Disease detection devices

Abstract

A method for analyzing and detecting a disease or condition in a subject is described herein, the device comprising: a sample collection container for collecting a sample; a filtration unit in fluid communication with the sample collection container, the filtration unit comprising at least one filter for filtering the sample to generate a filtrate; and a filtrate collection container in fluid communication with the filtration unit for collecting the filtrate and contacting the filtrate with a preservative. A method for preserving a sample for detection of a disease or condition in a subject is also described herein.

Description

[Technical Field]

[0001] cross reference This application claims the benefit of U.S. Provisional Application No. 63 / 317,794, filed March 8, 2022, U.S. Provisional Application No. 63 / 333,711, filed April 22, 2022, U.S. Provisional Application No. 63 / 390,929, filed July 20, 2022, and U.S. Provisional Application No. 63 / 428,897, filed November 30, 2022, the contents of which are hereby incorporated by reference. [Background technology]

[0002] Cancer is one of the most prevalent diseases, affecting millions of people. For example, approximately 1 in 8 women in the United States will develop invasive breast cancer during their lifetime. Early detection and treatment can increase the survival rate of cancer patients. However, cancer detection can be tricky, and test results are prone to false positives or false negatives. Summary of the Invention

[0003] Thus, there remains a need for methods for detecting diseases such as cancer in biological samples that efficiently and accurately obtain, process, and analyze the biological samples. In some aspects, described herein are devices for collecting and stabilizing an analyte in a sample, the devices comprising: a sample collection container for collecting the sample; a filtration unit in fluid communication with the sample collection container, the filtration unit comprising at least one filter for filtering the sample to produce a filtrate; and a filtrate collection container in fluid communication with the filtration unit for collecting the filtrate and contacting the filtrate with a preservative. In some embodiments, the filtration unit comprises multiple filters arranged in order of pore size that successively passes smaller species. In some embodiments, the filtration unit comprises at least two filters of the same type. In some embodiments, the device comprises a prefiltration mechanism to prevent clogging of the smaller pore size filters. In some embodiments, the filtration unit comprises a single filter. In some embodiments, at least one filter comprises a depth filter. In some embodiments, at least one filter comprises an asymmetric filter. In some embodiments, at least one filter comprises a microporous filter. In some embodiments, at least one filter comprises a low nucleic acid binding material. In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 2 μm, at least about 3 μm, at least about 4 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, at least about 25 μm, at least about 30 μm, at least about 35 μm, at least about 40 μm, at least about 45 μm, at least about 50 μm, at least about 55 μm, at least about 60 μm, at least about 65 μm, at least about 70 μm, at least about 75 μm, at least about 80 μm, at least about 85 μm, at least about 90 μm, at least about 95 μm, or at least about 100 μm.In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 2 μm, at most about 3 μm, at most about 4 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, at most about 25 μm, at most about 30 μm, at most about 35 μm, at most about 40 μm, at most about 45 μm, at most about 50 μm, at most about 55 μm, at most about 60 μm, at most about 65 μm, at most about 70 μm, at most about 75 μm, at most about 80 μm, at most about 85 μm, at most about 90 μm, at most about 95 μm, or at most about 100 μm. In some embodiments, the size cutoff of at least one filter is between about 0.1 μm and about 100 μm. In some embodiments, the thickness of the filter is from about 50 μm to about 1000 μm, or from about 50 μm, about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, or about 950 μm to about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, up to about 950 μm, or about 100 μm, for example, about 355 μm to about 560 μm, for example, about 330 μm, for example, about 120 μm to about 170 μm, for example, about 230 μm to about 270 μm, for example, about 480 μm to about 640 μm. In some embodiments, the filter diameter is from about 10 mm to about 50 mm, such as from about 10 mm, about 15 mm, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, or about 45 mm to about 15 mm, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, about 45 mm, or about 50 mm.In some embodiments, the filtration unit has a diameter of about 120 μm, about 150 μm, about 175 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, about 950 μm, about 1000 μm, From about 1250 μm, about 1500 μm, about 1750 μm, about 2000 μm, about 2500 μm, about 3000 μm, about 3500 μm, about 4000 μm, about 4500 μm, about 5000 μm, about 5500 μm, about 6000 μm, about 6500 μm, about 7000 μm, about 7500 μm, about 8000 μm, about 8500 μm, about 9000 μm or about 9500 μm to about 150 μm, Approximately 175μm, approximately 200μm, approximately 250μm, approximately 300μm, approximately 350μm, approximately 400μm, approximately 450μm, approximately 500μm, approximately 550μm, approximately 600μm, approximately 650μm, approximately 70 0μm, about 750μm, about 800μm, about 850μm, about 900μm, about 950μm, about 1000μm, about 1250μm, about 1500μm, about 1750μm, about 2000μm, about 2 Filter stack heights include about 120 μm to about 10,000 μm, such as about 500 μm, about 3,000 μm, about 3,500 μm, about 4,000 μm, about 4,500 μm, about 5,000 μm, about 5,500 μm, about 6,000 μm, about 6,500 μm, about 7,000 μm, about 7,500 μm, about 8,000 μm, about 8,500 μm, about 9,000 μm, about 9,500 μm, or about 10,000 μm. In some embodiments, at least one filter is hydrophilic or hydrophobic. In some embodiments, at least one filter is comprised of polysulfone and / or polypropylene. In some embodiments, at least one filter retains a plurality of white blood cells. In some embodiments, at least one filter retains a plurality of red blood cells. In some embodiments, at least one filter retains a plurality of cells derived from solid tissue. In some embodiments, at least one filter retains a plurality of microorganisms. In some embodiments, at least one filter is constructed from a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, at least one filter is constructed from a biological material.In some embodiments, at least one filter does not contain biological material. In some embodiments, the biological material comprises cellulose. In some embodiments, the sample is a biological fluid including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the filtrate is a cell-free biological fluid or a cell-depleted biological fluid. In some embodiments, the filtrate is a cell-free plasma or a cell-depleted plasma. In some embodiments, the filtrate is a cell-free saliva or a cell-depleted saliva. In some embodiments, the filtrate is a cell-free urine or a cell-depleted urine. In some embodiments, the device further comprises a mechanism for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through at least one of the at least one filter. In some embodiments, the mechanism is for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through all filters in the device. In some embodiments, the mechanism comprises a plunger that engages with the sample collection container to force the sample through the filtration unit and into the filtrate collection container. In some embodiments, the plunger is integral with or separate from the sample collection container. In some embodiments, the sample collection container includes a funnel, which is integral with or connectable to the sample collection container. In some embodiments, the filtrate collection container includes a preservative. In some embodiments, the preservative includes at least one of the following: ethylenediaminetetraacetic acid (EDTA); an RNase inhibitor; an antimicrobial agent; a denaturant; an agent that inhibits nuclease activity; a sequestrant; a buffer; a salt; an osmotic agent; or a combination thereof. In some embodiments, the denaturant includes a nucleic acid denaturant or a protein denaturant. In some embodiments, the agent that inhibits nuclease activity includes one or more protein denaturants, EDTA, a detergent such as SDS, aurintricarboxylic acid (ATA), a chelating agent, or a combination thereof.In some embodiments, the one or more protein denaturants comprise one or more chaotropic agents, including a surfactant, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more protein denaturants comprise guanidine thiocyanate at a concentration of about 30% to about 70%. In some embodiments, the filtrate collection container is removable from the filtration unit. In some embodiments, the device further comprises a cap for the removed filtrate collection container. In some embodiments, the cap comprises a housing for storing a preservative that is released upon securing the cap onto the removed filtrate collection container. In some embodiments, the device further comprises a second container for decanting the filtrate. In some embodiments, the second container comprises a preservative. In some embodiments, the device stabilizes the analytes at a temperature range of about -20°C to about 50°C. In some embodiments, the analytes are stabilized for at least 5 days. In some embodiments, at least one analyte comprises a cell-free analyte. In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, the nucleic acid comprises cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell. In some embodiments, the filtration unit is removable to collect the retentate so that biomolecules and cells contained in the retentate can be preserved and analyzed. In some embodiments, the at least one filter reduces the viscosity of the filtrate and compares it to the sample. In some embodiments, the at least one filter removes mucin from the sample.

[0004] In some aspects, described herein are kits that include a device described herein and at least one of: a plunger; a cap for a filtrate collection container; a funnel; and / or a preservative.

[0005] In some aspects, methods are described herein for stabilizing an analyte in a sample, the methods comprising adding the sample to a device described herein or a sample collection container of a kit described herein, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

[0006] In some aspects, described herein are analytes that are stabilized by the methods described herein.

[0007] In some embodiments, the disclosure herein provides a method for detecting the presence of a medical condition or disease in a subject, the method comprising: (a) optionally collecting a bodily fluid sample from a subject; (b) optionally preserving the sample at the time of collection by adding a preservative; (c) optionally fractionating the sample; (d) optionally adding a preservative to the fractionated sample; (e) selecting one or more analytes in the sample, including but not limited to nucleic acid transcripts or genomic regions of interest; (f) qualitatively or quantitatively detecting the selected analytes in an assay, which may involve techniques including but not limited to biomolecule generation, biomolecule enrichment, biomolecule sequencing, PCR, quantitative PCR, isothermal amplification, mass spectrometry, antibody-based detection, or CRISPR and CRISPR-Cas systems, thereby generating data; and (g) analyzing the data using a computer to detect the presence or absence of the medical condition or disease, or analyzing the data to generate a likelihood score for the medical condition or disease.

[0008] Disclosed herein are methods for detecting a disease or condition in a subject, the methods optionally comprising: collecting a sample from the subject; detecting the presence of at least one analyte in the sample or measuring the abundance of at least one analyte in the sample; and generating a score for the likelihood of the subject having or developing the disease or condition, wherein the sample is collected from a sampling site different from the site of the disease or condition, and the abundance of the at least one analyte in the sample correlates with the presence of the at least one analyte at the site of the disease or condition, the abundance of the at least one analyte, or the outcome of the disease or condition. In some embodiments, the method comprises storing the sample prior to b). In some embodiments, storing the sample comprises contacting the sample with a preservative comprising at least one of the following: ethylenediaminetetraacetic acid (EDTA); an RNase inhibitor; an antimicrobial agent; a denaturant; an agent that inhibits nuclease activity; a sequestrant; a buffer; a salt; an osmolality agent; or a combination thereof. In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. In some embodiments, the method further comprises fractionating the sample prior to b). In some embodiments, the fractionating comprises separating the sample into two or more sample subsets. In some embodiments, at least one of the two or more sample subsets comprises a cell-containing fraction, wherein the cell-containing fraction comprises cells from the subject or cells not from the subject. In some embodiments, the cells from the subject are human cells. In some embodiments, the cells not from the subject are non-human cells. In some embodiments, the non-human cells comprise microbial cells. In some embodiments, the non-human cells comprise bacterial cells. In some embodiments, the non-human cells comprise mycelial cells. In some embodiments, the non-human cells comprise archaeal cells. In some embodiments, at least one of the two or more sample subsets comprises a cell-free fraction. In some embodiments, the fractionating comprises centrifuging the sample or filtering the sample. In some embodiments, the sample comprises a bodily fluid sample.In some embodiments, the bodily fluid sample comprises a saliva sample. In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, at least one analyte comprises a cell-free analyte. In some embodiments, the nucleic acid comprises cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell. In some embodiments, step b) comprises sequencing at least one analyte comprising at least one nucleic acid. In some embodiments, step b) comprises hybridizing at least one analyte, such as a nucleic acid, with a probe. In some embodiments, the disease or condition is cancer. In some embodiments, the cancer is breast cancer. In some embodiments, the disease or condition is a neurological disorder. In some embodiments, the disease or condition is an autoimmune disease. In some embodiments, the disease or condition is a metabolic disease. In some embodiments, the disease or condition is an endocrine disease. In some embodiments, the disease or condition is a gastrointestinal disease. In some embodiments, the disease or condition is an injury. In some embodiments, the disease or condition is pregnancy. In some embodiments, the score determines the origin of the disease or condition. In some embodiments, at least one analyte is the subject's DNA or cell-free saliva RNA, and both genetic and transcriptomic analysis are used to detect the presence of a disease or condition in a subject. In some embodiments, two or more samples are collected from a patient, and the samples are processed using different versions of the workflow described herein.

[0009] Also disclosed herein are methods for stabilizing nucleic acids in a sample, the methods comprising: a) filtering the sample so that the nucleic acids flow through the filter and remain in the filtrate; and b) adding one or more protein denaturants to the sample and / or filtrate to inhibit nuclease activity. In some embodiments, the nucleic acids in the sample are purified. In some embodiments, the sample is a biological fluid, including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the sample is filtered before adding one or more protein denaturants. In some embodiments, the sample is collected in or transferred to a device comprising a filtration unit comprising at least one filter, the device comprising: a prefiltration mechanism to prevent clogging of smaller pore size filters; one or more filters; multiple filters arranged in order with pore sizes that allow successively smaller species to pass; or at least two filters of the same type; and a mechanism for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through at least one filter. In some embodiments, filtration is achieved using depth filtration. In some embodiments, the method further comprises using a filtrate collection container pre-filled with one or more denaturing agents that enable rapid inactivation of nucleases upon contact with the filtrate. In some embodiments, the filtered sample is removed from the device or decanted into a second container. In some embodiments, the filtered sample is removed from the device or decanted into a second container containing a preservative. In some embodiments, the collection unit or filtration unit is separated from the post-filtration filtrate collection container to allow for the addition of a preservative. In some embodiments, the preservative is stored in a housing within the cap and released upon securing the cap onto the removed filtrate collection container. In some embodiments, the nucleic acids are stabilized at a temperature range of about -20°C to about 50°C. In some embodiments, the nucleic acids are stabilized for at least 5 days. In some embodiments, at least one filter comprises a low nucleic acid binding material.In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, or at least about 25 μm. In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, or at most about 25 μm. In some embodiments, the size cutoff of at least one filter is between about 0.1 μm and about 25 μm. In some embodiments, at least one filter retains a plurality of white blood cells. In some embodiments, at least one filter retains a plurality of red blood cells. In some embodiments, at least one filter retains a plurality of cells derived from solid tissue. In some embodiments, at least one filter retains a plurality of microorganisms. In some embodiments, at least one filter is constructed of a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, at least one or the filters are comprised of biological material. In some embodiments, filtering produces a cell-free or cell-depleted biological fluid. In some embodiments, filtering produces a cell-free or cell-depleted plasma. In some embodiments, filtering produces a cell-free or cell-depleted saliva. In some embodiments, filtering produces a cell-free or cell-depleted urine. In some embodiments, the nucleic acid is RNA. In some embodiments, the nucleic acid is DNA. In some embodiments, the one or more denaturing agents include one or more chaotropic agents, including surfactants, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 30% to about 70%. In some embodiments, the retentate can be collected, and the biomolecules and cells contained within the retentate can be preserved and analyzed.

[0010] Disclosed herein is an apparatus for detecting a disease or condition in a subject, the apparatus comprising a computer system comprising a hardware processor and a memory wherein instructions are coded to cause the hardware processor to perform the operations of: detecting the presence of at least one analyte or measuring the abundance of at least one analyte in a sample obtained from the subject; and generating a score for the likelihood of the subject having or developing the disease or condition, wherein the sample is obtained from a sampling site distinct from the site of the disease or condition, and wherein the presence of the at least one analyte or the abundance of the at least one analyte in the sample correlates with the presence of the at least one analyte, the abundance of the at least one analyte, or the outcome of the disease or condition at the site of the disease or condition.

