-traumatic stress disorder (PTSD); oncological conditions such as brain tumors, glioblastoma multiforme, primary CNS lymphoma, leptomeningeal carcinomatosis, and brain metastases; cerebrovascular diseases including stroke, small vessel disease, subarachnoid hemorrhage, and intracerebral hemorrhage; traumatic and structural injuries such as traumatic brain injury, spinal cord injury (SCI), and acute brain trauma; metabolic and genetic disorders including lysosomal storage diseases and mitochondrial encephalopathies; neurological conditions such as epilepsy, particularly in drug-resistant forms where BBB disruption may play a role, normal pressure hydrocephalus (NPH), and acute confusional states such as hospital-induced and postoperative delirium; treatment-induced or iatrogenic conditions including chemotherapy-induced cognitive impairment (“chemo brain”) and radiation-induced encephalopathy; and infectious and inflammatory conditions including encephalitis (e.g., herpes or HIV), meningitis (including tuberculous forms), septic encephalopathy, cerebral malaria, viral hemorrhagic fevers, COVID-19-associated encephalopathy, progressive multifocal leukoencephalopathy, and brain abscess, as well as any other neurological, psychiatric, autoimmune, oncological, traumatic, metabolic, or infectious conditions in which BBB dysfunction or restricted CNS drug delivery is observed or suspected.

[0201] In some embodiments, the method includes generating training data based on correlations between the reference levels and indicators for cerebral microhemorrhages; and training a machine learning model on the training data to output the condition score.

[0202] In some embodiments, the condition score indicates whether the subject is indicative for the specifically trained condition. In some embodiments, the assigning of the condition score to each of the biomarkers includes transforming the level of the one or more biomarkers to the condition score via the machine learning model. In some embodiments, the machine learning model includes an ensemble of classifiers. In some embodiments, the machine learning model includes a multivariate statistical test. In some embodiments, multivariate statistical test is a classifier. In some embodiments, the multivariate statistical test includes partial least squares discriminant analysis. In some embodiments, the multivariate statistical test includes random decision forests. In some embodiments, the multivariate statistical test includes a support vector machine (SVM). In some embodiments, the SVM includes a linear SVM. In some embodiments, the SVM includes a non-linear SVM. In some embodiments, the non-linear SVM includes a polynomial kernel. In some embodiments, the non-linear SVMincludes a radial basis function (RBF) kernel. In some embodiments, the non-linear SVM includes a sigmoid kernel. In some embodiments, the SVM includes a support vector regression (SVR).

[0203] In some embodiments, the glycocalyx is the endothelial glycocalyx. In some embodiments, the one or more PGs include CD44, or a fragment thereof. In some embodiments, the one or more GAGs include hyaluronic acid (HA), or a fragment thereof. In some embodiments, the one or more GAGs include heparan sulfate (HS), or a fragment thereof. In some embodiments, the one or more PGs include a syndecan, or a fragment thereof. In some embodiments, the one or more PGs include CD44 or a fragment thereof, wherein the one or more GAGs include HA, HS, or a fragment thereof or any combination of the aforementioned biomarkers, and wherein the one or more PGs include syndecan, perlecan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0204] In some embodiments, the one or more GPs includes a mucin or a fragment thereof. In some embodiments, the mucin includes Mucin 1, Mucin 16, Mucin 20, Mucin 5b, Mucin 6, Muc 18, Muc 14, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the one or more GPs include a sialomucin, or a fragment thereof. In some embodiments, the sialomucin includes podocalyxin, endoglycan, podoplanin, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the GP includes a IgSF, which includes a siglec, or a fragment thereof.

[0205] In some embodiments, the siglec includes Siglec-5, Siglec-9, Siglec-10, Siglec-15, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the one or more biomarkers includes a galectin, or a fragment thereof. In some embodiments, the galectin includes Galectin- 1, Galectin-2, Gal ectin-3, Galectin-4, Galectin-8, Galectin-9, Galectin-12, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the one or more biomarkers includes a sialic acid or a fragment thereof. In some embodiments, the sialic acid includes a polysialic acid or a fragment thereof.

[0206] In some embodiments, the one or more biomarkers includes a sphingolipid or a fragment thereof. In some embodiments, the one or more GLs includes a glycosphingolipid, a ganglioside, or a combination thereof. In some embodiments, the glycosphingolipid includes sulfatide or a fragment thereof. In some embodiments, the ganglioside includes GM1, GDlb, GT lb, or a combination thereof.

[0207] In some embodiments, the glycoprotein includes a selectin or a fragment thereof. In some embodiments, the selectin includes P-selectin, E-selectin, L-selectin, or a fragment thereof or any combination of the aforementioned biomarkers.

[0208] In some embodiments, the one or more PGs include a small leucine-rich proteoglycan (SLRP) or a fragment thereof. In some embodiments, the SLRP includes Fibromodulin, Asporin, Proline / arginine-rich end leucine-rich repeat protein (PRELP), Chondroadherin, Podocan, Opticin, Tsukushi, Testican (SPOCK), Epiphycan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Fibromodulin, Asporin, PRELP, Chondroadherin, Podocan, Opticin, Tsukushi, SPOCK, Epiphycan, Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0209] In some embodiments, the one or more PGs include a betaglycan, or a fragment thereof. In some embodiments, the one or more PGs include a Glypican or a fragment thereof. In some embodiments, the Glypican includes Glypican- 1, Glypican-2, Glypican-3, Glypican-4, Glypican-5, Glypican-6, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the one or more PGs includes a hyalectan or a fragment thereof. In some embodiments, the hyalectan includes aggrecan, brevican, neurocan, versican, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the one or more GAGs include Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the first dataset further includes one or more autoantibodies against one or more of the one or more biomarkers.

[0210] In some embodiments, the method includes reducing a dosage of a therapy, increasing a dosage interval of the therapy, or discontinuing the therapy if the indication status of the BB dysfunction or condition indicates a presence of the condition for the subject, or proceeding with the therapy if the indication status of the condition does not indicate the presence of the condition for the subject. In some embodiments, the method includes monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of a therapy; or a change in an efficacy of the therapy. In some embodiments, the method includes monitoring a change in a state of the subject during aclinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of the therapy; or a change in an efficacy of the therapy.

[0211] In some embodiments, the therapy includes administering a therapeutically effective amount of a composition (e.g., a pharmaceutical composition comprising an active agent and one or more pharmaceutically acceptable excipients) to the subject. The composition may be designed to remove A0 from the brain. In some embodiments, the composition includes a small molecule. In some embodiments, the composition includes a peptide. In some embodiments, the peptide includes a GLP-1 -receptor acting agent. In some embodiments, the composition includes an antibody, or antibody-drug conjugate. In some embodiments, the antibody includes a monoclonal antibody. In some embodiments, the antibody is directed against an A0 peptide. In some embodiments, the antibody includes lecanemab. In some embodiments, the antibody includes donanemab. In some embodiments, the antibody includes gantenerumab. In some embodiments, the antibody includes remtemetug. In some embodiments, the antibody includes solanezumab. In some embodiments, the antibody includes trontinemab. In some embodiments, the antibody includes crenezumab. In some embodiments, the antibody includes bapineuzumab. In some embodiments, the antibody includes ponezumab. In some embodiments, the antibody includes cevipabulin. In some embodiments, the antibody includes AAB-003. In some embodiments, the antibody includes AAB-004. In some embodiments, the antibody includes BAN2410. In some embodiments, the antibody includes BAN2402. In some embodiments, the antibody includes BAN2403. In some embodiments, the antibody includes SAR231893. In some embodiments, the antibody includes A4B7. In some embodiments, the antibody includes CB1. In some embodiments, the antibody includes MEDI1814. In some embodiments, the antibody includes ELND005. In some embodiments, the antibody includes aducanumab, lecanemab, donanemab, gantenerumab, solanezumab, crenezumab, bapineuzumab, ponezumab, cevipabulin, AAB-003, AAB-004, BAN2410, BAN2402, BAN2403, SAR231893, A4B7, CB1, MEDI1814, or ELND005, or any combination thereof. In some embodiments, the antibody includes a BBB shuttle molecule.In some embodiments, the BBB is disrupted or permeabilized intentionally using external or internal modulation techniques to enhance the delivery or detection of biomarkers, therapeutics, or imaging agents. Such techniques include, but are not limited to, focused ultrasound in combination with microbubbles (FUS+MB), which transiently and reversibly opens the BBB by inducing localized acoustic cavitation; osmotic disruption (e.g., mannitolinfusion); pharmacological agents (e.g., bradykinin analogs, VEGF); radiation-induced opening; nanoparticle-mediated delivery systems; and electrical or electromagnetic stimulation. In some embodiments, these techniques are employed in a controlled clinical or research setting to facilitate access of GLX-targeting agents, antibodies, or tracers to the CNS compartment.

[0212] In some embodiments, the therapeutic agent is a cell-based therapy, such as a chimeric antigen receptor T cell (CAR-T) therapy, designed to target CNS conditions, including malignancies, inflammatory diseases, or neurodegenerative disorders. CAR-T cells may be engineered to recognize antigens expressed within the CNS compartment, and may enter or act upon regions where the BBB is compromised due to disease-associated processes.

[0213] The present methods may provide more efficacious treatment regimens that avoid a variety of safety issues associated with certain drugs. For example, some drugs have central nervous system safety issues due to their BBB disruption or off-target cytotoxicity. Table 2 identifies several exemplary drug classes, indications, and exemplary drugs within those classes that have these safety issues.The present disclosure includes monitoring and treating individuals with drugs having CNS Safety Issues (e.g., those identified in Table 2). For example, the methods may comprise sample collection before, during, and / or after administration of any of these drugs. Following identification of a BBB-score indicating the presence of BBB leakage, administration of these drugs may be stopped or reduced. In certain embodiments, the methods comprise administering the drugs in the new identified treatment regime.

