Methods and systems for assessing sample quality

Abstract

Methods and systems disclosed herein can improve the quality of blood-derived samples (e.g., whole blood, serum, or plasma sample). In an aspect, the present disclosure provides a method of evaluating a plasma sample quality, comprising collecting a blood sample from a subject; processing the blood sample to separate a plasma sample from other blood components; quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and evaluating the quality of the plasma sample based at least in part on the measured amounts, wherein the measured amounts are indicative of the plasma sample quality. In another aspect, the present disclosure provides a method of adjusting for preanalytical bias utilizing a linear regression model to improve a signal to noise ratio, thereby potentially enhancing the performance of a classification model.

Description

[0001] Atty Dkt No.: 49407-791601

[0002] METHODS AND SYSTEMS FOR ASSESSING SAMPLE QUALITY

[0003] CROSS-REFERENCE

[0004] [1] This application claims the benefit of U.S. Provisional Application No. 63 / 692,812, filed September 10, 2024, which is incorporated by reference herein in its entirety.

[0005] BACKGROUND

[0006] [2] Real-world biological sample collection techniques may lead to suboptimal sample quality. Further, real-world biological sample collection techniques may impact sample integrity potentially resulting in signals associated with a disease status to be confounded by preanalytical factors.

[0007] SUMMARY

[0008] [3] Current sample collection techniques and methodologies may not provide for optimal sample quality. Recognizing a need for improved methods and systems to evaluate sample quality prior to sample analysis, the present disclosure provides methods and systems for evaluating the quality of a biological sample (e.g., a plasma sample). Disclosed herein are methods and systems to characterize preanalytical variables in real-world sample collection to reduce the impact of preanalytical confounders. The methods and systems disclosed herein can further improve early cancer detection in subjects.

[0009] [4] In an aspect, the present disclosure provides methods of evaluating a plasma sample quality, the method comprising: (a) collecting a blood sample from a subject, wherein the blood sample is collected in a collection device; (b) processing the blood sample to separate a plasma sample from other blood components; (c) quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and (d) evaluating the quality of the plasma sample based at least in part on the measured amounts of the one or more analytes present in the plasma sample, wherein the measured amounts of the one or more analytes present in the plasma sample are indicative of the plasma sample quality.

[0010] [5] In some embodiments, the method further comprises (e) based at least in part on the evaluating of (d), determining if the plasma sample is eligible as a candidate for use in a detection or diagnostic assay, wherein the detection or diagnostic assay is used to indicate an indication in the subject.

[0011] [6] In some embodiments, the one or more analytes comprise hemoglobin. Atty Dkt No.: 49407-791601

[0012] [7] In some embodiments, the one or more analytes comprise potassium (K).

[0013] [8] In some embodiments, the one or more analytes comprise lactate.

[0014] [9] In some embodiments, the one or more analytes comprise formaldehyde (CH2O).

[0015]

[0010] In some embodiments, the one or more analytes comprise hemoglobin and potassium

[0016] (K).

[0017]

[0011] In some embodiments, the one or more analytes comprise hemoglobin and formaldehyde (CH2O).

[0018]

[0012] In some embodiments, the one or more analytes comprise potassium (K) and formaldehyde (CH2O).

[0019]

[0013] In some embodiments, the one or more analytes comprise hemoglobin, potassium (K), lactate, and formaldehyde (CH2O).

[0020]

[0014] In some embodiments, the method further comprises comparing the measured amounts of the one or more analytes present in the plasma sample to a reference sample.

[0021]

[0015] In some embodiments, an elevated amount of hemoglobin in the plasma sample as compared to the reference sample is indicative of protein perturbation.

[0022]

[0016] In some embodiments, the elevated amount of the hemoglobin in the plasma sample is greater than 80 mg / dL.

[0023]

[0017] In some embodiments, an elevated amount of potassium (K) in the plasma sample as compared to the reference sample is indicative of protein perturbation.

[0024]

[0018] In some embodiments, the elevated amount of the potassium (K) in the plasma sample is greater than 50 mmol / L.

[0025]

[0019] In some embodiments, an elevated amount of lactate in the plasma sample as compared to the reference sample is indicative of protein perturbation.

[0026]

[0020] In some embodiments, the elevated amount of the lactate in the plasma sample is greater than 15.5 mmol / L.

[0027]

[0021] In some embodiments, a decreased amount of formaldehyde (CH2O) in the plasma sample as compared to the reference sample is indicative of protein perturbation.

[0028]

[0022] In some embodiments, the decreased amount of formaldehyde (CH2O) in the plasma sample is less than 3500 pM.

[0029]

[0023] In some embodiments, the protein perturbation comprises protein degradation or protein aggregation.

[0030]

[0024] In some embodiments, the protein perturbation comprises the protein degradation.

[0031]

[0025] In some embodiments, the protein perturbation comprises the protein aggregation.

[0032]

[0026] In some embodiments, the protein perturbation is a result of cell lysis. Atty Dkt No.: 49407-791601

[0033]

[0027] In some embodiments, the cell lysis comprises red blood cell (RBC) lysis.

[0034]

[0028] In some embodiments, the quantitative measuring of the amount of the hemoglobin comprises use of an Ultraviolet-visible spectroscopy (UV / Vis) spectrum assay.

[0035]

[0029] In some embodiments, the measured amount of the hemoglobin comprises a Hemolysis index.

[0036]

[0030] In some embodiments, the quantitative measuring of the amount of the lactate comprises use of an enzymatic colorimetric assay.

[0037]

[0031] In some embodiments, the quantitative measuring of the amount of the potassium (K) comprises use of a turbidimetric assay or a potassium (K) ion selective electrode (ISE) assay.

[0038]

[0032] In some embodiments, the quantitative measuring of the amount of the formaldehyde (CH2O) comprises use of a fluorescence-based assay.

[0039]

[0033] In some embodiments, the subject is a mammal.

[0040]

[0034] In some embodiments, the mammal is a human.

[0041]

[0035] In some embodiments, the detection or diagnostic assay is selected from the group consisting of: a quantitative immunoassay, an enzyme-linked immunosorbent assay (ELISA), an electrochemiluminescence immunoassay (ECLIA), a proximity extension assay (PEA), a protein microarray, mass spectrometry, and a cell-free Protein Immuno-Quant ELISA.

[0042]

[0036] In some embodiments, the indication comprises a cancer selected from the group consisting of: colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer.

[0043]

[0037] In some embodiments, the cancer comprises the colorectal cancer.

[0044]

[0038] In some embodiments, the cancer comprises the liver cancer.

[0045]

[0039] In some embodiments, the cancer comprises the lung cancer.

[0046]

[0040] In some embodiments, the cancer comprises the pancreatic cancer.

[0047]

[0041] In some embodiments, the cancer comprises the breast cancer.

[0048]

[0042] In some embodiments, the cancer comprises the bladder cancer.

[0049]

[0043] In some embodiments, the cancer comprises the gastric cancer.

[0050]

[0044] In some embodiments, the cancer comprises the esophageal cancer.

[0051]

[0045] In some embodiments, the cancer comprises the uterine cancer.

[0052]

[0046] In some embodiments, the cancer comprises the endometrial cancer.

[0053]

[0047] In some embodiments, the cancer comprises the kidney cancer

[0054]

[0048] In some embodiments, the cancer comprises a stage of the cancer. Atty Dkt No.: 49407-791601

[0055]

[0049] In some embodiments, the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer.

[0056]

[0050] In some embodiments, the processing the blood sample in (b) is completed in a time between less than one (1) hour and seven (7) days after the collecting of the blood sample in (a).

[0057]

[0051] In some embodiments, the collection device is a blood collection tube (BCT).

[0058]

[0052] In an aspect, the present disclosure provides provide non-transitory computer-readable memory storing one or more instructions executable by one or more processors, that when executed by the one or more processors cause the one or more processors to perform processing comprising: (a) collecting a blood sample from a subject, wherein the blood sample is collected in a collection device; (b) processing the blood sample to separate a plasma sample from other blood components; (c) quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and (d) evaluating the quality of the plasma sample based at least in part on the measured amounts of the one or more analytes present in the plasma sample, wherein the measured amounts of the one or more analytes present in the plasma sample are indicative of the plasma sample quality.

[0059]

[0053] In an aspect, the present disclosure provides computer systems for evaluating a plasma sample quality, the system comprising: (a) a non-transitory memory; and (b) a processor in communication with the non-transitory memory, the processor configured to execute the following operations in order to effectuate a method comprising the operations of: (i) collecting a blood sample from a subject, wherein the blood sample is collected in a collection device; (ii) processing the blood sample to separate a plasma sample from other blood components; (iii) quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and (iv) evaluating the quality of the plasma sample based at least in part on the measured amounts of the one or more analytes present in the plasma sample, wherein the measured amounts of the one or more analytes present in the plasma sample are indicative of the plasma sample quality.

[0060]

[0054] In an aspect, the present disclosure provides methods for detecting an indication in a subject, the method comprising: (a) obtaining a plasma sample obtained or derived from the subject; (b) measuring amounts of one or more analytes in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements; and (c) computer processing the one or more analyte measurements using a machine learning model trained to distinguish between samples to Atty Dkt No.: 49407-791601 be analyzed in a subsequent detection or diagnostic assay and samples not to be analyzed in a subsequent detection or diagnostic assay, wherein the subsequent detection or diagnostic assay can detect or diagnose the presence or absence of the indication in the subject.

[0061]

[0055] In some embodiments, the indication comprises a cancer selected from the group consisting of: colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer.

[0062]

[0056] In some embodiments, the cancer comprises the colorectal cancer.

[0063]

[0057] In some embodiments, the cancer comprises the liver cancer.

[0064]

[0058] In some embodiments, the cancer comprises the lung cancer.

[0065]

[0059] In some embodiments, the cancer comprises the pancreatic cancer.

[0066]

[0060] In some embodiments, the cancer comprises the breast cancer.

[0067]

[0061] In some embodiments, the cancer comprises the bladder cancer.

[0068]

[0062] In some embodiments, the cancer comprises the gastric cancer.

[0069]

[0063] In some embodiments, the cancer comprises the esophageal cancer.

[0070]

[0064] In some embodiments, the cancer comprises the uterine cancer.

[0071]

[0065] In some embodiments, the cancer comprises the endometrial cancer.

[0072]

[0066] In some embodiments, the cancer comprises the kidney cancer.

[0073]

[0067] In some embodiments, the cancer comprises a stage of the cancer.

[0074]

[0068] In some embodiments, the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer.

[0075]

[0069] In an aspect, the present disclosure provides methods for detecting an indication in a subject, the method comprising: (a) obtaining a plasma sample obtained or derived from the subject; (b) measuring an amount of one or more proteins in the plasma sample, thereby providing a protein profile of the subject; (c) measuring an amount of one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements; (d) computer processing the protein profile and the one or more analyte measurements using a regression model; and (e) computer processing the protein profile using a machine learning model trained to detect a presence or an absence of the indication in the subject, based at least in part on an output of the regression model.

[0076]

[0070] In some embodiments, the indication comprises a cancer selected from the group consisting of: colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast Atty Dkt No.: 49407-791601 cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer.

[0077]

[0071] In some embodiments, the cancer comprises the colorectal cancer.

[0078]

[0072] In some embodiments, the cancer comprises the liver cancer.

[0079]

[0073] In some embodiments, the cancer comprises the lung cancer.

[0080]

[0074] In some embodiments, the cancer comprises the pancreatic cancer.

[0081]

[0075] In some embodiments, the cancer comprises the breast cancer.

[0082]

[0076] In some embodiments, the cancer comprises the bladder cancer.

[0083]

[0077] In some embodiments, the cancer comprises the gastric cancer.

[0084]

[0078] In some embodiments, the cancer comprises the esophageal cancer.

[0085]

[0079] In some embodiments, the cancer comprises the uterine cancer.

[0086]

[0080] In some embodiments, the cancer comprises the endometrial cancer.

[0087]

[0081] In some embodiments, the cancer comprises the kidney cancer.

[0088]

[0082] In some embodiments, the cancer comprises a stage of the cancer.

[0089]

[0083] In some embodiments, the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer.

[0090]

[0084] In some embodiments, the one or more analytes comprises potassium.

[0091]

[0085] In some embodiments, the one or more analytes comprises hemoglobin.

[0092]

[0086] In some embodiments, the machine learning model comprises a logistic regression model.

[0093]

[0087] In some embodiments, the regression model comprises a logistic regression model.

[0094]

[0088] In some embodiments, an amount of at least one protein of the one or more proteins comprises a correlation with the one or more analyte measurements.

[0095]

[0089] In some embodiments, the regression model is configured to control for the correlation between the amount of the at least one protein and the one or more analyte measurements.

[0096]

[0090] In some embodiments, the method further comprises determining a strength of the correlation between the amount of the at least one protein and the one or more analyte measurements.

[0097]

[0091] In some embodiments, the strength of the correlation is indicative of a quality of the plasma sample.

[0098]

[0092] In some embodiments, the regression model is configured to control for a correlation between an amount of at least one protein of the one or more proteins and one or both of age and sex. Atty Dkt No.: 49407-791601

[0099]

[0093] In some embodiments, the method further comprises obtaining a methylation score associated with the plasma sample.

[0100]

[0094] In some embodiments, the machine learning model is trained to detect the presence or the absence of the indication in the subject based at least in part on the methylation score.

[0101]

[0095] In an aspect, the present disclosure provides computer systems for detecting an indication in a subject, the system comprising: (a) a non-transitory memory; and (b) a processor in communication with the non-transitory memory, the processor configured to execute the following operations in order to effectuate a method comprising the operations of: (i) obtaining a plasma sample obtained or derived from the subject; (ii) measuring an amount of one or more proteins in the plasma sample, thereby providing a protein profile of the subject; (iii) measuring an amount of one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements; (iv) computer processing the protein profile and the one or more analyte measurements using a regression model; and (iv) computer processing the protein profile using a machine learning model trained to detect a presence or an absence of the indication in the subject, based at least in part on an output of the regression model.

[0102]

[0096] Additional aspects and advantages of the present disclosure will become readily apparent to those skilled in this art from the following detailed description, wherein only illustrative examples of the present disclosure are shown and disclosed. As will be realized, the present disclosure is capable of other and different examples, and its several details are capable of modifications in various obvious respects, all without departing from the disclosure. Accordingly, the drawings and description are to be regarded as illustrative in nature, and not as restrictive.

[0103] INCORPORATION BY REFERENCE

[0104]

[0097] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. To the extent publications and patents or patent applications incorporated by reference contradict the disclosure contained in the specification, the specification is intended to supersede and / or take precedence over any such contradictory material.

[0105] BRIEF DESCRIPTION OF THE DRAWINGS

[0106]

[0098] The features of the inventive concepts are set forth with particularity in the appended claims. A better understanding of the features and advantages of the present methods and Atty Dkt No.: 49407-791601 systems will be obtained by reference to the following detailed description that sets forth illustrative examples, in which the principles of the methods and systems are utilized, and the accompanying drawings (also “figure” and “FIG.” herein), of which:

[0107]

[0099] FIG. 1 shows a computer system that is programmed or otherwise configured to perform methods of the present disclosure.

[0108]

[0100] FIG. 2 shows an example of a graph that illustrates a comparison between K (mmol (millimole) / L (liter)) (x-axis) and an immunoregulatory cytokine protein (pg (picogram) / mL (milliliter)) (y-axis).

[0109]

[0101] FIG. 3 shows an example of a graph that illustrates a comparison between time to processing (x-axis) and an immunoregulatory cytokine protein (pg / mL) (y-axis).

[0110]

[0102] FIG. 4 shows an example of a graph that illustrates a comparison between hemoglobin (Hb) (mg (milligram) / dL (deciliter)) (x-axis) and an immunoregulatory cytokine protein (pg / mL) (y-axis).

[0111]

[0103] FIG. 5 shows an example of a graph that illustrates a comparison between potassium (K0 (mmol / L) (x-axis) and an immunoregulatory cytokine protein (pg / mL) (y-axis).

[0112]

[0104] FIG. 6 shows an example of a graph that shows plasma formaldehyde (CH2O) levels in a BCT in a time / condition Time at Temperature (T@T) study.

[0113]

[0105] FIG. 7 shows an example of an image illustrating a BCT Component and a Lysine Containing Protein.

[0114]

[0106] FIG. 8 shows an example of a workflow used in the methods and systems of the present disclosure.

[0115]

[0107] FIG. 9 shows an example of a graph illustrating a K assay and its response to time and temperature perturbations. The graph in FIG. 9 illustrates a comparison between treatment (x- axis) and K (mmol / L) (y-axis).

[0116]

[0108] FIG. 10 shows an example of a graph illustrating a Hb assay and its response to time and temperature perturbations. The graph in FIG. 10 illustrates a comparison between treatment (x- axis) and average Hb (mg / dL) (y-axis).

[0117]

[0109] FIG. 11 shows an example of a graph illustrating a CH2O assay and its response to time and temperature perturbations. The graph in FIG. 11 illustrates a comparison between treatment (x-axis) and CH2O (pM) (y-axis)

[0118]

[0110] FIG. 12 shows an example of a graph illustrating a comparison between treatment (x- axis) and an immunoregulatory cytokine protein (log2) concentration (y-axis) in response to ambient, summer, and winter conditions. Atty Dkt No.: 49407-791601

[0119]

[0111] FIG. 13 shows an example of a graph illustrating a comparison between treatment (x- axis) and a growth factor protein (log2) concentration (y-axis) in response to ambient, summer, and winter conditions.

