Molecular diagnosis signal real-time classification method

Abstract

Disclosed herein are methods, systems, compositions, algorithms, and kits suitable for real-time classification of molecular diagnostic signals. The method may include receiving time series data from an instrument in real time. The time series data includes more than one data point forming the sample curve. The method may include calculating two or more quantitative metrics for each data point in real time. The method may include calculating a likelihood ratio (LR) at each data point in real time. The calculating step may include employing a defined classifier for each of two or more quantitative metrics derived from (i) a reference population of positive curves and (ii) a reference population of negative curves. The method may include determining a sample profile in real time. When the LR calculated for the data point exceeds a defined LR threshold, the sample curve may be determined as positive in real time.

Description

[0001] Related applications This application claims the benefit of U.S. Provisional Patent Application Serial No. 63 / 496,001, filed April 13, 2023, pursuant to 35 USC §119(e), the contents of which are incorporated herein by reference in their entirety for all purposes.

[0002] background field This disclosure generally pertains to the field of decision-making algorithms.

[0003] Description of related technologies Existing molecular diagnostic technologies either employ end-state data processing or use relatively simple real-time methods to generate positive / negative determinations. The use of endpoint processing inherently prevents the system from making a determination before all data is collected, prolonging the time required to report results. In the point-of-care diagnostics market, where rapid results reporting is highly valued, endpoint processing can severely disadvantage products. Real-time methods utilizing a single metric (such as the amplitude or gradient of fluorescence data points) often fail to capture the necessary signal characteristics that define amplified versus non-amplified behavior. Even when more than one metric is used in real-time scenarios, they are typically evaluated separately, each with an independent threshold, limiting the potential sensitivity and specificity of the test. There is a need for methods, systems, algorithms, compositions, and kits for real-time classification of molecular diagnostic signals.

[0004] Overview In some embodiments, a real-time determination method is provided. In some embodiments, the method includes: receiving time-series data from an instrument in real time, wherein the time-series data includes more than one data point forming a sample curve. The method may include: calculating two or more quantitative measures for each data point in real time. The method may include: calculating the likelihood ratio (LR) at each data point in real time. The calculation step may include applying a defined classifier to each of the two or more quantitative measures derived from a reference population of (i) positive curves and (ii) negative curves, the classifier optionally derived via quadratic discriminant analysis (QDA). The method may include: determining the sample curve in real time. In some embodiments, the sample curve is determined to be positive in real time when the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, the sample curve is determined to be negative in real time if the LR calculated for all data points of the sample curve falls at or below a defined LR threshold.

[0005] In some embodiments, quantitative measures include amplitude (A), gradient (G), and / or span (S), optionally, S is the variation of A within a defined window. In some embodiments, time-series data originate from instrumental analysis of a sample (optionally a sample suspected of containing the target analyte). In some embodiments, the instrument is configured to generate time-series data by analyzing the sample; optionally, the instrument is configured to perform molecular diagnostic assays. In some embodiments, instrumental analysis of the sample includes subjecting the sample to one or more reactions, optionally, the reactions being configured to detect the presence and / or amount of the target analyte in the sample. In some embodiments, the instrument includes one or more sensors configured to detect signals originating from the sample, and wherein the time-series data includes time-series signal data, optionally, the signals being generated by the one or more reactions. In some embodiments, the signal is a calorimetric signal, a potentiometric signal, an amperometric signal, an optical signal (e.g., a fluorescence signal and / or a colorimetric signal), a piezoelectric signal, or any combination thereof. In some embodiments, the signal is generated in the presence of the target analyte. In some embodiments, the signal is generated in the absence of the target analyte.

[0006] In some implementations, instrumental analysis includes one or more of the following: spectroscopy, Raman spectroscopy, FFT (Fast Fourier Transform) spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), infrared spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, electrochemical detection, polynucleotide detection, volatile organic compound methods, fluorescence anisotropy, fluorescence resonance energy transfer, electron transfer, enzyme assays, magnetism, conductivity, electrochemical detection, isoelectric focusing, lateral flow assay (LFA), microfluidics, amino acid sequencing, nucleic acid sequencing, flow cytometry, chromatography, immunoprecipitation, immunodissociation, aptamer binding, filtration, electrophoresis, using a CCD camera, immunoassay, enzyme-linked immunosorbent assay (ELISA), Gram staining, immunostaining, microscopy, immunofluorescence, size / weight / charge detection, Western blotting, polymerase chain reaction (PCR), RT-PCR, isothermal amplification, sequencing, fluorescence in situ hybridization, mass spectrometry, surface plasmon resonance (SPR), and localized surface plasmon resonance (LSPR). In some implementations, the instrument includes a thermal cycler, such as a thermal cycler configured for real-time PCR amplification and fluorescence monitoring.

[0007] In some embodiments, a positive sample curve indicates the presence of the target analyte in the sample. In some embodiments, a negative sample curve indicates the absence of the target analyte in the sample. In some embodiments, the target analyte is a target nucleic acid sequence. In some embodiments, one or more reactions include a nucleic acid detection reaction. In some embodiments, the sample curve includes a nucleic acid amplification curve, and the signal includes a fluorescent signal indicating the amplification of the target nucleic acid sequence. In some embodiments, a positive sample curve indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction, and thus indicates the presence and / or amount of the target nucleic acid sequence in the sample. In some embodiments, a negative sample curve indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction, and thus indicates the absence of the target nucleic acid sequence in the sample.

[0008] In some implementations, the step of calculating two or more quantitative measures includes applying a median filter (e.g., a 3-point median filter) to the time series data to generate median-filtered data. In some implementations, the 3-point median filter includes: .

[0009] In some implementations, the median filter provides smoothing, removal of single-point spikes, and / or removal of system noise. In some implementations, the step of calculating two or more quantitative metrics includes applying a Savitzky-Golay (SG) filter (e.g., a 7-point SG filter) to the time-series data and / or the median-filtered data to generate a smoothed magnitude value (SG). Amp ) and the smoothed gradient value (SG) Grad In some implementations, the application of the SG filter includes: shifting a sliding window across the time series data and / or the median-filtered data, and applying a second-order linear regression to each window. .

[0010] In some implementations, the x-values ​​in each window are encoded as {-3,…3}, and the regression fit uses the following as input: .

[0011] In some implementations, for the center point of each window (k = 0), the smoothed amplitude value (SG) Amp ) and the smoothed gradient value (SG) Grad The calculation is as follows: .

[0012] In some implementations, the method includes: calculating the average signal value (e.g., SG) over a defined time window t1 and t2. Amp The initial signal average is generated by taking a value (e.g., a signal value). In some implementations, the determination step includes: if the initial signal average does not fall within the range specified by the signal... 最小值 and signal 最大值 If the signal falls within the defined signal window, the sample curve is considered invalid. In some implementations, the signal... 最小值 and signal 最大值 The distribution of the initial signal average value between a reference population containing positive and negative curves is determined based on the distribution of the average value between defined time windows t1 and t2. In some implementations, the signal... 最小值 and signal 最大值 The value was set to a limit equal to the mean of the reference population ± 3.6σ.

[0013] In some implementations, the step of calculating two or more quantitative measures includes calculating the span (S) value by employing the following formula: in, a Let Σ be a span half-window defined in units of points, where Σ is defined if any step shift is detected within the interval. J i The sum of the amplitudes of all step shifts identified within the interval. In some embodiments, the defined span half-window is determination-specific. In some embodiments, the defined span half-window is about 6 points to about 12 points, optionally about 10 points. In some embodiments, if the boundary points of the defined span window are located within a defined number of points around the step shifts, the step of calculating two or more quantitative measures includes calculating the span (S) value by using the median-filtered signal value in the following formula ( ): The boundary points of the span window are defined as k + a or k – a.

[0014] In some embodiments, the method includes providing a defined classifier for each of two or more quantitative measures. In some embodiments, the defined classifier is provided via a measurement definition document (ADF). In some embodiments, the defined classifier includes quadratic discriminant analysis (QDA) coefficients. In some embodiments, the defined classifier includes linear discriminant analysis (LDA) coefficients. In some embodiments, the defined classifier is measurement-specific and / or defined for each curve. In some embodiments, the defined classifier includes... , , and Two or more of them, and among them: .

[0015] In some embodiments, the reference populations for positive and negative curves each comprise at least about 10 curves. In some embodiments, the reference populations for positive and negative curves are generated using representative target analyte concentrations from appropriate sample types. In some embodiments, for each curve in the reference populations for positive and negative curves, a defined classifier is provided, including identifying characteristic peaks y via an iterative process. PLR (where LR is the largest), this characteristic peak best distinguishes the positive and negative reference curves; and at the characteristic peak y PLR The system identifies A, G, and S metrics. In some implementations, the provision also includes calculating the mean and covariance matrix of said metrics.

[0016] In some implementations, the defined classifier includes: (a) estimating initial coefficients from the distribution of A, G, and S values ​​measured at all points in the reference population of the negative curve. and (b) From the point y in each positive reference curve M峰 The distribution of A, G, and S values ​​measured at the location is used to estimate the initial coefficients. and , where y M峰 For relative to and The point with the maximum Mahalanobis distance satisfies the condition that in y M峰 (c) The requirement that A, G, and S at each point must all be greater than 0; Calculate the initial LR for all points in the negative reference curve; (d) calculate the LR from the points y in each negative reference curve. PLR Calculation of the distribution of A, G and S values ​​measured at the location and (e) Using input coefficients Calculate the initial LR for all points in the positive reference curve; (f) calculate the LR from the points y in each positive reference curve. PLR Calculation of the distribution of A, G and S values ​​measured at the location and , where y PLRThe point where LR is maximized; and (g1) will be the output coefficient of one iteration. Used as input coefficients for the next iteration Repeat steps (c)-(f) until the parameter values ​​converge, or (g2) outputs the coefficients of one iteration. Used as input coefficients for the next iteration Repeat steps (e)-(f) until the parameter values ​​converge.

[0017] In some implementations, calculating LR includes calculating for each data point y 测试 = {A 测试 G 测试 , S 测试}calculate Q 0 / Q 1 ,in: , and and The corresponding Mahalanobis distance to each reference group is given by the following: .

[0018] In some implementations, real-time calculation of the LR at each data point includes calculating the LR at each data point after t2. In some implementations, at point y, the LR is calculated under the following condition: 测试 Calculate LR: y 测试 Occurs at or after the defined minimum decision loop; A 测试 G 测试 and S 测试 All > 0; and at a distance of y 测试 No step shift occurred within 3 points. In some implementations, the method includes detecting a step shift, wherein the step shift is detected by measuring the pairwise difference between adjacent median-filtered points: , Apply 3-point median filtering to the paired difference curves; subtract the smoothed difference Y from the unsmoothed difference. s ; and Y– Y s Any point in the system whose value exceeds a defined step shift threshold is identified as a step shift with an amplitude of J. In some implementations, the step shift represents macroscopic system noise.

[0019] In some implementations, the determination step includes: if two or more consecutive missing points exist in the time series data prior to the sample curve being determined to be positive; and / or if more than two missing points exist in the time series data prior to the sample curve being determined to be positive (optionally, the missing points are consecutive or discontinuous), then the sample curve is determined to be invalid. In some implementations, one or more defined classifiers, defined LR thresholds, t1, t2, and signal... 最小值 ,Signal 最大值 The defined step shift threshold, the defined minimum decision loop, and the defined span half-window are provided via an assay definition file (ADF). In some embodiments, the method includes multiplexing decision, which includes: receiving two or more sets of time-series data from the instrument in real time, wherein each set of time-series data includes more than one data point forming a sample curve; and making real-time decision on each of the two or more sample curves, optionally each of the sample curves being a nucleic acid amplification curve associated with a different target nucleic acid sequence.

[0020] In some implementations, this method can identify a curve as positive at least approximately 1 minute, 2 minutes, 5 minutes, 10 minutes, 15 minutes, 20 minutes, 25 minutes, 30 minutes, 35 minutes, 40 minutes, 45 minutes, 50 minutes, 55 minutes, or 60 minutes earlier than methods employing final-state processing of time series data. In some implementations, step detection and correction make no assumptions about the correct and / or absolute signal baseline.

[0021] In some implementations, the instrument is capable of: amplifying a target nucleic acid sequence in an amplification reaction mixture to produce a nucleic acid amplification product, optionally, the nucleic acid amplification product being produced at a detectable level within about 20 minutes, about 15 minutes, or about 10 minutes; and detecting the nucleic acid amplification product with a signal-generating oligonucleotide, wherein the signal-generating oligonucleotide is capable of hybridizing with the nucleic acid amplification product, optionally being a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide.

[0022] In some embodiments, the signal-generating oligonucleotide comprises a label, optionally comprising a quenchable label, and even more optionally, the quenchable label is a fluorophore. Optionally, the signal-generating oligonucleotide comprises a quencher capable of quenching the signal generated by the label when the quencher and the label are in close proximity. In some embodiments, the label is capable of generating a detectable signal when: (i) the signal-generating oligonucleotide hybridizes with a nucleic acid amplification product; and / or (ii) the nucleic acid amplification product is extended to generate an extended nucleic acid amplification product hybridizing with the signal-generating oligonucleotide, optionally the signal being fluorescence. In some embodiments, amplifying the target nucleic acid sequence includes generating a nucleic acid amplification product at a detectable level within about 20 minutes, about 15 minutes, or about 10 minutes.

[0023] The method may include: contacting a sample containing a biological entity with a lysis buffer to produce a treated sample, wherein the lysis buffer contains one or more lysis agents capable of lysing the biological entity to release sample nucleic acids contained therein, and wherein the sample nucleic acids are suspected of containing a target nucleic acid sequence; and contacting a reagent composition with the treated sample to produce an amplification reaction mixture, wherein the reagent composition contains one or more amplification reagents.

