Disease detection device

Abstract

Described herein are methods of analyzing and detecting a disease or condition of a subject, comprising a sample collection container for collecting a sample; the filtering unit is in fluid communication with the sample collecting container, comprises at least one filter and is used for filtering the sample to obtain filtrate; and a filtrate collection container in fluid communication with the filtration unit for collecting the filtrate and contacting the filtrate with the preservative. In addition, methods of preservation of a sample for detecting a disease or condition in a subject are described herein.

Description

[0001] Cross-references

[0002] This application claims the benefit of U.S. Provisional Application Serial No. 63 / 317,794, filed on March 8, 2022, the benefit of U.S. Provisional Application Serial No. 63 / 333,711, filed on April 22, 2022; the benefit of U.S. Provisional Application Serial No. 63 / 390,929, filed on July 20, 2022; and the benefit of U.S. Provisional Application Serial No. 63 / 428,897, filed on November 30, 2022, the entire contents of which are incorporated into this application by reference. Background Art

[0003] Cancer is one of the most common diseases that affects millions of people. For example, in the United States, approximately one in eight women will develop invasive breast cancer in her lifetime. Early detection and early treatment can improve the survival rate of cancer patients. However, cancer testing can be cumbersome and prone to false positive or false negative test results. Summary of the invention

[0004] Therefore, there is still a need for a method for detecting diseases such as cancer in biological samples, in which biological samples are obtained, processed and analyzed efficiently and accurately. In some aspects, a device for collecting and stabilizing analytes in a sample is described herein, the device comprising: a sample collection container for collecting a sample; a filtration unit in fluid communication with the sample collection container, the filtration unit comprising at least one filter for filtering the sample to obtain a filtrate; a filtrate collection container in fluid communication with the filtration unit for collecting the filtrate and contacting the filtrate with a preservative. In some embodiments, the filtration unit comprises a plurality of filters arranged in order of pore size to allow smaller substances to pass through in sequence. In some embodiments, the filtration unit comprises at least two filters of the same type. In some embodiments, the device comprises a pre-filtration mechanism to prevent clogging of smaller pore size filters. In some embodiments, the filtration unit comprises a single filter. In some embodiments, at least one filter comprises a depth filter. In some embodiments, at least one filter comprises an asymmetric filter. In some embodiments, at least one filter comprises a microporous filter. In some embodiments, at least one filter comprises a low nucleic acid binding material. In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 2 μm, at least about 3 μm, at least about 4 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, at least about 25 μm, at least about 30, at least about 35 μm, at least about 40 μm, at least about 45 μm, at least about 50 μm, at least about 55 μm, at least about 60 μm, at least about 65 μm, at least about 70 μm, at least about 75 μm, at least about 80 μm, at least about 85 μm, at least about 90 μm, at least about 95 μm, or at least about 100 μm. In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 2 μm, at most about 3 μm, at most about 4 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, at most about 25 μm, at most about 30, 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 about 0.1 μm to 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 250μm, about 300μm, about 350μm, about 400μm, about 450μm, about 500μm, about 550μm, about 600μm, about 650μm, about 700μm, about 750μm, about 800μm, about 850μm, about 900μm, about 950μm or about 100μm, for example, about 355μm to about 560μm, for example, about 330μm, for example, about 120μm to about 170μm, for example, about 230μm to about 270μm, for example, about 480μm to about 640μm. In some embodiments, the diameter of the filter is about 10 mm to about 50 mm, for example, 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 comprises a filter stack height of about 120 μm to about 10000 μm, such as 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. m, about 900 μm, about 950 μm, about 1000 μm, 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. um, about 9000 μm or about 9500 μm to 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 125 In some embodiments, the filter comprises a plurality of filters, such as a filter having a diameter of about 0 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, about 9500 μm or about 10000 μm. In some embodiments, at least one filter is hydrophilic or hydrophobic. In some embodiments, at least one filter comprises polysulfone and / or polypropylene. In some embodiments, at least one filter retains a plurality of white blood cells. In some embodiments, at least one filter retains a plurality of red blood cells. In some embodiments, at least one filter retains a plurality of cells derived from solid tissue. In some embodiments, at least one filter retains a plurality of microorganisms. In some embodiments, at least one filter comprises a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, at least one filter comprises a biological material. In some embodiments, at least one filter is free of biological material. In some embodiments, the biological material comprises cellulose. In some embodiments, the sample is a biological fluid, including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the filtrate is a cell-free biological fluid or a cell-free biological fluid. In some embodiments, the filtrate is cell-free plasma or a cell-free plasma. In some embodiments, the filtrate is cell-free saliva or a cell-free saliva.In some embodiments, the filtrate is acellular urine or decellularized 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 used to apply 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, which engages with the sample collection container to push the sample through the filtration unit and enter the filtrate collection container. In some embodiments, the plunger is integrated or separated from the sample collection container. In some embodiments, the sample collection container comprises a funnel, wherein the funnel is integrated or connectable with the sample collection container. In some embodiments, the filtrate collection container comprises a preservative. In some embodiments, the preservative comprises at least one of: ethylenediaminetetraacetic acid (EDTA), ribonuclease inhibitors, antimicrobial agents, denaturants, reagents that inhibit nuclease activity, sequestration agents, buffers, salts, permeabilizers or combinations thereof. In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. In some embodiments, the reagent that inhibits nuclease activity includes one or more protein denaturants, EDTA, detergents such as SDS, aurin tricarboxylic acid (ATA), chelating agents, or combinations thereof. In some embodiments, one or more protein denaturants include one or more chaotropic agents, including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodecanidine, or guanidine hydrochloride. In some embodiments, 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 separable from the filtration unit. In some embodiments, the device further includes a lid for a separate filtrate collection container. In some embodiments, the lid includes an encapsulation for storing a preservative, which releases the preservative after the lid is fixed to the separate filtrate collection container. In some embodiments, the device further includes a second container for pouring the filtrate. In some embodiments, the second container contains a preservative. In some embodiments, the device stabilizes the analyte at about -20°C to about 50°C. In some embodiments, the analyte is stable for at least 5 days. In some embodiments, at least one analyte includes a cell-free analyte. In some embodiments, at least one analyte includes a nucleic acid. In some embodiments, the nucleic acid includes cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite.In some embodiments, at least one analyte comprises cells. In some embodiments, the filtration unit is detachable for recovering the retentate so that biomolecules and cells contained in the retentate can be stored and analyzed. In some embodiments, at least one filter reduces the viscosity of the filtrate compared to the sample. In some embodiments, at least one filter removes mucin from the sample.

[0005] In some aspects, described herein is a kit comprising a device described herein and at least one of: a plunger, a cap for a filtrate collection container, a funnel, and / or a preservative.

[0006] In some aspects, described herein is a method of stabilizing an analyte in a sample, the method comprising applying the sample to a sample collection container of a device described herein or a kit described herein, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

[0007]

[0013] Described herein, in some aspects, are analytes stabilized by the methods described herein.

[0008] 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 the subject; (b) optionally, preserving the sample by adding a preservative during collection; (c) optionally, separating the sample; (d) optionally, adding a preservative to the separated sample; (e) selecting one or more analytes in the sample, including but not limited to nucleic acid transcripts or genomic regions of interest; (f) using an assay to qualitatively or quantitatively detect the selected analytes, wherein the assay may include techniques involving but not limited to biomolecule purification, 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) using a computer to analyze the data to detect the presence or absence of a medical condition or disease, or to analyze the data to generate a likelihood score for a medical condition or disease.

[0009] Disclosed herein is a method for detecting a disease or condition in a subject, wherein the method comprises: optionally, collecting a sample from the subject; detecting the presence of at least one analyte or measuring the abundance of at least one analyte in the sample; generating a score for the probability of the subject having or developing a disease or condition, wherein the sample is collected from a sample collection site different from the disease or condition site, and wherein the presence of at least one analyte or the abundance of at least one analyte in the sample is associated with the presence of at least one analyte in the disease or condition site, the abundance of at least one analyte, or the consequence of the disease or condition. In some embodiments, the method comprises preserving the sample before b). In some embodiments, preserving the sample comprises contacting the sample with a preservative, the preservative comprising at least one of: ethylenediaminetetraacetic acid (EDTA), a ribonuclease inhibitor, an antimicrobial agent, a denaturant, an agent that inhibits nuclease activity, a masking agent, a buffer, a salt, a permeabilizing agent, or a combination thereof. In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. In some embodiments, the method comprises separating the sample before b). In some embodiments, the separation comprises dividing 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 portion, wherein the cell-containing portion comprises cells derived from the subject or cells not derived from the subject. In some embodiments, the cells derived from the subject are human cells. In some embodiments, the cells not derived 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 fungal 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 portion. In some embodiments, separation comprises centrifuging the sample or filtering the sample. In some embodiments, the sample comprises a body fluid sample. In some embodiments, the body fluid sample comprises a saliva sample. In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, at least one analyte comprises a cell-free analyte. In some embodiments, the nucleic acid comprises a cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell. In some embodiments, 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 digestive tract 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 source of the disease or condition. In some embodiments, at least one analyte is DNA or cell-free salivary RNA of the subject, and genetic and transcriptomic analysis is used to detect the presence of the subject's disease or condition. In some embodiments, multiple samples are collected from the patient and the samples are processed using different versions of the workflow described herein.

[0010] Also disclosed herein is a method for stabilizing nucleic acids in a sample, wherein the method comprises: 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 the filtrate to inhibit nuclease activity. In some embodiments, nucleic acids in the sample are purified. In some embodiments, the sample is a biological fluid, including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the sample is filtered before adding one or more protein denaturants. In some embodiments, the sample is collected in or transferred to a device having a filtration unit, the filtration unit comprising at least one filter, wherein the device comprises a pre-filtration mechanism to prevent clogging of a smaller pore size filter; one or more filters; a plurality of filters arranged in order of pore size to allow smaller substances to pass through in sequence; 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, filtration is achieved using depth filtration. In some embodiments, the method further comprises using a filtrate collection container pre-loaded with one or more denaturants, so that the nuclease can be rapidly inactivated when in contact with the filtrate. In some embodiments, the filtered sample is removed from the device or poured into a second container. In some embodiments, the filtered sample is removed from the device or poured into a second container containing a preservative. In some embodiments, the collection unit or the filtration unit is separated from the filtrate collection container after filtration to allow the addition of a preservative. In some embodiments, the preservative is stored in an enclosure in the lid and released after the lid is fixed to the separated filtrate collection container. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 50°C. In some embodiments, the nucleic acid is stable for at least 5 days. In some embodiments, at least one filter includes a low nucleic acid binding material. In some embodiments, the size cutoff value 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 about 0.1 μm to about 25 μm. In some embodiments, at least one filter retains a plurality of white blood cells. In some embodiments, at least one filter retains a plurality of red blood cells. In some embodiments, at least one filter retains a plurality of cells derived from solid tissue. In some embodiments, at least one filter retains a plurality of microorganisms. In some embodiments, at least one filter comprises a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, at least one or more filters comprise biological materials.In some embodiments, filtration obtains acellular biological fluid or acellular biological fluid. In some embodiments, filtration obtains acellular plasma or acellular plasma. In some embodiments, filtration obtains acellular saliva or acellular saliva. In some embodiments, filtration obtains acellular urine or acellular urine. In some embodiments, the nucleic acid is RNA. In some embodiments, the nucleic acid is DNA. In some embodiments, the one or more denaturants include one or more chaotropic agents, including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodecanidine or guanidine hydrochloride. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 30% to about 70%. In some embodiments, the retentate can be recovered, and the biomolecules and cells contained in the retentate can be stored and analyzed.

[0011] Disclosed herein is an apparatus for detecting a disease or condition in a subject, the apparatus comprising: a computer system including a hardware processor and a memory, instructions encoded on the memory causing the hardware processor to perform the following operations: detecting the presence of at least one analyte or measuring the abundance of at least one analyte in a sample obtained from the subject; generating a score for the likelihood that the subject has or develops a disease or condition, wherein the sample is collected from a sample collection site different from a disease or condition site, and wherein the presence of at least one analyte or the abundance of at least one analyte in the sample is correlated with the presence of at least one analyte in the disease or condition site, the abundance of at least one analyte, or the outcome of the disease and condition.

[0012] Disclosed herein is a method for detecting a disease or condition in a subject, the method comprising: obtaining a sample from the subject; utilizing a computer system comprising a hardware processor and a memory, encoding instructions on the memory to cause the hardware processor to perform the following operations: detecting the presence of at least one analyte or measuring the abundance of at least one analyte in the sample; generating a score for the likelihood that the subject has or develops the disease or condition, wherein the sample is collected from a sample collection site different from the disease or condition site, and wherein the presence of at least one analyte or the abundance of at least one analyte in the sample is correlated with the presence of at least one analyte in the disease or condition site, the abundance of at least one analyte, or the consequence of the disease or condition.

[0013] Disclosed herein is a method for detecting a disease or condition in a subject, the method comprising: a) detecting the presence of at least one analyte or measuring the abundance of at least one analyte in a sample of the subject; b) generating a score for the probability of the subject having or developing a disease or condition, wherein the sample is from a sample collection site different from the disease or condition site, and wherein the presence of at least one analyte in the sample or the abundance of at least one analyte is associated with the presence of at least one analyte in the disease or condition site, the abundance of at least one analyte, or the consequence of the disease or condition. In some embodiments, prior to a), the method comprises preserving the sample. In some embodiments, preserving the sample comprises contacting the sample with a preservative, the preservative comprising at least one of: ethylenediaminetetraacetic acid (EDTA), a ribonuclease inhibitor, an antimicrobial agent, a denaturant, an agent that inhibits nuclease activity, a masking agent, a buffer, a salt, a permeabilizing agent, or a combination thereof. In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. In some embodiments, prior to a), the method further comprises separating the sample. In some embodiments, separation comprises dividing 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 portion, wherein the cell-containing portion comprises cells derived from the subject or cells not derived from the subject. In some embodiments, the cells derived from the subject are human cells. In some embodiments, the cells not derived 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 fungal 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 portion. In some embodiments, separation comprises centrifuging the sample or filtering the sample. In some embodiments, the sample comprises a biological fluid. In some embodiments, the biological fluid comprises blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the biological fluid comprises saliva. In some embodiments, at least one analyte comprises a cell-free analyte. In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, the nucleic acid comprises a cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell.In some embodiments, a) comprises sequencing at least one analyte, wherein 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 digestive tract 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 source of the disease or condition. In some embodiments, at least one analyte is DNA or cell-free salivary RNA of the subject, and genetic and transcriptomic analysis is used to detect the presence of the subject's disease or condition. 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 collecting samples from the subject.