[0120]

[0112] FIG. 14A shows examples of graphs illustrating associations between an Hb assay and immunoregulatory cytokine protein and growth factor protein concentrations, where log2 Hb (mg / dL) is the x-axis and log2 of immunoregulatory cytokine protein / growth factor protein concentration is the y-axis.

[0121]

[0113] FIG. 14B shows examples of graphs illustrating associations between a K assay and immunoregulatory cytokine protein and growth factor protein concentrations, where log2 K (mmol / L) is the x-axis and log2 of immunoregulatory cytokine protein / growth factor protein concentration is the y-axis.

[0122]

[0114] FIG. 15 shows examples of graphs illustrating associations between a CH2O assay and immunoregulatory cytokine protein and growth factor protein concentrations, where CH2O (uM) is the x-axis and log2 of immunoregulatory cytokine protein / growth factor protein concentration is the y-axis.

[0123]

[0115] FIG. 16 shows an example of a graph showing associations of donor and three QC plasma assays (e.g., K, Hb, and CH2O) with proteins indicative of the lysis of red blood cells (RBCs), platelets and other proteins not associated with blood cell lysis (including, but not limited to, immunoregulatory cytokine proteins, growth factor proteins, glycoproteins, chemokines, mucin proteins, and acidic proteins).

[0124]

[0116] FIG. 17 shows examples of graphs showing immunoregulatory cytokine protein (all batches) and growth factor protein (batches 1 and 3).

[0125]

[0117] FIG. 18 shows examples of graphs illustrating associations between a Hb assay and immunoregulatory cytokine protein and growth factor protein concentrations in CRC (colorectal cancer) subjects and healthy subjects, where log2 Hb (mg / dL) is the x-axis and log2 of immunoregulatory cytokine protein / growth factor protein concentration is the y-axis.

[0126]

[0118] FIG. 19 shows examples of graphs illustrating associations between a K assay and immunoregulatory cytokine protein and growth factor protein concentrations in CRC subjects and healthy subjects, where log2 K (mmol / L) is the x-axis and log2 of immunoregulatory cytokine protein / growth factor protein concentration is the y-axis.

[0127]

[0119] FIG. 20 shows examples of graphs illustrating associations between a CH2O assay and immunoregulatory cytokine protein and growth factor protein concentrations in CRC subjects and healthy subjects, where CH2O (pM) is the x-axis and log2 of immunoregulatory cytokine protein / growth factor protein concentration is the y-axis. Atty Dkt No.: 49407-791601

[0128]

[0120] FIG. 21 shows an example of a chart illustrating a comparison between the association of assays and labels, with or without K.

[0129]

[0121] FIG. 22 shows examples of graphs that show adjusting for the K assay increases CRC signal in immunoregulatory cytokine protein, but not in growth factor protein.

[0130]

[0122] FIG. 23 shows an example of a chart illustrating a comparison between the association of assays and labels, with or without Hb.

[0131]

[0123] FIG. 24 shows examples of graphs that show adjusting for the Hb assay causes the CRC effect to become negative.

[0132]

[0124] FIG. 25 shows an example of a chart illustrating a comparison between the association of assays and labels, with or without CH2O.

[0133]

[0125] FIG. 26 shows examples of graphs that show adjusting for the CH2O assay increases CRC signal in both immunoregulatory cytokine protein and growth factor protein proteins.

[0134]

[0126] FIG. 27A shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for glycoproteins.

[0135]

[0127] FIG. 27B shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for chemokines.

[0136]

[0128] FIG. 27C shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for cytokines.

[0137]

[0129] FIG. 27D shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for kallikreins.

[0138]

[0130] FIG. 27E shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for surface proteins.

[0139]

[0131] FIG. 27F shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for blood vessel proteins.

[0140]

[0132] FIG. 27G shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for growth factor proteins.

[0141]

[0133] FIG. 27H shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for growth proteins.

[0142]

[0134] FIG. 271 shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for immunoregulatory cytokine proteins.

[0143]

[0135] FIG. 27J shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for mucin proteins.

[0144]

[0136] FIG. 27K shows an example of a graph comparing K (mmol / L, dilution corrected) (x- axis) and concentration (log2) (y-axis) for acidic proteins. Atty Dkt No.: 49407-791601

[0145]

[0137] FIG. 28A shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for glycoproteins.

[0146]

[0138] FIG. 28B shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for chemokines.

[0147]

[0139] FIG. 28C shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for cytokines.

[0148]

[0140] FIG. 28D shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for kallikreins.

[0149]

[0141] FIG. 28E shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for surface proteins.

[0150]

[0142] FIG. 28F shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for blood vessel proteins.

[0151]

[0143] FIG. 28G shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for growth factor proteins.

[0152]

[0144] FIG. 28H shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for growth proteins.

[0153]

[0145] FIG. 281 shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for immunoregulatory cytokine proteins.

[0154]

[0146] FIG. 28J shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for mucin proteins.

[0155]

[0147] FIG. 28K shows an example of a graph comparing average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) for acidic proteins.

[0156]

[0148] FIG. 29A shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for glycoproteins.

[0157]

[0149] FIG. 29B shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for chemokines.

[0158]

[0150] FIG. 29C shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for cytokines.

[0159]

[0151] FIG. 29D shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for kallikreins.

[0160]

[0152] FIG. 29E shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for surface proteins.

[0161]

[0153] FIG. 29F shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for blood vessel proteins. Atty Dkt No.: 49407-791601

[0162]

[0154] FIG. 29G shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for growth factor proteins.

[0163]

[0155] FIG. 29H shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for growth proteins.

[0164]

[0156] FIG. 291 shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for immunoregulatory cytokine proteins.

[0165]

[0157] FIG. 29J shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for mucin proteins.

[0166]

[0158] FIG. 29K shows an example of a graph comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) for acidic proteins.

[0167]

[0159] FIG. 30A shows examples of graphs illustrating immunoregulatory cytokine protein (log2) vs. K for all samples.

[0168]

[0160] FIG. 30B shows examples of graphs illustrating growth factor protein (log2) vs. K (log2 transformed) for batch 1.

[0169]

[0161] FIG. 31A shows examples of graphs illustrating immunoregulatory cytokine protein (log2) vs. Hb (log2 transformed) for all samples.

[0170]

[0162] FIG. 31B shows examples of graphs illustrating growth factor protein (log2) vs. Hb (log2 transformed) for batch 1.

[0171]

[0163] FIG. 32A shows examples of graphs illustrating immunoregulatory cytokine protein (log2) vs. CH2O for all samples.

[0172]

[0164] FIG. 32B shows examples of graphs illustrating growth factor protein (log2) vs. CH2O (xA2 transformed) for batch 1.

[0173]

[0165] FIG. 33A shows an example of a graph illustrating plasma QC assays vs. exact time to plasma separation protocol (PSP) for K.

[0174]

[0166] FIG. 33B shows an example of a graph illustrating plasma QC assays vs. exact time to PSP for Hb.

[0175]

[0167] FIG. 33C shows an example of a graph illustrating plasma QC assays vs. exact time to PSP for CH2O.

[0176]

[0168] FIG. 34A shows examples of graphs illustrating Hb vs. concentration, per protein for all samples. FIG. 34B shows examples of graphs illustrating K vs. concentration, per protein for all samples. FIG. 34C shows examples of graphs illustrating CH2O vs. concentration, per protein for all samples.

[0177]

[0169] FIG. 35 shows an example of a graph illustrating transformed K vs. concentration for all samples. Atty Dkt No.: 49407-791601

[0178]

[0170] FIG. 36A shows an example of graphs comparing time to PSP vs. analyte concentrations for a lab-controlled cohort.

[0179]

[0171] FIG. 36B shows an example of graphs comparing time to PSP vs. analyte concentrations for a test cohort.

[0180]

[0172] FIG. 37A shows an example of a graph illustrating correlations between proteins and analytes.

[0181]

[0173] FIG. 37B shows an example of graphs illustrating correlations between a protein and K and Hb.

[0182]

[0174] FIG. 38 shows an example of graphs illustrating differences in analyte concentration based on disease status.

[0183]

[0175] FIG. 39 shows an example of graphs illustrating machine learning model performance with vs. without controlling for analyte concentrations.

[0184]

[0176] FIG. 40A shows an example of a graph illustrating machine learning model performance with vs. without controlling for analyte concentrations.

[0185]

[0177] FIG. 40B shows an example of a chart illustrating a machine learning model performance with vs. without controlling for analyte concentrations.

[0186]

[0178] FIG. 40C shows an example of a chart illustrating a multimodal machine learning model performance with vs. without controlling for analyte concentrations.

[0187]

[0179] FIG. 41 shows an example of a chart illustrating performances of various machine learning models with vs. without controlling for analyte concentrations.

[0188]

[0180] FIG. 42 shows an example of a graph illustrating correlations between proteins and analytes in two cohorts.

[0189]

[0181] FIG. 43A shows an example of a graph illustrating correlations between a protein and K in two cohorts.

[0190]

[0182] FIG. 43B shows an example of a graph illustrating correlations between a protein and K in two cohorts.

[0191]

[0183] FIG. 43C shows an example of a graph illustrating correlations between a protein and K in two cohorts.

[0192]

[0184] FIG. 44A shows an example of a graph illustrating differences in time to PSP between two cohorts.

[0193]

[0185] FIG. 44B shows an example of a chart illustrating differences in sample cooling between two cohorts. Atty Dkt No.: 49407-791601

[0194] DETAILED DESCRIPTION

[0195]

[0186] While various embodiments of the inventive concepts have been shown and disclosed herein, it will be obvious to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions may occur to those skilled in the art without departing from the inventive concepts. It should be understood that various alternatives to the embodiments of the inventive concepts disclosed herein may be employed.

[0196]

[0187] In an aspect, the present disclosure provides methods of evaluating a plasma sample quality, comprising: collecting a blood sample from a subject, wherein the blood sample is collected in a collection device (e.g., blood collection tube (BCT)); processing the blood sample to separate a plasma sample from other blood components; quantitatively measuring one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin (Hb), potassium (K), lactate, or formaldehyde (CH2O); and evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample, wherein the amount of the one or more analytes present in the plasma sample is indicative of the plasma sample quality.

[0197]

[0188] In an aspect, the present disclosure provides a non-transitory computer-readable memory storing one or more instructions executable by one or more processors, that when executed by the one or more processors cause the one or more processors to perform processing comprising: collecting a blood sample from a subject, wherein the blood sample is collected in a collection device (e.g., blood collection tube (BCT)); processing the blood sample to separate a plasma sample from other blood components; quantitatively measuring one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample, wherein the amount of the one or more analytes present in the plasma sample is indicative of the plasma sample quality.

[0198]

[0189] In an aspect, the present disclosure provides computer systems for evaluating a plasma sample quality, the system comprising: a non-transitory memory; and a processor in communication with the non-transitory memory, the processor configured to execute the following operations in order to effectuate a method comprising the operations of: collecting a blood sample from a subject, wherein the blood sample is collected in a collection device (e.g., blood collection tube (BCT)); processing the blood sample to separate a plasma sample from other blood components; quantitatively measuring one or more analytes in the plasma sample, Atty Dkt No.: 49407-791601 wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample, wherein the amount of the one or more analytes present in the plasma sample is indicative of the plasma sample quality.

[0199]

[0190] As used in the specification and claims, the singular form “a”, “an”, and “the” include plural references unless the context clearly dictates otherwise. For example, the term “a nucleic acid” includes a plurality of nucleic acids, including mixtures thereof.

[0200]

[0191] Where values are disclosed as ranges, it will be understood that such disclosure includes the disclosure of all possible sub-ranges within such ranges, as well as specific numerical values that fall within such ranges irrespective of whether a specific numerical value or specific sub-range is expressly stated.

[0201]

[0192] As used herein, the term “nucleic acid” generally refers to a polynucleotide comprising two or more nucleotides. The nucleic acid may be DNA or RNA. The nucleic acid may be a polymeric form of nucleotides of any length, either deoxyribonucleotides (dNTPs) or ribonucleotides (rNTPs), or analogs thereof. Nucleic acids may have any three-dimensional structure, and may perform any function, known or unknown. Non-limiting examples of nucleic acids include deoxyribonucleic (DNA), ribonucleic acid (RNA), coding or non-coding regions of a gene or gene fragment, loci (locus) defined from linkage analysis, exons, introns, messenger RNA (mRNA), transfer RNA, ribosomal RNA, short interfering RNA (siRNA), short-hairpin RNA (shRNA), micro-RNA (miRNA), ribozymes, cDNA, recombinant nucleic acids, branched nucleic acids, plasmids, vectors, isolated DNA of any sequence, isolated RNA of any sequence, nucleic acid probes, and primers. A nucleic acid may comprise one or more modified nucleotides, such as methylated nucleotides and nucleotide analogs. If present, modifications to the nucleotide structure may be made before or after assembly of the nucleic acid. The sequence of nucleotides of a nucleic acid may be interrupted by non-nucleotide components. A nucleic acid may be further modified after polymerization, such as by conjugation or binding with a reporter agent. A “variant” nucleic acid may be a polynucleotide having a nucleotide sequence identical to that of its original nucleic acid except having at least one nucleotide modified, for example, deleted, inserted, or replaced, respectively. The variant may have a nucleotide sequence at least about 80%, 90%, 95%, or 99%, identity to the nucleotide sequence of the original nucleic acid.

[0202]

[0193] As used herein, the terms “polyamino acid”, “polypeptide”, and “protein” generally refer to a class of biomolecules and / or macromolecules that comprise one or more chains of amino acids. Atty Dkt No.: 49407-791601

[0203]

[0194] As used herein, the term “subject” generally refers to an individual, entity or a medium that has or is suspected of having testable or detectable genetic information or material. A subject can be a person, individual, or patient. The subject can be a vertebrate, such as, for example, a mammal. Non-limiting examples of mammals include humans, simians, farm animals, sport animals, rodents, and pets. The subject may be displaying a symptom(s) indicative of a health or physiological state or condition of the subject, such as a cancer or a stage of a cancer of the subject. As an alternative, the subject can be asymptomatic with respect to such health or physiological state or condition.

[0204]

[0195] As used herein, the term “sample” generally refers to a biological sample obtained from or derived from one or more subjects. Biological samples may be cell-free biological samples or substantially cell-free biological samples, or may be processed or fractionated to produce cell- free biological samples. For example, cell-free biological samples may include cell-free ribonucleic acid (cfRNA), cell-free deoxyribonucleic acid (cfDNA), cell-free protein and / or cell- free polypeptides. A biological sample may be tissue (e.g., tissue obtained by biopsy), blood (e.g., whole blood), plasma, serum, sweat, urine, saliva, or a derivative thereof. Cell-free biological samples may be obtained or derived from subjects using an ethylenediaminetetraacetic acid (EDTA) collection tube, or a cell-free DNA collection tube (e.g., Streck®, Roche®, PAXgene®, Norgen), a cell-free RNA collection tube (e.g., Streck® RNA tube). Cell-free biological samples may be derived from whole blood samples by fractionation. Biological samples or derivatives thereof may contain cells. For example, a biological sample may be a blood sample or a derivative thereof (e.g., blood collected by a collection tube or blood drops), a tumor sample, a tissue sample, a urine sample, or a cell (e.g., tissue) sample.

[0205]

[0196] As used herein, the term “kit” is not limited to any specific device and includes any device suitable for implementing systems and methods of the present disclosure such as, but not limited to, microarrays, bioarrays, biochips, biochip arrays, or bead-based assays.

[0206]

[0197] As used herein, the term “collection device” includes any device suitable for collecting a blood sample including, but not limited to, an ethylenediaminetetraacetic acid (EDTA) collection tube, a cell-free RNA collection tube (e.g., Streck®), a cell-free DNA collection tube (e.g., Streck®, Roche®, PAXgene®, Norgen), or a cell-free RNA collection tube (e.g., Streck® RNA tube).

[0207]

[0198] In an aspect, the present disclosure provides methods of evaluating a sample quality. The sample may be a blood sample. The sample may be a plasma sample. The methods may include collecting a blood sample from a subject in a collection device. The blood sample may be collected wherein the collection device is a tube. The tube may be a blood collection tube (BCT). Atty Dkt No.: 49407-791601

[0208] The methods may include processing the blood sample. The processing may include separating a plasma sample from other blood components of the blood sample. The methods may include quantitatively measuring one or more analytes in the plasma sample. The one or more analytes may comprise hemoglobin. The one or more analytes may comprise potassium (K). The one or more analytes may comprise lactate (which, as used herein, is intended to be synonymous with lactic acid) or lactate derivatives. The one or more analytes may comprise formaldehyde (CH2O) or CH2O) derivatives. The methods may include evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample. The amount of the one or more analytes present in the plasma sample may be indicative of the plasma sample quality.

[0209]

[0199] The methods may include obtaining a sample from a subject. The sample may be a blood sample. The blood sample may include plasma. The blood sample may include serum. The sample may be cell-free or substantially cell-free. The sample may include nucleic acids, such as DNA or RNA. The sample may include cell-free nucleic acids, such as cell-free DNA or cell- free RNA and cell-free proteins. The sample may be a tissue sample (e.g., a tissue obtained by a biopsy). The sample may be blood (e.g., whole blood), plasma, serum, or any combination or derivative thereof.