[0214] Additionally, the BBB analysis provided herein may afford access to certain treatment modalities that were previously thought unable to cross the BBB or have variable crossing mechanics across the BBB. These drugs often require specific treatment protocols, such as high dosing or intrathecal injection, to overcome their limited BBB crossing ability. Table 3 provides several of these exemplary drug classes and drugs.

[0215] The present disclosure includes identifying, monitoring, and treating individuals with drugs that have difficulty crossing the BBB normally (e.g. but not limited to those identified in Table 3). For example, the methods may comprise the identification of a BBB state in a subject where influx of the drugs becomes possible. Following identification of a BBB-score indicating the presence of drug influx from the vascular lumen into the brain parenchyma,administration of these drugs via the blood (e.g., intravenous administration) may begin. In certain embodiments, the methods comprise administering low BBB permeability drugs (e.g., intravenously) to patients identified as having BBB characteristics that would allow their influx through the BBB to the brain parenchyma.

[0216] In some embodiments, the method further includes assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the BBB dysfunction or condition. In some embodiments, the method includes imaging a brain of the subject using the MRI to obtain MRI images, wherein the MRI images indicate signal abnormalities related to the cerebral microhemorrhage known as Amyloid- Related Imaging Abnormality (ARIA). In some embodiments, the subject has been diagnosed with a neurological disease or disorder, or sequela of the neurological disease or disorder.

[0217] In some embodiments, the method determines a risk of developing ARIA prior to the administrating step of the therapy. In some embodiments, the condition score determines the risk of developing ARIA. In some embodiments, the sample for the first dataset is obtained from the subject prior to the administrating step of the therapy. In some embodiments, the sample for the first dataset is obtained from the subject after the administrating step of the therapy to determine whether or not the subject has ARIA. In some embodiments, the sample for the first dataset is obtained from the subject after the subject is indicated or suspected of having a risk of developing ARIA to determine whether the subject is ARIA-negative or continues to be ARIA-positive.

[0218] In some embodiments, the sample includes a biological fluid, such as a blood sample, plasma sample, serum sample, a cerebrospinal fluid sample, a saliva sample, a urine sample, a tear sample, or any combination thereof.

[0219] In some embodiments, the method achieves a high sensitivity, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a sensitivity within a range defined by any two of the aforementioned values. In some embodiments, the method achieves a high specificity, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a specificity within a range defined by any two of the aforementioned values.

[0220] In some embodiments, the method achieves a high positive predictive value, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a specificity within a range defined by any two of the aforementioned values. In some embodiments, the method achievesa high negative predictive value, such as 55%, 60%, 65%, 70%, 75%, 80%, 85%, 90%, 95%, 100%, or a specificity within a range defined by any two of the aforementioned values.

[0221] With reference to FIG. 3, in some embodiments, a method 300 for identifying a change in a BBB that allows an influx of a composition across the BBB from the vascular lumen into the brain parenchyma is provided, the method 300 including providing 302 a first dataset from a sample obtained by a subject, the first dataset including a level of a one or more biomarkers, wherein the one or more biomarkers includes one or more GLX-related GAGs, one or more GLX-related PGs, one or more GLX-related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof, or any combination of the aforementioned biomarkers; classifying 310 each biomarker from the one or more biomarkers to an influxcondition, the classifying 310 including providing 312 a second dataset including a reference level of the same one or more biomarkers analyzed in the first dataset; comparing the levels of the one or more biomarkers from the first dataset to the reference level of the one or more biomarkers from the second dataset; and assigning 314, under control of one or more processors, an BBB influx-condition score to each of the biomarkers of the one or more biomarkers of the first dataset, wherein the influx-condition score is based on a deviation of the level of each of the one or more biomarkers of the first dataset from the reference level of the second dataset; and determining 320 an indication status of the influx of the composition based on the BBB influx-condition score.

[0222] In some embodiments, the second dataset includes a level of the same one or more biomarkers analyzed in the first dataset, wherein the level of the same one or more biomarkers is from a sample obtained from the subject at a different time. In some embodiments, if the subject does not have or is not suspected of having the change in the BBB that allows an influx of the composition across the BBB from the vascular lumen into the brain parenchyma, then the reference level includes the level of the same one or more biomarkers from a sample obtained from the subject at a different time. In some embodiments, the different time includes a time prior to the sample obtained from the subject for the first dataset. In some embodiments, the different time includes a time after the sample obtained from the subj ect for the first dataset.

[0223] With reference to FIG. 4, in some embodiments, a method 400 for identifying a change in the BBB that allows an efflux of a composition across the BBB from the brain parenchyma into the vascular lumen is provided. The method 400 includes providing 402 a first dataset from a sample obtained by a subject, the first dataset including a level of a one or morebiomarkers, wherein the one or more biomarkers includes one or more GLX-related GAGs, one or more GLX-related PGs, one or more GLX-related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof, or any combination of the aforementioned biomarkers; classifying 410 each biomarker from the one or more biomarkers to an influx-condition, the classifying 410 including providing 412 a second dataset including a reference level of the same one or more biomarkers analyzed in the first dataset; comparing the level of the one or more biomarkers from the first dataset to the reference level of the second dataset; and assigning 414, under control of one or more processors, an BBB efflux-condition score to each of the one or more biomarkers of the first dataset, wherein the efflux-condition score is based on a deviation of the levels of each of the biomarkers of the one or more biomarkers of the first dataset from the reference levels of the second dataset; and determining 420 an indication status of the efflux of the composition based on the BBB efflux-condition score.

[0224] In some embodiments, the second dataset includes a level of the same one or more biomarkers analyzed in the first data, wherein the level of the same one or more biomarkers is from a sample obtained from the subject at a different time. In some embodiments, if the subject does not have or is not suspected of having the change in the BBB that allows an efflux of the composition across the BBB from the brain parenchyma into the vascular lumen, then the reference level includes the level of the same one or more biomarkers from a sample obtained from the subject at a different time. In some embodiments, the different time includes a time prior to the sample obtained from the subject for the first dataset. In some embodiments, the different time includes a time after the sample obtained from the subject for the first dataset.

[0225] Methods for identifying a cerebral edema and / or a cerebral microhemorrhage in a subject can include providing a sample obtained from the subject, the sample including one or more biomarkers, wherein the one or more biomarkers includes one or more GLX-related GAGs, one or more GLX-related PGs, one or more GLX-related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof, or any combination of the aforementioned biomarkers; measuring or detecting a level of one or more biomarkers from the one or more biomarkers in the sample; comparing the levels of the one or more biomarkers measured or detected to a reference level for the one or more biomarkers; and identifying the subject as having a cerebral edema or a cerebral microhemorrhage if the level of the one ormore of the biomarkers measured or detected from the sample are different than the reference level for the one or more biomarkers.

[0226] In some embodiments, the concentration of the one or more biomarkers is obtained from a biochemical analysis of the sample. The level of the one or more biomarkers may be determined in the sample by different methods, or a combination of different methods. In some embodiments, the level for each of the one or more biomarkers are determined by an in vitro method. In some embodiments, the level for each of the one or more biomarkers are determined by a method selected from the group consisting of: binding agents, fluorescence detection, immunoassays, antibody recognition, immunostrips, lateral flow assays, multiplexing, dot blotting, beads, microspheres, immunoturbidity magnetic beads, electrochemical sensors, fluorescence polarization, fluorescent linking, turbidity, immunoturbidity magnetic beads, agglutination PCR, nucleic acid coupled assays, lanthanides, lectins, GAG-binding molecules, Alcian Blue, Toluidine blue, dimethylmethylene blue, single-molecule array technology, mass spectrometry (GC-MS, Liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ES-MS / MS), LC-MS, MALDLMS, HPLC-MS), HPLC, Raman spectroscopy, NIR spectroscopy, or NMR spectroscopy. By using a multiplex immunoassay, multiple analytes may be measured simultaneously from a single sample and in a single experiment. In a non-limiting example, microspheres of designated colors are coated with antibodies that are specific to the biomarkers to be quantified. The beads are quantifiable and qualitatively distinguishable by flow cytometry according to their signal intensity and fluorescent signature. The number of biomarkers measured typically determines the number of bead colors required. In a non-limiting example, the binding agent, including but not limited to one or more antibodies, binds to a specific epitope of the biomarker. The binding agent itself may be labelled with a detectable reporter, and / or secondary binding agents can be used to signal the biomarker-binding agent complex, e.g. via chemiluminescence detection system based on HRP enzymatic activity. Any number of methods, individually or in combination, described herein can be used.

[0227] In some embodiments, the level of one or more biomarkers are detected in one sample (i.e., multiplexing). In some embodiments, the level of one or more biomarkers are detected by a device. In some embodiments, the device is configured for multiplexing. In some embodiments, the device includes microfluidics. In some embodiments, the device includes magnetic beads. In some embodiments, the device involves DNA-coupling, separation on asurface, e.g., chromatography, different wavelengths, fluorescence polarization, or a combination thereof. In some embodiments, the level of a one or more biomarkers are captured by a binding agent and then tested for post-translational modification (PTMs), such as a glycosylation pattern in a manner described as “profiling” or “glycoprofiling.” The sample is ideally concentrated for the detection of the one or more biomarkers, preferably from Table 1, and thereafter probed for PTMs.

