Methods for disease detection

Abstract

Methods for analyzing and detecting disease or condition of a subject are described herein. Methods for storing samples for detecting disease or condition of a subject are also described herein. Detecting disease or condition includes: a) detecting the presence of at least one analyte in a sample from a subject, or measuring the abundance of at least one analyte in a sample; and b) generating a score for the likelihood of the subject having disease or condition or developing disease or condition.

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 more than millions of people. For example, about 1 in 8 women in the United States will develop invasive breast cancer in their lifetime. Early detection and early treatment can increase the survival rate of cancer patients. However, cancer detection can be troublesome and prone to false positive or false negative test results. Summary of the Invention

[0003] Thus, there remains a need for a method for detecting a disease, such as cancer, in a biological sample, which method efficiently and accurately obtains, processes, and analyzes the biological sample. In some aspects, a method for detecting a disease or condition in a subject is described herein, which includes detecting the presence of at least one analyte in a sample from a subject, or measuring the abundance of at least one analyte, and generating a score for the likelihood of the subject having or developing a disease or condition, wherein the sample is from a sampling site different from the site of the disease or condition, and the presence of at least one analyte or the abundance of at least one analyte in the sample correlates with the presence of at least one analyte at the site of the disease or condition, the abundance of at least one analyte, or the outcome of the disease or condition. In some embodiments, prior to detecting, the method includes storing the sample. In some embodiments, storing the sample includes contacting the sample with a preservative comprising at least one of the following: ethylenediaminetetraacetic acid (EDTA); RNase inhibitors; antibacterial agents; denaturants; agents that inhibit nuclease activity; sequestrants; buffers; salts; osmolytes; or combinations thereof. In some embodiments, the denaturing agent comprises a nucleic acid denaturing agent or a protein denaturing agent. In some embodiments, prior to detecting, 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, the cell-containing fraction comprising 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, the at least one analyte comprises a cell-free analyte. In some embodiments, the 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, the 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, the at least one analyte comprises a small molecule. In some embodiments, the at least one analyte comprises a metabolite. In some embodiments, the at least one analyte comprises a cell. In some embodiments, the detecting comprises sequencing at least one analyte, the at least one analyte comprising at least one nucleic acid. In some embodiments, the detecting 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 DNA or cell-free saliva RNA of the subject, and both genetic and transcriptomic analysis are used to detect the presence of a disease or condition in the subject. In some embodiments, multiple samples from the subject are processed using different versions of the workflow described herein. In some embodiments, the method further comprises obtaining a sample from the subject.

[0004] In some aspects, methods are described herein for detecting a disease or condition in a subject, the methods comprising using a computer system comprising a hardware processor and a memory in which instructions are coded to cause the hardware processor to perform the following operations: detect the presence of at least one analyte in a sample from the subject or measure the abundance of at least one analyte in the sample, 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 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 in 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 methods further comprise generating a machine learning model that is iteratively trained to detect the disease or condition in the sample. In some embodiments, the methods further comprise generating a machine learning model that is iteratively trained to generate a score for the likelihood of the subject having the disease or condition. In some embodiments, the methods further comprise generating a machine learning model that is iteratively trained to generate a score for 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.

[0005] In some aspects, a device for detecting a disease or condition in a subject is described herein, the device comprising a computer system comprising a hardware processor and a memory in which instructions are coded to cause the hardware processor to perform the following operations: detect the presence of at least one analyte in a sample obtained from the subject, or measure the abundance of at least one analyte in the sample, and generate a score for the likelihood of the subject having or developing the disease or condition, where 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 in 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 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 for 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 for 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.

[0006] 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 a nucleic acid transcript or genomic region of interest; (f) qualitatively or quantitatively detecting the selected analytes in an assay, which may include 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.

[0007] 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 a 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 at least one analyte in the sample correlates with the presence of at least one analyte at the site of the disease or condition, the abundance of 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 sequestering agent; a buffer; a salt; an osmolyte; 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, 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, the cell-containing fraction comprising 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 bodily fluid sample.In some embodiments, the bodily fluid sample comprises a saliva sample. In some embodiments, the at least one analyte comprises a nucleic acid. In some embodiments, the at least one analyte comprises a cell-free analyte. In some embodiments, the nucleic acid comprises a cell-free RNA. In some embodiments, the nucleic acid comprises an mRNA, a small RNA, a miRNA, a snoRNA, a snRNA, a rRNA, a tRNA, an siRNA, an hnRNA, a long non-coding RNA, an shRNA, a fragment thereof, or a combination thereof. In some embodiments, the 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, the at least one analyte comprises a small molecule. In some embodiments, the at least one analyte comprises a metabolite. In some embodiments, the at least one analyte comprises a cell. In some embodiments, b) comprises sequencing at least one analyte comprising at least one nucleic acid. In some embodiments, 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 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, two or more samples are taken from a patient, and the samples are processed using different versions of the workflow described herein.

[0008] Also disclosed herein is a method for stabilizing nucleic acid in a sample, the method comprising: a) filtering the sample so that the nucleic acid flows through the filter and remains 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 acid in the sample is 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 with a filtration unit comprising at least one filter, the device comprising a prefiltration mechanism to prevent clogging of the smaller pore size filter; one or more filters; a plurality of filters arranged in sequence with pore sizes that allow successively smaller species to pass; or at least two filters of the same type; 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 accomplished using depth filtration. In some embodiments, the method further comprises using a filtrate collection vessel pre-filled with one or more denaturing agents that allow 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 vessel. In some embodiments, the filtered sample is removed from the device or decanted into a second vessel containing a preservative. In some embodiments, the collection unit or filtration unit is separated from the post-filtration filtrate collection vessel 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 vessel. In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about 50°C. In some embodiments, the nucleic acid is 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 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 acellular or cell-depleted biological fluid. In some embodiments, filtering produces acellular or cell-depleted plasma. In some embodiments, filtering produces acellular or cell-depleted saliva. In some embodiments, filtering produces acellular 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.

[0009] 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 in which 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 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.

[0010] 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 in which instructions are coded to cause the hardware processor to perform the operations of: detecting the presence of at least one analyte in the sample or measuring the abundance of at least one analyte, and generating 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.