[0210]

[0200] The subject may be a human. The subject may be an adult (e.g., at least 18 years old). The subject may not be an adult (e.g., less than 18 years old). The subject may be male. The subject may be female. The subject may also be referred to herein as a person, an individual, or a patient. The subject may be a mammal, for example, a rat, a mouse, a gerbil, a hamster, a guinea pig, a fox, a bear, a dog, a cow, a pig, a sheep, a monkey, or a human. The subject may be a primate, for example, an ape or a monkey. The primate may include a chimpanzee, a bonobo, an orangutan, or a baboon. The subject may be a vertebrate. The subject may be a bird, a reptile, or an amphibian. The subject may be a farm animal, a sport animal, a rodent, or a pet.

[0211]

[0201] One or more samples may be obtained from one or more subjects. For example, more than or equal to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24,

[0212] 25 samples may be collected or obtained from more than or equal to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10,

[0213] 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25 subjects. As yet another example, less than or equal to 25, 24, 23, 22, 21, 20, 19, 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 samples may be collected or obtained from less than or equal to 25, 24, 23, 22, 21, 20, 19,

[0214] 18, 17, 16, 15, 14, 13, 12, 11, 10, 9, 8, 7, 6, 5, 4, 3, 2, or 1 subjects. Multiple samples may be obtained from one subject, for example, more than or equal to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 samples may be obtained from one subject. In certain embodiments, more than or Atty Dkt No.: 49407-791601 equal to 1-5 samples may be collected or obtained from more than or equal to 25-100, 100-500, 500-1,000, 1,000-5,000, 5,000-10,000, 10,000-50,000, 50,000-100,000, 100,000-500,00 subjects.

[0215]

[0202] The methods may include processing a blood sample. The processing may include separating a blood sample into one or more portions. For example, the processing may include separating a blood sample into more than or equal to 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, or 15 portions. The processing may include separating a plasma sample from the blood sample. The processing may include separating a plasma sample from other blood components in a blood sample. Other blood components may include, but are not limited to, buffy coat, white blood cells, lymphocytes, blood platelets, red blood cells, granulocytes, or the like. In some cases, other blood components may include plasma.

[0216]

[0203] The processing may include use of centrifugation. Centrifugation may be referred to as an action or process of using a centrifuge to separate fluids of different densities or liquids from solids. Centrifugation may be used to separate a blood sample to a plasma sample and other blood components. The processing may include centrifugation at a speed, also referred to as the number of revolutions per minute (rpm). The processing may include a rpm of more than or equal to 300 rpm, 500 rpm, 800 rpm, 1,000 rpm, 1,250 rpm, 1,500 rpm, 1,750 rpm, 2,000 rpm, 2,250 rpm, 2,500 rpm, 2750 rpm, 3,000 rpm, 3,250 rpm, 3,500 rpm, 3,750 rpm, 4,000 rpm, 4,250 rpm, 4,500 rpm, 4,750 rpm, 5,000 rpm, 5,250 rpm, 5,500 rpm, 5,750 rpm, 6,000 rpm, 6,250 rpm,

[0217] 6.500 rpm, 6,750 rpm, 7,000 rpm, 7,250 rpm, 7,500 rpm, 7,750 rpm, 8,000 rpm, 8,250 rpm,

[0218] 8.500 rpm, 8,750 rpm, 9,000 rpm, 9,250 rpm, 9,500 rpm, 9,750 rpm, or 10,000 rpm. The processing may include a rpm of less than or equal to 10,000 rpm, 9,750 rpm, 9,500 rpm, 9,250 rpm, 9,000 rpm, 8,750 rpm, 8,500 rpm, 8,250 rpm, 8,000 rpm, 7,750 rpm, 7,500 rpm, 7,250 rpm,

[0219] 7,000 rpm, 6,750 rpm, 6,500 rpm, 6,250 rpm, 6,000 rpm, 5,750 rpm, 5,500 rpm, 5,250 rpm,

[0220] 5,000 rpm, 4,750 rpm, 4,500 rpm, 4,250 rpm, 4,000 rpm, 3,750 rpm, 3,500 rpm, 3,250 rpm,

[0221] 3,000 rpm, 2,750 rpm, 2,500 rpm, 2,250 rpm, 2,000 rpm, 1,750 rpm, 1,500 rpm, 1,250 rpm,

[0222] 1,000 rpm, 800 rpm, 500 rpm, or 300 rpm.

[0223]

[0204] The methods may include collecting a blood sample from a subject and processing the blood sample after a period of time. In some embodiments, the blood sample is processed in a time between less than (1) hour and seven (7) days after collecting the blood sample. In some embodiments, the blood sample is processed in a time more than or equal to 10 minutes, 20 minutes, 30 minutes, 40 minutes 50 minutes, 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, 9 hours, 10 hours, 11 hours, 12 hours, 13 hours, 14 hours, 15 hours, 16 hours, 17 hours, 18 hours, 19 hours, 20 hours, 21 hours, 22 hours, 23 hours, or 24 after collecting the blood sample. In some embodiments, the blood sample is processed in a time less than or equal Atty Dkt No.: 49407-791601 to 24 hours, 23 hours, 22 hours, 21 hours, 20 hours, 19 hours, 18 hours, 17 hours, 16 hours, 15 hours, 14 hours, 13 hours, 12 hours, 11 hours, 10 hours, 9 hours, 8 hours, 7 hours, 6 hours, 5 hours, 4 hours, 3 hours, 2 hours, 1 hours, 50 minutes, 40 minutes, 30 minutes, 20 minutes, or 10 minutes after collecting the blood sample. In some embodiments, the blood sample is processed in a time more than or equal to 1 day, 2 days, 3 days, 4 days, 5 days, 6 days, 1 week, 2 weeks, 3 weeks, 4 weeks, 5 weeks, 6 weeks, 7 weeks, 8 weeks, 9 weeks, or 10 weeks after collecting the blood sample. In some embodiments, the blood sample is processed in a time less than or equal to 10 weeks, 9 weeks, 8 weeks, 7 weeks, 6 weeks, 5 weeks, 4 weeks, 3 weeks, 2 weeks, 1 week, 6 days, 5 days, 4 days, 3 days, 2 days, or 1 day after collecting the blood sample.

[0224]

[0205] The methods may include measuring one or more analytes in a sample. The sample may comprise a blood sample, a plasma sample, a serum sample, or a combination thereof. An amount of the one or more analytes may be measured. A level of the one or more analytes may be measured. The one or more analytes may comprise hemoglobin. The one or more analytes may comprise potassium (K). The one or more analytes may comprise lactate (lactic acid) and lactate derivatives. The one or more analytes may comprise formaldehyde (CH2O) and CH2O derivatives. The one or more analytes may comprise hemoglobin, potassium (K), lactate, and formaldehyde (CH2O). The one or more analytes may comprise hemoglobin, potassium (K), lactate, formaldehyde (CH2O), or combinations thereof. The one or more analytes may comprise the hemoglobin and the potassium (K). The one or more analytes may comprise the hemoglobin and the formaldehyde (CH2O). The one or more analytes may comprise the potassium (K) and the formaldehyde (CH2O).

[0225]

[0206] The methods may include measuring one or more analytes in a blood sample using various measurement techniques. In certain embodiments, the measuring of one or more analytes can comprise clinical chemistry analysis. Clinical chemistry, as used herein, refers to the biochemical analysis of body fluids. It uses chemical reactions to determine the levels of various analytes in bodily fluids and is used to detect and quantify different analytes in blood, serum, plasma, urine and other specimen types in clinical chemistry. Clinical chemistry analysis comprises techniques such as spectrophotometry, immunoassays, and electrophoresis to measure the concentration of analytes in various specimen types.

[0226]

[0207] In certain embodiments, measuring may include use of an immunoassay. The measuring may include use of an ELISA. The measuring may include use of Luminex. The measuring may include use of a blood stain. The measuring may include a spectroscopy assay. The measuring may include a turbidimetry assay. The measuring may include a fluorescence-based assay. The measuring may include a flame photometer or ion-selective electrode. The measuring may Atty Dkt No.: 49407-791601 include chromatography. The measuring may include gas chromatography. The measuring may include liquid chromatography. The measuring may include capillary electrophoresis-mass spectrometry. The measuring may include ion mobility. The measuring may include electrochemiluminescence immunoassay (ECLIA) technology. The measuring may include mass spectrometry, for example, ionization, electron ionization, TOF MS, tandem MS, mass analyzer, MALDI, linear ion trap, or the like. The measuring may include immunoassays, for example enzyme-linked immunosorbent assays (ELISAs, radioimmunoassays and the like (see Immunoassay, E. Diamandis and T. Christopoulus, Academic Press, Inc., San Diego, Calif., 1996, the contents of which are incorporated herein by reference), as well as proximity extension assay (PEA), protein microarrays. The methods may include use of an Ultraviolet-visible spectroscopy (UV / Vis) spectrum assay. The UV / Vis assay may be used to measure one or more analytes in a blood sample, for example, hemoglobin. UV-Vis spectroscopy may be referred to as an analytical technique that aims to measure the amount of discrete wavelengths of UV or visible light that may be absorbed by or transmitted through a sample in comparison to a reference sample (e.g., a blank sample). The UV-Vis spectroscopy assay may indicate the contents of a blood sample and at what concentration. The UV-Vis spectroscopy assay may use light. The UV-Vis spectroscopy assay may include use of a light source, a wavelength selector, a sample, a detector, a computer, an electric current, a semiconductor, or any combination thereof. The UV-Vis spectroscopy assay may include use of one or more filters, for example a monochromator, an absorption filter, an interference filter, a cutoff filter, a bandpass filter, or any combination thereof. The measured amount of the hemoglobin may comprise a hemolysis index. The hemolysis index may comprise a semi quantitative index. A greater value of the hemolysis index may be indicative of a larger amount of hemoglobin in the blood sample. The methods may include use of an enzymatic colorimetric assay to measure one or more analytes in a blood sample, for example, lactate.

[0227]

[0208] The methods may include use of a turbidimetric assay. The methods may include use of an Ion-Selective Electrode (ISE) assays. These assays may be used to measure one or more analytes in a blood sample, for example potassium (K). Potassium (K) is an essential ion that is involved in several physiological processes, including for example, nerve function, fluid balance, cardiovascular health, and the like. Turbidimetry may be referred to methods in analytical chemistry for determining an amount of cloudiness, or turbidity, in a solution based upon measuring of the effect of turbidity upon the transmission and scattering of light. The turbidimetric assay may measure the turbidity of a sample (e.g., a blood sample) to determine a level of an analyte (e.g., potassium). The turbidimetric assay may determine the concentration of Atty Dkt No.: 49407-791601 an analyte in a solution. The turbidimetric assay may use a light source. ISE assays may incorporate a sensor (e.g., pH electrodes) to quantify the presence of an analyte. Potassium ISE electrode may be intended for measuring potassium ion concentrations and activities in aqueous solutions. Potassium ISE’s can be used in aqueous solutions over a wide temperature range, for example, up to 40 °C.

[0228]

[0209] The methods may include use of a fluorescence-based assay. The fluorescence-based assay may be used to measure one or more analytes in a blood sample, for example formaldehyde (CH2O). The fluorescence-based assay may include fluorescence intensity, fluorescence resonance energy transfer (FRET), time-resolved fluorescence (TFR), fluorescence correlation methods (FCM), or the like. The fluorescence-based assay may use light emitting diodes, a fluorescence spectrometer (e.g., white light source, excitation monochromator, sample chamber, emission monochromator, a detector, and the like), flow cytometry, and the like.

[0229]

[0210] The methods may include evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample. The amount of the one or more analytes present in the plasma sample may be indicative of the plasma sample quality for use in protein, DNA and RNA assays.

[0230] [2H] The methods may include determining if a plasma sample is eligible as a candidate for use in a detection or diagnostic assay based upon the amount or presence of certain analytes in the plasma sample. The determination may be based at least in part on an evaluation of the amount of potassium, formaldehyde, and / or hemoglobin in the sample to determine quality of the plasma sample for use in downstream detection or diagnostic assays.

[0231]

[0212] The methods may include use of a detection or diagnostic assay. The detection or diagnostic assay may be used to indicate an indication in the subject. In some embodiments, the detection or diagnostic assay is a quantitative immunoassay. In some embodiments, the detection or diagnostic assay is an enzyme-linked immunosorbent assay (ELISA). In some embodiments, the detection or diagnostic assay is a proximity extension assay (PEA). In some embodiments, the detection or diagnostic assay is a protein microarray. In some embodiments, the detection or diagnostic assay is mass spectrometry. In some embodiments, the detection or diagnostic assay is a cell-free Protein Immuno-Quant ELISAs. In some embodiments, the detection or diagnostic assay is an electrochemiluminescence immunoassay (ECLIA).

[0232]

[0213] In other embodiments, the methods may include the use of a detection or diagnostic assay that is a genomic assay. In some embodiments, the genomic assay comprises molecular assays. In some embodiments, the molecular assay includes, but is not limited to, single variant assays, single gene assays, gene panel assays, epigenetic analysis assays (methylation-based assays Atty Dkt No.: 49407-791601 comprising either whole methylome and / or targeted methylation assays), and whole exome sequencing or whole genome sequencing.

[0233]

[0214] The detection or diagnostic assay may be used to indicate an indication in a subject. Such indications can include, for example, cancer, gut-associated diseases, immune-mediated inflammatory diseases, neurological diseases, kidney diseases, prenatal diseases, metabolic diseases, or a combination thereof.

[0234]

[0215] In some embodiments, the indication is cancer. The cancer may be colorectal cancer (CRC). The cancer may be liver cancer. The cancer may be lung cancer. The cancer may be pancreatic cancer. The cancer may be breast cancer. The cancer may be bladder cancer. The cancer may be gastric cancer. The cancer may be esophageal cancer. The cancer may be uterine cancer. The cancer may be endometrial cancer. The cancer may be kidney cancer. The cancer may be leukemia. The cancer may be melanoma. The cancer may be prostate cancer. The cancer may be thyroid cancer. The cancer may be non-small cell lung cancer (NSCLC). The cancer may be bile duct cancer. The cancer may be brain cancer. The cancer may be cervical cancer. The cancer may be esophageal cancer. The cancer may be eye cancer. The cancer may be gallbladder cancer. The cancer may be testicular cancer. The cancer may be head and neck cancer. The cancer may be parathyroid cancer. The cancer may be rectal cancer. The cancer may be skin cancer. The cancer may be small intestine cancer. The cancer may be stomach cancer. The cancer may comprise a stage of a cancer. For example, the cancer may be stage 0 cancer, stage I cancer, stage II cancer, stage III cancer, or stage IV cancer. In some embodiments, the indication is a stage of a cancer. In some embodiments, the indication is a tumor. The tumor may be benign. The tumor may be metastatic.

[0235]

[0216] In other embodiments, the indications can include gut-associated diseases including Crohn's disease, colitis, ulcerative colitis (UC), inflammatory bowel disease (IBD), irritable bowel syndrome (IBS), and celiac disease. In some examples, the disease is inflammatory bowel disease (IBD), colitis, ulcerative colitis, Crohn's disease, microscopic colitis, collagenous colitis, lymphocytic colitis, diversion colitis, Behcet's disease, indeterminate colitis, or a combination thereof.

[0236]

[0217] In certain embodiments, the indications can include immune-mediated inflammatory diseases such as psoriasis, sarcoidosis, rheumatoid arthritis, asthma, rhinitis (hay fever), food allergy, eczema, lupus, multiple sclerosis, fibromyalgia, type 1 diabetes, Lyme disease, or a combination thereof. The indications may include neurological diseases. Non-limiting examples of neurological diseases can include Parkinson's disease, Huntington's disease, multiple sclerosis, Alzheimer's disease, stroke, epilepsy, neurodegeneration, and neuropathy. Atty Dkt No.: 49407-791601

[0237]

[0218] In other embodiments, the indications can be kidney diseases including interstitial nephritis, acute kidney failure, nephropathy, or a combination thereof. The indications may include prenatal diseases. In certain embodiments, prenatal diseases can include Down syndrome, aneuploidy, spina bifida, trisomy, Edwards syndrome, teratomas, sacrococcygeal teratoma (SCT), ventriculomegaly, renal agenesis, cystic fibrosis, and hydrops fetalis. The indications may include metabolic diseases. In still other embodiments, metabolic diseases that can be inferred by the disclosed methods and systems may include cystinosis, Fabry disease, Gaucher disease, Lesch-Nyhan syndrome, Niemann-Pick disease, phenylketonuria, Pompe disease, Tay-Sachs disease, von Gierke disease, obesity, diabetes, and heart disease.

[0238]

[0219] The detection or diagnostic assay may be used to indicate an indication in a subject at an accuracy. The accuracy may be expressed as a percentage. In some cases, the methods comprise indicating an indication in a subject with an accuracy of more than or equal to 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%,

[0239] 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%,

[0240] 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0241]

[0220] The detection or diagnostic assay may be used to indicate an indication in a subject at a positive predictive value (PPV). In some cases, the methods comprise indicating an indication in a subject with a PPV of more than or equal to 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%.