[0011] Disclosed herein are methods for detecting a disease or condition in a subject, the method comprising obtaining a sample from a subject using a computer system comprising a hardware processor and a memory wherein instructions are coded to cause the hardware processor to perform the following operations: detect the presence of at least one analyte in the sample or measure the abundance of at least one analyte, and generate a score for the likelihood of the subject having or developing the disease or condition, wherein the sample is taken from a sampling site distinct from the site of the disease or condition, and wherein the presence of the at least one analyte or the abundance of the at least one analyte in the sample correlates with the presence of the at least one analyte, the abundance of the at least one analyte, or the outcome of the disease or condition at the site of the disease or condition.

[0012] Disclosed herein are methods for detecting a disease or condition in a subject, the methods comprising: a) detecting the presence of at least one analyte or measuring the abundance of at least one analyte in a sample from the subject; and b) generating a score for the likelihood of the subject having or developing the disease or condition, wherein the sample is from a sampling site different from the site of the disease or condition, and the presence of the at least one analyte or the abundance of the at least one analyte in the sample correlates with the presence of the at least one analyte, the abundance of the at least one analyte at the site of the disease or condition, or the outcome of the disease or condition. In some embodiments, prior to a), the method comprises storing the sample. In some embodiments, storing the sample comprises contacting the sample with a preservative comprising at least one of the following: ethylenediaminetetraacetic acid (EDTA); an RNase inhibitor; an antimicrobial agent; a denaturant; an agent that inhibits nuclease activity; a sequestrant; a buffer; a salt; an osmolality agent; or a combination thereof. In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. In some embodiments, prior to a), the method further comprises fractionating the sample. In some embodiments, fractionating comprises separating the sample into two or more sample subsets. In some embodiments, at least one of the two or more sample subsets comprises a cell-containing fraction, wherein the cell-containing fraction comprises cells from the subject or cells not from the subject. In some embodiments, the cells from the subject are human cells. In some embodiments, the cells not from the subject are non-human cells. In some embodiments, the non-human cells comprise microbial cells. In some embodiments, the non-human cells comprise bacterial cells. In some embodiments, the non-human cells comprise mycelial cells. In some embodiments, the non-human cells comprise archaeal cells. In some embodiments, at least one of the two or more sample subsets comprises a cell-free fraction. In some embodiments, fractionating comprises centrifuging the sample or filtering the sample. In some embodiments, the sample comprises a biological fluid.In some embodiments, the biological fluid comprises blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the biological fluid comprises saliva. In some embodiments, at least one analyte comprises a cell-free analyte. In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, the nucleic acid comprises cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell. In some embodiments, a) comprises sequencing at least one analyte, and at least one analyte comprises at least one nucleic acid. In some embodiments, a) comprises hybridizing at least one nucleic acid to a probe. In some embodiments, the disease or condition is cancer. In some embodiments, the cancer is breast cancer. In some embodiments, the disease or condition is a neurological disease. In some embodiments, the disease or condition is an autoimmune disease. In some embodiments, the disease or condition is a metabolic disease. In some embodiments, the disease or condition is an endocrine disease. In some embodiments, the disease or condition is a gastrointestinal disease. In some embodiments, the disease or condition is an injury. In some embodiments, the disease or condition is pregnancy. In some embodiments, the score determines the origin of the disease or condition. In some embodiments, at least one analyte is the subject's DNA or cell-free saliva RNA, and both genetic and transcriptomic analysis are used to detect the presence of a disease or condition in a subject.In some embodiments, multiple samples from a subject are processed using different versions of the workflow described herein. In some embodiments, the method further comprises obtaining a sample from the subject.

[0013]

[0003] Disclosed herein are methods for detecting a disease or condition in a subject, the method comprising using a computer system comprising a hardware processor and a memory having instructions encoded thereon to cause the hardware processor to perform the following operations: detect the presence of at least one analyte or measure the abundance of at least one analyte in a sample from the subject, and generate a score related to the likelihood of the subject having or developing the disease or condition, wherein the sample is collected from a sampling site distinct from the site of the disease or condition, and the presence of the at least one analyte or the abundance of the at least one analyte in the sample correlates with the presence of the at least one analyte, the abundance of the at least one analyte at the site of the disease or condition, or an outcome of the disease or condition. In some embodiments, the method further comprises generating a machine learning model iteratively trained to detect the disease or condition in the sample. In some embodiments, the method further comprises generating a machine learning model iteratively trained to generate a score related to the likelihood of the subject having the disease or condition. In some embodiments, the method further comprises generating a machine learning model iteratively trained to generate a score related to the likelihood of the subject developing the disease or condition. In some embodiments, the machine learning model includes at least one of an XGBoost algorithm, a logistic regression model, and a random forest algorithm.

[0014] Also described herein is an apparatus for detecting a disease or condition in a subject, the apparatus comprising a computer system comprising a hardware processor and a memory having instructions encoded thereon to cause the hardware processor to perform the following operations: detect the presence of at least one analyte or measure the abundance of at least one analyte in a sample obtained from the subject, and generate a score related to the likelihood of the subject having or developing the disease or condition, wherein the sample is from a sampling site different from the site of the disease or condition, and the presence of the at least one analyte or the abundance of the at least one analyte in the sample correlates with the presence of the at least one analyte, the abundance of the at least one analyte at the site of the disease or condition, or the outcome of the disease or condition. In some embodiments, the hardware processor generates a machine learning model that is iteratively trained to detect the disease or condition in the sample. In some embodiments, the hardware processor generates a machine learning model that is iteratively trained to generate a score related to the likelihood of the subject having the disease or condition. In some embodiments, the hardware processor generates a machine learning model that is iteratively trained to generate a score related to the likelihood of the subject developing the disease or condition. In some embodiments, the machine learning model includes at least one of an XGBoost algorithm, a logistic regression model, and a random forest algorithm.

[0015] Also described herein are devices for collecting and stabilizing an analyte in a sample, the devices comprising: a) a sample collection container for collecting a sample; b) a filtration unit in fluid communication with the sample collection container, the filtration unit comprising at least one filter for filtering the sample to produce a filtrate; c) a filtration unit; and a filtrate collection container in fluid communication with the filtration unit for collecting the filtrate and contacting the filtrate with a preservative. In some embodiments, the filtration unit comprises multiple filters arranged in order of pore size that successively allows smaller species to pass through. In some embodiments, the filtration unit comprises at least two filters of the same type. In some embodiments, the device comprises a prefiltration mechanism to prevent clogging of the smaller pore size filters. In some embodiments, the filtration unit comprises a single filter. In some embodiments, at least one filter comprises a depth filter. In some embodiments, at least one filter comprises an asymmetric filter. In some embodiments, at least one filter comprises a microporous filter. In some embodiments, at least one filter comprises a low nucleic acid binding material. In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 2 μm, at least about 3 μm, at least about 4 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, at least about 25 μm, at least about 30, at least about 35 μm, at least about 40 μm, at least about 45 μm, at least about 50 μm, at least about 55 μm, at least about 60 μm, at least about 65 μm, at least about 70 μm, at least about 75 μm, at least about 80 μm, at least about 85 μm, at least about 90 μm, at least about 95 μm, or at least about 100 μm.In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 2 μm, at most about 3 μm, at most about 4 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, at most about 25 μm, at most about 30 μm, at most about 35 μm, at most about 40 μm, at most about 45 μm, at most about 50 μm, at most about 55 μm, at most about 60 μm, at most about 65 μm, at most about 70 μm, at most about 75 μm, at most about 80 μm, at most about 85 μm, at most about 90 μm, at most about 95 μm, or at most about 100 μm. In some embodiments, the size cutoff of at least one filter is between about 0.1 μm and about 100 μm. In some embodiments, the thickness of the filter is from about 50 μm to about 1000 μm, or from about 50 μm, about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, or about 950 μm to about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, up to about 950 μm, or about 100 μm, for example, about 355 μm to about 560 μm, for example, about 330 μm, for example, about 120 μm to about 170 μm, for example, about 230 μm to about 270 μm, for example, about 480 μm to about 640 μm. In some embodiments, the filter diameter is from about 10 mm to about 50 mm, such as from about 10 mm, about 15 mm, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, or about 45 mm to about 15 mm, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, about 45 mm, or about 50 mm.In some embodiments, the filtration unit has a diameter of about 120 μm, about 150 μm, about 175 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, about 950 μm, about 1000 μm, From about 1250 μm, about 1500 μm, about 1750 μm, about 2000 μm, about 2500 μm, about 3000 μm, about 3500 μm, about 4000 μm, about 4500 μm, about 5000 μm, about 5500 μm, about 6000 μm, about 6500 μm, about 7000 μm, about 7500 μm, about 8000 μm, about 8500 μm, about 9000 μm or about 9500 μm to about 150 μm, Approximately 175μm, approximately 200μm, approximately 250μm, approximately 300μm, approximately 350μm, approximately 400μm, approximately 450μm, approximately 500μm, approximately 550μm, approximately 600μm, approximately 650μm, approximately 70 0μm, about 750μm, about 800μm, about 850μm, about 900μm, about 950μm, about 1000μm, about 1250μm, about 1500μm, about 1750μm, about 2000μm, about 2 Filter stack heights include about 120 μm to about 10,000 μm, such as about 500 μm, about 3,000 μm, about 3,500 μm, about 4,000 μm, about 4,500 μm, about 5,000 μm, about 5,500 μm, about 6,000 μm, about 6,500 μm, about 7,000 μm, about 7,500 μm, about 8,000 μm, about 8,500 μm, about 9,000 μm, about 9,500 μm, or about 10,000 μm. In some embodiments, at least one filter is hydrophilic or hydrophobic. In some embodiments, at least one filter is comprised of polysulfone and / or polypropylene. In some embodiments, at least one filter retains a plurality of white blood cells. In some embodiments, at least one filter retains a plurality of red blood cells. In some embodiments, at least one filter retains a plurality of cells derived from solid tissue. In some embodiments, at least one filter retains a plurality of microorganisms. In some embodiments, at least one filter is constructed from a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, at least one filter is constructed from a biological material.In some embodiments, at least one filter does not contain biological material. In some embodiments, the biological material comprises cellulose. In some embodiments, the sample is a biological fluid including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the filtrate is a cell-free biological fluid or a cell-depleted biological fluid. In some embodiments, the filtrate is a cell-free plasma or a cell-depleted plasma. In some embodiments, the filtrate is a cell-free saliva or a cell-depleted saliva. In some embodiments, the filtrate is a cell-free urine or a cell-depleted urine. In some embodiments, the device further comprises a mechanism for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through at least one of the at least one filter. In some embodiments, the mechanism is for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through all filters in the device. In some embodiments, the mechanism comprises a plunger that engages with the sample collection container to force the sample through the filtration unit and into the filtrate collection container. In some embodiments, the plunger is integral with or separate from the sample collection container. In some embodiments, the sample collection container includes a funnel, which is integral with or connectable to the sample collection container. In some embodiments, the filtrate collection container includes a preservative. In some embodiments, the preservative includes at least one of the following: ethylenediaminetetraacetic acid (EDTA); an RNase inhibitor; an antimicrobial agent; a denaturant; an agent that inhibits nuclease activity; a sequestrant; a buffer; a salt; an osmotic agent; or a combination thereof. In some embodiments, the denaturant includes a nucleic acid denaturant or a protein denaturant. In some embodiments, the agent that inhibits nuclease activity includes one or more protein denaturants, EDTA, a detergent such as SDS, aurintricarboxylic acid (ATA), a chelating agent, or a combination thereof.In some embodiments, the one or more protein denaturants comprise one or more chaotropic agents, including a surfactant, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more protein denaturants comprise guanidine thiocyanate at a concentration of about 30% to about 70%. In some embodiments, the filtrate collection container is removable from the filtration unit. In some embodiments, the device further comprises a cap for the removed filtrate collection container. In some embodiments, the cap comprises a housing for storing a preservative that is released upon securing the cap onto the removed filtrate collection container. In some embodiments, the device further comprises a second container for decanting the filtrate. In some embodiments, the second container comprises a preservative. In some embodiments, the device stabilizes the analytes at a temperature range of about -20°C to about 50°C. In some embodiments, the analytes are stabilized for at least 5 days. In some embodiments, at least one analyte comprises a cell-free analyte. In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, the nucleic acid comprises cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell. In some embodiments, the filtration unit is removable to collect the retentate so that biomolecules and cells contained in the retentate can be preserved and analyzed. In some embodiments, the at least one filter reduces the viscosity of the filtrate and compares it to the sample. In some embodiments, the at least one filter removes mucin from the sample.

[0016] Also described herein are kits that include a device described herein and at least one of: a) a plunger; b) a cap for a filtrate collection container; c) a funnel; and / or a preservative.

[0017] Also described herein are methods for stabilizing an analyte in a sample, the methods comprising adding the sample to a sample collection container of a device or kit described herein, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

[0018] Also described herein are analytes stabilized by the methods described herein.

[0019] Incorporation by Reference 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 herein. [Brief explanation of the drawings]

[0020] [Figure 1] 1 shows a flow diagram providing an overview of the workflow steps.

[0021] [Figure 2] 1 shows a flow diagram providing an overview of the workflow steps.

[0022] [Figure 3] FIG. 1 is a flow chart for identifying tissue-specific and tissue-enriched transcripts present in patient samples.

[0023] [Figure 4]Figure 1 shows that tissue-specific transcripts in saliva demonstrate the detectability of a wide range of diseases. Specifically, the figure shows a hierarchical clustering heat map of tissues and samples, where red represents high-level mRNA overlap between tissue-specific transcripts and cell-free saliva from an individual, and blue represents low-level mRNA overlap.

[0024] [Figure 5] Figure 1 shows that tissue-enriched transcripts in saliva demonstrate the detectability of a wide range of diseases. Specifically, the figure shows a hierarchical clustering heat map of tissues and samples, where red represents high-level mRNA overlap between tissue-enriched transcripts and cell-free saliva from an individual, and blue represents low-level mRNA overlap.

[0025] [Figure 6] A diagram illustrating the contents of whole saliva is shown.

[0026] [Figure 7] A diagram showing the device concept with procedures is shown.

[0027] [Figure 8] A diagram illustrating a preliminary saliva collection and storage experiment is shown. In the experiment, saliva was filtered to remove human cells, followed by the addition of a chaotropic preservative to preserve the resulting cell-free saliva. BioAnalyzer analysis was used to quantify and assess degradation. To assess degradation, ACTB, FOSL2, and NAMPT transcript levels were monitored by RT-qPCR.

[0028] [Figure 9] Preliminary data for the nucleic acid preservation embodiment described herein are presented. Sample filtration provided similar results to removal by centrifugation. Addition of preservative protected transcripts for 4 days.

[0029] [Figure 10]Figure 1 shows a graph illustrating the improvement of human sequence coverage by exome capture. Pilot experiments were performed to demonstrate that exome enrichment using hybrid capture resulted in significantly greater coverage of the GENCODE gene. Future optimization of hybridization conditions and probes may further improve performance.

[0030] [Figure 11] A diagram illustrating the bioinformatics pipeline is shown. Human alignments were performed using hg38. Microbial alignments were performed using the Human Oral Microbiome database.

[0031] [Figure 12] A diagram illustrating the overall view of sequencing reads is shown. Reads were widely spread across samples. HOM reads were consistent across samples, comprising 20% ​​of total reads. The greatest variation was observed for hg38-mapped reads, with an approximately 100-fold difference between maximum and minimum coverage.

[0032] [Figure 13] A diagram illustrating the final reads after alignment is shown. Deduplication reduced the number of reads by approximately 8-fold, but removed amplification bias. "Assigned to gene" represents the final feature.

[0033] [Figure 14A] 1 shows a graph illustrating final feature counts across 332 samples. [Figure 14B] 1 shows a graph illustrating final feature counts across 332 samples.

[0034] [Figure 15A] Two figures illustrating the final feature sequence QC are shown. [Figure 15B]Two figures illustrating sequence QC of the final features are shown. GC profiles were consistent across samples. Coverage across gene lengths was consistent across samples. A bias was observed towards higher coverage at the 5' end of the transcript.