[0024] In some embodiments, the method is: carried out in a single reaction vessel; does not include the use of any enzymes other than reverse transcriptase and enzymes with hyperthermophilic biopolymerase activity; does not include the use of any enzymes other than enzymes with hyperthermophilic biopolymerase activity; does not include thermal denaturation and / or enzymatic denaturation of nucleic acids during the amplification step; and / or does not include contacting nucleic acids with single-stranded DNA-binding proteins.

[0025] In some embodiments, amplification is performed over a period of approximately 5 to approximately 60 minutes, optionally over a period of approximately 15 minutes; and / or under isothermal amplification conditions free of helicases, single-strand binding proteins, cleavage agents, and recombinases. In some embodiments, amplification is performed using methods selected from the group consisting of: polymerase chain reaction (PCR), ligase chain reaction (LCR), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), replicase-mediated amplification, immunoassay amplification, sequence-based amplification (NASBA), autonomous sequence replication (3SR), rolling circle amplification, and transcription-mediated amplification (TMA), optionally the PCR being real-time PCR and / or quantitative real-time PCR (QRT-PCR).

[0026] In some embodiments, the biological entity includes one or more of prokaryotic cells, eukaryotic cells, viral particles, exosomes, protoplasts, and microvesicles. In some embodiments, the biological entity includes viruses, bacteria, fungi, protozoa, portions thereof, or any combination thereof. In some embodiments, the target nucleic acid sequence is a nucleic acid sequence of a virus, bacteria, fungi, or protozoa, optionally the sample nucleic acid is derived from a virus, bacteria, fungi, or protozoa. In some embodiments, the virus is SARS-CoV-2, human immunodeficiency virus type 1 (HIV-1), human T-cell lymphotropic virus type 1 (HTLV-1), hepatitis B virus (HBV), hepatitis C virus (HCV), herpes simplex virus, herpesvirus 6, herpesvirus 7, Epstein-Barr virus, respiratory syncytial virus (RSV), cytomegalovirus, varicella-zoster virus, JC virus, parvovirus B19, influenza A virus, influenza B virus, influenza C virus, rotavirus, human adenovirus, rubella virus, human enterovirus, genital human papillomavirus (HPV), or hantavirus. In some implementations, the bacteria include Mycobacterium tuberculosis (Mycobacterium tuberculosis) Mycobacteria tuberculosis ), Rickettsia rickettsii ( Rickettsia rickettsii ), Chafielich body ( Ehrlichia chaffeensis ), Breospirochete ( Borrelia burgdorferi Yersinia pestis (Yersinia pestis) Yersinia pestis ), pale spirochetes ( Treponema pallidum ), Chlamydia trachomatis ( Chlamydia trachomatis ), Chlamydia pneumoniae ( Chlamydia pneumoniae Mycoplasma pneumoniae () Mycoplasma pneumoniae ), Mycoplasma genus and species ( Mycoplasma sp. Legionella pneumophila ( Legionella pneumophila Legionella dumović ( ) Legionella dumoffii ), fermentation mycoplasma ( Mycoplasma fermentans ), Ehrlichia species ( Ehrlichia sp. Haemophilus influenzae ( ) Haemophilus influenzae ), Neisseria meningitidis ( Neisseria meningitidis ), Neisseria gonorrhoeae ( Neisseria gonorrhoeae Streptococcus pneumoniae () Streptococcus pneumonia ), agalactococcus ( S. agalactiae ), and Listeria monocytogenes ( Listeria monocytogenes One or more of the fungi. In some embodiments, the fungi include Cryptococcus neoformans (…). Cryptococcus neoformans Pneumocystis carinii ( Pneumocystis carinii Histoplasma capsulatum ( ) Histoplasma capsulatum ), dermatitis blastomyces ( Blastomyces dermatitidis ), Coccidioides immitis ( Coccidioides immitis), and Trichophyton rubrum ( Trichophyton rubrum One or more of the following. In some embodiments, the protozoa include Trypanosoma cruzi (…). Trypanosoma cruzi Leishmania species Leishmania sp. ), Plasmodium ( Plasmodium ), Entamoeba histolytica ( Entamoeba histolytica ), babesiidae (a type of volcano) Babesia microti ), Giardia lamblia ( Giardia lamblia ), Cyclospora species ( Cyclospora sp. ) or species of the genus Eimeria ( Eimeria sp. One or more of them.

[0027] In some embodiments, the sample is a biological sample or an environmental sample. In some embodiments, the environmental sample is, or is obtained from, the following: food samples, beverage samples, paper surfaces, fabric surfaces, metal surfaces, wood surfaces, plastic surfaces, soil samples, freshwater samples, wastewater samples, saline samples, samples exposed to atmospheric air or other gases, their cultures, or any combination thereof. In some embodiments, the biological sample is, or is obtained from, the following: tissue samples, saliva, blood, plasma, serum, feces, urine, sputum, mucus, lymph, synovial fluid, cerebrospinal fluid, ascites, pleural effusion, seroma, pus, swabs from skin or mucous membrane surfaces, their cultures, or any combination thereof.

[0028] In some embodiments, the amplification does not include one or more of the following: archaea polymerase amplification (APA), loop-mediated isothermal amplification (LAMP), helicase-dependent amplification (HDA), recombinase polymerase amplification (RPA), strand substitution amplification (SDA), nucleic acid sequence-based amplification (NASBA), transcription-mediated amplification (TMA), nickase amplification reaction (NEAR), rolling circle amplification (RCA), multiple substitution amplification (MDA), branching amplification (RAM), circular helicase-dependent amplification (cHDA), single primer isothermal amplification (SPIA), signal-mediated RNA amplification technology (SMART), autonomous sequence replication (3SR), genome exponential amplification reaction (GEAR), and isothermal multiple substitution amplification (IMDA), optionally excluding LAMP. In some embodiments, the amplification includes one or more of the following: APA, LAMP, HDA, RPA, SDA, NASBA, TMA, NEAR, RCA, MDA, RAM, cHDA, SPIA, SMART, 3SR, GEAR, and IMDA, optionally excluding LAMP. In some embodiments, the method includes and / or excludes one or more of the following: (i) dilution of the treated sample; (ii) dilution of the amplification reaction mixture; (iii) thermal denaturation of the treated sample; (iv) acoustic treatment of the treated sample; (v) acoustic treatment of the amplification reaction mixture; (vi) addition of a ribonuclease inhibitor to the treated sample; (vii) addition of a ribonuclease inhibitor to the amplification reaction mixture; (viii) purification of the sample; (ix) purification of the sample nucleic acid; (x) purification of the nucleic acid amplification product; (xi) removal of one or more cleavage agents from the treated sample or the amplification reaction mixture; (xii) thermal denaturation and / or enzymatic denaturation of the sample nucleic acid before and / or during amplification; and (xiii) addition of ribonuclease H to the treated sample or the amplification reaction mixture.

[0029] In some implementations, a system for real-time determination is provided. This system may include an instrument configured to generate time-series data by analyzing samples. The system may also include a processor including a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to perform the methods provided herein.

[0030] In some implementations, a computer system for real-time determination is provided. The computer system may include: a hardware processor; and a non-transitory memory having stored instructions thereon, which, when executed by the hardware processor, cause the processor to perform the methods provided herein.

[0031] In some embodiments, a computer-readable medium is provided that includes code for performing the methods provided herein. In some embodiments, a determination definition file (ADF) for the methods provided herein is provided, which includes a defined classifier, a defined LR threshold, t1, t2, and a signal. 最小值 ,Signal 最大值 One or more of the following: a defined step shift threshold, a defined minimum decision loop, and a defined span half-window. Brief description of the attached diagram Figure 1 A non-limiting exemplary functional block diagram of an exemplary processor is depicted.

[0033] Figure 2 A non-limiting exemplary block diagram of an exemplary computing system is depicted.

[0034] Figures 3A-3B A non-limiting exemplary schematic diagram is depicted related to the decision algorithm provided in this paper: initial mean test (whether the curve is valid); Figure 3A ) and assertion tests (whether the curve is amplified; Figure 3B ).

[0035] Figure 4 A non-restrictive, exemplary flowchart of initial mean tests and assertion tests is depicted. Nodes correspond to the relevant decision algorithm data and / or metrics. Black annotations are functions used to calculate metrics based on the data or other metrics. Blue annotations correspond to the ADF inputs used in these functions / calculations.

[0036] Figures 5A-5B This paper depicts a non-limiting exemplary application of median filtering to raw fluorescence data. The raw fluorescence data (…) is shown. Figure 5A ) and median filtered data ( Figure 5B Median filtering can be particularly useful for removing transient fluorescence spikes that represent system noise from raw fluorescence data.

[0037] Figure 6 An unrestricted exemplary distribution of the initial mean is depicted. The dataset consists of 100 asserted curves and 41 unasserted curves. The initial mean is measured between 75 seconds and 120 seconds.

[0038] Figure 7 A non-limiting exemplary step shift curve is depicted.

[0039] Figures 8A-8F A non-limiting exemplary step detection function is described. For real-world amplification (… Figure 8A ) and step shift ( Figure 8B The results show that: paired difference calculation ( Figure 8C , Figure 8D(blue curve), differential median filtering ( Figure 8C , Figure 8D (Red curve), and subtracting the smoothed data from the unsmoothed data ( Figure 8E , Figure 8F ).

[0040] Figure 9 Non-limiting exemplary asserted and unassertified curves are depicted. The LR peaks in the reference data are plotted in three dimensions, with the magnitude, gradient, and span values ​​of the reference positive (blue) and reference negative (red) curves at their likelihood ratio peaks plotted in a 3D graph. The hyperplane (green) separating these groups corresponds to the defined likelihood ratio threshold within this space.

[0041] Figures 10A-10B A non-restrictive exemplary higher-order decision logic for FluA / B testing is described. A logic for FluA ( Figure 10A ) and FluB ( Figure 10B FluA employs a higher-level decision-making logic. The FluA test, along with its internal control test, is a duplex test.

[0042] Figure 11 A non-limiting exemplary plot depicting the initial likelihood ratio peak is shown. The y-values ​​in the curve population are plotted. PLR The amplitude, gradient, and span values ​​are represented by a single point for each curve. The peaks of the amplification curves are shown in blue, while the peaks of the non-amplification curves are shown in red.

[0043] Figures 12A-12B A non-limiting exemplary plot of the refined likelihood ratio peaks is shown. After three rounds of refinement, the population of PLR peaks in the amplification curve almost collapses to one dimension. Figure 12A From the distribution of amplitude, gradient, and span values ​​at these points, we can obtain... The final value. A hyperplane (green) is drawn at the threshold of LR = 8. Figure 12B ).

[0044] Detailed Explanation In the following detailed description, reference is made to the accompanying drawings, which form part of this document. In the drawings, like symbols generally identify like components unless the context otherwise indicates. The illustrative embodiments described in the detailed description, drawings, and claims are not intended to be limiting. Other embodiments may be utilized, and other changes may be made, without departing from the spirit or scope of the subject matter set forth herein. It will be readily understood that aspects of this disclosure as generally described herein and illustrated in the drawings can be arranged, substituted, combined, separated, and designed in a variety of different configurations, all of which are expressly contemplated herein and form part of this disclosure.

[0045] All patents, published patent applications, other publications, and sequences from GenBank and other databases mentioned in this article concerning relevant technologies are incorporated herein by reference in their entirety.

[0046] Unless otherwise defined, the technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art to which this disclosure pertains. See, for example Singleton et al., Dictionary of Microbiology and Molecular Biology, 2nd ed., J. Wiley & Sons (New York, NY 1994); Sambrook et al., Molecular Cloning, A Laboratory Manual, Cold Spring Harbor Press (Cold Spring Harbor, NY 1989). For the purposes of this disclosure, the following terms are defined below.

[0047] In some embodiments, a real-time determination method is provided. In some embodiments, the method includes: receiving time-series data from an instrument in real time, wherein the time-series data includes more than one data point forming a sample curve. The method may include: calculating two or more quantitative measures for each data point in real time. The method may include: calculating the likelihood ratio (LR) at each data point in real time. The calculation step may include applying a defined classifier to each of the two or more quantitative measures derived from a reference population of (i) positive curves and (ii) negative curves, the classifier optionally derived via quadratic discriminant analysis (QDA). The method may include: determining the sample curve in real time. In some embodiments, the sample curve is determined to be positive in real time when the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, the sample curve is determined to be negative in real time if the LR calculated for all data points of the sample curve falls at or below a defined LR threshold.

[0048] In some implementations, a system for real-time determination is provided. This system may include an instrument configured to generate time-series data by analyzing samples. The system may also include a processor, the processor including memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to perform the methods provided herein.

[0049] In some implementations, a computer system for real-time determination is provided. The computer system may include: a hardware processor; and a non-transitory memory having stored instructions thereon, which, when executed by the hardware processor, cause the processor to perform the methods provided herein.

[0050] In some embodiments, a computer-readable medium is provided that includes code for performing the methods provided herein. In some embodiments, a determination definition file (ADF) for the methods provided herein is provided, which includes a defined classifier, a defined LR threshold, t1, t2, and a signal. 最小值 ,Signal 最大值 One or more of the following: a defined step shift threshold, a defined minimum decision loop, and a defined span half-window.

[0051] Methods for real-time classification of molecular diagnostic signals In some implementations, methods, systems, compositions, algorithms, and kits are provided for real-time classification of molecular diagnostic signals using an algorithm based on quadratic discriminant analysis and iterative refinement, employing local step detection and correction. This document provides robust methods for evaluating fluorescence signals generated by molecular diagnostic tests. This document provides decision algorithms that, when adopted by a diagnostic platform, can be used to report positive and negative results. The methods provided herein can detect target DNA amplification in real time and reliably distinguish true amplification from background noise.