[0014] Disclosed herein is a method for detecting a disease or condition in a subject, the method comprising: utilizing a computer system comprising a hardware processor and a memory, encoding instructions on the memory to cause the hardware processor to perform the following operations: detecting the presence of at least one analyte in a sample from the subject or measuring the abundance of at least one analyte; generating a score for the likelihood that the subject has or develops a disease or condition, wherein the sample is from a sample collection site different from the disease or condition site, and wherein the presence of at least one analyte in the sample or the abundance of at least one analyte is associated with the presence of at least one analyte in the disease or condition site, the abundance of at least one analyte, or the consequence of the disease or condition. In some embodiments, the method further comprises the step of generating an iteratively trained machine learning model to detect a disease or condition in the sample. In some embodiments, the method further comprises the step of generating an iteratively trained machine learning model to generate a score for the likelihood that the subject has a disease or condition. In some embodiments, the method further comprises the step of generating an iteratively trained machine learning model to generate a score for the likelihood that the subject develops a disease or condition. In some embodiments, the machine learning model comprises at least one of an XGBoost algorithm, a logistic regression model, and a random forest algorithm.

[0015] Also described herein is a device for detecting a disease or condition in a subject, the device comprising: a computer system comprising a hardware processor and a memory, instructions encoded on the memory causing the hardware processor to perform the following operations: detecting the presence of at least one analyte or measuring the abundance of at least one analyte in a sample obtained from the subject; generating a score for the likelihood that the subject has or develops a disease or condition, wherein the sample is from a sample collection site different from the disease or condition site, and wherein the presence of at least one analyte or the abundance of at least one analyte in the sample is associated with the presence of at least one analyte in the disease or condition site, the abundance of at least one analyte, or the consequence of the disease or condition. In some embodiments, the hardware processor generates an iteratively trained machine learning model to detect a disease or condition in a sample. In some embodiments, the hardware processor generates an iteratively trained machine learning model to generate a score for the likelihood that the subject has a disease or condition. In some embodiments, the hardware processor generates an iteratively trained machine learning model to generate a score for the likelihood that the subject develops a disease or condition. In some embodiments, the machine learning model comprises at least one of an XGBoost algorithm, a logistic regression model, and a random forest algorithm.

[0016] Also described herein is a device for collecting and stabilizing analytes in a sample, the device comprising: a) a sample collection container for collecting the sample; b) a filtration unit in fluid communication with the sample collection container, the filtration unit comprising at least one filter for filtering the sample to obtain a filtrate; c) a filtrate collection container in fluid communication with the filtration unit, for collecting the filtrate and contacting the filtrate with a preservative. In some embodiments, the filtration unit comprises a plurality of filters arranged in order of pore size to sequentially allow smaller substances 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 pre-filtration mechanism to prevent clogging of smaller pore size filters. In some embodiments, the filtration unit comprises a single filter. In some embodiments, at least one filter comprises a depth filter. In some embodiments, at least one filter comprises an asymmetric filter. In some embodiments, at least one filter comprises a microporous filter. In some embodiments, at least one filter comprises a low nucleic acid binding material. In some embodiments, the size cutoff of at least one filter is at least about 0.1 μm, at least about 1 μm, at least about 2 μm, at least about 3 μm, at least about 4 μm, at least about 5 μm, at least about 10 μm, at least about 15 μm, at least about 20 μm, at least about 25 μm, at least about 30, at least about 35 μm, at least about 40 μm, at least about 45 μm, at least about 50 μm, at least about 55 μm, at least about 60 μm, at least about 65 μm, at least about 70 μm, at least about 75 μm, at least about 80 μm, at least about 85 μm, at least about 90 μm, at least about 95 μm, or at least about 100 μm. In some embodiments, the size cutoff of at least one filter is at most about 0.1 μm, at most about 1 μm, at most about 2 μm, at most about 3 μm, at most about 4 μm, at most about 5 μm, at most about 10 μm, at most about 15 μm, at most about 20 μm, at most about 25 μm, at most about 30, 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 about 0.1 μm to 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 ... The present invention relates to a method for preparing the present invention to a method for preparing the present invention. The present invention relates to a method for preparing the present invention to a method for preparing the present invention. The present invention relates to a method for preparing the present invention to a method for preparing the present invention. In some embodiments, the diameter of the filter is about 10 mm to about 50 mm, for example, 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 comprises a filter stack height of about 120 μm to about 10000 μm, such as 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. m, about 900 μm, about 950 μm, about 1000 μm, 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. um, about 9000 μm or about 9500 μm to 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 125 In some embodiments, the filter comprises a plurality of filters, such as a filter having a diameter of about 0 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, about 9500 μm or about 10000 μm. In some embodiments, at least one filter is hydrophilic or hydrophobic. In some embodiments, at least one filter comprises polysulfone and / or polypropylene. In some embodiments, at least one filter retains a plurality of white blood cells. In some embodiments, at least one filter retains a plurality of red blood cells. In some embodiments, at least one filter retains a plurality of cells derived from solid tissue. In some embodiments, at least one filter retains a plurality of microorganisms. In some embodiments, at least one filter comprises a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, at least one filter comprises a biological material. In some embodiments, at least one filter is free of biological material. In some embodiments, the biological material comprises cellulose. In some embodiments, the sample is a biological fluid, including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid. In some embodiments, the filtrate is a cell-free biological fluid or a cell-free biological fluid. In some embodiments, the filtrate is cell-free plasma or a cell-free plasma. In some embodiments, the filtrate is cell-free saliva or a cell-free saliva.In some embodiments, the filtrate is acellular urine or decellularized urine. In some embodiments, the device also includes 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 used to apply 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 includes a plunger engaged with the sample collection container to push the sample through the filter unit and enter the filtrate collection container. In some embodiments, the plunger is integrated or separated from the sample collection container. In some embodiments, the sample collection container includes a funnel, wherein the funnel is integrated or connectable to the sample collection container. In some embodiments, the filtrate collection container includes a preservative. In some embodiments, the preservative includes at least one of: ethylenediaminetetraacetic acid (EDTA), ribonuclease inhibitors, antibacterial agents, denaturants, agents that inhibit nuclease activity, masking agents, buffers, salts, permeabilizers, or combinations thereof. In some embodiments, the denaturant includes a nucleic acid denaturant or a protein denaturant. In some embodiments, the reagent that inhibits nuclease activity includes one or more protein denaturants, EDTA, detergents such as SDS, aurin tricarboxylic acid (ATA), chelating agents, or combinations thereof. In some embodiments, one or more protein denaturants include one or more chaotropic agents, including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodecanidine, or guanidine hydrochloride. In some embodiments, 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 separable from the filtration unit. In some embodiments, the device further includes a lid for a separate filtrate collection container. In some embodiments, the lid includes an encapsulation for storing a preservative, which releases the preservative after the lid is fixed to the separate filtrate collection container. In some embodiments, the device further includes a second container for pouring the filtrate. In some embodiments, the second container contains a preservative. In some embodiments, the device stabilizes the analyte at a temperature range of about -20°C to about 50°C. In some embodiments, the analyte is stable for at least 5 days. In some embodiments, at least one analyte includes a cell-free analyte. In some embodiments, at least one analyte includes a nucleic acid. In some embodiments, the nucleic acid includes cell-free RNA. In some embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof. In some embodiments, at least one analyte comprises a polypeptide. In some embodiments, the polypeptide is a protein. In some embodiments, the polypeptide is a metabolite. In some embodiments, at least one analyte comprises a small molecule. In some embodiments, at least one analyte comprises a metabolite. In some embodiments, at least one analyte comprises a cell.In some embodiments, the filtration unit can be used separately to recover the retentate so that the biomolecules and cells contained in the retentate can be preserved and analyzed. In some embodiments, at least one filter reduces the viscosity of the filtrate compared to the sample. In some embodiments, at least one filter removes mucin from the sample.

[0017] Also described herein is a kit comprising a device as described herein and at least one of: a) a plunger; b) a lid for a filtrate collection container; c) a funnel; and / or d) a preservative.

[0018] Also described herein is a method of stabilizing an analyte in a sample, the method comprising applying the sample to a sample collection container of a device or kit described herein, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

[0019] Also described herein are analytes stabilized by the methods described herein.

[0020] Incorporation by Reference

[0021] All publications, patents, and patent applications mentioned in this specification are herein incorporated by reference to the same extent as if each individual publication, patent, or patent application was specifically and individually indicated to be incorporated by reference. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart providing an overview of the workflow steps is shown.

[0023] Figure 2 A flowchart providing an overview of the workflow steps is shown.

[0024] Figure 3 A flow chart for identifying tissue-specific and tissue-enriched transcripts present in patient samples is shown.

[0025] Figure 4 A diagram illustrating the potential of tissue-specific transcripts in saliva for broad disease detection is shown. In particular, the figure illustrates a hierarchical clustering heat map of tissues and samples, where red features indicate a high level of mRNA overlap between an individual's tissue-specific transcripts and cell-free saliva, while blue features indicate a low level of mRNA overlap.

[0026] Figure 5 A plot illustrating the potential of tissue-enriched transcripts in saliva for broad disease detection is shown. In particular, the figure illustrates a hierarchical clustering heat map of tissues and samples, where red features indicate a high level of mRNA overlap between an individual's tissue-enriched transcripts and cell-free saliva, while blue features indicate a low level of mRNA overlap.

[0027] Figure 6A graph illustrating the content of whole saliva is shown.

[0028] Figure 7 Diagrams describing device concepts and procedures are shown.

[0029] Figure 8 Figures illustrating preliminary experiments of saliva collection and preservation are shown. In the experiment, after saliva filtration to remove human cells, the resulting cell-free saliva was preserved by adding a chaotropic preservative. BioAnalyzer analysis was used to quantify and assess degradation. ACTB, FOSL2 and NAMPT transcript levels were monitored by RT-qPCR to assess degradation.

[0030] Fig. 9 Preliminary data on the preservation of nucleic acids described herein are shown. The yield of sample filtration was similar to that of centrifugal clearing. Addition of a preservative protected transcripts for four days.

[0031] Fig.10 A graph illustrating the improvement in human sequence coverage with exome capture is shown. Pilot experiments were performed showing that exon-enriched sets using hybridization capture significantly improved coverage of GENCODE genes. Future optimization of hybridization conditions and probes may further improve performance.

[0032] Fig.11 A diagram illustrating the bioinformatics pipeline is shown. Human alignment was performed using hg38. Microbial alignment was performed using the Human Oral Microbiome Database.

[0033] Fig.12 A graph illustrating sequencing reads is shown. The samples have a wide range of read lengths. HOM reads are consistent across samples, accounting for 20% of the total reads. The hg38 mapping has the most variable reads: the difference between the highest and lowest coverage is about 100-fold.

[0034] Fig.13 A graph illustrating the final read lengths after alignment is shown. Deduplication reduced the number of reads by about 8-fold but eliminated amplification bias. "Gene assignment" represents the final features.

[0035] Fig.14A and Fig. 14B A graph illustrating the final number of features for 332 samples is shown.

[0036] Fig.15A and Fig. 15B Two plots illustrating the final characterization of sequence QC are shown. Figure 1 The coverage of each sample was consistent across the entire gene length. A bias was observed towards higher coverage on the 5' end of the transcript.

[0037] Fig.16Shown is a graph illustrating an NGS study of 301 patients, including 115 breast cancer patients and 186 non-cancer patients.

[0038] Fig.17A and Fig. 17B Two graphs illustrating the classification results are shown. Fig.17A In the study, a machine learning classifier using 20 cancer and 20 non-cancer cohorts showed good performance with an AUC of 0.763. A 10-fold cross validation (CV) was performed. Fig. 17B In the , classification was performed using random disease labels. The average AUC for 100 iterations was 0.486, indicating that the results with the correct labels were non-random. Increasing the hg38 mapped read length by analytical optimization could generalize this performance across all samples.

[0039] Fig.18 A table illustrating that gene set enrichment analysis revealed that multiple gene sets were enriched in the cancer group is shown.

[0040] Fig.19 Plots illustrating ensemble classifiers including logistic regression, random forest, and XGBoost are shown.

[0041] Fig. 20 A graph illustrating 10-fold cross validation performed 100 times with shuffling is shown.

[0042] Fig.21A The performance of a logistic regression model using 20 breast cancer patients and 20 non-cancer patients is illustrated. Fig.21B The top 157 genes contributing to the classifier performance are shown. A positive coefficient indicates that the gene is commonly up-regulated in cancer patients, and a negative coefficient indicates down-regulation. Fig. 21C The relationship between classifier discrimination and feature exclusion is shown. The discrimination disappears after removing about 250 top features.

[0043] Fig. 22 A non-limiting example of a computing device is shown; in this case, a device having one or more processors, memory, storage, and a network interface.

[0044] Fig.23 A non-limiting example of a web / mobile application provisioning system is shown; in this case, a browser-based and / or native mobile user interface system is provided.

[0045] Fig.24 A non-limiting example of a cloud-based web / mobile application provisioning system is shown; in this case, the system includes elastic load balancing, automatically scaling web server and application server resources, and synchronously replicated databases.