[0035] [Figure 16] A chart representing an NGS study of 301 patients, including 115 breast cancer patients and 186 non-cancer patients, is shown.

[0036] [Figure 17A] Graphs showing classification results are shown. In Figure 17A, the machine learning classifier using 20 cancer and 20 non-cancer cohorts performed well, with an AUC of 0.763. 10-fold cross validation (CV) was performed. [Figure 17B] Graphs showing classification results are shown. In Figure 17B, classification was performed using randomized disease labels. The average AUC over 100 iterations was 0.486, suggesting that the results with accurate labels were non-random. This performance may be generalized across all samples by increasing the number of hg38-mapped reads through assay optimization.

[0037] [Figure 18] 1 shows a table illustrating that gene set enrichment analysis shows multiple gene sets enriched in the cancer group.

[0038] [Figure 19] A diagram showing an ensemble of classifiers including logistic regression, random forest, and XGBoost is shown.

[0039] [Figure 20] A diagram illustrating 10-fold cross-validation performed 100 times with shuffling is shown.

[0040] [Figure 21A]The performance of the logistic regression model using 20 breast cancer patients and 20 non-cancer patients is shown. [Figure 21B] The 157 genes with the highest coefficients contributing to the performance of the classifier are illustrated. Positive coefficients represent genes that are commonly upregulated in cancer patients, while negative coefficients represent downregulation. [Figure 21C] The discrimination of the classifier as a function of feature removal is shown. Discrimination was lost after removing approximately 250 top features.

[0041] [Figure 22] A non-limiting example of a computing device is shown, where the device comprises one or more processors, memory, storage, and a network interface.

[0042] [Figure 23] A non-limiting example of a web / mobile application delivery system is shown, where the system provides a browser-based and / or native mobile user interface.

[0043] [Figure 24] 1 illustrates a non-limiting example of a cloud-based web / mobile application delivery system, where the system comprises elastically load balanced, auto-scaling web server and application server resources, as well as a synchronously replicated database.

[0044] [Figure 25A] Figure 25A shows the improved preservation of nucleic acids in saliva samples. Figure 25A shows the degradation of RNA encoding actin B over a 3 day period. [Figure 25B] Figure 25B shows a schematic outlining a nucleic acid stability study over a 7 day period of the method described herein for preserving nucleic acids, demonstrating improved preservation of nucleic acids in saliva samples. [Figure 25C]Improved preservation of nucleic acids in saliva samples is shown in Figure 25C, which shows an exemplary profile from one donor over a 7-day period, with the filter and preservative combination showing the most stable profile over the 7-day period. [Figure 25D] Figure 25D shows the improved preservation of nucleic acids in saliva samples. Figure 25D shows the preservation of spike-in controls stored over 7 days. [Figure 25E] Figure 25E shows the stability of endogenous transcripts over a 7-day period, demonstrating improved preservation of nucleic acids in saliva samples. DETAILED DESCRIPTION OF THE INVENTION

[0045] overview In some aspects, methods are described herein for detecting a disease or condition in a subject in need thereof. In some aspects, the methods include analyzing a sample obtained from a subject to detect a disease or condition in the subject. In some aspects, the methods include analyzing a sample obtained from a subject to detect or determine the subject's likelihood of developing a disease or condition in the future. In some embodiments, the methods include analyzing a sample for a disease or condition from a location different from where the sample was obtained. For example, a method can detect or determine a subject's likelihood of having or developing breast cancer by analyzing a sample obtained from a non-breast sample (e.g., saliva). In some embodiments, the methods include analyzing nucleic acids, such as RNA transcripts, in the sample. In some embodiments, the methods include preserving nucleic acids in the sample. In some embodiments, preserving nucleic acids in the sample includes inactivating nucleases using at least one denaturant or filtration through at least one filter or a combination thereof. Figures 1-3 show exemplary workflows utilizing the methods described herein for analyzing a sample to detect a disease or condition in a subject. In some embodiments, the methods involve determining the over- or under-expression of a transcript in a sample, where over- or under-expression of a transcript in a sample may correspond to over- or under-expression of the same transcript in a different body location than where the sample was taken. For example, Figures 4 and 5 show that transcripts found in saliva overlap significantly with transcripts found in blood and esophageal mucosal tissue.

[0046] In some embodiments, saliva (also called spit) is an extracellular fluid produced and secreted by salivary glands in the mouth. In some embodiments, in humans, saliva comprises water and solids. In some embodiments, the solids may include salts and buffers. In some embodiments, the solids may also include organic compounds. In some embodiments, the organic compounds may include enzymes and proteins. In some embodiments, the organic compounds may include metabolic products and nitrogenous substances. In some embodiments, the organic compounds may also include hormones and signaling molecules. In some embodiments, the organic compounds may also include nucleic acids. In some embodiments, the nucleic acids may include RNA or DNA. In some embodiments, the RNA or DNA may be derived from apoptotic / necrotic cells or released due to signal transduction. In some embodiments, the solids may also include cells and vesicles. In some embodiments, the cells and vesicles may include extracellular vesicles. In some embodiments, the extracellular vesicles may be actively secreted by cells. In some embodiments, the cells and vesicles may include or be derived from epithelial cells or white blood cells. In some embodiments, the epithelial cells or white blood cells are derived from tissues or blood lining the oral cavity. In some embodiments, the cells and vesicles may comprise or be derived from microorganisms, hi some embodiments, the microorganisms may comprise cells of the oral microbiome.

[0047] In some embodiments, saliva may include acellular components and intact cells. In some embodiments, the acellular components may include salts and buffers, organic compounds, and some cells and vesicles in saliva. In some embodiments, the acellular components may include enzymes and proteins. In some embodiments, the acellular components may include metabolic products and nitrogenous materials. In some embodiments, the acellular components may also include hormones and signaling molecules. In some embodiments, the acellular components may also include nucleic acids. In some embodiments, the acellular components may also include extracellular vesicles.

[0048] In some embodiments, the intact cells may include epithelial cells or leukocytes. In some embodiments, the intact cells may also include microorganisms.

[0049] In some embodiments, the method includes preserving nucleic acids in a sample obtained from a subject. Figures 7 and 8 show various combinations of fractionation processes (e.g., by centrifugation and / or filtration), preservation using denaturing agents, and quality control experiments to preserve and determine the integrity of nucleic acids in a sample. Figures 8 and 9 show experiments measuring the integrity of nucleic acids preserved by the methods described herein. Figure 10 shows a chart comparing the percent GENCODE coverage before and after enrichment.

[0050] In some aspects, methods for analyzing a sample are described herein. In some embodiments, the methods include using computer-implemented methods or machine learning-based algorithms to analyze, train, and improve the disease or condition detection methods described herein (e.g., FIGS. 11-20). FIGS. 16, 17A, 17B, 21A, 21B, and 21C show examples of utilizing the methods described herein for classifying a clinical sample. FIGS. 22-24 show non-limiting examples of computing devices, applications, or systems for utilizing the methods described herein. In some embodiments, the methods increase the sensitivity for detecting a disease or condition in a sample. In some embodiments, the methods increase the specificity for detecting a disease or condition in a sample. In some embodiments, the methods reduce false positives for detecting a disease or condition in a sample. In some embodiments, the methods reduce false negatives for not detecting a disease or condition in a sample. In some embodiments, the methods include generating a classifier based on overexpression or underexpression of transcripts detected in the sample. In some embodiments, the disease or condition is cancer. In some embodiments, the disease or condition is the likelihood of developing cancer.

[0051] In some aspects, described herein are methods for detecting the presence of a medical condition or disease in a subject, the methods including: (a) optionally collecting a bodily fluid sample from a subject; (b) optionally preserving the sample at the time of collection by adding a preservative; (c) optionally fractionating the sample; (d) optionally adding a preservative to the fractionated sample; (e) selecting one or more analytes in the sample, including but not limited to, nucleic acid transcripts or genomic regions of interest; (f) qualitatively or quantitatively detecting the selected analytes in an assay, which may involve techniques including but not limited to biomolecule generation, biomolecule enrichment, biomolecule sequencing, PCR, quantitative PCR, isothermal amplification, mass spectrometry, antibody-based detection, or CRISPR and CRISPR-Cas systems, thereby generating data; and (g) analyzing the data using a computer to detect the presence or absence of the medical condition or disease, or to analyze the data and generate a likelihood score for the medical condition or disease.

[0052] In some embodiments, the sample comprises a bodily fluid sample, also referred to as a biological fluid, hi some embodiments, the bodily fluid sample comprises a saliva sample.

[0053] In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, at least one analyte comprises cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, the nucleic acid comprises mRNA. In some embodiments, the nucleic acid comprises small RNA. In some embodiments, the nucleic acid comprises miRNA. In some embodiments, the nucleic acid comprises snoRNA. In some embodiments, the nucleic acid comprises snRNA. In some embodiments, the nucleic acid comprises rRNA. In some embodiments, the nucleic acid comprises tRNA. In some embodiments, the nucleic acid comprises siRNA. In some embodiments, the nucleic acid comprises hnRNA. In some embodiments, the nucleic acid comprises long non-coding RNA. In some embodiments, the nucleic acid comprises shRNA. Fragments of any such nucleic acids are also contemplated.

[0054] In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite.

[0055] In some embodiments, at least one analyte comprises a small molecule.

[0056] In some embodiments, the at least one analyte comprises a cell.

[0057] Also disclosed herein is a method for stabilizing nucleic acid in a sample, wherein one or more protein denaturants are added to the sample to inhibit nuclease activity.In some embodiments, the nucleic acid is RNA.In some embodiments, the nucleic acid is DNA.In some embodiments, the nucleic acid is purified.

[0058] In some embodiments, the one or more denaturing agents comprise one or more chaotropic agents. In some embodiments, the one or more chaotropic agents comprise a detergent, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more chaotropic agents comprise a detergent. In some embodiments, the one or more chaotropic agents comprise urea. In some embodiments, the one or more chaotropic agents comprise thiourea. In some embodiments, the one or more chaotropic agents comprise guanidine thiocyanate. In some embodiments, the one or more chaotropic agents comprise guanidine hydrochloride.

[0059] In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 30% to about 70%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 40% to about 70%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 50% to about 70%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 60% to about 70%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 30% to about 60%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 30% to about 50%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 30% to about 40%.

[0060] In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 30%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 35%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 40%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 45%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 50%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 55%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 60%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 65%. In some embodiments, the one or more denaturing agents comprise guanidine thiocyanate at a concentration of about 70%.

[0061] In some embodiments, the sample is a biological fluid, hi some embodiments, the biological fluid comprises blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid.

[0062] In some embodiments, the sample is filtered prior to the addition of one or more protein denaturants.

[0063] In some embodiments, the sample is collected or transferred to a device equipped with a filtration unit comprising at least one filter. In some embodiments, the device comprises a prefiltration mechanism to prevent, reduce, or inhibit clogging of smaller pore size filters; one or more filters; multiple filters arranged in sequence with pore sizes that allow successively smaller species to pass; or at least two filters of the same type; and a mechanism for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through at least one filter. In some embodiments, the filtration is achieved using depth filtration. In some embodiments, the mechanism further comprises using a filtrate collection container pre-filled with one or more denaturants that enable rapid inactivation of nucleases upon contact with the filtrate. In some embodiments, the filtered sample is removed from the device or decanted into a second container. In some embodiments, the filtered sample is removed from the device or decanted into a second container containing a preservative. In some embodiments, the collection unit or filtration unit is separated from the filtrate collection container after filtration to allow for the addition of a preservative. In some embodiments, the preservative is stored in a housing within the cap and is released when the cap is secured onto the removed filtrate collection vessel.

[0064] In some embodiments, the nucleic acid is stabilized in a temperature range of about -20°C to about 50°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about -10°C to about 50°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about 0°C to about 50°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about 10°C to about 50°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about 20°C to about 50°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about 30°C to about 50°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about -20°C to about 40°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about -20°C to about 30°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about -20°C to about 20°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about -20°C to about 10°C. In some embodiments, the nucleic acid is stabilized in a temperature range of about -20°C to about 0°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about -10°C.

[0065] In some embodiments, the nucleic acid is stabilized at about -20°C. In some embodiments, the nucleic acid is stabilized at about -15°C. In some embodiments, the nucleic acid is stabilized at about -10°C. In some embodiments, the nucleic acid is stabilized at about -5°C. In some embodiments, the nucleic acid is stabilized at about 0°C. In some embodiments, the nucleic acid is stabilized at about 5°C. In some embodiments, the nucleic acid is stabilized at about 10°C. In some embodiments, the nucleic acid is stabilized at about 15°C. In some embodiments, the nucleic acid is stabilized at about 20°C. In some embodiments, the nucleic acid is stabilized at about 25°C. In some embodiments, the nucleic acid is stabilized at about 30°C. In some embodiments, the nucleic acid is stabilized at about 35°C. In some embodiments, the nucleic acid is stabilized at about 40°C. In some embodiments, the nucleic acid is stabilized at about 45°C. In some embodiments, the nucleic acid is stabilized at about 50°C.

[0066] In some embodiments, the nucleic acid is stabilized for the final 5 days.

[0067] In some embodiments, the nucleic acid is stabilized for the final 1 day. In some embodiments, the nucleic acid is stabilized for the final 2 days. In some embodiments, the nucleic acid is stabilized for the final 3 days. In some embodiments, the nucleic acid is stabilized for the final 4 days. In some embodiments, the nucleic acid is stabilized for at least 5 days.

[0068] In some embodiments, at least one filter comprises a low nucleic acid binding material.

[0069] In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 0.5 μm, at least about 1 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, or at least about 25 μm. In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm. In some embodiments, the size cutoff of at least one filter is at least about 0.5 μm. In some embodiments, the size cutoff of at least one filter is at least about 1 μm. In some embodiments, the size cutoff of at least one filter is at least about 5 μm. In some embodiments, the size cutoff of at least one filter is at least about 10 μm. In some embodiments, the size cutoff of at least one filter is at least about 15 μm. In some embodiments, the size cutoff of at least one filter is at least about 20 μm. In some embodiments, the size cutoff of at least one filter is at least about 25 μm.

[0070] In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 0.5 μm, at most about 1 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, or at most about 25 μm. In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm. In some embodiments, the size cutoff of at least one filter is at most about 0.5 μm. In some embodiments, the size cutoff of at least one filter is at most about 1 μm. In some embodiments, the size cutoff of at least one filter is at most about 5 μm. In some embodiments, the size cutoff of at least one filter is at most about 10 μm. In some embodiments, the size cutoff of at least one filter is at most about 15 μm. In some embodiments, the size cutoff of at least one filter is at most about 20 μm. In some embodiments, the size cutoff of at least one filter is at most about 25 μm.

[0071] In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 20 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 15 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 10 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 9 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 8 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 7 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 6 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 5 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 4 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 3 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 2 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 1 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 0.5 μm. In some embodiments, the size cutoff of at least one filter is about 0.5 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 1 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 2 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 3 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is from about 4 μm to about 25 μm.In some embodiments, the size cutoff of at least one filter is about 5 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 6 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 7 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 8 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 9 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 10 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 15 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 20 μm to about 25 μm.