[0228] In some embodiments, the concentration or levels of the one or more biomarkers are determined from pixel intensities of the one or more biomarkers obtained from an image analysis with or without a labeled binding agent or detection agent. In some embodiments, the levels of the one or more biomarkers are determined by evaluating the pixel intensities of the biomarkers obtained from an image analysis, and optionally these detected biomarker levels are compared to a trained database of pixel intensities for the biomarkers and disease states associated therewith (e.g., cerebral edema or cerebral microhemorrhage) and a classification score is generated outputting a predilection or prediction of cerebral edema or cerebral microhemorrhage. In some embodiments, the image analysis includes measuring the pixel intensities by a program executed by one or more processors. In some embodiments, the program includes Imaged.

[0229] In some embodiments, the one or more biomarkers include 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1. In some embodiments, the level of the one or more biomarkers include levels of 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1.

[0230] In some embodiments, the glycocalyx is the endothelial glycocalyx.

[0231] In some embodiments, the one or more PGs include CD44, or a fragment thereof. In some embodiments, the one or more GAGs include hyaluronic acid (HA), or a fragmentthereof. In some embodiments, the one or more GAGs include heparan sulfate (HS), or a fragment thereof. In some embodiments, the one or more PGs include a syndecan, or a fragment thereof. In some embodiments, the one or more PGs include CD44 or a fragment thereof, wherein the one or more GAGs include HA, HS, or a fragment thereof or any combination of the aforementioned biomarkers, and wherein the one or more PGs include syndecan, perlecan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0232] In some embodiments, the one or more biomarkers includes an IgSF, or a fragment thereof. In some embodiments, the IgSF includes a mucin or a fragment thereof. In some embodiments, the mucin includes Mucin 1, Mucin 16, Mucin 20, Mucin 5b, Mucin 6, Muc 18, Muc 14, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the IgSF includes a siaolmucin, or a fragment thereof. In some embodiments, the siaolmucin includes endoglycan, podocalyxin, podoplanin, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the IgSF includes a siglec, or a fragment thereof. In some embodiments, the siglec includes Siglec-5, Siglec-9, Siglec-10, Siglec- 15, or a fragment thereof or any combination of the aforementioned biomarkers.

[0233] In some embodiments, the one or more biomarkers includes a galectin, or a fragment thereof. In some embodiments, the galectin includes Galectin- 1, Galectin-2, Galectin-3, Galectin-4, Galectin-8, Galectin-9, Galectin- 12, or a fragment thereof or any combination of the aforementioned biomarkers.

[0234] In some embodiments, the one or more biomarkers includes a sialic acid or a fragment thereof. In some embodiments, the sialic acid includes a polysialic acid or a fragment thereof.

[0235] In some embodiments, the one or more biomarkers includes a sphingolipid or a fragment thereof. In some embodiments, the one or more GLs includes a glycosphingolipid, a ganglioside, or a combination thereof. In some embodiments, the glycosphingolipid includes sulfatide or a fragment thereof. In some embodiments, the ganglioside includes GM1, GDlb, GT lb, or a combination thereof.

[0236] In some embodiments, the glycoprotein includes a selectin or a fragment thereof. In some embodiments, the selectin includes P-selectin, E-selectin, L-selectin, or a fragment thereof or any combination of the aforementioned biomarkers.

[0237] In some embodiments, the one or more PGs include a small leucine-rich proteoglycan (SLRP) or a fragment thereof. In some embodiments, the SLRP includes Fibromodulin, Asporin, Proline / arginine-rich end leucine-rich repeat protein (PRELP), Chondroadherin, Podocan, Opticin, Tsukushi, Testican (SPOCK), Epiphycan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Fibromodulin, Asporin, PRELP, Chondroadherin, Podocan, Opticin, Tsukushi, SPOCK, Epiphycan, Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0238] In some embodiments, the one or more PGs include a betaglycan, or a fragment thereof. In some embodiments, the one or more PGs include a Glypican or a fragment thereof. In some embodiments, the Glypican includes Glypican- 1, Glypican-2, Glypican-3, Glypican-4, Glypican-5, Glypican-6, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the one or more PGs includes a hyalectan or a fragment thereof.

[0239] In some embodiments, the hyalectan includes aggrecan, brevican, neurocan, versican, or a fragment thereof or any combination of the aforementioned biomarkers.

[0240] In some embodiments, the one or more GAGs include Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, or a fragment thereof or any combination of the aforementioned biomarkers.

[0241] In some embodiments, the first dataset further includes one or more autoantibodies against one or more of the one or more biomarkers.

[0242] In some embodiments, the method includes reducing a dosage of a therapy, increasing a dosage interval of the therapy, or discontinuing administrating the therapy if the indication status of the cerebral edema indicates a presence of the cerebral edema for the subject, reducing the dosage of the therapy, increasing the dosage interval of the therapy, or discontinuing the administrating of the therapy if the indication status of the cerebral microhemorrhage indicates a presence of the cerebral microhemorrhage for the subject, proceeding with the therapy if the indication status of the cerebral edema does not indicate the presence of the cerebral edema forthe subject, or proceeding with the therapy if the indication status of the cerebral edema does not indicate the presence of the cerebral microhemorrhage for the subject.

[0243] In some embodiments, the method includes monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of a therapy; or a change in an efficacy of the therapy.

[0244] In some embodiments, the method includes monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of the therapy; or a change in an efficacy of the therapy.

[0245] In some embodiments, the therapy includes administering a therapeutically effective amount of a composition to the subject. In some embodiments, the composition is designed to treat a BBBx disease. In some embodiments, the composition includes a small molecule. In some embodiments, the composition includes a peptide. In some embodiments, the peptide includes a GLP-1 -based peptide. In some embodiments, the composition includes a large molecule. In some embodiments, the composition includes an antibody. In some embodiments, the antibody includes a monoclonal antibody. In some embodiments, the composition includes a shuttling molecule to transport the composition across the BBB. In some embodiments, the composition includes contrasting agents used for imaging. In some embodiments, the contrasting agent includes gadolinium.

[0246] In some embodiments, the composition is designed to remove A0 from the brain. In some embodiments, the antibody is directed against an A0 peptide. In some embodiments, the antibody includes lecanemab. In some embodiments, the antibody includes donanemab. In some embodiments, the antibody includes gantenerumab. In some embodiments, the antibody includes remtemetug. In some embodiments, the antibody includes solanezumab. In some embodiments, the antibody includes trontinemab. In some embodiments, the antibody includes crenezumab. In some embodiments, the antibody includes bapineuzumab. In some embodiments, the antibody includes ponezumab. In some embodiments, the antibody includes cevipabulin. In some embodiments, the antibody includes AAB-003. In some embodiments, the antibody includes AAB-004. In some embodiments, the antibody includes BAN2410. In some embodiments, the antibody includes BAN2402. In some embodiments, the antibody includes BAN2403. In some embodiments, the antibody includes SAR231893. In some embodiments, the antibody includes A4B7. In some embodiments, the antibody includes CB1.In some embodiments, the antibody includes MEDI1814. In some embodiments, the antibody includes ELND005. In some embodiments, the antibody includes aducanumab, lecanemab, donanemab, gantenerumab, solanezumab, crenezumab, bapineuzumab, ponezumab, cevipabulin, AAB-003, AAB-004, BAN2410, BAN2402, BAN2403, SAR231893, A4B7, CB1, MEDI1814, or ELND005, or any combination thereof.

[0247] In some embodiments, the antibody is conjugated to the shuttling molecule.

[0248] In some embodiments, the method includes assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the cerebral edema or the cerebral microhemorrhage. In some embodiments, the method includes imaging a brain of the subject using the MRI to obtain MRI images. In some embodiments, the MRI images indicate signal abnormalities caused by the cerebral edema, the signal abnormalities being related to Amyloid-Related Imaging Abnormality (ARIA). In some embodiments, the MRI images indicate signal abnormalities related to the cerebral hemorrhage, the signal abnormalities being related to Amyloid-Related Imaging Abnormality (ARIA). In some embodiments, the subject has been diagnosed with a neurological disease or disorder, or sequela of the neurological disease or disorder.

[0249] In some embodiments, the method determines a risk of developing ARIA prior to the administrating step of the therapy. In some embodiments, the BBB influx-condition score determines the risk of developing ARIA. In some embodiments, the BBB efflux-condition score determines the risk of developing ARIA. In some embodiments, the sample for the first dataset is obtained from the subject prior to the administrating step of the therapy. In some embodiments, the sample for the first dataset is obtained from the subject after the administrating step of the therapy to determine whether or not the subject has ARIA. In some embodiments, the sample for the first dataset is obtained from the subject after the subject is indicated or suspected of having a risk of developing ARIA to determine whether the subject is ARIA-negative or continues to be ARIA-positive.

[0250] In some embodiments, the sample includes a biological fluid, such as a blood sample, plasma sample, serum sample, a cerebrospinal fluid sample, a saliva sample, a urine sample, a tear sample, or any combination thereof.

[0251] In some embodiments, a method for identifying a change in a BBB that allows an influx of a composition across the BBB from the vascular lumen into the brain parenchyma of asubject or for identifying a change in the BBB that allows an efflux of a composition across the BBB from the brain parenchyma into the vascular lumen of the subject is provided. The method includes providing a sample obtained from the subject, including a level of a one or more biomarkers, wherein the one or more biomarkers includes one or more GLX-related GAGs, one or more GLX-related PGs, one or more GLX-related GPs, one or more GLX-related GLs, one or more GLX-related GCs, or one or more fragments thereof, or any combination of the aforementioned biomarkers; measuring or detecting the level of one or more biomarkers in the sample; comparing the levels of the one or more biomarkers measured or detected to a reference level for the one or more biomarkers; and predicting or providing an indication status of the influx or efflux of the composition across the BBB when the level of the one or more of the biomarkers measured or detected from the sample is different than the reference level for the one or more biomarkers.