[0052] The methods, systems, compositions, algorithms, and suites presented in this paper can integrate more than one quantitative metric to provide comprehensive analysis of signals (e.g., fluorescence signals). Specifically, as part of the likelihood ratio test (also known as the Wilks test), three metrics—amplitude, gradient, and span (the change in amplitude within a defined window)—can be computed in real time at each point and compared with values ​​from reference datasets of known positive and negative results. If an effective curve contains at least one point with a likelihood ratio above a defined threshold, the curve can be considered asserted (amplified). However, if all points within the effective curve fall below the threshold, the curve can be considered unassertified (unamplified).

[0053] Likelihood ratios can be calculated at each point using classifiers derived from known positive and negative reference populations. These classifiers can be obtained via quadratic discriminant analysis (QDA), a statistical method for binning multidimensional data into two or more groups. To classify an entire curve consisting of more than one data point, QDA can be applied to measures at characteristic peaks to optimally distinguish between amplified and non-amplified curves. These characteristic peaks can be identified using an iterative method, where the point in each curve corresponding to the peak (maximum) likelihood ratio in one round is selected as the input for the next round of refinement. In this iterative method, the QDA coefficients for the non-amplified reference population are initially defined based on the distribution of the measure observed across the set of all negative points, rather than the measure at any particular peak. The corresponding input for the amplified population can be initially calculated based on the point in each curve that is statistically most different from the non-amplified population (with the largest Mahalanobis distance (multidimensional generalization of standard deviation)). Using this set of initial coefficients, initial likelihood ratios can be calculated for all points in the non-amplified and amplified training sets. Subsequently, the points in each curve corresponding to the peak likelihood ratio can be used to define revised QDA coefficients for the amplified and (optionally) non-amplified populations, which can then be used to generate revised likelihood ratios for each point. This process can be performed more than once—calculating peak likelihood ratios to generate the revised set of QDA coefficients—until the coefficients converge to their final values. This iterative process can produce a set of model parameters optimized to distinguish amplified and non-amplified curves based on the likelihood ratio metric. Furthermore, because the likelihood ratio can be calculated for individual points, this method allows for real-time decision-making, where a single point above a defined cutoff value is sufficient for a positive decision.

[0054] The methods, systems, compositions, algorithms, and kits presented herein provide a real-time detection and correction method for step shifts (sudden shifts in baseline fluorescence unrelated to true amplification). Step shifts can represent macroscopic system noise, such as bubbles blocking the optical path or condensate droplets falling into the reaction; without correction, they pose a risk of false positives. As presented herein, step shifts can be detected by measuring pairwise differences between adjacent median-filtered points. The smoothed differences can then be subtracted from the original differences, and the resulting values ​​can be compared to defined thresholds to identify sudden jumps in the signal. Notably, this method applies real-time correction to these jumps, subtracting the amplitude of any step detected within a locally defined window from a span metric.

[0055] The likelihood ratio test has never been used or considered as a method for real-time molecular diagnostic determination. Real-time implementation has proven challenging because QDA coefficients must be generated from an object described by a single (multidimensional) metric, which is typically only computed when all points of the signal have been measured. The method presented in this paper circumvents this problem by applying QDA to each point relative to the characteristic peaks in a reference population. In some implementations, and without being bound by any particular theory, the likelihood ratio test can be applied in real-time scenarios by using the rule that there exists a single point above a defined threshold sufficient to assign a curve to a population.

[0056] The characteristic peaks used to generate QDA coefficients can be obtained through an iterative process that has never been considered or employed before for population differentiation. This method optimizes the QDA coefficients based on the likelihood ratio peak—a specific data point in the reference signal most relevant to classifying the test signal as positive or negative.

[0057] The real-time method for step correction represents an improvement over the prior art because it makes no assumptions about the correct (absolute) fluorescence baseline. Previous methods either assumed the baseline prior to the step was "correct" and adjusted all subsequent points; or they assumed the baseline after the step represented the true baseline and that the signal prior to the step needed correction. In some implementations, since the method presented herein only corrects the total amplitude difference over a defined window, no assumptions about the true baseline are required.

[0058] The method described in this article allows for real-time detection of DNA amplification by integrating multiple metrics into a single score (likelihood ratio) in a statistically reasonable manner. Using this method, users can more comprehensively capture the diverse fluorescence signals and behaviors present in real-world clinical data and robustly distinguish between positive and negative results in real time.

[0059] In some implementations, when a reference population of a known type is available for training, an algorithm is provided for classifying signals into two or more populations in real time. In some implementations, a decision algorithm is provided for rapid molecular diagnostic tests, including those tests subjected to significant noise (which limits the sensitivity and specificity of simpler methods).

[0060] In some implementations, metrics other than amplitude, gradient, and span can be used to describe points within a real-time curve. Additionally, in some implementations, such as for signal processing applications where the expected population has similar variance and covariance, linear discriminant analysis (LDA) can be used instead of quadratic discriminant analysis (QDA) for simplicity.

[0061] This document discloses a real-time determination method. In some embodiments, the method includes: receiving time-series data from an instrument in real time, wherein the time-series data includes more than one data point forming a sample curve. The method may include: calculating two or more quantitative measures for each data point in real time. The method may include: calculating the likelihood ratio (LR) at each data point in real time. The calculation step may include applying a defined classifier to each of the two or more quantitative measures derived from (i) a reference population of positive curves and (ii) a reference population of negative curves, said classifier optionally derived via quadratic discriminant analysis (QDA). The method may include: determining the sample curve in real time. In some embodiments, the sample curve is determined to be positive in real time when the LR calculated for a data point exceeds a defined LR threshold. In some embodiments, the sample curve is determined to be negative in real time if the LR calculated for all data points of the sample curve falls at or below a defined LR threshold.

[0062] In some implementations, the methods provided herein (e.g., decision algorithms) are operated on-board on an instrument (e.g., a clinical instrument) to determine in real time whether a result is positive, negative, or invalid. In some implementations, the methods apply both an initial mean test and an assertion test to each fluorescence curve ( Figures 3A-3B Assertions obtained from the detection curve and the internal control curve can be used as input to a higher-level decision logic that determines the result as positive, negative, or invalid.

[0063] Assertion testing, the core of the decision-making algorithm, relies on metrics derived from a reference dataset with known positive and negative outcomes. Specifically, three metrics—amplitude, gradient, and span—can be calculated in real-time at each point. The likelihood ratio test (also known as the Wilks test) can then be applied to the real-time values ​​to distinguish between amplified and non-amplified responses. Specifically, the likelihood ratio at each point can be calculated by providing the measurement parameters of each metric derived from the known positive and negative reference populations via quadratic discriminant analysis (QDA) through the ADF. The likelihood ratio represents the relative likelihood of points appearing in the amplified population versus the non-amplified population. A valid curve is considered asserted if the likelihood ratio of at least one point is above a defined threshold (specified in the ADF). However, if all points within a valid curve fall below the threshold, the curve is considered unasserted.

[0064] Figure 4 A flowchart outlining the initial mean test and assertion test calculations used for real-time assertions is depicted. The following sections present descriptions of the metrics and the functions used to generate them, followed by a discussion of the higher-level logic used to make positive, negative, or invalid determinations.

[0065] Initial processing of raw signal data First, a 3-point median filter can be applied to the raw signal data (e.g., raw fluorescence data). Figures 5A-5B This provides smoothing and removes single-point spikes. .

[0066] A 7-point Savitzky-Golay (SG) filter can then be applied to the median-filtered data to provide low-variance estimates of the magnitude and gradient values ​​at each point. In some implementations, and without being bound by any particular theory, the basic principle behind the SG filter is to move a sliding window through the data and apply a second-order linear regression to each window: .

[0067] In some implementations, the x-values ​​in each window are encoded as {-3,…3}. That is, the regression fit uses the following as input: .

[0068] The smoothing magnitude and gradient value of the center point (k = 0) of each window can be calculated using the following formula: .

[0069] Initial Mean Test Before assertion testing, the real-time decision algorithm can apply an initial mean test to each curve. Figure 3A This test can confirm that a signal (e.g., a fluorescence signal) begins within a specified range and guard against serious system failures (e.g., distribution failures, the presence of foreign objects, etc.). The initial mean test can use four inputs defined by an ADF—t1, t2, and the signal. 最小值 and signal 最大值 And these can be applied to The curve passes the average signal test if the average signal within the time window falls within the specified range. However, if the average signal falls outside the specified range, the curve fails the test and is considered invalid.

[0070] When designing an ADF, a suitable signal can be identified based on the initial mean distribution in the reference dataset (with t1 and t2 as boundaries). 最小值 and signal 最大值 enter( Figure 6 ).Signal最小值 and signal 最大值 These cutoffs can be set to equal to the mean of the reference population ± 3.6σ. Assuming the initial mean is normally distributed, these cutoffs are expected to produce only one invalidation judgment per 3000 runs in the absence of systemic failures. Since a typical assay includes three curves (two test curves and one internal control curve), this means that the probability of a clinical test producing an invalidation assertion is 1 / 1000 in the absence of a true systemic failure.

[0071] Span measurement Span metric describes the net increment of amplitude within a defined interval. Except for the special cases described below, span can be defined as: in, a For a span half-window (in points), and Σ J i This is the sum of the magnitudes of all step shifts (if any) identified within this interval (see the "Step Detection" section). a The value should be comparable to the duration of the "rising" portion of the amplified curve. A span window that is too short may reduce the ability to distinguish amplified from non-amplified signals, while a span window that is too long may delay the decision and fail to provide any distinguishing benefit. The span half-window can be set within the ADF, typically 10 points, but a range of 6 to 12 is permissible. Assuming a sampling duration of 5 seconds, these correspond to a full width of 1 to 2 minutes.

[0072] In the special case where the boundary points (k + a or k – a) of the span window are located near a step shift (see the "Step Detection" section), median-filtered fluorescence can be used instead of Savitzky-Golay amplitude estimation. For example, if point k + a is located near a step shift, the span can be calculated as: Likelihood ratio ADF Input – Distribution of the metric in the reference population Assertion tests can be viewed as binary hypothesis tests applied to each point within the curve. They utilize the distribution of amplitude, gradient, and span values ​​at characteristic peaks in known positive and negative curves to determine the relative likelihood that the test curve belongs to each population. Typically, SME selects a set of at least 100 amplification curves and 100 non-amplification curves as a suitable reference. To ensure optimal sensitivity and specificity in a clinical setting, reference curves can be collected using representative target concentrations from appropriate sample types.

[0073] For each curve in the reference group, the characteristic peak y can be found PLRThe amplitude, gradient, and span are identified at a point; this characteristic peak can be identified through an iterative process (see the section "Derivation of Input Parameters for the Decision Algorithm"), used to maximally separate known positive and negative samples within the A, G, and S spaces. The mean and covariance matrices of these measures can then be calculated for both amplified and non-amplified populations. .

[0074] The result can be a set of ADF coefficients. This can be used to distinguish between amplification curves and non-amplification curves via a likelihood ratio test. These coefficients (totaling 18 inputs) The three The three, and each (symmetric) covariance matrix The six (test curves) can be assay-specific and can be defined in the assay definition document (ADF) for each test curve and internal control curve.

[0075] Calculate the likelihood ratio The decision algorithm can calculate the Mahalanobis distance between each point in the test curve and the known distribution of amplified and non-amplified curves. This is a multidimensional generalization of the number of standard deviations that separate a point from the distribution mean. The calculation can be performed on three dimensions corresponding to amplitude, gradient, and span. This method assumes that these measures are normally distributed across the amplified and non-amplified populations but is relatively robust to deviations from normality.

[0076] point y 测试 = {A 测试 G 测试 , S 测试 The relative log-likelihood of belonging to the negative or positive group can be obtained from... Q 0 and Q 1 Give, Q 0 and Q 1 They are respectively: , in and The corresponding Mahalanobis distance to each reference group can be given by the following formula: .

[0077] Likelihood ratio (LR) It can be defined at each point in the test curve. Q 0 / Q 1 The prerequisite is that the following conditions are met: (a) as defined in ADF, y 测试 (b) A occurs during or after the minimum decision loop; 测试 G 测试 and S test All > 0; and (c) at a distance of y 测试 No step shift occurred within 3 points ( See “ Step detection (Partial). If these conditions are not met, then y 测试 LR can be defined as zero.

[0078] Step detection The curves collected on the system may occasionally exhibit step shifts, i.e., sudden shifts in the baseline signal unrelated to the actual amplification. Figure 7 These can represent macroscopic system noise, such as bubbles in the detection window or condensate droplets falling into the reaction. To prevent step shifts from triggering erroneous assertion decisions, some implementations provide methods for identifying and correcting these signal noises.

[0079] Step shifts can be detected by measuring the pairwise differences between adjacent median-filtered points.

[0080] .

[0081] Real-world amplification responses exhibit pairwise difference functions with broad peaks, corresponding to a steady increase in signal at more than one time point. Figure 8A and Figure 8C Conversely, step shift is characterized by a sharp, single-point peak above the baseline due to the (almost) instantaneous shift of the signal. Figure 8B and Figure 8D ).

[0082] To identify step shifts, a 3-point median filter can be applied to pairwise difference curves. Figure 8C and Figure 8D -Red). Then the smoothed difference Y can be... s Subtracting from the unsmoothed difference, thus retaining only the "sharp" peaks. Figure 8E and Figure 8F Y – Y sAny point in the array with a value higher than the corresponding threshold defined in the ADF can be identified as a step shift, and its magnitude J is recorded. The detection of step shifts alters the calculation of the span and likelihood ratio, as described above. Span measurement and Calculate the likelihood ratio Partially discussed.