[0072] In some embodiments, the size cutoff of at least one filter is about 0.1 μm. In some embodiments, the size cutoff of at least one filter is about 0.5 μm. In some embodiments, the size cutoff of at least one filter is about 1 μm. In some embodiments, the size cutoff of at least one filter is about 2 μm. In some embodiments, the size cutoff of at least one filter is about 3 μm. In some embodiments, the size cutoff of at least one filter is about 4 μm. In some embodiments, the size cutoff of at least one filter is about 5 μm. In some embodiments, the size cutoff of at least one filter is about 6 μm. In some embodiments, the size cutoff of at least one filter is about 7 μm. In some embodiments, the size cutoff of at least one filter is about 8 μm. In some embodiments, the size cutoff of at least one filter is about 9 μm. In some embodiments, the size cutoff of at least one filter is about 10 μm. In some embodiments, the size cutoff of at least one filter is about 12 μm. In some embodiments, the size cutoff of at least one filter is about 14 μm. In some embodiments, the size cutoff of at least one filter is about 16 μm. In some embodiments, the size cutoff of at least one filter is about 18 μm. In some embodiments, the size cutoff of at least one filter is about 20 μm. In some embodiments, the size cutoff of at least one filter is about 22 μm. In some embodiments, the size cutoff of at least one filter is about 24 μm. In some embodiments, the size cutoff of at least one filter is about 25 μm.

[0073] In some embodiments, the filter retains a plurality of white blood cells. In some embodiments, the filter retains a plurality of red blood cells. In some embodiments, the filter retains a plurality of cells from solid tissue. In some embodiments, the filter retains a plurality of microorganisms.

[0074] In some embodiments, the filter is constructed from synthetic materials to minimize the introduction of contaminating nucleic acids. In some embodiments, the filter is constructed from biological materials.

[0075] In some embodiments, the filtering produces a cell-free or cell-depleted biological fluid. In some embodiments, the filtering produces a cell-free or cell-depleted plasma. In some embodiments, the filtering produces a cell-free or cell-depleted saliva. In some embodiments, the filtering produces a cell-free or cell-depleted urine.

[0076] In some embodiments, the disease or condition (or medical condition) is cancer. In some embodiments, the type of cancer is a solid cancer or a hematological malignancy. In some embodiments, the type of cancer is a metastatic cancer or a recurrent or refractory cancer. In some embodiments, the type of cancer is acute myeloid leukemia (LAML or AML), acute lymphoblastic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial cancer (BLCA), brain stem glioma, brain lower grade glioma (LGG), brain tumor, breast cancer (BRCA), bronchial tumor, Burkitt's lymphoma, carcinoma of unknown primary site, carcinoid tumor, carcinoma of unknown primary site, central nervous system atypical teratoid tumor / rhabdoid tumor, central nervous system embryonal tumor, cervical squamous cell carcinoma, endocervical adenocarcinoma Adenocarcinoma (CESC), childhood cancer, cholangiocarcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic myeloid leukemia, chronic myeloproliferative disorders, colon (adenocarcinoma) cancer (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine pancreatic islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), esthesioneuroblastoma, Ewing's sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic biliary tract tumor, gallbladder cancer, digestive (gastric) cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioblastoma multiforme glioma, GBM), hairy cell leukemia, head and neck cancercancer (HNSD), cardiac cancer, Hodgkin's lymphoma, hypopharyngeal cancer, intraocular melanoma, pancreatic islet tumor, Kaposi's sarcoma, renal cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytic bone cancer, medulloblastoma, medulloepithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous cell carcinoma of the head and neck of unknown primary origin, oral cancer, multiple endocrine neoplasia, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative disorders, nasal cavity cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cavity cancer Cancer of the oropharynx, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell cancer, ovarian low malignant potential tumor, pancreatic cancer, papilloma, sinus cancer, parathyroid cancer, bone marrow cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), intermediate pineal parenchymal tumor, pineoblastoma, pituitary tumor, plasma cell neoplasm / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma, prostate cancer including prostate adenocarcinoma (PRAD), rectal cancer, renal cancer, renal cell (kidney) cancer, renal cell carcinoma, airway cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma (SARC), Sézary syndrome, skin cutaneous melanoma melanoma, SKCM), small cell lung cancer, small intestine cancer, soft tissue tumor, squamous cell carcinoma, head and neck squamous cell carcinoma, gastric (gastrointestinal) cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer, testicular germ cell tumor (TGCT), head and neck cancer, thymic carcinoma, thymoma (THYM), thyroid cancercancer (THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, choriocarcinoma, ureteral cancer, urethral cancer, uterine cancer, uterine cancer, uveal melanoma (UVM), vaginal cancer, vulvar cancer, Waldenstrom's hypergammaglobulinemia, or Wilms' tumor. In some embodiments, the type of cancer comprises acute lymphocytic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, cholangiocarcinoma (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, renal cancer, liver cancer, lung cancer, lymphoma, melanoma, myeloma, ovarian cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, squamous cell carcinoma, testicular cancer, gastric cancer, or thyroid cancer. In some embodiments, the type of cancer comprises bladder cancer, breast cancer, cervical cancer, cholangiocarcinoma (CHOL), colon cancer, esophageal cancer, head and neck cancer, renal cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, gastric cancer, or thyroid cancer.

[0077] In some embodiments, the disease or condition is breast cancer, hi some embodiments, the cancer is lung cancer, esophageal cancer, or head and neck cancer.

[0078] In some embodiments, the disease or condition is a neurological disease. In some embodiments, the disease or condition is an autoimmune disease. In some embodiments, the disease or condition is a metabolic disease. In some embodiments, the disease or condition is an endocrine disease. In some embodiments, the disease or condition is a gastrointestinal disease.

[0079] In some embodiments, the disease or condition is an injury.

[0080] In some embodiments, the disease or condition is pregnancy.

[0081] Nucleic acid storage In some aspects, methods for preserving nucleic acids are described herein in which a solution containing nucleic acids is protected from degradation by a preservative for extended periods of time without the need for refrigeration. The biological solution may be filtered before the addition of the preservative. Filtration is particularly advantageous in the case of biological fluids, where it can separate cells and leave primarily cell-free nucleic acids for analysis. Nucleases present in biological samples are the primary underlying cause of nucleic acid degradation. Their action can be inhibited by the addition of nuclease inhibitors, as described below.

[0082] In some embodiments, metal chelators such as ethylenediaminetetraacetic acid (EDTA) can inhibit nucleases that depend on divalent cations for their activity, although in some embodiments, this method does not inhibit all nucleases, as there are some nucleases that do not require divalent ions.

[0083] In some embodiments, competitive inhibitors that bind to the enzyme active site may also be used, however, the existence of many different types of nucleases requires a large number of competitive inhibitors, making it difficult to universally inhibit nucleolytic activity using this approach.

[0084] An alternative strategy to prevent nucleic acid degradation is to destroy the nuclease by one of three means: (i) heat denaturation of the nuclease; (ii) digestion of the nuclease with a protease; and (iii) chemical denaturation of the nuclease.

[0085] In some aspects, the method of heat denaturation of nucleases is limited in that some nucleases can revert to their native conformation upon cooling, and the process itself can damage nucleic acids. This method can be particularly slow when sample volumes are large, and the nuclease may have an opportunity to substantially degrade the nucleic acid before it is inactivated.

[0086] In some aspects, digestion of nucleases with proteases can be effective in degrading the nucleases, but due to its inherently slow process, the nucleases may have an opportunity to substantially degrade the nucleic acid before it is completely digested.

[0087] In some embodiments, chemical denaturation of nucleases, such as the addition of agents that denature proteins, can render the nuclease inactive. This strategy has the advantage of completely inhibiting nuclease activity without damaging the nucleic acids until they can be extracted.

[0088] In some embodiments, preserving the sample comprises contacting the sample with a preservative. In some embodiments, the preservative comprises at least one of the following: ethylenediaminetetraacetic acid (EDTA); an RNase inhibitor; an antimicrobial agent; a denaturant; an agent that inhibits nuclease activity; a sequestrant; a buffer; a salt; an osmolyte; or a combination thereof. In some embodiments, the preservative comprises ethylenediaminetetraacetic acid (EDTA). In some embodiments, the preservative comprises an RNase inhibitor. In some embodiments, the preservative comprises an antimicrobial agent. In some embodiments, the preservative comprises a denaturant. In some embodiments, the preservative comprises an agent that inhibits nuclease activity. In some embodiments, the preservative comprises a sequestrant. In some embodiments, the preservative comprises a buffer. In some embodiments, the preservative comprises a salt. In some embodiments, the preservative comprises an osmolyte.

[0089] In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. The denaturant comprises a nucleic acid denaturant. The denaturant comprises a protein denaturant.

[0090] device A device is also described herein, an example of which is shown in Figure 7. The device is for collecting and stabilizing an analyte in a sample, and includes: a) a sample collection container 2 for collecting the sample; b) a filtration unit 3 in fluid communication with the sample collection container 2, the filtration unit 3 including at least one filter for filtering the sample to produce a filtrate; and c) a filtrate collection container 4 in fluid communication with the filtration unit 3 for collecting the filtrate and contacting the filtrate with a preservative.

[0091] In embodiments, filtration unit 2 comprises multiple filters arranged in order by pore size that allows successively smaller species to pass through. In additional or alternative embodiments, filtration unit 2 comprises at least two filters of the same type. In embodiments, the device comprises a prefiltration mechanism to prevent clogging of the smaller pore size filters. For example, filtration unit 2 may comprise a prefiltration mechanism, or the prefiltration mechanism may be separate from the cell filtration unit. In some embodiments, the filtration unit comprises a single filter, two filters, three filters, four filters, five filters, six filters, seven filters, eight filters, nine filters, ten filters, or more filters.

[0092] The filters may be of any known type suitable for filtering biological fluid samples. For example, in embodiments, at least one filter comprises a depth filter, an asymmetric filter, a microporous filter, or a combination thereof. In embodiments, at least one filter comprises a low nucleic acid binding material.

[0093] In embodiments, at least one filter may have a size cutoff selected to exclude or pass any desired components based on size. For example, in embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 2 μm, at least about 3 μm, at least about 4 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, at least about 25 μm, at least about 30 μm, at least about 35 μm, at least about 40 μm, at least about 45 μm, at least about 50 μm, at least about 55 μm, at least about 60 μm, at least about 65 μm, at least about 70 μm, at least about 75 μm, at least about 80 μm, at least about 85 μm, at least about 90 μm, at least about 95 μm, or at least about 100 μm. In additional or alternative embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 2 μm, at most about 3 μm, at most about 4 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, at most about 25 μm, at most about 30 μm, at most about 35 μm, at most about 40 μm, at most about 45 μm, at most about 50 μm, at most about 55 μm, at most about 60 μm, at most about 65 μm, at most about 70 μm, at most about 75 μm, at most about 80 μm, at most about 85 μm, at most about 90 μm, at most about 95 μm, or at most about 100 μm. For example, in embodiments, the size cutoff of at least one filter is between about 0.1 μm and about 100 μm.

[0094] The at least one filter may similarly have any desired thickness. For example, in embodiments, the thickness of the filter may range from about 50 μm to about 1000 μm, or from about 50 μm, about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, or about 950 μm to about 100 μm, about 150 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, up to about 950 μm, or about 100 μm, for example, about 355 μm to about 560 μm, for example, about 330 μm, for example, about 120 μm to about 170 μm, for example, about 230 μm to about 270 μm, for example, about 480 μm to about 640 μm.

[0095] Two or more filters can be stacked together, resulting in a stack height that is the thickness of the combined filters. In embodiments, the filtration unit 2 has a thickness of about 120 μm, about 150 μm, about 175 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, about 950 μm, about 1000 μm, about 1200 μm, about 1500 μm, about 1750 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 650 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, about 950 μm, about 1000 μm, about 1200 μm, about 1500 μm, about 1600 μm, about 1750 μm, about 1800 μm, about 1900 μm, about 2000 μm, about 2100 μm, about 2200 μm, about 2300 μm, about 2400 μm, about 2500 μm, about 2600 μm, about 2700 μm, about 2800 μm, about 2900 μm, about 300 50 μm, about 1500 μm, about 1750 μm, about 2000 μm, about 2500 μm, about 3000 μm, about 3500 μm, about 4000 μm, about 4500 μm, about 5000 μm, about 5500 μm, about 6000 μm, about 6500 μm, about 7000 μm, about 7500 μm, about 8000 μm, about 8500 μm, about 9000 μm or about 9500 μm to about 150 μm, about 1 75μm, about 200μm, about 250μm, about 300μm, about 350μm, about 400μm, about 450μm, about 500μm, about 550μm, about 600μm, about 650μm, about 700μ m, approximately 750μm, approximately 800μm, approximately 850μm, approximately 900μm, approximately 950μm, approximately 1000μm, approximately 1250μm, approximately 1500μm, approximately 1750μm, approximately 2000μm, approximately 25 The present invention also includes filter stack heights from about 120 μm to about 10,000 μm, such as about 3000 μm, about 3500 μm, about 4000 μm, about 4500 μm, about 5000 μm, about 5500 μm, about 6000 μm, about 6500 μm, about 7000 μm, about 7500 μm, about 8000 μm, about 8500 μm, about 9000 μm, about 9500 μm or about 10,000 μm.

[0096] Most filters are circular and have a diameter. While filters can be of any shape, they are typically circular and have a diameter of about 10 mm to about 50 mm, such as about 10 mm, about 15 mm, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, or about 45 mm, to about 15 mm, about 20 mm, about 25 mm, about 30 mm, about 35 mm, about 40 mm, about 45 mm, or about 50 mm.

[0097] In embodiments, the at least one filter is hydrophilic or hydrophobic. In additional or alternative embodiments, the at least one filter is composed of polysulfone and / or polypropylene. In some embodiments, the at least one filter is composed of a synthetic material to minimize the introduction of contaminating nucleic acids. In other embodiments, the at least one filter is composed of biological materials, such as cellulose, or is free of biological materials. In some embodiments, contaminating components from biological materials, such as contaminating nucleic acids that may be found in cellulose or other biological materials, can confound results, especially when isolating or stabilizing low-abundance analytes. Therefore, in some embodiments, it is advantageous to avoid the use of biological materials in the filter material.

[0098] In embodiments, at least one filter retains a plurality of white blood cells. In further or alternative embodiments, at least one filter retains a plurality of red blood cells. In further or alternative embodiments, at least one filter retains a plurality of solid tissue-derived cells. In further or alternative embodiments, at least one filter retains a plurality of microorganisms.

[0099] The sample is typically a biological fluid, which can include any bodily fluid, examples of which include blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid.

[0100] The filtrate collected by the device, in embodiments, is a cell-free or cell-depleted biological fluid. In embodiments, the filtrate is a cell-free or cell-depleted plasma. In embodiments, the filtrate is a cell-free or cell-depleted saliva. In embodiments, the filtrate is a cell-free or cell-depleted urine.

[0101] In embodiments, the device further comprises a mechanism for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through at least one of the at least one filter. In embodiments, the mechanism is for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through all filters in the device. In some embodiments, the mechanism comprises a plunger 5 that engages with the sample collection container 2 to force the sample through the filtration unit 3 and into the filtrate collection container 4. In embodiments, the plunger 5 is integral with the sample collection container 2 or is separate from the sample collection container 2.

[0102] In an embodiment, the sampling container 2 comprises a funnel 1 , which is integral with the sampling container 2 or which can be connected to the sampling container 2 .

[0103] In some embodiments, the filtrate collection container 4 includes a preservative 6. In other embodiments, the preservative 6 is provided separately, for example, in its own container. It will be appreciated that the preservative may be added to the sample before it is placed in the sample collection container 2, may be added to the sample in the sample collection container 2, may be already present in the filtrate collection container 4, or may be added separately to the filtrate collection container 4 before, during, or after the filtrate is collected in the filtrate collection container. For example, in embodiments, the filtrate collection container 4 is removable from the filtration unit 2. In embodiments, the device further includes a cap 7 for the removed filtrate collection container 2. In some embodiments, the cap 7 includes a housing for storing the preservative 6, which is released when the cap 7 is secured onto the removed filtrate collection container 4. In additional or alternative embodiments, the device further includes a second container for decanting the filtrate. In embodiments, the second container includes the preservative 6.