[0252] In some embodiments, the concentration of the one or more biomarkers is obtained from a biochemical analysis of the sample. The level of the one or more biomarkers may be determined in the sample by different methods, or a combination of different methods. In some embodiments, the level for each of the one or more biomarkers are determined by an in vitro method. In some embodiments, the level for each of the one or more biomarkers are determined by a method selected from the group consisting of: binding agents, fluorescence detection, immunoassays, antibody recognition, immunostrips, lateral flow assays, multiplexing, dot blotting, beads, microspheres, immunoturbidity magnetic beads, electrochemical sensors, fluorescence polarization, fluorescent linking, turbidity, immunoturbidity magnetic beads, agglutination PCR, nucleic acid coupled assays, lanthanides, lectins, GAG-binding molecules, Alcian Blue, Toluidine blue, dimethylmethylene blue, single-molecule array technology, mass spectrometry (GC-MS, Liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ES-MS / MS), LC-MS, MALDLMS, HPLC-MS)), HPLC, Raman spectroscopy, NIR spectroscopy, or NMR spectroscopy. By using a multiplex immunoassay, multiple analytes may be measured simultaneously from a single sample and in a single experiment. In a non-limiting example, microspheres of designated colors are coated with antibodies that are specific to the biomarkers to be quantified. The beads are quantifiable and qualitatively distinguishable by flow cytometry according to their signal intensity and fluorescent signature. The number of biomarkers measured determines the number of bead colors required. In a non-limiting example, the binding agent, including but not limited to one or more antibodies, binds to a specific epitope of the biomarker. The binding agent itself maybe labelled with a detectable reporter, and / or secondary binding agents can be used to signal the biomarker-binding agent complex, e.g. via chemiluminescence detection system based on HRP enzymatic activity. Any number of methods, individually or in combination, described herein can be used.

[0253] In some embodiments, the level of one or more biomarkers are detected in one sample (i.e., multiplexing). In some embodiments, the level of one or more biomarkers are detected by a device. In some embodiments, the device is configured for multiplexing. In some embodiments, the device includes microfluidics. In some embodiments, the device includes magnetic beads. In some embodiments, the device involves DNA-coupling, separation on a surface, e.g., chromatography, different wavelengths, fluorescence polarization, or a combination thereof. In some embodiments, the level of a one or more biomarkers are captured by a binding agent and then tested for post-translational modification (PTMs), such as a glycosylation pattern in a manner described as “profiling” or “glycoprofiling.” The sample is ideally concentrated for the detection of the one or more biomarkers, preferably from Table 1, and thereafter probed for PTMs.

[0254] In some embodiments, the concentration or levels of the one or more biomarkers are determined from pixel intensities of the one or more biomarkers obtained from an image analysis with or without a labeled binding agent or detection agent. In some embodiments, the levels of the one or more biomarkers are determined by evaluating the pixel intensities of the biomarkers obtained from an image analysis, and optionally these detected biomarker levels are compared to a trained database of pixel intensities for the biomarkers and disease states associated therewith (e.g., predicting an influx of a composition across a BBB from the vascular lumen into the brain parenchyma) and a classification score is generated outputting a predilection or prediction of cerebral edema or cerebral microhemorrhage. In some embodiments, the image analysis includes measuring the pixel intensities by a program executed by one or more processors. In some embodiments, the program includes Imaged.

[0255] In some embodiments, the one or more biomarkers include 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, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers selected from Table 1. In some embodiments, the level of the one or more biomarkers include levels of two or more, three or more, four or more, fiveor more, six or more, seven or more, eight or more, nine or more, ten or more, eleven or more, twelve or more, thirteen or more, fourteen or more, fifteen or more, sixteen or more, seventeen or more, eighteen or more, nineteen or more, or twenty or more biomarkers, or a plurality of biomarkers, preferably selected from the biomarkers set forth in Table 1.

[0256] In some embodiments, the glycocalyx is the endothelial glycocalyx.

[0257] In some embodiments, the one or more PGs include CD44, or a fragment thereof. In some embodiments, the one or more GAGs include hyaluronic acid (HA), or a fragment thereof. In some embodiments, the one or more GAGs include heparan sulfate (HS), or a fragment thereof. In some embodiments, the one or more PGs include a syndecan, or a fragment thereof. In some embodiments, the one or more PGs include CD44 or a fragment thereof, wherein the one or more GAGs include HA, HS, or a fragment thereof or any combination of the aforementioned biomarkers, and wherein the one or more PGs include syndecan, perlecan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0258] In some embodiments, the one or more biomarkers includes an IgSF, or a fragment thereof. In some embodiments, the IgSF includes a mucin or a fragment thereof. In some embodiments, the mucin includes Mucin 1, Mucin 16, Mucin 20, Mucin 5b, Mucin 6, Muc 18, Muc 14, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the IgSF includes a siaolmucin, or a fragment thereof. In some embodiments, the siaolmucin includes endoglycan, podocalyxin, podoplanin, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the IgSF includes a siglec, or a fragment thereof. In some embodiments, the siglec includes Siglec-5, Siglec-9, Siglec-10, Siglec- 15, or a fragment thereof or any combination of the aforementioned biomarkers.

[0259] In some embodiments, the one or more biomarkers includes a galectin, or a fragment thereof. In some embodiments, the galectin includes Galectin- 1, Galectin-2, Galectin-3, Galectin-4, Galectin-8, Galectin-9, Galectin- 12, or a fragment thereof or any combination of the aforementioned biomarkers.

[0260] In some embodiments, the one or more biomarkers includes a sialic acid or a fragment thereof. In some embodiments, the sialic acid includes a polysialic acid or a fragment thereof.

[0261] In some embodiments, the one or more biomarkers includes a sphingolipid or a fragment thereof. In some embodiments, the one or more GLs includes a glycosphingolipid, a ganglioside, or a combination thereof. In some embodiments, the glycosphingolipid includes sulfatide or a fragment thereof. In some embodiments, the ganglioside includes GM1, GDlb, GT lb, or a combination thereof.

[0262] In some embodiments, the glycoprotein includes a selectin or a fragment thereof. In some embodiments, the selectin includes P-selectin, E-selectin, L-selectin, or a fragment thereof or any combination of the aforementioned biomarkers.

[0263] In some embodiments, the one or more PGs include a small leucine-rich proteoglycan (SLRP) or a fragment thereof. In some embodiments, the SLRP includes Fibromodulin, Asporin, Proline / arginine-rich end leucine-rich repeat protein (PRELP), Chondroadherin, Podocan, Opticin, Tsukushi, Testican (SPOCK), Epiphycan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers. In some embodiments, the SLRP includes Fibromodulin, Asporin, PRELP, Chondroadherin, Podocan, Opticin, Tsukushi, SPOCK, Epiphycan, Biglycan, Mimecan, Decorin, Lumican, Keratocan, or a fragment thereof or any combination of the aforementioned biomarkers.

[0264] In some embodiments, the one or more PGs include a betaglycan, or a fragment thereof. In some embodiments, the one or more PGs include a Glypican or a fragment thereof. In some embodiments, the Glypican includes Glypican- 1, Glypican-2, Glypican-3, Glypican-4, Glypican-5, Glypican-6, or a fragment thereof or any combination of the aforementioned biomarkers.

[0265] In some embodiments, the one or more PGs includes a hyalectan or a fragment thereof. In some embodiments, the hyalectan includes aggrecan, brevican, neurocan, versican, or a fragment thereof or any combination of the aforementioned biomarkers.

[0266] In some embodiments, the one or more GAGs include Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, or a fragment thereof or any combination of the aforementioned biomarkers.-n-

[0267] In some embodiments, the first dataset further includes one or more autoantibodies against one or more of the one or more biomarkers.

[0268] In some embodiments, the method includes reducing a dosage of the composition, increasing a dosage interval of the composition, or discontinuing administrating the composition if the indication status of the efflux of the composition across the BBB indicates a BBB dysfunction related to the efflux, reducing the dosage of the composition, increasing the dosage interval of the composition, or discontinuing administrating the composition if the indication status of the influx of the composition across the BBB indicates a BBB dysfunction related to the influx, proceeding with the administrating of the composition if the indication status of the efflux of the composition across the BBB does not indicate the BBB dysfunction related to the efflux, or proceeding with the administrating of the composition if the indication status of the efflux of the composition across the BBB does not indicate the BBB dysfunction related to the influx.

[0269] In some embodiments, the method includes monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of the composition; or a change in an efficacy of the composition.

[0270] In some embodiments, the method includes monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of the composition; or a change in an efficacy of the composition.