[0242]

[0221] The detection or diagnostic assay may be used to indicate an indication in a subject at an Area Under the Curve (AUC) of a Receiver Operating Characteristic (ROC) curve. In some cases, the methods comprise indicating an indication in a subject with an AUC value of more than or equal to about 0.50, about 0.525, about 0.55, about 0.575, about 0.60, about 0.625, about 0.65, about 0.675, about 0.70, about 0.725, about 0.75, about 0.775, about 0.80, about 0.825, about 0.85, about 0.875, about 0.90, about 0.925, about 0.95, about 0.975, or about 0.99.

[0243]

[0222] The detection or diagnostic assay may be used to indicate an indication in a subject at a specificity. In some cases, the methods comprise indicating an indication in a subject with a specificity of more than or equal to 50%, 51%, 52%, 53%, 54%, 55%, 56%, 57%, 58%, 59%, 60%, 61%, 62%, 63%, 64%, 65%, 66%, 67%, 68%, 69%, 70%, 71%, 72%, 73%, 74%, 75%, 76%, 77%, 78%, 79%, 80%, 81%, 82%, 83%, 84%, 85%, 86%, 87%, 88%, 89%, 90%, 91%, 92%, 93%, 94%, 95%, 96%, 97%, 98%, or 99%. Atty Dkt No.: 49407-791601

[0244]

[0223] The methods may include comparing the amount of one or more analytes present in a plasma sample to the amount in a reference sample or reference standard. The reference sample may be a sample from a subject known not to have the indication. The reference sample may be a control sample. The reference sample may be a healthy sample. The reference standard may comprise contrived samples (e.g., spike-in with specific analytes).

[0245]

[0224] In some embodiments, the methods may include comparing an amount of hemoglobin in a plasma sample and comparing said amount of hemoglobin to an amount of hemoglobin in a reference sample or a known reference standard. In some embodiments, an elevated amount of the hemoglobin in the plasma sample as compared to the reference sample or reference standard may be indicative of protein perturbation.

[0246]

[0225] In some embodiments, the methods may include comparing an amount of potassium (K) in a plasma sample and comparing said amount of potassium to an amount of potassium in a reference sample or a known reference standard. In some embodiments, an elevated amount of the potassium (K) in the plasma sample as compared to the reference sample or reference standard may be indicative of protein perturbation.

[0247]

[0226] In some embodiments, the methods may include comparing an amount of lactate (e.g., lactic acid) in a plasma sample and comparing said amount of lactate to an amount of lactate in a reference sample or a known reference standard. In some embodiments, an elevated amount of the lactate (e.g., lactic acid) in the plasma sample as compared to the reference sample or reference standard may be indicative of protein perturbation.

[0248]

[0227] In some embodiments, the methods may include comparing an amount of formaldehyde (CH2O) in a plasma sample and comparing said amount of formaldehyde (CH2O) to an amount of formaldehyde (CH2O) in a reference sample or a known reference standard. In some embodiments, a decreased amount of the formaldehyde (CH2O) in the plasma sample as compared to the reference sample or reference standard may be indicative of protein perturbation.

[0249]

[0228] The methods may include assessing protein perturbation. In some embodiments, the protein perturbation may comprise protein degradation or protein aggregation. In some embodiments, the protein perturbation may comprise protein degradation. In some embodiments, the protein perturbation may comprise protein aggregation. In some embodiments, the protein perturbation is a result of cell lysis. In some embodiments, the cell lysis may comprise red blood cell (RBC) lysis. The cell lysis may comprise platelet lysis. In some embodiments, the protein perturbation may be a result of platelet activation. Atty Dkt No.: 49407-791601

[0250]

[0229] In an aspect, the present disclosure provides a non-transitory computer-readable memory storing one or more instructions executable by one or more processors, that when executed by the one or more processors cause the one or more processors to perform processing. The processing may include collecting a blood sample from a subject. The blood sample may be collected in a collection device (e.g., a blood collection tube (BCT)). The processing may include processing the blood sample to separate a plasma sample from other blood components. The processing may include quantitatively measuring one or more analytes in the plasma sample. The one or more analytes may comprise hemoglobin. The one or more analytes may comprise potassium (K). The one or more analytes may comprise lactate. The one or more analytes may comprise formaldehyde (CH2O). The processing may include evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample. The amount of the one or more analytes present in the plasma sample may be indicative of the plasma sample quality.

[0251]

[0230] In an aspect, the present disclosure provides computer systems for evaluating a plasma sample quality. The system may include a non-transitory memory. The system may include a processor in communication with the non-transitory memory. The processor may be configured to execute the following operations in order to effectuate a method. The method may include collecting a blood sample from a subject. The blood sample may be collected in a collection device (e.g., a blood collection tube (BCT)). The method may include processing the blood sample to separate a plasma sample from other blood components. The method may include quantitatively measuring one or more analytes in the plasma sample. The one or more analytes may include hemoglobin. The one or more analytes may include potassium (K). The one or more analytes may comprise lactate. The one or more analytes may include formaldehyde (CH2O). The method may include evaluating the quality of the plasma sample. The evaluating the quality of the plasma sample may be based at least in part on the amount of the one or more analytes present in the plasma sample. The amount of the one or more analytes present in the plasma sample may be indicative of the plasma sample quality.

[0252] Machine Learning Modeling

[0253]

[0231] In another aspect, the present disclosure provides methods for detecting an indication in a subject. The method may comprise obtaining a plasma sample obtained or derived from the subject. The method may comprise measuring an amount of one or more proteins in the plasma sample. The method may comprise providing a protein profile of the subject. The method may comprise measuring an amount of one or more analytes in the plasma sample. In some embodiments, the one or more analytes may comprise hemoglobin, potassium (K), lactate, or Atty Dkt No.: 49407-791601 formaldehyde (CH20). The method may comprise providing one or more analyte measurements. The method may comprise computer processing the protein profile and the one or more analyte measurements using a regression model. The method may comprise computer processing the protein profile using a machine learning model trained to detect a presence or an absence of the indication in the subject, based at least in part on an output of the regression model.

[0254]

[0232] In some embodiments, the indication comprises a cancer selected from the group consisting of colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer. In some embodiments, the cancer comprises the colorectal cancer. In some embodiments, the cancer comprises the liver cancer. In some embodiments, the cancer comprises the lung cancer. In some embodiments, the cancer comprises the pancreatic cancer. In some embodiments, the cancer comprises the breast cancer. In some embodiments, the cancer may be bladder cancer. In some embodiments, the cancer may be gastric cancer. In some embodiments, the cancer may be esophageal cancer. In some embodiments, the cancer may be uterine cancer. In some embodiments, the cancer may be endometrial cancer. In some embodiments, the cancer may be kidney cancer. In some embodiments, the cancer comprises a stage of the cancer. In some embodiments, the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer. In some embodiments, the stage of the cancer comprises stage I cancer. In some embodiments, the stage of the cancer comprises stage II cancer. In some embodiments, the stage of the cancer comprises stage III cancer. In some embodiments, the stage of the cancer comprises stage IV cancer. The cancer may comprise any of the types or the stages of cancer disclosed herein.

[0255]

[0233] In some embodiments, the one or more analytes comprises potassium. In some embodiments, the one or more analytes comprise hemoglobin. The one or more analytes may comprise potassium and hemoglobin. The one or more analytes may comprise any of the analytes disclosed herein. The amount of the hemoglobin may be indicative of a level of hemolysis.

[0256]

[0234] In some embodiments, the machine learning model comprises a logistic regression model. The machine learning model may comprise a classification model. The machine learning model may output a binary value. The binary value may be indicative of the presence or the absence of the indication. The machine learning model may output a continuous value. The continuous value may be indicative of a likelihood of the indication or a severity of the indication. The machine learning model may output a multinomial value. The multinomial value may correspond to an indication type or subgroup. Atty Dkt No.: 49407-791601

[0257]

[0235] In some embodiments, the regression model comprises a logistic regression model. The regression model may be validated via cross-validation. The regression model may be validated via 1-fold, 2-fold, 3-fold, 4-fold, 5-fold, 6-fold, 7-fold, 8-fold, 9-fold, 10-fold, 15-fold, or 20-fold cross-validation. The regression model may comprise a Lasso penalty, a Ridge penalty, or an ElasticNet penalty. The regression model may comprise a regularization hyperparameter. The regularization hyperparameter may be less than or equal to about 10, less than or equal to about 1, less than or equal to about 0.1, less than or equal to about 0.01, less than or equal to about 0.001, or less than or equal to about 0.0001.

[0258]

[0236] In some embodiments, the method comprises pre-processing the protein profile. The preprocessing may comprise imputing one or more missing values based on a minimum value of the protein profile. The preprocessing may comprise applying a log2 transformation. The preprocessing may comprise controlling for one or more variables. The preprocessing may comprise mean-centering the protein profile. The preprocessing may comprise scaling the protein profile to unit variance.

[0259]

[0237] In some embodiments, an amount of at least one protein of the one or more proteins may comprise a correlation with the one or more analyte measurements. The one or more analyte measurements may be associated with pre- analytical factors. The pre-analytical factors may have an effect on the measured amounts of the one or more proteins. The pre-analytical factors may reduce an accuracy or reliability of the one or more proteins as a biomarker for detecting the indication. The pre-analytical factors may comprise temperature or time to plasma separation process (PSP). The pre-analytical factors may be negatively associated with the amount of the at least one protein. For example, time or temperature may be associated with blood cell lysis, protein denaturation, protein degradation, protein reaction with a collection tube medium, or reduced protein detectability due to cross-linking. The one or more analyte measurements may be used to account for the pre-analytical factors. The one or more analyte measurements may comprise a consistent association with the pre-analytical factors. For example, the one or more analyte measurements may be directly proportional with the pre-analytical factors. A greater value of the one or more analyte measurements may be associated with a larger effect of the pre- analytical factors on the measured amounts of the one or more proteins.

[0260]

[0238] In some embodiments, the machine learning model or the regression model is configured to control for the correlation between the amount of the at least one protein and the one or more analyte measurements. The one or more analyte measurements may be used to explain away at least a portion of the variability of the at least one protein. The one or more analyte measurements may provide a more generalizable predictor of the indication. The one or more Atty Dkt No.: 49407-791601 analyte measurements may be used to increase an effect size of the at least one protein, which may improve a performance of the machine learning model or the regression model. The machine learning model or the regression model may be configured to control for the correlation by regressing out the one or more analyte measurements. The machine learning model or the regression model may adjust the amount of the at least one protein in a sample, based on the one or more analyte measurements in the sample. For example, if the one or more analyte measurements is higher in a sample, it may be indicative that the pre-analytical factors had a greater effect on the measured amount of the at least one protein in the sample. In this example, the machine learning model or the regression model may be configured to adjust the measured amount of the at least one protein to be higher to account for the greater effect of the pre- analytical factors. It may be advantageous to control for the correlation because it may allow the machine learning model or the logistic regression model to perform consistently on plasma samples which may be handled or processed differently.

[0261]

[0239] In some embodiments, controlling for the correlation between the amount of the at least one protein and the one or more analyte measurements may improve a performance of the machine learning model or the regression model. The performance may comprise an area under the curve (AUC), an accuracy, a sensitivity, or a specificity. In some embodiments, controlling for the correlation between the amount of the at least one protein and the one or more analyte measurements may increase a partial AUC of the at least one protein. In some embodiments, controlling for the correlation between the amount of the at least one protein and the one or more analyte measurements may increase an effect size of the at least one protein on the model prediction.

[0262]

[0240] In some embodiments, the methods comprise determining a strength of the correlation between the amount of the at least one protein and the one or more analyte measurements. The methods may comprise determining a correlation coefficient between the at least one protein and the one or more analyte measurements. The methods may comprise determining a p-value of the correlation between the at least one protein and the one or more analyte measurements. The correlation coefficient or the p-value may be determined by the machine learning model. The correlation coefficient or the p-value may be indicative of the strength of the correlation. The machine learning model or the regression model may control for the one or more analyte measurements, based at least in part on the strength of the correlation.

[0263]

[0241] In some embodiments, the strength of the correlation is indicative of a quality of the plasma sample. The quality may comprise an impact of pre-analytical stress on the amount of the at least one protein in the plasma sample. A stronger correlation may be indicative of a greater Atty Dkt No.: 49407-791601 impact of the pre-analytical stress. This may decrease a performance of the machine learning model or the regression model if the pre-analytical stress is not accounted for.

[0264]

[0242] In some embodiments, the machine learning model or the regression model is configured to control for a correlation between an amount of at least one protein of the one or more proteins and one or both of age and sex. The machine learning model or the regression model may be configured to control for the correlation by regressing out the one or more analyte measurements. The machine learning model or the regression model may be configured to determine a strength of the correlation between age or sex and the measured amount of the one or more proteins. The machine learning model or the regression model may be configured to adjust the measured amounts of the one or more proteins, based on the determined strength of the correlation.

[0265]

[0243] In some embodiments, the methods comprise obtaining a methylation score associated with the plasma sample. The methylation score may be determined using a methylation score model. The methylation score may be indicative of a level of methylation of one or more nucleic acid molecules in the plasma sample or another biological sample obtained or derived from the subject. In some embodiments, the machine learning model is configured to obtain methylation data and generate the methylation score. The methylation score may be normalized or scaled.

[0266]

[0244] In some embodiments, the machine learning model or the regression model is trained to detect the presence or the absence of the indication in the subject based at least in part on the methylation score. The methylation score may be combined with the protein profile to provide a combined input data. For example, the methylation score may be concatenated with a vector comprising the measured amount of the one or more proteins. The machine learning model or the regression model may be configured to receive as input the combined input data and detect the presence or the absence of the indication based on the combined input. The combined input may improve a performance of the machine learning model or the regression model, compared to a machine learning model or the regression model that does not account for the methylation score or the protein profile.

[0267]

[0245] In another aspect, the present disclosure provides computer systems for detecting an indication in a subject. The system may comprise a non-transitory memory. The system may comprise a processor in communication with the non-transitory memory. The processor may be configured to execute one or more operations in order to effectuate a method. The method may comprise an operation of obtaining a plasma sample obtained or derived from the subject. The method may comprise an operation of measuring an amount of one or more proteins in the plasma sample, thereby providing a protein profile of the subject. The method may comprise an operation of measuring an amount of one or more analytes in the plasma sample, wherein the one Atty Dkt No.: 49407-791601 or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements. The method may comprise an operation of computer processing the protein profile and the one or more analyte measurements using a regression model. The method may comprise an operation of computer processing the protein profile using a machine learning model trained to detect a presence or an absence of the indication in the subject, based at least in part on an output of the regression model.

[0268] Examples of Machine Learning Methodologies

[0269]

[0246] As used in this specification and the appended claims, the terms “artificial intelligence,” “artificial intelligence techniques,” “artificial intelligence operation,” and “artificial intelligence algorithm” generally refer to any system or computational procedure that may take one or more actions that simulate human intelligence processes for enhancing or maximizing a chance of achieving a goal. The term “artificial intelligence” may include “generative modeling,” “machine learning” (ML), or “reinforcement learning” (RL). As used in this specification and the appended claims, the terms “machine learning,” “machine learning techniques,” “machine learning operation,” and “machine learning model” generally refer to any system or analytical or statistical procedure that may progressively improve computer performance of a task.

[0270]

[0247] In some cases, ML may generally involve identifying and recognizing patterns in existing data in order to facilitate making predictions for subsequent data. ML may include a ML model (which may include, for example, a ML algorithm). Machine learning, whether analytical or statistical in nature, may provide deductive or abductive inference based on real or simulated data. The ML model may be a trained model. ML techniques may comprise one or more supervised, semi-supervised, self-supervised, or unsupervised ML techniques. For example, an ML model may be a trained model that is trained through supervised learning (e.g., various parameters are determined as weights or scaling factors). ML may comprise one or more of regression analysis, regularization, classification, dimensionality reduction, ensemble learning, meta learning, association rule learning, cluster analysis, anomaly detection, deep learning, or ultra-deep learning. ML may comprise: k-means, k-means clustering, k-nearest neighbors, learning vector quantization, linear regression, non-linear regression, least squares regression, partial least squares regression, logistic regression, stepwise regression, multivariate adaptive regression splines, ridge regression, principal component regression, least absolute shrinkage and selection operation (LASSO), least angle regression, canonical correlation analysis, factor analysis, independent component analysis, linear discriminant analysis, multidimensional scaling, non-negative matrix factorization, principal components analysis, principal coordinates analysis, projection pursuit, Sammon mapping, t-distributed stochastic neighbor embedding, Atty Dkt No.: 49407-791601

[0271] AdaBoosting, boosting, gradient boosting, bootstrap aggregation, ensemble averaging, decision trees, conditional decision trees, boosted decision trees, gradient boosted decision trees, random forests, stacked generalization, Bayesian networks, Bayesian belief networks, naive Bayes, Gaussian naive Bayes, multinomial naive Bayes, hidden Markov models, hierarchical hidden Markov models, support vector machines, encoders, decoders, auto-encoders, stacked autoencoders, perceptrons, multi-layer perceptrons, artificial neural networks, feedforward neural networks, convolutional neural networks, recurrent neural networks, residual neural networks, physics-informed neural networks, long short-term memory, deep belief networks, deep Boltzmann machines, deep convolutional neural networks, deep recurrent neural networks, large language models, transformer models, vision transformers, or generative adversarial networks.