[0104] Any preservative may be used as understood by those skilled in the art. In some embodiments, the preservative comprises at least one of the following: ethylenediaminetetraacetic acid (EDTA); RNase inhibitors; antimicrobial agents; denaturants; agents that inhibit nuclease activity; sequestrants; buffers; salts; osmolytes; or combinations thereof. For example, in embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. In some embodiments, the agent that inhibits nuclease activity comprises one or more protein denaturants, a detergent such as EDTA, SDS, aurintricarboxylic acid (ATA), a chelating agent, or a combination thereof. In embodiments, the one or more protein denaturants comprise one or more chaotropic agents, including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more protein denaturants comprise guanidine thiocyanate at a concentration of about 30% to about 70%, such as from about 30%, about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, or about 65%, to about 35%, about 40%, about 45%, about 50%, about 55%, about 60%, about 65%, or about 70%.

[0105] In embodiments, the device stabilizes the analyte at a desired temperature range, such as freezing, refrigerator, room, body temperature, or temperatures above body temperature. For example, in embodiments, the device stabilizes the analyte at a temperature between about -20°C and about 50°C, such as from about -20°C, about -10°C, about -5°C, about 0°C, about 2°C, about 4°C, about 8°C, about 10°C, about 15°C, about 20°C, about 30°C, about 37°C, or about 40°C to about -10°C, about -5°C, about 0°C, about 2°C, about 4°C, about 8°C, about 10°C, about 15°C, about 20°C, about 30°C, about 37°C, about 40°C, or about 50°C.

[0106] In embodiments, the device stabilizes the analyte for at least 5 days, such as at least 1 day, at least 2 days, at least 3 days, at least 4 days, at least 5 days, at least 6 days, at least 7 days, at least 10 days, at least 2 weeks, at least 1 month, at least 2 months, at least 3 months, at least 6 months, at least 1 year or more.

[0107] In embodiments, the at least one analyte comprises a cell-free analyte.

[0108] In embodiments, at least one analyte comprises a nucleic acid. Any nucleic acid or fragment of a nucleic acid is contemplated. For example, in embodiments, the nucleic acid comprises cell-free RNA. In embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof.

[0109] In embodiments, at least one analyte comprises a polypeptide. In embodiments, the polypeptide is a protein. In further or alternative embodiments, the polypeptide is a metabolite.

[0110] In embodiments, at least one analyte comprises a small molecule. In embodiments, at least one analyte comprises a metabolite. In embodiments, at least one analyte comprises a cell.

[0111] In an embodiment, the filtration unit is removable to collect the retentate liquid so that the biomolecules and cells contained therein can be preserved and analyzed.

[0112] In some embodiments, the at least one filter reduces the viscosity of the filtrate compared to the sample. For example, the at least one filter may remove components of the sample that contribute to viscosity, such as mucin.

[0113] In embodiments, the devices described herein are provided as a kit, which in embodiments includes the device including the sample collection container, the filtration unit, and the filtrate collection container, along with at least one additional component, such as a plunger, a cap for the filtrate collection container, a funnel, a preservative, and / or instructions for use.

[0114] Also described herein are methods for stabilizing an analyte in a sample, the methods including adding the sample to a sample collection container of a device or kit described herein, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

[0115] When the above methods are used to stabilize an analyte, the stabilized analyte is collected, and therefore, the analyte stabilized by the methods is also described herein.

[0116] Computing System 22, a block diagram is shown illustrating an exemplary machine including an executing computer system 2700 (e.g., a processing or computing system) on which a set of instructions is executable to cause the device to perform or execute any one or more of the aspects and / or methodologies for static code scheduling of the present disclosure. The components of FIG. 22 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic element, or combination of two or more such elements to implement a particular embodiment.

[0117] The computer system 2700 may include one or more processors 2701, memory 2703, and storage 2708, which communicate with each other and with other components via a bus 2740. The bus 2740 may also link a display 2732, one or more input devices 2733 (which may include, for example, a keypad, keyboard, mouse, stylus, etc.), one or more output devices 2734, one or more storage devices 2735, and various tangible storage media 2736. All of these elements may interface to the bus 2740 directly or through one or more interfaces or adapters. For example, the various tangible storage media 2736 may interface with the bus 2740 through a storage media interface 2726. The computer system 2700 may have any suitable physical form, including, but not limited to, one or more integrated circuits (ICs), a printed circuit board (PCB), a mobile handheld device (such as a mobile phone or PDA), a laptop or notebook computer, a distributed computer system, computing device, or server.

[0118] Computer system 2700 includes one or more processors 2701 (e.g., a central processing unit (CPU) or a general-purpose graphics processing unit (GPGPU)) that perform functions. Processor 2701 optionally contains a cache memory unit 2702 for temporary local storage of instructions, data, or computer addresses. Processor 2701 is configured to support the execution of computer-readable instructions. Computer system 2700 may provide the functionality of the components depicted in FIG. 23 as a result of processor 2701 executing non-transitory processor-executable instructions embodied in one or more tangible computer-readable storage media, such as memory 2703, storage 2708, storage device 2735, and / or storage medium 2736. The computer-readable medium may store, and processor 2701 may execute, software that implements particular embodiments. Memory 2703 may read software from one or more other computer-readable media (such as mass storage devices 2735, 2736) or from one or more other sources through a suitable interface, such as network interface 2720. The software may cause processor 2701 to perform one or more processes, or one or more steps of one or more processes, described or illustrated herein. Performing such processes or steps may include defining data structures stored in memory 2703 and modifying the data structures as directed by the software.

[0119] The memory 2703 may include various components (e.g., machine-readable media), including, but not limited to, random access memory components (e.g., RAM 2704) (e.g., static RAM (SRAM)), dynamic RAM (DRAM), ferroelectric random access memory (FRAM®), phase change random access memory (PRAM), etc.), read-only memory components (e.g., ROM 2705), and any combination thereof. The ROM 2705 may act to communicate data and instructions unidirectionally to the processor 2701, and the RAM 2704 may act to communicate data and instructions bidirectionally with the processor 2701. The ROM 2705 and RAM 2704 may include any suitable tangible computer-readable media, as described below. In one example, a basic input / output system 2706 (BIOS), containing the basic routines that help transfer information between elements within the computer system 2700, such as during start-up, may be stored in the memory 2703.

[0120] Persistent storage 2708 is optionally coupled bidirectionally to processor 2701 via storage control unit 2707. Persistent storage 2708 provides additional data storage capacity and may also include any suitable tangible computer-readable media described herein. Storage 2708 may be used to store operating system 2709, executables 2710, data 2711, applications 2712 (application programs), etc. Storage 2708 may also include an optical disk drive, a solid-state memory device (e.g., a flash-based system), or any combination of the above. The information in storage 2708 may also be incorporated as virtual memory in memory 2703, where appropriate.

[0121] In one example, storage device(s) 2735 may be removably interfaced to computer system 2700 via storage device interface 2725 (e.g., via an external port connector (not shown)). In particular, storage device(s) 2735 and associated machine-readable media may provide non-volatile and / or volatile storage of machine-readable instructions, data structures, program modules, and / or other data for computer system 2700. In one example, software may reside, completely or partially, within the machine-readable media on storage device 2735. In another example, software may reside, completely or partially, within processor 2701.

[0122] The bus 2740 connects a wide range of subsystems. As used herein, references to a bus may, where appropriate, encompass one or more digital signal lines carrying a common function. The bus 2740 may be any of several types of buses, including, but not limited to, a memory bus, a memory controller, a peripheral bus, a local bus, and any combination thereof, using any of a variety of bus architectures. By way of example and not limitation, such architectures include an Industry Standard Architecture (ISA) bus, an Enhanced ISA (EISA) bus, a Micro Channel Architecture (MCA) bus, a Video Electronics Standards Association local bus (VLB), a Peripheral Component Interconnect (PCI) bus, a PCI-Express (PCI-X) bus, an Accelerated Graphics Port (AGP) bus, a HyperTransport (HTX) bus, a serial advanced technology attachment (SATA) bus, and any combination thereof.

[0123] Computer system 2700 may also include input devices 2733. In one example, a user of computer system 2700 may input commands and / or other information into computer system 2700 via input devices 2733. Examples of input devices 2733 include, but are not limited to, an alphanumeric input device (e.g., a keyboard), a pointing device (e.g., a mouse or touchpad), a touchpad, a touchscreen, a multi-touch screen, a joystick, a stylus, a gamepad, an audio input device (e.g., a microphone, a voice response system, etc.), an optical scanner, a video or still image capture device (e.g., a camera), and any combination thereof. In some embodiments, input devices are Kinect, Leap Motion, etc. Input devices 2733 may be interfaced to bus 2740 via any of a variety of input interfaces 2723 (e.g., input interface 2723), including, but not limited to, serial, parallel, gameport, USB, FIREWIRE®, THUNDERBOLT®, or any combination of the above.

[0124] In particular embodiments, when computer system 2700 is connected to network 2730, computer system 2700 may communicate with other devices connected to network 2730, such as mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, etc. Communications to and from computer system 2700 may be transmitted through network interface 2720. For example, network interface 2720 may receive incoming communications (e.g., requests or responses from other devices) in the form of one or more packets (e.g., Internet Protocol (IP) packets) from network 2730, and computer system 2700 may store the incoming communications in memory 2703 for processing. Computer system 2700 may similarly store outgoing communications (e.g., requests or responses from other devices) communicated from network interface 2720 to network 2730 in the form of one or more packets in memory 2703. Processor 2701 may access these communication packets stored in memory 2703 for processing.

[0125] Examples of network interface 2720 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of network 2730 or network segment 2730 include, but are not limited to, a distributed computing system, a cloud computing system, a wide area network (WAN) (e.g., the Internet, an enterprise network), a local area network (LAN) (e.g., a network associated with an office, building, campus, or other relatively small geographic space), a telephone network, a direct connection between two computing devices, a peer-to-peer network, and any combination thereof. A network such as network 2730 may utilize wired and / or wireless communication modes. In general, any network topology may be used.

[0126] Information and data can be displayed through display 2732. Examples of display 2732 include, but are not limited to, a cathode ray tube (CRT), a liquid crystal display (LCD), a thin film transistor liquid crystal display (TFT-LCD), an organic liquid crystal display (OLED) such as a passive-matrix OLED (PMOLED) or an active-matrix OLED (AMOLED) display, a plasma display, and any combination thereof. Display 2732 can be interfaced to processor 2701, memory 2703 and persistent storage 2708, and other devices such as input device(s) 2733 via bus 2740. Display 2732 is linked to bus 2740 via video interface 2722, and data transfer between display 2732 and bus 2740 can be controlled via graphics control 2721. In some embodiments, the display is a video projector. In some embodiments, the display is a head-mounted display (HMD), such as a VR headset. In further embodiments, suitable VR headsets include, by way of non-limiting example, HTC Vive, Oculus Rift, Samsung Gear VR, Microsoft HoloLens, Razer OSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headsets, etc. In further embodiments, the display is a combination of devices, such as those disclosed herein.

[0127] In addition to the display 2732, the computer system 2700 may include one or more other peripheral output devices 2734, including, but not limited to, audio speakers, printers, storage devices, and any combination thereof. Such peripheral output devices may be connected to the bus 2740 via an output interface 2724. Examples of the output interface 2724 include, but are not limited to, a serial port, a parallel connection, a USB port, a FIREWIRE® port, a THUNDERBOLT® port, and any combination thereof.

[0128] In some embodiments, computer system 2700 may additionally or alternatively provide functionality as a result of hardwired or otherwise embodied logic in circuitry, which may operate in place of or in conjunction with software performing one or more processes or one or more steps of one or more processes described or illustrated herein. References to software in this disclosure may encompass logic, and vice versa. Furthermore, in some embodiments, references to computer-readable media may encompass circuitry (such as an IC) storing executable software, circuitry embodying executable logic, or both, as appropriate. The present disclosure encompasses any suitable combination of hardware, software, or both.

[0129] In some embodiments, those skilled in the art will understand that the various illustrative logical blocks, modules, circuits, and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or a combination of both. To clearly illustrate this interchangeability of hardware and software, the various illustrative components, blocks, modules, circuits, and steps have been described above generally in terms of their functionality.

[0130] In some embodiments, the various illustrative logic blocks, modules, and circuits described in connection with the embodiments disclosed herein may be implemented or performed using a general-purpose processor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. In some embodiments, a general-purpose processor may be a microprocessor, but alternatively, the processor may be any conventional processor, controller, microcontroller, or state machine. A processor may also be implemented as a combination of computing devices, such as, for example, a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

[0131] In some embodiments, the steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by one or more processors, or in a combination of the two. In some embodiments, a software module may reside in RAM memory, flash memory, ROM memory, EPROM memory, EEPROM memory, registers, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art. In some embodiments, an exemplary storage medium is coupled to the processor such that the processor can read information from, and write information to, the storage medium. Alternatively, the storage medium may be integral to the processor. In some embodiments, the processor and the storage medium may reside in an ASIC. The ASIC may reside in a user terminal. Alternatively, the processor and the storage medium may reside as discrete components in a user terminal.

[0132] In some embodiments, in accordance with the description herein, suitable computing devices include, by way of non-limiting example, server computers, desktop computers, laptop computers, notebook computers, subnotebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet appliances, mobile smartphones, tablet computers, personal digital assistants, video game consoles, and vehicles. In some embodiments, those skilled in the art will recognize that select televisions, video players, and digital music players with any computer network connection are suitable for use with the systems described herein. Suitable tablet computers, in various embodiments, include, but are not limited to, booklet, slate, and convertible configurations known to those skilled in the art.

[0133] In some embodiments, a computing device includes an operating system configured to execute executable instructions. An operating system is software, including programs and data, that manages the device's hardware and provides services for running applications, for example. In some embodiments, those skilled in the art will recognize that suitable server operating systems include, by way of non-limiting example, FreeBSD, OpenBSD, NetBSD®, Linux®, Apple® Mac OS X Server®, Oracle® Solaris®, Windows Server®, and Novell® NetWare®. In some embodiments, those skilled in the art will recognize that suitable personal computer operating systems include, by way of non-limiting example, UNIX-like operating systems, such as Microsoft® Windows®, Apple® Mac OS X®, UNIX®, and GNU / Linux®. In some embodiments, the operating system is provided by cloud computing. In some embodiments, those skilled in the art will also recognize that suitable mobile smartphone operating systems include, by way of non-limiting example, Nokia® Symbian® OS, Apple® iOS®, Research In Motion® BlackBerry® OS®, Google® Android®, Microsoft® Windows Phone® OS, Microsoft® Windows Mobile® OS, Linux®, and Palm® WebOS®.In some embodiments, skilled artisans will also recognize that suitable media streaming device operating systems include, by way of non-limiting example, Apple TV®, Roku®, Boxee®, Google TV®, Google Chromecast®, Amazon Fire®, and Samsung® HomeSync®. In some embodiments, skilled artisans will also recognize that suitable video game console operating systems include, by way of non-limiting example, Sony® PS3®, Sony® PS4®, Microsoft® Xbox 360®, Microsoft Xbox One, Nintendo® Wii®, Nintendo® Wii U®, and Ouya®.

[0134] Non-transitory computer-readable storage medium In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transitory computer-readable storage media encoding a program including instructions that are optionally executable by an operating system of a networked computing device. In further embodiments, the computer-readable storage medium is a tangible component of the computing device. In further embodiments, the computer-readable storage medium is optionally removable from the computing device. In some embodiments, the computer-readable storage medium includes, by way of non-limiting example, CD-ROMs, DVDs, flash memory devices, solid-state memory, magnetic disk drives, magnetic tape drives, optical disk drives, distributed computing systems including cloud computing systems and services, and the like. In some cases, the programs and instructions are encoded on the medium permanently, substantially permanently, semi-permanently, or non-transitoryly.