[0011] 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 a disease or condition, the sample being from a sampling site different from the site of the disease or condition, and the presence of at least one analyte or the abundance of at least one analyte in the sample correlates with the presence of at least one analyte at the site of the disease or condition, the abundance of at least one analyte, 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 sequestering agent; a buffer; a salt; an osmolyte; 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, the cell-containing fraction comprising 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, the at least one analyte comprises a cell-free analyte. In some embodiments, the at least one analyte comprises a nucleic acid. In some embodiments, the nucleic acid comprises a cell-free RNA. In some embodiments, the nucleic acid comprises an mRNA, a small RNA, a miRNA, a snoRNA, a snRNA, a rRNA, a tRNA, an siRNA, an hnRNA, a long non-coding RNA, a shRNA, a fragment thereof, or a combination thereof. In some embodiments, the 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, the at least one analyte comprises a small molecule. In some embodiments, the at least one analyte comprises a metabolite. In some embodiments, the at least one analyte comprises a cell. In some embodiments, a) comprises sequencing the at least one analyte, and the at least one analyte comprises at least one nucleic acid. In some embodiments, a) comprises hybridizing at least one 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 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, the at least one analyte is DNA or cell-free saliva RNA of the subject, 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 a workflow described herein. In some embodiments, the method further comprises obtaining a sample from the subject.

[0012] Disclosed herein is a method for detecting a disease or condition in a subject, the method comprising using a computer system comprising a hardware processor and a memory in which instructions are coded to cause the hardware processor to perform the following operations: detect the presence of at least one analyte in a sample from the subject, 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, the sample being collected 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 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 further comprises generating a machine learning model that is iteratively trained to detect the disease or condition in the sample. In some embodiments, the method further comprises generating a machine learning model that is iteratively trained to generate a score for the likelihood of the subject having the disease or condition. In some embodiments, the method further comprises generating a machine learning model that is iteratively trained to generate a score for 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.

[0013] 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 in which instructions are coded 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 a subject, and generate a score for the likelihood of the subject having or developing a disease or condition, the sample being 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 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 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 for 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 for 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 a device for collecting and stabilizing an analyte in a sample, the device comprising: a) a sample collection vessel for collecting a sample; b) a filtration unit in fluid communication with the sample collection vessel, the filtration unit comprising at least one filter for filtering the sample to generate a filtrate; c) a filtration unit; and a filtrate collection vessel 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 a plurality of filters arranged in sequence with pore sizes that successively allow 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 filter. 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, 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, 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 The filter stack heights may range from 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 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, hi 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 acellular or cell-depleted biological fluid. In some embodiments, the filtrate is acellular or cell-depleted plasma. In some embodiments, the filtrate is acellular or cell-depleted saliva. In some embodiments, the filtrate is acellular or 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 the sample collection container or is separate from the sample collection container. In some embodiments, the sample collection container includes a funnel, which is integral with the sample collection container or is 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 sequestering agent; a buffer; a salt; an osmolyte; 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 include one or more chaotropic agents including surfactants, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more protein denaturants include 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 over 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, the at least one analyte comprises a cell-free analyte. In some embodiments, the 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, the 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, the at least one analyte comprises a small molecule. In some embodiments, the at least one analyte comprises a metabolite. In some embodiments, the 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 is compared to the sample. In some embodiments, the at least one filter removes mucin from the sample.

[0015] Also described herein are kits that include a device as 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.

[0016] Also described herein are methods for stabilizing an analyte in a sample, the methods including adding a 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.

[0017] Also described herein are analytes that are stabilized by the methods described herein.

[0018] Incorporation by Reference All publications, patents, and patent applications mentioned in this specification are incorporated herein 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 description of the drawings]

[0019] [Figure 1] 1 shows a flow diagram providing an overview of the workflow steps.

[0020] [Diagram 2] 1 shows a flow diagram providing an overview of the workflow steps.

[0021] [Diagram 3] FIG. 1 is a flow chart for identifying tissue-specific and tissue-enriched transcripts present in patient samples.

[0022] [Figure 4]Figure 1 shows that tissue-specific transcripts in saliva demonstrate broad disease detectability. Specifically, the figure shows a hierarchical clustering heat map of tissues and samples, where red represents high levels of mRNA overlap between tissue-specific transcripts from an individual and acellular saliva, and blue represents low levels of mRNA overlap.

[0023] [Diagram 5] Figure 1 shows that tissue enriched transcripts in saliva demonstrate broad disease detectability. Specifically, the figure shows a hierarchical clustering heat map of tissues and samples, where red represents high levels of mRNA overlap between tissue enriched transcripts from an individual and acellular saliva, and blue represents low levels of mRNA overlap.

[0024] [Figure 6] A diagram illustrating the contents of whole saliva is shown.

[0025] [Figure 7] A diagram showing the device concept with steps is shown.

[0026] [Figure 8] A diagram illustrating a preliminary saliva collection and storage experiment is shown. In the experiment, saliva filtration to remove human cells was followed by the addition of a chaotropic preservative to preserve the resulting cell-free saliva. BioAnalyzer analysis was used to assess quantification and degradation. To assess degradation, ACTB, FOSL2 and NAMPT transcript levels were monitored by RT-qPCR.

[0027] [Figure 9] Preliminary data for the nucleic acid preservation embodiment described herein is presented. Sample filtration provided similar results as removal by centrifugation. Addition of preservative protected the transcripts for 4 days.

[0028] [Figure 10]Graphs illustrating the improvement of human sequence coverage by exome capture are shown. 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.

[0029] [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.

[0030] [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, constituting 20% ​​of total reads. The greatest variation was observed for hg38 mapped reads, with a difference of approximately 100-fold between maximum and minimum coverage.

[0031] [Figure 13] A diagram illustrating the final reads after alignment is shown. De-duplication reduced the number of reads by about 8-fold, but removed amplification bias. "Assigned to gene" represents the final feature.

[0032] [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.

[0033] [Figure 15A]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. Bias was observed towards higher coverage at the 5' end of the transcript. [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. Bias was observed towards higher coverage at the 5' end of the transcript.

[0034] [Figure 16] A chart is shown representing an NGS study of 301 patients, including 115 breast cancer patients and 186 non-cancer patients.

[0035] [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] Graph showing classification results. In Figure 17B, classification was performed using randomized disease labels. The average AUC over 100 iterations was 0.486, suggesting that the results with correct labels are non-random. This performance may be generalized across all samples by increasing the number of hg38-mapped reads through assay optimization.

[0036] [Figure 18] 1 shows a table illustrating that gene set enrichment analysis shows multiple gene sets enriched in the cancer group.

[0037] [Figure 19] A diagram showing an ensemble classifier including logistic regression, random forest, and XGBoost is shown.

[0038] [Figure 20] A diagram illustrating 10-fold cross-validation performed 100 times with shuffling is shown.

[0039] [Figure 21A] The performance of a 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 represented genes that were commonly upregulated in cancer patients, while negative coefficients represented downregulation. [Figure 21C] We show the discrimination of the classifier as a function of features removed. Discrimination was lost after removing approximately 250 top features.