[0046] Fig.25A-E shows the improvement of nucleic acid preservation in saliva samples. Fig.25A Degradation of RNA encoding actin B over three days is shown. Fig.25B Schematic diagram showing a seven-day nucleic acid stability study of the nucleic acid storage method described herein. Fig.25C An exemplary profile for one donor over seven days is shown, indicating that the combination of filter and preservative conditions showed the most stable profile over seven days. Fig.25D The seven-day storage of the spiked controls is shown. Fig.25E The stability of endogenous transcripts over seven days is shown. DETAILED DESCRIPTION

[0047] Overview

[0048] In some aspects, described herein is a method for detecting a disease or condition in a subject in need. In some aspects, the method includes analyzing a sample obtained from the subject for detecting a disease or condition in the subject. In some aspects, the method includes analyzing a sample obtained from the subject for detecting or determining the likelihood of the subject developing a disease or condition in the future. In some embodiments, the method includes analyzing a disease or condition of a sample derived from a site different from the site from which the sample was obtained. For example, the method 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 method includes analyzing nucleic acids in the sample, such as RNA transcripts. In some embodiments, the method includes preserving nucleic acids in the sample. In some embodiments, the preservation of nucleic acids in the sample includes inactivating a nuclease using at least one denaturant or filtering through at least one filter or a combination thereof. Figure 1-3 An exemplary workflow for analyzing a sample for detecting a disease or condition in a subject using the methods described herein is shown. In some embodiments, the method includes determining overexpression or underexpression of a transcript in a sample, wherein the overexpression or underexpression of a transcript in the sample can correspond to overexpression or underexpression of the same transcript in a body site different from the site where the sample was collected. For example, Figure 4 and Figure 5 The transcripts in saliva overlapped significantly with those found in blood and esophageal mucosal tissue.

[0049] In some embodiments, saliva (also referred to as saliva) is an extracellular fluid produced and secreted by salivary glands in the mouth. In some embodiments, in humans, saliva comprises water and solids. In some embodiments, solids may comprise salts and buffers. In some embodiments, solids may also comprise organic compounds. In some embodiments, organic compounds may include enzymes and proteins. In some embodiments, organic compounds may also include metabolites and nitrogen-containing substances. In some embodiments, organic compounds may also include hormones and signaling molecules. In some embodiments, organic compounds may also include nucleic acids. In some embodiments, nucleic acids may include RNA or DNA. In some embodiments, RNA or DNA may be derived from apoptotic / necrotic cells or released for signal transduction. In some embodiments, solids may also comprise cells and vesicles. In some embodiments, cells and vesicles may include extracellular vesicles. In some embodiments, extracellular vesicles may be actively secreted by cells. In some embodiments, cells and vesicles may also include epithelial cells or leukocytes or be derived from epithelial cells or leukocytes. In some embodiments, epithelial cells or leukocytes are derived from oral lining membranes or blood. In some embodiments, cells and vesicles may also include microorganisms or be derived from microorganisms. In some embodiments, microorganisms may include cells of the oral microbiome.

[0050] In some embodiments, saliva may contain cell-free components and intact cells. In some embodiments, the cell-free components may include salts and buffers, organic compounds, and some cells and vesicles in saliva. In some embodiments, the cell-free components may include enzymes and proteins. In some embodiments, the cell-free components may also include metabolites and nitrogen-containing substances. In some embodiments, the cell-free components may also include hormones and signaling molecules. In some embodiments, the cell-free components may also include nucleic acids. In some embodiments, the cell-free components may also include extracellular vesicles.

[0051] In some embodiments, intact cells may include epithelial cells or leukocytes. In some embodiments, intact cells may also include microorganisms.

[0052] In some embodiments, the method comprises preserving nucleic acid in a sample obtained from the subject. Figure 7 and Figure 8 Various combinations of separation processes (eg, by centrifugation and / or filtration), preservation using denaturing agents, and quality control experiments for preserving and determining the integrity of the nucleic acids in the samples are shown. Figure 8 and Fig. 9 An experiment measuring the integrity of nucleic acids preserved by the methods described herein is shown. Fig.10 Shown is a graphical comparison of the percentage of GENCODE coverage before and after enrichment.

[0053] In some aspects, a method of analyzing a sample is described herein. In some embodiments, the method comprises using a computer-implemented method or a machine learning-based algorithm to analyze, train, and improve a detection method for a disease or condition described herein (e.g., Figure 11-20 ). Fig.16 , 17A , 17B, 21A, 21B and 21C show examples of classifying clinical samples using the methods described herein. Figure 22-24 Non-limiting examples of computing devices, applications, or systems utilizing the methods described herein are shown. In some embodiments, the methods improve the sensitivity of detecting a disease or condition in a sample. In some embodiments, the methods improve the specificity of detecting a disease or condition in a sample. In some embodiments, the methods reduce false positives of detecting a disease or condition in a sample. In some embodiments, the methods reduce false negatives of 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 a sample. In some embodiments, the disease or condition is cancer. In some embodiments, the disease or condition is the likelihood of developing cancer.

[0054] In some aspects, described herein is 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 the subject; (b) optionally preserving the sample by adding a preservative during collection; (c) optionally separating the sample; (d) optionally adding a preservative to the separated sample; (e) selecting one or more analytes in the sample, including but not limited to nucleic acid transcripts or genomic regions of interest; (f) using an assay to qualitatively or quantitatively detect the selected analytes, wherein the assay may include techniques involving but not limited to biomolecule purification, 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) using a computer to analyze the data to detect the presence or absence of a medical condition or disease, or to analyze the data to generate a likelihood score for a medical condition or disease.

[0055] In some embodiments, the sample comprises a bodily fluid sample, also referred to as a biological fluid. In some embodiments, the bodily fluid sample comprises a saliva sample.

[0056] In some embodiments, at least one analyte comprises a nucleic acid. In some embodiments, at least one analyte comprises a cell-free RNA. In some embodiments, the nucleic acid comprises an mRNA, a small RNA, a miRNA, a snoRNA, a snRNA, a rRNAs, a tRNA, a siRNA, a hnRNA, a long non-coding RNA, a 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 a 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 rRNAs. 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 of the nucleic acids are also contemplated.

[0057] 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.

[0058] In some embodiments, at least one analyte comprises a small molecule.

[0059] In some embodiments, at least one analyte comprises a cell.

[0060] Also disclosed herein is a method for stabilizing nucleic acids in a sample, wherein one or more protein denaturants are added to the sample to inhibit nuclease activity. In some embodiments, the nucleic acid is RNA. In some embodiments, the nucleic acid is DNA. In some embodiments, the nucleic acid is purified.

[0061] In some embodiments, one or more denaturants include one or more chaotropic agents. In some embodiments, one or more chaotropic agents include detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodecanedine or guanidine hydrochloride. In some embodiments, one or more chaotropic agents include detergents. In some embodiments, one or more chaotropic agents include urea. In some embodiments, one or more chaotropic agents include thiourea. In some embodiments, one or more chaotropic agents include guanidine thiocyanate. In some embodiments, one or more chaotropic agents include guanidine hydrochloride.

[0062] In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 30% to about 70%. In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 40% to about 70%. In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 50% to about 70%. In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 60% to about 70%. In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 30% to about 60%. In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 30% to about 50%. In some embodiments, one or more denaturants include guanidine thiocyanate at a concentration of about 30% to about 40%.

[0063] In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 30%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 35%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 40%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 45%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 50%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 55%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 60%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 65%. In some embodiments, the one or more denaturants include guanidine thiocyanate at a concentration of about 70%.

[0064] In some embodiments, the sample is a biological fluid. In some embodiments, the biological fluid comprises blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid.

[0065] In some embodiments, the sample is filtered prior to adding one or more protein denaturing agents.

[0066] In some embodiments, the sample is collected or transferred to a device having a filtration unit, the filtration unit comprising at least one filter. In some embodiments, the device comprises a pre-filtration mechanism to prevent, reduce or inhibit clogging of smaller pore size filters; one or more filters; multiple filters arranged in order of pore size to allow smaller substances to pass through in sequence; 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 achieved using depth filtration. In some embodiments, the mechanism also includes using a filtrate collection container pre-filled with one or more denaturants to allow rapid inactivation of nucleases when in contact with the filtrate. In some embodiments, the filtered sample is removed from the device or poured into a second container. In some embodiments, the filtered sample is removed from the device or poured into a second container containing a preservative. In some embodiments, the collection unit or filtration unit is separated from the filtrate collection container after filtration to allow the addition of a preservative. In some embodiments, the preservative is stored in an encapsulation within the lid and released after the lid is secured to a separate filtrate collection container.

[0067] In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 50°C. In some embodiments, the nucleic acid is stable at a temperature range of about -10°C to about 50°C. In some embodiments, the nucleic acid is stable at a temperature range of about 0°C to about 50°C. In some embodiments, the nucleic acid is stable at a temperature range of about 10°C to about 50°C. In some embodiments, the nucleic acid is stable at a temperature range of about 20°C to about 50°C. In some embodiments, the nucleic acid is stable at a temperature range of about 30°C to about 50°C. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 40°C. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 30°C. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 20°C. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 10°C. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about 0°C. In some embodiments, the nucleic acid is stable at a temperature range of about -20°C to about -10°C.

[0068] In some embodiments, the nucleic acid is stable at about -20°C. In some embodiments, the nucleic acid is stable at about -15°C. In some embodiments, the nucleic acid is stable at about -10°C. In some embodiments, the nucleic acid is stable at about -5°C. In some embodiments, the nucleic acid is stable at about 0°C. In some embodiments, the nucleic acid is stable at about 5°C. In some embodiments, the nucleic acid is stable at about 10°C. In some embodiments, the nucleic acid is stable at about 15°C. In some embodiments, the nucleic acid is stable at about 20°C. In some embodiments, the nucleic acid is stable at about 25°C. In some embodiments, the nucleic acid is stable at about 30°C. In some embodiments, the nucleic acid is stable at about 35°C. In some embodiments, the nucleic acid is stable at about 40°C. In some embodiments, the nucleic acid is stable at about 45°C. In some embodiments, the nucleic acid is stable at about 50°C.

[0069] In some embodiments, the nucleic acid is stable for at least 5 days.

[0070] In some embodiments, the nucleic acid is stable for at least 1 day. In some embodiments, the nucleic acid is stable for at least 2 days. In some embodiments, the nucleic acid is stable for at least 3 days. In some embodiments, the nucleic acid is stable for at least 4 days. In some embodiments, the nucleic acid is stable for at least 5 days.

[0071] In some embodiments, at least one filter comprises a low nucleic acid binding material.

[0072] 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.

[0073] 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.

[0074] In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 20 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 15 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 10 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 9 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 8 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 7 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 6 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 5 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 4 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 3 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 2 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 1 μm. In some embodiments, the size cutoff of at least one filter is about 0.1 μm to about 0.5 μm. In some embodiments, the size cutoff of at least one filter is about 0.5 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 1 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 2 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 3 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 4 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 5 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 6 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 7 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 8 μm to about 25 μm. In some embodiments, the size cutoff of at least one filter is about 9 μm to about 25 μm. In some embodiments, at least one filter has a size cutoff of about 10 μm to about 25 μm. In some embodiments, at least one filter has a size cutoff of about 15 μm to about 25 μm. In some embodiments, at least one filter has a size cutoff of about 20 μm to about 25 μm.

[0075] 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.

[0076] 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 derived from solid tissue. In some embodiments, the filter retains a plurality of microorganisms.

[0077] In some embodiments, the filter comprises a synthetic material to minimize the introduction of contaminating nucleic acids. In some embodiments, the filter comprises a biological material.

[0078] In some embodiments, filtration obtains acellular biological fluid or acellular biological fluid. In some embodiments, filtration obtains acellular plasma or acellular plasma. In some embodiments, filtration obtains acellular saliva or acellular saliva. In some embodiments, filtration obtains acellular urine or acellular urine.

[0079] In some embodiments, the disease or condition (or medical condition) is cancer. In some embodiments, the cancer type is a solid cancer type or a hematological malignant cancer type. In some embodiments, the cancer type is a metastatic cancer type or a relapsed or refractory cancer type. In some embodiments, the cancer types include acute myeloid leukemia (LAML or AML), acute lymphocytic leukemia (ALL), adrenocortical carcinoma (ACC), bladder urothelial carcinoma (BLCA), brain stem glioma, brain low-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, atypical teratoma / rhabdoid tumor of the central nervous system, embryonal tumor of the central nervous system, cervical squamous cell carcinoma, endocervical adenocarcinoma (CESC), childhood cancer, bile duct carcinoma (CHOL), chordoma, chronic lymphocytic leukemia, chronic myeloid Leukemia, chronic myeloproliferative disease, colon cancer (adenocarcinoma) (COAD), colorectal cancer, craniopharyngioma, cutaneous T-cell lymphoma, endocrine islet cell tumor, endometrial cancer, ependymoblastoma, ependymoma, esophageal cancer (ESCA), olfactory neuroblastoma, Ewing sarcoma, extracranial germ cell tumor, extragonadal germ cell tumor, extrahepatic bile duct cancer, gallbladder cancer, gastric (gastric) cancer, gastrointestinal carcinoid tumor, gastrointestinal stromal cell tumor, gastrointestinal stromal tumor (GIST), gestational trophoblastic tumor, glioma multiforme (GBM), hairy cell leukemia, head and neck cancer (HNSD), heart cancer, Hodgkin lymphoma, hypopharyngeal cancer, intraocular melanoma, islet cell tumor, Kaposi's sarcoma, kidney cancer, Langerhans cell histiocytosis, laryngeal cancer, lip cancer, liver cancer, lymphoma diffuse large B-cell lymphoma (DLBCL), malignant fibrous histiocytoma bone cancer, medulloblastoma, medullary epithelioma, melanoma, Merkel cell carcinoma, Merkel cell skin cancer, mesothelioma (MESO), metastatic squamous neck cancer with occult primary, oral cancer, multiple endocrine neoplasia syndrome, multiple myeloma, multiple myeloma / plasmacytoma, mycosis fungoides, myelodysplastic syndrome, myeloproliferative neoplasms, nasal cancer, nasopharyngeal cancer, neuroblastoma, non-Hodgkin's lymphoma, non-melanoma skin cancer, non-small cell lung cancer, oral cancer, oral cancer, oral Pharyngeal cancer, osteosarcoma, other brain and spinal cord tumors, ovarian cancer, ovarian epithelial cancer, ovarian germ cell tumors, ovarian low malignant potential tumors, pancreatic cancer, papillomatosis, paranasal sinus cancer, parathyroid cancer, pelvic cancer, penile cancer, pharyngeal cancer, pheochromocytoma and paraganglioma (PCPG), moderately differentiated pineal parenchymal tumor, pinealoblastoma, pituitary tumor, plasmacytoma / multiple myeloma, pleuropulmonary blastoma, primary central nervous system (CNS) lymphoma, primary hepatocellular carcinoma, prostate cancer such as prostate adenocarcinoma (PRAD), rectal cancer, kidney cancer, renal cell (kidney) cancer, renal cell carcinoma, respiratory tract cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer,Sarcoma (SARC), Sezary syndrome, cutaneous melanoma (SKCM), small cell lung cancer, small intestine cancer, soft tissue sarcoma, squamous cell carcinoma, squamous neck cancer, stomach (gastric) cancer, supratentorial primitive neuroectodermal tumor, T-cell lymphoma, testicular cancer testicular germ cell tumor (TGCT), pharyngeal cancer, thymic cancer, thymoma (THYM), thyroid cancer (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 macroglobulinemia, or Wilms' tumor. In some embodiments, the cancer type comprises acute lymphocytic leukemia, acute myeloid leukemia, bladder cancer, breast cancer, brain cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, colorectal cancer, endometrial cancer, esophageal cancer, gastrointestinal cancer, glioma, glioblastoma, head and neck cancer, kidney cancer, liver cancer, lung cancer, lymphoma, melanoma, myeloid cell tumor, 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 cancer type comprises bladder cancer, breast cancer, cervical cancer, bile duct cancer (CHOL), colon cancer, esophageal cancer, head and neck cancer, kidney cancer, liver cancer, lung cancer, pancreatic cancer, pheochromocytoma and paraganglioma (PCPG), prostate cancer, rectal cancer, sarcoma, skin cancer, gastric cancer, or thyroid cancer.