[0135] computer program In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program, or the use thereof. In some embodiments, a computer program includes a sequence of instructions written to perform specified tasks and executable by one or more processors of a computing device's CPU. In some embodiments, computer-readable instructions may be implemented as program modules, such as functions, objects, application program interfaces (APIs), computing data structures, etc., that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, those skilled in the art will understand that computer programs may be written in a variety of languages ​​and in a variety of versions.

[0136] In some embodiments, the functionality of the computer-readable instructions may be combined or distributed as desired in various environments. In some embodiments, a computer program includes one sequence of instructions. In some embodiments, a computer program includes multiple sequences of instructions. In some embodiments, a computer program is provided from one location. In other embodiments, a computer program is provided from multiple locations. In various embodiments, a computer program includes one or more software modules. In various embodiments, a computer program includes, 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 a combination thereof.

[0137] Web Applications In some embodiments, the computer program comprises a web application. In light of the disclosure provided herein, those skilled in the art will recognize that web applications, in various embodiments, utilize one or more software frameworks and one or more database systems. In some embodiments, the web application is built on a software framework such as Microsoft® .NET or Ruby on Rails (RoR). In some embodiments, the web application utilizes one or more database systems, including, by way of non-limiting example, relational, non-relational, object-oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting example, Microsoft® SQL Server, mySQL™, and Oracle®. Those skilled in the art will also recognize that, in various embodiments, web applications are written in one or more languages ​​in one or more versions. Web applications may be written in one or more markup languages, presentation definition languages, client-side scripting languages, server-side coding languages, database query languages, or combinations thereof. In some embodiments, a web application is written in part in a markup language such as Hypertext Markup Language (HTML), Extensible Hypertext Markup Language (XHTML), or Extensible Markup Language (XML). In some embodiments, a web application is written in part in a presentation definition language such as Cascading Style Sheets (CSS).In some embodiments, the web application is written in part in a client-side scripting language such as Asynchronous Javascript and XML (AJAX), Flash® Actionscript, Javascript, or Silverlight®. In some embodiments, the web application is written in part in a server-side coding language such as Active Server Pages (ASP), ColdFusion®, Perl, Java™, JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python™, Ruby, Tcl, Smalltalk, WebDNA®, or Groovy. In some embodiments, the web application is written in part in a database query language such as Structured Query Language (SQL). In some embodiments, the web application integrates with an enterprise server product such as IBM® Lotus Domino®. In some embodiments, the web application includes a media player component. In various further embodiments, the media player element utilizes one or more of many suitable multimedia technologies, including, by way of non-limiting example, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

[0138] 23 , in particular embodiments, the application delivery system includes one or more databases 2800 accessed by a relational database management system (RDBMS) 2810. In some embodiments, suitable RDBMSs include Firebird, MySQL®, PostgreSQL, SQLite, Oracle Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase, Teradata, or the like. In this embodiment, the application delivery system further includes one or more application servers 2820 (e.g., a Java server, a .NET server, a PHP server, or the like) and one or more web servers 2830 (e.g., Apache, IIS, GWS, or the like). In some embodiments, the web servers optionally expose one or more web services via app application program interfaces (APIs) 2840. Over a network such as the Internet, the system provides browser-based and / or mobile-native user interfaces.

[0139] Referring to FIG. 24, in certain embodiments, the application delivery system alternatively has a distributed cloud-based architecture 2900 with elastically load balanced, auto-scaling web server resources 2910 and application server resources 2920, as well as a synchronously replicated database 2930.

[0140] Mobile Applications In some embodiments, the computer program comprises a mobile application provided to the mobile computing device. In some embodiments, the mobile application is provided to the mobile computing device at the time of manufacture. In other embodiments, the mobile application is provided to the mobile computing device via a computer network as described herein.

[0141] In light of the disclosure provided herein, mobile applications are generated using hardware, languages, and development environments known in the art and by techniques known to those skilled in the art. In some embodiments, those skilled in the art will understand that mobile applications are written in several languages. In some embodiments, suitable programming languages ​​include, by way of non-limiting example, C, C++, C#, Objective-C, Java™, Javascript, Pascal, Object Pascal, Python™, Ruby, VB.NET, WML with or without CSS, and XHTML / HTML, or combinations thereof.

[0142] In some embodiments, suitable mobile application development environments are available from several sources. Commercially available development environments include, by way of non-limiting example, Airplay SDK, alcheMo, Appcelerator®, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are available free of charge, including, by way of non-limiting example, Lazarus, MobiFlex, MoSync, and Phonegap. Mobile device manufacturers also distribute software developer kits, including, by way of non-limiting example, the iPhone® and iPad® (iOS) SDK, Android™ SDK, BlackBerry® SDK, BREW SDK, Palm® OS SDK, Symbian SDK, webOS SDK, and Windows® Mobile SDK.

[0143] In some embodiments, those skilled in the art will recognize that several commercial forums are available for distributing mobile applications, including, by way of non-limiting example, the Apple® App Store, Google® Play, Chrome WebStore, BlackBerry® App World, App Store for Palm devices, App Catalog for webOS, Windows® Marketplace for Mobile, Ovi Store for Nokia® devices, Samsung® App, and Nintendo® Dsi Shop.

[0144] Standalone Applications In some embodiments, the computer program comprises a standalone application, which is a program that runs as a dependent computer process rather than as an add-on to an existing process, e.g., a plug-in. In some embodiments, those skilled in the art will recognize that standalone applications are often compiled. In some embodiments, a compiler is a computer program that converts source code written in a programming language into binary object code, such as assembly language or machine language. In some embodiments, suitable compiled programming languages ​​include, by way of non-limiting example, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java™, Lisp, Python™, Visual Basic, and VB.NET, or combinations thereof. Compilation is often performed, at least in part, to generate an executable program. In some embodiments, the computer program comprises one or more executable compiled applications.

[0145] Web browser plugin In some embodiments, the computer program includes a web browser plug-in (e.g., an extension, etc.). In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Software application manufacturers support plug-ins to allow third-party developers to create the ability to extend their applications, support easy addition of new features, and reduce the size of the application. When supported, plug-ins allow the functionality of the software application to be customized. For example, plug-ins are commonly used in web browsers to play video, generate interactivity, scan for viruses, and display specific file types. In some embodiments, those skilled in the art are familiar with several web browser plug-ins, including Adobe® Flash® Player, Microsoft® Silverlight®, and Apple® QuickTime®. In some embodiments, the toolbar includes one or more web browser extensions, add-ins, or add-ons. In some embodiments, the toolbar includes one or more explorer bars, tool bands, or desk bands.

[0146] In light of the disclosure provided herein, those skilled in the art will recognize that several plug-in frameworks are available that allow for the development of plug-ins in a variety of programming languages, including, by way of non-limiting example, C++, Delphi, Java™, PHP, Python™, and VB.NET, or combinations thereof.

[0147] In some embodiments, a web browser (also called an Internet browser) is a software application designed for use with networked computing devices to search, display, and traverse information resources on the World Wide Web. In some embodiments, suitable web browsers include, by way of non-limiting example, Microsoft® Internet Explorer®, Mozilla®, Firefox®, Google® Chrome, Apple® Safari®, Opera Software® Opera®, and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also called microbrowsers, minibrowsers, and wireless browsers) are designed for use on mobile computing devices, including, by way of non-limiting example, handheld computers, tablet computers, netbook computers, subnotebook computers, smartphones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include, by way of non-limiting example, Google® Android® browser, RIM BlackBerry® Browser, Apple® Safari®, Palm® Blazer, Palm® WebOS® Browser, Mozilla® Firefox® for mobile, Microsoft® Internet Explorer® Mobile, Amazon® Kindle® Basic Web, Nokia® Browser, Opera Software® Opera® Mobile, and Sony® PSP™ browser.

[0148] Software Module In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules, or the use thereof. In light of the disclosure provided herein, software modules are generated using machines, software, and languages ​​known in the art and by techniques known to those skilled in the art. In some embodiments, the software modules disclosed herein are implemented in numerous ways. In various embodiments, a software module comprises a file, a section of code, a programming object, a programming structure, or a combination thereof. In further various embodiments, a software module comprises multiple files, multiple sections of code, multiple programming objects, multiple programming structures, or a combination thereof. In various embodiments, one or more software modules include, by way of non-limiting examples, a web application, a mobile application, and a standalone application. In some embodiments, a software module resides within one computer program or application. In other embodiments, a software module resides within two or more computer programs or applications. In some embodiments, a software module is hosted on one machine. In other embodiments, a software module is hosted on two or more machines. In further embodiments, a software module is hosted on a distributed computing platform, such as a cloud computing platform. In some embodiments, the software modules are hosted on one or more machines in one location. In other embodiments, the software modules are hosted on one or more machines in two or more locations.

[0149] Database In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more databases or the use thereof. In light of the disclosure provided herein, those skilled in the art will recognize that many databases are suitable for storing and retrieving information. In various embodiments, suitable databases include, by way of non-limiting example, relational databases, non-relational databases, object-oriented databases, object databases, entity-relationship model databases, associative databases, and XML databases. Further non-limiting examples include SQL, PostgreSQL, MySQL®, Oracle, DB2, and Sybase. In some embodiments, the database is internet-based. In further embodiments, the database is web-based. In further embodiments, the database is cloud computing-based. In certain embodiments, the database is a distributed database. In other embodiments, the database is based on one or more local computer storage devices.

[0150] Using a computer In some embodiments, the methods and software described herein may utilize one or more computers. In some embodiments, a computer may be used for managing customer and sample information, such as sample or customer tracking, for database management, for analyzing molecular profiling data, for analyzing cytological data, for data storage, for advertising, for marketing, for reporting results, for storing results, or a combination thereof. In some embodiments, a computer may include a monitor or other graphical interface for displaying data, results, advertising information, marketing information (e.g., demographics), customer information, or sample information. In some embodiments, a computer may also include a means for inputting data or information. In some embodiments, a computer may include a processing unit and fixed or removable media, or a combination thereof. In some embodiments, a computer may be accessed by a user in physical proximity to the computer, for example, via a keyboard and / or mouse, or by a user who does not necessarily have access to the physical computer through a communications medium, such as a modem, an Internet connection, a telephone connection, or a wired or wireless communications signal carrier. In some cases, a computer may be connected to a server or other communications device for relaying information from users to the computer or from the computer to users. In some cases, a user may store data or information obtained from a computer through a communication medium on a medium such as a removable medium. In some embodiments, it is contemplated that data related to a method may be transmitted over such a network or connection for receipt and / or review by a party. In some embodiments, the receiving party may be, but is not limited to, an individual, a healthcare provider, or a healthcare administrator. In one example, the computer-readable medium includes a medium suitable for transmitting biological sample analysis results. In some embodiments, the medium may include subject results, such results obtained using the methods described herein.

[0151] In some embodiments, an entity obtaining sample information can enter the information into a database for one or more of the following purposes: enrollment tracking, assay result tracking, order tracking, customer management, customer service, advertising and promotion, and sales. Sample information can include, but is not limited to, customer name, specific customer identification, customer-associated medical professional, indicated assay, assay result, validity status, indicated validity test, individual's medical history, preliminary diagnosis, suspected diagnosis, sample history, insurance provider, healthcare provider, third-party testing center, or any information suitable for storage in a database. In some embodiments, sample history can include, but is not limited to, sample age, sample type, acquisition method, storage method, or transportation method.

[0152] In some embodiments, the database may be accessible by customers, medical professionals, insurance providers, or other third parties. In some embodiments, database access may take the form of digitally processed communications, such as a computer or telephone. In some embodiments, the database may be accessed through an intermediary, such as a customer service representative, company representative, consultant, independent testing center, or medical professional. In some embodiments, the availability or extent of database access or sample information, such as assay results, may vary upon payment for products and services provided or to be offered. In some embodiments, the extent of database access or sample information may be limited to comply with generally accepted or legal requirements for patient or customer confidentiality.

[0153] Machine Learning In some embodiments, systems, methods, software, and platforms as described herein may include computer-implemented methods of supervised or unsupervised learning methods, including SVM, random forests, clustering algorithms (or software modules), gradient boosting, logistic regression, and / or decision trees. In some embodiments, the machine learning methods described herein may improve the generation of recommendations based on the recording and analysis of any of the identifiers, test results, patient outcomes, or any other relevant medical information described herein. In some cases, the machine learning methods may intentionally group or separate treatment options. In some embodiments, some treatment options may be intentionally clustered or removed from any one of multiple stages of the medical encounter.

[0154] In some embodiments, a supervised learning algorithm may be an algorithm that relies on the use of a set of labeled, corresponding training data examples to infer relationships between input data and output data. In some embodiments, an unsupervised learning algorithm may be an algorithm used to draw inferences from a training data set for output data. In some embodiments, an unsupervised learning algorithm may include cluster analysis, which may be used for exploratory data analysis to find hidden patterns or groupings in process data. An example of an unsupervised learning method may include principal component analysis. In some embodiments, principal component analysis may include reducing the dimensionality of one or more variables. In some embodiments, the dimensionality of a given variable may be at least 1, 5, 10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800, or more. In some embodiments, the dimensionality of a given variable may be at most 1800, 1600, 1500, 1400, 1300, 1200, 1100, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, 10 or less.

[0155] In some embodiments, the computer-implemented method can include statistical techniques, which can include linear regression, classification, resampling methods, subset selection, shrinkage, dimensionality reduction, non-linear models, tree-based methods, support vector machines, unsupervised learning, or any combination thereof.

[0156] In some embodiments, linear regression can be a method of predicting a target variable by fitting the best linear relationship between a dependent variable and independent variables. In some embodiments, the best fit can mean that the sum of all distances between the shape and the actual observations at each point is the smallest. In some embodiments, linear regression can include single linear regression and multiple linear regression. In some embodiments, single linear regression can use a single independent variable to predict a dependent variable. In some embodiments, multiple linear regression can use two or more independent variables to predict a dependent variable by fitting the best linear relationship.

[0157] In some embodiments, classification can be a data mining technique that assigns categories to a collection of data to achieve accurate predictions and analysis. In some embodiments, classification techniques can include logistic regression and discriminant analysis. Logistic regression can be used when the dependent variable is dichotomous (binary). In some embodiments, logistic regression can be used to discover and describe relationships between a dependent binary variable and one or more nominal, ordinal, interval, or proportional independent variables. In some embodiments, resampling can be a method that involves drawing replicate samples from the original data sample. In some embodiments, resampling can involve utilizing a comprehensive distribution table to calculate approximate probability values. In some embodiments, resampling can generate a unique sampling distribution based on actual data. In some embodiments, resampling can use experimental rather than analytical methods to generate a unique sampling distribution. In some embodiments, resampling techniques can include bootstrapping and cross-validation. In some embodiments, bootstrapping is performed by sampling with replacement from the original data and can take "unselected" data points as test cases. In some embodiments, cross-validation can be performed by dividing the training data into multiple portions.

[0158] In some embodiments, subset selection can identify a subset of predictors associated with the response. In some embodiments, subset selection can include best subset selection, forward regression, backward regression, hybrid methods, or any combination thereof. In some cases, shrinkage fits a model that includes all predictors, but the estimated coefficients shrink toward zero relative to least-squares estimation. In some embodiments, this shrinkage can reduce variance. In some embodiments, shrinkage can include ridge regression and lasso regression. In some embodiments, dimensionality reduction can reduce the problem of estimating n+1 coefficients to the simpler problem of m+1 coefficients, where m is less than n. This can be obtained by computing different linear combinations of the n variables, or their projections. These n projections are then used as predictors to fit a linear regression model via least squares. In some embodiments, dimensionality reduction can include principal component regression and partial least squares. In some embodiments, principal component regression can be used to derive a low-dimensional set of features from a large set of variables. In some embodiments, the principal components used in principal component regression use linear combinations of the data followed by orthogonal methods to capture the most variance in the data. In some embodiments, partial least squares can be a supervised alternative to principal component regression because it can use response variables to identify novel features.