[0040] [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.

[0041] [Diagram 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.

[0042] [Figure 24] A non-limiting example of a cloud-based web / mobile application delivery system is shown, where the system comprises elastically load balanced, auto-scaling web server and application server resources, as well as a synchronously replicated database.

[0043] [Figure 25A] 25A shows the improved preservation of nucleic acids in saliva samples, and FIG 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, illustrating the 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] 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 25D shows improved preservation of nucleic acids in saliva samples. Figure 25E shows the stability of endogenous transcripts over a 7 day period. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0044] 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 likelihood of the subject to develop the 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 is obtained. For example, the methods can detect or determine the likelihood of a subject 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 the nucleic acids in the sample. Figures 1-3 show an exemplary workflow utilizing the methods described herein for analyzing a sample to detect a disease or condition in a subject. In some embodiments, the methods include determining overexpression or underexpression of a transcript in the sample, which may correspond to overexpression or underexpression of the same transcript in a location of the body different from where the sample was taken. For example, Figures 4 and 5 show that transcripts found in saliva overlap greatly with transcripts found in blood and esophageal mucosal tissue.

[0045] In some embodiments, saliva (also called spit) is an extracellular liquid produced and secreted by the salivary glands in the mouth. In some embodiments, in humans, saliva includes 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 materials. 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 signaling. 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.

[0046] In some embodiments, the 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 the 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.

[0047] In some embodiments, the intact cells may include epithelial cells or white blood cells. In some embodiments, the intact cells may also include microorganisms.

[0048] In some embodiments, the method includes preserving nucleic acid in a sample obtained from a subject. Figures 7 and 8 show various combinations of fractionation (e.g., by centrifugation and / or filtration), preservation using denaturing agents, and quality control experiments to preserve and determine the integrity of nucleic acid in a sample. Figures 8 and 9 show experiments to measure the integrity of nucleic acid preserved by the methods described herein. Figure 10 shows a chart comparing the percentage of GENCODE coverage before and after enrichment.

[0049] 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 for analyzing, training, and improving the disease or condition detection methods described herein (e.g., Figures 11-20). Figures 16, 17A, 17B, 21A, 21B, and 21C show examples of utilizing the methods described herein for classifying clinical samples. Figures 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.

[0050] 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 a nucleic acid transcript or genomic region of interest; (f) qualitatively or quantitatively detecting the selected analytes in an assay, which may include 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.

[0051] 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.

[0052] 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 an mRNA, a small RNA, a miRNA, a snoRNA, a snRNA, a rRNA, a tRNA, an siRNA, an hnRNA, a long non-coding RNA, an shRNA, a fragment thereof, or a combination thereof. In some embodiments, the nucleic acid comprises an mRNA. In some embodiments, the nucleic acid comprises a small RNA. In some embodiments, the nucleic acid comprises an miRNA. In some embodiments, the nucleic acid comprises a snoRNA. In some embodiments, the nucleic acid comprises a snRNA. In some embodiments, the nucleic acid comprises an rRNA. In some embodiments, the nucleic acid comprises a tRNA. In some embodiments, the nucleic acid comprises an siRNA. In some embodiments, the nucleic acid comprises an hnRNA. In some embodiments, the nucleic acid comprises a long non-coding RNA. In some embodiments, the nucleic acid comprises an shRNA. Fragments of any such nucleic acids are also contemplated.

[0053] 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.

[0054] In some embodiments, at least one analyte comprises a small molecule.

[0055] In some embodiments, the at least one analyte comprises a cell.

[0056] Also disclosed herein is a method for stabilizing nucleic acid in a sample, in which 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.

[0057] In some embodiments, the one or more denaturing agents include one or more chaotropic agents. In some embodiments, the one or more chaotropic agents include a detergent, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodaine, or guanidine hydrochloride. In some embodiments, the one or more chaotropic agents include a detergent. In some embodiments, the one or more chaotropic agents include urea. In some embodiments, the one or more chaotropic agents include thiourea. In some embodiments, the one or more chaotropic agents include guanidine thiocyanate. In some embodiments, the one or more chaotropic agents include guanidine hydrochloride.

[0058] 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 one or more denaturing agents include guanidine thiocyanate at a concentration of about 40% to about 70%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 50% to about 70%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 60% to about 70%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 30% to about 60%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 30% to about 50%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 30% to about 40%.

[0059] In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 30%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 35%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 40%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 45%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 50%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 55%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 60%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 65%. In some embodiments, the one or more denaturing agents include guanidine thiocyanate at a concentration of about 70%.

[0060] 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.

[0061] In some embodiments, the sample is filtered prior to the addition of one or more protein denaturing agents.

[0062] In some embodiments, the sample is collected or transferred to a device that includes a filtration unit that includes at least one filter. In some embodiments, the device includes a prefiltration mechanism to prevent, reduce, or inhibit clogging of smaller pore size filters; one or more filters; a plurality of 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 includes using a filtrate collection vessel that is pre-filled with one or more denaturants that allow 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 vessel. In some embodiments, the filtered sample is removed from the device or decanted into a second vessel containing a preservative. In some embodiments, the collection unit or filtration unit is separated from the filtrate collection vessel 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 upon securing the cap over a removed filtrate collection vessel.

[0063] In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about 50°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about -10°C to about 50°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about 0°C to about 50°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about 10°C to about 50°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about 20°C to about 50°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about 30°C to about 50°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about 40°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about 30°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about 20°C. In some embodiments, the nucleic acid is stabilized at a temperature range of about -20°C to about 10°C. In some embodiments, the nucleic acid is stabilized at 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.

[0064] 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.

[0065] In some embodiments, the nucleic acid is stabilized for the final 5 days.

[0066] 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.

[0067] In some embodiments, at least one filter comprises a low nucleic acid binding material.

[0068] 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.

[0069] 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.

[0070] In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 20 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 15 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 10 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 9 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 8 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 7 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 6 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 5 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 4 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 3 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 2 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 1 μm. In some embodiments, the size cutoff of the at least one filter is about 0.1 μm to about 0.5 μm. In some embodiments, the size cutoff of the at least one filter is about 0.5 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 1 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 2 μm to about 25 μm. In some embodiments, the size cutoff of the 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 the at least one filter is about 5 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 6 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 7 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 8 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 9 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 10 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 15 μm to about 25 μm. In some embodiments, the size cutoff of the at least one filter is about 20 μm to about 25 μm.

[0071] 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.

[0072] 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.