[0272]

[0248] Training the ML model may include, in some cases, selecting one or more untrained data models to train using a training data set. The selected untrained data models may include any type of untrained ML models for supervised, semi-supervised, self-supervised, or unsupervised machine learning. The selected untrained data models may be specified based upon input (e.g., user input) specifying relevant parameters to use as predicted variables or other variables to use as potential explanatory variables. For example, the selected untrained data models may be specified to generate an output (e.g., a prediction) based upon the input. Conditions for training the ML model from the selected untrained data models may likewise be selected, such as limits on the ML model complexity or limits on the ML model refinement past a certain point. The ML model may be trained (e.g., via a computer system such as a server) using the training data set. In some cases, a first subset of the training data set may be selected to train the ML model. The selected untrained data models may then be trained on the first subset of training data set using appropriate ML techniques, based upon the type of ML model selected and any conditions specified for training the ML model. In some cases, due to the processing power requirements of training the ML model, the selected untrained data models may be trained using additional computing resources (e.g., cloud computing resources). Such training may continue, in some cases, until at least one aspect of the ML model is validated and meets selection criteria to be used as a predictive model.

[0273]

[0249] In some cases, one or more aspects of the ML model may be validated using a second subset of the training data set (e.g., distinct from the first subset of the training data set) to determine accuracy and robustness of the ML model. Such validation may include applying the ML model to the second subset of the training data set to make predictions derived from the second subset of the training data. The ML model may then be evaluated to determine whether performance is sufficient based upon the derived predictions. The sufficiency criteria applied to Atty Dkt No.: 49407-791601 the ML model may vary depending upon the size of the training data set available for training, the performance of previous iterations of trained models, or user-specified performance requirements. If the ML model does not achieve sufficient performance, additional training may be performed. Additional training may include refinement of the ML model or retraining on a different first subset of the training dataset, after which the new ML model may again be validated and assessed. When the ML model has achieved sufficient performance, in some cases, the ML may be stored for present or future use. The ML model may be stored as sets of parameter values or weights for analysis of further input (e.g., further relevant parameters to use as further predicted variables, further explanatory variables, further user interaction data, etc.), which may also include analysis logic or indications of model validity in some instances. In some cases, a plurality of ML models may be stored for generating predictions under different sets of input data conditions. In some cases, the ML model may be stored in a database (e.g., associated with a server).

[0274] Examples of Decision Trees and Random Forests

[0275]

[0250] As described above, the machine learning model may implement a decision tree. A decision tree may be a supervised ML algorithm that can be applied to both regression and classification problems. A decision tree may grow from a root (base condition), and when it meets a condition (internal node / feature), it may split into multiple branches. The end of the branch that does not split anymore may be an outcome (leaf). A decision tree can be generated using a training data set according to the following operations: (1) starting from a root node (the entire dataset), the algorithm may split the dataset in two branches using a decision rule or branching criterion; (2) each of these two branches may generate a new child node; (3) for each new child node, the branching process may be repeated until the dataset cannot be split any further; (4) each branching criterion may be chosen to maximize information gain (e.g., a quantification of how much a branching criterion reduces a quantification of how mixed the labels are in the children nodes). The labels may be the data or the classification that is predicted by the decision tree.

[0276]

[0251] A random forest regression is an extension of the decision tree model that tends to yield more robust predictions by stretching the use of the training data partition. Whereas a decision tree may make a single pass through the data, a random forest regression may bootstrap 50% of the data (e.g., with replacement) and build many trees. Rather than using all explanatory variables as candidates for splitting, a random subset of candidate variables may be used for splitting, which may enable trees that have different data and different variables (hence the term random). The predictions from the trees, which may be collectively referred to as the Atty Dkt No.: 49407-791601

[0277] “forest,” may then be averaged to produce a final prediction. Many trees (e.g., ten trees, fifty trees, one hundred trees, one thousand trees, etc.) may be included in a random forest model, with a number (e.g., 3, 6, 10, etc.) of terms sampled per split, a minimum of number (e.g., 1, 2, 4, 10, etc.) of splits per tree, and a minimum split size (e.g., 16, 32, 64, 128, 256, etc.). Random forests may be trained in a similar way as decision trees. Specifically, training a random forest may include the following operations: (1) randomly select k features from the total number of features; (2) create a decision tree from these k features using the same operations as for generating a decision tree; and (3) repeat the previous two operations until a target number of trees is created.

[0278]

[0252] As disclosed, a random forest classifier, which may comprise a plurality of decision trees wherein the output prediction may be the mode of the predicted classifications of the individual trees, can be helpful in reducing overfitting to training data. In some cases, an ensemble of decision trees can be constructed using a random subset of features at each split or decision node. The Gini criterion may be employed, in some cases, to choose the best partition, wherein decision nodes having the lowest calculated Gini impurity index are selected. The Gini impurity can be used, in some cases, as a criterion to find informative features based on which the splits in each decision tree may be constructed.

[0279]

[0253] In some cases, each decision tree of a random forest may comprise one or more decision nodes, wherein each decision node specifies a predicate condition. For example, decision node may predicate the condition that, for a given dataset, the outcome to an question is a specific outcome. At each decision node, a decision tree can be split based on whether the predicate condition attached to the decision node holds true, leading to various prediction nodes. Each prediction node can comprise output values that represent “votes” for one or more of the classifications or conditions being evaluated by the assessment model. At prediction time, a “vote” can be taken over all of the decision trees, and the majority vote (or mode of the predicted classifications) can be output as the predicted classification.

[0280]

[0254] In some cases, when the dataset being queried in the assessment model reaches a “leaf’, or a final prediction node with no further downstream splits, the output values of the leaf can be output as the votes for the particular decision tree. Since a random forest model comprises a plurality of decision trees, the final votes across all trees in the forest can be summed to yield the final votes and the corresponding classification of the subject. A large number of decision trees can help reduce overfitting of the assessment model to the training data, by reducing the variance of each individual decision tree. For example, an assessment model can comprise, for example, at least about 3 decision trees, at least about 5 decision trees, at least about 10 decision trees, at Atty Dkt No.: 49407-791601 least about 20 decision trees, at least about 50 decision trees, at least about 100 decision trees, etc.

[0281] Examples of Support Vector Machines

[0282]

[0255] As also described above, the machine learning model may implement support vector machine learning techniques. In machine learning, support vector machines (SVMs) may be supervised learning models with associated learning algorithms that analyze data for classification and regression analysis. SVMs may be a robust prediction method, being based on statistical learning. SVMs may be well-suited for domains characterized by the existence of large amounts of data, noisy patterns, or the absence of general theories.

[0283]

[0256] In general terms, SVMs may map input vectors into high dimensional feature space through non-linear mapping function, chosen a priori. In this high dimensional feature space, an optimal separating hyperplane may be constructed. The optimal hyperplane may then be used to determine, for example, class separations, regression fit, accuracy in density estimation, etc. More formally, a SVM may construct a hyperplane or set of hyperplanes in a high or infinitedimensional space, which can be used for classification, regression, or other tasks like outlier detection.

[0284]

[0257] Support vectors may be defined as the data points that lie closest to the decision surface (or hyperplane). Support vectors may therefore be the data points that are most difficult to classify and may have direct bearing on an optimum location of the decision surface. Given a set of training examples, each marked as belonging to one of two categories, an SVM training algorithm may build a model that assigns new examples to one category or the other, making it a non-probabilistic binary linear classifier (although methods such as Platt scaling exist to use SVM in a probabilistic classification setting). In some cases, SVMs may map training examples to points in space so as increase (e.g., maximize) the width of the gap between the two categories. New examples may then be mapped into that same space and predicted to belong to a category based on which side of the gap the new examples fall. In addition to performing linear classification, SVMs can efficiently perform a non-linear classification using what may be referred to as a kernel trick, implicitly mapping their inputs into high-dimensional feature spaces.

[0285]

[0258] Within a support vector machine, the dimensionally of the feature space may be large. For example, a fourth-degree polynomial mapping function may cause a 200-dimensional input space to be mapped into a 1.6 billionth dimensional feature space. The kernel trick and the Vapnik-Chervonenkis dimension may allow the SVM to thwart the “curse of dimensionality” limiting other methods and effectively derive generalizable answers from this very high dimensional feature space. Accordingly, SVMs may assist in discovering knowledge from vast Atty Dkt No.: 49407-791601 amounts of input data.

[0286] Computer Systems

[0287]

[0259] The present disclosure provides computer systems that are programmed to implement methods disclosed herein. FIG. 1 shows a computer system 101 that is programmed or otherwise configured to perform methods of the present disclosure, such as, for example, collecting a blood sample, processing the blood sample, quantitatively measuring one or more analytes in the blood sample, and evaluating the quality of the sample. In some embodiments, the computer systems herein are configured to perform methods such as collecting a blood sample from a subject, wherein the blood sample is collected in a collection device (e.g., a blood collection tube (BCT)); processing the blood sample to separate a plasma sample from other blood components; quantitatively measuring one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and evaluating the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample, wherein the amount of the one or more analytes present in the plasma sample is indicative of the plasma sample quality. The computer system 101 can process various aspects of subject data, including blood sample data, plasma sample data, analyte data, and the like. The computer system 101 can be an electronic device of a user or a computer system that is remotely located with respect to the electronic device. The electronic device can be a mobile electronic device.

[0288]

[0260] The computer system 101 may include a central processing unit (CPU, also called a “processor” and “computer processor” herein) 105, which can be a single core or a multi core processor, or a plurality of processors for parallel processing. The computer system 101 may also include a memory or a memory location 110 (e.g., random-access memory, read-only memory, flash memory), electronic storage unit 115 (e.g., hard disk), communication interface 120 (e.g., network adapter) for communicating with one or more other systems, and peripheral devices 125, such as cache, other memory, data storage and / or electronic display adapters. The memory 110, storage unit 115, interface 120 and peripheral devices 125 may be in communication with the CPU 105 through a communication bus (solid lines), such as a motherboard. The storage unit 115 can be a data storage unit (or data repository) for storing data. The computer system 101 can be operatively coupled to a computer network (“network”) 130 with the aid of the communication interface 120. The network 130 can be the Internet, an internet and / or extranet, or an intranet and / or extranet that is in communication with the Internet. The network 130, in some embodiments, may be a telecommunication and / or data network. The network 130 can include one or more computer servers, which can enable distributed computing, such as cloud Atty Dkt No.: 49407-791601 computing. The network 130, in some embodiments, with the aid of the computer system 101, can implement a peer-to-peer network, which may enable devices coupled to the computer system 101 to behave as a client or a server.

[0289]

[0261] The CPU 105 can execute a sequence of machine-readable instructions, which can be embodied in a program or software. The instructions may be stored in a memory location, such as the memory 110. The instructions can be directed to the CPU 105, which can subsequently program or otherwise configure the CPU 105 to implement methods of the present disclosure. Examples of operations performed by the CPU 105 can include fetch, decode, execute, and writeback.

[0290]

[0262] The CPU 105 can be part of a circuit, such as an integrated circuit. One or more other components of the system 101 can be included in the circuit. In some examples, the circuit is an application specific integrated circuit (ASIC).

[0291]

[0263] The storage unit 115 can store files, such as drivers, libraries and saved programs. The storage unit 115 can store user data, e.g., user preferences and user programs. The computer system 101, in some embodiments, can include one or more additional data storage units that are external to the computer system 101, such as located on a remote server that is in communication with the computer system 101 through an intranet or the Internet.

[0292]

[0264] The computer system 101 can communicate with one or more remote computer systems through the network 130. For instance, the computer system 101 can communicate with a remote computer system of a user. Examples of remote computer systems include personal computers (e.g., portable PC), slate or tablet PC’s (e.g., Apple® iPad, Samsung® Galaxy Tab), telephones, Smart phones (e.g., Apple® iPhone, Android-enabled device, Blackberry®), or personal digital assistants. The user can access the computer system 101 via the network 130.

[0293]

[0265] Methods as disclosed herein can be implemented by way of machine (e.g., computer processor) executable code stored on an electronic storage location of the computer system 101, such as, for example, on the memory 110 or electronic storage unit 115. The machine executable or machine-readable code can be provided in the form of software. During use, the code can be executed by the processor 105. In some examples, the code can be retrieved from the storage unit 115 and stored on the memory 110 for ready access by the processor 105. In some embodiments, the electronic storage unit 115 can be precluded, and machine-executable instructions are stored on memory 110.

[0294]

[0266] The code can be pre-compiled and configured for use with a machine having a processer adapted to execute the code or can be interpreted or compiled during runtime. The code can be Atty Dkt No.: 49407-791601 supplied in a programming language that can be selected to enable the code to execute in a precompiled, interpreted, or as-compiled fashion.

[0295]

[0267] Aspects of the systems and methods provided herein, such as the computer system 101, can be embodied in programming. Various aspects of the technology may be thought of as “products” or “articles of manufacture” which may be in the form of machine (or processor) executable code and / or associated data that is carried on or embodied in a type of machine readable medium. Machine-executable code can be stored on an electronic storage unit, such as memory (e.g., read-only memory, random-access memory, flash memory) or a hard disk. “Storage” type media can include any or all of the tangible memory of the computers, processors or the like, or associated modules thereof, such as various semiconductor memories, tape drives, disk drives and the like, which may provide non- transitory storage at any time for the software programming. All or portions of the software may at times be communicated through the Internet or various other telecommunication networks. Such communications, for example, may enable loading of the software from one computer or processor into another, for example, from a management server or host computer into the computer platform of an application server. Thus, another type of media that may bear the software elements includes optical, electrical and electromagnetic waves, such as used across physical interfaces between local devices, through wired and optical landline networks and over various air-links. The physical elements that carry such waves, such as wired or wireless links, optical links or the like, also may be considered as media bearing the software. As used herein, unless restricted to non-transitory, tangible “storage” media, terms such as computer or machine “readable medium” refer to any medium that participates in providing instructions to a processor for execution.

[0296]

[0268] Hence, a machine readable medium, such as computer-executable code, may take many forms, including but not limited to, a tangible storage medium, a carrier wave medium or physical transmission medium. Non-volatile storage media include, for example, optical or magnetic disks, such as any of the storage devices in any computer(s) or the like, such as may be used to implement the databases, etc. shown in the drawings. Volatile storage media include dynamic memory, such as main memory of such a computer platform. Tangible transmission media include coaxial cables; copper wire and fiber optics, including the wires that comprise a bus within a computer system. Carrier-wave transmission media may take the form of electric or electromagnetic signals, or acoustic or light waves such as those generated during radio frequency (RF) and infrared (IR) data communications. Common forms of computer- readable media therefore include for example: a floppy disk, a flexible disk, hard disk, magnetic tape, any other magnetic medium, a CD-ROM, DVD or DVD-ROM, any other optical medium, punch Atty Dkt No.: 49407-791601 cards paper tape, any other physical storage medium with patterns of holes, a RAM, a ROM, a PROM and EPROM, a FLASH-EPROM, any other memory chip or cartridge, a carrier wave transporting data or instructions, cables or links transporting such a carrier wave, or any other medium from which a computer may read programming code and / or data. Many of these forms of computer readable media may be involved in carrying one or more sequences of one or more instructions to a processor for execution.

[0297]

[0269] The computer system 101 can include or be in communication with an electronic display 135 that comprises a user interface (UI) 140. Examples of UI’s include, without limitation, a graphical user interface (GUI) and web-based user interface.

[0298]

[0270] Methods and systems of the present disclosure can be implemented by way of one or more algorithms. An algorithm can be implemented by way of software upon execution by the central processing unit 105. The algorithm can, for example, collect a blood sample from a subject, wherein the blood sample is collected in a blood collection tube (BCT); process the blood sample to separate a plasma sample from other blood components; measure one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and evaluate the quality of the plasma sample based at least in part on the amount of the one or more analytes present in the plasma sample, wherein the amount of the one or more analytes present in the plasma sample is indicative of the plasma sample quality

[0299]

[0271] In some examples, the subject matter disclosed herein can include at least one computer program or use of the same. A computer program can be a sequence of instructions, executable in the digital processing device’s CPU, GPU, or TPU, written to perform a specified task. Computer- readable instructions can be implemented as program modules, such as functions, objects, Application Programming Interfaces (APIs), data structures, and the like, that perform particular tasks or implement particular abstract data types. For example, a computer program can be written in various versions of various languages.

[0300]

[0272] The functionality of the computer-readable instructions can be combined or distributed as desired in various environments. In some examples, a computer program can be provided from one location. In some examples, a computer program can be provided from a plurality of locations. In some examples, a computer program can include one or more software modules. In some examples, a computer program can include, in part or in whole, one or more web applications, one or more mobile applications, one or more standalone applications, one or more web browser plug-ins, extensions, add- ins, or add-ons, or combinations thereof. Atty Dkt No.: 49407-791601

[0301]

[0273] In some examples, the computer processing can be a method of statistics, mathematics, biology, or any combination thereof. In some examples, the computer processing method includes a dimension reduction method including, for example, logistic regression, dimension reduction, principal component analysis, autoencoders, singular value decomposition, Fourier bases, singular value decomposition, wavelets, discriminant analysis, support vector machine, tree-based methods, random forest, gradient boost tree, logistic regression, matrix factorization, network clustering, and neural network.

[0302]

[0274] In some examples, the computer processing may include a supervised machine learning method including, for example, a regression, support vector machine, tree-based method, and network.

[0303]

[0275] In some examples, the computer processing may include an unsupervised machine learning method including, for example, clustering, network, principal component analysis, and matrix factorization.