[0080] In some embodiments, the disease or condition is breast cancer. In some embodiments, the cancer is lung cancer, esophageal cancer, or head and neck cancer.

[0081] 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 digestive tract disease.

[0082] In some embodiments, the disease or condition is an injury.

[0083] In some embodiments, the disease or condition is pregnancy.

[0084] Nucleic acid storage

[0085] In some aspects, a method for preserving nucleic acids is described herein, wherein a solution containing nucleic acids is not degraded for a long time by a preservative without the need for refrigeration. Before adding a preservative, the biological solution can be filtered. In the case of biological fluids, if cells can be separated out to leave mainly cell-free nucleic acids for analysis, then filtering is particularly advantageous. Nucleases present in biological samples are the main underlying cause of nucleic acid degradation. Their effects can be inhibited by adding nuclease inhibitors as described below.

[0086] In some aspects, metal chelators, such as ethylenediaminetetraacetic acid (EDTA), can inhibit nucleases whose activity depends on divalent cations. However, in some aspects, this approach cannot inhibit all nucleases because some nucleases do not require divalent ions.

[0087] In some aspects, competitive inhibitors that bind to the enzyme active site can also be used, but because there are many different types of nucleases, many competitive inhibitors are required and it is difficult to universally inhibit nucleolytic activity using this approach.

[0088] Another strategy to prevent nucleolytic degradation is to destroy the nuclease in one of three ways: (i) thermal denaturation of the nuclease; (ii) digestion of the nuclease by a protease; and (iii) chemical denaturation of the nuclease.

[0089] In some respects, the heat denaturation method for nucleases is limited because some nucleases may renature to their native conformation upon cooling, and the treatment itself can damage the nucleic acids. This method can also be slow, especially with larger sample volumes, and the nucleases may have a chance to degrade the nucleic acids extensively before they are inactivated.

[0090] In certain aspects, nuclease digestion by proteases can effectively degrade nucleases, but due to their inherently slow process, nucleases may have the opportunity to substantially degrade nucleic acids before they are fully digested.

[0091] In certain aspects, chemical denaturation of the nuclease, such as the addition of a reagent that denatures the protein, inactivates the nuclease. The advantage of this strategy is that nuclease activity can be completely inhibited without damaging the nucleic acid prior to extraction.

[0092] In some embodiments, preserving the sample includes contacting the sample with a preservative. In some embodiments, the preservative includes at least one of ethylenediaminetetraacetic acid (EDTA), a ribonuclease inhibitor, an antimicrobial, a denaturant, an agent that inhibits nuclease activity, a masking agent, a buffer, a salt, a permeating agent, or a combination thereof. In some embodiments, the preservative includes ethylenediaminetetraacetic acid (EDTA). In some embodiments, the preservative includes a ribonuclease inhibitor. In some embodiments, the preservative includes an antimicrobial. In some embodiments, the preservative includes a denaturant. In some embodiments, the preservative includes an agent that inhibits nuclease activity. In some embodiments, the preservative includes a masking agent. In some embodiments, the preservative includes a buffer. In some embodiments, the preservative includes a salt. In some embodiments, the preservative includes a permeating agent.

[0093] In some embodiments, the denaturant comprises a nucleic acid denaturant or a protein denaturant. The denaturant comprises a nucleic acid denaturant. The denaturant comprises a protein denaturant.

[0094] Device

[0095] This article also describes an apparatus, an example of which is Figure 7 The device is used to collect and stabilize analytes in a sample, and the device comprises: 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 obtain a filtrate; and c) a filtrate collection container 4 in fluid communication with the filtration unit 3, for collecting the filtrate and bringing the filtrate into contact with a preservative.

[0096] In an embodiment, the filter unit 2 includes a plurality of filters arranged in order of pore size to allow smaller substances to pass through in sequence. In an additional or alternative embodiment, the filter unit 2 includes at least two filters of the same type. In an embodiment, the device includes a pre-filter mechanism to prevent clogging of smaller pore size filters. For example, the filter unit 2 may include a pre-filter mechanism, or the pre-filter mechanism may be separated from the cell filtration unit. In certain embodiments, the filter unit includes a single filter, two filters, three filters, four filters, five filters, six filters, seven filters, eight filters, nine filters, 10 filters or more than 10 filters.

[0097] The filter can be any known type suitable for filtering a biological fluid sample. For example, in an embodiment, at least one filter comprises a depth filter, an asymmetric filter, a microporous filter, or a combination thereof. In an embodiment, at least one filter comprises a low nucleic acid binding material.

[0098] In embodiments, at least one filter may have a size cutoff value selected to reject or pass any desired portion according to size. For example, in embodiments, the size cutoff value 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 an embodiment, the size cutoff of at least one filter is about 0.1 μm to about 100 μm.

[0099] At least one filter can similarly have any desired thickness. For example, in embodiments, the thickness of the filter is about 50 μm to about 1000 μm, or 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, or about 950 μm to about 100 μm, about 150 μm, about 200 μm, about The present invention relates to a method for preparing the present invention to a method for preparing the present invention. The present invention relates to a method for preparing the present invention to a method for preparing the present invention. The present invention relates to a method for preparing the present invention to a method for preparing the present invention.

[0100] More than one filter can be stacked together, thereby creating a stack height, which is the combined thickness of the multiple filters. In an embodiment, the filter unit 2 includes a filter stack height of about 120 μm to about 10000 μm, such as 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 900μm, about 950μm, about 1000μm, about 1250μm, about 1500μm, about 1750μm, about 2000μm, about 2500μm, about 3000μm, about 3500μm, about 4000μm, about 4500μm, about 5000μm, about 5500μm, about 6000μm, about 6500μm, about 7000μm, about 7500μm, about 8000μm, about 8500μm m, about 9000 μm or about 9500 μm to 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 125 0μm, about 1500μm, about 1750μm, about 2000μm, about 2500μm, about 3000μm, about 3500μm, about 4000μm, about 4500μm, about 5000μm, about 5500μm, about 6000μm, about 6500μm, about 7000μm, about 7500μm, about 8000μm, about 8500μm, about 9000μm, about 9500μm or about 10000μm.

[0101] Most filters are circular and have a diameter. Although the filters can be 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.

[0102] In an embodiment, at least one filter is hydrophilic or hydrophobic. In additional or alternative embodiments, at least one filter comprises polysulfone and / or polypropylene. In some embodiments, at least one filter comprises a synthetic material to minimize the introduction of contaminating nucleic acids. In other embodiments, at least one filter comprises a biological material or does not contain a biological material, such as cellulose. In some embodiments, particularly when separating or stabilizing small amounts of analytes, contaminating components from biological materials, such as contaminating nucleic acids that may be present in cellulose or other biological materials, may confound the results. Therefore, in some embodiments, it is advantageous to avoid using biological materials in the filter material.

[0103] In an embodiment, at least one filter retains a plurality of white blood cells. In an additional or alternative embodiment, at least one filter retains a plurality of red blood cells. In an additional or alternative embodiment, at least one filter retains a plurality of cells derived from solid tissue. In an additional or alternative embodiment, at least one filter retains a plurality of microorganisms.

[0104] The sample is typically a biological fluid. A biological fluid may include any body fluid, examples of which include blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen, or cerebrospinal fluid.

[0105] In embodiments, the filtrate collected in the device is a cell-free biological fluid or a cell-depleted biological fluid. In embodiments, the filtrate is cell-free plasma or a cell-depleted plasma. In embodiments, the filtrate is cell-free saliva or a cell-depleted saliva. In embodiments, the filtrate is cell-free urine or a cell-depleted urine.

[0106] In an embodiment, 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 filters. In an embodiment, 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, which engages with the sample collection container 2 to push the sample through the filtration unit 3 and into the filtrate collection container 4. In an embodiment, the plunger 5 is integrated with or separate from the sample collection container 2.

[0107] In an embodiment, the sample collecting container 2 comprises a funnel 1 , wherein the funnel 1 is integrated with or connectable to the sample collecting container 2 .

[0108] In some embodiments, the filtrate collection container 4 contains a preservative 6. In other embodiments, the preservative 6 is provided separately, for example in its own container. It should be understood that the preservative can be added to the sample before the sample is placed in the sample collection container 2, it can be added to the sample in the sample collection container 2, it can also be already present in the filtrate collection container 4, or it can 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 separable from the filtration unit 2. In an embodiment, the device also includes a lid 7 for the separate filtrate collection container 2. In some embodiments, the lid 7 includes an enclosure for storing the preservative 6, and the preservative 6 is released after the lid 7 is fixed to the separate filtrate collection container 4. In additional or alternative embodiments, the device also includes a second container for pouring the filtrate. In an embodiment, the second container contains a preservative 6.

[0109] It will be appreciated by those skilled in the art that any preservative can be used. In certain embodiments, the preservative includes at least one of ethylenediaminetetraacetic acid (EDTA), ribonuclease inhibitors, antimicrobial agents, denaturants, agents that inhibit nuclease activity, masking agents, buffers, salts, permeants, or combinations thereof. For example, in an embodiment, the denaturant includes a nucleic acid denaturant or a protein denaturant. In certain embodiments, the agent that inhibits nuclease activity includes one or more protein denaturants, EDTA, detergents such as SDS, aurintricarboxylic acid (ATA), chelating agents, or combinations thereof. In an embodiment, one or more protein denaturants include one or more chaotropic agents, including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodecylguanidine, or guanidine hydrochloride. In some embodiments, the one or more protein denaturants include guanidine thiocyanate at a concentration of about 30% to about 70%, such as 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%.

[0110] In an embodiment, the device stabilizes the analyte at a desired temperature range, such as a freezing temperature, a refrigerated temperature, room temperature, body temperature, or a temperature above body temperature. For example, in an embodiment, the device stabilizes the analyte at about -20°C to about 50°C, such as 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.

[0111] In an embodiment, the device stabilizes the analyte for at least 5 days, e.g., 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 a year, or longer.

[0112] In embodiments, the at least one analyte comprises a cell-free analyte.

[0113] In embodiments, at least one analyte comprises a nucleic acid. Any nucleic acid or nucleic acid fragment is contemplated. For example, in embodiments, the nucleic acid comprises cell-free RNA. In embodiments, the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof.

[0114] In embodiments, at least one analyte comprises a polypeptide. In embodiments, the polypeptide is a protein. In additional or alternative embodiments, the polypeptide is a metabolite.

[0115] 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.

[0116] In an embodiment, the filtration unit is detachable for recovering the retentate so that the biomolecules and cells contained in the retentate can be stored and analyzed.

[0117] In some embodiments, at least one filter reduces the viscosity of the filtrate compared to the sample. For example, at least one filter can remove components of the sample that contribute to viscosity, such as mucin.

[0118] In an embodiment, the device described herein is provided as a kit. The kit in the embodiment includes a device, which includes a sample collection container, a filtration unit and a filtrate collection container and at least one additional component, such as a plunger; a cover for the filtrate collection container; a funnel; a preservative; and / or instructions for use.

[0119] Also described herein is a method of stabilizing an analyte in a sample. The method comprises applying a sample to a sample collection container of a device or kit described herein, filtering the sample through a filter unit sample, and contacting the filtrate with a preservative.

[0120] When the analyte is stabilized using the above method, the stabilized analyte is collected.Thus, the analyte stabilized by the method is also described herein.

[0121] Computing System

[0122] refer to Fig. 22, a block diagram illustrating an exemplary machine is shown, the machine including a computer system 2700 (e.g., a processing or computing system) within which a set of instructions can be executed to cause the device to implement or perform any one or more aspects and / or methods of static code scheduling of the present invention. Fig. 22 The components in the drawings are merely examples and do not limit the scope of use or functionality of any hardware, software, embedded logic component, or combination of two or more such components, that implement a particular embodiment.