[0159] In some embodiments, the nonlinear regression may be a form of regression analysis in which the observed data is modeled with a function that is a nonlinear combination of model parameters and that depends on one or more independent variables, hi some embodiments, the nonlinear regression may include a step function, a piecewise function, a spline, a generalized additive model, or any combination thereof.

[0160] In some embodiments, tree-based methods may be used for both regression and classification problems. In some embodiments, regression and classification problems may involve stratifying or segmenting the predictor space into several simple regions. In some embodiments, tree-based methods may include bagging, boosting, random forests, or any combination thereof. In some embodiments, bagging may reduce the variance of predictions by generating additional data for training from the original dataset using a combination of iterations to generate multiple stages of the same cardinality / size as the original data. In some embodiments, boosting may calculate outputs using several different models and then average the results using a weighted average approach. In some embodiments, a random forest algorithm may draw random bootstrap samples of the training set. In some embodiments, a support vector machine may be a classification technique. In some embodiments, a support vector machine may involve finding a hyperplane that best separates two classes of points with a maximum margin. In some embodiments, a support vector machine may constrain the optimization problem to be objective, with the margin maximized relative to the constraint of perfectly classifying the data.

[0161] In some embodiments, an unsupervised method may be a method for drawing inferences from a dataset that includes input data without labeled responses, hi some embodiments, the unsupervised method may include clustering, principal component analysis, k-means clustering, hierarchical clustering, or any combination thereof.

[0162] definition Unless otherwise defined, all terms, notations, and other technical and scientific or terminology used herein are intended to have the same meaning as commonly understood by one of ordinary skill in the art to which the claimed subject matter belongs. In some instances, terms having a commonly understood meaning are defined herein for clarity and / or ease of reference, and the inclusion of such definitions herein should not necessarily be construed as representing a substantial departure from what is commonly understood in the art.

[0163] Throughout this application, various embodiments may be expressed in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the present disclosure. Accordingly, the description of a range should be considered to have specifically disclosed all possible subranges and individual numerical values ​​within that range. For example, the description of a range such as 1 to 6 should be considered to have specifically disclosed subranges such as 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc., as well as individual numbers within that range, e.g., 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0164] As used in this specification and the appended claims, the singular forms "a," "an," and "the" include plural referents unless the context clearly dictates otherwise. For example, the term "sample" includes multiple samples, including mixtures of samples.

[0165] The terms "determining," "measuring," "evaluating," "assessing," "assaying," and "analyzing" are often used interchangeably herein and refer to forms of measurement. The terms include determining whether an element is present or absent (e.g., detecting). These terms can include quantitative, qualitative, or both quantitative and qualitative determinations. Evaluating can be relative or absolute. "Detecting the presence of" can include determining the amount of something present in addition to determining presence or absence, depending on the context.

[0166] The terms "subject," "individual," or "patient" are often used interchangeably herein. A "subject" can be a biological entity containing expressed genetic material. The biological entity can be a plant, animal, or microorganism, including, for example, bacteria, viruses, fungi, and protozoa. A subject can be tissues, cells, and their progeny of a biological entity obtained in vivo or cultured in vitro. A subject can be a mammal. A subject can be a human. A subject can be diagnosed or suspected of being at high risk for a disease. In some cases, a subject is not necessarily diagnosed or suspected of being at high risk for a disease.

[0167] As used herein, the term "about" a number refers to ±15% of that number. The term "about" refers to the range minus 15% of the lowest value and plus 15% of the highest value.

[0168] As used herein, the terms "treatment" or "treating" are used in reference to a pharmaceutical or other intervention regimen to obtain a beneficial or desired result in a recipient. Beneficial or desired results include, but are not limited to, therapeutic benefit and / or prophylactic benefit. Therapeutic benefit may refer to the eradication or amelioration of the condition being treated or its underlying disease. In addition, therapeutic benefit may be achieved along with the eradication or amelioration of one or more physiological symptoms associated with the underlying disease, such that an improvement is observed in a subject, even though the subject may still be afflicted with the underlying disease. Prophylactic benefit includes delaying, preventing, or eliminating the appearance of a disease or condition, delaying, preventing, or eliminating the onset of symptoms of a disease or condition, slowing, halting, or reversing the progression of a disease or condition, or any combination thereof. For prophylactic benefit, subjects at risk of developing a particular disease or reporting one or more physiological symptoms of a disease can receive treatment even if the disease has not been diagnosed.

[0169] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0170] Embodiment Embodiment 1. A method for detecting the presence of a medical condition or disease in a subject, the method comprising: optionally collecting a bodily fluid sample from the subject; optionally preserving the sample at the time of collection by adding a preservative; optionally fractionating the sample; optionally adding a preservative to the fractionated sample; selecting an analyte in the sample, including but not limited to a nucleic acid transcript or genomic region of interest; qualitatively or quantitatively detecting the selected analyte in an assay, which may involve techniques including but not limited to biomolecule generation, biomolecule enrichment, biomolecule sequencing, PCR, quantitative PCR, isothermal amplification, mass spectrometry, antibody-based detection, or CRISPR and CRISPR-Cas systems, thereby generating data; and analyzing the data using a computer to detect the presence or absence of the medical condition or disease, or analyzing the data to generate a likelihood score for the medical condition or disease.

[0171] Embodiment 2. The method of embodiment 1, wherein the bodily fluid is saliva.

[0172] Embodiment 3. The method of embodiment 1 or 2, wherein multiple samples are taken from the subject in a longitudinal fashion to generate an individual's health profile that can be monitored for changes indicative of health changes.

[0173] Embodiment 4. The method of embodiment 1 or 2, wherein signal changes caused by circadian cycles are distinguished from signals resulting from a medical or disease state.

[0174] Embodiment 5. The method of embodiment 1, wherein a preservative is added to the sample after collection.

[0175] Embodiment 6. The method of embodiment 1, wherein a preservative is present in the collection container prior to collection.

[0176] Embodiment 7. The method of embodiment 1, wherein the preservative comprises at least one or more of: EDTA for inhibiting nucleases; poly(vinyl sulfonic acid, sodium salt); RNase inhibitors such as, but not limited to, dUppAp, pdUppAp, and pTppAp, ribonucleoside vanadyl complexes, aurintricarboxylic acid, Rnasin, SUPERaseIN, or the like; agents for preventing microbial growth, such as isothiazolinones and formaldehyde-releasing agents, such as, but not limited to, Germall Plus, DMDM ​​hydantoin, imadozolidinyl urea, diazolidinyl urea, and Proclin 300; protein denaturants, such as, but not limited to, nucleic acid denaturants, urea, guanidine thiocyanate, and guanidinium chloride; agents for sequestration of catabolic proteins; buffers; detergents; reducing agents; antioxidants; cryoprotectants; or osmolytes that are salts.

[0177] Embodiment 8. The method of embodiment 1, wherein the sample is fractionated after collection.

[0178] Embodiment 9. The method of embodiment 1, wherein the sample is fractionated into two or more portions through the application of centrifugal force, and each fraction is removed separately, and the isolated fractions may comprise a cell-free fraction and a cell-containing fraction.

[0179] Embodiment 10. The method of embodiment 1, wherein the sample is fractionated by filtration.

[0180] Embodiment 11. The method of embodiment 10, wherein the mechanism for filtration is incorporated into the device, and one or more filters may be used for filtration. Multiple filters may be arranged in sequence with pore sizes that allow successively smaller species to pass through. Filtration is achieved through the application of mechanical force, centrifugal force, or vacuum, or through capillary action. Optionally, a preservative, such as that described in embodiment 7, is added to the filtrate simultaneously with or immediately after filtration. Optionally, a preservative, such as that described in embodiment 7, is added to the retentate.

[0181] Embodiment 12. The method of embodiment 1, wherein the saliva is fractionated to produce a cell-free portion and a cell-containing portion using the method of any one of embodiments 9-11.

[0182] Embodiment 13 The method of embodiment 1, wherein at least one class of analytes comprises RNA.

[0183] Embodiment 14 The method of embodiment 1, wherein at least one class of analytes comprises cell-free RNA.

[0184] Embodiment 15. The method of embodiment 13, wherein the RNA is selected from the group consisting of mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, tRNA fragments, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, and any combination thereof.

[0185] Embodiment 16. The method of embodiment 14, wherein the RNA is selected from the group consisting of mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, tRNA fragments, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, and any combination thereof.

[0186] Embodiment 17. The method of embodiment 1, wherein at least one class of analytes comprises DNA.

[0187] Embodiment 18 The method of embodiment 1, wherein at least one class of analytes comprises cell-free DNA.

[0188] Embodiment 19. The method of embodiment 1, wherein the analyte is endogenous (derived from the subject), exogenous (e.g., the subject's microbiome), or a mixture thereof.

[0189] Embodiment 20 The method of embodiment 1, wherein at least one class of analytes comprises proteins.

[0190] Embodiment 21 The method of embodiment 1, wherein at least one class of analytes comprises small molecules.

[0191] Embodiment 22. The method of embodiment 1, wherein at least one class of analytes comprises hormones.

[0192] Embodiment 23 The method of embodiment 1, wherein at least one class of analytes comprises metabolites.

[0193] Embodiment 24. The method of embodiment 1, wherein at least one class of analytes comprises cells (endogenous or exogenous).

[0194] Embodiment 25. The method of embodiment 1, wherein the disease is cancer.

[0195] Embodiment 26. The method of embodiment 1, wherein the disease is breast cancer.

[0196] Embodiment 27. The method of embodiment 26, wherein the bodily fluid is saliva.

[0197] Embodiment 28. The method of embodiment 27, wherein the sample is fractionated to obtain cell-free saliva.

[0198] Embodiment 29 The method of embodiment 26, wherein at least one analyte class is RNA.

[0199] Embodiment 30. The method of embodiment 26, wherein at least one analyte class is cell-free.

[0200] Embodiment 31. The method of embodiment 29 or 30, wherein the RNA is analyzed using sequencing.

[0201] Embodiment 32. The method of embodiment 31, wherein sequencing is multiplexed.

[0202] Embodiment 33. The method of embodiment 31, wherein the sequencing is high-throughput.

[0203] Embodiment 34. The method of embodiment 31, wherein a molecular barcode (UMI) is used to identify a single RNA species that is represented multiple times.

[0204] Embodiment 35. The method of embodiment 29 or 30, wherein the RNA is analyzed using PCR.

[0205] Embodiment 36. The method of embodiment 29 or 30, wherein the RNA is analyzed using a microarray.

[0206] Embodiment 37. The method of embodiment 26, wherein the patient has dense breast tissue.

[0207] Embodiment 38 The method of embodiment 13 or 14, wherein tissue-specific contributions to the RNA profile are determined.

[0208] Embodiment 39. The method of embodiment 38, wherein tissue-specific contributions to the RNA profile are subtracted, either through assay or computationally, to distinguish between signals and diseases or medical conditions.

[0209] Embodiment 40. The method of embodiment 38, wherein the tissue-specific contribution is used directly to identify the presence of a disease or medical condition.

[0210] Embodiment 41 The method of embodiment 25, wherein tissue-specific contributions to the RNA profile are used to identify the cancer tissue of origin.

[0211] Embodiment 42. The method of embodiment 1, wherein the disease is an infectious disease.

[0212] Embodiment 43. The method of embodiment 1, wherein the disease or medical condition involves the brain or nervous system.

[0213] Embodiment 44. The method of embodiment 1, wherein the medical condition involves the brain or nervous system.

[0214] Embodiment 45. The method of embodiment 1, wherein the medical condition is pregnancy.

[0215] Embodiment 46. The method of embodiment 1, wherein the medical condition is organ trauma or injury.

[0216] Embodiment 47. The method of embodiment 1, wherein the disease is an autoimmune disease.

[0217] Embodiment 48. The method of embodiment 1, wherein the disease is metabolic in nature.

[0218] Embodiment 49. The method of embodiment 1, wherein the disease is a disease of the endocrine system.

[0219] Embodiment 50. The method of embodiment 1, wherein the disease is a disease of the gastrointestinal tract.

[0220] Embodiment 51. The method of embodiment 1, wherein the subject is a human.

[0221] Embodiment 52. The method of embodiment 1, wherein the subject is a non-human.

[0222] Embodiment 53. The method of embodiment 1, wherein the sample is collected at home.

[0223] Embodiment 54. The method of embodiment 1, wherein the sample is collected in a medical facility.

[0224] Embodiment 55. The method of embodiment 1, wherein the sample is collected by a dentist or dental hygienist.

[0225] Embodiment 56. The method of embodiment 1, wherein the sample is collected by a veterinarian.

[0226] Embodiment 57. The method of embodiment 1, wherein the preservative comprises some or all of the following: (1) a reducing agent, such as tris(2-carboxyethyl)phosphine hydrochloride, β-mercaptoethanol, or dithiothreitol; (2) an antioxidant, such as ascorbate or ascorbic acid; (3) an antimicrobial agent, such as Proclin 300 or an isothiazolinone; (4) a buffer to maintain a pH of 4 to 9; (5) a nuclease inhibitor, such as EDTA, aurintricarboxylic acid, RNaseIn, or the like; (6) an osmolyte, such as betaine; and (7) a cryoprotectant.

[0227] Embodiment 58. The method of embodiment 1, wherein the preservative comprises one or more of a denaturing agent, such as guanidine thiocyanate or urea, (1) EDTA, (2) a buffering agent, (3) a surfactant, and (4) a reducing agent.

[0228] 2. The method of embodiment 1, wherein the analytes are DNA and RNA of cell-free saliva of the patient, and both genetic and transcriptomic analysis are used to detect the presence of a disease or medical condition.

[0229] 2. The method of embodiment 1, wherein two or more samples are taken from the patient, and the samples are processed using different versions of the workflow described in embodiment 1.

[0230] 2. The method of embodiment 1, wherein the sample is taken from a body site different from the body site of the disease or condition.

[0231] example The following examples are included for illustrative purposes only and are not intended to limit the scope of the invention.

[0232] Example 1. Tissue-specific and tissue-enriched transcripts in saliva The GTEx data were analyzed to identify genes that are highly specific to a small group of tissues. Tissue-specific transcripts were detected in patient saliva from multiple organs (Figure 4). The greatest overlap observed was with blood and esophagus. Tissue-specific transcripts in saliva demonstrated the potential for broad disease detection.

[0233] The GTEx data were analyzed to identify genes enriched across multiple tissues, which expands the ability to use saliva to analyze multiple tissues (Figure 5).

[0234] Transcript enrichment (e.g., identification of tissue-enriched transcripts) was assessed using a correlation-weighted entropy calculation: CWE=Σ i ((p i logp i ) / max{Σ j r ij ,0}) And, During the ceremony: i represents the type of tissue; p represents the normalized TPM in the tissue, r ij is the Pearson correlation coefficient between tissue types i and j, The total was calculated at least in part based on calculations across all tissue types. For the numerator entropy term, lower values ​​indicate expression in fewer tissues. The denominator is a weighting factor for highly correlated tissues. This reduces the CWE for genes expressed in groups of correlated tissue types (e.g., brain regions). For tissue-enriched transcripts, the lowest 30 percentile for entropy was chosen. If a gene was present at 50% or more of the maximum value, it was included as an enriched tissue.

[0235] Example 2. Breast cancer proof-of-concept study A clinical research trial was conducted with over 2,600 patients enrolled at two sites. A total of 2,623 samples were collected. In an NGS study of 301 patients, 115 breast cancer patients and 186 non-cancer patients were analyzed (Figure 16). Genes that were differentially enriched in the two groups were identified. GSEA analysis further demonstrated that hallmarks of the cancer gene catalog were enriched in the cancer group (Figure 18).