[0304] Machine Learning Systems and Models

[0305]

[0276] In another aspect, the present disclosure provides systems and methods comprising use of a classifier. The classifier may form part of a predictive engine for distinguishing groups in a population based on features (e.g., analytes) identified in biological samples (e.g., blood samples) to determine sample quality.

[0306]

[0277] In some embodiments, a classifier is created by normalizing information by formatting similar portions of the information into a unified format and a unified scale; storing the information in a database; training a prediction engine by applying one or more machine learning operations to the stored information, the prediction engine mapping, for a particular population, a combination of one or more features; applying the prediction engine to the accessed field information to identify a sample associated with a group (e.g., pass / fail metrics for use in downstream analysis); and classifying the sample into a group.

[0307]

[0278] The trained classifier may be configured to accept a plurality of input variables and to produce one or more output values based on the plurality of input variables. The plurality of input variables may comprise one or more datasets. For example, an input variable may comprise one or more analytes present in a blood sample. The plurality of input variables may also include data of a subject.

[0308]

[0279] A trained algorithm provided herein may comprise a classifier, such that each of the one or more output values comprises one of a fixed number of possible values (e.g., a linear classifier, a logistic regression classifier, etc.) indicating a classification of a sample by the classifier. The trained algorithm may comprise a binary classifier, such that each of the one or Atty Dkt No.: 49407-791601 more output values comprises one of two values (e.g., {0, 1 }, {positive, negative}, or {high-risk, low-risk}) indicating a classification of the sample by the classifier. The trained algorithm may be another type of classifier, such that each of the one or more output values comprises one of more than two values (e.g., {0, 1, 2}, {positive / pass, negative, / fail or indeterminate}, or {high- risk, intermediate-risk, or low-risk}) indicating a classification of the sample by the classifier. The output values may comprise descriptive labels, numerical values, or a combination thereof. Some of the output values may comprise descriptive labels. Some descriptive labels may be mapped to numerical values, for example, by mapping “positive or pass” to 1 and “negative or fail” to 0.

[0309]

[0280] Some of the output values may comprise numerical values, such as binary, integer, or continuous values. Such binary output values may comprise, for example, {0, 1 }, {positive, negative}, or {pass, fail}. Such integer output values may comprise, for example, {0, 1, 2}. Such continuous output values may comprise, for example, a probability value of at least 0 and no more than 1. Such continuous output values may comprise, for example, an un-normalized probability value of at least 0. Some numerical values may be mapped to descriptive labels, for example, by mapping 1 to “positive / pass” and 0 to “negative / fail.”

[0310]

[0281] In some embodiments, the subject matter disclosed herein can include a digital processing device or use of the same. In some embodiments, the digital processing device can include one or more hardware central processing units (CPU), graphics processing units (GPU), or tensor processing units (TPU) that carry out the device’s functions. In some embodiments, the digital processing device can include an operating system configured to perform executable instructions. In some embodiments, the digital processing device may be connected a computer network. In some embodiments, the digital processing device may be connected to the Internet. In some embodiments, the digital processing device may be connected to a cloud computing infrastructure. In some embodiments, the digital processing device may be connected to an intranet. In some embodiments, the digital processing device may be connected to a data storage device.

[0311]

[0282] Non-limiting examples of suitable digital processing devices include server computers, desktop computers, laptop computers, notebook computers, sub-notebook computers, netbook computers, netpad computers, set-top computers, handheld computers, Internet appliances, mobile smartphones, and tablet computers. Suitable tablet computers can include, for example, those with booklet, slate, and convertible configurations.

[0312]

[0283] In some embodiments, the digital processing device can include an operating system configured to perform executable instructions. For example, the operating system can include Atty Dkt No.: 49407-791601 software, including programs and data, which manages the device’s hardware and provides services for execution of applications. Non-limiting examples of operating systems include Ubuntu, FreeBSD, OpenBSD, NetBSD®, Linux, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. Non-limiting examples of suitable personal computer operating systems include Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and UNIX-like operating systems such as GNU / Linux®. In some embodiments, the operating system can be provided by cloud computing, and cloud computing resources can be provided by one or more service providers.

[0313]

[0284] In some embodiments, the device can include a storage and / or memory device. The storage and / or memory device can be one or more physical apparatuses used to store data or programs on a temporary or permanent basis. In some embodiments, the device can be volatile memory and require power to maintain stored information. In some embodiments, the device can be non-volatile memory and retain stored information when the digital processing device is not powered. In some embodiments, the non-volatile memory can include flash memory. In some embodiments, the non-volatile memory can include dynamic random-access memory (DRAM). In some embodiments, the non-volatile memory can include ferroelectric random-access memory (FRAM). In some embodiments, the non-volatile memory can include phase-change random access memory (PRAM). In some embodiments, the device can be a storage device including, for example, CD-ROMs, DVDs, flash memory devices, magnetic disk drives, magnetic tapes drives, optical disk drives, and cloud computing-based storage. In some embodiments, the storage and / or memory device can be a combination of devices such as those disclosed herein. In some embodiments, the digital processing device can include a display to send visual information to a user. In some embodiments, the display can be a cathode ray tube (CRT). In some embodiments, the display can be a liquid crystal display (LCD). In some embodiments, the display can be a thin film transistor liquid crystal display (TFT-LCD). In some embodiments, the display can be an organic light emitting diode (OLED) display. In some embodiments, an OLED display can be a passive- matrix OLED (PMOLED) or active-matrix OLED (AMOLED) display. In some embodiments, the display can be a plasma display. In some embodiments, the display can be a video projector. In some embodiments, the display can be a combination of devices such as those disclosed herein.

[0314]

[0285] In some embodiments, the digital processing device can include an input device to receive information from a user. In some examples, the input device can be a keyboard. In some embodiments, the input device can be a pointing device including, for example, a mouse, trackball, trackpadjoystick, game controller, or stylus. In some embodiments, the input device Atty Dkt No.: 49407-791601 can be a touch screen or a multi-touch screen. In some embodiments, the input device can be a microphone to capture voice or other sound input. In some embodiments, the input device can be a video camera to capture motion or visual input. In some embodiments, the input device can be a combination of devices such as those disclosed herein.

[0315]

[0286] In some embodiments, the subject matter disclosed herein can include one or more non- transitory computer-readable storage media encoded with a program including instructions executable by the operating system. The operating system may be part of a networked digital processing device. In some examples, a computer-readable storage medium can be a tangible component of a digital processing device. In some embodiments, a computer-readable storage medium may be removable from a digital processing device. In some embodiments, a computer- readable storage medium can include, for example, CD-ROMs, DVDs, flash memory devices, solid state memory, magnetic disk drives, magnetic tape drives, optical disk drives, cloud computing systems and services, and the like. In some embodiments, the program and instructions can be permanently, substantially permanently, semi- permanently, or non- transitorily encoded on the media.

[0316] KITS

[0317]

[0287] Provided herein are kits that relate to the methods and systems described herein. The kits herein may be used to measure one or more analytes in a blood sample, as further described in the methods and systems herein. In some embodiments, the kits may comprise one or more clinical chemistry analysis methods. In some embodiments, the kits may comprise one or more buffers, one or more reagents, instructions for use, a manual, a protocol, or a combination thereof. In some embodiments, the kits may comprise a tube (such as a blood collection tube), a bottle, a glass jar, a container, or a combination thereof. In some embodiments, the kits may comprise a centrifuge, a shaker, an incubator, or a combination thereof. In some embodiments, the kits may comprise one or more containers.

[0318]

[0288] In some embodiments, the kits may comprise a potassium (K) turbidimetric assay kit. The potassium (K) turbidimetric assay kit may comprise sodium tetraphenylborate, a centrifuge, a micro pipettor, a vortex mixer, a microplate reader, one or more buffers, one or more reagents, and the like. The potassium (K) turbidimetric assay kit may be designed for the quantitative measurement of potassium (K) in various sample types, such as serum, plasma, tissues, cells, and the like. The potassium (K) turbidimetric assay kit may comprise potassium ions in a sample (e.g., a biological sample), under alkaline conditions, which may react with sodium tetraphenylborate to form potassium tetraphenylborate, which is a turbid white solution with little solubility. The turbidity may be measured by the kit, which may be proportional to the Atty Dkt No.: 49407-791601 potassium ion concentration in the sample. The potassium ion concentration may be calculated by measuring the OD (optical density) value at 450 nm (nanometers).

[0319]

[0289] The kits may comprise a formaldehyde assay kit. The formaldehyde assay kit may comprise a microplate, one or more buffers, one or more reagents, one or more containers, a tube (such as a blood collection tube), instructions for use, a manual, a protocol, a centrifuge, a shaker, an incubator, and the like.

[0320]

[0290] The kits may comprise a hemoglobin absorbance assay kit. The hemoglobin absorbance assay kit may comprise a microplate, one or more buffers, one or more reagents, one or more containers, a tube (such as a blood collection tube), instructions for use, a manual, a protocol, a centrifuge, a shaker, an incubator, and the like.

[0321]

[0291] The kits may comprise a lactate assay kit. The lactate assay kit may comprise a microplate, one or more buffers, one or more reagents, one or more containers, a tube (such as a blood collection tube), instructions for use, a manual, a protocol, a centrifuge, a shaker, an incubator, and the like.

[0322] EXAMPLES

[0323] EXAMPLE 1: Sample QC Study

[0324] 1. Background

[0325]

[0292] Using methods and systems of the present disclosure, sample QC assays were developed and used to understand and improve sample quality of biological samples. Currently, real -world sample collection may lead to suboptimal sample quality. Real-world sample collection may further cause signals associated with a disease status to be confounded by preanalytical factors.

[0326]

[0293] One goal of this study is to develop assays to characterize extrinsic and intrinsic variables in real-world sample collection, while also reducing the impact of preanalytical confounders. Relatedly, an additional goal of this study is to improve preanalytical / sample processes related to assays developed for the early detection of cancer.

[0327]

[0294] For a preanalytical workflow, additional goals include: (1) guiding sample collection and processing workflow improvement, and (2) guiding analyte stability evaluation and sample stabilization research. For clinical samples, additional goals include: (1) flagging samples that are impacted by preanalytical workflow, and (2) serving as proxies to correct cancer signals by regressing out preanalytical noise.

[0328]

[0295] Sample QC assays can be used for protein signal correction. An additional goal of this study is to demonstrate a computational approach to improving early cancer signal detection in serum / plasma samples (e.g., serum, EDTA, Streck® tubes) by using reliable sample QC assays Atty Dkt No.: 49407-791601 to identify, measure, reduce, and / or remove preanalytical sources of variability. For instance, and as shown in FIG. 2, which provides a hypothetical plot comparing an immunoregulatory cytokine protein and potassium (K), when a sample comprises a high amount of an immunoregulatory cytokine protein, it calls into question whether the high amount of an immunoregulatory cytokine protein is a true cancer signal, or if it is confounded by an immunoregulatory cytokine protein released due to preanalytical variables (e.g., red blood cell (RBC) lysis). Further, a K assay can quantitatively assess RBC lysis and correlates with an immunoregulatory cytokine protein released due to RBC lysis, then, it can be determined, based at least on the amount of an immunoregulatory cytokine protein, whether each sample is confounded by RBC lysis or not. In summary, and as shown in FIG. 2, a high immunoregulatory cytokine protein but low K may be due to a true biological signal. Further, a high immunoregulatory cytokine protein but also a high K may be due to mostly preanalytical noise.

[0329]

[0296] In a study, it was observed that an immunoregulatory cytokine protein increased 3.5-fold in both cold and warm conditions. Proteins with time-to-PSP instability (e.g., an immunoregulatory cytokine protein) likely reflect cell lysis, and increase regardless of warm or cold temperature stress. Growth factor protein increased 1.7-fold in cold and 3-fold in warm conditions. Proteins (e.g., growth factor protein) demonstrate some temperature-dependent instability. In order to correct for the range of instability patterns observed in both temperatureindependent and temperature-dependent, assays may be helpful.

[0330]

[0297] Hemoglobin (Hb) and K can quantitatively measure blood cell lysis under Time at Temperature (T@T). For example, proteins increased under T@T likely due to blood cell lysis. Immunoregulatory cytokine protein and growth factor protein is stable in cell-free plasma but may show an increase when blood cells are present (e.g., post draw whole blood).

[0331]

[0298] Hemoglobin may be used as an indicator of hemolysis. Hemolysis in vivo may be inherited (e.g., defects of RBC membrane) or acquired (e.g., infections, drugs, toxins, and the like). Post blood draw hemolysis may include use of a collection device, a storage time, and a transportation temperature.

[0332]

[0299] Extracellular K may change as an indicator of change of cell resting membrane potential and functions. K may change in vivo, for example, a small change may significantly depolarize or hyperpolarize cells, (e.g., implication of cardiac function). In a post blood draw, K may increase, for example, during blood cell lysis (e.g., RBC lysis).

[0333]

[0300] FIG. 3 shows a graph illustrating a plasma immunoregulatory cytokine protein over an extended time (plasma separated from whole blood and cell-free plasma), where time-to-PSP is the x-axis and an immunoregulatory cytokine protein (pg / mL) is the y-axis. FIG. 4 shows Hb Atty Dkt No.: 49407-791601 compared to an immunoregulatory cytokine protein for four donors, where Hb is the x-axis and an immunoregulatory cytokine protein is the y-axis. FIG. 5 shows K compared to an immunoregulatory cytokine protein for four donors, where K is the x-axis and an immunoregulatory cytokine protein is the y-axis.

[0334]

[0301] Formaldehyde (CH2O) may quantitatively reflect time-to-PSP while also capturing sample temperature stressors. FIG. 6 shows a bar graph illustrating plasma CH2O levels in in a time / condition study. FIG. 7 shows an image illustrating BCT component becoming lysine containing protein under conditions of temperature acceleration and CH2O consumption. CH2O may be from a CH2O releaser-based collection device, which may be used in preservation BCTs (e.g., Streck cfDNA BCT).

[0335] 2. Study Design

[0336]

[0302] Goals of this study include (1) prototype at least one sample QC assay that can reliably measure preanalytical variables, e.g., cell lysis and Time at Temperature (T@T) and (2) establish a computational approach using the QC assay to reduce / remove preanalytical variability and improve signal detection of Fn-immunoassay proteins (e.g., especially an immunoregulatory cytokine protein and growth factor protein) in late-stage colorectal cancer (CRC) samples.

[0337]

[0303] Samples were collected from 14 healthy subjects (donors). Out of the 14 healthy subjects, 9 were male and 5 were female. Samples were collected at 2 local collection sites in the bay area at 2 collection dates. 13 T@T stressors were used, as shown in Table 2 below. FIG. 8 shows an example of a workflow used to collect the healthy samples. In total, 209 healthy samples were collected for this study. Atty Dkt No.: 49407-791601

[0338] Table 1. T@T Treatment and BCT Replicates

[0339]

[0304] Additional samples were collected from late-stage CRC subjects and used for per-protein effect size analysis. To perform a preliminary growth factor protein and immunoregulatory cytokine protein effect size check using an independent cohort, 21 CRC subjects at stage III or stage IV cancer were used. 10 healthy subjects were used. With regard to the T@T stresses used, all BCTs that were collected were shipped to under real -world shipping (FIG. 8). Further, some samples had additional T@T stress applied to BCTs after being received. In total, 25 CRC and 15 healthy samples were used.

[0340] 3. Data Analysis: Sample QC Assay

[0341]

[0305] Three sample QC assays were executed on the healthy cohort. The hemoglobin absorbance assay used measures the UV / Vis spectrum of the sample and isolates hemoglobin based on a 410 nm wavelength. The K turbidity assay was used to measure turbidity after potassium ions reacting with sodium tetraphenylborate and forming white particle with low solubility. Streck cell-free DNA BCT contained K in K3EDTA anticoagulant (15 mmol / L), which was not subtracted for this study. The CH2O assay was used to measure fluorescent signal after free CH2O in Streck plasma was oxidized to produce a stable fluorescent product. CH2O in Streck plasma was due to BCT additive reaction with blood post draw.

[0342]

[0306] For all three assays (e.g., Hb, K, and CH2O), duplicates were used in a 96-well plate format. Further, 7 total batches were used with randomized plate layout. For the Hb assay, the %CV < 20%, with a higher CV for low [Hb], For the K and CH2O assays, the RA2 of all batches standard curve > 0.99 and the %CV < 5%. The %CV by batch is summarized below in Table 2.

[0343] Table 2. Assays and %CV by batch

[0344]

[0307] All three plasma QC assays were time / temperature perturbed (e.g., RT (room temperature), summer, and winter) under the following in conditions set forth in Table 3. Atty Dkt No.: 49407-791601

[0345] Table 3. Treatment Conditions

[0346]

[0308] However, each assay resulted in different dynamics to the time / temperature perturbations applied. The summer and winter conditioned samples were kept at summer or winter temperatures for 3 days, and then further incubated at RT for 0, 3, or 5 days.

[0347]

[0309] As shown in FIG. 9, K vs. time / temp, the K assay increased over time. Further, as shown in FIG. 10, Hb vs. time / temp, the Hb assay increased over time, although the Hb assay took longer to start responding to the perturbation than the K assay did. Further, as shown in FIG. 11, CH2O vs. time / temp, the CH2O assay decreased over time. The CH2O assay responded quickly to the perturbations (similar to the K assay). However, in the summer conditions, the Hb assay may have reached a saturation point by the 74-hour mark.