[0123] The computer system 2700 may include one or more processors 2701, memory 2703, and storage 2708, which communicate with each other and other components via a bus 2740. The bus 2740 may also connect a display 2732, one or more input devices 2733 (for example, which may include a keypad, a keyboard, a mouse, a 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 be connected to the bus 2740 directly or via one or more interfaces or adapters. For example, various tangible storage media 2736 may be connected to the bus 2740 via a storage medium 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), laptops or notebook computers, distributed computer systems, computing grids, or servers.

[0124] Computer system 2700 includes one or more processors 2701 (e.g., central processing units (CPUs) or general purpose graphics processing units (GPGPUs)) that perform functions. Processor 2701 optionally includes cache memory unit 2702 for temporary local storage of instructions, data, or computer addresses. Processor 2701 is configured to assist in executing computer-readable instructions. As processor 2701 executes non-transient processor-executable instructions in one or more tangible computer-readable storage media (e.g., memory 2703, storage 2708, storage device 2735, and / or storage medium 2736), computer system 2700 may be Fig.23 The components in the computer readable medium can store software that implements a specific embodiment, and the processor 2701 can execute the software. The memory 2703 can read the software from one or more other computer-readable media (such as mass storage devices 2735, 2736) or from one or more other sources through a suitable interface (such as a network interface 2720). The software can cause the processor 2701 to perform one or more processes described or shown herein or one or more steps of one or more processes. Executing the process or step may include defining a data structure stored in the memory 2703 and modifying the data structure as instructed by the software.

[0125] 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 be used to transfer data and instructions unidirectionally to the processor 2701, and the RAM 2704 may be used to transfer data and instructions bidirectionally with the processor 2701. The ROM 2705 and the RAM 2704 may include any suitable tangible computer-readable media 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 startup, may be stored in the memory 2703.

[0126] Fixed memory 2708 is optionally bidirectionally connected to processor 2701 via memory control unit 2707. Fixed memory 2708 provides additional data storage capacity and may also include any suitable tangible computer-readable medium described herein. Memory 2708 may be used to store operating system 2709, executable file 2710, data 2711, application program 2712 (application program), etc. Memory 2708 may also include an optical drive, a solid-state storage device (e.g., a flash memory-based system), or a combination of any of the above. Where appropriate, the information in memory 2708 may be merged into memory 2703 as virtual memory.

[0127] In one example, storage device 2735 can be removably connected 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 can 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, the software may reside in whole or in part on the machine-readable media on storage device 2735. In another example, the software may reside in whole or in part within processor 2701.

[0128] Bus 2740 connects various subsystems. Here, where appropriate, the bus may include one or more digital signal lines serving common functions. Bus 2740 may be any of several types of bus structures using any of various bus architectures, including but not limited to a memory bus, a memory controller, a peripheral bus, a local bus, and any combination thereof. As an example and not limitation, the architecture includes an industry standard architecture (ISA) bus, an enhanced ISA (EISA) bus, a microchannel architecture (MCA) bus, a video electronics standard 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.

[0129] The computer system 2700 may also include an input device 2733. In one example, a user of the computer system 2700 may input commands and / or other information to the computer system 2700 via the input device 2733. Examples of the input device 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 game controller, 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, a Leap Motion, etc. The input device 2733 may be connected to the bus 2740 via any of a variety of input interfaces 2723 (e.g., input interface 2723), which may include, but are not limited to, a serial interface, a parallel interface, a game port, a USB interface, a FIREWIRE interface, a THUNDERBOLT interface, or any combination thereof.

[0130] In certain embodiments, when the computer system 2700 is connected to the network 2730, the computer system 2700 can communicate with other devices connected to the network 2730, particularly mobile devices and enterprise systems, distributed computing systems, cloud storage systems, cloud computing systems, etc. Communications with the computer system 2700 can be sent through the network interface 2720. For example, the network interface 2720 can receive incoming communications (such as requests or responses from other devices) in the form of one or more data packets (such as Internet Protocol (IP) packets) from the network 2730, and the computer system 2700 can store the incoming communications in the memory 2703 for processing. The computer system 2700 can similarly store outgoing communications (such as requests or responses to other devices) in the form of one or more data packets in the memory 2703 and transmit them from the network interface 2720 to the network 2730. The processor 2701 can access these communication data packets stored in the memory 2703 for processing.

[0131] Examples of network interface 2720 include, but are not limited to, network interface cards, modems, and any combination thereof. Examples of network 2730 or network segment 2730 include, but are not limited to, distributed computing systems, cloud computing systems, wide area networks (WANs) (e.g., the Internet, enterprise networks), local area networks (LANs) (e.g., networks associated with offices, buildings, campuses, or other relatively small geographic spaces), telephone networks, direct connections between two computing devices, peer-to-peer networks, and any combination thereof. Networks, such as network 2730, may employ wired and / or wireless communication modes. In general, any network topology may be used.

[0132] Information and data may be displayed via display 2732. Examples of display 2732 include, but are not limited to, cathode ray tubes (CRTs), liquid crystal displays (LCDs), thin film transistor liquid crystal displays (TFT-LCDs), organic liquid crystal displays (OLEDs) such as passive matrix OLEDs (PMOLEDs) or active matrix OLEDs (AMOLEDs) displays, plasma displays, and any combination thereof. Display 2732 may be connected to processor 2701, memory 2703, and fixed storage 2708, as well as other devices such as input devices 2733, via bus 2740. Display 2732 is connected to bus 2740 via video interface 2722, and data transfer between display 2732 and bus 2740 may be controlled via graphics controls 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, but are not limited to, HTC Vive, Oculus Rift, Samsung GearVR, Microsoft HoloLens, RazerOSVR, FOVE VR, Zeiss VR One, Avegant Glyph, Freefly VR headsets, etc. In further embodiments, the display is a combination of those disclosed herein.

[0133] 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. The peripheral output devices may be connected to the bus 2740 via an output interface 2724. Examples of output interface 2724 include but are not limited to a serial port, a parallel connection, a USB port, a FireWire port, a Thunderbolt port, and any combination thereof.

[0134] In some embodiments, additionally or alternatively, the computer system 2700 may provide the functionality of hardwired logic or otherwise implemented logic in circuits that may replace software or operate in conjunction with software to perform one or more processes or one or more steps of one or more processes described or illustrated herein. The software in the present invention may include logic, and logic may include software. Furthermore, in some embodiments, where appropriate, a computer-readable medium may include a circuit (such as an IC) storing execution software, a circuit implementing execution logic, or both. The present invention includes any suitable combination of hardware, software, or both.

[0135] In some embodiments, it will be appreciated by those skilled in the art that the various illustrative logic blocks, modules, circuits, and algorithm steps described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, computer software, or a combination of both. In order to clearly illustrate this interchangeability of hardware and software, various illustrative components, blocks, modules, circuits, and steps have been generally described above according to their functions.

[0136] In some embodiments, the various illustrative logical blocks, modules, and circuits described in conjunction with the embodiments disclosed herein may be implemented or executed 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, the general purpose processor may be a microprocessor, but in an alternative, the processor may be any conventional processor, controller, microcontroller, or state machine. The processor may also be implemented as a combination of computing devices, such as a combination of a DSP and a microprocessor, multiple microprocessors, one or more microprocessors in combination with a DSP core, or any other such configuration.

[0137] In some embodiments, the steps of the method or algorithm described in conjunction with the embodiments disclosed herein can be implemented directly in hardware, a software module executed by one or more processors, or a combination of the two. In some embodiments, the software module can reside in a RAM memory, a flash memory, a ROM memory, an EPROM memory, an EEPROM memory, a register, 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 connected to a processor so that the processor can read information from the storage medium and write information to the storage medium. In an alternative, the storage medium can be integrated into the processor. In some embodiments, the processor and the storage medium can reside in an ASIC. The ASIC can reside in a user terminal. In an alternative, the processor and the storage medium can reside in a user terminal as discrete components.

[0138] According to the description herein, in some embodiments, suitable computing devices include, but are not limited to, server computers, desktop computers, laptop computers, notebook computers, small notebook computers, netbook computers, netpad computers, set-top computers, media streaming devices, handheld computers, Internet devices, mobile smart phones, tablet computers, personal digital assistants, video game consoles, and vehicles. In some embodiments, those skilled in the art will also recognize that selected televisions, video players, and digital music players with optional computer network connections are suitable for use in the systems described herein. In various embodiments, suitable tablet computers include, but are not limited to, tablet computers with netbook, tablet, and convertible configurations known to those skilled in the art.

[0139] In some embodiments, the computing device includes an operating system configured to execute executable instructions. For example, an operating system is software including programs and data that manages the hardware of the device and provides services for the execution of application programs. In some embodiments, those skilled in the art will recognize that suitable server operating systems include, but are not limited to, FreeBSD, OpenBSD, Linux, Mac OS X Windows and In some embodiments, those skilled in the art will recognize that suitable personal computer operating systems include, but are not limited to Mac OS and UNIX-like operating systems, such as, 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 but are not limited to OS, Research In BlackBerry Windows OS, Windows OS, and In some embodiments, those skilled in the art will also recognize that suitable media streaming device operating systems include, but are not limited to, Apple Google Google Amazon and In some embodiments, those skilled in the art will also recognize that suitable video game console operating systems include, but are not limited to Xbox MicrosoftXbox One, Wii and

[0140] Non-transitory computer readable storage medium

[0141] In some embodiments, the platforms, systems, media, and methods disclosed herein include one or more non-transient computer-readable storage media encoded with a program, the program including instructions executable by an operating system of an optionally networked computing device. In a further embodiment, the computer-readable storage medium is a tangible component of the computing device. In a further embodiment, the computer-readable storage medium is optionally removable from the computing device. In some embodiments, the computer-readable storage medium includes, but is not limited to, a CD-ROM, a DVD, a flash memory device, a solid-state memory, a disk drive, a tape drive, an optical drive, a distributed computing system, including cloud computing systems and services, etc. In some cases, the program and instructions are permanently, substantially permanently, semi-permanently, or non-temporarily encoded on the medium.

[0142] Computer Programs

[0143] In some embodiments, the platforms, systems, media, and methods disclosed herein include at least one computer program or its use. In some embodiments, the computer program includes a series of instructions executable by one or more processors of a CPU of a computing device, written to perform specified tasks. In some embodiments, computer-readable instructions can be implemented as program modules that perform specific tasks or implement specific abstract data types, such as functions, objects, application programming interfaces (APIs), computational data structures, etc. Based on the disclosure provided herein, those skilled in the art will recognize that computer programs can be written in various versions of various languages.

[0144] In some embodiments, the functions of the computer readable instructions can be combined or distributed in various environments as needed. In some embodiments, the computer program includes an instruction sequence. In some embodiments, the computer program includes multiple instruction sequences. In some embodiments, the computer program is provided from one location. In some 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 one or more web applications, one or more mobile applications, one or more stand-alone applications, one or more web browser plug-ins, extensions, plug-ins or add-ons or a combination thereof in part or in whole.

[0145] Web Applications

[0146] In some embodiments, the computer program includes a web application. Based on the disclosure provided herein, those skilled in the art will recognize that in various embodiments, the web application utilizes one or more software frameworks and one or more database systems. In some embodiments, the web application is built on a software framework, such as NET or Ruby on Rails (RoR). In some embodiments, the web application utilizes one or more database systems, including but not limited to relational, non-relational, object-oriented, associative, and XML database systems. In further embodiments, suitable relational database systems include but are not limited to SQL Server, mySQL TM and Those skilled in the art will also recognize that in various embodiments, the web application is written in one or more versions of one or more languages. The 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 to some extent 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 to some extent in a presentation definition language, such as Cascading Style Sheets (CSS). In some embodiments, the web application is written to some extent in a client-side scripting language, such as Asynchronous Javascript and XML (AJAX), Actionscript, Javascript, or In some embodiments, the web application is written in some way using a server-side coding language, such as Active Server Pages (ASP), Perl, Java TM , JavaServer Pages (JSP), Hypertext Preprocessor (PHP), Python TM , Ruby, Tcl, Smalltalk, In some embodiments, the web application is written in a database query language, such as Structured Query Language (SQL). In some embodiments, the web application is integrated with an enterprise server product, such as Lotus In some embodiments, the web application includes a media player component. In various further embodiments, the media player component utilizes one or more of a number of suitable multimedia technologies, including but not limited to HTML 5, Java TM and

[0147] refer to Fig.23 In a specific embodiment, the application providing 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, OracleDatabase, Microsoft SQL Server, IBM DB2, IBM Informix, SAP Sybase, SAP Sybases, Teradata, etc. In this embodiment, the application providing system also 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 server optionally exposes one or more web services via an application program interface (API) 2840. Through a network such as the Internet, the system provides a browser-based and / or mobile local user interface.

[0148] refer to Fig.24 In a particular embodiment, the application providing system instead has a distributed, cloud-based architecture 2900 and includes elastic load balancing, automatically scaling web server resources 2910 and application server resources 2920 and a synchronously replicated database 2930.

[0149] Mobile Apps

[0150] In some embodiments, the computer program includes a mobile application provided to the mobile computing device. In some embodiments, the mobile application is provided when the mobile computing device is manufactured. In other embodiments, the mobile application is provided to the mobile computing device via a computer network as described herein.

[0151] In view of the disclosure provided herein, mobile applications are created using hardware, languages, and development environments known in the art using techniques known to those skilled in the art. In some embodiments, those skilled in the art will recognize that mobile applications are written in several languages. In some embodiments, suitable programming languages ​​include, but are not limited to, C, C++, C#, Objective-C, Java, and the like.TM ,Javascript,Pascal,Object Pascal,Python TM , Ruby, VB.NET, WML and XHTML / HTML with or without CSS or a combination thereof.

[0152] In some embodiments, suitable mobile application development environments are available from a variety of sources. Commercial development environments include, but are not limited to, AirplaySDK, alcheMo, Celsius, Bedrock, Flash Lite, .NET Compact Framework, Rhomobile, and WorkLight Mobile Platform. Other development environments are free, including but not limited to Lazarus, MobiFlex, MoSync, and Phonegap. In addition, mobile device manufacturers sell software development kits, including but not limited to iPhone and iPad (iOS) SDK, Android TM SDK, SDK, BREWSDK, OSSDK, Symbian SDK, webOSSDK and Mobile SDK.