[0073] In some embodiments, the filter is constructed from synthetic materials to minimize the introduction of contaminating nucleic acids, hi some embodiments, the filter is constructed from biological materials.

[0074] 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.

[0075] In some embodiments, the disease or condition (or medical condition) is cancer. In some embodiments, the type of cancer is a solid cancer type or a hematological malignancy. In some embodiments, the type of cancer is a metastatic cancer type or a recurrent or refractory cancer type. 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 adenocar ... adenocarcinoma (CESC), childhood cancer, cholangiocarcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic myelogenous 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 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 head and neck squamous cell carcinoma of unknown primary site, oral cancer, multiple endocrine neoplasia, multiple myeloma, multiple myeloma / plasma cell neoplasm, mycosis fungoides, myelodysplastic syndrome, myeloproliferative disorder, nasal cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cavity cancer cancer, oropharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell cancer, ovarian low malignant potential tumor, pancreatic cancer, papilloma, paranasal 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 cancer, thymoma (THYM), thyroid cancercancer, THCA), transitional cell carcinoma, transitional cell carcinoma of the renal pelvis and ureter, trophoblastic tumor, 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.

[0076] 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.

[0077] 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.

[0078] In some embodiments, the disease or condition is an injury.

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

[0080] Nucleic Acid Preservation 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 mainly cell-free nucleic acids for analysis. Nucleases present in biological samples are the main underlying cause of nucleic acid degradation. Their action can be inhibited by the addition of nuclease inhibitors as described below.

[0081] In some embodiments, metal chelators such as ethylenediaminetetraacetic acid (EDTA) can inhibit nucleases that are dependent 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.

[0082] In some embodiments, competitive inhibitors that bind to the enzyme active site may also be used, however, because there are many different types of nucleases, a large number of competitive inhibitors would be required, making it difficult to universally inhibit nucleolytic activity using this approach.

[0083] 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.

[0084] In some aspects, the method of heat denaturation of nucleases has limitations in that some nucleases can revert to their native conformation upon cooling, and the process itself can be damaging to nucleic acids. The 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.

[0085] 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.

[0086] In some embodiments, chemical denaturation of the nuclease, such as the addition of a drug that denatures the protein, can render the nuclease inactive. This strategy has the advantage that nuclease activity can be completely inhibited without damaging the nucleic acid until it can be extracted.

[0087] 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.

[0088] In some embodiments, the denaturing agent comprises a nucleic acid denaturing agent or a protein denaturing agent. The denaturing agent comprises a nucleic acid denaturing agent. The denaturing agent comprises a protein denaturing agent.

[0089] device Also described herein are devices, an example of which is shown in Figure 7. The device is for collecting and stabilizing an analyte in a sample, the device comprising: a) a sample collection container 2 for collecting a sample; b) a filtration unit 3 in fluid communication with the sample collection container 2, the filtration unit 3 comprising 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.

[0090] In embodiments, the filtration unit 2 comprises multiple filters arranged in order with pore sizes that allow successively smaller species to pass through. In additional or alternative embodiments, the 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 filter. For example, the 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.

[0091] 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.

[0092] 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, 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, 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.

[0093] The at least one filter may have any desired thickness as well. For example, in embodiments, the thickness of the filter may be 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, 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.

[0094] Two or more filters can be stacked together, resulting in a stack height that is the thickness of the combined filters. In an embodiment, the filtration unit 2 is 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 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 μ 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 In some embodiments, the filter stack height may be from about 120 μm to about 10,000 μm, such as about 100 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, about 9500 μm, or about 10,000 μm.

[0095] Most filters are circular and have a diameter. Although 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.

[0096] 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 synthetic materials 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 small amounts of analytes. Thus, in some embodiments, it is advantageous to avoid the use of biological materials in the filter material.

[0097] 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.

[0098] The sample is typically a biological fluid. The biological fluid may include any bodily fluid, examples of which include blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid.

[0099] The filtrate collected by the device is, in embodiments, acellular or cell-depleted biological fluid. In embodiments, the filtrate is cell-free or cell-depleted plasma. In embodiments, the filtrate is cell-free or cell-depleted saliva. In embodiments, the filtrate is cell-free or cell-depleted urine.

[0100] 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 push 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.

[0101] 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 .

[0102] 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 prior to placing the sample 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 an embodiment, the filtrate collection container 4 is removable from the filtration unit 2. In an embodiment, the device further comprises a cap 7 for the removed filtrate collection container 2. In some embodiments, the cap 7 comprises a housing for storing the preservative 6 that is released upon securing the cap 7 onto the removed filtrate collection container 4. In additional or alternative embodiments, the device further comprises a second container for decanting the filtrate. In an embodiment, the second container comprises a preservative 6.

[0103] 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 combinations thereof. In embodiments, the one or more protein denaturants comprise one or more chaotropic agents, including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodain, 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%.

[0104] In embodiments, the device stabilizes the analyte at a desired temperature range, such as freezing temperatures, refrigerator temperatures, room temperature, 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.

[0105] 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.

[0106] In embodiments, the at least one analyte comprises a cell-free analyte.

[0107] In an embodiment, at least one analyte comprises a nucleic acid. Any nucleic acid or fragment of a nucleic acid is contemplated. For example, in an embodiment, the nucleic acid comprises cell-free RNA. In an embodiment, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof.

[0108] 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.

[0109] 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.

[0110] 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.

[0111] 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.

[0112] 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, together 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.

[0113] Also described herein are methods for stabilizing an analyte in a sample, the methods including adding a sample to a sample collection vessel of a device or kit described herein, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

[0114] When the methods are used to stabilize an analyte, the stabilized analyte is collected, and thus, the analyte stabilized by the methods is also described herein.

[0115] Computing System Referring to Figure 22, a block diagram is shown illustrating an example machine including an executing computer system 2700 (e.g., a processing system 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 Figure 22 are examples only and do not limit the scope of use or functionality of any hardware, software, embedded logic elements, or combinations of two or more such components to implement a particular embodiment.

[0116] The computer system 2700 may include one or more processors 2701, memory 2703, and storage 2708 that 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), printed circuit boards (PCBs), mobile handheld devices (such as mobile phones or PDAs), laptop or notebook computers, distributed computer systems, computing or servers.

[0117] The 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. The processor 2701 optionally contains a cache memory unit 2702 for temporary local storage of instructions, data, or computer addresses. The processor 2701 is configured to support the execution of computer-readable instructions. The computer system 2700 may provide the functionality of the components depicted in FIG. 23 as a result of the 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 media may store software implementing certain embodiments, and the processor 2701 may execute the software. Memory 2703 may read software from one or more other computer-readable media (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.