[0271] In some embodiments, the therapy includes administering a therapeutically effective amount of a composition to the subject. The composition is designed to remove A0 from the brain. In some embodiments, the composition includes a small molecule. In some embodiments, the composition includes a peptide. In some embodiments, the peptide includes a GLP-1 -based peptide. In some embodiments, the composition includes an antibody. In some embodiments, the antibody includes a monoclonal antibody. In some embodiments, the antibody is directed against an A0 peptide. In some embodiments, the antibody includes lecanemab. In some embodiments, the antibody includes donanemab. In some embodiments, the antibody includes gantenerumab. In some embodiments, the antibody includes remternetug. In some embodiments, the antibody includes solanezumab. In some embodiments, the antibody includes trontinemab. In some embodiments, the antibody includes crenezumab. In some embodiments, the antibody includes bapineuzumab. In some embodiments, the antibodyincludes ponezumab. In some embodiments, the antibody includes cevipabulin. In some embodiments, the antibody includes AAB-003. In some embodiments, the antibody includes AAB-004. In some embodiments, the antibody includes BAN2410. In some embodiments, the antibody includes BAN2402. In some embodiments, the antibody includes BAN2403. In some embodiments, the antibody includes SAR231893. In some embodiments, the antibody includes A4B7. In some embodiments, the antibody includes CB1. In some embodiments, the antibody includes MEDI1814. In some embodiments, the antibody includes ELND005. In some embodiments, the antibody includes aducanumab, lecanemab, donanemab, gantenerumab, solanezumab, crenezumab, bapineuzumab, ponezumab, cevipabulin, AAB-003, AAB-004, BAN2410, BAN2402, BAN2403, SAR231893, A4B7, CB1, MEDI1814, or ELND005, or any combination thereof.

[0272] In some embodiments, the method includes assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the BBB disruption. In some embodiments, the method includes imaging a brain of the subject using the MRI to obtain MRI images, wherein the MRI images indicate signal abnormalities related to the cerebral microhemorrhage known as Amyloid-Related Imaging Abnormality (ARIA). In some embodiments, the subject has been diagnosed with a neurological disease or disorder, or sequela of the neurological disease or disorder.

[0273] In some embodiments, the method determines a risk of developing ARIA prior to the administrating step of the therapy. In some embodiments, the indication status of the influx or efflux of the composition across the BBB determines the risk of developing ARIA. In some embodiments, the sample for the first dataset is obtained from the subject prior to the administrating step of the therapy. In some embodiments, the sample for the first dataset is obtained from the subject after the administrating step of the therapy to determine whether or not the subject has ARIA. In some embodiments, the sample for the first dataset is obtained from the subject after the subject is indicated or suspected of having a risk of developing ARIA to determine whether the subject is ARIA-negative or continues to be ARIA-positive.

[0274] In some embodiments, the sample includes a biological fluid, such as a blood sample, plasma sample, serum sample, a cerebrospinal fluid sample, a saliva sample, a urine sample, a tear sample, or any combination thereof.

[0275] In some embodiments, a kit is provided. In some embodiments, the kit includes a panel of one or more biomarkers listed in Table 1. In some embodiments, the kit tests the sample for the one or more biomarkers. In some embodiments, the kit includes a detection device constructed to detect a disease or condition of the disease in a patent. In some embodiments, the detection device includes a plurality of layers, the plurality of layers including a plurality of channels disposed therein or thereon; a sample input channel into which the sample is introduced. In some embodiments, the detection device detects the level of the one or more biomarkers. In some embodiments, the detection device includes an output module. In some embodiments, detection device includes a display that is communicative with the output module. In some embodiments, the output module produces a readout of each of the one or more biomarkers. In some embodiments, the readout includes the level of the one or more biomarkers. In some embodiments, the detection device compares the level of the one or more biomarkers to a reference level and determines whether a given BBBx disease is at-risk, present, active, progressing, changing or, in the case of treatment response, in remission or changing. In some embodiments, the detection includes communication device that is communicative with the output module. In some embodiments, the communication device is configured to transmit a signal via remote sensor technology to a receiving sensor. In some embodiments, the remote sensor technology includes Bluetooth. In some embodiments, the communication device is configured to transmit the signal via a wired connection with another electronic device. In some embodiments, the electronic device is an electronic storage device.EXAMPLES

[0276] Some aspects of the embodiments discussed above are disclosed in further detail in the following examples, which are not in any way intended to limit the scope of the present disclosure. Those in the art will appreciate that many other embodiments also fall within the scope of the disclosure, as it is described herein above and in the claims.Example 1 : Assessment of BBB permeability

[0277] Assessment of BBB permeability was conducted on an organ-on-a-chip microfluidic device (“BBB chip”), which forms realistic BBB phenotypes with human cells. The BBB chip was seeded with human cells to form a complex, multicellular, three-dimensional blood vessel with realistic fluidic flow (bottom channel, blood chamber, human brain endothelial cells) that interfaces with brain cells (top channel, brain chamber, human astrocytes + human pericytes).BBB permeability was assayed in real-time by the flow of a fluorescent tracer that is added to the bottom channel (blood vessel side) and detected on the top channel (brain side).

[0278] As shown in FIG 5A, “control” conditions represent no addition of an inflammatory cocktail to the BBB chip. In “control” conditions, the fluorescent tracer detection on the ‘brain side’ the in vitro BBB model was set as the control level of BBB vascular leakiness. As shown in FIG. 5B, pathological BBB breakdown is induced by introducing a cocktail of inflammatory factors in the inflow of the bottom channel. In the ‘BBB Breakdown’ experiment, leakiness of the BBB increased by 20%. In the ‘BBB Protection’ experiment, as shown in FIG. 5C, the same inflammatory cocktail was infused into the BBB chip as shown in FIG. 5B, and thereafter a drug was added to the inflow that improves the BBB integrity and reduced the amount of fluorescent tracer that travelled from the blood-side to the brain-side and thus, BBB leakiness was assayed. Samples for GLX analyses are taken from the Bottom-Inflow and Bottom- Outflow and Top-Inflow and Top-Outflow and frozen for processing.

[0279] GLX-related markers were changed in the in vitro model of the blood-brain barrier (BBB) in both the blood vessel chamber (FIGS. 6A-6C) and the brain-side chamber (FIGS. 6D-6F) when BBB is broken down, and drug that protects the BBB is similar to control levels. Samples were taken from the BBB model described in FIG. 5 and Bottom-Inflow and Bottom- Outflow and Top-Inflow and Top-Outflow and frozen for processing. GLX-related markers Hyaluronic Acid (HA, a glycosaminoglycan), Perlecan (a proteoglycan), CD44 (a proteoglycan) were measured by sandwich ELISA with GAG-binding peptides (HA) or sandwich luminex immunoassays (Perlecan, CD44) in duplicate and the concentration in ng / ml was interpolated from a 7-point calibration curve and the duplicates were averaged. “Fold Change” was calculated as the division of the sample in “Outflow” divided by the “Inflow” to control for signal in the Inflow material. Data is presented as a bar graph representing the “Fold Change” in the Outflow relative to the Inflow. Error bars are S.E.M. for conditions where there are multiple experiments.

[0280] FIGS. 6A-6C show how all GLX markers were shed from the blood vessel under BBB breakdown inflammatory conditions, confirmed by a fluorescent tracer that showed increased BBB leakiness. Under BBB protection conditions, the GLX markers move towards control levels during inflammatory conditions where a BBB protection drug was introduced, which was confirmed by a fluorescent tracer that showed less BBB leakiness. FIGS. 6D-6F show how all GLX markers on the brain side were changed from the control conditions under BBBbreakdown inflammatory conditions, confirmed by a fluorescent tracer that showed increased BBB leakiness. Under BBB protection conditions, the GLX markers moved towards control levels during inflammatory conditions where a BBB protection drug was introduced, confirmed by a fluorescent tracer that showed less BBB leakiness.

[0281] With reference to FIGS. 7A-7E, combining GLX-related markers from FIG. 5 in an in vitro model of the BBB with differing levels of BBB leakiness, as detected by fluorescent tracer permeability, showed an increased ability to classify which group has a BBB breakdown and increased tracer permeability. The data presented in FIGS. 7A-7E stems from the BBB model described in Figure 1 and GLX analyses in FIGS 6A-6B. Fold Change data for each GLX marker is plotted in a 3D scatter to illustrate the additive power for classifying groups that have increased BBB leakiness (BBB breakdown) versus control (Control) and partial recovery (BBB protection) for the blood chamber (FIG. 7A) and the brain chamber (FIG. 7B). FIG. 7C provides data includes Multivariate vector analysis and Euclidean distances computed for each group when combining GLX markers and the angles, the data illustrating a large deviation between Control and BBB breakdown and a return to the control in BBB protection group for the blood chamber and the brain chamber. FIGS. 7D and 7E show results from Principal Component Analysis (PCA) performed with the GLX markers, where two PCAs explained -100% of the variance and the control group was highly separated from the BBB breakdown group and BBB protection represented a partial recovery in both the blood chamber (FIG. 7D) and brain chamber (FIG. 7E).Example 2: Pre-Treatment Risk Assessment and Serial Monitoring of ARIA Using Glycocalyx-Associated Biomarkers

[0282] A 72-year-old ApoE4-positive individual scheduled to begin monoclonal antibody therapy targeting beta-amyloid. Prior to initiating treatment, a baseline blood sample is collected and processed to serum. A panel of glycocalyx-associated biomarkers (e.g., perlecan, hyaluronic acid (ha), syndecan-2 (syn2), podocalyxin (podo), biglycan, glypican-1 (GPC1), syndecan-1 (synl), syndecan-4 (syn4), chondroitin sulfate (CS) and keratan sulfate) is measured via immunoassay.

[0283] A composite GLX Index is computed from these markers using a predefined normalization and scoring algorithm. The resulting value is compared to a reference threshold established from a population where ARIA did or did not develop with anti-amyloid treatment.The subject’s pre-treatm ent GLX Index is below the high-risk cutoff, indicating low likelihood of ARIA-related complications. The subject proceeds with therapy.

[0284] During treatment, repeat blood samples are collected prior to each administration (e.g., every two weeks). At administration visit 4, the GLX Index shows a significant increase above the monitoring threshold, despite the subject being clinically asymptomatic. Based on the elevated GLX Index and its known negative predictive value, the care team initiates confirmatory MRI imaging, which reveals ARIA-E (edema). Infusions are adjusted or discontinued per clinical protocol, e.g. where ARIA is severe.