[0153] In some embodiments, those skilled in the art will recognize that several commercial forums may sell mobile applications, including but not limited to App Store, Play, Chrome WebStore, AppWorld, App Store for Palm devices, App Catalog for webOS, Mobile market, Ovi Store on your device, Apps and Dsi Shop.

[0154] Standalone Application

[0155] In some embodiments, the computer program includes a stand-alone application, which is a program that runs as an independent computer process, rather than an add-on to an existing process, e.g., not a plug-in. In some embodiments, those skilled in the art will recognize that stand-alone applications are typically compiled. In some embodiments, a compiler is a computer program that converts source code written in a programming language into binary object code (e.g., assembly language or machine code). In some embodiments, suitable compiled programming languages ​​include, but are not limited to, C, C++, Objective-C, COBOL, Delphi, Eiffel, Java, and the like. TM , Lisp, Python TM , Visual Basic and VB.NET or a combination thereof. Compilation is usually performed at least in part to create an executable program. In some embodiments, the computer program includes one or more executable compiled applications.

[0156] Web Browser Plug-ins

[0157] In some embodiments, a computer program includes a web browser plug-in (e.g., extension, etc.). In computing, a plug-in is one or more software components that add specific functionality to a larger software application. Manufacturers of software applications support plug-ins to enable third-party developers to create extensions to the functionality of the application, to enable easy addition of new features, and to reduce the size of the application. Where supported, plug-ins can customize the functionality of the software application. For example, plug-ins are commonly used in web browsers to play videos, generate interactivity, scan for viruses, and display specific file types. In some embodiments, those skilled in the art will be familiar with several web browser plug-ins, including Player, and In certain embodiments, the toolbar includes one or more web browser extensions, plug-ins or add-ons. In certain embodiments, the toolbar includes one or more browser bars, tool belts or desktop belts.

[0158] In view of the disclosure provided herein, those skilled in the art will recognize that plug-ins can be developed using a variety of plug-in frameworks in a variety of programming languages, including but not limited to C++, Delphi, Java, and the like. TM , PHP, Python TM and VB.NET or a combination thereof.

[0159] In some embodiments, a web browser (also known as an Internet browser) is a software application designed for use with a network-connected computing device for retrieving, presenting, and traversing information resources on the World Wide Web. In some embodiments, suitable web browsers include, but are not limited to Internet Chrome, Opera and KDE Konqueror. In some embodiments, the web browser is a mobile web browser. Mobile web browsers (also known as micro-browsers, mini-browsers, and wireless browsers) are designed for use with mobile computing devices, including but not limited to handheld computers, tablet computers, laptops, subnotebook computers, smart phones, music players, personal digital assistants (PDAs), and handheld video game systems. Suitable mobile web browsers include but are not limited to Browser, RIM Browser, Blazer, Browsers, for mobile devices Internet Mobile, Basic Web, Browser, Opera Mobile and PSP TM Browser.

[0160] Software Modules

[0161] In some embodiments, the platforms, systems, media, and methods disclosed herein include software, server, and / or database modules or their uses. In view of the disclosure provided herein, the software modules are created using machines, software, and languages ​​known in the art by techniques known to those skilled in the art. In some embodiments, the software modules disclosed herein are implemented in a variety of ways. In various embodiments, the software modules include files, code segments, programming objects, programming structures, or combinations thereof. In further various embodiments, the software modules include multiple files, multiple code segments, multiple programming objects, multiple programming structures, or combinations thereof. In various embodiments, one or more software modules include, but are not limited to, web applications, mobile applications, and standalone applications. In some embodiments, the software modules are located in one computer program or application. In other embodiments, the software modules are located in multiple computer programs or applications. In some embodiments, the software modules are located on one machine. In some embodiments, the software modules are located on multiple machines. In further embodiments, the software modules are located on a distributed computing platform, such as a cloud computing platform. In some embodiments, the software modules are located on one or more machines at one location. In other embodiments, the software modules are located on one or more machines at multiple locations.

[0162] database

[0163] In some embodiments, the platform, system, medium and method disclosed herein include one or more databases or their uses. In view of the disclosure provided herein, those skilled in the art will recognize that many databases are suitable for storing and retrieving information. In various embodiments, suitable databases include, but are not limited to, relational databases, non-relational databases, object-oriented databases, object databases, entity relationship model databases, associated databases, and XML databases. Other non-limiting examples include SQL, PostgreSQL, MySQL, Oracle, DB2, and Sybase. In some embodiments, the database is based on the Internet. In other embodiments, the database is based on web. In other embodiments, the database is based on cloud computing. In specific embodiments, the database is a distributed database. In other embodiments, the database is based on one or more local computer storage devices.

[0164] Methods using computers

[0165] In some embodiments, the methods and software described herein can utilize one or more computers. In some embodiments, computers can be used to manage customer and sample information, such as sample or customer tracking, database management, analyzing molecular spectrum data, analyzing cytological data, storing data, billing, marketing, reporting results, storing results or a combination thereof. In some embodiments, the computer can include a monitor or other graphical interface for displaying data, results, billing information, marketing information (such as demographics), customer information or sample information. In some embodiments, the computer can also include a device for data or information input. In some embodiments, the computer can include a processing unit and a fixed or removable medium or a combination thereof. In some embodiments, the computer can be accessed by a user physically close to the computer, such as by a keyboard and / or a mouse, or by a user who does not necessarily access a physical computer through a communication medium (such as a modem, an Internet connection, a telephone connection or a wired or wireless communication signal carrier). In some cases, the computer can be connected to a server or other communication device for relaying information from a user to a computer or from a computer to a user. In some cases, the user can store the data or information obtained from the computer on a medium such as a removable medium through a communication medium. In some embodiments, it is conceivable that the data associated with these methods can be transmitted through the network or connection for receiving and / or review by one party. In some embodiments, the recipient can be, but is not limited to, an individual, a healthcare provider, or a healthcare manager. In one example, the computer-readable medium includes a medium suitable for transmitting the results of the biological sample analysis. In some embodiments, the medium can include the results of the subject, wherein the results are the results obtained using the methods described herein.

[0166] In some embodiments, the entity that obtains the sample information may enter the results into a database for one or more of the following purposes: inventory tracking, test result tracking, order tracking, customer management, customer service, billing, and sales. The sample information may include, but is not limited to: customer name, unique customer identity, medical professional associated with the customer, one or more tests indicated, test results, adequacy status, adequacy of the indicated test, personal 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, the sample history may include, but is not limited to: sample age, sample type, collection method, storage method, or transportation method.

[0167] In some embodiments, the database can be accessed by a client, a medical professional, an insurance provider, or other third party. In some embodiments, database access can take the form of digital processing communications, such as a computer or telephone. In some embodiments, the database can be accessed by an intermediary, such as a customer service representative, a business representative, a consultant, an independent testing center, or a medical professional. In some embodiments, the provision or extent of database access or sample information (such as test results) can change after payment of the fees for products and services provided or to be provided. In some embodiments, the extent of database access or sample information can be limited to meet generally accepted or legal requirements for patient or customer confidentiality.

[0168] Machine Learning

[0169] In some embodiments, the systems, methods, software and platforms described herein may include computer-implemented supervised or unsupervised learning methods, including SVM, random forests, clustering algorithms (or software modules), gradient enhancement, logistic regression and / or decision trees. In some embodiments, the machine learning methods described herein may improve the generation of recommendations based on recording and analyzing any identification, laboratory results, patient results, or any other relevant medical information described herein. In some cases, the machine learning methods may intentionally group or separate treatment options. In some embodiments, some treatment options may be intentionally grouped together or removed from any of the multiple stages of the treatment experience.

[0170] In some embodiments, a supervised learning algorithm may be an algorithm that relies on using a set of labeled paired training data examples to infer the relationship between input data and output data. In some embodiments, an unsupervised learning algorithm may be an algorithm for drawing inferences from a training data set to output data. In some embodiments, an unsupervised learning algorithm may include cluster analysis, which may be used for exploratory data analysis to discover implicit patterns or groupings in processed data. An example of an unsupervised learning method may include principal component analysis. In some embodiments, principal component analysis may include reducing the dimension of one or more variables. In some embodiments, the dimension of a given variable may be at least 1, 5, 10, 50, 100, 200, 300, 400, 500, 600, 700, 800, 900, 1000, 1100, 1200, 1300, 1400, 1500, 1600, 1700, 1800 or more. In some embodiments, the dimensionality of a given variable may be at most 1800, 1600, 1500, 1400, 1300, 1200, 1100, 1000, 900, 800, 700, 600, 500, 400, 300, 200, 100, 50, 10, or less.

[0171] In some embodiments, the computer-implemented method may include statistical techniques. In some embodiments, the statistical techniques may include linear regression, classification, resampling methods, subset selection, shrinkage, dimensionality reduction, nonlinear models, tree-based methods, support vector machines, unsupervised learning, or any combination thereof.

[0172] In some embodiments, linear regression can be a method for predicting a target variable by fitting the best linear relationship between a dependent variable and an independent variable. In some embodiments, the best fit can mean that the sum of all distances between the shape and the actual observed values ​​at each point is minimal. In some embodiments, linear regression can include simple linear regression and multiple linear regression. In some embodiments, simple linear regression can use a single independent variable to predict the dependent variable. In some embodiments, multiple linear regression can predict the dependent variable by fitting the best linear relationship using multiple independent variables.

[0173] In some embodiments, classification can be a data mining technique that assigns categories to a data set to enable accurate prediction and analysis. In some embodiments, classification techniques can include logistic regression and discriminant analysis. Logistic regression can be used when the dependent variable is binary. In some embodiments, logistic regression can be used to discover and describe the relationship between a binary dependent variable and one or more nominal, ordinal, interval, or ratio independent variables. In some embodiments, resampling can be a method that includes drawing repeated samples from an original data sample. In some embodiments, resampling can involve using a common distribution table to calculate approximate probability values. In some embodiments, resampling can generate a unique sampling distribution based on actual data. In some embodiments, resampling can use an experimental method instead of 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 replacing samples from the original data and using the "not selected" data points as test cases. In some embodiments, cross-validation can be performed by dividing the training data into multiple parts.

[0174] In some embodiments, subset selection can identify a subset of predictors that are related to the response. In some embodiments, subset selection can include best subset selection, forward stepwise selection, backward stepwise selection, hybrid methods, or any combination thereof. In some cases, shrinkage fitting involves a model with all predictors, but the estimated coefficients shrink towards zero relative to the least squares estimates. In some embodiments, this shrinkage can reduce variance. In some embodiments, shrinkage can include ridge regression and lasso. In some embodiments, dimensionality reduction can simplify the problem of estimating n + 1 coefficients into a simpler problem of estimating m + 1 coefficients, where m < n. This can be achieved by computing n different linear combinations or projections of the variables. Then, these n projections are 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 feature set from a larger set of variables. In some embodiments, the principal components used in principal component regression can capture the maximum variance in the data using linear combinations of the data in the subsequent orthogonal directions. In some embodiments, partial least squares can be a supervised alternative to principal component regression because partial least squares can utilize the response variable to identify new features.

[0175] In some embodiments, non-linear regression can be a form of regression analysis in which the observed data is modeled by a function that is a non-linear combination of the model parameters and depends on one or more independent variables. In some embodiments, non-linear regression can include step functions, piecewise functions, spline functions, generalized additive models, or any combination thereof.

[0176] In some embodiments, tree-based methods can be used for regression and classification problems. In some embodiments, regression and classification problems may involve stratifying or segmenting the predictor space into multiple simple regions. In some embodiments, tree-based methods may include bagging, boosting, random forests, or any combination thereof. In some embodiments, bagging can reduce the variance of predictions by using a combination of repetitions to generate additional data for training from the original data set to produce a multi-step method with the same cardinality / size as the original data. In some embodiments, boosting can use several different models to calculate outputs, and then average the results using a weighted average method. In some embodiments, a random forest algorithm can extract a random bootstrap sample of a training set. In some embodiments, a support vector machine can be a classification technique. In some embodiments, a support vector machine can include a hyperplane that finds the best separation of two classes of points with the maximum margin. In some embodiments, a support vector machine can constrain the optimization problem so that the margin is maximized under the constraint of perfectly classified data.

[0177] In some embodiments, the unsupervised method may be a method of drawing inferences from a data set that includes input data without labeled responses. In some embodiments, the unsupervised method may include clustering, principal component analysis, k-means clustering, hierarchical clustering, or any combination thereof.

[0178] definition

[0179] Unless otherwise defined, all technical terms, symbols and other technical and scientific terms or terminology used herein 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 with commonly understood meanings are defined herein for clarity and / or ease of reference, and the inclusion of such definitions herein should not necessarily be construed as indicating a substantial difference from definitions commonly understood in the art.

[0180] In the present application, various embodiments can be presented in a range format. It should be understood that the description of the range format is only for convenience and brevity and should not be interpreted as a mandatory limitation on the scope of the invention. Therefore, the description of the range should be considered to have specifically disclosed all possible sub-ranges and each numerical value within the range. For example, the description of a range (e.g., 1 to 6) should be considered to specifically disclose each sub-range (e.g., 1 to 3, 1 to 4, 1 to 5, 2 to 4, 2 to 6, 3 to 6, etc.) and each numerical value within the range, such as 1, 2, 3, 4, 5 and 6. Regardless of the breadth of the range, this applies.

[0181] As used in the specification and claims, the singular forms "a," "an," and "the" include plural references unless the context clearly dictates otherwise. For example, the term "a sample" includes a plurality of samples, including mixtures thereof.

[0182] The terms "determine," "measure," "assess," "evaluate," "assay," and "analyze" are often used interchangeably herein to refer to forms of measurement. These terms include determining whether an element is present (e.g., detecting). These terms may include quantitative, qualitative, or both. An assessment may be relative or absolute. "Detecting the presence of" may include determining the amount of something present in addition to determining whether it is present in context.