[0118] 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), including 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.

[0119] Persistent storage 2708 is optionally connected 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), and the like. 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 be incorporated as virtual memory in memory 2703, where appropriate.

[0120] In one example, storage device 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 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.

[0121] 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.

[0122] The computer system 2700 may also include input devices 2733. In one example, a user of the computer system 2700 may input commands and / or other information into the computer system 2700 via the input devices 2733. Examples of the 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 touch screen, 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, the input device is a Kinect, Leap Motion, or the like. The input devices 2733 may be interfaced to the bus 2740 via any of a variety of input interfaces 2723 (e.g., input interface 2723), including, but not limited to, serial, parallel, game port, USB, FIREWIRE®, THUNDERBOLT®, or any combination of the above.

[0123] In certain 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 (such as requests or responses from other devices) in the form of one or more packets (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 (such as 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.

[0124] Examples of network interfaces 2720 include, but are not limited to, a network interface card, a modem, and any combination thereof. Examples of networks 2730 or network segments 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, a building, a 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.

[0125] Information and data can be displayed through a display 2732. Examples of the 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 active-matrix OLED (AMOLED) display, a plasma display, and any combination thereof. The display 2732 can be interfaced to the processor 2701, memory 2703 and fixed storage 2708, and other devices such as input device(s) 2733 via a bus 2740. The display 2732 is linked to the bus 2740 via a video interface 2722, and data transfer between the display 2732 and the bus 2740 can be controlled via a 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.

[0126] 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 output interface 2724. Examples of output interface 2724 include, but are not limited to, serial ports, parallel connections, USB ports, FIREWIRE® ports, THUNDERBOLT® ports, and any combination thereof.

[0127] In some embodiments, additionally or alternatively, computer system 2700 may provide functionality as a result of hardwired logic or logic otherwise embodied 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 references to logic may encompass software. Furthermore, in some embodiments, references to computer-readable media may encompass circuitry (such as ICs) storing software for execution, circuitry embodying logic for execution, or both, as appropriate. This disclosure encompasses any suitable combination of hardware, software, or both.

[0128] In some embodiments, those skilled in the art will appreciate that the various illustrative logical blocks, modules, circuits, and algorithmic 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.

[0129] In some embodiments, the various example 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.

[0130] 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, the software modules 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.

[0131] 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, sub-notebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet appliances, mobile smart phones, 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.

[0132] In some embodiments, the 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 examples, 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 examples, 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 examples, 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, one skilled in the art 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, one skilled in the art 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®.

[0133] 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 encoded with 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 examples, CD-ROMs, DVDs, flash memory devices, solid-state memories, 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.

[0134] Computer Programs 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 that are written to perform a specified task and are executable by one or more processors of a CPU of a computing device. In some embodiments, computer readable instructions may be implemented as program modules, such as functions, objects, application program interfaces (APIs), computing data structures, and the like, that perform particular tasks or implement particular abstract data types. In light of the disclosure provided herein, one of ordinary skill in the art will understand that computer programs may be written in a variety of languages ​​and in a variety of versions.

[0135] In some embodiments, the functionality of the computer readable instructions may be combined or distributed as desired in various environments. In some embodiments, the computer program includes one sequence of instructions. In some embodiments, the computer program includes multiple sequences of instructions. In some embodiments, the computer program is provided from one location. In other embodiments, the computer program is provided from multiple locations. In various embodiments, the computer program includes one or more software modules. In various embodiments, the 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.

[0136] Web Applications In some embodiments, the computer program comprises a web application. In light of the disclosure provided herein, one skilled in the art will recognize that a web application, in various embodiments, utilizes one or more software frameworks and one or more database systems. In some embodiments, the web application is created 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 examples, relational, non-relational, object-oriented, associative databases, and XML database systems. In further embodiments, suitable relational database systems include, by way of non-limiting examples, Microsoft® SQL Server, mySQL®, and Oracle®. One skilled in the art will also recognize that, in various embodiments, a web application is written in one or more languages ​​in one or more versions. A web application 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, the 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, the 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 a number of suitable multimedia technologies, including, by way of non-limiting example, Adobe® Flash®, HTML 5, Apple® QuickTime®, Microsoft® Silverlight®, Java™, and Unity®.

[0137] 23, in a particular embodiment, the application provisioning system includes one or more databases 2800 accessed by a relational database management system (RDBMS) 2810. In some embodiments, suitable RDBMS include Firebird, MySQL, PostgreSQL, SQLite, Oracle Database, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase, Teradata, etc. In this embodiment, the application provisioning system further includes one or more application servers 2820 (e.g., Java server, .NET server, PHP server, etc.) and one or more web servers 2830 (e.g., Apache, IIS, GWS, etc.). 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 a browser-based and / or mobile native user interface.

[0138] Referring to FIG. 24, in a particular embodiment, 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.

[0139] 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.

[0140] In light of the disclosure provided herein, mobile applications are generated by techniques known to those of skill in the art using hardware, languages, and development environments known in the art. In some embodiments, those of skill in the art will appreciate that mobile applications are written in a number of languages. In some embodiments, suitable programming languages ​​include, by way of non-limiting examples, 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.

[0141] In some embodiments, suitable mobile application development environments are available from a number of 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 at no cost, 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.

[0142] 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 examples, 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.

[0143] Standalone Applications In some embodiments, the computer program includes a standalone application, which is a program that runs as a dependent computer process rather than an add-on to an existing process, e.g., a plug-in. In some embodiments, one 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 code. In some embodiments, suitable compiled programming languages ​​include, by way of non-limiting examples, 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 includes one or more executable compiled applications.

[0144] Web browser plugins 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. Makers of software applications support plug-ins to allow third party developers to create the ability to extend the application, to support easy addition of new features, and to 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.

[0145] In light of the disclosure provided herein, one of skill 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.

[0146] 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, sub-notebook 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.

[0147] 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, the software modules are generated by techniques known to those skilled in the art using machines, software, and languages ​​known in the art. In some embodiments, the software modules disclosed herein are implemented in a number of ways. In various embodiments, the software modules include files, sections of code, programming objects, programming structures, or combinations thereof. In further various embodiments, the software modules include multiple files, multiple sections of code, multiple programming objects, multiple programming structures, or combinations thereof. In various embodiments, the one or more software modules include, by way of non-limiting examples, web applications, mobile applications, and standalone applications. In some embodiments, the software modules are present in one computer program or application. In other embodiments, the software modules are present in more than one computer program or application. In some embodiments, the software modules are hosted on one machine. In other embodiments, the software modules are hosted on more than one machine. In further embodiments, the software modules are 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.