[0285] Over the next six weeks, serial blood tests show GLX Index values returning to baseline, and follow-up MRI confirms resolution of ARIA. The subject resumes treatment with continued biomarker monitoring.

[0286] As can be seen, both pre-treatment risk stratification and longitudinal safety are monitored using the composite biomarker panel derived from glycocalyx-associated molecules. The method offers a minimally invasive strategy to assess CNS vascular vulnerability, triage imaging needs, and personalized treatment in patients receiving antiamyloid therapies.Example 3: Assessing Blood-Brain Barrier Permeability and Glycocalyx Degradation in a Human-Derived Microfluidic System

[0287] An in vitro model of the human blood-brain barrier (BBB) was established using a dualchamber microfluidic platform comprising a first chamber representative of the vascular endothelium (hereinafter, “Endo”) and a second chamber representative of the brain compartment (hereinafter, “Brain”). The chambers were separated by a semipermeable membrane and perfused under shear-controlled flow conditions using human-derived endothelial and neural cell types. This configuration facilitated the physiological emulation of the BBB.

[0288] Each chamber included independent inflow and outflow ports allowing for:• Continuous unidirectional media perfusion;• Selective administration of reagents;Independent sample collection from inflow and effluent media.

[0289] After a defined culture period, tight junction integrity of the BBB was verified using light microscopy imaging and baseline permeability measurements prior to experimental intervention.

[0290] Two experimental conditions were conducted:1. Healthy Control: Standard culture media administered to both Endo and Brain chambers.2. GLX Cocktail: A glycocalyx-degrading enzymatic cocktail was administered to the Blood-side chamber. The GLX Cocktail included at least the following components at the calibrated concentrations:Matrix metalloproteinase-2 (MMP2), MMP9, MMP12, MMP14, ADAM17, Hyaluronidase- 1, Heparanase, Chondroitinase ABC, Keratanase II

[0291] Each treatment group was conducted in biological replicate using a minimum of two microfluidic chips per condition.

[0292] A fluorescent tracer (Lucifer Yellow) was introduced in the inflow of the Endo chamber and measured every 24 and 48 hours as described below using spectrofluorometric methods. Apparent permeability coefficients (P_app) were calculated in accordance with standard formulae A change in P app of >20% from baseline was deemed to be BBB open.

[0293] Inflow and effluent media from both the Endo and Brain chambers were collected and stored at -80°C until analysis. The collected samples were assayed for glycocalyx-associated biomarkers using various immunoassay methods including ELISA, Luminex, and immunoblotting.

[0294] FIGS. 8-10 provide several glycocalyx-associated biomarker concentrations in the endo compartment (panel B) and the brain compartment (panel C). The concentrations of each biomarker are compared when the BBB is Closed (black) and Open (gray; 20% increased permeability; FIG. 8-10, panel A), for the natural control (BBB closed (N=l l) BBB open (N=6)) and following use of the GLX Cocktail Application (FIG. 9, BBB closed (N=7) and BBB open (N=2)). FIG. 10 is the biomarker levels of the Endo and Brain combined for the natural and the GLX-enzymes. Various GLX biomarkers were changed in the open versusclosed BBB states and various patterns emerged from the Endo, Brain and combined levels. For the most part, a BBB-open state had increased levels of biomarkers at these timepoints.

[0295] Using the glycocalyx associated biomarker levels measured on the endo and brain side as well as combined (endo + brain) when comparing the BBB closed (N=l l) and BBB open (N=6) state, machine learning (ML) models were trained to identify open vs. closed based on the biomarker levels.

[0296] The GLX ML pipeline represents a specialized machine learning framework designed for biomarker analysis and medical diagnostics. The key advantage of the system lies in the cross-validation methodology and the custom GLX score output.

[0297] When data is sparse, standard cross-validation approaches often introduce systematic biases that can lead to overoptimistic performance estimates and poor generalization to real clinical scenarios. Custom Cross Validation (GLX LIPOC) and Double Cross Validation addresses these limitations through a novel approach to test set construction and fold generation.Custom Cross Validation (GLX LIPOC)

[0298] Unlike conventional k-fold cross-validation that randomly partitions data and can produce test sets with highly variable class ratios, GLX LIPOC systematically constructs test sets containing exactly one representative from each class label, achieving 1 : 1 balance regardless of underlying dataset composition. This systematic sampling strategy eliminates class imbalance bias that typically produces misleadingly optimistic performance estimates due to disproportionate influence of majority classes, while significantly reducing the variance associated with random fold generation that can lead to overconfident performance estimates and poor generalization. By ensuring consistent test set composition across all validation folds, GLX LIPOC provides more reliable estimates of diagnostic accuracy across all patient populations, reduces the random component of performance estimation, and enables more accurate confidence interval estimation.Double Cross Validation

[0299] The GLX pipeline implements a sophisticated double cross-validation framework specifically designed to address the challenge of creating representative and trustworthy testsets in small-sample biomarker studies. In small datasets, removing any portion of data for testing leads to a wide range of performance results due to high sensitivity to sample composition (For larger datasets, alternative test set sizes are employed to optimize statistical power while maintaining robust validation, (e.g. 80 / 20 percent train and test splits)). However, omitting the test set entirely and relying only on training and validation sets creates significant risk of information leakage during hyperparameter optimization, scaling, and feature selection processes.

[0300] This challenge is solved through the implementation of a nested double cross-validation loop architecture. The inner loop independently fits the scaler and feature selector while conducting an isolated hyperparameter optimization event using Bayesian optimization techniques. Subsequently, the trained scaler and feature selector are applied to the outer test set, and the optimized model undergoes final evaluation. The outer loop employs the GLX L1POC cross-validation technique to ensure robust and unbiased performance estimation across the entire validation process. This nested approach maintains strict separation between model development and evaluation while maximizing the utility of limited data resources.System Architecture and Methodology

[0301] The system operates through a series of configurable steps, orchestrated by a main run function that iterates through different models and preprocessing configurations. A core component is the run inner function, which encapsulates the model building pipeline. The system can operate in two primary modes: a single cross-validation mode (scv=True) for final model generation, and a more rigorous nested / double cross-validation mode for robust performance estimation.1. Data Ingestion and Initial PreparationProcess: Input data is ingested2. Outer Cross-Validation Setup:The dataset is shuffled and split into (features X, target y). It's then prepared for an outer cross-validation loop using the GLX L1POC cross-validator.3. Data NormalizationA Scaler module is utilized which fits and transforms each marker in the training data between 0 and 1.4. Sequential Feature SelectionSequential Feature Selection is applied to the training data, which recursively prunes markers from the dataset to find the optimal subset of final markers. It uses an internal GLX L20 cross-validation strategy to evaluate the performance of feature subsets with an embedded tree based estimator (e.g., XGBoost).5. Model TrainingA suite of different models are trained (Random Forest Classifier, XGBoost Classifier, Logistic Regression, Linear Discriminant Analysis, Gradient Boosting Classifier, K- Nearest Neighbors Classifier) on the train data, and the trained models, scaler and feature selector is saved.6. Evaluation & OutputThe scaling and feature selection is applied to the test data and the model is evaluated. The continuous predictive probabilities are then transformed into discrete scale between 1-10. By doing so, we preserve the granularity necessary for meaningful risk stratification while presenting results in a format that aligns with established clinical scoring systems. This process is repeated for all outer folds and the results are combined.

[0302] Table 4 presents the independent evaluation of machine learning pipelines trained to classify blood-brain barrier (BBB) state (open vs. closed) based on multivariate patterns of glycocalyx biomarker expression. Data were collected from a human-derived microfluidic model of the BBB across three compartments: the endothelial chamber (“Endo”), the brain chamber (“Brain”), and a combined dataset (“Combined”).

[0303] Table 4a shows cross-validated model performance on the base dataset derived from natural-state chips. Each row represents a distinct ML pipeline defined by:• Scaler: either Standard (z-score normalization) or MinMax (0-1 scaling);• SFS: use of Sequential Feature Selection to optimize biomarker input sets;• Model: algorithm used (Random Forest, XGBoost, Logistic Regression, LDA, Gradient Boosting).Performance metrics are reported separately for Endo, Brain, and Combined data inputs.

[0304] Cross-validation was performed using the GLX L1POC method, a custom leave-one- per-class-out approach that enforces 1 : 1 class balance across folds to avoid overfitting in small, imbalanced datasets. Within each fold, model optimization included nested Bayesian hyperparameter tuning and strict separation between training and evaluation steps. For larger datasets, alternative test set sizes are employed to optimize statistical power while maintaining robust validation, (e.g. 80 / 20 percent train and test splits).

[0305] Table 4b evaluates the same trained models on a second, unseen test dataset generated using GLX-degrading enzymes. This tests the models’ ability to generalize across biologically perturbed states of the BBB.

[0306] Table 4c presents the clinically interpretable GLXBBB Score, a 1-10 discrete risk scale derived from model prediction probabilities.• Scores of 1-3 indicate BBB opening• Scores of 4-6 reflect an indeterminate state• Scores of 7-10 indicate an intact BBB

[0307] Reported performance metrics across all tables include: area under the ROC curve (ROC AUC), Fl score, accuracy, precision, recall (sensitivity), specificity, positive predictive value (PPV), negative predictive value (NPV), true / false positive rate (TPR / FPR), and true / false negative rate (TNR / FNR).