[0183] The terms "subject," "individual," or "patient" are often used interchangeably herein. A "subject" can be a biological entity containing expressed genetic material. A biological entity can be a plant, an animal, or a microorganism, including, for example, bacteria, viruses, fungi, and protozoa. A subject can be a tissue, a cell, and the progeny thereof of a biological entity obtained in vivo or cultured in vitro. A subject can be a mammal. A mammal can be a human. A subject may be at high risk for diagnosis or suspicion of having a disease. In some cases, a subject may not necessarily be at high risk for diagnosis or suspicion of having a disease.

[0184] As used herein, the term "about" plus a numerical value refers to the numerical value ±15%. The term "about" plus a range refers to the range minus 15% of its lowest value and plus 15% of its highest value.

[0185] The term "treatment" as used herein is used to refer to a drug or other intervention regimen that enables the recipient to obtain a beneficial or desired result. Beneficial or desired results include, but are not limited to, therapeutic benefit and / or preventive benefit. Therapeutic benefit may refer to the eradication or improvement of the symptoms or underlying disease being treated. In addition, therapeutic benefit can be achieved by eradicating or improving one or more physiological symptoms associated with the underlying disease, so that improvement is observed in the subject, although the subject may still suffer from the underlying disease. Preventive effects include delaying, preventing or eliminating the appearance of a disease or condition, delaying or eliminating the onset of a disease or condition, slowing, stopping or reversing the progression of a disease or condition, or any combination thereof. As for preventive benefit, a subject at risk of developing a particular disease, or a subject reporting one or more physiological symptoms of a disease, may also receive treatment even though the disease may not have been diagnosed yet.

[0186] The section headings used herein are for organizational purposes only and are not to be construed as limiting the subject matter described.

[0187] Example

[0188] Embodiment 1. A method for detecting the presence of a medical condition or disease in a subject, the method comprising: collecting a bodily fluid sample from the subject; optionally, preserving the sample by adding a preservative during collection; optionally, separating the sample; optionally, adding a preservative to the separated sample; selecting one or more analytes in the sample, including but not limited to nucleic acid transcripts or genomic regions of interest; using an assay to qualitatively or quantitatively detect the selected analytes, wherein the assay may include technologies involving but not limited to biomolecule purification, biomolecule enrichment, biomolecule sequencing, PCR, quantitative PCR, isothermal amplification, mass spectrometry, antibody-based detection, or CRISPR and CRISPR-Cas systems, thereby generating data; using a computer to analyze the data to detect the presence or absence of a medical condition or disease, or to analyze the data to generate a likelihood score for a medical condition or disease.

[0189] Embodiment 2. The method according to embodiment 1, wherein the body fluid is saliva.

[0190] Embodiment 3. The method of embodiment 1 or 2, wherein multiple samples are collected from the subject in a longitudinal manner to create a personal health profile that can monitor changes indicative of health changes.

[0191] Embodiment 4. The method of embodiment 1 or 2, wherein the signal changes caused by the circadian cycle are different from the signal caused by the medical condition or disease state.

[0192] Embodiment 5. The method according to embodiment 1, wherein a preservative is added to the sample after collection.

[0193] Embodiment 6. The method of embodiment 1, wherein a preservative is present in the collection container prior to collection.

[0194] Embodiment 7. The method according to embodiment 1, wherein the preservative comprises at least one or more of the following: EDTA to inhibit nuclease; ribonuclease inhibitors, such as but not limited to poly (vinyl sulfonic acid, sodium salt), dUppAp, pdUppAp and pTppAp, vanadyl ribonucleoside complex, aurintricarboxylic acid, Rnasin, SUPERaseIN or the like; agents to prevent microbial growth, such as but not limited to isothiazolinone and formaldehyde releasers, such as Germall Plus, DMDM ​​hydantoin, imidazolidinyl urea, diazolidinyl urea and Proclin 300; nucleic acid denaturants; protein denaturants, such as but not limited to detergents, urea, guanidine thiocyanate, guanidine hydrochloride; catabolic protein masking agents; buffers; detergents; reducing agents; antioxidants; cryoprotectants; or salt permeabilizing agents.

[0195] Embodiment 8. The method according to embodiment 1, wherein the sample is separated after collection.

[0196] Embodiment 9. The method according to embodiment 1, wherein the sample is separated into two or more parts by applying centrifugal force, and each part is taken out separately, wherein the separated parts may include a cell-free part and a cell-containing part.

[0197] Embodiment 10. The method according to embodiment 1, wherein the sample is separated by filtration.

[0198] Embodiment 11. The method of embodiment 10, wherein the filtering mechanism is integrated in the device and one or more filters can be used. Multiple filters can be arranged in order of pore size to allow smaller substances to pass through in sequence. Filtration is achieved by applying mechanical force, centrifugal force or vacuum or by capillary action. Optionally, a preservative such as described in embodiment 7 is added to the filtrate during or shortly after filtration. Optionally, a preservative such as described in embodiment 7 is added to the retentate.

[0199] Embodiment 12. The method according to embodiment 1, wherein the saliva is separated using the method of any one of embodiments 9-11 to obtain a cell-free portion and a cell-containing portion.

[0200] Embodiment 13. The method of embodiment 1, wherein at least one type of analyte comprises RNA.

[0201] Embodiment 14. The method of embodiment 1, wherein at least one type of analyte comprises cell-free RNA.

[0202] Embodiment 15. The method according to embodiment 13, wherein the RNA is selected from the group consisting of: mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, tRNA fragments, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof and any combination thereof.

[0203] Embodiment 16. The method according to embodiment 14, wherein the RNA is selected from the group consisting of: mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, tRNA fragments, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof and any combination thereof.

[0204] Embodiment 17. The method of embodiment 1, wherein at least one type of analyte comprises DNA.

[0205] Embodiment 18. The method of embodiment 1, wherein at least one type of analyte comprises cell-free DNA.

[0206] Embodiment 19. The method of embodiment 1, wherein the analyte is endogenous (derived from the subject), exogenous (eg, the subject's microbiome), or a mixture thereof.

[0207] Embodiment 20. The method of embodiment 1, wherein at least one type of analyte comprises proteins.

[0208] Embodiment 21. The method of embodiment 1, wherein at least one type of analyte comprises small molecules.

[0209] Embodiment 22. The method of embodiment 1, wherein at least one type of analyte comprises hormones.

[0210] Embodiment 23. The method of embodiment 1, wherein at least one type of analyte comprises metabolites.

[0211] Embodiment 24. The method of embodiment 1, wherein at least one type of analyte comprises cells (endogenous or exogenous).

[0212] Embodiment 25. The method of embodiment 1, wherein the disease is cancer.

[0213] Embodiment 26. The method according to embodiment 1, wherein the disease is breast cancer.

[0214] Embodiment 27. The method of embodiment 26, wherein the body fluid is saliva.

[0215] Embodiment 28. The method according to embodiment 27, wherein the sample is separated to obtain cell-free saliva.

[0216] Embodiment 29. The method of embodiment 26, wherein at least one type of analyte is RNA.

[0217] Embodiment 30. The method of embodiment 26, wherein at least one type of analyte is cell-free RNA.

[0218] Embodiment 31. A method according to embodiment 29 or 30, wherein the RNA is analyzed using sequencing.

[0219] Embodiment 32. The method of embodiment 31, wherein the sequencing is multiplexed.

[0220] Embodiment 33. The method according to embodiment 31, wherein the sequencing is high-throughput.

[0221] Embodiment 34. A method according to embodiment 31, wherein a single molecule tag (UMI) is used to identify a single RNA represented multiple times.

[0222] Embodiment 35. The method of embodiment 29 or 30, wherein the RNA is analyzed using PCR.

[0223] Embodiment 36. The method of embodiment 29 or 30, wherein the RNA is analyzed using a microarray.

[0224] Embodiment 37. The method of embodiment 26, wherein the patient has dense breast tissue.

[0225] Embodiment 38. A method according to embodiment 13 or 14, wherein tissue-specific contributions to the RNA profile are determined.

[0226] Embodiment 39. A method according to embodiment 38, wherein the signal is distinguished from a disease or medical condition by determining or calculating subtracting the tissue-specific contribution to the RNA profile.

[0227] Embodiment 40. The method of embodiment 38, wherein tissue-specific contributions are used directly to identify the presence of a disease or medical condition.

[0228] Embodiment 41. A method according to embodiment 25, wherein tissue-specific contributions to RNA profiles are used to identify cancer tissue of origin.

[0229] Embodiment 42. The method of Embodiment 1, wherein the disease is an infectious disease.

[0230] Embodiment 43. The method of Embodiment 1, wherein the disease or medical condition involves the brain or nervous system.

[0231] Embodiment 44. The method of Embodiment 1, wherein the medical condition involves the brain or nervous system.

[0232] Embodiment 45. The method of Embodiment 1, wherein the medical condition is pregnancy.

[0233] Embodiment 46. The method of Embodiment 1, wherein the medical condition is organ trauma or injury.

[0234] Embodiment 47. The method of embodiment 1, wherein the disease is an autoimmune disease.

[0235] Embodiment 48. The method of embodiment 1, wherein the disease is metabolic in nature.

[0236] Embodiment 49. The method according to embodiment 1, wherein the disease is an endocrine system disease.

[0237] Embodiment 50. The method of embodiment 1, wherein the disease is a gastrointestinal disease.

[0238] Embodiment 51. The method of embodiment 1, wherein the subject is human.

[0239] Embodiment 52. A method according to embodiment 1, wherein the subject is non-human.

[0240] Embodiment 53. The method according to embodiment 1, wherein the sample is collected at home.

[0241] Embodiment 54. The method according to embodiment 1, wherein the sample is collected in a medical institution.

[0242] Embodiment 55. The method of embodiment 1, wherein the sample is collected in a dental clinic or by a dentist.

[0243] Embodiment 56. The method of embodiment 1, wherein the sample is collected by a veterinarian.

[0244] Example 57. A method according to Example 1, wherein the preservative includes 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 antibacterial agent, such as Proclin 300 or isothiazolinone, (4) a buffer that maintains the pH value between 4 and 9, (5) a nuclease inhibitor, such as EDTA, aurintricarboxylic acid, RNaseIn, etc., (6) an osmotic agent, such as betaine, and (7) a cryoprotectant.

[0245] Embodiment 58. A method according to embodiment 1, wherein the preservative comprises a denaturing agent, such as guanidine thiocyanate or urea, and one or more of: (1) EDTA, (2) a buffer, (3) a detergent and (4) a reducing agent.

[0246] A method according to embodiment 1, wherein the analytes are DNA and cell-free salivary RNA of the patient and genetic and transcriptional analysis is used to detect the presence of the disease or medical condition.

[0247] According to the method described in Example 1, multiple samples are collected from a patient, and the samples are processed using different versions of the workflow described in Example 1.

[0248] The method of embodiment 1, wherein the sample is collected from a body site different from the body site of the disease or condition.

[0249] Example

[0250] The following examples are for illustrative purposes only and are not intended to limit the scope of the present invention.

[0251] Example 1. Tissue-specific and tissue-enriched transcripts in saliva

[0252] GTEx data were analyzed to identify genes that are highly specific to a small group of tissues. Tissue-specific transcripts were detected in patient saliva from multiple organs ( Figure 4 ). The greatest overlap was observed in blood and esophagus. Tissue-specific transcripts in saliva demonstrate its potential for broad disease detection.

[0253] GTEx data were analyzed to identify genes enriched in multiple tissues. This analysis expands the ability to analyze more tissues using saliva ( Figure 5 ).

[0254] Transcript enrichment (e.g., identifying tissue-enriched transcripts) is calculated at least in part based on a correlation weighted entropy calculation:

[0255] CWE=-Σ i ((p i logp i ) / max{Σ j r ij ,0})

[0256] in:

[0257] i is the organization type,

[0258] p is the normalized TPM in the tissue,

[0259] r ij is the Pearson correlation coefficient between tissue types i and j,

[0260] The total is the sum of all tissue types.

[0261] 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 related groups of tissue types (such as brain sections). For tissue-enriched transcripts, the lowest 30% of entropy was selected. If a gene was present at 50% or more of the maximum value, it was included as an enriched tissue.

[0262] Example 2. Breast cancer proof-of-concept study

[0263] The clinical study enrolled more than 2,600 patients at two sites. A total of 2,623 samples were collected. 115 breast cancer patients and 186 non-cancer patients were analyzed in the NGS study of 301 patients ( Fig.16 ). Genes with large differences between the two groups were identified. GSEA analysis further demonstrated that the cancer signature gene list was enriched in the cancer group ( Fig.18 ).

[0264] Example 3. Detection of breast cancer

[0265] A method of detecting the presence of a medical condition or disease in a subject, the method comprising: obtaining saliva from the subject; purifying nucleic acids from the saliva; measuring the level of at least a portion of the nucleic acids using at least one or more of the following methods: sequencing, qPCR, microarray; and analyzing the data using computer and statistical methods to detect the presence of a medical condition, wherein all measurements or subsets of measurements can result in the detection of a condition. Although this method can be used to detect a variety of diseases, this application focuses on the method and RNA signature for breast cancer. The method can be used for patients in all risk groups who need to undergo breast cancer screening or diagnostic workup. Therefore, it can be performed as a stand-alone test or in conjunction with imaging methods (such as mammography, MRI or ultrasound) to improve overall accuracy. Saliva collection can be performed at home, in the field, or in a medical setting. In all cases, the collection, processing, and storage of saliva must maintain the integrity of the analyte. In this case, the analyte of interest is nucleic acid that may be derived from whole saliva or cell-free saliva. Proper collection and nucleic acid preservation methods are critical. It is desirable to use a method that inactivates nucleases as much as possible and preserves nucleic acids for a long time over a wide temperature range. Other aspects of sample collection, such as time or fasting state, may also play an important role and must be considered.