[0148] 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, one of skill in the art will appreciate that many databases are suitable for storing and retrieving information. In various embodiments, suitable databases include, by way of non-limiting examples, 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.

[0149] How to use a computer In some embodiments, the methods and software described herein may utilize one or more computers. In some embodiments, the computer may be used for managing customer and sample information, such as sample or customer tracking, for database management, for analysis of molecular profiling data, for analysis of cytological data, for data storage, for advertising, for marketing, for reporting results, for storing results, or combinations thereof. In some embodiments, the 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, the computer may also include a means for inputting data or information. In some embodiments, the computer may include a processing unit and fixed or removable media, or combinations thereof. In some embodiments, the 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, the computer may be connected to a server or other communications device for relaying information from the user to the computer or from the computer to the user. 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 the 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 health care provider, or a health care 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.

[0150] In some embodiments, an entity obtaining sample information may 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 may include, but is not limited to, customer name, specific customer identification, customer associated medical professional, assay indicated, assay result, validity status, validity test indicated, individual medical history, preliminary diagnosis, suspected diagnosis, sample history, insurance provider, medical provider, third party testing center, or any information suitable for storage in a database. In some embodiments, sample history may include, but is not limited to, sample age, sample type, acquisition method, storage method, or transportation method.

[0151] 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 communication, 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 rendered. In some embodiments, the extent of database access or sample information may be restricted to comply with commonly accepted or legal requirements for patient or customer confidentiality.

[0152] Machine Learning In some embodiments, the 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 results, or any other relevant medical information described herein. In some cases, the machine learning methods may purposefully group or separate treatment options. In some embodiments, some treatment options may be purposefully clustered or removed from any one of multiple stages of medical encounters.

[0153] 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 up to 1800, 1600, 1500, 1400, 1300, 1200, 1100, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, 10 or less.

[0154] 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.

[0155] In some embodiments, linear regression can be a method of predicting a target variable by fitting the best linear relationship between the dependent 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.

[0156] 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 explain relationships between one dependent binary variable and one or more nominal, ordinal, interval, or proportional level independent variables. In some embodiments, resampling can be a method that includes drawing replicate samples from the original data sample. In some embodiments, resampling can include utilizing a comprehensive distribution table to calculate approximate probability values. In some embodiments, resampling can generate a unique sampling distribution based on the actual data. In some embodiments, resampling can use an empirical method rather than an analytical method to generate a unique sampling distribution. In some embodiments, resampling techniques can include bootstrapping and cross-validation. In some embodiments, bootstrapping can be performed by sampling with replacement from the original data and taking "unselected" data points as test cases. In some embodiments, cross-validation can be performed by splitting the training data into multiple portions.

[0157] In some embodiments, subset selection can identify a subset of predictors that are associated with the response. In some embodiments, subset selection can include best subset selection, forward elimination, backward elimination, hybrid methods, or any combination thereof. In some cases, shrinkage fits a model that includes all predictors, but the estimated coefficients shrink toward zero for least squares estimation. In some embodiments, this shrinkage can reduce variance. In some embodiments, shrinkage can include ridge regression and lasso. 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 projections thereof. These n projections are then used as predictors to fit a linear regression model by 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 a linear combination of the data followed by an orthogonal method to capture the most variance in the data. In some embodiments, partial least squares can be a supervised alternative to principal component regression since it can use the response variables to identify novel features.

[0158] 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 depends on one or more independent variables, hi some embodiments, the nonlinear regression may include step functions, piecewise functions, splines, generalized additive models, or any combination thereof.

[0159] In some embodiments, tree-based methods may be used for both regression and classification problems. In some embodiments, regression and classification problems may include 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 to train on from the original data set using a combination with repetition to generate multiple stages of the same cardinality / size as the original data. In some embodiments, boosting may calculate the output 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 include 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 the objective with the margin maximized against the constraint of perfectly classifying the data.

[0160] 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, an unsupervised method may include clustering, principal component analysis, k-means clustering, hierarchical clustering, or any combination thereof.

[0161] definition Unless otherwise defined, all terms, notations, and other technical and scientific or specialist terms 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 cases, 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 difference from what is commonly understood in the art.

[0162] 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 disclosure. Thus, 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 1-6 should be considered to have specifically disclosed subranges such as 1-3, 1-4, 1-5, 2-4, 2-6, 3-6, and the individual numbers within that range, for example, 1, 2, 3, 4, 5, and 6. This applies regardless of the breadth of the range.

[0163] 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 a plurality of samples, including a mixture of samples.

[0164] 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.

[0165] The terms "subject," "individual," or "patient" are often used interchangeably herein. A "subject" may be a biological entity that contains expressed genetic material. The biological entity may be a plant, animal, or microorganism, including, for example, bacteria, viruses, fungi, and protozoa. A subject may be tissues, cells, and their progeny, of a biological entity obtained in vivo or cultured in vitro. A subject may be a mammal. A subject may be a human. A subject may 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.

[0166] As used herein, the term "about" a number refers to ±15% of that number. The term "about" refers to the range minus 15% of its lowest value and plus 15% of its highest value.

[0167] 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 outcome in a recipient. Beneficial or desired outcome includes, but is not limited to, therapeutic benefit and / or prophylactic benefit. Therapeutic benefit may refer to eradication or amelioration of the condition being treated or its underlying disease. In addition, therapeutic benefit may be achieved with eradication or amelioration of one or more of the physiological symptoms associated with the underlying disease such that an improvement is observed in the subject, even though the subject may still be suffering from 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, delaying, 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 of the physiological symptoms of a disease may receive treatment even if the disease has not been diagnosed.

[0168] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0169] 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 include 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; 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.

[0170] Embodiment 2. The method of embodiment 1, wherein the bodily fluid is saliva.

[0171] Embodiment 3. The method of embodiment 1 or 2, wherein a plurality of 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.

[0172] Embodiment 4. The method of embodiment 1 or 2, wherein the signal changes caused by the circadian cycle are distinct from signals resulting from a medical or disease state.

[0173] Embodiment 5. The method of embodiment 1, wherein a preservative is added to the sample after collection.

[0174] Embodiment 6 The method of embodiment 1, wherein a preservative is present in the collection container prior to collection.

[0175] Embodiment 7. The method of embodiment 1, wherein the preservative comprises at least one or more of the following: EDTA for inhibiting nucleases; poly(vinylsulfonic 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 to prevent microbial growth, such as isothiazolinones and formaldehyde releasers, 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, guanidinium chloride; agents for sequestration of catabolic proteins; buffers; detergents; reducing agents; antioxidants; cryoprotectants; or osmolytes that are salts.