[0348] 4. Data Analysis: Computational Approach

[0349]

[0310] An immunoassay -based approach was used for this study. The analytes of interest included an immunoregulatory cytokine protein and growth factor protein. The assay was performed with duplicates in a 384-well plate format. The assay was preliminary QC passed in 2 test runs (see below Table 4). Regarding these test runs: (1) bead count: all analytes > 35, (2) RA2 of standard curve from immunoassay-based software: > 0.94, and (3) %CV: < 10% for growth factor protein and immunoregulatory cytokine protein (no Dev threshold applicable).

[0350] [3H] A randomization was based on donor and T@T stresses for 2 immunoassay batches. The 2 batches, which included 209 samples, were based on: (1) gender, (2) collection site, (3) Atty Dkt No.: 49407-791601 collection date, and (4) age. For each individual donor, baseline and stressed samples were in the same batch to reduce batch effect. Within each batch, samples were randomized across donors and T@T stresses. In summary, 7 donors were used per batch x 15 samples per donor x 2 batches.

[0351]

[0312] For this analysis, growth factor protein and immunoregulatory cytokine protein were the primary focus, among other analytes in an RUO kit. Growth factor protein and immunoregulatory cytokine protein passed all other QC criteria, with one exception for growth factor protein. In batch 2, there was high variability in many of the Standard 6 replicates. The clinical range for immunoregulatory cytokine protein did not overlap with these standards, so it likely did not impact immunoregulatory cytokine protein. However, the baseline samples for growth factor protein did overlap with the standard 6 range. Therefore, for the growth factor protein analyses, samples from batch 2 were excluded.

[0352]

[0313] Both immunoregulatory cytokine protein and growth factor protein increased in concentration over time, regardless of the temperatures. The summer and winter conditioned samples were kept at summer or winter temperatures for 3 days, and then further incubated at RT (room temperature) for 0, 3, or 5 days. Increasing concentration over time was seen in ambient, summer, and winter conditions. The dynamics differed slightly depending on the outside temperature. Growth factor protein had larger differences between summer / winter dynamics than immunoregulatory cytokine protein did. FIG. 12 shows a summary for immunoregulatory cytokine protein. FIG. 13 shows a summary for growth factor protein.

[0353]

[0314] An analysis was conducted to assess associations between immunoregulatory cytokine protein and growth factor protein with the plasma QC assays. In some instances, the plasma QC measurement was transformed in order to achieve a linear fit. For example, for immunoregulatory cytokine protein, log2 transformed Hb. For growth factor protein, log2 transformed K and Hb. OLS diagnostics were used to assess the validity of these fits. Follow-up analyses will aim to improve the model fits and explain the observed relationships. FIG. 14A shows examples of scatter plots that summarize Hb vs. concentration for immunoregulatory cytokine protein and growth factor protein. FIG. 14B shows examples of scatter plots that summarize K vs. concentration for immunoregulatory cytokine protein and growth factor protein. FIG. 15 shows examples of scatter plots that summarize CH2O vs. concentration for immunoregulatory cytokine protein and growth factor protein.

[0354]

[0315] As illustrated in the graph shown in FIG. 16, all three plasma QC assays (e.g., K, Hb, and CH2O) dominated the observed variability in growth factor protein and immunoregulatory cytokine protein but contributed marginally to other assays. For immunoregulatory cytokine Atty Dkt No.: 49407-791601 protein and growth factor protein, all three of the plasma QC assays explained a large percentage of the variation. The association with donor was observed to be much weaker in comparison. For all the other assays (e.g., glycoproteins, chemokines, mucin proteins, and acidic proteins), their association with the plasma QC assays were weak compared to their association with the donor. This was expected for proteins that are considered less sensitive to T@T based on our previous experimentation.

[0355]

[0316] Independent, late-stage CRC samples were not distinguishable from healthy for either immunoregulatory cytokine protein or growth factor protein. 219 healthy samples and 25 CRC samples were used, and all CRC samples were either stage III or stage IV CRC. The CRC samples had between 0 and 8 days of time / temperature stress prior to PSP. The effect sizes were minimal: (1) immunoregulatory cytokine protein (All batches): 0.07; and (2) growth factor protein (Batch 1 & 3): 0.12. In contrast, other proteins (e.g., glycoproteins, cytokines, mucin proteins, acidic proteins) were observed to have effect sizes >1, as expected for late-stage CRC. These other biomarkers were much less sensitive to time / temperature perturbation than the immunoregulatory cytokine protein or growth factor protein were. FIG. 17 illustrates graphs summarizing data and results for immunoregulatory cytokine protein (all batches) and growth factor protein (batch 1 and 3).

[0356]

[0317] Growth factor protein and immunoregulatory cytokine protein were plotted alongside the plasma QC measurements. For example, growth factor protein and immunoregulatory cytokine protein were plotted against the Hb assay (FIG. 18), the K assay (FIG. 19), and the CH2O assay (FIG. 20). From the plots shown in FIGs. 18-20, some separation between the CRC and healthy controls was observed. As such, the K and CH2O assays may help improve the separation between CRC and healthy controls.

[0357]

[0318] As shown in the graphs and tables in FIGs. 21-22, adjusting for the K assay increased CRC signal in immunoregulatory cytokine protein, but not in growth factor protein.

[0358]

[0319] As shown in the graphs and tables in FIGs. 23-24, adjusting for the Hb assay caused the CRC effect to become negative. Further, while Hb was unable to rescue CRC signal here, this may be due to the specific proteins / donors used in this study. A better model fit may improve signal observed in this study.

[0359]

[0320] As shown in the graphs and tables in FIGs. 25-26, adjusting for the CH2O assay increased CRC signal in both immunoregulatory cytokine protein and growth factor protein proteins. Atty Dkt No.: 49407-791601

[0360]

[0321] CH2O rescued CRC signal in growth factor protein. K was unable to do so. This may be due to growth factor protein’s high sensitivity to time to PSP. Further, the CH2O assay appeared to be a good proxy for time to PSP.

[0361] 5. Conclusion

[0362]

[0322] Computational approaches were developed that reduces preanalytical variability and improves signal detection. Further, three sample QC assays were developed, with results showing that 2 of the 3 (e.g., K and CH2O) may be used for signal correction in late-stage CRCs. Tables 8 and 9 summarize metrics of each of the QC assays (e.g., Hb, K, and CH2O) used in this study.

[0363]

[0323] Further, reliable sample QC assays were developed that reflect cell lysis and T@T with low biological variability. Sample QC measurements added a second dimension to existing protein measurement to reflect preanalytical factors on plasma, that may be beneficial for a classifier once they are regressed out computationally.

[0364] Table 4. Summary of Metrics of Study

[0365]

[0324] FIGs. 27A-27K show examples of graphs that compare K (mmol / L, dilution corrected) (x-axis) and concentration (log2) (y-axis) of various analytes in healthy and colorectal cancer (CRC) subjects. For example, FIG. 27A shows a graph for glycoproteins (r = 0.04, p = 0.52), FIG. 27B shows a graph for chemokines (r = 0.19, p = 0.0025), FIG. 27C shows a graph for cytokines (r = 0.05, p = 0.45), FIG. 27D shows a graph for kallikreins (r = 0.43, p = 2.5e-12), FIG. 27E shows a graph for surface proteins (r = 0.10, p = 0.13), FIG. 27F shows a graph for Atty Dkt No.: 49407-791601 blood vessel proteins (r = 0.20, p = 0.0019), FIG. 27G shows a graph for growth factor proteins (r = 0.74, p = l.le-43), FIG. 27H shows a graph for growth proteins (r = 0.28, p = 9.2e-06), FIG. 271 shows a graph for a immunoregulatory cytokine proteins (r = 0.88, p = 1.9e-79), FIG. 27J shows a graph for mucin proteins (r = 0.22, p = 0.00044), and FIG. 27K shows a graph for acidic proteins (r = -0.09, p = 0.14).

[0366]

[0325] FIGs. 28A-28K show examples of graphs that compare average Hb (mg / dL) (x-axis) and concentration (log2) (y-axis) of various analytes in healthy and colorectal cancer (CRC) subjects. For example, FIG. 28 A shows a graph for glycoproteins (r = -0.10, p =0.11), FIG. 28B shows a graph for chemokines (r = 0.24, p = 0.0002), FIG. 28C shows a graph for cytokines (r = 0.10, p = 0.14), FIG. 28D shows a graph for kallikreins (r = 0.32, p = 2.8e-07), FIG. 28E shows a graph for surface proteins (r = -0.10, p = 0.11), FIG. 28F shows a graph for blood vessel proteins (r = 0.11, p = 0.084), FIG. 28G shows a graph for growth factor proteins (r = 0.49, p = 2.3e-16), FIG. 28H shows a graph for growth proteins (r = 0.20, p = 0.0015), FIG. 281 shows a graph for immunoregulatory cytokine proteins (r = 0.71, p = 5e-39), FIG. 28J shows a graph for mucin proteins (r = 0.22, p = 0.00063), and FIG. 28K shows a graph for acidic proteins (r = -0.09, p = 0.18).

[0367]

[0326] FIGs. 29A-29K shows examples of graphs that comparing CH2O (pM) (x-axis) and concentration (log2) (y-axis) of various analytes in healthy and colorectal cancer (CRC) subjects. For example, FIG. 29A shows a graph for glycoproteins (r = -0.07, p = 0.28), FIG. 29B shows a graph for chemokines (r = -0.13, p = 0.04), FIG. 29C shows a graph for cytokines (r = 0.01, p = 0.87), FIG. 29D shows a graph for kallikreins (r = -0.28, p = l.le-05), FIG. 29E shows a graph for surface proteins(r = 0.12, p = 0.054), FIG. 29F shows a graph for blood vessel proteins (r = - 0.10, p = 0.12), FIG. 29G shows a graph for growth factor proteins (r = -0.64, p = le-29), FIG. 29H shows a graph for growth proteins (r = -0.09, p = 0.18), FIG. 291 shows a graph for immunoregulatory cytokine proteins (r = -0.73, p = 3.1e-42), FIG. 29J shows a graph for mucin proteins (r = -0.12, p = 0.061), and FIG. 29K shows a graph for acidic proteins (r = -0.03, p = 0.6).

[0368]

[0327] FIG. 30A shows examples of graphs comparing immunoregulatory cytokine proteins (log2) vs. K for all samples. FIG. 30A shows graphs illustrating residuals vs. fitted (top left), normal Q-Q (top right), scale-location (bottom left), and residuals vs. leverage (bottom right). FIG. 30B shows examples of graphs comparing growth factor proteins (log2) vs. K (log2 transformed), batch 1. FIG. 30B shows graphs illustrating residuals vs. fitted (top left), normal Q-Q (top right), scale-location (bottom left), and residuals vs. leverage (bottom right). Atty Dkt No.: 49407-791601

[0369]

[0328] FIG. 31 A shows examples of graphs comparing immunoregulatory cytokine proteins (log2) vs. Hb (log2 transformed) for all samples. FIG. 31A shows graphs illustrating residuals vs. fitted (top left), normal Q-Q (top right), scale-location (bottom left), and residuals vs. leverage (bottom right). FIG. 31B shows examples of graphs comparing growth factor proteins (log2) vs. Hb (log2 transformed), batch 1. FIG. 31B shows graphs illustrating residuals vs. fitted (top left), normal Q-Q (top right), scale-location (bottom left), and residuals vs. leverage (bottom right).

[0370]

[0329] FIG. 32A shows examples of graphs comparing immunoregulatory cytokine proteins (log2) vs. CH2O for all samples. FIG. 32A shows graphs illustrating residuals vs. fitted (top left), normal Q-Q (top right), scale-location (bottom left), and residuals vs. leverage (bottom right). FIG. 32B shows examples of graphs comparing growth factor proteins (log2) vs. CH2O (xA2 transformed), batch 1. FIG. 32B shows graphs illustrating residuals vs. fitted (top left), normal Q-Q (top right), scale-location (bottom left), and residuals vs. leverage (bottom right).

[0371]

[0330] FIGs. 33A-C shows examples of graphs comparing plasma QC assays vs. exact time to PSP for analytes K (FIG. 33A) (r = -0.19, p = 0.22), Hb (FIG. 33B) (r = -0.00, p = 0.98), and CH2O (FIG. 33C) (r = -0.66, p = 1.6e-06).

[0372]

[0331] FIG. 34A shows a graph illustrating Hb vs. concentration for immunoregulatory cytokine protein (r = 0.72, p = 1.5e-34) (left) and growth factor protein (r = 0.50, p = 7.3e-15).

[0373]

[0332] FIG. 34B shows a graph illustrating K vs. concentration for immunoregulatory cytokine protein (r = 0.88, p = 2. le-70) and growth factor protein (r = 0.74, p = 3e-37).

[0374]

[0333] FIG. 34C shows a graph illustrating CH2O vs. concentration for immunoregulatory cytokine protein (r = -0.33, p = 7.6e-55) and growth factor protein (r = -0.81, p = 2.9e-49).

[0375]

[0334] FIG. 35 shows a graph illustrating K (mmol / L, dilution corrected) (x-axis) compared to concentration (log2) (y-axis).

[0376]

[0335] Based upon the foregoing, it has been shown that a sample rejection threshold for certain pre-analytical markers can be as follows: (i) K: Greater than 50 mmol / L; (ii) Hemoglobin: Greater than 80 mg / dL; (iii) lactate: Greater than 15.5 mmol / L; and (iv) CH2O: Less than 3500 uM. The threshold is determined by using the 95 percentile values of K and hemoglobin, and the median value of CH2O, from a set of samples with known pre-analytical stress (e.g., plasma separated from whole blood 8 days after blood draw). Using the threshold mentioned above in an independent clinical sample set, it was observed that 0.39%, 2.55%, and 2.74% sample rejection rates were based on K, hemoglobin, or CH2O, respectively. Atty Dkt No.: 49407-791601

[0377] EXAMPLE 2: Machine Learning Modeling Study

[0378]

[0336] An example study may apply any of the systems, methods, computer-readable media, and techniques disclosed herein to detect a presence or an absence of an indication in one or more cohorts of subjects. The presence or the absence of the indication may be detected based on measuring one or more proteins to obtain a protein profile. Plasma samples may be obtained from subjects in each cohort of the one or more cohorts. The plasma samples obtained from subjects in different cohorts may be processed or handled differently. For example, there may be differences in the transit or storage temperature, or the time to plasma separation process (PSP).

[0379]

[0337] To determine analytes that may be useful for accounting for pre-analytical factors like time and temperature, the study may comprise determining associations between various analytes and the pre-analytical factors.

[0380]

[0338] FIG. 36A shows an example of graphs comparing time to PSP vs. analyte concentrations for a lab-controlled cohort. As shown in FIG. 36A, a Hemolysis index (which corresponds to a Hemoglobin concentration), K, and lactate are shown to be positively associated with time to PSP. The example in FIG. 36A illustrates that the Hemolysis index and lactate concentration may vary based on temperature, as the Hemolysis index may be higher in colder temperatures (“winter”) while the lactate concentration may be lower in colder temperatures.

[0381]

[0339] FIG. 36B shows an example of graphs comparing time to PSP vs. analyte concentrations for a test cohort. As shown in FIG. 36B, Hemolysis index, K, and lactate was also positively associated with time to PSP in the test cohort. The example in FIG. 36B shows that the Hemolysis index may be higher when cooling is performed and that the lactate concentrations may be lower when cooling is performed, which is consistent with the lab-controlled cohort shown in the example of FIG. 36A.

[0382]

[0340] The study may comprise determining correlations between the one or more proteins and the analytes. This may be useful in determining whether the analytes may be able to account for a portion of the variation of the measured amounts of the one or more proteins.

[0383]

[0341] FIG. 37A shows an example of a graph illustrating correlations between proteins and analytes. The graph shows Spearman’s rank correlation values between each protein of a panel of proteins and lactate, K, and Hemolysis. Adjusted p-values were obtained for each correlation using Benjamini -Hochberg FDR correction. Correlation values associated with an adjusted p- value of less than 0.1 are bolded. As shown in the example of FIG. 37A, several statistically significant correlation values were determined. For example, amounts of protein 3 were significantly negatively correlated with lactate, K, and Hemolysis. Amounts of protein 7, 8, 9, and 12 were also significantly negatively correlated with at least one analyte. FIG. 37B shows an Atty Dkt No.: 49407-791601 example of graphs illustrating correlations between a protein and K and Hb. As shown in the example of FIG. 37B, further analysis showed that amounts of protein 3 was negatively correlated with K with a p-value of 0.0001, and negatively correlated with Hemolysis with a p- value of 0.0370.

[0384]

[0342] The study may comprise determining whether the distribution of analyte measurements may differ based on disease status. This may provide an indication of whether pre-analytical factors have different effect sizes on protein measurements from healthy vs. disease subjects. If the effect sizes are significantly different, this may indicate that the pre-analytical factors are a confounding factor during disease detection.

[0385]

[0343] FIG. 38 shows an example of graphs illustrating differences in analyte concentration based on disease status. As shown in FIG. 38, there may be statistically significant differences in the concentration of analytes between healthy subjects and subjects having cancer. In particular, K may be higher in subjects having cancer, compared to healthy subjects, with a p-value of 0.0146. Hemolysis may be higher in subjects having cancer, compared to healthy subjects, with a p-value of 0.0127.