[0308] Table 4: Performance of machine learning pipelines for BBB integrity classification and scoring using glycocalyx biomarker data from a physiologically relevant in vitro model.Table 4 A:Table 4 A - continued:Table 4 B:Table 4B - continued:Table 4B - continued:1 01 01 [33666666 6]Table 4B - continued:1 01 1 0 0 L 01Table 4 C:Table 4 C - continued:Table 4 C - continued:Table 4 C - continued:

[0309] This example demonstrates a clinically translatable system for assessing blood-brain barrier (BBB) integrity using glycocalyx-associated biomarkers in a physiologically relevant, human-derived in vitro model. The dual-chamber microfluidic platform replicates key aspects of human BBB biology, including endothelial-glial architecture, directional perfusion, and dynamic barrier properties.

[0310] Multivariate biomarker patterns from both the vascular (Endo) and brain compartments were used to train a suite of machine learning classifiers to distinguish open from closed BBB states, using a >20% increase in Lucifer Yellow permeability as a validated threshold. Compartment-specific responses revealed distinct biological signatures, while elevated biomarker levels consistently tracked with increased permeability, supporting a mechanistic and dose-responsive relationship between glycocalyx degradation and barrier dysfunction.

[0311] A key output — the GLX BBB Score — translates model probabilities into a clinically interpretable 1-10 scale. While scores of 1-3 (BBB open), 4-6 (indeterminate), and 7-10 (BBB closed) are used illustratively, this scoring system is non-limiting. Future implementations may use alternative formats or thresholds depending on indication, regulatory context, or clinical workflow.

[0312] Notably, the machine learning pipelines demonstrated highly concordant performance across models, preprocessing methods, and compartment inputs, underscoring the robustness and reproducibility of the biomarker signal. All models were trained using a nested double cross-validation framework with GLX L1POC sampling, which enforces 1 : 1 class balance and prevents overfitting in small datasets. In larger datasets, we anticipate using alternative traintest split strategies — including stratified k-fold, holdout sets, external validation cohorts, or prospective testing — tailored to scale, data diversity, and intended use.

[0313] The trained classifiers also generalized effectively to an independent dataset derived from GLX enzyme-induced barrier disruption, confirming their ability to detect mechanistically relevant BBB compromise beyond the training context.

[0314] Clinical and translational applications include:• Objective BBB Assessment: A standardized, quantitative measure that may supplement or guide imaging decisions• Safety Monitoring: Enables serial, minimally invasive tracking of BBB status during therapeutic intervention• Therapeutic Stratification: Supports patient selection for CNS-targeted therapies based on predicted permeability• Clinical Trial Enrichment: Enables inclusion / exclusion criteria based on vascular vulnerability• Flexible Deployment: Adaptable across sample types, timepoints, and clinical settings

[0315] Supported by rigorous controls, biological replicates, and cross-condition validation, this example establishes a robust, modular framework for BBB assessment. It connects mechanistic biology with clinical actionability and supports a wide range of diagnostic and monitoring embodiments across CNS-related diseases.Example 4: Predicting Therapeutic Response in Glioblastoma via GLX-BBB Score

[0316] A 56-year-old patient with newly diagnosed IDH-wildtype glioblastoma is evaluated for systemic therapy. Among available options is erlotinib, an EGFR inhibitor with limited central nervous system (CNS) efficacy due to poor blood-brain barrier (BBB) penetration.

[0317] Prior to initiating treatment, a baseline blood sample is collected and analyzed for a panel of glycocalyx-associated biomarkers (e.g., CD44, perlecan, hyaluronic acid, syndecan- 2, podocalyxin, biglycan, glypican-1, syndecan-1, syndecan-4, chondroitin sulfate, keratan sulfate). These data are processed through a pre-trained scoring algorithm (GLX BBB Score) designed to estimate individual BBB permeability.

[0318] The patient’s GLX BBB Score indicates increased BBB permeability and a higher likelihood of CNS drug delivery. Based on this profile, treatment is initiated with erlotinib.

[0319] At six-week follow-up, MRI shows significant tumor regression and reduced peritumoral edema. Serial GLX testing confirms continued elevated permeability. In contrast, a second patient where the GLX BBB Score predicts restricted BBB permeability is started instead on an alternate therapy e.g. standard chemoradiotherapy (e.g., temozolomide plus radiation), which does not rely on enhanced BBB penetration for efficacy.

[0320] The GLX-based BBB Score enables stratified selection of a previously underperforming systemic therapy (erlotinib) for patients most likely to benefit, while guiding others to more appropriate first-line options. This approach improves therapeutic precision and avoids unnecessary exposure to ineffective agents.Example 5: GLX-Based Stratification in a Clinical Trial of a Novel ALS Therapy

[0321] A Phase lib multicenter trial tests the efficacy of a new small-molecule antiinflammatory for Amyotrophic Lateral Sclerosis (ALS). Preclinical data show limited BBB penetration without inflammation-driven leakage.

[0322] Prior to randomization, all participants undergo GLX screening. A machine learning model classifies participants as:• BBB-Open— Assigned to systemic oral therapy or placebo• BBB-Closed— Randomized to alternative arm using intrathecal administration versus placebo.

[0323] Interim analysis at 12 weeks shows greater ALSFRS-R preservation in the BBB-Open group with treatment (A= +3.5 points) versus placebo. The BBB-Closed group matches the BBB-Open group in efficacy.

[0324] Conclusion: GLX stratification personalizes delivery modality, enhances signal detection in trials, and supports targeted inclusion criteria based on BBB state.

[0325] It should be appreciated that all combinations of the foregoing concepts and additional concepts discussed in greater detail below (provided such concepts are not mutually inconsistent) are contemplated as being part of the inventive subject matter disclosed herein. In particular, all combinations of claimed subject matter appearing at the end of this disclosure are contemplated as being part of the inventive subject matter disclosed herein. It should also be appreciated that terminology explicitly employed herein that also may appear in any disclosure incorporated by reference should be accorded a meaning most consistent with the particular concepts disclosed herein.

[0326] All of the methods and tasks described herein may be, at least in part, performed and automated by a computer system. The computer system may, in some cases, include multiple distinct computers or computing devices (e.g., physical servers, workstations, storage arrays, cloud computing resources, etc.) that communicate and interoperate over a network to perform the described functions. Each such computing device typically includes a processor (or multiple processors) that executes program instructions or modules stored in a memory or other non- transitory computer-readable storage medium or device (e.g., solid state storage devices, disk drives, etc.). The various functions disclosed herein may be embodied in such program instructions or may be implemented in application-specific circuitry (e.g., ASICs or FPGAs) of the computer system. Where the computer system includes multiple computing devices, these devices may, but need not, be co-located. The results of the disclosed methods and tasks may be persistently stored by transforming physical storage devices, such as solid state memory chips or magnetic disks, into a different state. In some embodiments, the computer system may be a cloud-based computing system whose processing resources are shared by multiple distinct business entities or other users.

[0327] Depending on the embodiment, certain acts, events, or functions of any of the processes or algorithms described herein can be performed in a different sequence, can be added, merged, or left out altogether (e.g., not all described operations or events are necessary for the practice of the algorithm). Moreover, in certain embodiments, operations or events can be performed concurrently, e.g., through multi-threaded processing, interrupt processing, or multiple processors or processor cores or on other parallel architectures, rather than sequentially.

[0328] The various illustrative logical blocks, modules, routines, and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware (e.g., ASICs or FPGA devices), computer software that runs on computer hardware, or combinations of both. Moreover, the various illustrative logical blocks and modules described in connection with the embodiments disclosed herein can be implemented or performed by a machine, such as a processor device, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A processor device can be a microprocessor, but in the alternative, the processor device can be a controller, microcontroller, or logic circuitry that implements a state machine, combinations of the same,or the like. A processor device can include electrical circuitry configured to process computerexecutable instructions. In another embodiment, a processor device includes an FPGA or other programmable device that performs logic operations without processing computer-executable instructions. A processor device can also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. Although described herein primarily with respect to digital technology, a processor device may also include primarily analog components. For example, some or all of the rendering techniques described herein may be implemented in analog circuitry or mixed analog and digital circuitry. A computing environment can include any type of computer system, including, but not limited to, a computer system based on a microprocessor, a mainframe computer, a digital signal processor, a portable computing device, a device controller, or a computational engine within an appliance, to name a few.

[0329] The elements of a method, process, routine, or algorithm described in connection with the embodiments disclosed herein can be embodied directly in hardware, in a software module executed by a processor device, or in a combination of the two. A software module can reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, hard disk, a removable disk, a CD-ROM, or any other form of a non-transitory computer-readable storage medium. An exemplary storage medium can be coupled to the processor device such that the processor device can read information from, and write information to, the storage medium. In the alternative, the storage medium can be integral to the processor device. The processor device and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In the alternative, the processor device and the storage medium can reside as discrete components in a user terminal.

[0330] Reference throughout the specification to “one example”, “another example”, “an example”, and so forth, means that a particular element (e.g., feature, structure, and / or characteristic) described in connection with the example is included in at least one example described herein, and may or may not be present in other examples. In addition, it is to be understood that the described elements for any example may be combined in any suitable manner in the various examples unless the context clearly dictates otherwise. While several examples have been described in detail, it is to be understood that the disclosed examples may be modified. Therefore, the foregoing description is to be considered non-limiting.

[0331] Features, materials, characteristics, or groups described in conjunction with a particular aspect, or example are to be understood to be applicable to any other aspect or example described in this section or elsewhere in this specification unless incompatible therewith. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and / or all of the steps of any method or process so disclosed, may be combined in any combination, except combinations where at least some of such features and / or steps are mutually exclusive. The protection is not restricted to the details of any foregoing examples. The protection extends to any novel one, or any novel combination, of the features disclosed in this specification (including any accompanying claims, abstract and drawings), or to any novel one, or any novel combination, of the steps of any method or process so disclosed.