[0083] Curve level determination Based on the results of the initial mean test, the likelihood ratio at each point, and the LR threshold defined in the ADF, a single curve can be determined in real time as asserted, unasserted, or invalid.

[0084] In some implementations, the initial mean test is performed before the assertion test, indicating that the value of t2 of the former is less than or equal to the minimum decision loop. If the curve fails the initial mean test, the curve can be deemed invalid. In other rare cases, the curve may also be deemed invalid, such as: (a) before the assertion test, F... 原始 The memory contains ≥2 consecutive missing points; and / or (b) before the assertion is evaluated, F 原始 There are more than 2 missing points (contiguous or non-contiguous) in memory.

[0085] Provided the curve passes the initial mean test (i.e., the curve is valid), the curve can be asserted once a point is determined to have a likelihood ratio higher than the threshold defined in the ADF. A single point with a likelihood ratio higher than the threshold is sufficient to trigger an assertion decision. If the likelihood ratio of any point does not exceed the defined threshold at the end of the run, the curve can be determined as unassertified.

[0086] Figure 9 This provides a more intuitive view of the mathematical principles governing assertion decisions. The figure shows the magnitude, gradient, and span values ​​of the reference positive (blue) and reference negative (red) curves at their likelihood ratio peaks in a 3D plot. The hyperplane (green) separating these groups corresponds to the defined likelihood ratio threshold within this space. When a point crossing the LR threshold is identified in this 3D space, the decision algorithm can make an assertion decision.

[0087] Algorithm output metric The instrument can track and record the following curve-level algorithmic metrics in the raw output file.

[0088] initial average As part of the initial average test, the instrument records the average signal between times t1 and t2.

[0089] Initial mean assertion The result of the initial mean test. In a typical implementation, a value of 1 corresponds to a successful test, and a value of 0 corresponds to a failed test.

[0090] QDA assertion The result of the assertion test. In a typical implementation, a value of 1 corresponds to an assertion, a value of 0 corresponds to an unasserted statement, and a value "!" corresponds to invalidity.

[0091] QDA assertion time The time it takes to make an assertion during runtime. In some implementations, since the likelihood ratio is calculated from data collected over a time window, there may be a delay between the time it takes to measure a point and the time it takes to specify the LR. The QDA assertion time can correspond to the latter, i.e., the real-time time when the system actually makes the assertion.

[0092] Example The system collects data at 5-second intervals and employs a decision algorithm with a span half-window of 8 points. The system "looks ahead" at 8 + 3 + 1 = 12 points (span half-window + Savitzky-Golay half-window + median-filtered half-window) to assign the likelihood ratio. Therefore, in some implementations, if a curve crosses the LR threshold at the 3-minute time point, no assertion will be made until another 12 points (1 minute) of data are collected. Thus, in some implementations, the QDA assertion time will occur at the 4-minute mark.

[0093] As used herein, the term "real-time" should be given its ordinary meaning, and should also refer to processing and providing information within a time interval short enough that the user cannot perceive it. In some implementations, real-time means "near real-time".

[0094] Peak likelihood ratio (PLR) The maximum likelihood ratio calculated at all points within the curve. Since LR is defined as zero before the minimum decision loop, PLR occurs afterward.

[0095] Occasionally, all points within the effective curve may have a likelihood ratio equal to zero. In these cases, the PLR ​​is assigned a zero value, and the corresponding PLR time, PLR amplitude, PLR gradient, and PLR span values ​​are undefined.

[0096] PLR time The time taken to measure the point with the peak likelihood ratio.

[0097] PLR amplitude SG at the point where the peak likelihood ratio is measured. Amp The value of .

[0098] PLR gradient SG at the point where the peak likelihood ratio is measured. Grad The value of .

[0099] PLR span The span value at the point where the peak likelihood ratio is measured.

[0100] Step shift The total number (integer) of step shifts above the cutoff value defined by ADF detected within the curve.

[0101] Test decision logic Figures 10A-10B A non-restrictive exemplary higher-order decision logic for FluA / B testing is described. A logic for FluA ( Figure 10A ) and FluB ( Figure 10B This involves a higher-level decision-making logic. In this embodiment, FluA is tested in conjunction with an internal control test.

[0102] Derivation of input parameters for the decision algorithm include The assertion test inputs can be obtained from a reference population with known amplified and non-amplified curves. They can be derived using a method designed to maximize the algorithm's ability to classify curves according to the LR metric, requiring that a single point above a defined LR threshold should be considered sufficient to assert the curve. In short, the computation of these inputs can be performed as follows: Step 1 From the population without amplification curves All points The distribution of the measured amplitude, gradient, and span values ​​is used to estimate the initial coefficients. and .

[0103] Step 2 From the point y in each amplification curve M峰 The distribution of the measured amplitude, gradient, and span values ​​is used to estimate the initial coefficients. and , where y M峰 For relative to and The point with the maximum Mahalanobis distance satisfies the condition that in y M峰 The amplitude, gradient, and span at the point must all be greater than 0.

[0104] Step 3 : Use input coefficients Calculate the preliminary likelihood ratio for all points on the non-amplified reference curve.

[0105] Step 4 From point y in each non-amplification curve PLR Calculation of the distribution of amplitude, gradient and span values ​​measured at the location and , where y PLR It is the point where the likelihood ratio is maximized.

[0106] Step 5 : Use input coefficients Calculate the preliminary likelihood ratio for all points on the amplification reference curve.

[0107] Step 6 From the point y in each amplification curve PLR Calculation of the distribution of amplitude, gradient and span values ​​measured at the location and , where y PLR It is the point where the likelihood ratio is maximized.

[0108] Step 7 Further iterations of steps #3-#6 (as needed) will convert the output coefficients of one iteration into... Used as input coefficients for the next iteration Optionally, in additional iterations of steps #5-#6 (as needed), the output coefficients of one iteration will be... Used as input coefficients for the next iteration The calculation can be repeated until the values ​​of the coefficients converge.

[0109] Estimate initial coefficients By definition, a non-amplified curve is a curve with no signal. In some implementations, and without being bound by any particular theory, the expected value at each point within these curves should be the same, and the variability in amplitude, gradient, and span is directly attributed to system noise.

[0110] To fully capture the noise from which the signal needs to be identified, initial coefficients can be obtained by calculating the mean, variance, and covariance of the amplitude, gradient, and span at all points in the unamplified curve set. and .

[0111] The amplified curves contain a specific signal relative to the background, which subject matter experts have determined to be distinct from the non-amplified curves and belong to a type that should be asserted by the decision algorithm. This can be achieved by first identifying the group of non-amplified curves within each curve. exist Most statistically different To calculate the initial coefficients, use the points. and On this point, y M峰 , is the point in each curve that has the maximum Mahalanobis distance (d) relative to the non-amplified curve group, satisfying y M峰 The amplitude, gradient, and span at each point must all be positive. .

[0112] From the set of amplification curves, y M峰 The initial coefficients are determined by the mean, variance, and covariance of the observed amplitude, gradient, and span values. and .

[0113] Calculation of preliminary likelihood ratio Mastering the coefficients In this case, " Calculate likelihood Compare The method outlined in this section assigns an initial likelihood ratio to all points on both the amplified and non-amplified reference curves. Based on this, the "most positive" point in each curve—the peak likelihood ratio (y)—can be identified. PLR The point where y is located. PLR A 3D graph of the amplitude, gradient, and span values ​​of the points is presented in Figure 11 In the diagram, the population with amplification curves is shown in blue, and the population without amplification curves is shown in red.

[0114] Iterative refinement of algorithm coefficients The amplified and non-amplified populations can be used to analyze y PLR The mean, variance, and covariance of the points are used to generate a set of refined coefficients. Importantly, the peak likelihood ratio-based refinement model has the effect of separating amplified and non-amplified populations to the greatest extent possible according to the metric ultimately used to make the decision. Given the requirement that a single point above the LR threshold is sufficient for a curve to be asserted, in some implementations, and without being bound by any particular theory, it is best to use y for the non-amplified curve. PLR Instead of using all points to generate the distribution (the points most likely to trigger the assertion), it uses a distribution of points (the points most likely to trigger the assertion) instead of generating the distribution as initially did. and For the amplification curve, based on the likelihood... Compare Refining the data rather than based on the absolute Mahalanobis distance produces a tighter result. The effect of coefficient distribution enhances the discriminative power of assertion algorithms. This process involves more than one iteration—from a refined set of... Calculate a new set of y coefficients PLR The value can be used to further refine the assertion algorithm input to ensure optimal discrimination.

[0115] Figures 12A-12B It showed the generation Figure 11 y identified using the same dataset PLR The magnitude, gradient, and span values ​​of the points, but this is the result after three rounds of refinement. In this refinement model, y PLR The points have collapsed into an almost linear distribution in 3D space. Figure 12B The hyperplane (green) is shown as defined by these groups for an assertion algorithm with an LR cutoff value of 8. For this algorithm, an "assertion" decision occurs when a single point in the test curve can be mapped to the "blue" side of the hyperplane. A "non-assertion" decision occurs when all points can be mapped to the "red" side.

[0116] Real-time determination system This disclosure includes aspects of systems for real-time decision-making. In some embodiments, the system includes instruments (e.g., devices) configured to generate data and processors configured to analyze the data.

[0117] The system disclosed herein can beneficially provide rapid target detection. In a clinical setting, the method presented herein can also help avoid the delays associated with routine nucleic acid testing, enabling clinicians to determine a diagnosis within the typical timeframe of a patient's office visit. Therefore, the disclosed system allows clinicians to develop a treatment plan for a patient during their initial office visit, rather than requiring them to wait hours or even days to receive test results from the laboratory. For example, when a patient visits a clinic, a nurse or other healthcare professional can collect a sample from the patient and begin testing using the described system. The system can provide test results while the patient consults with their physician or clinician to determine a treatment plan. Particularly when used to diagnose rapidly progressing pathologies, the disclosed system avoids the delays associated with laboratory tests that could negatively impact patient treatment and outcomes.

[0118] In some implementations, disclosed systems can be used outside clinical settings (e.g., in the field, in rural environments where established healthcare clinics are not easily accessible) to detect health conditions such as infectious diseases (e.g., Ebola), enabling appropriate personnel to take immediate action to prevent or mitigate the spread of the infectious disease. Similarly, disclosed systems can be used in the field or on-site to rapidly determine whether a sample contains a suspected hazardous contaminant (e.g., anthrax), enabling appropriate personnel to take immediate action to prevent or mitigate human exposure to the contaminant. Additionally, disclosed systems can be used to detect contaminants in blood or plasma supplies or in the food industry. It should be understood that disclosed systems can provide similar benefits in other scenarios where real-time detection of the target analyte enables more efficient results than delayed detection by sending samples to an off-site laboratory.

[0119] instrument In some embodiments, an instrument is provided configured to generate time-series data by analyzing a sample. The time-series data may originate from instrumental analysis of a sample (e.g., a sample suspected of containing a target analyte). The instrument may be configured to generate time-series data by analyzing the sample. The instrument may be configured to perform molecular diagnostic assays. Instrumental analysis of the sample may include subjecting the sample to one or more reactions, and the reactions may be configured to detect the presence and / or amount of the target analyte in the sample. The instrument may include one or more sensors configured to detect signals originating from the sample. The time-series data may include time-series signal data, and the signals may be generated from the one or more reactions. The signals may be calorimetric signals, potentiometric signals, amperometric signals, optical signals (e.g., fluorescence signals and / or colorimetric signals), piezoelectric signals, or any combination thereof. Depending on the embodiment, the signal is generated in the presence or absence of the target analyte. The real-time determination methods, compositions, systems, and kits provided herein can advantageously detect target analytes at low detection thresholds. The term "detection threshold" is used herein to describe the minimum amount of the target analyte (e.g., a nucleic acid containing a target nucleic acid sequence) that must be present in the sample for detection to occur. For example, when the detection threshold is 10 nM, a signal can be detected when the target nucleic acid is present in the sample at a concentration of 10 nM or higher. In some cases, the detection threshold is less than or equal to 5 nM, 1 nM, 0.5 nM, 0.1 nM, 0.05 nM, 0.01 nM, 0.005 nM, 0.001 nM, 0.0005 nM, 0.0001 nM, 0.00005 nM, 0.00001 nM, 10 pM, 1 pM, 500 fM, 250 fM, 100 fM, 50 fM, 10 fM, 5 fM, 1 fM, 500 attomoles / liter (aM), 100 aM, 50 aM, 10 aM, or 1 aM.In some cases, the detection thresholds are in the following ranges: 1 aM to 1 nM, 1 aM to 500 pM, 1 aM to 200 pM, 1 aM to 100 pM, 1 aM to 10 pM, 1 aM to 1 pM, 1 aM to 500 fM, 1 aM to 100 fM, 1 aM to 1 fM, 1 aM to 500 aM, 1 aM to 100 aM, 1 aM to 50 aM, 1 aM to 10 aM, 10 aM to 1 nM, 10 aM to 500 pM, 10 aM to 200 pM, 10 aM to 100 pM, 10 aM to 10 pM, 10 aM to 10 pM, 10 aM to 1 pM, 10 aM to 500 fM, 10 aM to 100 fM, 10 aM to 1 fM, 10 aM to 500 aM, 10 aM to 1 ... aM to 50 aM, 100 aM to 1 nM, 100 aM to 500 pM, 100 aM to 200 pM, 100 aM to 100 pM, 100 aM to 10 pM, 100 aM to 1 pM, 100 aM to 500 fM, 100 aM to 100 fM, 100 aM to 1 fM, 100 aM to 500 aM, 500 aM to 1 nM, 500 aM to 500 pM, 500 aM to 200 pM, 500 aM to 100 pM, 500 aM to 10 pM, 500 aM to 10 pM, 500 aM to 1 pM, 500 aM to 500 fM, 500 aM to 100 fM, 500 aM to 1 fM, 1 fM to 1 nM, 1 fM to 500 pM, 1 fM to 200 pM, 1 fM to 100 pM, 1 fM to 10 pM, 1 fM to 1 pM, 10 fM to 1 nM, 10 fM to 500 pM, 10 fM to 200 pM, 10 fM to 100 pM, 10 fM to 10 pM, 10 fM to 1 pM, 500 fM to 1 nM, 500 fM to 500 pM, 500 fM to 200 pM, 500 fM to 100 pM, 500 fM to 10 pM, 500 fM to 1 pM, 800 fM to 1 nM, 800 fM to 500 pM, 800 fM to 200 pM, 800 fM to 100 pM, 800 fM to 10 pM, 800 fM to 1 pM, from 1 pM to 1 nM, 1 The range is from 500 pM, 1 pM to 200 pM, 1 pM to 100 pM, or 1 pM to 10 pM.In some cases, the detection threshold is in the range of 800 fM to 100 pM, 1 pM to 10 pM, 10 fM to 500 fM, 10 fM to 50 fM, 50 fM to 100 fM, 100 fM to 250 fM, or 250 fM to 500 fM.