[0386]

[0344] The study may aim to determine whether accounting for the correlations between the analytes and the one or more proteins affects a performance of a machine learning model for detecting an indication.

[0387]

[0345] FIG. 39 shows an example of graphs illustrating machine learning model performance with vs. without controlling for analyte concentrations. The example of FIG. 39 shows correlation values between a panel of proteins and the analytes, as well as partial AUCs of single-variable machine learning models trained using each protein. As shown in the example of FIG. 39, the machine learning models achieved higher partial AUCs on almost all of the proteins when controlling for the analytes. In particular, the partial AUC was statistically significantly higher for protein 4 and protein 6 when controlling for the analytes. This indicates that the analyte measurements may improve a model performance.

[0388]

[0346] The study may determine an effect of the analyte measurements on the performance of a multivariate model. The multivariate model may be a protein model or a multimodal model. The protein model may receive as input the measured amounts of the one or more proteins. The multimodal model may receive as input the measured amounts of the one or more proteins as well as a methylation score. The multivariate model may comprise any of the machine learning models disclosed herein.

[0389]

[0347] FIG. 40A shows an example of a graph illustrating machine learning model performance with vs. without controlling for analyte concentrations. As shown in the example of FIG. 40A, Atty Dkt No.: 49407-791601 both the protein model and the multimodal model may perform better than when controlling for analyte concentrations. FIG. 40B shows an example of a chart illustrating a machine learning model performance with vs. without controlling for analyte concentrations. As shown in the example of FIG. 40B, the protein model may achieve a higher partial AUC when controlling for the analyte concentrations. A 95-percent confidence interval may comprise a lower bound of 0.007, and an upper bound of 0.039, showing that the protein model achieves a statistically significant higher partial AUC. FIG. 40C shows an example of a chart illustrating a multimodal machine learning model performance with vs. without controlling for analyte concentrations. As shown in the example of FIG. 40C, the multimodal model may also achieve a higher partial AUC when controlling for the analyte concentrations.

[0390]

[0348] FIG. 41 shows an example of a chart illustrating performances of various machine learning models with vs. without controlling for analyte concentrations. As shown in the example of FIG. 41, at a specificity of 75%, the multimodal model may achieve a higher partial AUC on an intended use population (IUP) cohort, an adenocarcinoma cohort, and a squamous cell carcinoma cohort. At the specificity of 75%, the protein model may achieve a higher partial AUC on the IUP cohort, the adenocarcinoma cohort, and a small cell lung cancer cohort. As shown in the example of FIG. 41, at a specificity of 50%, the multimodal model may achieve a higher partial AUC on the IUP cohort and the adenocarcinoma cohort. At the specificity of 50%, the protein model may achieve a higher partial AUC on the IUP cohort, the adenocarcinoma cohort, and the squamous cell carcinoma cohort.

[0391]

[0349] The study may assess the correlations between measured amounts of proteins and analyte measurements across multiple cohorts to assess reproducibility.

[0392]

[0350] FIG. 42 shows an example of graphs illustrating correlations between proteins and analytes in two cohorts. The graphs show Spearman’s rank correlation values between each protein of a panel of proteins and lactate, K, and Hemolysis. Adjusted p-values were obtained for each correlation using Benjamini -Hochberg FDR correction. Correlation values associated with an adjusted p-value of less than 0.1 are bolded. As shown in the example of FIG. 42, the correlations between the proteins and the analytes may be similar across the two cohorts. For example, protein 3 is significantly negatively associated with lactate, K, and Hemolysis in cohort 1, and is shown to be negatively associated with lactate and K in cohort 2.

[0393]

[0351] FIG. 43A shows an example of a graph illustrating correlations between a protein and K in two cohorts. As shown in the example of FIG. 43 A, measured amounts of protein 3 is significantly negatively associated with K in cohort 1 and also negatively associated with K in cohort 2. FIG. 43B shows an example of a graph illustrating correlations between a protein and Atty Dkt No.: 49407-791601

[0394] K in two cohorts. As shown in the example of FIG. 43B, the measured amounts of protein 5 is not significantly correlated with K in cohort 1 or cohort 2. FIG. 43C shows an example of a graph illustrating correlations between a protein and K in two cohorts. As shown in the example of FIG. 43C, the measured amounts of protein 7 may be negatively associated with K in cohort 1 and may not be significantly correlated with K in cohort 2.

[0395]

[0352] FIG. 44A shows an example of a graph illustrating differences in time to PSP between two cohorts. As shown in FIG. 44, the distribution of time to PSP may be different between cohort 1 and cohort 2 for both healthy subjects and subjects having cancer. FIG. 44B shows an example of a chart illustrating differences in sample cooling between two cohorts. As shown in the example of FIG. 44B, there may be differences in the cooling status between cohort 1 and cohort 2. The differences in time to PSP and cooling status may account for the differences in the correlation values between cohort 1 and cohort 2.

[0396]

[0353] While certain examples of methods and systems have been shown and disclosed herein, one of skill in the art will realize that these are provided by way of example only and not intended to be limiting within the specification. Numerous variations, changes, and substitutions will now occur to those skilled in the art without departing from the scope disclosed herein. Furthermore, it shall be understood that all aspects of the disclosed methods and systems are not limited to the specific depictions, configurations or relative proportions set forth herein which depend upon a variety of conditions and variables and the description is intended to include such alternatives, modifications, variations or equivalents.

Claims

Atty Dkt No.: 49407-791601CLAIMSWHAT IS CLAIMED IS:

1. A method of evaluating a plasma sample quality, the method comprising:(a) collecting a blood sample from a subject, wherein the blood sample is collected in a collection device;(b) processing the blood sample to separate a plasma sample from other blood components;(c) quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and(d) evaluating the quality of the plasma sample based at least in part on the measured amounts of the one or more analytes present in the plasma sample, wherein the measured amounts of the one or more analytes present in the plasma sample are indicative of the plasma sample quality.

2. The method of claim 1, further comprising (e) based at least in part on the evaluating of (d), determining if the plasma sample is eligible as a candidate for use in a detection or diagnostic assay, wherein the detection or diagnostic assay is used to indicate an indication in the subject.

3. The method of claim 1 or 2, wherein the one or more analytes comprise hemoglobin.

4. The method of claim 1 or 2, wherein the one or more analytes comprise potassium (K).

5. The method of claim 1 or 2, wherein the one or more analytes comprise lactate.

6. The method of claim 1 or 2, wherein the one or more analytes comprise formaldehyde (CH2O).

7. The method of claim 1 or 2, wherein the one or more analytes comprise hemoglobin and potassium (K).

8. The method of claim 1 or 2, wherein the one or more analytes comprise hemoglobin and formaldehyde (CH2O).

9. The method of claim 1 or 2, wherein the one or more analytes comprise potassium (K) and formaldehyde (CH2O).

10. The method of claim 1 or 2, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, and formaldehyde (CH2O).

11. The method of any one of claims 1-10, further comprising comparing the measured amounts of the one or more analytes present in the plasma sample to a reference sample.

12. The method of claim 11, wherein an elevated amount of hemoglobin in the plasma sample as compared to the reference sample is indicative of protein perturbation.Atty Dkt No.: 49407-79160113. The method of claim 12, wherein the elevated amount of the hemoglobin in the plasma sample is greater than 80 mg / dL.

14. The method of claim 11, wherein an elevated amount of potassium (K) in the plasma sample as compared to the reference sample is indicative of protein perturbation.

15. The method of claim 14, wherein the elevated amount of the potassium (K) in the plasma sample is greater than 50 mmol / L.

16. The method of claim 11, wherein an elevated amount of lactate in the plasma sample as compared to the reference sample is indicative of protein perturbation.

17. The method of claim 16, wherein the elevated amount of the lactate in the plasma sample is greater than 15.5 mmol / L.

18. The method of claim 11, wherein a decreased amount of formaldehyde (CH2O) in the plasma sample as compared to the reference sample is indicative of protein perturbation.

19. The method of claim 18, wherein the decreased amount of formaldehyde (CH2O) in the plasma sample is less than 3500 pM.

20. The method of any one of claims 12 to 19, wherein the protein perturbation comprises protein degradation or protein aggregation.

21. The method of claim 20, wherein the protein perturbation comprises the protein degradation.

22. The method of claim 20, wherein the protein perturbation comprises the protein aggregation.

23. The method of any one of claims 12 to 22, wherein the protein perturbation is a result of cell lysis.

24. The method of claim 23, wherein the cell lysis comprises red blood cell (RBC) lysis.

25. The method of claim 1 or 2, wherein the quantitative measuring of the amount of the hemoglobin comprises use of an Ultraviolet-visible spectroscopy (UV / Vis) spectrum assay.

26. The method of claim 1 or 2, wherein the measured amount of the hemoglobin comprises a Hemolysis index.

27. The method of claim 1 or 2, wherein the quantitative measuring of the amount of the lactate comprises use of an enzymatic colorimetric assay.

28. The method of claim 1 or 2, wherein the quantitative measuring of the amount of the potassium (K) comprises use of a turbidimetric assay or a potassium (K) ion selective electrode (ISE) assay.

29. The method of claim 1 or 2, wherein the quantitative measuring of the amount of the formaldehyde (CH2O) comprises use of a fluorescence-based assay.

30. The method of any one of claims 1 to 29, wherein the subject is a mammal.

31. The method of claim 30, wherein the mammal is a human.Atty Dkt No.: 49407-79160132. The method of any one of claims 2 to 31, wherein the detection or diagnostic assay is selected from the group consisting of: a quantitative immunoassay, an enzyme-linked immunosorbent assay (ELISA), an electrochemiluminescence immunoassay (ECLIA), a proximity extension assay (PEA), a protein microarray, mass spectrometry, and a cell-free Protein Immuno-Quant ELISA.

33. The method of any one of claims 2 to 32, wherein the indication comprises a cancer selected from the group consisting of: colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer.

34. The method of claim 33, wherein the cancer comprises the colorectal cancer.

35. The method of claim 33, wherein the cancer comprises the liver cancer.

36. The method of claim 33, wherein the cancer comprises the lung cancer.

37. The method of claim 33, wherein the cancer comprises the pancreatic cancer.

38. The method of claim 33, wherein the cancer comprises the breast cancer.

39. The method of claim 33, wherein the cancer comprises the bladder cancer.

40. The method of claim 33, wherein the cancer comprises the gastric cancer.

41. The method of claim 33, wherein the cancer comprises the esophageal cancer.

42. The method of claim 33, wherein the cancer comprises the uterine cancer.

43. The method of claim 33, wherein the cancer comprises the endometrial cancer.

44. The method of claim 33, wherein the cancer comprises the kidney cancer45. The method of any one of claims 33 to 44, wherein the cancer comprises a stage of the cancer.

46. The method of claim 45, wherein the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer.

47. The method of any one of claims 1 to 46, wherein the processing the blood sample in (b) is completed in a time between less than one (1) hour and seven (7) days after the collecting of the blood sample in (a).

48. The method of any one of claims 1 to 47, wherein the collection device is a blood collection tube (BCT).

49. A non-transitory computer-readable memory storing one or more instructions executable by one or more processors, that when executed by the one or more processors cause the one or more processors to perform processing comprising:(a) collecting a blood sample from a subject, wherein the blood sample is collected in a collection device;Atty Dkt No.: 49407-791601(b) processing the blood sample to separate a plasma sample from other blood components;(c) quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and(d) evaluating the quality of the plasma sample based at least in part on the measured amounts of the one or more analytes present in the plasma sample, wherein the measured amounts of the one or more analytes present in the plasma sample are indicative of the plasma sample quality.

50. A computer system for evaluating a plasma sample quality, the system comprising:(a) a non-transitory memory; and(b) a processor in communication with the non-transitory memory, the processor configured to execute the following operations in order to effectuate a method comprising the operations of:(i) collecting a blood sample from a subject, wherein the blood sample is collected in a collection device;(ii) processing the blood sample to separate a plasma sample from other blood components;(iii) quantitatively measuring amounts of one or more analytes present in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O); and(iv) evaluating the quality of the plasma sample based at least in part on the measured amounts of the one or more analytes present in the plasma sample, wherein the measured amounts of the one or more analytes present in the plasma sample are indicative of the plasma sample quality.

51. A method for detecting an indication in a subject, the method comprising:(a) obtaining a plasma sample obtained or derived from the subject;(b) measuring amounts of one or more analytes in the plasma sample, wherein the one or more analytes comprise hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements; and(c) computer processing the one or more analyte measurements using a machine learning model trained to distinguish between samples to be analyzed in a subsequent detection or diagnostic assay and samples not to be analyzed in a subsequent detection or diagnostic assay,Atty Dkt No.: 49407-791601 wherein the subsequent detection or diagnostic assay can detect or diagnose the presence or absence of the indication in the subject.

52. The method of claim 51, wherein the indication comprises a cancer selected from the group consisting of: colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer.

53. The method of claim 52, wherein the cancer comprises the colorectal cancer.

54. The method of claim 52, wherein the cancer comprises the liver cancer.

55. The method of claim 52, wherein the cancer comprises the lung cancer.

56. The method of claim 52, wherein the cancer comprises the pancreatic cancer.

57. The method of claim 52, wherein the cancer comprises the breast cancer.

58. The method of claim 52, wherein the cancer comprises the bladder cancer.

59. The method of claim 52, wherein the cancer comprises the gastric cancer.

60. The method of claim 52, wherein the cancer comprises the esophageal cancer.

61. The method of claim 52, wherein the cancer comprises the uterine cancer.

62. The method of claim 52, wherein the cancer comprises the endometrial cancer.

63. The method of claim 52, wherein the cancer comprises the kidney cancer.

64. The method of any one of claims 52 to 63, wherein the cancer comprises a stage of the cancer.

65. The method of claim 64, wherein the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer.

66. A method for detecting an indication in a subject, the method comprising:(a) obtaining a plasma sample obtained or derived from the subject;(b) measuring an amount of one or more proteins in the plasma sample, thereby providing a protein profile of the subject;(c) measuring an amount of one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements;(d) computer processing the protein profile and the one or more analyte measurements using a regression model; and(e) computer processing the protein profile using a machine learning model trained to detect a presence or an absence of the indication in the subject, based at least in part on an output of the regression model.Atty Dkt No.: 49407-79160167. The method of claim 66, wherein the indication comprises a cancer selected from the group consisting of: colorectal cancer (CRC), liver cancer, lung cancer, pancreatic cancer, breast cancer, bladder cancer, gastric cancer, esophageal cancer, uterine cancer, endometrial cancer, and kidney cancer.

68. The method of claim 67, wherein the cancer comprises the colorectal cancer.

69. The method of claim 67, wherein the cancer comprises the liver cancer.

70. The method of claim 67, wherein the cancer comprises the lung cancer.

71. The method of claim 67, wherein the cancer comprises the pancreatic cancer.

72. The method of claim 67, wherein the cancer comprises the breast cancer.

73. The method of claim 67, wherein the cancer comprises the bladder cancer.

74. The method of claim 67, wherein the cancer comprises the gastric cancer.

75. The method of claim 67, wherein the cancer comprises the esophageal cancer.

76. The method of claim 67, wherein the cancer comprises the uterine cancer.

77. The method of claim 67, wherein the cancer comprises the endometrial cancer.

78. The method of claim 67, wherein the cancer comprises the kidney cancer.

79. The method of any one of claims 67 to 78, wherein the cancer comprises a stage of the cancer.

80. The method of claim 79, wherein the stage of the cancer comprises stage I cancer, stage II cancer, stage III cancer, or stage IV cancer.

81. The method of claim 66, wherein the one or more analytes comprises potassium.

82. The method of claim 66, wherein the one or more analytes comprises hemoglobin.

83. The method of claim 66, wherein the machine learning model comprises a logistic regression model.

84. The method of claim 66, wherein the regression model comprises a logistic regression model.

85. The method of claim 66, wherein an amount of at least one protein of the one or more proteins comprises a correlation with the one or more analyte measurements.

86. The method of claim 85, wherein the regression model is configured to control for the correlation between the amount of the at least one protein and the one or more analyte measurements.

87. The method of claim 85, further comprising determining a strength of the correlation between the amount of the at least one protein and the one or more analyte measurements.

88. The method of claim 87, wherein the strength of the correlation is indicative of a quality of the plasma sample.Atty Dkt No.: 49407-79160189. The method of claim 66, wherein the regression model is configured to control for a correlation between an amount of at least one protein of the one or more proteins and one or both of age and sex.

90. The method of claim 66, further comprising obtaining a methylation score associated with the plasma sample.

91. The method of claim 90, wherein the machine learning model is trained to detect the presence or the absence of the indication in the subject based at least in part on the methylation score.

92. A computer system for detecting an indication in a subject, the system comprising:(a) a non-transitory memory; and(b) a processor in communication with the non-transitory memory, the processor configured to execute the following operations in order to effectuate a method comprising the operations of:(i) obtaining a plasma sample obtained or derived from the subject;(ii) measuring an amount of one or more proteins in the plasma sample, thereby providing a protein profile of the subject;(iii) measuring an amount of one or more analytes in the plasma sample, wherein the one or more analytes comprises hemoglobin, potassium (K), lactate, or formaldehyde (CH2O), thereby providing one or more analyte measurements;(iv) computer processing the protein profile and the one or more analyte measurements using a regression model; and(iv) computer processing the protein profile using a machine learning model trained to detect a presence or an absence of the indication in the subject, based at least in part on an output of the regression model.

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