[0332] Furthermore, certain features that are described in this disclosure in the context of separate implementations can also be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation can also be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described above as acting in certain combinations, one or more features from a claimed combination can, in some cases, be excised from the combination, and the combination may be claimed as a sub-combination or variation of a sub-combination.

[0333] Moreover, while operations may be depicted in the drawings or described in the specification in a particular order, such operations need not be performed in the particular order shown or in sequential order, or that all operations be performed, to achieve desirable results. Other operations that are not depicted or described can be incorporated in the example methods and processes. For example, one or more additional operations can be performed before, after, simultaneously, or between any of the described operations. Further, the operations may be rearranged or reordered in other implementations. Those skilled in the art will appreciate that in some examples, the actual steps taken in the processes illustrated and / or disclosed may differ from those shown in the figures. Depending on the example, certain of the steps described above may be removed or others may be added. Furthermore, the features and attributes of the specific examples disclosed above may be combined in different ways to form additional examples, all of which fall within the scope of the present disclosure.

[0334] For purposes of this disclosure, certain aspects, advantages, and novel features are described herein. Not necessarily all such advantages may be achieved in accordance with anyparticular example. Thus, for example, those skilled in the art will recognize that the disclosure may be embodied or carried out in a manner that achieves one advantage or a group of advantages as taught herein without necessarily achieving other advantages as may be taught or suggested herein.

[0335] Conditional language, such as “can,” “could,” “might,” or “may,” unless specifically stated otherwise, or otherwise understood within the context as used, is generally intended to convey that certain examples include, while other examples do not include, certain features, elements, and / or steps. Thus, such conditional language is not generally intended to imply that features, elements, and / or steps are in any way required for one or more examples or that one or more examples necessarily include logic for deciding, with or without user input or prompting, whether these features, elements, and / or steps are included or are to be performed in any particular example.

[0336] Conjunctive language such as the phrase “at least one of X, Y, and Z,” unless specifically stated otherwise, is otherwise understood with the context as used in general to convey that an item, term, etc. may be either X, Y, or Z. Thus, such conjunctive language is not generally intended to imply that certain examples require the presence of at least one of X, at least one of Y, and at least one of Z.

[0337] Language of degree used herein, such as the terms “approximately,” “about,” “generally,” and “substantially” represent a value, amount, or characteristic close to the stated value, amount, or characteristic that still performs a desired function or achieves a desired result.

[0338] Throughout this disclosure, including in the Summary, the Detailed Description Section, and the claims, reference is made to some illustrative embodiments and features. It is to be understood that the disclosure in this specification includes all possible combinations of such particular features. For example, where a particular feature is disclosed in the context of a particular aspect or embodiment, or a particular claim, that feature can also be used, to the extent possible, in combination with and / or in the context of other particular aspects and embodiments, and in general.

[0339] The scope of the present disclosure is not intended to be limited by the specific disclosures of preferred examples in this section or elsewhere in this specification, and may be defined by claims as presented in this section or elsewhere in this specification or as presentedin the future. The language of the claims is to be interpreted broadly based on the language employed in the claims and not limited to the examples described in the present specification or during the prosecution of the application, which examples are to be construed as nonexclusive.

[0340] The described embodiments and examples of the present disclosure are intended to be illustrative rather than restrictive and are not intended to represent every embodiment or example of the present disclosure, and thus, are not to be limited in scope by the specific embodiments and examples described herein. While the fundamental novel features of the disclosure as applied to various specific embodiments thereof have been shown, described, and pointed out, it will also be understood that various omissions, substitutions, and changes in the details of the compositions and methods that are disclosed, may become apparent and may be made by those skilled in the art without departing from the spirit of the disclosure. For example, it is expressly intended that all combinations of those method steps that perform substantially the same function in substantially the same way to achieve the same results are within the scope of the disclosure. Moreover, it should be recognized that method steps shown and / or described in connection with any disclosed form or embodiment of the disclosure may be incorporated in any other disclosed or described or suggested form or embodiment as a general matter of design choice. Further, various modifications and variations can be made without departing from the spirit or scope of the disclosure as set forth in the following claims both literally and in equivalents recognized in law.

Claims

WHAT IS CLAIMED IS:

1. A method of identifying a change in the blood-brain barrier (BBB), the method comprising: providing (e.g., receiving) a dataset from a sample obtained from a subject, the dataset comprising the combination of biomarker information indicating glycocalyx shedding, which combination of biomarker information is information indicating the level of two or more of a proteoglycan (PG) or fragment thereof, a glycosaminoglycan (GAG) or fragment thereof, a glycoprotein (GP) or fragment thereof, a glycoconjugate (GC) or fragment thereof, a glycolipids (GL) or fragment thereof, a galectin or fragment thereof; correlating the levels of the one or more biomarkers associated with the glycocalyx with the BBB permeability of the subject.

2. A method for identifying a change in a blood-brain barrier (BBB) that allows influx or efflux of a composition across the BBB, the method comprising: providing (e.g. receiving) a first dataset from a sample obtained from a subject, the first dataset comprising the combination of biomarker information indicating glycocalyx shedding, which combination of biomarker information is information indicating the level of two or more of re GLX-related GAGs, GLX-related PGs, GLX- related GPs, GLX-related GLs, GLX-related GCs, or fragments thereof, or any combination of the aforementioned biomarkers; classifying each biomarker from the one or more biomarkers to an influx- or efflux condition, the classifying comprising: providing a second dataset comprising a reference level of the same combination of biomarker information analyzed in the first dataset; comparing the levels of the combination of biomarker information from the first dataset to the reference level of the second dataset; and assigning, under control of one or more processors, an BBB influx-or efflux-condition score to each of the biomarkers of the one or more biomarkers of the first dataset, wherein the BBB influx-condition score is based on a deviation of the level of the combination of the biomarker information of the first dataset from the reference level of the second dataset; anddetermining an indication status of the influx or efflux of the composition based on the BBB influx- or efflux-condition score.

3. The method of claim 1 or 2, wherein the levels of the one or more biomarkers comprise concentrations of the one or more biomarkers.

4. The method of claim 3, wherein the concentrations of the one or more biomarkers are obtained from a biochemical analysis of the sample.

5. The method of claim 3, wherein the concentrations of the one or more biomarkers are determined from pixel intensities of the biomarkers obtained from an image analysis.

6. The method of any one of claims 1-5, wherein the levels of the one or more biomarkers comprise pixel intensities of the biomarkers obtained from an image analysis.

7. The method of any one of claims 1-6, wherein the correlating the levels and / or assigning of the BBB influx-or efflux-condition score are performed using a correlation module identified by a machine learning model with training data based on the reference levels of biomarkers and indicators for the influx or efflux of the composition across the BBB; wherein the machine learning model used the training data to determine how to output the BBB influx- or efflux-condition score.

8. The method of claim 7, wherein the BBB condition score indicates whether the subject is indicative for a BBB dysfunction, and wherein a symptom of the BBB dysfunction comprises the influx or efflux of the composition across a blood-brain barrier.

9. The method of claim 8, wherein the assigning of the BBB condition score comprises transforming the levels of the one or more biomarkers to the BBB condition score via the machine learning model.

10. The method of claim 7, wherein the machine learning model comprises a multivariate statistical test.

11. The method of any one of claims 1-10, wherein the glycocalyx is the endothelial glycocalyx.

12. The method of any one of claims 1-11, wherein the combination comprises information for two or more of CD44, hyaluronic acid (HA), heparan sulfate (HS), Chondroitin Sulfate, Dermatan Sulfate, Keratan Sulfate, a syndecan, a glypican, podocalyxin, perlecan, or any fragment thereof, or any combination of the aforementioned biomarkers.

13. The method of claim 9, further comprising: reducing a dosage of the composition, increasing a dosage interval of the composition, or discontinuing administrating the composition if the indication status of the influx of the composition across the BBB indicates the BBB dysfunction.

14. The method of claim 9, further comprising: monitoring a change in a state of the subject during a clinical trial with respect to: a change in a disease extent of the subject; a change in a side effect of the composition; or a change in an efficacy of the composition.

15. The method of claim 14, wherein the composition comprises an antibody.

16. The method of any one of claims 1-15, further comprising assigning or advising the subject to receive one or more confirmatory magnetic resonance imaging (MRI) sessions if the subject is indicated for the BBB dysfunction.

17. The method of claim 16, further comprising imaging a brain of the subj ect using the MRI to obtain MRI images, wherein the MRI images indicate signal abnormalities related to the BBB dysfunction, wherein the signal abnormalities relate to an Amyloid-Related Imaging Abnormality (ARIA).

18. A method of treating a disease, disorder, or condition with a drug that crosses the BBB in a subject in need thereof, the method comprising: providing (e.g., receiving) a dataset from a sample obtained from a subject, the dataset comprising the combination of biomarker information indicating glycocalyxshedding, which combination of biomarker information is information indicating the level of two or more of a proteoglycan (PG) or fragment thereof, a glycosaminoglycan (GAG) or fragment thereof, a glycoprotein (GP) or fragment thereof, a glycoconjugate (GC) or fragment thereof, a glycolipids (GL) or fragment thereof, a galectin or fragment thereof; correlating the levels of the one or more biomarkers associated with the glycocalyx with BBB permeability of the subject; determining the permeability of the BBB to the drug; and administering the drug to the patient based on the permeability of the BBB to the drug (e.g., beginning administration of the drug, stopping administration of the drug, reducing administration frequency and / or dosage of the drug, increasing administration frequency and / or dosage of the drug).

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