[0176] Embodiment 8. The method of embodiment 1, wherein the sample is fractionated after collection.

[0177] Embodiment 9. The method of embodiment 1, wherein the sample is fractionated into two or more portions through application of centrifugal force, and each fraction is removed separately, and the isolated fractions may include a cell-free fraction and a cell-containing fraction.

[0178] Embodiment 10. The method of embodiment 1, wherein the sample is fractionated by filtration.

[0179] 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 filtration or immediately after filtration. Optionally, a preservative, such as that described in embodiment 7, is added to the retentate.

[0180] 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 to 11.

[0181] Embodiment 13 The method of embodiment 1, wherein at least one class of analytes comprises RNA.

[0182] Embodiment 14 The method of embodiment 1, wherein at least one class of analytes comprises cell-free RNA.

[0183] 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.

[0184] 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.

[0185] Embodiment 17 The method of embodiment 1, wherein at least one class of analytes comprises DNA.

[0186] Embodiment 18 The method of embodiment 1, wherein at least one class of analytes comprises cell-free DNA.

[0187] Embodiment 19. The method of embodiment 1, wherein the analyte is endogenous (derived from the subject), exogenous (e.g., the microbiome of the subject), or a mixture thereof.

[0188] Embodiment 20 The method of embodiment 1, wherein at least one class of analytes comprises proteins.

[0189] Embodiment 21 The method of embodiment 1, wherein at least one class of analytes comprises small molecules.

[0190] Embodiment 22 The method of embodiment 1, wherein at least one class of analytes comprises hormones.

[0191] Embodiment 23 The method of embodiment 1, wherein at least one class of analytes comprises metabolites.

[0192] Embodiment 24. The method of embodiment 1, wherein at least one class of analytes comprises cells (endogenous or exogenous).

[0193] Embodiment 25 The method of embodiment 1, wherein the disease is cancer.

[0194] Embodiment 26 The method of embodiment 1, wherein the disease is breast cancer.

[0195] Embodiment 27 The method of embodiment 26, wherein the bodily fluid is saliva.

[0196] Embodiment 28. The method of embodiment 27, wherein the sample is fractionated to obtain acellular saliva.

[0197] Embodiment 29 The method of embodiment 26, wherein at least one analyte class is RNA.

[0198] Embodiment 30. The method of embodiment 26, wherein at least one analyte class is cell-free.

[0199] Embodiment 31. The method of embodiment 29 or 30, wherein the RNA is analyzed using sequencing.

[0200] Embodiment 32. The method of embodiment 31, wherein sequencing is multiplexed.

[0201] Embodiment 33 The method of embodiment 31, wherein the sequencing is high-throughput.

[0202] 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.

[0203] Embodiment 35. The method of embodiment 29 or 30, wherein the RNA is analyzed using PCR.

[0204] Embodiment 36 The method of embodiment 29 or 30, wherein the RNA is analyzed using a microarray.

[0205] Embodiment 37. The method of embodiment 26, wherein the patient has dense breast tissue.

[0206] Embodiment 38 The method of embodiment 13 or 14, wherein tissue-specific contributions to the RNA profile are determined.

[0207] Embodiment 39. The method of embodiment 38, wherein tissue-specific contributions to the RNA profile are subtracted, either through an assay or computationally, to distinguish between signals and diseases or medical conditions.

[0208] 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.

[0209] Embodiment 41. The method of embodiment 25, wherein tissue-specific contributions to the RNA profile are used to identify the cancer tissue of origin.

[0210] Embodiment 42 The method of embodiment 1, wherein the disease is an infectious disease.

[0211] Embodiment 43. The method of embodiment 1, wherein the disease or medical condition involves the brain or nervous system.

[0212] Embodiment 44. The method of embodiment 1, wherein the medical condition involves the brain or nervous system.

[0213] Embodiment 45. The method of embodiment 1, wherein the medical condition is pregnancy.

[0214] Embodiment 46 The method of embodiment 1, wherein the medical condition is organ trauma or injury.

[0215] Embodiment 47. The method of embodiment 1, wherein the disease is an autoimmune disease.

[0216] Embodiment 48. The method of embodiment 1, wherein the disease is metabolic in nature.

[0217] Embodiment 49. The method of embodiment 1, wherein the disease is a disease of the endocrine system.

[0218] Embodiment 50. The method of embodiment 1, wherein the disease is a disease of the gastrointestinal tract.

[0219] Embodiment 51. The method of embodiment 1, wherein the subject is a human.

[0220] Embodiment 52. The method of embodiment 1, wherein the subject is a non-human.

[0221] Embodiment 53. The method of embodiment 1, wherein the sample is collected at home.

[0222] Embodiment 54. The method of embodiment 1, wherein the sample is collected in a medical facility.

[0223] Embodiment 55. The method of embodiment 1, wherein the sample is collected by a dentist or dental hygienist.

[0224] Embodiment 56 The method of embodiment 1, wherein the sample is collected by a veterinarian.

[0225] 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.

[0226] 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.

[0227] 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.

[0228] 2. The method of embodiment 1, wherein two or more samples are taken from a patient and the samples are processed using different versions of the workflow described in embodiment 1.

[0229] 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.

[0230] example The following examples are included for illustrative purposes only and are not intended to limit the scope of the invention.

[0231] Example 1. Tissue-specific and tissue-enriched transcripts in saliva The GTEx data was analyzed to identify genes that are highly specific to a small group of tissues. Tissue-specific transcripts were detected from multiple organs in the saliva of patients (Figure 4). The largest overlap observed was with blood and esophagus. Tissue-specific transcripts in saliva demonstrated the potential for broad disease detection.

[0232] The GTEx data were analyzed to identify genes that were enriched across multiple tissues, extending the ability to use saliva to analyze multiple tissues (Figure 5).

[0233] Transcript enrichment (e.g., identification of tissue enriched transcripts) was analyzed 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, Totals were 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 sections). 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.

[0234] Example 2. Breast cancer proof-of-concept study A clinical research study was conducted with over 2600 patients enrolled at two sites. A total of 2623 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 the hallmarks of the cancer gene catalog were enriched in the cancer group (Figure 18).

[0235] Example 3. Breast cancer detection A method for detecting the presence of a medical condition or disease in a subject, the method includes obtaining saliva from a subject, purifying nucleic acid 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, microarray, and analyzing the data using computer and statistical methods to detect the presence or absence of a medical condition where all or a subset of the measurements may result in the detection of the condition. Although this approach can be used to detect a large number of 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 combination 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 from whole saliva or acellular saliva. Proper collection and storage methods for preserving nucleic acid are important. 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 conditions, may also play an important role and must be considered.