[0266] Prior to analysis, the preserved saliva sample can be separated to obtain cell-free saliva. Nucleic acids can be extracted from the sample using various methods. Some methods are for RNA, some are for DNA, and some methods allow separation of both. In the case of isolating RNA, it is essential to use a method to remove any contaminating DNA, and in the case of isolating DNA, it is essential to use a method to remove contaminating RNA. Once separated, the nucleic acid can be further analyzed using various methods (including but not limited to sequencing, qPCR and microarrays). In the case of genome-scale technology, it may be desirable to sub-select genes or regions of interest in detection or bioinformatics. This may be to reduce background noise or for cost reasons. Regardless of the detection method used, any algorithm used to determine the presence of a disease or condition can be determined using only partial data.

[0267] Example 3 illustrates the detection of breast cancer using RNA transcripts found in cell-free saliva. Using RNA-Seq data from 20 breast cancer patients and 20 non-cancer patients, machine learning logistic regression was used to identify genes that can be used in the classifier for breast cancer detection (Table 1). Fig.21A The ROC curves for the 10-fold cross validation results are shown, which are averaged over 100 replicates of the different training and testing splits. Cross validation provides an assessment of how the model performs on new data and prevents overfitting. Fitting all the data resulted in a model using 157 genes with logistic regression coefficients as Fig.21BAs shown. While this set of 157 genes was used for the best performing classifier, an expanded set of 250 genes was also found to provide 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 on the remaining genes. At each iteration, the performance of the classifier degraded. This process was repeated until the classification result was no better than random. This random performance was found to occur when 250 genes were removed ( Fig. 21C ). Based on these findings, it is concluded that the expression levels of the genes listed in Table 1 can predict the presence of breast cancer. It is worth pointing out that the data of these gene subsets may be sufficient for classifiers. The final classifier may only use a portion of these genes for prediction. Alternatively, the classifier may include many or all genes, with only a few providing useful information for disease prediction in any one sample.

[0268] Table 1. Exemplary gene list for detecting breast cancer

[0269]

[0270]

[0271] Example 4 Nucleic Acid Preservation

[0272] Two things are demonstrated by the following examples: adding a denaturing agent, such as a chaotropic agent, to a biological sample preserves RNA in the sample for at least four days; and sample filtration can be used to prepare cell-free or cell-depleted RNA.

[0273] Saliva collected from multiple individuals was pooled and divided into two parts, A and B (see Figure 8 ). Spin part A, harvest cell-free saliva and divide it into two parts, A1 and A2. Add equal volumes of PBS (preservative (-) condition) and chaotropic agent (preservative (+) condition) to A1 and A2, respectively. Filter part B using a syringe-based filter to remove cells. Divide the filtrate into two parts, B1 and B2. Add equal volumes of PBS (-preservative condition) and chaotropic agent (+ preservative condition) to B1 and B2, respectively. Take aliquots from A1, A2, B1 and B2 on days 0, 1, 2 and 4 and freeze at -80°C. Extract samples and analyze using qPCR of three genes (ACTB, FOSL2 and NAMPT).

[0274] The qPCR results of the experiment showed that the condition containing the denaturing chaotropic agent (preservative+) showed no decrease in the transcription level of any of the three genes, while the condition containing PBS (preservative-) showed a decrease in transcription level over time ( Fig. 9Filtration resulted in transcript levels for ACT similar to those of cell-free saliva preparations by centrifugation, whereas transcript levels for FOSL2 and NAMPT were higher. In addition, whole saliva was included as a control, and all three transcripts generally showed higher levels.

[0275] Example 5. Nucleic acid preservation under filtration and storage conditions

[0276] This example illustrates the preservation of whole saliva, which can be used for downstream analysis of nucleic acids, donor cells, microbiome, etc. Whole saliva is preserved with a combination of additives to stabilize the components: nuclease inhibitors; antimicrobial agents to inhibit microbial growth; and fixatives to prevent cell lysis. Fig.25A Degradation of RNA encoding actin B over three days is shown. Fig.25B A schematic diagram of a seven-day nucleic acid stability study outlining the nucleic acid preservation method described herein is shown. Saliva samples from three donors were obtained. The quality of the preserved nucleic acids was tested by qPCR for measuring the abundance of synthetic spike-in and endogenous genes. Fig.25C An exemplary curve for one donor over seven days is shown, indicating that the combination filter and preservative condition showed the most stable curve over seven days. The condition lacking preservative showed an increase in signal over time (e.g., from bacterial growth). The filter and preservative conditions were essentially unchanged. Fig.25D Storage of the spiked control over seven days is shown. The spiked control showed rapid degradation in the absence of a preservative. Even in the presence of a preservative, the stability of the spiked control in the filtered samples was better than the spun samples. Stability was observed for seven days. Fig.25E The stability of endogenous transcripts over seven days is shown. Endogenous genes gradually degrade in the absence of preservatives. In the presence of preservatives, transcript levels remain stable.

[0277] Although preferred embodiments of the present invention are shown and described herein, it will be apparent to those skilled in the art that these embodiments are intended to be examples only. Those skilled in the art may conceive of numerous variations, changes, and substitutions without departing from the present invention. It should be understood that various substitutions of the embodiments of the present invention described herein may be employed in the practice of the present invention. The claims are intended to define the scope of the present invention and encompass methods and structures within the scope of these claims and their equivalents.

Claims

1. A device for collecting and stabilizing an analyte in a sample, the device comprising: a) a sample collection container for collecting samples; b) a filtration unit in fluid communication with the sample collection container, the filtration unit comprising at least one filter for filtering the sample to obtain a filtrate; and c) a filtrate collection container in fluid communication with the filtration unit for collecting the filtrate and contacting the filtrate with a preservative.

2. The device according to claim 1, wherein the filtering unit comprises a plurality of filters arranged in order of pore size to sequentially allow smaller substances to pass therethrough.

3. The device according to claim 1 or 2, wherein the filter unit comprises at least two filters of the same type.

4. The device according to any one of claims 1 to 3, wherein the device comprises a pre-filtering mechanism to prevent clogging of smaller pore size filters. The device of claim 1 , wherein the filtration unit comprises a single filter.

6. The device of any one of claims 1 to 5, wherein at least one filter comprises a depth filter.

7. The device of any one of claims 1 to 6, wherein at least one filter comprises an asymmetric filter.

8. The device of any one of claims 1 to 7, wherein at least one filter comprises a microporous filter.

9. The device of any one of claims 1 to 8, wherein at least one filter comprises a low nucleic acid binding material.

10. The device of any one of claims 1 to 9, wherein 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.

11. The device of any one of claims 1 to 10, wherein 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.

12. The device of any one of claims 1 to 11, wherein at least one filter has a size cutoff of about 0.1 μm to about 100 μm.

13. The device according to any one of claims 1 to 12, wherein the thickness of the filter is about 50 μm to about 1000 μm, or 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 200μm, about 250μm, about 300μm, about 350μm, about 400μm, about 450μm, about 500μm, about 550μm, about 600μm, about 650μm, about 700μm, about 750μm, about 800μm, about 850μm, about 900μm, about 950μm or about 100μm, for example, about 355μm to about 560μm, for example, about 330μm, for example, about 120μm to about 170μm, for example, about 230μm to about 270μm, for example, about 480μm to about 640μm.

14. A device according to any one of claims 1 to 13, wherein the diameter of the filter is from about 10 mm to about 50 mm, for example 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.

15. The device according to any one of claims 1 to 14, wherein the filtration unit comprises a filter stack height of about 120 μm to about 10000 μm, such as 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 8 ... about 800 μm, about 850 μm, about 900 μm, about 950 μm, about 1000 μm, about 1250 μm, about 1500 μm, about 1750 μm, about 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 μm, about 8500 μm, about 9000 μm or about 9500 μm to 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 1250μm, about 1500μm, about 1750μm, about 2000μm, about 2500μm, about 3000μm, about 3500μm, about 4000μm, about 4500μm, about 5000μm, about 5500μm, about 6000μm, about 6500μm, about 7000μm, about 7500μm, about 8000μm, about 8500μm, about 9000μm, about 9500μm or about 10000μm.

16. The device according to any one of claims 1 to 15, wherein at least one filter is hydrophilic or hydrophobic.

17. The device according to any one of claims 1 to 16, wherein at least one filter comprises polysulfone and / or polypropylene.

18. The device of any one of claims 1 to 17, wherein at least one filter retains a plurality of leukocytes.

19. The device of any one of claims 1 to 18, wherein at least one filter retains a plurality of red blood cells.

20. The device of any one of claims 1 to 19, wherein at least one filter retains a plurality of cells derived from a solid tissue.

21. The device of any one of claims 1 to 20, wherein at least one filter retains a plurality of microorganisms.

22. The device of any one of claims 1 to 21, wherein at least one filter comprises a synthetic material to minimize the introduction of contaminating nucleic acids.

23. The device of any one of claims 1 to 22, wherein at least one filter comprises biological material.

24. The device of any one of claims 1 to 23, wherein at least one filter is free of biological material.

25. A device according to claim 23 or 24, wherein the biological material comprises cellulose.

26. The device of any one of claims 1 to 25, wherein the sample is a biological fluid including blood, serum, plasma, saliva, urine, sweat, tears, breast milk, colostrum, semen or cerebrospinal fluid.

27. The device of any one of claims 1 to 26, wherein the filtrate is a cell-free biological fluid or a decellularized biological fluid.

28. The device of claim 27, wherein the filtrate is cell-free plasma or cell-depleted plasma.

29. The device of claim 27, wherein the filtrate is cell-free saliva or decellularized saliva.

30. The device of claim 27, wherein the filtrate is cell-free urine or decellularized urine.

31. The device of any one of claims 1 to 30, further comprising a mechanism for applying at least one of mechanical force, centrifugal force, vacuum, capillary action, or radial or axial flow for filtering the sample through at least one of the at least one filters.

32. The device of claim 31, wherein the mechanism is used to apply mechanical force, centrifugal force, vacuum, capillary action or radial or axial flow to filter the sample through all filters in the device.

33. The device of claim 32, wherein the mechanism comprises a plunger that engages the sample collection container to push the sample through the filtration unit and into the filtrate collection container.

34. The device of claim 33, wherein the plunger is integral with or separate from the sample collection container.

35. The device according to any one of claims 1 to 34, wherein the sample collection container comprises a funnel, wherein the funnel is integral with or connectable to the sample collection container.

36. The device according to any one of claims 1 to 35, wherein the filtrate collection container contains a preservative.

37. The device of any one of claims 1 to 36, wherein the preservative comprises at least one of ethylenediaminetetraacetic acid (EDTA), a ribonuclease inhibitor, an antimicrobial agent, a denaturing agent, an agent that inhibits nuclease activity, a masking agent, a buffer, a salt, an osmotic agent, or a combination thereof.

38. The device of claim 37, wherein the denaturant comprises a nucleic acid denaturant or a protein denaturant.

39. The device of claim 37 or 38, wherein the agent that inhibits nuclease activity comprises one or more protein denaturants, EDTA, detergents such as SDS, aurintricarboxylic acid (ATA), chelating agents, or a combination thereof.

40. The device of claim 39, wherein the one or more protein denaturing agents comprises one or more chaotropic agents including detergents, urea, thiourea, guanidine thiocyanate, dodecylguanidine, dodine, or guanidine hydrochloride.

41. The device of claim 40, wherein the one or more protein denaturing agents comprises guanidine thiocyanate at a concentration of about 30% to about 70%.

42. The device according to any one of claims 1 to 41, wherein the filtrate collection container is separable from the filtration unit.

43. The apparatus of claim 42, further comprising a cover for a separate filtrate collection container.

44. The apparatus of claim 43, wherein the cover includes an enclosure storing a preservative that is released after the cover is secured to the separate filtrate collection container.

45. The apparatus of any one of claims 1 to 44, further comprising a second container for pouring the filtrate.

46. ​​The device of claim 45, wherein the second container comprises a preservative.

47. The device of any one of claims 1 to 46, wherein the device stabilizes the analyte at a temperature range of about -20°C to about 50°C.

48. The device of any one of claims 1 to 47, wherein the analyte is stable for at least 5 days.

49. The device of any one of claims 1 to 48, wherein at least one analyte comprises a cell-free analyte.

50. The device of any one of claims 1 to 49, wherein at least one analyte comprises a nucleic acid.

51. The device of claim 50, wherein the nucleic acid comprises cell-free RNA.

52. The device of claim 50 or 51, wherein the nucleic acid comprises mRNA, small RNA, miRNA, snoRNA, snRNA, rRNAs, tRNA, siRNA, hnRNA, long non-coding RNA, shRNA, fragments thereof, or combinations thereof.

53. The device of any one of claims 1 to 52, wherein at least one analyte comprises a polypeptide.

54. The device of claim 53, wherein the polypeptide is a protein.

55. The device of claim 53 or 54, wherein the polypeptide is a metabolite.

56. The device of any one of claims 1 to 55, wherein at least one analyte comprises a small molecule.

57. The device of any one of claims 1 to 56, wherein at least one analyte comprises a metabolite.

58. The device of any one of claims 1 to 57, wherein at least one analyte comprises a cell.

59. The device according to any one of claims 1 to 58, wherein the filtration unit is detachable for recovering the retentate, thereby enabling the preservation and analysis of biomolecules and cells contained in the retentate.

60. The device of any one of claims 1 to 59, wherein at least one filter reduces the viscosity of the filtrate compared to the sample.

61. The device of any one of claims 1 to 60, wherein at least one filter removes mucin from the sample.

62. A kit comprising the device of any one of claims 1 to 61 and at least one of the following: a) plunger; b) a cover for the filtrate collection container; c) Funnel; and / or d) Preservatives.

63. A method of stabilizing an analyte in a sample, the method comprising applying the sample to a sample collection container of the device of any one of claims 1 to 61 or the kit of claim 62, filtering the sample through a filtration unit, and contacting the filtrate with a preservative.

64. An analyte stabilized by the method of claim 63.

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