[0120] The instruments may include spectrometers, electrochemical detection devices, polynucleotide detection devices, fluorescence anisotropy devices, fluorescence resonance energy transfer devices, electron transfer devices, enzyme assays, lateral flow assays, magnetic devices, conductivity devices, isoelectric focusing devices, chromatographs, immunoprecipitation devices, immunoseparation devices, aptamer binding devices, filtration devices, electrophoresis devices, CCD cameras, immunoassays, ELISA, Gram staining devices, immunostaining devices, flow cytometers, microscopes, immunofluorescence devices, Western blotting devices, polymerase chain reaction (PCR) devices, RT-PCR devices, isothermal amplification devices, fluorescence in situ hybridization devices, sequencing devices, next-generation sequencing devices, mass spectrometers, ion mobility spectrometers, surface plasmon resonance devices and local surface plasmon resonance devices, or any combination thereof.

[0121] Instrumental analysis may include one or more of the following: spectroscopy, Raman spectroscopy, FFT (Fast Fourier Transform) spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), infrared spectroscopy, nuclear magnetic resonance (NMR) spectroscopy, electrochemical detection, polynucleotide detection, volatile organic compound methods, fluorescence anisotropy, fluorescence resonance energy transfer, electron transfer, enzyme assays, magnetism, conductivity, electrochemical detection, isoelectric focusing, lateral flow assay (LFA), microfluidics, amino acid sequencing, nucleic acid sequencing, flow cytometry, chromatography, immunoprecipitation, immunodissociation, aptamer binding, filtration, electrophoresis, using a CCD camera, immunoassay, enzyme-linked immunosorbent assay (ELISA), Gram staining, immunostaining, microscopy, immunofluorescence, size / weight / charge detection, Western blotting, polymerase chain reaction (PCR), RT-PCR, isothermal amplification, sequencing, fluorescence in situ hybridization, mass spectrometry, surface plasmon resonance (SPR), and localized surface plasmon resonance (LSPR). Target analytes can be cells, cancer cells, viruses, bacteria, fungi, proteins, nucleic acids, DNA molecules, RNA molecules, miRNA molecules, mRNA molecules, peptides, polypeptides, antibodies, tissues, nanoparticles, drug metabolites, lipids, carbohydrates, hormones, vitamins, fragments thereof, or any combination thereof. Target analytes can be detected using labels selected from the group consisting of: light-emitting labels, fluorescent labels, dyes, quantum dots, luminescent labels, electroluminescent labels, chemiluminescent labels, beads, electromagnetic radiation emitters, optical labels, electrical labels, enzymes that can generate optical or electrical signals, nanoparticles, colorimetric labels, enzyme-linked reagents, multicolor reagents, and detection reagents related to avidin-streptavidin. A positive sample curve indicates the presence of the target analyte in the sample. A negative sample curve indicates the absence of the target analyte in the sample.

[0122] The instrument may include a thermal cycler, such as, for example, a thermal cycler configured for real-time PCR amplification and fluorescence monitoring. The target analyte is a target nucleic acid sequence. One or more reactions may include nucleic acid detection reactions. The sample curve may include a nucleic acid amplification curve, and the signal may include a fluorescence signal indicating the amplification of the target nucleic acid sequence. In some embodiments, a positive sample curve indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction, and thus indicates the presence and / or amount of the target nucleic acid sequence in the sample. In some embodiments, a negative sample curve indicates the absence of target nucleic acid amplification in the nucleic acid detection reaction, and thus indicates the absence of the target nucleic acid sequence in the sample. In some embodiments, one or more reactions may include isothermal amplification reactions. The term "isothermal amplification reaction" should be given its usual meaning and should also include reactions in which the temperature does not change significantly during the reaction. In some embodiments, the temperature deviation of the isothermal amplification reaction during the main enzymatic reaction step in which amplification occurs does not exceed 10°C, for example, not exceeding 5°C or not exceeding 2°C. Depending on the method of isothermal amplification of nucleic acids, different enzymes may be used for amplification. Isothermal amplification compositions and methods are described in WO2017176404, the contents of which are incorporated herein by reference in their entirety. The instrument is capable of amplifying a target nucleic acid sequence in an amplification reaction mixture to produce nucleic acid amplification products, optionally at detectable levels within about 20 minutes, about 15 minutes, or about 10 minutes. The instrument is capable of detecting the nucleic acid amplification products with signal-generating oligonucleotides (e.g., TaqMan detection probe oligonucleotides, molecular beacon detection probe oligonucleotides, or molecular torch detection probe oligonucleotides), wherein the signal-generating oligonucleotides are capable of hybridizing with the nucleic acid amplification products.

[0123] In some implementations, the instrument is a mass spectrometer. Various configurations of mass spectrometers can be used to detect target analytes. Several types of mass spectrometers are available, or can be manufactured in various configurations. Typically, a mass spectrometer has the following main components: a sample inlet, an ion source, a mass analyzer, a detector, a vacuum system, an instrument control system, and a data system. The differences in the sample inlet, ion source, and mass analyzer generally define the type of instrument and its capabilities. For example, the inlet can be a capillary column liquid chromatography source, or it can be a direct probe or stage, such as that used in matrix-assisted laser desorption / sorption spectroscopy (MALDI). Common ion sources are, for example, electrospray ionization, including nanosprays and microsprays, or MALDI. Common mass analyzers include quadrupole mass filters, ion trap mass analyzers, and time-of-flight mass analyzers. Other mass spectrometry methods are well known in the art (see Burlingame et al., Anal. Chem. 70:647 R-716R (1998); Kinter and Sherman, New York (2000)). Protein biomarkers and biomarker values ​​can be detected and measured using any of the following methods: electrospray ionization mass spectrometry (ESI-MS), ESI-MS / MS, ESI-MS / (MS)n, matrix-assisted laser desorption / ionization time-of-flight mass spectrometry (MALDI-TOF-MS), surface-enhanced laser desorption / ionization time-of-flight mass spectrometry (SELDI-TOF-MS), silicon desorption / ionization (DIOS), secondary ion mass spectrometry (SIMS), quadrupole time-of-flight (Q-TOF), tandem time-of-flight (TOF / TOF) technology (known as ultraflex III TOF / TOF), atmospheric pressure chemical ionization mass spectrometry (APCI-MS), APCI-MS / MS, APCI-(MS).sup.N, atmospheric pressure photoionization mass spectrometry (APPI-MS), APPI-MS / MS and APPI-(MS).sup.N, quadrupole mass spectrometry, Fourier transform mass spectrometry (FTMS), quantitative mass spectrometry, and ion trap mass spectrometry.

[0124] In some implementations, the instrument is configured to perform immunoassays or immunodetection assays. Immunoassay methods are based on the reaction of an antibody with its corresponding target or analyte and can detect the analyte in a sample according to a specific assay format. Immunoassays have been designed for a wide range of biological sample matrices. Immunoassay formats have been designed to provide qualitative, semi-quantitative, and quantitative results. Many immunoassay formats have been designed. ELISA or EIA can be used for the detection and quantification of a target analyte. This method relies on the attachment of a label to the analyte or antibody, and the label component may include an enzyme directly or indirectly. ELISA tests can be formatted for direct, indirect, competitive, or sandwich detection of the analyte. Other methods rely on labels, such as, for example, radioisotopes (I125) or fluorescence. Additional techniques include, for example, agglutination, turbidimetry, immunoblotting, immunocytochemistry, immunohistochemistry, flow cytometry, Luminex assays, and other techniques (see ImmunoAssay: A Practical Guide, edited by Brian Law, published by Taylor & Francis, Ltd., 2005). Exemplary assays include enzyme-linked immunosorbent assay (ELISA), radioimmunoassay, fluorescence, chemiluminescence and fluorescence resonance energy transfer (FRET) or time-resolved FRET (TR-FRET) immunoassay.

[0125] In some implementations, the instrument is a nucleic acid sequencing platform. The sequencing system can be any sequencing system of interest, including Sanger sequencing systems, next-generation sequencing (NGS) systems, etc. In some respects, the sequencing system is an NGS system. NGS systems of interest include, but are not limited to, sequencing systems provided by Illumina® (e.g., HiSeq™, MiSeq™ and / or Genome Analyzer™ sequencing systems); Ion Torrent™ (e.g., Ion PGM™ and / or Ion Proton™ sequencing systems); Pacific Biosciences (e.g., PACBIO RS II sequencing system); Life Technologies™ (e.g., SOLiD sequencing system); Roche (e.g., 454 GS FLX+ and / or GS Junior sequencing systems) or any other suitable NGS system.

[0126] In some implementations, the instrument is a flow cytometer. Suitable flow cytometry systems may include, but are not limited to, those in Ormerod (ed.). Flow Cytometry: A Practical Approach , Oxford Univ. Press(1997); Jaroszeski et al.(eds.), Flow Cytometry Protocols, Methods in Molecular Biology No. 91, Humana Press (1997); Practical Flow Cytometry 3rd ed., Wiley-Liss (1995); Virgo et al. (2012) Ann Clin Biochem Jan; 49(pt 1):17-28; Linden, et al. Semin Throm Hemost. 2004 Oct;30(5):502-11; Alison, et al., J Pathol, 2010 Dec; 222(4):335-344; and Herbig, et al. (2007) Crit Rev Ther Drug Carrier Cyst.Those described in 24(3):203-255; their public content is incorporated herein by reference. In some cases, flow cytometry systems of interest include the BD Biosciences FACSCanto™ II flow cytometer, BDDAccuri™ flow cytometer, BD Biosciences FACSCelesta™ flow cytometer, BD Biosciences FACSLyric™ flow cytometer, BD Biosciences FACSVerse™ flow cytometer, BD Biosciences FACSymphony™ flow cytometer, BD Biosciences LSRFortessa™ flow cytometer, BD Biosciences LSRFortess™ X-20 flow cytometer, BD Biosciences FACSCalibur™ cell sorter, BD Biosciences FACSCount™ cell sorter, BD Biosciences FACSLyric™ cell sorter, BD Biosciences Via™ cell sorter, BD Biosciences Influx™ cell sorter, BD Biosciences Jazz™ cell sorter, BD Biosciences Aria™ cell sorter, and BD Biosciences FACSMelody™ cell sorter. In some implementations, the subject particle sorting system is a flow cytometry system, such as that described in U.S. Patent No. 9,952,076;9 Those described in 933,341; 9,726,527; 9,453,789; 9,200,334; 9,097,640; 9,095,494; 9,092,034; 8,975,595; 8,753,573; 8,233,146; 8,140,300; 7,544,326; 7,201,875; 7,129,505; 6,821,740; 6,813,017; 6,809,804; 6,372,506; 5,700,692; 5,643,796; 5,627,040; 5,620,842; 5,602,039; the public information thereof is incorporated herein by reference in its entirety.

[0127] processor In some implementations, the system (e.g., isothermal amplification system, flow cytometry system, nucleic acid sequencing system) additionally includes a processor having a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to perform the real-time decision method provided herein.

[0128] In the implementation scheme, after time-series data (e.g., molecular diagnostic signals) are generated by instruments (e.g., by isothermal amplification devices, by flow cytometers, by nucleic acid sequencing platforms), the processor is configured to execute the real-time determination method provided herein. Figure 1 A functional block diagram of an example processor 100 for analyzing and displaying data is shown. Processor 100 can be configured to implement various processes for controlling the graphical display of time-series data (e.g., molecular diagnostic signals). Instrument 102 can be configured to acquire data by analyzing biological samples (e.g., as described above). Instrument can be configured to provide time-series data (e.g., molecular diagnostic signals) to processor 100. A data communication channel can be included between instrument 102 and processor 100. Data can be provided to processor 100 via the data communication channel. Processor 100 can be configured to provide a graphical display, including heatmaps and / or plots, to display device 106. Display device 106 can be implemented as a monitor, tablet computer, smartphone, or other electronic device configured to present a graphical interface. Processor 100 can be connected to storage device 104. Storage device 104 can be configured to receive and store data from processor 100. Storage device 104 can also be configured to allow processor 100 to retrieve data, such as time-series data (e.g., molecular diagnostic signals). Display device 106 can be configured to receive display data from processor 100. The display data may include graphs of time-series data (e.g., molecular diagnostic signals). Display device 106 can also be configured to change the presented information based on input received from processor 100 in combination with input from instrument 102, storage device 104, keyboard 108, and / or mouse 110.