[0236] Example 3. Breast cancer detection A method for detecting the presence of a medical condition or disease in a subject includes obtaining saliva from a subject, purifying nucleic acids from the saliva, measuring the level of at least a portion of the nucleic acid using at least one of the following methods: sequencing, qPCR, or microarray, and analyzing the data using computational and statistical methods to detect the presence or absence of a medical condition in which all or a subset of the measurements may result in the detection of the condition. While this approach can be used to detect numerous diseases, this application focuses on the method and RNA signature for breast cancer. This method may be used for patients of all risk groups who need to undergo screening or diagnostic workup for breast cancer. Therefore, it may be performed as a stand-alone test or in conjunction with imaging methods such as mammography, MRI, or ultrasound to improve overall accuracy. Saliva collection may be performed at home, in the field, or in a medical facility. In all cases, the saliva must be collected, handled, and stored in a manner that preserves the integrity of the analyte. In this case, the analyte of interest is nucleic acid, which may be derived from whole saliva or cell-free saliva. Appropriate collection and storage methods are important for preserving nucleic acids. It is desirable to use methods that inactivate nucleases as much as possible and preserve nucleic acids over a wide range of temperatures and for long periods of time. Other aspects of sample collection, such as time or fasting state, may also play an important role and must be considered.

[0237] Stored saliva samples can be fractionated to obtain cell-free saliva before analysis. Nucleic acids may be extracted from the sample using a variety of methods. Some methods are specific for RNA, some for DNA, and some allow for the isolation of both. For RNA isolation, it is important to use a method that removes any contaminating DNA, and for DNA isolation, it is important to use a method that removes contaminating RNA. Once isolated, nucleic acids can be further analyzed using a variety of methods, including, but not limited to, sequencing, qPCR, and microarrays. For genome-scale techniques, it may be preferable to subselect genes or regions of interest either by assay or bioinformatically. This may be to reduce background noise or for cost reasons. Regardless of the assay, any algorithm used to determine the presence of a disease or condition may use only a portion of the data to make a decision.

[0238] Example 3 illustrates breast cancer detection using RNA transcripts found in cell-free saliva. Using RNA-Seq data from a set of 20 breast cancer patients and 20 non-cancer patients, machine learning logistic regression was used to identify genes that could be used in a classifier for breast cancer detection (Table 1). Figure 21A shows the ROC curve for 10-fold cross-validation results, averaged over 100 iterations split across different training runs. Cross-validation provided an estimate of how the model would perform on new data and how it guarded against overfitting. Fitting all the data resulted in a model using 157 genes with the logistic regression coefficients shown in Figure 21B. While these 157 genes were used in the best-performing classifier, an expanded set of 250 genes was also identified that provided discrimination between cancer and non-cancer patients. This set was identified by iteratively removing the most important features used by the classifier and refitting the classifier to the remaining genes. With each iteration, the classifier's performance deteriorated. This process was repeated until the classification results were sub-random. We found that this random performance occurred when 250 genes were removed (Figure 21C). Based on these findings, we concluded that the expression levels of the genes listed in Table 1 can predict the presence of breast cancer. Note that data from a subset of these genes may be sufficient for the classifier. The final classifier may use only a portion of these genes for prediction. Alternatively, the classifier may include many or all genes, with only a small portion being useful for disease prediction in any one sample. [Table 1-1] [Table 1-2] [Table 1-3]

[0239] Example 4. Preservation of nucleic acids The following examples demonstrate two things: adding a denaturing agent, such as a chaotropic agent, to a biological sample preserves the RNA in the sample for at least 4 days, and sample filtration can be used to prepare cell-free or cell-depleted RNA.

[0240] Saliva collected from multiple individuals was pooled and divided into two fractions, A and B (see Figure 8). Fraction A was spun to obtain cell-free saliva, which was then divided into two portions, A1 and A2. Equal volumes of PBS (preservative-negative condition) and chaotropic agent (preservative-positive condition) were added to A1 and A2, respectively. Fraction B was filtered using a syringe-based filter to remove cells. The filtrate was divided into two portions, B1 and B2. Equal volumes of PBS (preservative-negative condition) and chaotropic agent (preservative-positive condition) were added to B1 and B2, respectively. Aliquots were removed from A1, A2, B1, and B2 on days 0, 1, 2, and 4 and frozen at -80°C. Samples were extracted and analyzed using qPCR for three genes: ACTB, FOSL2, and NAMPT.

[0241] Experimental qPCR results demonstrated that conditions containing PBS (preservative-) showed a decrease in transcript levels over time, while conditions containing a denaturing chaotropic agent (preservative+) showed no decrease in transcript levels for any of the three genes (Figure 9). Filtration yielded transcript levels for ACT similar to cell-free saliva preparations using centrifugation, and higher levels for FOSL2 and NAMPT. Whole saliva was also included as a control and generally showed higher levels of all three transcripts.

[0242] Example 5. Nucleic Acid Preservation Using Filtration and Storage Conditions This example illustrates the preservation of whole saliva, which can be used for downstream analysis of nucleic acids, donor cells, microbiome, etc. Whole saliva was preserved using a combination of additives to stabilize components (nuclease inhibitors, antimicrobial agents to inhibit microbial growth, and fixatives to prevent cell lysis). Figure 25A shows the degradation of RNA encoding actin B over a 3-day period. Figure 25B shows a schematic outlining a 7-day nucleic acid stability study of the method described herein for preserving nucleic acids. Saliva samples were obtained from these three donors. The quality of the preserved nucleic acids was tested via qPCR to measure abundant synthetic spike-in and endogenous genes. Figure 25C shows an exemplary profile over 7 days from one donor, demonstrating that the filter and preservative combination condition provided the most stable profile over 7 days. The preservative-free condition showed an increase in signal over time (e.g., from microbial growth). The filter and preservative condition showed little change. Figure 25D shows the preservation of the spike-in control stored over 7 days. The spike-in control showed rapid degradation in the absence of preservatives, and the spike-in control had better stability in the filtered samples than in the spun samples, even in the presence of preservatives. Stability was achieved for 7 days. Figure 25E shows the stability of endogenous transcripts over 7 days. The endogenous gene showed gradual degradation in the absence of preservatives. Transcript levels were stable in the presence of preservatives.

[0243] While preferred embodiments of the present invention have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. Numerous variations, changes, and substitutions will occur to those skilled in the art without departing from the invention. It is understood that various alternatives to the embodiments described herein can be employed in practicing the present disclosure. It is intended that the following claims define the scope of the invention, and that methods and structures within the scope of these claims and their equivalents be covered thereby.

Claims

1. A device for collecting and stabilizing analytes in a sample, a) A sample collection container for collecting the aforementioned sample; b) A filtration unit having fluid communication with the sample collection container, comprising at least one filter for filtering the sample to produce a filtrate; and c) A filtrate recovery container that is in fluid communication with the filtration unit for collecting the filtrate and bringing the filtrate into contact with the preservative. A device equipped with the following features.

2. The aforementioned filtration unit (a) Multiple filters arranged in sequence with pore sizes that allow smaller seeds to pass through in succession; (b) The device according to claim 1, comprising at least two filters of the same type; or a single filter.

3. The device according to claim 1, wherein the device comprises a pre-filtration mechanism for preventing clogging of filters with smaller pore sizes.

4. The aforementioned at least one filter, (a) Depth filter; (b) Asymmetric filter (c) Microporous filter, and (d) The device according to claim 1, comprising at least one low nucleic acid binding material.

5. (a) The size cutoff of the at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 2 μm, at least about 3 μm, at least about 4 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, at least about 25 μm, at least about 30 μm, at least about 35 μm, at least about 40 μm, at least about 45 μm, at least about 50 μm, at least about 55 μm, at least about 60 μm, at least about 65 μm, at least about 70 μm, at least about 75 μm, at least about 80 μm, at least about 85 μm, at least about 90 μm, at least about 95 μm, or at least about 100 μm; (b) The size cutoff of the at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 2 μm, at most about 3 μm, at most about 4 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, at most about 25 μm, at most about 30 μm, at most about 35 μm, at most about 40 μm, at most about 45 μm, at most about 50 μm, at most about 55 μm, at most about 60 μm, at most about 65 μm, at most about 70 μm, at most about 75 μm, at most about 80 μm, at most about 85 μm, at most about 90 μm, at most about 95 μm, or at most about 100 μm; preferably the size cutoff of the at least one filter is at most about 0.1 μm to about 100 μm; (c) The filter thickness is approximately 50 μm to approximately 1000 μm, or approximately 50 μm, approximately 100 μm, approximately 150 μm, approximately 200 μm, approximately 250 μm, approximately 300 μm, approximately 350 μm, approximately 400 μm, approximately 450 μm, approximately 500 μm, approximately 550 μm, approximately 600 μm, approximately 650 μm, approximately 700 μm, approximately 750 μm, approximately 800 μm, approximately 850 μm, approximately 900 μm, or approximately 950 μm, to approximately 100 μm, approximately 150 μm, approximately 200 μm, approximately 250 μm m, approximately 300 μm, approximately 350 μm, approximately 400 μm, approximately 450 μm, approximately 500 μm, approximately 550 μm, approximately 600 μm, approximately 650 μm, approximately 700 μm, approximately 750 μm, approximately 800 μm, approximately 850 μm, approximately 900 μm, approximately 950 μm, or approximately 100 μm, for example approximately 355 μm to approximately 560 μm, for example approximately 330 μm, for example approximately 120 μm to approximately 170 μm, for example approximately 230 μm to approximately 270 μm, for example approximately 480 μm to approximately 640 μm; and (d) The diameter of the filter is approximately 10 mm to approximately 50 mm, for example, approximately 10 mm, approximately 15 mm, approximately 20 mm, approximately 25 mm, approximately 30 mm, approximately 35 mm, approximately 40 mm or approximately 45 mm, or approximately 15 mm, approximately 20 mm, approximately 25 mm, approximately 30 mm, approximately 35 mm, approximately 40 mm, approximately 45 mm or approximately 50 mm. The device according to claim 1, which is at least one of the following.

6. The filtration unit has a filtration area of ​​approximately 120 μm to approximately 10,000 μm, for example, approximately 120 μm, approximately 150 μm, approximately 175 μm, approximately 200 μm, approximately 250 μm, approximately 300 μm, approximately 350 μm, approximately 400 μm, approximately 450 μm, approximately 500 μm, approximately 550 μm, approximately 600 μm, approximately 650 μm, approximately 700 μm, approximately 750 μm, approximately 800 μm, approximately 850 μm, approximately 900 μm, approximately 950 μm, From approximately 1000 μm, 1250 μm, 1500 μm, 1750 μm, 2000 μm, 2500 μm, 3000 μm, 3500 μm, 4000 μm, 4500 μm, 5000 μm, 5500 μm, 6000 μm, 6500 μm, 7000 μm, 7500 μm, 8000 μm, 8500 μm, 9000 μm, or 9500 μm , about 150 μm, about 175 μm, about 200 μm, about 250 μm, about 300 μm, about 350 μm, about 400 μm, about 450 μm, about 500 μm, about 550 μm, about 600 μm, about 65 0 μm, about 700 μm, about 750 μm, about 800 μm, about 850 μm, about 900 μm, about 950 μm, about 1000 μm, about 1250 μm, about 1500 μm, about 1750 μm, about 20 The device according to claim 5, comprising a filter stack height of 00 μm, approximately 2500 μm, approximately 3000 μm, approximately 3500 μm, approximately 4000 μm, approximately 4500 μm, approximately 5000 μm, approximately 5500 μm, approximately 6000 μm, approximately 6500 μm, approximately 7000 μm, approximately 7500 μm, approximately 8000 μm, approximately 8500 μm, approximately 9000 μm, approximately 9500 μm, or approximately 10000 μm.

7. The aforementioned at least one filter, (a) being hydrophilic or hydrophobic; and (b) Free from biomaterials, preferably containing cellulose; at least one of the following, and / or The aforementioned at least one filter, (a) Polysulfone and / or polypropylene; (b) Synthetic materials for minimizing the introduction of contaminated nucleic acids; and (c) Biomolecules The device according to claim 1, comprising at least one of the following.

8. The aforementioned at least one filter, (a) Multiple white blood cells; (b) Multiple red blood cells; (c) Cells derived from multiple solid tissues; and (d) Multiple microorganisms The device according to claim 1, which holds at least one of the following.

9. The device according to claim 1, wherein the sample is a biological fluid including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid.

10. The filtrate is (a) It is a cell-free biofluid or a cell-depleted biofluid; (b) Cell-free plasma or cell-depleted plasma; (c) It is cell-free or cell-depleted saliva; and (d) Acellular urine or cellular depletion urine, The device according to claim 1, which is at least one of the following.

11. The device according to claim 1, further comprising a mechanism for applying mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow to filter the sample through at least one of the at least one filters.

12. The mechanism is for applying mechanical force, centrifugal force, vacuum, capillary action or radial or axial flow to filter the sample through all filters in the device, and / or The mechanism includes a plunger that engages with the sample collection container so as to push the sample through the filtration unit into the filtrate collection container, The device according to claim 11, wherein the plunger is either integrated with the sample collection container or independent of the sample collection container.

13. The device according to claim 1, wherein the sample collection container is equipped with a funnel, and the funnel is integrated with the sample collection container or can be coupled to the sample collection container.

14. The filtrate collection container contains the preservative, or The device according to claim 1, wherein the filtrate collection container contains: ethylenediaminetetraacetic acid (EDTA); RNase inhibitors; antibacterial agents; denaturants; agents that inhibit nuclease activity; metal ion chelating agents; buffers; salts; osmotic pressure modifiers; or preservatives selected from at least one of these combinations.

15. The device according to claim 14, wherein at least one of the following applies: (a) The denaturing agent includes a nucleic acid denaturing agent or a protein denaturing agent; (b) The agent that inhibits nuclease activity includes one or more protein denaturants, surfactants such as EDTA and SDS, aurintricarboxylic acid (ATA), chelating agents, or combinations thereof; (c) The agent that inhibits nuclease activity comprises one or more protein denaturants comprising a surfactant, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodyne, or guanidine hydrochloride; and (d) The agent that inhibits nuclease activity contains one or more protein denaturants, including guanidine thiocyanate, at a concentration of about 30% to about 70%.

16. The device according to claim 1, wherein at least one of the following applies: (a) The filtrate collection container is removable from the filtration unit; (b) further comprising a cap for the filtrate collection container from which the device has been removed; (c) further comprising a cap for the filtrate collection container from which the device has been removed, and a housing for storing a preservative released when the cap is fixed onto the filtrate collection container from which the cap has been removed; (d) further comprising a second container for decanting the filtrate; and (e) further comprising a second container for decanting the filtrate, wherein the second container contains a preservative.

17. The device according to claim 1, wherein at least one of the following applies: (a) The device stabilizes the analyte in a temperature range of approximately -20°C to approximately 50°C; (b) The analyte is stabilized for at least five days; (c) The analyte comprises polypeptides, proteins, metabolites, low molecular weights, cells, cell-free analytes, or nucleic acids; and (d) The analyte comprises nucleic acids including cell-free RNA, mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof.

18. The device according to claim 1, wherein at least one of the following applies: (a) The filtration unit is removable to recover the residual liquid so that biomolecules and cells contained in the residual liquid can be preserved and analyzed; (b) The at least one filter reduces the viscosity of the filtrate and is compared with the sample: and (c) The at least one filter removes mucin from the sample.

19. The device according to claim 1, and: a) Plunger; b) A cap for the filtrate collection container; c) Funnel; and / or d) Preservatives, A kit containing at least one of the following.

20. A method for stabilizing an analyte in a sample, the method comprising: adding the sample to a sample collection container of the device described in claim 1 or the kit described in claim 19; filtering the sample through a filtration unit; and contacting the filtrate with a preservative.

21. An analyte stabilized by the method described in claim 20.

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