[0236] Stored saliva samples can be fractionated to obtain cell-free saliva prior to 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. In the case of 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, the nucleic acids may be further analyzed using a variety of methods, including but not limited to sequencing, qPCR, and microarrays. In the case of genome-scale techniques, it may be preferable to either assay or bioinformatically downselect genes or regions of interest. This may be for cost reasons, to reduce background noise. Regardless of the assay, any algorithm used to determine the presence of a disease or condition may only use a portion of the data to make the determination.

[0237] 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 splitting the different training trials. 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 logistic regression coefficients shown in Figure 21B. These 157 genes were used in the best performing classifier, but 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 performance of the classifier 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 includes 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]

[0238] Example 4. Preservation of nucleic acids The following examples demonstrate two things: the addition of a denaturing agent, such as a chaotropic agent, to a biological sample preserved the RNA in the sample for at least four days, and sample filtration can be used to prepare cell-free or cell-depleted RNA.

[0239] Saliva from multiple individuals was pooled and divided into two fractions A and B (see FIG. 8). Fraction A was spun to obtain cell-free saliva, which was divided into two parts A1 and A2. Equal volumes of PBS (preservative (-) condition) and chaotropic agent (preservative (+) 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 parts B1 and B2. Equal volumes of PBS (-preservative condition) and chaotropic agent (+preservative 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.

[0240] Experimental qPCR results demonstrated that conditions containing PBS (preservative-) showed a decrease in transcript levels over time, whereas conditions containing denaturing chaotropic agent (preservative+) showed no decrease in transcript levels for any of the three genes (Figure 9). Filtration yielded similar levels of transcripts for ACT as cell-free saliva preparations using centrifugation, and higher levels for FOSL2 and NAMPT. Whole saliva was also included as a control and generally shows higher levels of all three transcripts.

[0241] 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 from these three donors were obtained. The quality of the preserved nucleic acids was tested via qPCR to measure the abundance of synthetic spike-in and endogenous genes. Figure 25C shows an exemplary profile over 7 days from one donor, showing that the combined filter and preservative condition was shown to be 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 obtained for 7 days. Figure 25E shows the stability of endogenous transcripts over 7 days. Endogenous genes showed gradual degradation in the absence of preservatives. Transcript levels were stable in the presence of preservatives.

[0242] 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 present invention. It is understood that various alternatives to the embodiments described herein can be employed in practicing this 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 are covered thereby.

Claims

1. A method for detecting a target disease or condition, wherein the method is a) detecting the presence of at least one analyte in a sample from the subject, or measuring the amount of at least one analyte present; and b) To generate a score relating to the likelihood of a subject having the disease or condition or a subject developing the disease or condition. Includes, The aforementioned sample is taken from a site different from the site of the disease or condition. The presence or amount of the at least one analyte in the sample correlates with the presence or amount of the at least one analyte in the site of the disease or condition, or with the outcome of the disease or condition. method.

2. The method according to claim 1, wherein the method includes preserving the sample before a).

3. The method according to claim 2, wherein preserving the sample involves contacting the sample with a preservative comprising at least one of the following: ethylenediaminetetraacetic acid (EDTA); RNase inhibitors; antibacterial agents; denaturants; agents that inhibit nuclease activity; metal ion chelating agents; buffers; salts; osmotic pressure modulating substances; or a combination thereof.

4. The method according to claim 3, wherein the denaturing agent comprises a nucleic acid denaturing agent or a protein denaturing agent.

5. a) Prior to this, the method further includes fractionating the sample, and the fractionation includes centrifugation of the sample or filtration of the sample, or The method according to claim 1, wherein the fractionation includes separating the sample into two or more subsets of the sample.

6. The method according to claim 5, wherein at least one of the subsets of the two or more samples includes a cell-free fraction, or at least one of the subsets of the two or more samples includes a cell-containing fraction, wherein the cell-containing fraction includes cells derived from the subject or cells not derived from the subject.

7. The cells derived from the subject are human cells, or The method according to claim 6, wherein the cells that are not derived from the subject are non-human cells.

8. The aforementioned non-human cells (a) Microbial cells; (b) Bacterial cells; (C) Mycelial cells; and (d) Archaeal cells The method according to claim 7, comprising at least one of the following.

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

10. The method according to claim 1, wherein the at least one analyte comprises at least one of a cell-free analyte, polypeptide, protein, metabolite, small molecule, cell, and nucleic acid; preferably the nucleic acid comprises cell-free RNA, mRNA, small molecule RNA, miRNA, snoRNA, snRNA, rRNA, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof.

11. a) comprising sequencing the at least one analyte, wherein the at least one analyte contains at least one nucleic acid, a) The method according to claim 1, wherein the method comprises hybridizing the at least one nucleic acid with a probe.

12. The method according to claim 1, wherein the disease or condition is cancer, neurological disease, autoimmune disease, metabolic disease, endocrine disease, gastrointestinal disease, injury, or pregnancy.

13. The method according to claim 12, wherein the cancer is breast cancer.

14. The method according to claim 1, wherein the score determines the origin of the disease or condition.

15. The method according to claim 1, wherein the at least one analyte is the DNA or cell-free salivary RNA of the subject, and both genetic analysis and transcriptome analysis are used to detect the presence of the disease or condition in the subject.

16. The method according to claim 1, wherein multiple samples from the subject are processed using different versions of the workflow described in claim 1.

17. The method according to claim 1, further comprising taking the sample from the subject.

18. A method for detecting a target disease or condition, wherein the method is A hardware processor, and the following operations performed on the hardware processor: To detect the presence of at least one analyte in a sample from the target, or to measure the amount of said at least one analyte, and To generate a score relating to the likelihood of a subject having the aforementioned disease or condition, or a subject developing the aforementioned disease or condition. This includes using a computer system that has memory in which instructions are coded to execute, The aforementioned sample is taken from a site different from the site of the disease or condition. The presence or amount of at least one analyte in the sample correlates with the presence or amount of at least one analyte in the site of the disease or condition, or with the outcome of the disease or condition. method.

19. (a) A step of generating a machine learning model that is repeatedly trained to detect the disease or condition in the sample; (b) A step of generating a machine learning model that is iteratively trained to generate the score relating to the likelihood of the subject having the disease or condition; and / or (c) A step of generating a machine learning model that is iteratively trained to generate a score relating to the likelihood of the subject developing the disease or condition; preferably, The method according to claim 18, further comprising the step that the machine learning model includes at least one of the XGBoost algorithm, a logistic regression model, and a random forest algorithm.

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