[0129] Computer system for real-time determination The present invention includes a system comprising a computer having a computer-readable storage medium on which a computer program is stored, wherein when the computer program is loaded onto the computer, the computer program includes instructions for performing the real-time determination method provided herein. Aspects of this disclosure also include computer-controlled systems, wherein the systems further include one or more computers for full or partial automation.

[0130] In the implementation scheme, the system includes an input module, a processing module, and an output module. The subject system may include both hardware components and software components, wherein the hardware components may take the form of one or more platforms, for example, in the form of a server, such that the functional elements, i.e., those elements of the system that perform specific tasks of the system (such as managing the input and output of information, processing information, etc.), can be executed by executing software applications on one or more computer platforms represented by the system and across one or more computer platforms represented.

[0131] The system may include a display and an operator input device. For example, the operator input device may be a keyboard, mouse, etc. The processing module includes a processor that can access memory storing instructions thereon for performing steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory / storage devices and input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or will become available. The processor executes the operating system in a well-known manner, and the operating system interacts with firmware and hardware, facilitating the processor's coordination and execution of the functions of various computer programs written in various programming languages, such as Java, Perl, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system typically cooperates with the processor to coordinate and execute the functions of other components of the computer. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all according to known technologies. The processor may be any suitable analog or digital system.

[0132] System memory can be any of a variety of known or future memory / storage devices. Examples include any generally available random access memory (RAM), magnetic media such as resident hard disks or magnetic tapes, optical media such as optical discs, flash memory devices, or other memory / storage devices. Memory / storage devices can be any of a variety of known or future devices, including optical disc drives, magnetic tape drives, removable hard disk drives, or floppy disk drives. This type of memory / storage device typically reads from and / or writes to program storage media (not shown) such as optical discs, magnetic tapes, removable hard disks, or floppy disks. Any of these program storage media, or other program storage media currently in use or that may be developed in the future, can be considered a computer program product. As will be understood, these program storage media typically store computer software programs and / or data computer software programs, also known as computer control logic, which are typically stored in system memory and / or program storage devices used in conjunction with memory / storage devices.

[0133] In some embodiments, a computer program product is described, comprising a computer-usable medium in which control logic (computer software program, including program code) is stored. When executed by a computer's processor, the control logic causes the processor to perform the functions described herein. In other embodiments, some functions are implemented primarily in hardware using, for example, a hardware state machine. Implementing a hardware state machine to perform the functions described herein will be apparent to those skilled in the art.

[0134] The memory can be any suitable device in which a processor can store and retrieve data, such as magnetic, optical, or solid-state storage devices (including disks, optical discs, magnetic tapes, RAM, or any other suitable fixed or portable device). The processor can include a general-purpose digital microprocessor appropriately programmed from a computer-readable medium carrying the necessary program code. Programming can be provided to the processor remotely via a communication channel or using a computer program product previously stored in any of those devices associated with the memory, such as the memory or some other portable or fixed computer-readable storage medium. For example, a disk or optical disc can carry a program and can be read by a disk burner / reader. The system provided herein also includes, for example, programming algorithms for practicing the methods described above in the form of a computer program product. The programming provided herein can be recorded on a computer-readable medium, such as any medium that can be directly read and accessed by a computer. Such media include, but are not limited to: magnetic storage media, such as floppy disks, hard disk storage media, and magnetic tape; optical storage media, such as CD-ROMs; electrical storage media, such as RAM and ROM; portable flash drives; and mixtures of these categories, such as magnetic / optical storage media.

[0135] The processor can also access communication channels to communicate with users at remote locations. A remote location refers to a location where the user does not have direct contact with the system and relays input information from an external device to the input manager, such as a computer connected to a wide area network (“WAN”), telephone network, satellite network, or any other suitable communication channel, including mobile phones (i.e., smartphones).

[0136] In some embodiments, the system according to this disclosure may be configured to include a communication interface. In some embodiments, the communication interface includes a receiver and / or transmitter for communicating with a network and / or another device. The communication interface may be configured for wired or wireless communication, including but not limited to radio frequency (RF) communication, such as RFID, Zigbee communication protocol, WiFi, infrared, wireless universal serial bus (USB), ultra-wideband (UWB), Bluetooth® communication protocol, and cellular communication, such as Code Division Multiple Access (CDMA) or Global System for Mobile Communications (GSM).

[0137] In one implementation, the communication interface is configured to include one or more communication ports, such as physical ports or interfaces, such as USB ports, RS-232 ports, or any other suitable electrical connection ports, to allow data communication between the subject system and other external devices (such as computer terminals, for example, in a doctor's office or in a hospital environment) configured for similar complementary data communication.

[0138] In one implementation, the communication interface is configured for infrared communication, Bluetooth® communication, or any other suitable wireless communication protocol to enable the subject system to communicate with other devices, such as computer terminals and / or networks, communication-enabled mobile phones, personal digital assistants, or any other communication devices that the user can use in conjunction with them.

[0139] In one implementation, the communication interface is configured to provide connectivity for data transmission using the Internet Protocol (IP) via a mobile network, short message service (SMS), a wireless connection of a personal computer (PC) on a local area network (LAN) connected to the Internet, or a WiFi connection to the Internet at a WiFi hotspot.

[0140] In one implementation, the subject system is configured to wirelessly communicate with a server device via a communication interface, for example, using a common standard such as 802.11 or Bluetooth® RF protocol or IrDA infrared protocol. The server device can be another portable device, such as a smartphone, personal digital assistant (PDA), or laptop computer; or a larger device, such as a desktop computer, appliance, etc. In some implementations, the server device has a display, such as a liquid crystal display (LCD), and input devices, such as buttons, keyboard, mouse, or touchscreen.

[0141] In some implementations, the communication interface is configured to automatically or semi-automatically communicate data stored in the subject system (e.g., in an optional data storage unit) with a network or server device using one or more of the communication protocols and / or mechanisms described above.

[0142] The output controller may include a controller for presenting information to a user from a variety of known display devices, whether human or machine, local or remote. If one of the display devices provides visual information, this information may typically be logically and / or physically organized as an array of picture elements. The graphical user interface (GUI) controller may include any of a variety of known or future software programs for providing a graphical input and output interface between the system and the user, and for processing user input. The functional elements of the computer may communicate with each other via a system bus. Some of these communications may be implemented using networks or other types of remote communication in alternative embodiments. According to known technologies, the output manager may also provide information generated by the processing module to a user at a remote location, for example, via the Internet, telephone, or satellite networks. The presentation of data by the output manager may be implemented according to a variety of known technologies. As some examples, the data may include SQL, HTML, or XML documents, emails or other files, or other forms of data. The data may include Internet URL addresses, allowing the user to retrieve additional SQL, HTML, XML, or other files or data from a remote source. One or more platforms present in the subject system may be any type of known computer platform or type to be developed in the future, although they will typically be a class of computers commonly referred to as servers. However, they can also be mainframes, workstations, or other computer types. They can be connected via any known or future type of cable or other communication system, including wireless systems, whether networked or otherwise. They can be co-located, or they can be physically separated. A variety of operating systems can be used on any computer platform, depending on the type and / or brand of the computer platform chosen. Suitable operating systems include Windows NT, Windows XP, Windows 7, Windows 8, iOS, Sun Solaris, Linux, OS / 400, Compaq Tru64 Unix, SGI IRIX, Siemens Reliant Unix, etc.

[0143] Figure 2 The general architecture of an example computing device 200 according to certain implementation schemes is depicted. Figure 2The general architecture of the computing device 200 depicted includes the arrangement of computer hardware and software components. However, it is not necessary to show all these generally conventional elements for the purpose of providing feasible disclosure. As shown, the computing device 200 includes a processing unit 210, a network interface 220, a computer-readable media drive 230, an input / output device interface 240, a display 250, and an input device 260, all of which can communicate with each other via a communication bus. The network interface 220 can provide connectivity to one or more networks or computing systems. Thus, the processing unit 210 can receive information and instructions from other computing systems or services via the network. The processing unit 210 can also communicate round-trip with a memory 270 and also provide output information to an optional display 250 via the input / output device interface 240. For example, analysis software (e.g., data analysis software or programs) stored as executable instructions in the non-transitory memory of an analysis system can display time-series data (e.g., molecular diagnostic signals) to a user. Input / output device interface 240 may also accept input from optional input device 260, such as a keyboard, mouse, digital pen, microphone, touchscreen, gesture recognition system, voice recognition system, game controller, accelerometer, gyroscope, or other input device. Memory 270 may contain computer program instructions (grouped into modules or components in some embodiments) that are executed by processing unit 210 to implement one or more embodiments of the real-time decision-making methods provided herein. Memory 270 typically includes RAM, ROM, and / or other persistent, auxiliary, or non-transitory computer-readable media. Memory 270 may store operating system 272, which provides computer program instructions for use by processing unit 210 in the general management and operation of computing device 200. Data may be stored in data storage device 290. Memory 270 may also include computer program instructions and other information for implementing aspects of this disclosure.

[0144] Computer-readable storage media This disclosure also includes non-transitory computer-readable storage media having instructions for practicing the disclosed real-time decision-making method. Computer-readable storage media can be employed on one or more computers for the complete or partial automation of systems practicing the methods described herein. In some embodiments, instructions according to the methods described herein can be encoded in a “programmed” form onto a computer-readable medium, wherein the term “computer-readable medium” as used herein refers to any non-transitory storage medium that participates in providing instructions and data to a computer for execution and processing. Examples of suitable non-transitory storage media include floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network-attached storage (NAS), whether these devices are internal or external to a computer. In some cases, instructions can be provided on an integrated circuit device. In some cases, the integrated circuit device of interest may include a reconfigurable field-programmable gate array (FPGA), an application-specific integrated circuit (ASIC), or a complex programmable logic device (CPLD). Files containing information can be “stored” on a computer-readable medium, where “stored” means recording information such that it can be accessed and retrieved by a computer at a later date. The computer-implemented methods described herein can be executed using programming languages ​​that can be written in one or more of any number of computer programming languages. Such languages ​​include, for example, Java (Sun Microsystems, Inc., Santa Clara, Calif.), Visual Basic (Microsoft Corp., Redmond, Wash.), and C++ (AT&T Corp., Bedminster, NJ), as well as many other languages. In some embodiments, the computer-readable storage medium of interest includes a computer program stored thereon, wherein the computer program, when loaded onto a computer, includes instructions for performing the real-time decision-making methods provided herein.

[0145] In this implementation, the system is configured to analyze data within software or analytical tools used for analyzing time-series data (e.g., molecular diagnostic signals). The initial data can be analyzed within data analysis software or tools (e.g., FlowJo®, SeqGeq®) using appropriate means, such as manual gating, cluster analysis, or other computational techniques. This system, or portions thereof, can be implemented as software components for data analysis software, such as FlowJo® or SeqGeq®.

[0146] Computer-readable storage media may be employed on one or more computer systems having a display and an operator input device. For example, the operator input device may be a keyboard, mouse, etc. The processing module includes a processor that can access memory storing instructions for performing steps of the subject method. The processing module may include an operating system, a graphical user interface (GUI) controller, system memory, memory / storage devices and input / output controllers, cache memory, data backup units, and many other devices. The processor may be a commercially available processor, or it may be one of other processors that are available or will become available. The processor executes the operating system in a well-known manner, and the operating system interacts with firmware and hardware, facilitating the processor's coordination and execution of various computer programs written in various programming languages, such as Java, Perl, Python, C++, other high-level or low-level languages, and combinations thereof, as known in the art. The operating system also provides scheduling, input / output control, file and data management, memory management, and communication control and related services, all according to known techniques.

[0147] Measurement definition document An assay definition file (ADF) is a packaged file that defines the configuration of a molecular test or assay, including the assay name, technology platform configuration (e.g., next-generation sequencing (NGS), array type, chemical type), workflow steps (sample preparation, instrument script, analysis, reporting), analytical algorithm, regulatory labels (e.g., Research Use Only (RUO), in vitro diagnostics (IVD), Central Europe in vitro diagnostics (CE-IVD), Internal Use Only (IUO), etc.), target biomarkers(s), reference genome version, consumables, controls, QC thresholds, reporter genes, and variants. The assays and methods described herein can be defined in an ADF, which may include information describing how results are processed, what processing steps are performed, the order in which they are performed, the resulting interpretations, etc. An ADF may include defined classifiers, defined LR thresholds, t1, t2, signal... 最小值 ,Signal 最大值 One or more of the following are used in the methods provided herein: a defined step shift threshold, a defined minimum decision loop, and a defined span half-window. In some embodiments, the ADF may include software code modules for performing one or more steps of the methods provided herein.

[0148] In some implementations, the server system software can support packaged assay configurations, including assay name, assay type, panel, hotspot file (if any), reference name, control name (if any), quality control (QC) threshold, assay description (if any), data analysis parameters and values, instrument run script name, and other configurations defining the assay. This entire set of information is referred to as the assay definition. The assay configuration content and corresponding workflow can be delivered to the user as modular software components in an Assay Definition File (ADF). The server system software can import the Assay Definition File containing the assay configuration. The import process can be initiated via a zip file, which includes an encrypted Debian file and triggers the installation process. The user interface can provide the user with a page to select the ADF for import. An application store within a cloud-based support and resource system can store ADFs supporting various assays, panels, and workflows, which users can select to download to their local server system.

[0149] kit This disclosure also includes kits, which include storage media such as floppy disks, hard disks, optical disks, magneto-optical disks, CD-ROMs, CD-Rs, magnetic tapes, non-volatile memory cards, ROMs, DVD-ROMs, Blu-ray discs, solid-state drives, and network-attached storage (NAS). Any of these program storage media, or other program storage media currently in use or that may be developed thereafter, may be included in the subject kit. In embodiments, the program storage media includes instructions for performing the real-time decision-making methods provided herein. In embodiments, the instructions contained on a computer-readable medium provided in the subject kit or a portion thereof can be implemented as a software component for analyzing data, such as, for example, FlowJo® or SeqGeq®.

[0150] In addition to the components described above, theme kits may also include (in some implementations) instructions, such as instructions for installing plugins into existing software packages (e.g., FlowJo® or SeqGeq®). These instructions can exist in various forms within the theme kit, with one or more forms present in the kit. One form of these instructions is as printed information on a suitable medium or substrate, such as one or more sheets of paper with the information printed on them, in the component's packaging, in a packaging insert, etc. Another form of these instructions is as computer-readable media, such as floppy disks, optical discs (CDs), portable flash drives, etc., on which the information has already been recorded. Yet another form of these instructions is as a website address, which can be used via the Internet to access information at the removed site.

[0151] In at least some of the previously described embodiments, one or more elements used in one embodiment may be used interchangeably in another embodiment, unless such substitution is technically impractical. Those skilled in the art will understand that various other omissions, additions, and modifications may be made to the methods and structures described above without departing from the scope of the claimed subject matter. All such modifications and alterations are intended to fall within the scope of the subject matter defined by the appended claims.

[0152] Regarding the use of substantially any plural and / or singular terms herein, those skilled in the art can convert from plural to singular and / or from singular to plural where appropriate for the context and / or application. For clarity, various singular / plural arrangements may be explicitly set forth herein. As used in this specification and the appended claims, unless the context clearly indicates otherwise, the singular forms “a,” “an,” and “the” include plural euphemisms. Unless otherwise stated, any reference to “or” herein is intended to cover “and / or.”

[0153] Those skilled in the art will understand that, in general, the terminology used herein, and especially in the appended claims (e.g., the body of the appended claims), is typically intended as “open-ended” terminology (e.g., the term “including” should be interpreted as “including but not limited to”, the term “having” should be interpreted as “having at least”, the term “includes” should be interpreted as “including but not limited to”, etc.). Those skilled in the art will also understand that if a particular number of claims is anticipated, such anticipation will be explicitly stated in the claims, and if no such statement is present, such anticipation does not exist. For example, to aid understanding, the appended claims may include the prepositions “at least one” and “one or more” to introduce the claims. However, the use of such phrases should not be construed as implying that introducing a claim statement with the indefinite article “a” or “an” would limit any specific claim containing such an introduced claim statement to an embodiment containing only one such statement, even when the same claim includes the prepositions “one or more” or “at least one” and indefinite articles such as “a” or “an” (e.g., “a” and / or “an” should be interpreted as meaning “at least one” or “one or more”); the same applies to the use of definite articles to introduce claim statements. Furthermore, even if a specific number of introduced claim statements is explicitly stated, those skilled in the art will recognize that such a statement should be interpreted as meaning at least the number stated (e.g., simply stating “two statements” without other modifiers means at least two statements, or two or more statements). Furthermore, in cases where a convention similar to “at least one of A, B, and C” is used, such syntactic structures are generally expected to convey the meaning of the convention in a way that a person skilled in the art would understand (e.g., “a system having at least one of A, B, and C” will include, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.).In cases where conventions such as "at least one of A, B, or C" are used, such syntactic structures are generally intended to convey the meaning of the convention as would be understood by a person skilled in the art (e.g., "a system having at least one of A, B, or C" would include, but is not limited to, systems having only A, only B, only C, A and B together, A and C together, B and C together, and / or A, B, and C together, etc.). A person skilled in the art will also understand that, in practice, any transitional words and / or phrases presenting two or more alternative terms, whether in the specification, claims, or drawings, should be understood to account for the possibility of including one, any, or both terms.

[0154] Furthermore, when features or aspects of this disclosure are described in the Markush group, those skilled in the art will recognize that this disclosure is also described in the presence of any individual member of the Markush group or a subgroup of its members.

[0155] As those skilled in the art will understand, for any and all purposes, such as providing a written description, all scopes disclosed herein also include any and all possible subscopes and combinations thereof. Any listed scope can be readily identified as sufficiently descriptive and such scope can be decomposed into at least equal halves, thirds, quarters, fifths, tenths, etc. As a non-limiting example, each scope discussed herein can be readily decomposed into lower thirds, middle thirds, and upper thirds, etc. As those skilled in the art will also understand, all language, such as “up to,” “at least,” “greater than,” “less than,” etc., includes the stated numbers and refers to scopes that can subsequently be decomposed into subscopes as discussed above. Finally, as those skilled in the art will understand, a scope includes each individual member. Thus, for example, a group having 1-3 items means a group having 1, 2, or 3 items. Similarly, a group having 1-5 items means a group having 1, 2, 3, 4, or 5 items, and so on.

[0156] While various aspects and embodiments have been disclosed herein, other aspects and embodiments will be apparent to those skilled in the art. The various aspects and embodiments disclosed herein are for illustrative purposes and are not intended to limit the true scope and spirit pointed to by the appended claims.

Claims

1. A real-time determination method, comprising: The instrument receives time-series data in real time, wherein the time-series data includes more than one data point that forms the sample curve; Calculate two or more quantitative measures for each data point in real time; Calculate the likelihood ratio (LR) at each data point in real time. The calculation steps include applying a defined classifier to each of the two or more quantitative measures derived from (i) the reference population of the positive curve and (ii) the reference population of the negative curve, the classifier optionally being derived via quadratic discriminant analysis (QDA). as well as The sample curve is determined in real time. Specifically, when the LR calculated for a data point exceeds the defined LR threshold, the sample curve is immediately identified as positive, and Specifically, if the LR calculated for all data points of the sample curve falls at or below the defined LR threshold, the sample curve is determined to be negative in real time.

2. The method of claim 1, wherein the quantitative measure includes amplitude (A), gradient (G) and / or span (S), optionally, S is the variation of A within a defined window.

3. The method according to any one of claims 1-2, wherein the time series data is derived from instrumental analysis of a sample, optionally the sample being a sample suspected of containing the target analyte.

4. The method according to any one of claims 1-3, wherein the instrument is configured to generate time-series data by analyzing the sample, and optionally, the instrument is configured to perform molecular diagnostic assays.

5. The method according to any one of claims 1-4, wherein the instrumental analysis of the sample comprises subjecting the sample to one or more reactions, optionally, the reactions being configured to detect the presence and / or amount of a target analyte in the sample.

6. The method according to any one of claims 1-5, wherein the instrument comprises one or more sensors configured to detect signals originating from the sample, and wherein the time-series data comprises time-series signal data, optionally, the signals being generated by the one or more reactions.

7. The method according to any one of claims 1-6, wherein the signal is a calorimetric signal, a potential signal, an amperometric signal, an optical signal, a piezoelectric signal, or any combination thereof.

8. The method of claim 7, wherein the optical signal is a fluorescence signal and / or a colorimetric signal.

9. The method according to any one of claims 1-8, The signal is generated in the presence of the target analyte; or The signal is generated in the absence of the target analyte.

10. The method according to any one of claims 1-9, wherein the instrumental analysis includes spectroscopy, Raman spectroscopy, and FFT. One or more of the following: (Fast Fourier Transform) spectroscopy, Fourier Transform Infrared Spectroscopy (FTIR), Infrared Spectroscopy, Nuclear Magnetic Resonance (NMR) Spectroscopy, Electrochemical Detection, Polynucleotide Detection, Volatile Organic Compound Methods, Fluorescence Anisotropy, Fluorescence Resonance Energy Transfer, Electron Transfer, Enzyme Assay, Magnetism, Conductivity, Isoelectric Focusing, Lateral Flow Assay (LFA), Microfluidics, Amino Acid Sequencing, Nucleic Acid Sequencing, Flow Cytometry, Chromatography, Immunoprecipitation, Immunodissociation, Aptamer Binding, Filtration, Electrophoresis, Using a CCD Camera, Immunoassay, Enzyme-Linked Immunosorbent Assay (ELISA), Gram Staining, Immunostaining, Microscopy, Immunofluorescence, Size / Weight / Charge Detection, Western Blotting, Polymerase Chain Reaction (PCR), RT-PCR, Isothermal Amplification, Sequencing, Fluorescence In Situ Hybridization, Mass Spectrometry, Surface Plasmon Resonance (SPR), and Localized Surface Plasmon Resonance (LSPR), optionally, the instrument includes a thermal cycler, and optionally, the instrument includes a thermal cycler configured for real-time PCR amplification and fluorescence monitoring.

11. The method according to any one of claims 1-10, The curve of a sample that is deemed positive indicates the presence of the target analyte in the sample; and The curve of a sample that is determined to be negative indicates that the target analyte is not present in the sample.

12. The method according to any one of claims 1-11, wherein the target analyte is a target nucleic acid sequence.

13. The method according to any one of claims 1-12, wherein the one or more reactions comprise a nucleic acid detection reaction.

14. The method according to any one of claims 1-13, wherein the sample curve comprises a nucleic acid amplification curve, and wherein the signal comprises a fluorescence signal indicating the amplification of the target nucleic acid sequence.

15. The method according to any one of claims 1-14, The curve of a sample deemed positive indicates the presence of target nucleic acid amplification in the nucleic acid detection reaction, and thus indicates the presence and / or amount of the target nucleic acid sequence in the sample; and The curve of a sample that is determined to be negative indicates that the target nucleic acid is not amplified in the nucleic acid detection reaction, and thus indicates that the target nucleic acid sequence is not present in the sample.

16. The method according to any one of claims 1-15, wherein the step of calculating two or more quantitative measures comprises applying a median filter, optionally a three-point median filter, to the time series data to generate median-filtered data, and optionally the three-point median filter further comprises: Optionally, the median filter provides smoothing, removal of single-point spikes, and / or removal of system noise.

17. The method according to any one of claims 1-16, wherein, The steps for calculating two or more quantitative measures include applying a Savitzky-Golay (SG) filter to the time series data and / or the median-filtered data to generate smoothed magnitude values ​​(SG). Amp ) and the smoothed gradient value (SG) Grad Optionally, the SG filter is a 7-point SG filter.

18. The method according to any one of claims 1-17, wherein, The application of the SG filter includes moving a sliding window through the time series data and / or the median-filtered data, and applying second-order linear regression to each window: Optionally, the x-values ​​in each window are encoded as {-3,…3}, and the regression fit uses the following as input: 。 19. The method according to any one of claims 1-18, wherein for the center point of each window (k = 0), the smoothed amplitude value (SG) Amp ) and the smoothed gradient value (SG) Grad The calculation is as follows: 。 20. The method according to any one of claims 1-19, wherein the method comprises: Calculate the average signal value between the defined time windows t1 and t2 to generate an initial signal average value, optionally said signal value is SG. Amp value.

21. The method according to any one of claims 1-20, wherein, The determination process includes classifying the sample curve as invalid in the following cases: The initial signal average value did not fall within the range of the signal. 最小值 and signal 最大值 Within the defined signal window, and Optional signal 最小值 and signal 最大值 The distribution of the initial signal average value between a reference population containing positive and negative curves and defined time windows t1 and t2 is determined, and optionally the signal... 最小值 and signal 最大值 The value was set to a limit equal to the mean of the reference population ± 3.6σ.

22. The method according to any one of claims 1-21, wherein the step of calculating two or more quantitative measures comprises calculating the span (S) value by: Use the following formula in a For a defined span half-window, measured in points, and If any step shift is detected within the interval, then Σ J i This is the sum of the magnitudes of all step shifts identified within the interval. Optionally, the defined span half-window is for determination specificity. Alternatively, the defined span half-window is approximately 6 points to approximately 12 points, optionally approximately 10 points; or If the boundary points of the defined span window are located within an exclusion zone of approximately ±1 to approximately ±10 points around the step shift, then the median-filtered signal value is used in the following formula ( ): The boundary points of the span window are defined as k + a or k – a.

23. The method according to any one of claims 1-22, wherein the method includes providing a defined classifier for each of the two or more quantitative measures, optionally, the defined classifier being provided via a determination definition document (ADF).

24. The method according to any one of claims 1-23, wherein the defined classifier includes quadratic discriminant analysis (QDA) coefficients.

25. The method according to any one of claims 1-24, wherein the defined classifier includes linear discriminant analysis (LDA) coefficients.

26. The method according to any one of claims 1-25, wherein the defined classifier is specific to determination and / or defined for each curve, optionally the curves being test curves and / or internal control curves.

27. The method according to any one of claims 1-26, wherein the defined classifier comprises two or more of the following: , , and And among them: 。 28. The method according to any one of claims 1-27, wherein the reference population of the positive curves and the reference population of the negative curves each comprise at least about 10 curves.

29. The method according to any one of claims 1-28, wherein the reference population of the positive curve and the reference population of the negative curve are generated using representative target analyte concentrations in an appropriate sample type.

30. The method according to any one of claims 1-29, wherein, Providing a defined classifier for each curve in the reference group of the positive curves and the reference group of the negative curves includes: Characteristic peak y is identified through an iterative process. PLR The characteristic peaks optimally distinguish between positive and negative reference curves; and Determine the characteristic peak y PLR The A, G, and S measures at the location Optionally, the provision may further include calculating the mean and covariance matrix of the metric.

31. The method according to any one of claims 1-30, wherein, The classifiers provided by the definition include: (a) Estimate the initial coefficients from the distribution of A, G, and S values ​​measured at all points in the reference population of the negative curve. and ; (b) From the point y in each positive reference curve M峰 The distribution of A, G, and S values ​​measured at the location is used to estimate the initial coefficients. and , where y M峰 For relative to and The point with the maximum Mahalanobis distance satisfies the condition that in y M峰 The requirement that A, G, and S at each location must all be greater than 0; (c) Using input coefficients Calculate the preliminary LR for all points in the negative reference curve; (d) From the point y in each negative reference curve PLR Calculation of the distribution of A, G and S values ​​measured at the location and , where y PLR The point where LR is maximized; (e) Using input coefficients Calculate the preliminary LR for all points in the positive reference curve; (f) From the point y in each positive reference curve PLR Calculation of the distribution of A, G and S values ​​measured at the location and , where y PLR The point where LR is maximized; and (g1) output coefficients of one iteration Used as input coefficients for the next iteration Repeat steps (c)-(f) until the parameter values ​​converge, or (g2) The output coefficients of the first iteration Used as input coefficients for the next iteration Repeat steps (e)-(f) until the parameter values ​​converge.

32. The method according to any one of claims 1-31, wherein calculating the LR includes, for each data point y 测试 = {A 测试 G 测试 ,S 测试 }calculate Q 0 / Q 1 ,in: And among them and The corresponding Mahalanobis distance to each reference group is given by the following: 。 33. The method according to any one of claims 1-32, wherein, Real-time calculation of LR at each data point includes calculating LR at each data point after t2.

34. The method according to any one of claims 1-33, wherein, Under the following conditions, at point y 测试 Calculate LR at the location: y 测试 It occurs at or after the defined minimum decision loop; A 测试 G 测试 and S 测试 All > 0; and At a distance of y 测试 No step shift occurred within 3 points.

35. The method according to any one of claims 1-34, wherein the method includes detecting a step shift, and wherein the step shift is detected by: Measure the pairwise differences between adjacent median-filtered points: Apply three-point median filtering to the paired difference curves; Subtract the smoothed difference Y from the unsmoothed difference. s ;and Y – Y s Any point in the range whose value is higher than the defined step shift threshold is identified as having a step shift with magnitude J. Optionally, a step shift represents macroscopic system noise.

36. The method according to any one of claims 1-35, wherein, The determination process includes classifying the sample curve as invalid in the following cases: There are two or more consecutive missing points in the time series data prior to the sample curve being determined to be positive; and / or There are more than two missing points in the time series data prior to the sample curve being determined to be positive; optionally, the missing points may be continuous or discontinuous.

37. The method according to any one of claims 1-36, wherein, One or more of the defined classifiers, the defined LR threshold, t1, t2, and signal. 最小值 ,Signal 最大值 The defined step shift threshold, the defined minimum decision loop, and the defined span half-window are provided via the determination definition file (ADF).

38. The method according to any one of claims 1-37, wherein the method includes a multiplexing determination, the multiplexing determination comprising: The instrument receives two or more sets of time-series data in real time, wherein each set of time-series data includes more than one data point that forms the sample curve; as well as Real-time determination of each of two or more sample curves. Optionally, each of the sample curves is a nucleic acid amplification curve associated with a different target nucleic acid sequence.

39. The method according to any one of claims 1-38, wherein the method is capable of identifying a curve as positive at least about 1 minute, about 2 minutes, about 5 minutes, about 10 minutes, about 15 minutes, about 20 minutes, about 25 minutes, about 30 minutes, about 35 minutes, about 40 minutes, about 45 minutes, about 50 minutes, about 55 minutes, or about 60 minutes earlier than a method employing final state processing of the time series data.

40. The method according to any one of claims 1-39, wherein the step detection and correction make no assumptions about the correct and / or absolute signal baseline.

41. The method according to any one of claims 1-40, wherein the instrument is capable of: A target nucleic acid sequence is amplified in an amplification reaction mixture to produce a nucleic acid amplification product, optionally, said nucleic acid amplification product being produced at a detectable level within about 20 minutes, about 15 minutes, or about 10 minutes; and The nucleic acid amplification product is detected using a signal-generating oligonucleotide, wherein the signal-generating oligonucleotide is capable of hybridizing with the nucleic acid amplification product, and optionally the signal-generating oligonucleotide is a TaqMan detection probe oligonucleotide, a molecular beacon detection probe oligonucleotide, or a molecular torch detection probe oligonucleotide.

42. The method according to any one of claims 1-41, wherein the signal-generating oligonucleotide comprises a label, optionally the label comprises a quenchable label, further optionally the quenchable label is a fluorophore, and optionally the signal-generating oligonucleotide comprises a quencher capable of quenching the signal generated by the label when the quencher and the label are in close proximity.

43. The method according to any one of claims 1-42, wherein the marker is capable of generating a detectable signal under the following conditions: (i) The signal-generating oligonucleotide hybridizes with the nucleic acid amplification product; and / or (ii) The nucleic acid amplification product is extended to produce an extended nucleic acid amplification product that hybridizes with the signal-generating oligonucleotide. Optionally, the signal is fluorescence.

44. The method according to any one of claims 1-43, wherein amplifying the target nucleic acid sequence comprises generating the nucleic acid amplification product at a detectable level within about 20 minutes, about 15 minutes, or about 10 minutes.

45. The method according to any one of claims 1-44, the method comprising: A sample containing a biological entity is contacted with a lysis buffer to produce a treated sample, wherein the lysis buffer contains one or more lysis agents capable of lysing the biological entity to release the sample nucleic acids contained therein, and wherein the sample nucleic acids are suspected of containing the target nucleic acid sequence; and The reagent composition is brought into contact with the treated sample to generate the amplification reaction mixture, wherein the reagent composition comprises one or more amplification reagents.

46. ​​The method according to any one of claims 1-45, wherein the method: It is carried out in a single reaction vessel; It does not include the use of any enzymes other than the aforementioned reverse transcriptase and enzymes with hyperthermophilic biopolymerase activity; This excludes the use of any enzyme other than the enzyme described above that has superthermophilic biopolymerase activity; This does not include thermal denaturation and / or enzymatic denaturation of the nucleic acid during the amplification step; and / or This does not include contacting the nucleic acid with single-stranded DNA-binding proteins.

47. The method according to any one of claims 1-46, wherein the amplification: The amplification may be performed for a period of approximately 5 minutes to approximately 60 minutes, optionally for a period of approximately 15 minutes; and / or The amplification was performed under isothermal conditions free of helicase, single-strand binding proteins, cleavage agents, and recombinase.

48. The method according to any one of claims 1-47, wherein the amplification is performed using a method selected from the group consisting of: polymerase chain reaction (PCR), ligase chain reaction (LCR), loop-mediated isothermal amplification (LAMP), strand displacement amplification (SDA), replicase-mediated amplification, immune amplification, nucleic acid sequence-based amplification (NASBA), autonomous sequence replication (3SR), rolling circle amplification, and transcription-mediated amplification (TMA), optionally wherein the PCR is real-time PCR and / or quantitative real-time PCR (QRT-PCR).

49. The method according to any one of claims 1-48, wherein: The biological entities include one or more of the following: prokaryotic cells, eukaryotic cells, viral particles, exosomes, protoplasts, and microvesicles; The biological entities include viruses, bacteria, fungi, protozoa, parts thereof, or any combination thereof; and / or The target nucleic acid sequence is a nucleic acid sequence of a virus, bacteria, fungus, or protozoa, and optionally the sample nucleic acid is derived from a virus, bacteria, fungus, or protozoa.

50. The method according to any one of claims 1-49, wherein: The viruses mentioned are SARS-CoV-2, human immunodeficiency virus type 1 (HIV-1), human T-cell lymphotropic virus type 1 (HTLV-1), hepatitis B virus (HBV), hepatitis C virus (HCV), herpes simplex virus, herpesvirus 6, herpesvirus 7, Epstein-Barr virus, respiratory syncytial virus (RSV), cytomegalovirus, varicella-zoster virus, JC virus, parvovirus B19, influenza A virus, influenza B virus, influenza C virus, rotavirus, human adenovirus, rubella virus, human enterovirus, genital human papillomavirus (HPV), or hantavirus; The bacteria include Mycobacterium tuberculosis ( Mycobacteria tuberculosis ), Rickettsia rickettsii ( Rickettsia rickettsii ), Chafielich body ( Ehrlichia chaffeensis ), Breospirochete ( Borrelia burgdorferi Yersinia pestis (Yersinia pestis) Yersinia pestis ), pale spirochetes ( Treponema pallidum ), Chlamydia trachomatis ( Chlamydia trachomatis ), Chlamydia pneumoniae ( Chlamydia pneumoniae Mycoplasma pneumoniae () Mycoplasma pneumoniae ), Mycoplasma genus and species ( Mycoplasma sp.), Legionella pneumophila (sp.), Legionella pneumophila ( Legionella pneumophila Legionella dumović ( ) Legionella dumoffii ), fermentation mycoplasma ( Mycoplasma fermentans ), Ehrlichia species ( Ehrlichia sp.), Haemophilus influenzae (sp.), Haemophilus influenzae ( Haemophilus influenzae ), Neisseria meningitidis ( Neisseria meningitidis ), Neisseria gonorrhoeae ( Neisseria gonorrhoeae Streptococcus pneumoniae () Streptococcus pneumonia ), agalactococcus ( S. agalactiae ), and Listeria monocytogenes ( Listeria monocytogenes One or more of the following; The fungi include Cryptococcus neoformans (… Cryptococcus neoformans Pneumocystis carinii () Pneumocystis carinii Histoplasma capsulatum ( ) Histoplasma capsulatum ), dermatitis blastomyces ( Blastomyces dermatitidis ), Coccidioides immitis ( Coccidioides immitis ) and Trichophyton rubrum ( Trichophyton rubrum One or more of the following; and / or The protozoa include Trypanosoma cruzi ( Trypanosoma cruzi Leishmania species Leishmania sp.), Plasmodium genus ( Plasmodium ), Entamoeba histolytica ( Entamoeba histolytica ), babesiidae (a type of volcano) Babesia microti ), Giardia lamblia ( Giardia lamblia ), Cyclospora species ( Cyclospora sp.) and species of the genus Eimeria ( Eimeria One or more of the following (sp.).

51. The method according to any one of claims 1-50, wherein the sample is a biological sample or an environmental sample. The environmental samples mentioned therein are or are obtained from the following: food samples, beverage samples, paper surfaces, fabric surfaces, metal surfaces, wood surfaces, plastic surfaces, soil samples, freshwater samples, wastewater samples, saline samples, samples exposed to atmospheric air or other gases, their cultures, or any combination thereof; and / or The biological sample referred to herein is or is obtained from the following: tissue sample, saliva, blood, plasma, serum, feces, urine, sputum, mucus, lymph, synovial fluid, cerebrospinal fluid, ascites, pleural effusion, seroma, pus, swabs from the surface of the skin or mucous membrane, their cultures, or any combination thereof.

52. The method according to any one of claims 1-51, wherein the amplification does not include one or more of the following: archaea polymerase amplification (APA), loop-mediated isothermal amplification (LAMP), helicase-dependent amplification (HDA), recombinase polymerase amplification (RPA), strand substitution amplification (SDA), nucleic acid sequence-based amplification (NASBA), transcription-mediated amplification (TMA), nickase amplification reaction (NEAR), rolling circle amplification (RCA), multiple substitution amplification (MDA), branching amplification (RAM), circular helicase-dependent amplification (cHDA), single primer isothermal amplification (SPIA), signal-mediated RNA amplification technology (SMART), autonomous sequence replication (3SR), genome exponential amplification reaction (GEAR), and isothermal multiple substitution amplification (IMDA), optionally wherein the amplification does not include LAMP.

53. The method according to any one of claims 1-52, wherein the amplification comprises one or more of the following: APA, LAMP, HDA, RPA, SDA, NASBA, TMA, NEAR, RCA, MDA, RAM, cHDA, SPIA, SMART, 3SR, GEAR, and IMDA, optionally excluding LAMP.

54. The method according to any one of claims 1-53, wherein the method includes and / or excludes one or more of the following: (i) dilution of the treated sample; (ii) dilution of the amplification reaction mixture; (iii) Thermal denaturation of the treated sample; (iv) Acoustic treatment of the treated sample; (v) Acoustic treatment of the amplification reaction mixture; (vi) Addition of a ribonuclease inhibitor to the treated sample; (vii) Addition of a ribonuclease inhibitor to the amplification reaction mixture; (viii) Purification of the sample; (ix) Purification of the nucleic acid in the sample; (x) purification of the nucleic acid amplification product; (xi) removal of one or more lytic agents from the treated sample or the amplification reaction mixture; (xii) thermal denaturation and / or enzymatic denaturation of the nucleic acid in the sample before and / or during amplification; and (xiii) addition of ribonuclease H to the treated sample or the amplification reaction mixture.

55. A system for real-time determination, comprising: An instrument configured to generate time-series data by analyzing samples; and A processor, the processor including a memory operatively coupled to the processor, wherein the memory includes instructions stored thereon that, when executed by the processor, cause the processor to perform the method according to any one of claims 1-54.

56. A computer system for real-time decision-making, comprising: Hardware processor; as well as A non-transitory memory having instructions stored thereon, which, when executed by the hardware processor, cause the processor to perform the method according to any one of claims 1-54.

57. A computer-readable medium comprising code for performing the method according to any one of claims 1-54.

58. A determination definition document (ADF), comprising: Defined classifier, defined LR threshold, t1, t2, signal 最小值 ,Signal 最大值 One or more of the following can be used in the method of any one of claims 1-54: a defined step shift threshold, a defined minimum decision loop, and a defined span half-window.

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